serial_no int64 1 24.2k | cuda_source stringlengths 11 9.01M |
|---|---|
7,001 | #include <stdio.h>
#include <stdlib.h>
#include <time.h>
#include <assert.h>
#include <sys/time.h>
#include <thrust/host_vector.h>
#include <thrust/device_vector.h>
#include <thrust/generate.h>
#include <thrust/sort.h>
#include <thrust/copy.h>
#include <cstdlib>
#include <vector>
#include <ctime>
#include <string>
#include <vector>
#include <iterator>
#include <algorithm>
using namespace std;
#define THREADS 512
#ifdef __cplusplus
extern "C"
{
#endif
__global__ void gpu_sort(float *input, int *output, int* step) {
int index = blockIdx.x * blockDim.x + threadIdx.x;
output[index] = __float2int_rd(input[index] / *step);
}
int cuda_sort(int number_of_elements, float *a)
{
const int NUM_BUCKETS = 6;
float *d_in;
int *d_out;
int *out = (int *) malloc(sizeof(float) * number_of_elements);
int *d_step;
float max_num = *max_element(a, a + number_of_elements);
int step = ceil(max_num / number_of_elements);
vector<float> buckets[NUM_BUCKETS];
cudaMalloc(&d_in, sizeof(float) * number_of_elements);
cudaMalloc(&d_out, sizeof(int) * number_of_elements);
cudaMalloc(&d_step, sizeof(int) * 1);
cudaMemcpy(d_in, a, sizeof(float) * number_of_elements, cudaMemcpyHostToDevice);
cudaMemcpy(d_step, &step, sizeof(int) * 1, cudaMemcpyHostToDevice);
gpu_sort<<<number_of_elements/THREADS, THREADS>>>(d_in, d_out, d_step);
cudaMemcpy(out, d_out, sizeof(int) * number_of_elements, cudaMemcpyDeviceToHost);
for (int i = 0; i < number_of_elements; i++) {
buckets[out[i]].push_back(a[i]);
}
for (int i = 0; i < NUM_BUCKETS; i++) {
thrust::device_vector<float> d_vec = buckets[i];
thrust::sort(d_vec.begin(), d_vec.end());
thrust::copy(d_vec.begin(), d_vec.end(), buckets[i].begin());
}
int index = 0;
for (int i = 0; i < NUM_BUCKETS; i++)
{
for (vector<float>::iterator it = buckets[i].begin(); it != buckets[i].end(); it++)
{
a[index] = *it;
index++;
}
}
cudaFree(d_in);
cudaFree(d_out);
cudaFree(d_step);
free(out);
return 0;
}
#ifdef __cplusplus
}
#endif
|
7,002 | float h_A[]= {
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0.780349167841372, 0.870007240076699, 0.7023018777873804, 0.7602567102801869, 0.8689503422038376, 0.5056893815844674, 0.6495360109291132, 0.7444829968970539, 0.8246142162061156, 0.8363014615978499, 0.7502887510240339, 0.7624682379585823, 0.9367025512599882, 0.6484892548848851, 0.8812293800262152, 0.9930183929708626, 0.5475622525717101, 0.8637981353167001, 0.9120038157861974, 0.694686154363739, 0.709607241865603, 0.5529410180875867, 0.7355663889754933, 0.6505709514739018, 0.8820053312239113, 0.792206400636569, 0.551213822421663, 0.6498218176147907, 0.759221059668626, 0.7839459394125303, 0.5201035968370724, 0.9762371450256835, 0.6762188758632273, 0.7206818983289028, 0.5804559808131509, 0.8973517232688004, 0.5787534568725399, 0.7859753815192916, 0.8179473410826323, 0.9004332233486534, 0.8016217199454534, 0.8769053835274724, 0.8776975202369588, 0.7788065051179013, 0.9919428195689315, 0.9726998374536961, 0.8477663042499762, 0.9407826407574182, 0.6771965117061314, 0.551591336114613, 0.8694073020025672, 0.9536628316828418, 0.9172750140291568, 0.6344131681762548, 0.5992876352445069, 0.5421933241954782, 0.5498528946604987, 0.9025034380758583, 0.8936839706319681, 0.7841942061248366, 0.5192366835528193, 0.7669020000553849, 0.8588514476098277, 0.6805147593271987, 0.9337755192420201, 0.8701327951770499, 0.5480819775899733, 0.9304400521528425, 0.7355732733779683, 0.610800676150699, 0.522092845690102, 0.7898379838610021, 0.7397060154252053, 0.875945859005492, 0.9514387017422419, 0.9933925021922132, 0.814740869714861, 0.85944596615343, 0.7394631242297747, 0.8259223590371733, 0.5614152985389517, 0.8632213949316567, 0.8541572448552446, 0.6642726306382627, 0.956191015792424, 0.5691061188173906, 0.781403541318587, 0.6346971831852035, 0.5293048756613989, 0.5020104283608767, 0.7090148367624381, 0.6554363190816317, 0.851127770583713, 0.5846012723981909, 0.689040246815973, 0.8198666370563354, 0.8833915934938754, 0.8373025479424454, 0.9431496202761228, 0.559626039080807, 0.9065322977985837, 0.6287441772677312, 0.9788271222636256, 0.9837294134236683, 0.9487736756349356, 0.9113743101677716, 0.7582286352504184, 0.8528321819409188, 0.9737747828791876, 0.6127427539026944, 0.7500206409146744, 0.5988807686139643, 0.8659592666188406, 0.6176958932613691, 0.9001402937824703, 0.9364153099665067, 0.9247228385797688, 0.777687435025437, 0.6818715486180721, 0.6151887348412761, 0.8238280482437269, 0.8016646169309992, 0.570224841629391, 0.8156502977020306, 0.6666270350439887, 0.7038423029169886, 0.6248678621777212, 0.8138355900246507, 0.5602906558749673, 0.6414475950110732, 0.7837440240121363, 0.8910773343776097, 0.5058057876387897, 0.8692763491551136, 0.5370693314904669, 0.9668003509844674, 0.7861494616433332, 0.8371246168544523, 0.9122285215627469, 0.5443875309358115, 0.900287226953432, 0.8180556495655769, 0.5871025625771615, 0.5056212039120089, 0.6297578105191253, 0.8803248721013908, 0.5374554012568815, 0.64361009458991, 0.9685459033029095, 0.7654457638753602, 0.7148819391965082, 0.9885426683837868, 0.636953943066656, 0.9504938851160807, 0.8633145918554501, 0.6996439135038699, 0.7058337954657352, 0.883308780853836, 0.7450206882701533, 0.6488549288221854, 0.5740109248577263, 0.5504614389060458, 0.6545103088284392, 0.6590536133320977, 0.7999054731350888, 0.5829786466716367, 0.6090069461655271, 0.8147470150583054, 0.5270928777984527, 0.9969889399354555, 0.7285759356168245, 0.5452240664832716, 0.7048014838815876, 0.5406943908461401, 0.5088285146657496, 0.7220324558949538, 0.5340493284557943, 0.6005532912437546, 0.9436578282715612, 0.8213503006693446, 0.7449477524927209, 0.6091285916623101, 0.7117294319039233, 0.6637522565376707, 0.812027168506956, 0.8793571075221738, 0.8223707440525267, 0.8299943749291592, 0.6374775173440732, 0.6419289597554323, 0.6422576343671902, 0.7406341884004672, 0.9321084801472717, 0.8306730088995947, 0.5413111280962306, 0.9648927796948183, 0.6159889229534813, 0.8545801260537849, 0.8565449389423598, 0.9717545748716653, 0.5986171731697305, 0.9020219959519333, 0.8918028804443199, 0.9761908424177319, 0.9143909504006654, 0.7368828570523042, 0.5290538986042547, 0.763566906986761, 0.9416546839905474, 0.7681113034295417, 0.811604598407272, 0.8361708604980085, 0.7292046035823037, 0.884612143184243, 0.5351080914618125, 0.688083896049016, 0.9660030908450974, 0.5353247932665133, 0.9076921181307124, 0.8988732777637021, 0.8683653620418981, 0.6252172060202245, 0.5641644140180875, 0.7288447124623081, 0.9144853243948221, 0.6781442180168499, 0.8887473250254337, 0.6633837361398236, 0.6223212322572406, 0.8632855901037895, 0.8214603851185733, 0.9432379698047, 0.61943792929602, 0.8634495682081793, 0.7918841936459968, 0.8819802444532447, 0.5938700566765003, 0.570596627133785, 0.9457135566246646, 0.9917486013750947, 0.5025848453875833, 0.9827251253474731, 0.5527187464825631, 0.8566614289268231, 0.8052707606193352, 0.7728806156211929, 0.9825337169006471, 0.6620531210864176, 0.9588426431010579, 0.7956219817994913, 0.7427447828771707, 0.5487504580623104, 0.9272493244688829, 0.9336472969782658, 0.5928250740842247, 0.5713888332192794, 0.541659028290244, 0.5901839351193274, 0.7904422667941549, 0.9405294849160124, 0.6201300452365078, 0.7858986112556707, 0.5975878871955305, 0.9702503266847579, 0.6586556432293436, 0.9956497599747568, 0.6978759542517174, 0.9059709979755872, 0.6688965542272584, 0.5839445762843045, 0.6548557354215951, 0.5375183523448464, 0.506066663184865, 0.7897266254131836, 0.7326400222335515, 0.554375976283572, 0.58423226172105, 0.5869568351038761, 0.9468441517337098, 0.6938087671212849, 0.8228522589127406, 0.6870883523230851, 0.6396663518955529, 0.968207652952999, 0.8759945263828897, 0.8966550109679883, 0.520818845179397, 0.773955179825573, 0.5512775534086602, 0.508197638809688, 0.5475946555689448, 0.5257545255515642, 0.7467516823517995, 0.6162724048423774, 0.97782939210066, 0.894370732721054, 0.6032003676948522, 0.8366483585799409, 0.7467549035319726, 0.6859426426140609, 0.9580018111761224, 0.6821286308814964, 0.704981913560203, 0.6150213511834641, 0.7539266738618794, 0.9451071795104293, 0.7441605270895916, 0.9191282968417405, 0.8800573847634553, 0.6136606848602477, 0.5773511734816507, 0.8337334887773387, 0.6994047258223937, 0.6283673342656244, 0.986011332219525, 0.9909766271082865, 0.5109193492939983, 0.8619158359930958, 0.5565471539043381, 0.8605680699014842, 0.8309006705566542, 0.9963803920029655, 0.5909951506503875, 0.6336292712787386, 0.830344707070098, 0.9006348914901902, 0.788791779071409, 0.5628182563087647, 0.8684703131125417, 0.7139940746064344, 0.8193546084556432, 0.9567257785486439, 0.5677786557782285, 0.9602560969592742, 0.6094103209554815, 0.6837857864717005, 0.6206754171150803, 0.7659120948451856, 0.5022338983631505, 0.8636227995614105, 0.6206399716050182, 0.8624402978940369, 0.5712940374131545, 0.8461540163754201, 0.5160835878185663, 0.8784771637792778, 0.810320410908806, 0.711126184901366, 0.7722416472725557, 0.591304277944997, 0.5471252526622975, 0.8713356069257667, 0.50537220595093, 0.6705092972439213, 0.6232663105191483, 0.9902152755377354, 0.7622503012122699, 0.9503914103786008, 0.9858613552262636, 0.6683289995577562, 0.5703078986196166, 0.9092994908474263, 0.8897881422395105, 0.7399410746637103, 0.822468201935518, 0.6801590773932682, 0.8299411769553562, 0.9109012855841705, 0.6370198126503592, 0.5896139114142984, 0.7686566575445539, 0.6497709613904478, 0.9164630646485601, 0.9646586570771404, 0.9257953304267958, 0.9712267194575993, 0.6633004405869425, 0.7262394688730855, 0.6806291731563439, 0.9744491808238082, 0.6874978019822792, 0.6706707610494163, 0.5306191023173391, 0.6058134614812296, 0.9677410173587317, 0.5604664031321989, 0.8337070756384221, 0.8948284342470758, 0.8122038981571261, 0.8873879313030946, 0.931658494161136, 0.5506539054984612, 0.5293969090330095, 0.9947477786693769, 0.7417962413611408, 0.501184672462006, 0.9108789414306534, 0.6844345833193906, 0.9798792192774577, 0.6309266192558327, 0.8866686444753187, 0.9235104176127075, 0.8351255577119205, 0.910279851886868, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0};
int h_B[]= {
0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66, 68, 70, 72, 74, 76, 78, 80, 82, 84, 86, 88, 90, 92, 94, 96, 98, 100, 102, 104, 106, 108, 110, 112, 114, 116, 118, 120, 122, 124, 126, 128, 130, 132, 134, 136, 138, 140, 142, 144, 146, 148, 150, 152, 154, 156, 158, 160, 162, 164, 166, 168, 170, 172, 174, 176, 178, 180, 182, 184, 186, 188, 190, 192, 194, 196, 198, 200, 202, 204, 206, 208, 210, 212, 214, 216, 218, 220, 222, 224, 226, 228, 230, 232, 234, 236, 238, 240, 242, 244, 246, 248, 250, 252, 254, 256, 258, 260, 262, 264, 266, 268, 270, 272, 274, 276, 278, 280, 282, 284, 286, 288, 290, 292, 294, 296, 298, 300, 302, 304, 306, 308, 310, 312, 314, 316, 318, 320, 322, 324, 326, 328, 330, 332, 334, 336, 338, 340, 342, 344, 346, 348, 350, 352, 354, 356, 358, 360, 362, 364, 366, 368, 370, 374, 376, 378, 380, 382, 384, 386, 388, 390, 392, 394, 396, 398, 400, 402, 404, 406, 408, 410, 412, 414, 416, 418, 420, 422, 424, 426, 428, 430, 432, 434, 436, 438, 440, 442, 444, 446, 448, 450, 452, 454, 456, 458, 460, 462, 464, 466, 468, 470, 472, 474, 476, 478, 480, 482, 484, 486, 488, 490, 492, 494, 496, 498, 500, 502, 504, 506, 508, 510, 512, 514, 516, 518, 520, 522, 524, 526, 528, 530, 532, 534, 536, 538, 540, 542, 544, 546, 548, 550, 552, 554, 556, 558, 560, 562, 564, 566, 568, 570, 572, 574, 576, 578, 580, 582, 584, 586, 588, 590, 592, 594, 596, 598, 600, 602, 604, 606, 608, 610, 612, 614, 616, 618, 620, 622, 624, 626, 628, 630, 632, 634, 636, 638, 640, 642, 644, 646, 648, 650, 652, 654, 656, 658, 660, 662, 664, 666, 668, 670, 672, 674, 676, 678, 680, 682, 684, 686, 688, 690, 692, 694, 696, 698, 700, 702, 704, 706, 708, 710, 712, 714, 716, 718, 720, 722, 724, 726, 728, 730, 732, 734, 736, 738, 740, 742, 744, 746, 748, 750, 752, 754, 756, 758, 760, 762, 764, 766, 768, 770, 772, 774, 776, 778, 780, 782, 784, 786, 788, 790, 792, 794, 796, 798, 800, 802, 804, 806, 808, 810, 812, 814, 816, 818, 820, 822, 824, 826, 828, 830, 832, 834, 836, 838, 840, 842, 844, 846, 848, 850, 852, 854, 856, 858, 860, 862, 864, 866, 868, 870, 872, 874, 876, 878, 880, 882, 884, 886, 888, 890, 892, 894, 896, 898, 900, 902, 904, 906, 908, 910, 912, 914, 916, 918, 920, 922, 924, 926, 928, 930, 932, 934, 936, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 962, 964, 966, 968, 970, 972, 974, 976, 978, 980, 982, 984, 986, 988, 990, 992, 994, 996, 998, 1000, 1002, 1004, 1006, 1008, 1010, 1012, 1014, 1016, 1018, 1020, 1022, 1024, 1026, 1028, 1030, 1032, 1034, 1036, 1038, 1040, 1042, 1044, 1046, 1048, 1050, 1052, 1054, 1056, 1058, 1060, 1062, 1064, 1066, 1068, 1070, 1072, 1074, 1076, 1078, 1080, 1082, 1084, 1086, 1088, 1090, 1092, 1094, 1096, 1098, 1100, 1102, 1104, 1106, 1108, 1110, 1112, 1114, 1116, 1118, 1120, 1122, 1124, 1126, 1128, 1130, 1132, 1134, 1136, 1138, 1140, 1142, 1144, 1146, 1148, 1150, 1152, 1154, 1156, 1158, 1160, 1162, 1164, 1166, 1168, 1170, 1172, 1174, 1176, 1178, 1180, 1182, 1184, 1186, 1188, 1190, 1192, 1194, 1196, 1198, 1200, 1202, 1204, 1206, 1208, 1210, 1212, 1214, 1216, 1218, 1220, 1222, 1224, 1226, 1228, 1230, 1232, 1234, 1236, 1238, 1240, 1242, 1244, 1246, 1248, 1250, 1252, 1254, 1256, 1258, 1260, 1262, 1264, 1266, 1268, 1270, 1272, 1274, 1276, 1278, 1280, 1282, 1284, 1286, 1288, 1290, 1292, 1294, 1296, 1298, 1300, 1302, 1304, 1306, 1308, 1310, 1312, 1314, 1316, 1318, 1320, 1322, 1324, 1326, 1328, 1330, 1332, 1334, 1336, 1338, 1340, 1342, 1344, 1346, 1348, 1350, 1352, 1354, 1356, 1358, 1360, 1362, 1364, 1366, 1368, 1370, 1372, 1374, 1376, 1378, 1380, 1382, 1384, 1386, 1388, 1390, 1392, 1394, 1396, 1398, 1400, 1402, 1404, 1406, 1408, 1410, 1412, 1414, 1416, 1418, 1420, 1422, 1424, 1426, 1428, 1430, 1432, 1434, 1436, 1438, 1440, 1442, 1444, 1446, 1448, 1450, 1452, 1454, 1456, 1458, 1460, 1462, 1464, 1466, 1468, 1470, 1472, 1474, 1476, 1478, 1480, 1482, 1484, 1486, 1488, 1490, 1492, 1494, 1496, 1498, 1500, 1502, 1504, 1506, 1508, 1510, 1512, 1514, 1516, 1518, 1520, 1522, 1524, 1526, 1528, 1530, 1532, 1534, 1536, 1538, 1540, 1542, 1544, 1546, 1548, 1550, 1552, 1554, 1556, 1558, 1560, 1562, 1564, 1566, 1568, 1570, 1572, 1574, 1576, 1578, 1580, 1582, 1584, 1586, 1588, 1590, 1592, 1594, 1596, 1598, 1600, 1602, 1604, 1606, 1608, 1610, 1612, 1614, 1616, 1618, 1620, 1622, 1624, 1626, 1628, 1630, 1632, 1634, 1636, 1638, 1640, 1642, 1644, 1646, 1648, 1650, 1652, 1654, 1656, 1658, 1660, 1662, 1664, 1666, 1668, 1670, 1672, 1674, 1676, 1678, 1680, 1682, 1684, 1686, 1688, 1690, 1692, 1694, 1696, 1698, 1700, 1702, 1704, 1706, 1708, 1710, 1712, 1714, 1716, 1718, 1720, 1722, 1724, 1726, 1728, 1730, 1732, 1734, 1736, 1738, 1740, 1742, 1744, 1746, 1748, 1750, 1752, 1754, 1756, 1758, 1760, 1762, 1764, 1766, 1768, 1770, 1772, 1774, 1776, 1778, 1780, 1782, 1784, 1786, 1788, 1790, 1792, 1794, 1796, 1798, 1800, 1802, 1804, 1806, 1808, 1810, 1812, 1814, 1816, 1818, 1820, 1822, 1824, 1826, 1828, 1830, 1832, 1834, 1836, 1838, 1840, 1842, 1844, 1846, 1848, 1850, 1852, 1854, 1856, 1858, 1860, 1862, 1864, 1866, 1868, 1870, 1872, 1874, 1876, 1878, 1880, 1882, 1884, 1886, 1888, 1890, 1892, 1894, 1896, 1898, 1900, 1902, 1904, 1906, 1908, 1910, 1912, 1914, 1916, 1918, 1920, 1922, 1924, 1926, 1928, 1930, 1932, 1934, 1936, 1938, 1940, 1942, 1944, 1946, 1948, 1950, 1952, 1954, 1956, 1958, 1960, 1962, 1964, 1966, 1968, 1970, 1972, 1974, 1976, 1978, 1980, 1982, 1984, 1986, 1988, 1990, 1992, 1994, 1996, 1998, 2000, 2002, 2004, 2006, 2008, 2010, 2012, 2014, 2016, 2018, 2020, 2022, 2024, 2026, 2028, 2030, 2032, 2034, 2036, 2038, 2040, 2042, 2044, 2046, 2048, 2050, 2052, 2054, 2056, 2058, 2060, 2062, 2064, 2066, 2068, 2070, 2072, 2074, 2076, 2078, 2080, 2082, 2084, 2086, 2088, 2090, 2092, 2094, 2096, 2098, 2100, 2102, 2104, 2106, 2108, 2110, 2112, 2114, 2116, 2118, 2120, 2122, 2124, 2126, 2128, 2130, 2132, 2134, 2136, 2138, 2140, 2142, 2144, 2146, 2148, 2150, 2152, 2154, 2156, 2158, 2160, 2162, 2164, 2166, 2168, 2170, 2172, 2174, 2176, 2178, 2180, 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23386, 23390, 23393, 23396, 23399, 23401, 23402, 23408, 23410, 23414, 23416, 23419, 23422, 23424, 23427, 23431, 23435, 23438, 23441, 23444, 23445, 23447, 23449, 23454, 23455, 23457, 23460, 23463, 23467, 23470, 23472, 23473, 23476, 23479, 23482, 23485, 23486, 23488, 23490, 23492, 23494, 23495, 23498, 23501, 23504, 23507, 23508, 23510, 23512, 23517, 23518, 23520, 23523, 23526, 23532, 23535, 23538, 23541, 23544, 23547, 23549, 23551, 23554, 23556, 23558, 23559, 23564, 23567, 23570, 23573, 23575, 23579, 23581, 23587, 23590, 23592, 23594, 23598, 23600, 23610, 23612, 23614, 23617, 23620, 23623, 23626, 23628, 23629, 23635, 23638, 23641, 23644, 23646, 23648, 23651, 23653, 23655, 23656, 23661, 23664, 23667, 23670, 23672, 23678, 23682, 23685, 23687, 23690, 23692, 23694, 23700, 23701, 23707, 23709, 23712, 23715, 23717, 23721, 23722, 23726, 23729, 23731, 23733, 23738, 23741, 23744, 23746, 23750, 23754, 23756, 23761, 23764, 23767, 23769, 23775, 23777, 23782, 23785, 23788, 23795, 23797, 23799, 23802, 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22485, 22483, 22519, 22517, 22515, 24107, 22551, 22549, 24131, 24144, 24165, 24171, 24192, 24381, 24383, 24385, 24388, 24391, 24394, 24397, 24400, 24402, 24257, 24404, 24335, 24338, 24341, 24344, 24410, 24269, 24413, 24308, 24271, 24270, 24415, 24272, 24417, 24419, 24288, 24293, 24297, 24301, 24425, 24307, 24305, 24427, 24308, 24312, 24310, 24429, 24431, 24434, 24436, 24333, 24335, 24336, 24338, 24339, 24341, 24342, 24344, 24442, 24444, 24446, 24450, 24453, 24357, 24456, 24458, 24459, 24460, 24370, 24368, 24461, 24376, 24374, 24462, 24463, 24380, 24378, 24464, 24468, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 24484, 24488, 24490, 24491, 24494, 24495, 24500, 24502, 24503, 24507, 24510, 24511, 24515, 24516, 24521, 24524, 24525, 24526, 24532, 24534, 24540, 24542, 24547, 24556, 24562, 24571, 24572, 24573, 24575, 24576, 24578, 24580, 24581, 24584, 24585, 24586, 24588, 24589, 24590, 24596, 24608, 24610, 24614, 24615, 24616, 24623, 24624, 24629, 24630, 24631, 24632, 24639, 24640, 24641, 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24952, 24332, 25080, 25081, 25082, 25083, 25084, 25085, 25086, 25087, 25045, 25047, 24963, 24965, 25040, 25041, 25043, 25045, 25047, 25092, 25093, 25004, 25005, 25006, 25098, 25099, 25013, 25101, 25102, 25105, 25106, 25040, 25041, 25043, 25045, 25047, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 25263, 25264, 25265, 23044, 25160, 23045, 25266, 24482, 25267, 25120, 25276, 25277, 25221, 25223, 25225, 25280, 25259, 22705, 25262, 25281, 25199, 25282, 25225, 25283, 25259, 22705, 25262, 25284, 23073, 23069, 25286, 25125, 25287, 25288, 25289, 25241, 25290, 25242, 25292, 25293, 23097, 25127, 23106, 25295, 25296, 25299, 25300, 24508, 25130, 23135, 25301, 25302, 25303, 25133, 25306, 25307, 25308, 25241, 25309, 25242, 25311, 25312, 24522, 25160, 23171, 25313, 21455, 25314, 25137, 25317, 25319, 25320, 25322, 25139, 25323, 25325, 25326, 25328, 24543, 24541, 25329, 25142, 25332, 25333, 25334, 25335, 25336, 25337, 24558, 25338, 25340, 25342, 25343, 21586, 25345, 25347, 25350, 25351, 25353, 25354, 25146, 24574, 25149, 23336, 25146, 24574, 25149, 23336, 25151, 21654, 25153, 21663, 25262, 23358, 25151, 21654, 25153, 21663, 25262, 23358, 24591, 21676, 21672, 25357, 25358, 25159, 25361, 25363, 25364, 25365, 25366, 25367, 25368, 25371, 25160, 25373, 25374, 23426, 25376, 25248, 24876, 24617, 21767, 21763, 25378, 25380, 25381, 25166, 25382, 25383, 25384, 25167, 24633, 21815, 21811, 25385, 25386, 25171, 24643, 21841, 21837, 25387, 25389, 25390, 25176, 25391, 25392, 25177, 25394, 25395, 25396, 25397, 25398, 25399, 24662, 25401, 25402, 25403, 25404, 25406, 25407, 25408, 25411, 25412, 23597, 25413, 25415, 25418, 25421, 25422, 25423, 25424, 25425, 25426, 25427, 25428, 25431, 25432, 25433, 25434, 25435, 25436, 24694, 25437, 25439, 25440, 25441, 25442, 25444, 25446, 24705, 24703, 25449, 25450, 25183, 25453, 25185, 25454, 25186, 25456, 25457, 25458, 25187, 25460, 23748, 25462, 25463, 25189, 25465, 22162, 25466, 25468, 25191, 25470, 25471, 25472, 25193, 23805, 25196, 23816, 25199, 25474, 25225, 25475, 25259, 22705, 25262, 25476, 25201, 25203, 25205, 25479, 25206, 22261, 25209, 25211, 25213, 25482, 25214, 22300, 25217, 25483, 25219, 25484, 25217, 25485, 25219, 25486, 25221, 25223, 25225, 25489, 25259, 22705, 25217, 25490, 25219, 25491, 25221, 25223, 25225, 25494, 25259, 22705, 24787, 22359, 22355, 25495, 25496, 25499, 24796, 22387, 22383, 25500, 25501, 25504, 25233, 25505, 25506, 25234, 25508, 25509, 24015, 25510, 25512, 25513, 25514, 25517, 25518, 24039, 25519, 25521, 25522, 25523, 25526, 25527, 24064, 25528, 25529, 25530, 25531, 25532, 25535, 25536, 25537, 25538, 25539, 25540, 24839, 25541, 24115, 24111, 25544, 25545, 25546, 25241, 25547, 25242, 25549, 25243, 25551, 25552, 24859, 25554, 25555, 25245, 25557, 25558, 24869, 25560, 25248, 24876, 25251, 24880, 25254, 24884, 25257, 24236, 25259, 22705, 25262, 25562, 25566, 25567, 25568, 25569, 25571, 25573, 25575, 25579, 25581, 25584, 25270, 25585, 25271, 25586, 25272, 25273, 25274, 25275, 25591, 25592, 25595, 25285, 25597, 25599, 25600, 25601, 25602, 25603, 25604, 25377, 25613, 25564, 25614, 25615, 25616, 25617, 25618, 25561, 25619, 25563, 25620, 25564, 25621, 25624, 25625, 25626, 25627, 25629, 25630, 25632, 25634, 25635, 25561, 25636, 25563, 25637, 25564, 25638, 25608, 25606, 25612, 25610, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 25665, 25667, 25668, 25669, 25671, 25672, 25673, 25674, 25676, 25677, 25678, 25680, 25681, 25682, 25684, 25686, 25688, 25689, 25690, 25692, 25693, 25695, 25697, 25699, 25700, 25701, 25702, 25704, 25705, 25706, 25294, 25708, 25709, 25711, 25712, 25713, 25714, 25716, 25717, 25719, 25721, 25722, 25723, 25724, 25726, 25727, 25728, 25730, 25731, 25732, 25737, 25740, 25742, 25743, 25745, 25749, 25752, 25757, 25759, 25763, 25764, 25765, 25766, 25767, 25768, 25769, 25770, 25771, 25772, 25773, 25774, 25775, 25776, 25777, 25778, 25779, 25780, 25781, 25782, 25783, 25784, 25785, 25786, 25788, 25789, 25791, 25794, 25796, 25798, 25801, 25375, 25803, 25804, 25805, 25806, 25807, 25379, 25811, 25813, 25815, 25816, 25817, 25818, 25821, 25822, 25823, 25824, 25388, 25828, 25830, 25831, 25832, 25838, 25839, 25843, 25845, 25846, 25848, 25850, 25851, 25854, 25857, 25859, 25860, 25866, 25868, 25872, 25873, 25874, 25875, 25878, 25880, 25882, 25886, 25888, 25461, 25891, 25893, 25896, 25900, 25901, 25902, 25903, 25904, 25906, 25908, 25909, 25910, 25912, 25913, 25914, 25916, 25917, 25918, 25919, 25920, 25922, 25923, 25924, 25926, 25928, 25930, 25932, 25933, 25934, 25936, 25937, 25938, 25940, 25942, 25943, 25944, 25946, 25947, 25948, 25949, 25950, 25952, 25954, 25955, 25956, 25958, 25960, 25962, 25963, 25964, 25966, 25968, 25970, 25971, 25973, 25975, 25977, 25978, 25980, 25983, 25985, 25986, 25992, 25993, 25994, 25995, 25997, 25999, 26000, 26001, 26003, 26006, 25553, 26009, 26012, 25559, 26014, 26015, 26016, 26017, 26018, 26019, 26020, 26021, 26022, 26023, 26024, 25736, 25734, 25739, 25746, 25748, 25344, 25755, 25352, 25443, 25414, 25405, 25853, 25990, 25879, 25884, 25459, 25464, 25469, 25899, 25736, 25734, 25739, 25746, 25748, 25344, 25755, 25352, 26036, 26038, 26040, 26041, 26042, 26043, 25443, 25853, 25405, 25990, 25414, 25879, 25884, 25459, 25464, 25469, 25899, 26047, 25736, 25734, 25739, 25746, 25748, 25344, 25755, 25352, 25362, 25853, 25990, 25372, 26055, 26057, 25511, 25520, 25982, 25990, 25550, 25556, 26063, 26065, 26067, 25836, 25405, 25414, 25853, 25864, 25443, 25879, 25884, 25459, 25464, 25469, 25899, 25511, 25520, 25982, 25990, 25550, 25556, 26078, 26080, 26082, 26026, 26028, 25572, 25570, 25576, 25574, 26033, 26034, 26035, 26045, 26046, 26050, 26052, 26054, 26084, 26085, 26086, 26087, 26060, 26062, 26069, 26071, 26072, 26074, 26075, 26077, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 25670, 26131, 25729, 26164, 26170, 26171, 26192, 26195, 25800, 26205, 26212, 26216, 25837, 25865, 26240, 26286, 26289, 26290, 26293, 25991, 26312, 26005, 26011, 25696, 26137, 26135, 26141, 26139, 26143, 26147, 26145, 26149, 25718, 26154, 26152, 26158, 26156, 25733, 26335, 26336, 25738, 25741, 26337, 26338, 26166, 26339, 25751, 25753, 26340, 26341, 25758, 26342, 26175, 26173, 26179, 26177, 26183, 26181, 26185, 26189, 26187, 26191, 25829, 26222, 26215, 25812, 26211, 25810, 25827, 26343, 25870, 26239, 26344, 26229, 26231, 26345, 25841, 26227, 25856, 26346, 26234, 26347, 25988, 26348, 26242, 25881, 26349, 26244, 25887, 26350, 26247, 25892, 26351, 25894, 25897, 26352, 26353, 26204, 26129, 25687, 25685, 25691, 26258, 25907, 25905, 25911, 25696, 26137, 26135, 26115, 26113, 25718, 26154, 26152, 26158, 26156, 25733, 26354, 26355, 25738, 25741, 26356, 26357, 26166, 25751, 26358, 25753, 26359, 26360, 25758, 26361, 26175, 26173, 26179, 26177, 26183, 26181, 26185, 26189, 26187, 26191, 25812, 26211, 25829, 26222, 26215, 25827, 25810, 26368, 25870, 26239, 25856, 26369, 26234, 26370, 25841, 26227, 26371, 25988, 26372, 26229, 26231, 26373, 26242, 25881, 26374, 26244, 25887, 26375, 26247, 25892, 26376, 25894, 25897, 26377, 26378, 26124, 25679, 25279, 25278, 25683, 26129, 25687, 25685, 25691, 26258, 25907, 25905, 25911, 26204, 25696, 26137, 26135, 26141, 26139, 26143, 26147, 26145, 26149, 25718, 26154, 26152, 26158, 26156, 25733, 26380, 26381, 25738, 25741, 26382, 26383, 26166, 25751, 26384, 25753, 26385, 26386, 25758, 26387, 26175, 26173, 26179, 26177, 26183, 26181, 26185, 26189, 26187, 26191, 25961, 26296, 25793, 26388, 26199, 25856, 26389, 26234, 26390, 25988, 26317, 26315, 25799, 26391, 26333, 26331, 26025, 26204, 25961, 26296, 26394, 26298, 26300, 26395, 26302, 26304, 26396, 26306, 26308, 26397, 25988, 26317, 26315, 26004, 26398, 26010, 26399, 26325, 26327, 26329, 26333, 26331, 26025, 25810, 25812, 26211, 26215, 25827, 25829, 26222, 26403, 25834, 26404, 25841, 26227, 26405, 26229, 26231, 25856, 26406, 26234, 26407, 25862, 26408, 25870, 26239, 26409, 26242, 25881, 26410, 26244, 25887, 26411, 26247, 25892, 26412, 25894, 25897, 26413, 26414, 26254, 26252, 26258, 25907, 25905, 25911, 26264, 25915, 25478, 25477, 26269, 25921, 25481, 25480, 25925, 25927, 25929, 25931, 26278, 25935, 25488, 25487, 25939, 25941, 26285, 25945, 25493, 25492, 25961, 26296, 26415, 26298, 26300, 26416, 26302, 26304, 26417, 26306, 26308, 26418, 25988, 26317, 26315, 26004, 26419, 26010, 26420, 26325, 26327, 26329, 26333, 26331, 26025, 26424, 26425, 26426, 26427, 26428, 26429, 26430, 26431, 26432, 26037, 26039, 25588, 25587, 25590, 25589, 26433, 26434, 26048, 26435, 26436, 26437, 26438, 26440, 26058, 26056, 26442, 26443, 26064, 26068, 26066, 26444, 26445, 26446, 26447, 26448, 26449, 26079, 26083, 26081, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 26470, 26473, 26474, 26475, 26479, 26481, 26487, 25694, 26488, 26489, 26490, 26491, 26492, 26493, 26494, 26495, 26496, 26497, 26498, 26499, 26500, 26160, 26501, 26502, 26504, 26505, 25744, 26508, 26510, 26511, 26512, 26514, 25760, 26469, 26516, 26517, 26518, 26519, 26520, 26521, 26522, 26523, 26524, 26525, 26526, 26527, 26528, 26529, 26530, 26531, 26532, 26534, 26535, 26537, 26538, 26540, 26541, 26542, 26544, 26546, 26311, 25876, 26548, 26549, 26551, 26552, 26554, 26555, 26557, 26558, 26561, 26562, 26563, 26564, 26565, 26566, 26567, 26568, 26569, 26570, 25694, 26571, 26572, 26573, 26574, 26117, 26575, 26576, 26577, 26578, 26579, 26160, 26580, 26581, 26583, 26584, 25744, 26587, 26588, 26590, 26591, 26593, 25760, 26469, 26595, 26596, 26597, 26598, 26599, 26600, 26601, 26602, 26603, 26604, 26605, 26606, 26607, 26608, 26609, 26610, 26611, 26613, 26614, 26615, 26617, 26619, 26620, 26622, 26311, 26624, 26625, 25876, 26627, 26628, 26630, 26631, 26633, 26634, 26636, 26637, 26640, 26641, 26642, 26643, 26644, 26645, 26646, 26647, 26648, 26649, 26650, 26651, 26652, 26653, 26654, 25694, 26655, 26656, 26657, 26658, 26659, 26660, 26661, 26662, 26663, 26664, 26665, 26666, 26667, 26160, 26668, 26669, 26671, 26672, 25744, 26675, 26676, 26678, 26679, 26681, 25760, 26469, 26683, 26684, 26685, 26686, 26687, 26688, 26689, 26690, 26691, 26692, 26196, 26693, 26694, 26695, 26697, 26698, 26700, 26702, 26311, 25996, 26703, 26704, 26705, 26202, 26707, 26708, 26709, 26710, 25953, 25959, 26711, 26712, 26714, 26715, 26717, 26718, 26720, 26721, 26723, 26311, 25996, 26724, 26725, 26726, 26320, 26728, 26323, 26730, 26731, 26732, 26733, 26734, 26735, 26736, 26737, 26738, 26739, 26740, 26741, 26742, 26744, 25400, 26746, 26747, 26749, 26750, 26751, 26753, 26755, 25867, 26757, 26758, 25876, 26760, 26761, 26763, 26764, 26766, 26767, 26769, 26770, 26773, 26774, 26775, 26776, 26777, 26778, 26779, 26780, 26781, 26782, 26783, 26784, 26785, 26786, 26787, 26788, 26789, 26790, 26791, 26792, 26793, 26794, 26795, 26796, 26797, 26798, 26799, 26800, 25953, 25959, 26801, 26802, 26804, 26805, 26807, 26808, 26810, 26811, 26813, 26311, 25996, 26814, 26815, 26816, 26320, 26818, 26323, 26820, 26821, 26822, 26823, 26824, 26825, 26828, 26830, 26835, 26836, 26837, 26838, 26839, 26840, 26843, 26849, 26850, 26853, 26854, 26855, 26862, 26863, 26864, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 26887, 26888, 26890, 26893, 26897, 26899, 26901, 26905, 26906, 26507, 26509, 26912, 26913, 26914, 26916, 26918, 26921, 25819, 25808, 25825, 26533, 26536, 26539, 26937, 26545, 26940, 26941, 26547, 26550, 26945, 26947, 26949, 26951, 26955, 26960, 26961, 26963, 26965, 26967, 26969, 26971, 26975, 26976, 26586, 26978, 26982, 26983, 26984, 26986, 26988, 26991, 25819, 25825, 25808, 26612, 27003, 26618, 26621, 27008, 26623, 27011, 26626, 26629, 27015, 27017, 27019, 27020, 27022, 27025, 27029, 27035, 27036, 27038, 27041, 27045, 27047, 27049, 27053, 27054, 26674, 27056, 27060, 27061, 27062, 27064, 27066, 27069, 25787, 27072, 27075, 27077, 26701, 27080, 27081, 27082, 27084, 27085, 27086, 25951, 27090, 25957, 27091, 26713, 26716, 26719, 26722, 27101, 27102, 27103, 27105, 27106, 27107, 27108, 27112, 25808, 25819, 25825, 26743, 27123, 26745, 26748, 27128, 26754, 27131, 26756, 27134, 26759, 26762, 27138, 27140, 27142, 27143, 27145, 27149, 27151, 27153, 27155, 27161, 27163, 27167, 27169, 25951, 27171, 25957, 27172, 26803, 26806, 26809, 26812, 27182, 27183, 27184, 27186, 27187, 27188, 27189, 27193, 26903, 26911, 24407, 24409, 26925, 26928, 25577, 24414, 24416, 26973, 26981, 24422, 27200, 24424, 27202, 26995, 26997, 24426, 24428, 24430, 25598, 27051, 27059, 24439, 24441, 27074, 24443, 27205, 25089, 27093, 25090, 24449, 24448, 24452, 27208, 27117, 27121, 24457, 22992, 22991, 22994, 22993, 22999, 22998, 27174, 25107, 24467, 24466, 24470, 27211, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 26886, 26515, 27249, 27250, 27251, 27264, 27265, 26959, 26594, 27283, 27284, 27285, 27298, 27300, 27301, 27034, 26682, 27319, 27330, 27332, 27346, 27347, 27348, 27364, 27365, 27367, 27369, 27371, 27373, 27375, 26895, 26892, 27236, 27238, 27241, 27239, 27389, 27390, 26909, 25048, 25050, 27391, 24406, 27392, 24408, 27393, 27394, 26938, 27257, 26932, 26934, 26936, 27260, 27259, 26560, 26948, 26946, 27395, 27396, 27397, 27269, 27270, 27272, 27275, 27273, 27398, 27399, 26979, 25063, 25064, 27400, 24421, 27402, 24423, 27404, 27405, 27290, 27010, 27006, 27002, 27004, 27294, 27293, 26639, 27018, 27016, 27406, 27407, 27408, 27409, 27043, 27040, 27306, 27308, 27311, 27309, 27410, 27411, 27057, 25078, 25079, 27412, 24438, 27413, 24440, 27414, 27324, 27078, 27076, 27326, 27328, 27415, 25088, 27417, 27418, 27338, 27099, 27097, 27095, 27340, 27344, 27342, 27419, 27420, 27421, 27422, 25091, 27424, 27425, 27125, 27133, 27129, 27350, 27127, 27355, 27359, 27358, 26772, 27141, 27139, 25622, 27426, 27427, 27428, 27429, 27430, 27431, 27432, 27433, 27381, 27180, 27178, 27176, 27383, 27387, 27385, 27434, 27435, 27436, 27437, 25108, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 27233, 27486, 27487, 27488, 27489, 27490, 27491, 27244, 27494, 27495, 27496, 27498, 27500, 26930, 26926, 26929, 27503, 27504, 27505, 27506, 27507, 27508, 27509, 27510, 27511, 27512, 25057, 25061, 27267, 27516, 27517, 27518, 27519, 27520, 27278, 27523, 27524, 27525, 27527, 27529, 27000, 26999, 26998, 27532, 27533, 27534, 27535, 27536, 27537, 27538, 27539, 27540, 27541, 25069, 25072, 25076, 27303, 27546, 27547, 27548, 27549, 27550, 27551, 27314, 27554, 27555, 27556, 27558, 27560, 27320, 27562, 27563, 27564, 27565, 27566, 27568, 27333, 27331, 27571, 27572, 27573, 27574, 27575, 27576, 27577, 27579, 27582, 27118, 27115, 27119, 27583, 27585, 27586, 27587, 27588, 27589, 27590, 27591, 27592, 27593, 27594, 27595, 27596, 25094, 25096, 25095, 27598, 25100, 25103, 27376, 27374, 27605, 27606, 27607, 27608, 27609, 27610, 27611, 27613, 27616, 26832, 26844, 26851, 26852, 26861, 29, 30, 31, 27648, 27649, 27653, 27655, 27497, 27499, 27661, 27662, 27663, 27664, 27666, 27669, 27671, 27674, 27675, 27676, 27680, 27682, 27526, 27528, 27688, 27689, 27690, 27691, 27693, 27696, 27698, 27701, 27702, 27703, 27704, 27705, 27709, 27711, 27557, 27559, 27717, 27718, 27567, 27724, 27725, 27726, 27728, 27731, 27581, 27735, 27736, 27737, 27739, 27741, 27743, 27745, 27747, 27751, 27752, 27753, 27755, 27756, 27757, 27758, 27759, 27761, 27764, 27615, 27652, 26827, 26826, 27768, 27679, 27199, 27198, 27769, 27708, 26846, 26845, 27722, 27770, 27207, 27771, 26856, 27210, 27772, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 27778, 27779, 27501, 27783, 27785, 27788, 27514, 27515, 27792, 27793, 27796, 27797, 27799, 27802, 27542, 27543, 27544, 27808, 27809, 27561, 27813, 27570, 27817, 27821, 27824, 27828, 27597, 27830, 27832, 27833, 27604, 27836, 27840, 27777, 27197, 27196, 27841, 27842, 27844, 27677, 27403, 27401, 27845, 27846, 27848, 27807, 26848, 26847, 27849, 27850, 27851, 27416, 27819, 27423, 27853, 27855, 27838, 27438, 27856, 27, 28, 29, 30, 31, 27873, 27874, 27876, 27881, 27882, 27884, 27890, 27893, 27895, 27896, 27754, 27900, 27901, 27902, 27905, 27906, 27907, 27908, 27877, 26834, 26833, 27911, 27912, 27913, 27914, 27885, 27204, 26842, 26841, 27917, 27918, 27919, 27920, 27892, 27923, 27924, 27925, 27897, 26857, 27928, 27929, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 27937, 27940, 27944, 27936, 27951, 27954, 27955, 27956, 27939, 27958, 27961, 27962, 27963, 27964, 27942, 27966, 27969, 27852, 27894, 27972, 27973, 27974, 26859, 26860, 26858, 27903, 27976, 27, 28, 29, 30, 31, 28003, 28004, 27938, 28006, 28008, 28009, 27941, 27847, 28012, 28014, 28015, 27922, 28018, 28019, 27945, 28022, 28023, 28024, 28025, 28026, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 28032, 28034, 28035, 28036, 28038, 28039, 28041, 27971, 28046, 28021, 28048, 27975, 28017, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 28064, 28005, 28067, 28010, 28070, 28020, 28073, 28076, 28045, 28051, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 28102, 28042, 28066, 28037, 28033, 28104, 28105, 28069, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 28128, 28129, 28130, 28131, 28132, 28135, 28133, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 28160, 28103, 28163, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 28192, 28194, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 28224, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 28256, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 28288, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31};
int h_C[]= {
1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63, 65, 67, 69, 71, 73, 75, 77, 79, 81, 83, 85, 87, 89, 91, 93, 95, 97, 99, 101, 103, 105, 107, 109, 111, 113, 115, 117, 119, 121, 123, 125, 127, 129, 131, 133, 135, 137, 139, 141, 143, 145, 147, 149, 151, 153, 155, 157, 159, 161, 163, 165, 167, 169, 171, 173, 175, 177, 179, 181, 183, 185, 187, 189, 191, 193, 195, 197, 199, 201, 203, 205, 207, 209, 211, 213, 215, 217, 219, 221, 223, 225, 227, 229, 231, 233, 235, 237, 239, 241, 243, 245, 247, 249, 251, 253, 255, 257, 259, 261, 263, 265, 267, 269, 271, 273, 275, 277, 279, 281, 283, 285, 287, 289, 291, 293, 295, 297, 299, 301, 303, 305, 307, 309, 311, 313, 315, 317, 319, 321, 323, 325, 327, 329, 331, 333, 335, 337, 339, 341, 343, 345, 347, 349, 351, 353, 355, 357, 359, 361, 363, 365, 367, 369, 371, 375, 377, 379, 381, 383, 385, 387, 389, 391, 393, 395, 397, 399, 401, 403, 405, 407, 409, 411, 413, 415, 417, 419, 421, 423, 425, 427, 429, 431, 433, 435, 437, 439, 441, 443, 445, 447, 449, 451, 453, 455, 457, 459, 461, 463, 465, 467, 469, 471, 473, 475, 477, 479, 481, 483, 485, 487, 489, 491, 493, 495, 497, 499, 501, 503, 505, 507, 509, 511, 513, 515, 517, 519, 521, 523, 525, 527, 529, 531, 533, 535, 537, 539, 541, 543, 545, 547, 549, 551, 553, 555, 557, 559, 561, 563, 565, 567, 569, 571, 573, 575, 577, 579, 581, 583, 585, 587, 589, 591, 593, 595, 597, 599, 601, 603, 605, 607, 609, 611, 613, 615, 617, 619, 621, 623, 625, 627, 629, 631, 633, 635, 637, 639, 641, 643, 645, 647, 649, 651, 653, 655, 657, 659, 661, 663, 665, 667, 669, 671, 673, 675, 677, 679, 681, 683, 685, 687, 689, 691, 693, 695, 697, 699, 701, 703, 705, 707, 709, 711, 713, 715, 717, 719, 721, 723, 725, 727, 729, 731, 733, 735, 737, 739, 741, 743, 745, 747, 749, 751, 753, 755, 757, 759, 761, 763, 765, 767, 769, 771, 773, 775, 777, 779, 781, 783, 785, 787, 789, 791, 793, 795, 797, 799, 801, 803, 805, 807, 809, 811, 813, 815, 817, 819, 821, 823, 825, 827, 829, 831, 833, 835, 837, 839, 841, 843, 845, 847, 849, 851, 853, 855, 857, 859, 861, 863, 865, 867, 869, 871, 873, 875, 877, 879, 881, 883, 885, 887, 889, 891, 893, 895, 897, 899, 901, 903, 905, 907, 909, 911, 913, 915, 917, 919, 921, 923, 925, 927, 929, 931, 933, 935, 937, 939, 941, 943, 945, 947, 949, 951, 953, 955, 957, 959, 961, 963, 965, 967, 969, 971, 973, 975, 977, 979, 981, 983, 985, 987, 989, 991, 993, 995, 997, 999, 1001, 1003, 1005, 1007, 1009, 1011, 1013, 1015, 1017, 1019, 1021, 1023, 1025, 1027, 1029, 1031, 1033, 1035, 1037, 1039, 1041, 1043, 1045, 1047, 1049, 1051, 1053, 1055, 1057, 1059, 1061, 1063, 1065, 1067, 1069, 1071, 1073, 1075, 1077, 1079, 1081, 1083, 1085, 1087, 1089, 1091, 1093, 1095, 1097, 1099, 1101, 1103, 1105, 1107, 1109, 1111, 1113, 1115, 1117, 1119, 1121, 1123, 1125, 1127, 1129, 1131, 1133, 1135, 1137, 1139, 1141, 1143, 1145, 1147, 1149, 1151, 1153, 1155, 1157, 1159, 1161, 1163, 1165, 1167, 1169, 1171, 1173, 1175, 1177, 1179, 1181, 1183, 1185, 1187, 1189, 1191, 1193, 1195, 1197, 1199, 1201, 1203, 1205, 1207, 1209, 1211, 1213, 1215, 1217, 1219, 1221, 1223, 1225, 1227, 1229, 1231, 1233, 1235, 1237, 1239, 1241, 1243, 1245, 1247, 1249, 1251, 1253, 1255, 1257, 1259, 1261, 1263, 1265, 1267, 1269, 1271, 1273, 1275, 1277, 1279, 1281, 1283, 1285, 1287, 1289, 1291, 1293, 1295, 1297, 1299, 1301, 1303, 1305, 1307, 1309, 1311, 1313, 1315, 1317, 1319, 1321, 1323, 1325, 1327, 1329, 1331, 1333, 1335, 1337, 1339, 1341, 1343, 1345, 1347, 1349, 1351, 1353, 1355, 1357, 1359, 1361, 1363, 1365, 1367, 1369, 1371, 1373, 1375, 1377, 1379, 1381, 1383, 1385, 1387, 1389, 1391, 1393, 1395, 1397, 1399, 1401, 1403, 1405, 1407, 1409, 1411, 1413, 1415, 1417, 1419, 1421, 1423, 1425, 1427, 1429, 1431, 1433, 1435, 1437, 1439, 1441, 1443, 1445, 1447, 1449, 1451, 1453, 1455, 1457, 1459, 1461, 1463, 1465, 1467, 1469, 1471, 1473, 1475, 1477, 1479, 1481, 1483, 1485, 1487, 1489, 1491, 1493, 1495, 1497, 1499, 1501, 1503, 1505, 1507, 1509, 1511, 1513, 1515, 1517, 1519, 1521, 1523, 1525, 1527, 1529, 1531, 1533, 1535, 1537, 1539, 1541, 1543, 1545, 1547, 1549, 1551, 1553, 1555, 1557, 1559, 1561, 1563, 1565, 1567, 1569, 1571, 1573, 1575, 1577, 1579, 1581, 1583, 1585, 1587, 1589, 1591, 1593, 1595, 1597, 1599, 1601, 1603, 1605, 1607, 1609, 1611, 1613, 1615, 1617, 1619, 1621, 1623, 1625, 1627, 1629, 1631, 1633, 1635, 1637, 1639, 1641, 1643, 1645, 1647, 1649, 1651, 1653, 1655, 1657, 1659, 1661, 1663, 1665, 1667, 1669, 1671, 1673, 1675, 1677, 1679, 1681, 1683, 1685, 1687, 1689, 1691, 1693, 1695, 1697, 1699, 1701, 1703, 1705, 1707, 1709, 1711, 1713, 1715, 1717, 1719, 1721, 1723, 1725, 1727, 1729, 1731, 1733, 1735, 1737, 1739, 1741, 1743, 1745, 1747, 1749, 1751, 1753, 1755, 1757, 1759, 1761, 1763, 1765, 1767, 1769, 1771, 1773, 1775, 1777, 1779, 1781, 1783, 1785, 1787, 1789, 1791, 1793, 1795, 1797, 1799, 1801, 1803, 1805, 1807, 1809, 1811, 1813, 1815, 1817, 1819, 1821, 1823, 1825, 1827, 1829, 1831, 1833, 1835, 1837, 1839, 1841, 1843, 1845, 1847, 1849, 1851, 1853, 1855, 1857, 1859, 1861, 1863, 1865, 1867, 1869, 1871, 1873, 1875, 1877, 1879, 1881, 1883, 1885, 1887, 1889, 1891, 1893, 1895, 1897, 1899, 1901, 1903, 1905, 1907, 1909, 1911, 1913, 1915, 1917, 1919, 1921, 1923, 1925, 1927, 1929, 1931, 1933, 1935, 1937, 1939, 1941, 1943, 1945, 1947, 1949, 1951, 1953, 1955, 1957, 1959, 1961, 1963, 1965, 1967, 1969, 1971, 1973, 1975, 1977, 1979, 1981, 1983, 1985, 1987, 1989, 1991, 1993, 1995, 1997, 1999, 2001, 2003, 2005, 2007, 2009, 2011, 2013, 2015, 2017, 2019, 2021, 2023, 2025, 2027, 2029, 2031, 2033, 2035, 2037, 2039, 2041, 2043, 2045, 2047, 2049, 2051, 2053, 2055, 2057, 2059, 2061, 2063, 2065, 2067, 2069, 2071, 2073, 2075, 2077, 2079, 2081, 2083, 2085, 2087, 2089, 2091, 2093, 2095, 2097, 2099, 2101, 2103, 2105, 2107, 2109, 2111, 2113, 2115, 2117, 2119, 2121, 2123, 2125, 2127, 2129, 2131, 2133, 2135, 2137, 2139, 2141, 2143, 2145, 2147, 2149, 2151, 2153, 2155, 2157, 2159, 2161, 2163, 2165, 2167, 2169, 2171, 2173, 2175, 2177, 2179, 2181, 2183, 2185, 2187, 2189, 2191, 2193, 2195, 2197, 2199, 2201, 2203, 2205, 2207, 2209, 2211, 2213, 2215, 2217, 2219, 2221, 2223, 2225, 2227, 2229, 2231, 2233, 2235, 2237, 2239, 2241, 2243, 2245, 2247, 2249, 2251, 2253, 2255, 2257, 2259, 2261, 2263, 2265, 2267, 2269, 2271, 2273, 2275, 2277, 2279, 2281, 2283, 2285, 2287, 2289, 2291, 2293, 2295, 2297, 2299, 2301, 2303, 2305, 2307, 2309, 2311, 2313, 2315, 2317, 2319, 2321, 2323, 2325, 2327, 2329, 2331, 2333, 2335, 2337, 2339, 2341, 2343, 2345, 2347, 2349, 2351, 2353, 2355, 2357, 2359, 2361, 2363, 2365, 2367, 2369, 2371, 2373, 2375, 2377, 2379, 2381, 2383, 2385, 2387, 2389, 2391, 2393, 2395, 2397, 2399, 2401, 2403, 2405, 2407, 2409, 2411, 2413, 2415, 2417, 2419, 2421, 2423, 2425, 2427, 2429, 2431, 2433, 2435, 2437, 2439, 2441, 2443, 2445, 2447, 2449, 2451, 2453, 2455, 2457, 2459, 2461, 2463, 2465, 2467, 2469, 2471, 2473, 2475, 2477, 2479, 2481, 2483, 2485, 2487, 2489, 2491, 2493, 2495, 2497, 2499, 2501, 2503, 2505, 2507, 2509, 2511, 2513, 2515, 2517, 2519, 2521, 2523, 2525, 2527, 2529, 2531, 2533, 2535, 2537, 2539, 2541, 2543, 2545, 2547, 2549, 2551, 2553, 2555, 2557, 2559, 2561, 2563, 2565, 2567, 2569, 2571, 2573, 2575, 2577, 2579, 2581, 2583, 2585, 2587, 2589, 2591, 2593, 2595, 2597, 2599, 2601, 2603, 2605, 2607, 2609, 2611, 2613, 2615, 2617, 2619, 2621, 2623, 2625, 2627, 2629, 2631, 2633, 2635, 2637, 2639, 2641, 2643, 2645, 2647, 2649, 2651, 2653, 2655, 2657, 2659, 2661, 2663, 2665, 2667, 2669, 2671, 2673, 2675, 2677, 2679, 2681, 2683, 2685, 2687, 2689, 2691, 2693, 2695, 2697, 2699, 2701, 2703, 2705, 2707, 2709, 2711, 2713, 2715, 2717, 2719, 2721, 2723, 2725, 2727, 2729, 2731, 2733, 2735, 2737, 2739, 2741, 2743, 2745, 2747, 2749, 2751, 2753, 2755, 2757, 2759, 2761, 2763, 2765, 2767, 2769, 2771, 2773, 2775, 2777, 2779, 2781, 2783, 2785, 2787, 2789, 2791, 2793, 2795, 2797, 2799, 2801, 2803, 2805, 2807, 2809, 2811, 2813, 2815, 2817, 2819, 2821, 2823, 2825, 2827, 2829, 2831, 2833, 2835, 2837, 2839, 2841, 2843, 2845, 2847, 2849, 2851, 2853, 2855, 2857, 2859, 2861, 2863, 2865, 2867, 2869, 2871, 2873, 2875, 2877, 2879, 2881, 2883, 2885, 2887, 2889, 2891, 2893, 2895, 2897, 2899, 2901, 2903, 2905, 2907, 2909, 2911, 2913, 2915, 2917, 2919, 2921, 2923, 2925, 2927, 2929, 2931, 2933, 2935, 2937, 2939, 2941, 2943, 2945, 2947, 2949, 2951, 2953, 2955, 2957, 2959, 2961, 2963, 2965, 2967, 2969, 2971, 2973, 2975, 2977, 2979, 2981, 2983, 2985, 2987, 2989, 2991, 2993, 2995, 2997, 2999, 3001, 3003, 3005, 3007, 3009, 3011, 3013, 3015, 3017, 3019, 3021, 3023, 3025, 3027, 3029, 3031, 3033, 3035, 3037, 3039, 3041, 3043, 3045, 3047, 3049, 3051, 3053, 3055, 3057, 3059, 3061, 3063, 3065, 3067, 3069, 3071, 3073, 3075, 3077, 3079, 3081, 3083, 3085, 3087, 3089, 3091, 3093, 3095, 3097, 3099, 3101, 3103, 3105, 3107, 3109, 3111, 3113, 3115, 3117, 3119, 3121, 3123, 3125, 3127, 3129, 3131, 3133, 3135, 3137, 3139, 3141, 3143, 3145, 3147, 3149, 3151, 3153, 3155, 3157, 3159, 3161, 3163, 3165, 3167, 3169, 3171, 3173, 3175, 3177, 3179, 3181, 3183, 3185, 3187, 3189, 3191, 3193, 3195, 3197, 3199, 3201, 3203, 3205, 3207, 3209, 3211, 3213, 3215, 3217, 3219, 3221, 3223, 3225, 3227, 3229, 3231, 3233, 3235, 3237, 3239, 3241, 3243, 3245, 3247, 3249, 3251, 3253, 3257, 3259, 3261, 3263, 3265, 3267, 3269, 3271, 3273, 3275, 3277, 3279, 3281, 3283, 3285, 3287, 3289, 3291, 3293, 3295, 3297, 3299, 3301, 3303, 3305, 3307, 3309, 3311, 3313, 3315, 3317, 3319, 3321, 3323, 3325, 3327, 3329, 3331, 3333, 3335, 3337, 3339, 3341, 3343, 3345, 3347, 3349, 3351, 3353, 3355, 3357, 3359, 3361, 3363, 3365, 3367, 3369, 3371, 3373, 3375, 3377, 3379, 3381, 3383, 3385, 3387, 3389, 3391, 3393, 3395, 3397, 3399, 3401, 3403, 3405, 3407, 3409, 3411, 3413, 3415, 3417, 3419, 3421, 3423, 3425, 3427, 3429, 3431, 3433, 3435, 3437, 3439, 3441, 3443, 3445, 3447, 3449, 3451, 3453, 3455, 3457, 3459, 3461, 3463, 3465, 3467, 3469, 3471, 3473, 3475, 3477, 3479, 3481, 3483, 3485, 3487, 3489, 3491, 3493, 3495, 3497, 3499, 3501, 3503, 3505, 3507, 3509, 3511, 3514, 3516, 3518, 3520, 3522, 3524, 3526, 3528, 3530, 3532, 3534, 3536, 3538, 3540, 3542, 3544, 3546, 3548, 3550, 3552, 3554, 3556, 3558, 3560, 3563, 3565, 3567, 3569, 3571, 3573, 3576, 3578, 3580, 3582, 3585, 3587, 3590, 3592, 3597, 3599, 3608, 3610, 3612, 3614, 3617, 3619, 3622, 3624, 3630, 3632, 3634, 3636, 3639, 3641, 3644, 3646, 3652, 3654, 3656, 3658, 3660, 3662, 3664, 3666, 3668, 3670, 3672, 3674, 3676, 3678, 3680, 3682, 3684, 3686, 3688, 3690, 3693, 3695, 3697, 3699, 3702, 3704, 3706, 3708, 3710, 3712, 3714, 3716, 3718, 3720, 3722, 3724, 3726, 3728, 3731, 3733, 3735, 3737, 3739, 3741, 3743, 3745, 3747, 3749, 3751, 3753, 3755, 3757, 3759, 3761, 3763, 3765, 3767, 3769, 3771, 3773, 3775, 3777, 3779, 3781, 3783, 3785, 3788, 3790, 3793, 3795, 3798, 3800, 3803, 3805, 3808, 3810, 3812, 3814, 3817, 3819, 3822, 3824, 3829, 3831, 3834, 3836, 3840, 3842, 3845, 3847, 3850, 3852, 3854, 3856, 3859, 3861, 3863, 3865, 3869, 3871, 3874, 3876, 3879, 3881, 3884, 3886, 3888, 3890, 3892, 3894, 3896, 3898, 3900, 3902, 3904, 3906, 3908, 3910, 3913, 3915, 3917, 3919, 3921, 3923, 3925, 3927, 3929, 3931, 3933, 3935, 3937, 3939, 3941, 3943, 3945, 3947, 3949, 3951, 3953, 3955, 3957, 3959, 3961, 3963, 3965, 3967, 3969, 3971, 3973, 3975, 3978, 3980, 3982, 3984, 3986, 3988, 3990, 3992, 3995, 3997, 4000, 4002, 4008, 4010, 4012, 4014, 4017, 4019, 4021, 4023, 4026, 4028, 4031, 4033, 4038, 4040, 4042, 4044, 4047, 4049, 4052, 4054, 4060, 4062, 4068, 4070, 4073, 4075, 4077, 4079, 4081, 4083, 4085, 4087, 4089, 4091, 4093, 4095, 4097, 4099, 4101, 4103, 4108, 4110, 4112, 4114, 4116, 4118, 4121, 4123, 4126, 4128, 4134, 4136, 4141, 4143, 4146, 4148, 4150, 4152, 4154, 4156, 4158, 4160, 4162, 4164, 4166, 4168, 4174, 4176, 4179, 4181, 4184, 4186, 4189, 4191, 4197, 4199, 4201, 4203, 4205, 4207, 4209, 4211, 4213, 4215, 4217, 4219, 4221, 4223, 4225, 4227, 4229, 4231, 4233, 4235, 4237, 4239, 4241, 4243, 4245, 4247, 4249, 4251, 4253, 4255, 4257, 4259, 4261, 4263, 4265, 4267, 4269, 4271, 4273, 4275, 4277, 4279, 4281, 4283, 4285, 4287, 4289, 4291, 4293, 4295, 4297, 4299, 4301, 4303, 4305, 4307, 4309, 4311, 4313, 4315, 4317, 4319, 4321, 4323, 4325, 4327, 4329, 4331, 4333, 4335, 4337, 4339, 4341, 4343, 4345, 4347, 4349, 4351, 4353, 4355, 4357, 4359, 4361, 4363, 4365, 4367, 4369, 4371, 4373, 4375, 4377, 4379, 4381, 4383, 4386, 4388, 4390, 4392, 4394, 4396, 4398, 4400, 4402, 4404, 4406, 4408, 4410, 4412, 4414, 4416, 4418, 4420, 4422, 4424, 4426, 4428, 4430, 4432, 4434, 4436, 4438, 4440, 4442, 4444, 4447, 4449, 4451, 4453, 4456, 4458, 4460, 4462, 4465, 4467, 4469, 4471, 4473, 4475, 4477, 4479, 4482, 4484, 4486, 4488, 4491, 4493, 4496, 4498, 4503, 4505, 4507, 4509, 4511, 4513, 4515, 4517, 4520, 4522, 4524, 4526, 4529, 4531, 4534, 4536, 4541, 4543, 4545, 4547, 4549, 4551, 4554, 4556, 4559, 4561, 4564, 4566, 4569, 4571, 4574, 4576, 4579, 4581, 4584, 4586, 4005, 4005, 4591, 4593, 4595, 4597, 4599, 4601, 4603, 4605, 4607, 4609, 372, 372, 4613, 4615, 4617, 4619, 4621, 4623, 4625, 4627, 4629, 4631, 4633, 4635, 4637, 4639, 4641, 4643, 4645, 4647, 4649, 4651, 4653, 4655, 4657, 4659, 4661, 4663, 4665, 4667, 4682, 4684, 4686, 4688, 4690, 4692, 4694, 4696, 4698, 4700, 4702, 4704, 4706, 4708, 4710, 4712, 4714, 4716, 4718, 4720, 4722, 4724, 4726, 4728, 4730, 4732, 4734, 4736, 4738, 4740, 3993, 3993, 4005, 4005, 4035, 4035, 4005, 4005, 3993, 3993, 4776, 4778, 4780, 4782, 4784, 4786, 4788, 4790, 4792, 4794, 4796, 4798, 4800, 4802, 4804, 4806, 3561, 3561, 3561, 3561, 3561, 3561, 3603, 3603, 3603, 3603, 3603, 3603, 3615, 3627, 3637, 3637, 3649, 3649, 3615, 3627, 3637, 3637, 3649, 3649, 3976, 3976, 3993, 3993, 4194, 4194, 4005, 4005, 3976, 3976, 3976, 3976, 4005, 4005, 4057, 4057, 3512, 3512, 372, 372, 3512, 3512, 373, 373, 3627, 3627, 3637, 3637, 3649, 3649, 3976, 3976, 3976, 3976, 3976, 3976, 3993, 3993, 4057, 4057, 4045, 4065, 4065, 4065, 4065, 3512, 3512, 372, 372, 3512, 3512, 373, 373, 3512, 3512, 3615, 3615, 3627, 3627, 3832, 3837, 3583, 3594, 3627, 3627, 3615, 3615, 3649, 3649, 3637, 3637, 3691, 3691, 3700, 3700, 3561, 3561, 3561, 3561, 3561, 3561, 3603, 3603, 3603, 3603, 3603, 3603, 3627, 3627, 3615, 3615, 3637, 3637, 3649, 3649, 4005, 4005, 3993, 3993, 4035, 4035, 4015, 4015, 4045, 4045, 4057, 4057, 3993, 3993, 4005, 4005, 3993, 3993, 4005, 4005, 4015, 4015, 4045, 4045, 4169, 4169, 4035, 4035, 4015, 4015, 3976, 3976, 4171, 4171, 4015, 4015, 4015, 4015, 4057, 4057, 4045, 4045, 4065, 4065, 4065, 4065, 4005, 4005, 4015, 4015, 4035, 4035, 3976, 3976, 3976, 3976, 4119, 4119, 4131, 4131, 4171, 4171, 4171, 4171, 4171, 4171, 5251, 5253, 5255, 5257, 5259, 5261, 5263, 5265, 5267, 5269, 5271, 5273, 5275, 5277, 5279, 5281, 5283, 5285, 4384, 4384, 4463, 4463, 5340, 5342, 5344, 5346, 5348, 5350, 5352, 5354, 5356, 5358, 5360, 5362, 5364, 5366, 5368, 5370, 5372, 5374, 5376, 5378, 5380, 5382, 5384, 5386, 5388, 5390, 5392, 5394, 5396, 5398, 5400, 5402, 5404, 5406, 5408, 5410, 5412, 5414, 5416, 5418, 4384, 4384, 4463, 4463, 5479, 5481, 5483, 5485, 5487, 5489, 5491, 5493, 5495, 5497, 5499, 5501, 5503, 5505, 5507, 5509, 5512, 5514, 5516, 5518, 5520, 5522, 4005, 4005, 3976, 3976, 5543, 5545, 5547, 5549, 5551, 5553, 5555, 5557, 5559, 5561, 5563, 5565, 5567, 5569, 5571, 5573, 5575, 5577, 5579, 5581, 5583, 5585, 5587, 5589, 5591, 5593, 5595, 5597, 5599, 5601, 5603, 5605, 3627, 3627, 3615, 3615, 3615, 3615, 3691, 3691, 3700, 3700, 3561, 3561, 3561, 3561, 3561, 3561, 3603, 3603, 3615, 3615, 3691, 3691, 3700, 3700, 3627, 3627, 4005, 4005, 4035, 4035, 4065, 4065, 4035, 4035, 4015, 4015, 3976, 3976, 3976, 3976, 3976, 3976, 4065, 4065, 4035, 4035, 4045, 4045, 4057, 4057, 4065, 4065, 4035, 4035, 4015, 4015, 3976, 3976, 3993, 3993, 4005, 4005, 3976, 3976, 3976, 3976, 4005, 4005, 3993, 3993, 4065, 4065, 4005, 4005, 3993, 3993, 4035, 4035, 4005, 4005, 3993, 3993, 3976, 3976, 3976, 3976, 3993, 3993, 4005, 4005, 4065, 4065, 3512, 3512, 3512, 3512, 4194, 4194, 3615, 3615, 3627, 3627, 3637, 3637, 3615, 3615, 4005, 4005, 4171, 4171, 3993, 3993, 3976, 3976, 4045, 4045, 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24601, 24604, 24602, 9573, 9574, 9575, 24605, 9577, 24609, 24612, 9582, 23430, 24347, 24618, 9624, 24620, 24621, 24625, 23469, 23466, 24634, 24636, 24644, 9642, 24646, 24647, 24651, 23531, 9648, 24655, 24654, 24656, 24657, 24659, 24663, 9657, 24665, 24664, 24666, 24667, 9662, 24670, 24669, 9665, 9666, 9667, 24672, 24671, 24675, 9672, 9673, 9674, 9675, 9676, 9677, 9678, 24677, 24679, 24681, 24680, 24682, 24685, 24683, 9686, 9687, 9688, 24687, 24686, 24688, 24689, 24691, 24695, 9696, 9697, 24697, 24696, 24698, 24699, 9702, 23677, 9704, 9705, 9706, 9707, 24706, 9711, 9712, 9714, 23706, 24713, 9718, 23725, 23728, 24719, 9723, 24722, 9727, 23749, 24726, 9730, 24729, 9734, 9735, 24732, 9737, 24735, 24736, 9741, 9742, 24747, 24782, 24896, 9784, 9786, 24754, 9794, 9796, 24765, 24772, 24775, 24772, 24775, 9828, 9830, 24782, 24772, 24775, 9857, 9859, 24782, 24788, 23958, 9882, 9883, 24791, 24797, 23982, 9890, 9891, 24800, 24804, 23999, 9896, 24808, 24807, 24810, 9902, 24813, 24812, 9905, 9906, 9907, 24816, 24815, 24818, 9912, 24821, 24820, 9915, 9916, 9917, 24823, 24822, 24825, 24069, 24830, 24828, 9925, 9926, 9927, 24832, 24831, 24833, 24834, 24836, 24840, 9935, 9936, 9937, 24843, 24846, 24120, 24132, 9945, 24852, 9948, 24856, 24860, 9953, 24161, 24862, 9956, 24866, 24870, 9961, 24188, 24387, 24896, 24393, 24399, 10074, 24951, 24258, 24953, 22746, 10090, 22810, 10092, 22814, 10101, 22819, 10103, 24412, 10148, 25003, 10159, 10160, 10161, 10171, 24316, 24951, 24908, 10224, 10226, 10235, 10237, 21134, 10289, 10290, 10300, 10301, 10302, 24316, 24433, 21146, 24964, 24321, 24951, 24327, 24953, 10373, 10374, 10375, 10376, 10384, 10385, 10386, 10387, 24348, 24349, 21174, 24964, 21207, 22836, 22837, 21180, 21181, 24455, 10525, 25003, 21186, 24364, 10557, 10558, 20112, 10571, 10572, 10585, 10586, 21207, 22874, 22880, 21213, 21214, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 9241, 9266, 9267, 25134, 24523, 25135, 9271, 25161, 25268, 24483, 9314, 9315, 25121, 25222, 25224, 9321, 24888, 25260, 25261, 9329, 25198, 9344, 25224, 9346, 24888, 25260, 25261, 9354, 25123, 25122, 9380, 25124, 9382, 9383, 9384, 24847, 9386, 24850, 9389, 9390, 25126, 24501, 25128, 9395, 25297, 9399, 9400, 25129, 24509, 25131, 9404, 9405, 25304, 25132, 9410, 9411, 9412, 24847, 9414, 24850, 9417, 9418, 25134, 24523, 25135, 9422, 25161, 25315, 25136, 9428, 9430, 25321, 9433, 25138, 9435, 9437, 9438, 9440, 25141, 25140, 9443, 24546, 9447, 9448, 9449, 9450, 9451, 9452, 25143, 25339, 25341, 9458, 9459, 25144, 25346, 9464, 9467, 9468, 9470, 9471, 25145, 25147, 25148, 25150, 25145, 25147, 25148, 25150, 24579, 25152, 24583, 25154, 25261, 25155, 24579, 25152, 24583, 25154, 25261, 25155, 25158, 25157, 25156, 9561, 9562, 24595, 9566, 9568, 9569, 9570, 9571, 9572, 25369, 9576, 24607, 9579, 9580, 25161, 9583, 25247, 25249, 25164, 25163, 25162, 9623, 9625, 9626, 25165, 9628, 9629, 9630, 24628, 25170, 25169, 25168, 9635, 9636, 24638, 25174, 25173, 25172, 9641, 9643, 9644, 25175, 9646, 9647, 23534, 9650, 9651, 9652, 9653, 9654, 9655, 25178, 9658, 9659, 9660, 9661, 9663, 9664, 25409, 9668, 9669, 25179, 9671, 25416, 25419, 9679, 9680, 9681, 9682, 9683, 9684, 9685, 25429, 9689, 9690, 9691, 9692, 9693, 9694, 25180, 25438, 9698, 9699, 9700, 9701, 9703, 25447, 25182, 25181, 9710, 25451, 24708, 9715, 25184, 9717, 24715, 9720, 9721, 9722, 23737, 9725, 25188, 9728, 9729, 23760, 9732, 25190, 25467, 9736, 23781, 9739, 9740, 25473, 25192, 25194, 25195, 25197, 25198, 9761, 25224, 9763, 24888, 25260, 25261, 9771, 25200, 25202, 25204, 9788, 24755, 25207, 25208, 25210, 25212, 9798, 24766, 25215, 25216, 9804, 25218, 9808, 25216, 9820, 25218, 9824, 25220, 25222, 25224, 9832, 24888, 25260, 25216, 9849, 25218, 9853, 25220, 25222, 25224, 9861, 24783, 25260, 25228, 25227, 25226, 9880, 9881, 9884, 25231, 25230, 25229, 9888, 9889, 9892, 25232, 9894, 9895, 24002, 9898, 9899, 25235, 9901, 9903, 9904, 25515, 9908, 9909, 25236, 9911, 9913, 9914, 25524, 9918, 9919, 25237, 9921, 9922, 9923, 9924, 25533, 9928, 9929, 9930, 9931, 9932, 9933, 25238, 25542, 25240, 25239, 9940, 9941, 9942, 24847, 9944, 24850, 9947, 24854, 9950, 9951, 25244, 9954, 9955, 24864, 9958, 9959, 25246, 9962, 25247, 25249, 25250, 25252, 25253, 25255, 25256, 25258, 24888, 25260, 25261, 10015, 10075, 10080, 10081, 10089, 10091, 10100, 10102, 10149, 25582, 10172, 24321, 10209, 24327, 10215, 22746, 22810, 22814, 22819, 10288, 25593, 25596, 21186, 10313, 10318, 10319, 10358, 10359, 10364, 10365, 21167, 10416, 21170, 10418, 10422, 10423, 10454, 10455, 21177, 10463, 24356, 10474, 24396, 10476, 10526, 10536, 10537, 25628, 10570, 25631, 25633, 10617, 10618, 21210, 10626, 24390, 10637, 24396, 10639, 25607, 25605, 25611, 25609, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 25666, 9268, 9269, 9270, 9272, 25269, 9290, 25675, 9316, 9318, 9320, 9322, 9323, 9328, 9343, 9345, 9347, 9348, 9353, 9378, 9379, 9381, 25698, 9385, 25291, 9388, 25703, 9391, 9392, 9393, 25707, 25298, 25710, 9401, 9402, 9403, 25715, 25305, 9409, 25720, 9413, 25310, 9416, 25725, 9419, 9420, 9421, 9423, 25316, 9427, 9434, 25327, 9441, 9442, 9445, 25750, 9453, 9461, 25348, 25355, 9474, 9475, 9476, 9477, 9491, 9492, 9493, 9494, 9508, 9509, 9510, 9511, 9516, 9517, 9533, 9534, 9535, 9536, 9541, 9542, 9558, 9559, 9560, 25359, 9565, 25792, 25795, 25370, 9578, 9581, 25802, 9596, 9597, 9620, 9621, 9622, 25809, 9627, 25814, 9631, 9632, 9633, 9634, 9637, 9638, 9639, 9640, 25826, 9645, 25393, 9649, 25833, 9656, 25840, 25844, 25410, 25847, 9670, 25417, 25420, 25855, 25858, 25430, 25861, 9695, 25869, 25445, 25448, 9708, 9709, 9713, 9716, 9719, 9724, 9726, 25889, 9731, 9733, 9738, 9743, 9744, 9745, 9746, 9760, 9762, 9764, 9765, 9770, 9783, 9785, 9787, 9789, 9790, 9793, 9795, 9797, 9799, 9800, 9803, 9807, 9819, 9823, 9827, 9829, 9831, 9833, 9834, 9848, 9852, 9856, 9858, 9860, 9862, 9863, 9877, 9878, 9879, 25497, 9885, 9886, 9887, 25502, 9893, 25507, 9897, 25965, 9900, 25969, 25516, 25972, 9910, 25976, 25525, 25979, 9920, 25984, 25534, 25987, 9934, 25543, 9938, 9939, 25998, 9943, 25548, 9946, 9949, 9952, 26007, 9957, 9960, 26013, 9963, 9964, 9978, 9979, 9986, 9987, 10002, 10003, 10004, 10005, 10014, 25735, 25318, 25324, 25331, 25664, 25756, 25754, 25761, 25871, 25849, 25842, 25852, 25989, 25452, 25883, 25885, 25890, 25895, 25898, 25735, 25318, 25324, 25331, 25747, 25756, 25754, 25761, 10208, 10214, 10223, 10225, 10234, 10236, 25871, 25852, 25842, 25989, 25849, 25452, 25883, 25885, 25890, 25895, 25898, 10312, 25735, 25318, 25324, 25331, 25747, 25756, 25754, 25761, 25790, 25852, 25989, 25797, 10415, 10417, 25967, 25974, 25981, 25989, 26002, 26008, 10462, 10473, 10475, 25835, 25842, 25849, 25852, 25863, 25871, 25452, 25883, 25885, 25890, 25895, 25898, 25967, 25974, 25981, 25989, 26002, 26008, 10625, 10636, 10638, 25565, 26027, 26030, 26029, 26032, 26031, 25578, 25580, 25583, 26044, 25594, 26049, 26051, 26053, 10751, 10752, 10755, 10756, 26059, 26061, 25623, 26070, 25097, 26073, 25104, 26076, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 26116, 26132, 26159, 26165, 25349, 25356, 26193, 25360, 26201, 26206, 26213, 26217, 26224, 26236, 26241, 26287, 25498, 26291, 25503, 26310, 26313, 26319, 26322, 26133, 26136, 26134, 26140, 26138, 26142, 26146, 26144, 26148, 26150, 26153, 26151, 26157, 26155, 26161, 10053, 10054, 26162, 26163, 10057, 10059, 25330, 10061, 26167, 26168, 10064, 10065, 26169, 10067, 26174, 26172, 26178, 26176, 26182, 26118, 26184, 26188, 26186, 26190, 26220, 26221, 25820, 26209, 26210, 26208, 26219, 10114, 26237, 26238, 10117, 26119, 26230, 10120, 26225, 26226, 26232, 10124, 26233, 10126, 26309, 10130, 25877, 26243, 10133, 25455, 26245, 10136, 26246, 26248, 10139, 26249, 26250, 10142, 10143, 26203, 26128, 26127, 26126, 26130, 26257, 26256, 26255, 26259, 26133, 26136, 26134, 26114, 26112, 26150, 26153, 26151, 26157, 26155, 26161, 10187, 10188, 26162, 26163, 10191, 10193, 25330, 26167, 10196, 26168, 10198, 10199, 26169, 10201, 26174, 26172, 26178, 26176, 26182, 26118, 26184, 26188, 26186, 26190, 26209, 26210, 26220, 26221, 25820, 26219, 26208, 10248, 26237, 26238, 26232, 10252, 26233, 10254, 26225, 26226, 10257, 26309, 10260, 26119, 26230, 10264, 25877, 26243, 10267, 25455, 26245, 10270, 26246, 26248, 10273, 26249, 26250, 10276, 10277, 26123, 26122, 26121, 26120, 26125, 26128, 26127, 26126, 26130, 26257, 26256, 26255, 26259, 26203, 26133, 26136, 26134, 26140, 26138, 26142, 26146, 26144, 26148, 26150, 26153, 26151, 26157, 26155, 26161, 10337, 10338, 26162, 26163, 10341, 10343, 25330, 26167, 10346, 26168, 10348, 10349, 26169, 10351, 26174, 26172, 26178, 26176, 26182, 26180, 26184, 26188, 26186, 26190, 26294, 26295, 26197, 10393, 26198, 26232, 10396, 26233, 10398, 26309, 26316, 26314, 26200, 10405, 26332, 26330, 26334, 26203, 26294, 26295, 10430, 26297, 26299, 10433, 26301, 26303, 10436, 26305, 26307, 10439, 26309, 26316, 26314, 26318, 10446, 26321, 10449, 26324, 26326, 26328, 26332, 26330, 26334, 26208, 26209, 26210, 25820, 26219, 26220, 26221, 10487, 26223, 10490, 26225, 26226, 10493, 26228, 26230, 26232, 10497, 26233, 10499, 26235, 10502, 26237, 26238, 10506, 25877, 26243, 10509, 25455, 26245, 10512, 26246, 26248, 10515, 26249, 26250, 10518, 10519, 26253, 26251, 26257, 26256, 26255, 26259, 26263, 26262, 26261, 26260, 26268, 26267, 26266, 26265, 26270, 26271, 26272, 26273, 26277, 26276, 26275, 26274, 26279, 26280, 26284, 26283, 26282, 26281, 26294, 26295, 10593, 26297, 26299, 10596, 26301, 26303, 10599, 26305, 26307, 10602, 26309, 26316, 26314, 26318, 10609, 26321, 10612, 26324, 26326, 26328, 26332, 26330, 26334, 10652, 10654, 10657, 10658, 10661, 10662, 10679, 10682, 10685, 26362, 26363, 26365, 26364, 26367, 26366, 10725, 10728, 26379, 10733, 10746, 10748, 26439, 26441, 26393, 26392, 10769, 10781, 26400, 26402, 26401, 10806, 10809, 10814, 10818, 10822, 10834, 26421, 26423, 26422, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 26194, 26207, 26214, 26218, 26288, 26292, 10036, 26465, 10038, 10039, 10040, 10041, 10042, 10043, 10044, 10045, 10046, 10047, 10048, 10049, 10050, 26466, 10052, 26503, 10055, 10056, 26467, 10060, 10062, 10063, 26513, 10066, 26468, 25762, 10070, 10071, 10076, 10077, 10082, 10083, 10086, 10093, 10094, 10097, 10104, 10105, 10107, 10108, 10109, 10111, 10113, 10115, 10116, 10118, 10119, 10121, 10122, 10123, 10125, 10127, 26483, 26478, 10131, 10132, 10134, 10135, 10137, 10138, 10140, 10141, 10144, 10150, 10151, 10152, 10156, 10162, 10163, 10164, 10168, 10173, 26465, 10175, 10176, 10177, 10178, 26464, 10180, 10181, 10182, 10183, 10184, 26466, 10186, 26582, 10189, 10190, 26467, 10194, 10195, 10197, 26592, 10200, 26468, 25762, 10204, 10205, 10210, 10211, 10216, 10217, 10220, 10227, 10228, 10231, 10238, 10239, 10240, 10241, 10243, 10245, 10247, 10249, 10250, 10251, 10253, 10255, 10256, 10258, 26483, 10261, 10262, 26478, 10265, 10266, 10268, 10269, 10271, 10272, 10274, 10275, 10278, 10279, 10280, 10281, 10285, 10291, 10292, 10293, 10297, 10303, 10304, 10305, 10309, 10314, 10320, 26465, 10322, 10323, 10324, 10325, 10326, 10327, 10328, 10329, 10330, 10331, 10332, 10333, 10334, 26466, 10336, 26670, 10339, 10340, 26467, 10344, 10345, 10347, 26680, 10350, 26468, 25762, 10354, 10355, 10360, 10361, 10366, 10367, 10370, 10377, 10378, 10381, 26471, 10390, 10391, 10392, 10394, 10395, 10397, 10399, 26483, 26484, 10402, 10403, 10404, 26472, 10407, 10408, 10412, 10419, 26480, 26482, 10428, 10429, 10431, 10432, 10434, 10435, 10437, 10438, 10440, 26483, 26484, 10443, 10444, 10445, 26485, 10448, 26486, 10451, 10456, 10459, 10464, 10465, 10470, 10478, 10479, 10480, 10482, 10484, 10485, 10486, 10488, 26476, 10491, 10492, 10494, 10495, 10496, 10498, 10500, 26477, 10503, 10504, 26478, 10507, 10508, 10510, 10511, 10513, 10514, 10516, 10517, 10520, 10521, 10527, 10528, 10529, 10533, 10538, 10539, 10540, 10541, 10545, 10546, 10547, 10548, 10552, 10554, 10559, 10561, 10563, 10564, 10565, 10566, 10573, 10575, 10577, 10578, 10579, 10580, 26480, 26482, 10591, 10592, 10594, 10595, 10597, 10598, 10600, 10601, 10603, 26483, 26484, 10606, 10607, 10608, 26485, 10611, 26486, 10614, 10619, 10622, 10627, 10628, 10633, 26829, 26831, 10697, 10699, 10702, 10703, 10706, 10707, 10731, 10766, 10767, 10784, 10787, 10788, 10837, 10840, 10841, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 10037, 26889, 26891, 26894, 26898, 26900, 10051, 26506, 10058, 26907, 26908, 10068, 10069, 26915, 26917, 26919, 26922, 26882, 26881, 26883, 26931, 26933, 26935, 26543, 26939, 10128, 10129, 26942, 26944, 26553, 26556, 26559, 26952, 26956, 10174, 26962, 26964, 10179, 26968, 26970, 10185, 26585, 10192, 26977, 26589, 10202, 10203, 26985, 26987, 26989, 26992, 26882, 26883, 26881, 27001, 26616, 27005, 27007, 10259, 27009, 10263, 27012, 27014, 26632, 26635, 26638, 27021, 27023, 27026, 27030, 10321, 27037, 27039, 27042, 27046, 27048, 10335, 26673, 10342, 27055, 26677, 10352, 10353, 27063, 27065, 27067, 27070, 26880, 10389, 26696, 26699, 27079, 10400, 10401, 27083, 26706, 10406, 27087, 26884, 10425, 26885, 10427, 27094, 27096, 27098, 27100, 10441, 10442, 27104, 26727, 10447, 26729, 10450, 27113, 26881, 26882, 26883, 27122, 10489, 27124, 27126, 26752, 27130, 10501, 27132, 10505, 27135, 27137, 26765, 26768, 26771, 27144, 27146, 27150, 27152, 27154, 27156, 27162, 27164, 27168, 27170, 26884, 10588, 26885, 10590, 27175, 27177, 27179, 27181, 10604, 10605, 27185, 26817, 10610, 26819, 10613, 27194, 26902, 26910, 26920, 26923, 26924, 26927, 26950, 26954, 26958, 26972, 26980, 26990, 27201, 26993, 27203, 26994, 26996, 27024, 27028, 27032, 27033, 27050, 27058, 27068, 27071, 27073, 27088, 27206, 27089, 27092, 27109, 27111, 27110, 27114, 27209, 27116, 27120, 27148, 27158, 27157, 27160, 27159, 27166, 27165, 27173, 27190, 27192, 27191, 27195, 27212, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 27232, 27243, 10106, 10110, 10112, 26953, 26957, 27266, 27277, 10242, 10244, 10246, 27299, 27027, 27031, 27302, 27313, 10388, 10424, 10426, 10477, 10481, 10483, 27147, 27366, 27368, 27370, 27372, 10587, 10589, 27235, 27234, 26896, 27237, 27240, 26904, 10647, 10649, 27242, 27245, 27246, 10655, 27247, 10659, 27248, 10663, 10667, 27255, 27256, 27252, 27253, 27254, 26943, 27258, 27263, 27262, 27261, 10678, 10680, 10683, 27268, 26966, 27271, 27274, 26974, 10692, 10694, 27276, 27279, 27280, 10700, 27281, 10704, 27282, 10709, 10712, 27289, 27291, 27288, 27286, 27287, 27013, 27292, 27297, 27296, 27295, 10723, 10726, 10729, 10732, 27305, 27304, 27044, 27307, 27310, 27052, 10741, 10743, 27312, 27315, 27316, 10749, 27317, 10753, 27318, 10757, 27323, 27322, 27321, 27325, 27327, 10764, 27329, 10768, 10770, 27337, 27336, 27335, 27334, 27339, 27343, 27341, 10780, 10782, 10783, 10785, 27345, 10791, 10792, 27351, 27356, 27353, 27349, 27352, 27354, 27136, 27357, 27362, 27361, 27360, 27363, 10807, 10810, 10811, 10816, 10817, 10820, 10821, 10823, 27380, 27379, 27378, 27377, 27382, 27386, 27384, 10833, 10835, 10836, 10838, 27388, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 27456, 10641, 10642, 10643, 10644, 10645, 10646, 27457, 10650, 10651, 10653, 10656, 10660, 27460, 27458, 27459, 10668, 10669, 10670, 10671, 10672, 10673, 10674, 10675, 10676, 10677, 27461, 27462, 27463, 10687, 10688, 10689, 10690, 10691, 27464, 10695, 10696, 10698, 10701, 10705, 27467, 27466, 27465, 10713, 10714, 10715, 10716, 10717, 10718, 10719, 10720, 10721, 10722, 27468, 27469, 27470, 27471, 10735, 10736, 10737, 10738, 10739, 10740, 27472, 10744, 10745, 10747, 10750, 10754, 27473, 10759, 10760, 10761, 10762, 10763, 10765, 27475, 27474, 10773, 10774, 10775, 10776, 10777, 10778, 10779, 27580, 10786, 27477, 27476, 27478, 27584, 10794, 10795, 10796, 10797, 10798, 10799, 10800, 10801, 10802, 10803, 10804, 10805, 27479, 27481, 27480, 27599, 27482, 27483, 27485, 27484, 10826, 10827, 10828, 10829, 10830, 10831, 10832, 27614, 10839, 27513, 27545, 27569, 27578, 27612, 29, 30, 31, 10640, 27650, 27654, 10648, 27659, 27660, 10664, 10665, 10666, 27665, 27667, 27670, 27672, 10681, 10684, 10686, 27681, 10693, 27686, 27687, 10708, 10710, 10711, 27692, 27694, 27697, 27699, 10724, 10727, 10730, 10734, 27706, 27710, 10742, 27715, 27716, 10758, 27719, 27723, 10771, 10772, 27727, 27729, 27732, 27734, 10789, 10790, 10793, 27740, 27742, 27744, 27746, 27748, 10808, 10812, 10813, 10815, 10819, 10824, 10825, 27760, 27762, 27765, 27767, 27651, 27658, 27657, 10853, 27678, 27685, 27684, 10863, 27707, 27714, 27713, 27721, 10876, 27733, 10882, 27750, 27766, 10894, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 27492, 27493, 27782, 27784, 27786, 27673, 27789, 27790, 27521, 27522, 27530, 27798, 27800, 27700, 27803, 27804, 27805, 27552, 27553, 27812, 27720, 27815, 27818, 27822, 27825, 27749, 27829, 27831, 27600, 27602, 27834, 27837, 10843, 27776, 27781, 27780, 10847, 10848, 10855, 27791, 27795, 27794, 10859, 10860, 10868, 27806, 27811, 27810, 10872, 10873, 10874, 27814, 27730, 27820, 10881, 10888, 27763, 27839, 10893, 27, 28, 29, 30, 31, 27656, 27875, 27668, 27683, 27883, 27695, 27712, 27816, 27738, 27826, 27899, 27601, 27603, 27835, 10844, 10845, 10846, 27909, 27787, 27879, 27878, 10856, 10857, 10858, 27915, 27801, 27888, 27887, 27886, 10869, 10870, 10871, 27921, 27891, 10877, 10878, 10880, 27827, 27898, 10890, 10892, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 27502, 27531, 27823, 27872, 27952, 10849, 10851, 10852, 27880, 27959, 10861, 10864, 10865, 10866, 27889, 27967, 10875, 27970, 27943, 27926, 10883, 10885, 27947, 27948, 27946, 27949, 27930, 27, 28, 29, 30, 31, 10842, 27953, 28000, 28007, 10854, 27960, 28001, 28011, 28013, 10867, 27968, 28016, 10879, 27854, 28002, 10886, 10887, 10889, 10891, 27857, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 27904, 10850, 27843, 27910, 10862, 28040, 27916, 28044, 10884, 28047, 27927, 28050, 28043, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 27950, 28065, 27957, 28068, 27965, 28072, 28074, 10897, 28071, 28075, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 28049, 28100, 28097, 28098, 28096, 10901, 10902, 28099, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 28101, 10896, 10898, 10899, 10900, 10903, 28134, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 10895, 28162, 28164, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 28161, 28166, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 28193, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 28225, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 28165, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31};
bool h_Op[]= {
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1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
#define THREADS_PER_BLOCK 32
#define BLOCKS_PER_GRID 1
#define SIZE_OF_IN 10912
#define SIZE_OF_AC 17440
__device__ void
ac(float *A, const int *B, const int *C, const bool *Op, int n_iter) {
int i= blockDim.x * blockIdx.x + threadIdx.x;
__shared__ float R[886*THREADS_PER_BLOCK];
const int t= THREADS_PER_BLOCK;
__shared__ float final;
final=0;
R[i + 0*t] = A[i + 0*t];
R[i + 1*t] = A[i + 1*t];
R[i + 2*t] = A[i + 2*t];
R[i + 3*t] = A[i + 3*t];
R[i + 4*t] = A[i + 4*t];
R[i + 5*t] = A[i + 5*t];
R[i + 6*t] = A[i + 6*t];
R[i + 7*t] = A[i + 7*t];
R[i + 8*t] = A[i + 8*t];
R[i + 9*t] = A[i + 9*t];
R[i + 10*t] = A[i + 10*t];
R[i + 11*t] = A[i + 11*t];
R[i + 12*t] = A[i + 12*t];
R[i + 13*t] = A[i + 13*t];
R[i + 14*t] = A[i + 14*t];
R[i + 15*t] = A[i + 15*t];
R[i + 16*t] = A[i + 16*t];
R[i + 17*t] = A[i + 17*t];
R[i + 18*t] = A[i + 18*t];
R[i + 19*t] = A[i + 19*t];
R[i + 20*t] = A[i + 20*t];
R[i + 21*t] = A[i + 21*t];
R[i + 22*t] = A[i + 22*t];
R[i + 23*t] = A[i + 23*t];
R[i + 24*t] = A[i + 24*t];
R[i + 25*t] = A[i + 25*t];
R[i + 26*t] = A[i + 26*t];
R[i + 27*t] = A[i + 27*t];
R[i + 28*t] = A[i + 28*t];
R[i + 29*t] = A[i + 29*t];
R[i + 30*t] = A[i + 30*t];
R[i + 31*t] = A[i + 31*t];
R[i + 32*t] = A[i + 32*t];
R[i + 33*t] = A[i + 33*t];
R[i + 34*t] = A[i + 34*t];
R[i + 35*t] = A[i + 35*t];
R[i + 36*t] = A[i + 36*t];
R[i + 37*t] = A[i + 37*t];
R[i + 38*t] = A[i + 38*t];
R[i + 39*t] = A[i + 39*t];
R[i + 40*t] = A[i + 40*t];
R[i + 41*t] = A[i + 41*t];
R[i + 42*t] = A[i + 42*t];
R[i + 43*t] = A[i + 43*t];
R[i + 44*t] = A[i + 44*t];
R[i + 45*t] = A[i + 45*t];
R[i + 46*t] = A[i + 46*t];
R[i + 47*t] = A[i + 47*t];
R[i + 48*t] = A[i + 48*t];
R[i + 49*t] = A[i + 49*t];
R[i + 50*t] = A[i + 50*t];
R[i + 51*t] = A[i + 51*t];
R[i + 52*t] = A[i + 52*t];
R[i + 53*t] = A[i + 53*t];
R[i + 54*t] = A[i + 54*t];
R[i + 55*t] = A[i + 55*t];
R[i + 56*t] = A[i + 56*t];
R[i + 57*t] = A[i + 57*t];
R[i + 58*t] = A[i + 58*t];
R[i + 59*t] = A[i + 59*t];
R[i + 60*t] = A[i + 60*t];
R[i + 61*t] = A[i + 61*t];
R[i + 62*t] = A[i + 62*t];
R[i + 63*t] = A[i + 63*t];
R[i + 64*t] = A[i + 64*t];
R[i + 65*t] = A[i + 65*t];
R[i + 66*t] = A[i + 66*t];
R[i + 67*t] = A[i + 67*t];
R[i + 68*t] = A[i + 68*t];
R[i + 69*t] = A[i + 69*t];
R[i + 70*t] = A[i + 70*t];
R[i + 71*t] = A[i + 71*t];
R[i + 72*t] = A[i + 72*t];
R[i + 73*t] = A[i + 73*t];
R[i + 74*t] = A[i + 74*t];
R[i + 75*t] = A[i + 75*t];
R[i + 76*t] = A[i + 76*t];
R[i + 77*t] = A[i + 77*t];
R[i + 78*t] = A[i + 78*t];
R[i + 79*t] = A[i + 79*t];
R[i + 80*t] = A[i + 80*t];
R[i + 81*t] = A[i + 81*t];
R[i + 82*t] = A[i + 82*t];
R[i + 83*t] = A[i + 83*t];
R[i + 84*t] = A[i + 84*t];
R[i + 85*t] = A[i + 85*t];
R[i + 86*t] = A[i + 86*t];
R[i + 87*t] = A[i + 87*t];
R[i + 88*t] = A[i + 88*t];
R[i + 89*t] = A[i + 89*t];
R[i + 90*t] = A[i + 90*t];
R[i + 91*t] = A[i + 91*t];
R[i + 92*t] = A[i + 92*t];
R[i + 93*t] = A[i + 93*t];
R[i + 94*t] = A[i + 94*t];
R[i + 95*t] = A[i + 95*t];
R[i + 96*t] = A[i + 96*t];
R[i + 97*t] = A[i + 97*t];
R[i + 98*t] = A[i + 98*t];
R[i + 99*t] = A[i + 99*t];
R[i + 100*t] = A[i + 100*t];
R[i + 101*t] = A[i + 101*t];
R[i + 102*t] = A[i + 102*t];
R[i + 103*t] = A[i + 103*t];
R[i + 104*t] = A[i + 104*t];
R[i + 105*t] = A[i + 105*t];
R[i + 106*t] = A[i + 106*t];
R[i + 107*t] = A[i + 107*t];
R[i + 108*t] = A[i + 108*t];
R[i + 109*t] = A[i + 109*t];
R[i + 110*t] = A[i + 110*t];
R[i + 111*t] = A[i + 111*t];
R[i + 112*t] = A[i + 112*t];
R[i + 113*t] = A[i + 113*t];
R[i + 114*t] = A[i + 114*t];
R[i + 115*t] = A[i + 115*t];
R[i + 116*t] = A[i + 116*t];
R[i + 117*t] = A[i + 117*t];
R[i + 118*t] = A[i + 118*t];
R[i + 119*t] = A[i + 119*t];
R[i + 120*t] = A[i + 120*t];
R[i + 121*t] = A[i + 121*t];
R[i + 122*t] = A[i + 122*t];
R[i + 123*t] = A[i + 123*t];
R[i + 124*t] = A[i + 124*t];
R[i + 125*t] = A[i + 125*t];
R[i + 126*t] = A[i + 126*t];
R[i + 127*t] = A[i + 127*t];
R[i + 128*t] = A[i + 128*t];
R[i + 129*t] = A[i + 129*t];
R[i + 130*t] = A[i + 130*t];
R[i + 131*t] = A[i + 131*t];
R[i + 132*t] = A[i + 132*t];
R[i + 133*t] = A[i + 133*t];
R[i + 134*t] = A[i + 134*t];
R[i + 135*t] = A[i + 135*t];
R[i + 136*t] = A[i + 136*t];
R[i + 137*t] = A[i + 137*t];
R[i + 138*t] = A[i + 138*t];
R[i + 139*t] = A[i + 139*t];
R[i + 140*t] = A[i + 140*t];
R[i + 141*t] = A[i + 141*t];
R[i + 142*t] = A[i + 142*t];
R[i + 143*t] = A[i + 143*t];
R[i + 144*t] = A[i + 144*t];
R[i + 145*t] = A[i + 145*t];
R[i + 146*t] = A[i + 146*t];
R[i + 147*t] = A[i + 147*t];
R[i + 148*t] = A[i + 148*t];
R[i + 149*t] = A[i + 149*t];
R[i + 150*t] = A[i + 150*t];
R[i + 151*t] = A[i + 151*t];
R[i + 152*t] = A[i + 152*t];
R[i + 153*t] = A[i + 153*t];
R[i + 154*t] = A[i + 154*t];
R[i + 155*t] = A[i + 155*t];
R[i + 156*t] = A[i + 156*t];
R[i + 157*t] = A[i + 157*t];
R[i + 158*t] = A[i + 158*t];
R[i + 159*t] = A[i + 159*t];
R[i + 160*t] = A[i + 160*t];
R[i + 161*t] = A[i + 161*t];
R[i + 162*t] = A[i + 162*t];
R[i + 163*t] = A[i + 163*t];
R[i + 164*t] = A[i + 164*t];
R[i + 165*t] = A[i + 165*t];
R[i + 166*t] = A[i + 166*t];
R[i + 167*t] = A[i + 167*t];
R[i + 168*t] = A[i + 168*t];
R[i + 169*t] = A[i + 169*t];
R[i + 170*t] = A[i + 170*t];
R[i + 171*t] = A[i + 171*t];
R[i + 172*t] = A[i + 172*t];
R[i + 173*t] = A[i + 173*t];
R[i + 174*t] = A[i + 174*t];
R[i + 175*t] = A[i + 175*t];
R[i + 176*t] = A[i + 176*t];
R[i + 177*t] = A[i + 177*t];
R[i + 178*t] = A[i + 178*t];
R[i + 179*t] = A[i + 179*t];
R[i + 180*t] = A[i + 180*t];
R[i + 181*t] = A[i + 181*t];
R[i + 182*t] = A[i + 182*t];
R[i + 183*t] = A[i + 183*t];
R[i + 184*t] = A[i + 184*t];
R[i + 185*t] = A[i + 185*t];
R[i + 186*t] = A[i + 186*t];
R[i + 187*t] = A[i + 187*t];
R[i + 188*t] = A[i + 188*t];
R[i + 189*t] = A[i + 189*t];
R[i + 190*t] = A[i + 190*t];
R[i + 191*t] = A[i + 191*t];
R[i + 192*t] = A[i + 192*t];
R[i + 193*t] = A[i + 193*t];
R[i + 194*t] = A[i + 194*t];
R[i + 195*t] = A[i + 195*t];
R[i + 196*t] = A[i + 196*t];
R[i + 197*t] = A[i + 197*t];
R[i + 198*t] = A[i + 198*t];
R[i + 199*t] = A[i + 199*t];
R[i + 200*t] = A[i + 200*t];
R[i + 201*t] = A[i + 201*t];
R[i + 202*t] = A[i + 202*t];
R[i + 203*t] = A[i + 203*t];
R[i + 204*t] = A[i + 204*t];
R[i + 205*t] = A[i + 205*t];
R[i + 206*t] = A[i + 206*t];
R[i + 207*t] = A[i + 207*t];
R[i + 208*t] = A[i + 208*t];
R[i + 209*t] = A[i + 209*t];
R[i + 210*t] = A[i + 210*t];
R[i + 211*t] = A[i + 211*t];
R[i + 212*t] = A[i + 212*t];
R[i + 213*t] = A[i + 213*t];
R[i + 214*t] = A[i + 214*t];
R[i + 215*t] = A[i + 215*t];
R[i + 216*t] = A[i + 216*t];
R[i + 217*t] = A[i + 217*t];
R[i + 218*t] = A[i + 218*t];
R[i + 219*t] = A[i + 219*t];
R[i + 220*t] = A[i + 220*t];
R[i + 221*t] = A[i + 221*t];
R[i + 222*t] = A[i + 222*t];
R[i + 223*t] = A[i + 223*t];
R[i + 224*t] = A[i + 224*t];
R[i + 225*t] = A[i + 225*t];
R[i + 226*t] = A[i + 226*t];
R[i + 227*t] = A[i + 227*t];
R[i + 228*t] = A[i + 228*t];
R[i + 229*t] = A[i + 229*t];
R[i + 230*t] = A[i + 230*t];
R[i + 231*t] = A[i + 231*t];
R[i + 232*t] = A[i + 232*t];
R[i + 233*t] = A[i + 233*t];
R[i + 234*t] = A[i + 234*t];
R[i + 235*t] = A[i + 235*t];
R[i + 236*t] = A[i + 236*t];
R[i + 237*t] = A[i + 237*t];
R[i + 238*t] = A[i + 238*t];
R[i + 239*t] = A[i + 239*t];
R[i + 240*t] = A[i + 240*t];
R[i + 241*t] = A[i + 241*t];
R[i + 242*t] = A[i + 242*t];
R[i + 243*t] = A[i + 243*t];
R[i + 244*t] = A[i + 244*t];
R[i + 245*t] = A[i + 245*t];
R[i + 246*t] = A[i + 246*t];
R[i + 247*t] = A[i + 247*t];
R[i + 248*t] = A[i + 248*t];
R[i + 249*t] = A[i + 249*t];
R[i + 250*t] = A[i + 250*t];
R[i + 251*t] = A[i + 251*t];
R[i + 252*t] = A[i + 252*t];
R[i + 253*t] = A[i + 253*t];
R[i + 254*t] = A[i + 254*t];
R[i + 255*t] = A[i + 255*t];
R[i + 256*t] = A[i + 256*t];
R[i + 257*t] = A[i + 257*t];
R[i + 258*t] = A[i + 258*t];
R[i + 259*t] = A[i + 259*t];
R[i + 260*t] = A[i + 260*t];
R[i + 261*t] = A[i + 261*t];
R[i + 262*t] = A[i + 262*t];
R[i + 263*t] = A[i + 263*t];
R[i + 264*t] = A[i + 264*t];
R[i + 265*t] = A[i + 265*t];
R[i + 266*t] = A[i + 266*t];
R[i + 267*t] = A[i + 267*t];
R[i + 268*t] = A[i + 268*t];
R[i + 269*t] = A[i + 269*t];
R[i + 270*t] = A[i + 270*t];
R[i + 271*t] = A[i + 271*t];
R[i + 272*t] = A[i + 272*t];
R[i + 273*t] = A[i + 273*t];
R[i + 274*t] = A[i + 274*t];
R[i + 275*t] = A[i + 275*t];
R[i + 276*t] = A[i + 276*t];
R[i + 277*t] = A[i + 277*t];
R[i + 278*t] = A[i + 278*t];
R[i + 279*t] = A[i + 279*t];
R[i + 280*t] = A[i + 280*t];
R[i + 281*t] = A[i + 281*t];
R[i + 282*t] = A[i + 282*t];
R[i + 283*t] = A[i + 283*t];
R[i + 284*t] = A[i + 284*t];
R[i + 285*t] = A[i + 285*t];
R[i + 286*t] = A[i + 286*t];
R[i + 287*t] = A[i + 287*t];
R[i + 288*t] = A[i + 288*t];
R[i + 289*t] = A[i + 289*t];
R[i + 290*t] = A[i + 290*t];
R[i + 291*t] = A[i + 291*t];
R[i + 292*t] = A[i + 292*t];
R[i + 293*t] = A[i + 293*t];
R[i + 294*t] = A[i + 294*t];
R[i + 295*t] = A[i + 295*t];
R[i + 296*t] = A[i + 296*t];
R[i + 297*t] = A[i + 297*t];
R[i + 298*t] = A[i + 298*t];
R[i + 299*t] = A[i + 299*t];
R[i + 300*t] = A[i + 300*t];
R[i + 301*t] = A[i + 301*t];
R[i + 302*t] = A[i + 302*t];
R[i + 303*t] = A[i + 303*t];
R[i + 304*t] = A[i + 304*t];
R[i + 305*t] = A[i + 305*t];
R[i + 306*t] = A[i + 306*t];
R[i + 307*t] = A[i + 307*t];
R[i + 308*t] = A[i + 308*t];
R[i + 309*t] = A[i + 309*t];
R[i + 310*t] = A[i + 310*t];
R[i + 311*t] = A[i + 311*t];
R[i + 312*t] = A[i + 312*t];
R[i + 313*t] = A[i + 313*t];
R[i + 314*t] = A[i + 314*t];
R[i + 315*t] = A[i + 315*t];
R[i + 316*t] = A[i + 316*t];
R[i + 317*t] = A[i + 317*t];
R[i + 318*t] = A[i + 318*t];
R[i + 319*t] = A[i + 319*t];
R[i + 320*t] = A[i + 320*t];
R[i + 321*t] = A[i + 321*t];
R[i + 322*t] = A[i + 322*t];
R[i + 323*t] = A[i + 323*t];
R[i + 324*t] = A[i + 324*t];
R[i + 325*t] = A[i + 325*t];
R[i + 326*t] = A[i + 326*t];
R[i + 327*t] = A[i + 327*t];
R[i + 328*t] = A[i + 328*t];
R[i + 329*t] = A[i + 329*t];
R[i + 330*t] = A[i + 330*t];
R[i + 331*t] = A[i + 331*t];
R[i + 332*t] = A[i + 332*t];
R[i + 333*t] = A[i + 333*t];
R[i + 334*t] = A[i + 334*t];
R[i + 335*t] = A[i + 335*t];
R[i + 336*t] = A[i + 336*t];
R[i + 337*t] = A[i + 337*t];
R[i + 338*t] = A[i + 338*t];
R[i + 339*t] = A[i + 339*t];
R[i + 340*t] = A[i + 340*t];
__syncthreads();
for (int iter=0; iter< n_iter; iter++) {
R[i + 341*t] = Op[i + 0*t] ? R[B[i + 0*t]] * R[C[i + 0*t]] : R[B[i + 0*t]] + R[C[i + 0*t]];
R[i + 342*t] = Op[i + 1*t] ? R[B[i + 1*t]] * R[C[i + 1*t]] : R[B[i + 1*t]] + R[C[i + 1*t]];
R[i + 343*t] = Op[i + 2*t] ? R[B[i + 2*t]] * R[C[i + 2*t]] : R[B[i + 2*t]] + R[C[i + 2*t]];
R[i + 344*t] = Op[i + 3*t] ? R[B[i + 3*t]] * R[C[i + 3*t]] : R[B[i + 3*t]] + R[C[i + 3*t]];
R[i + 345*t] = Op[i + 4*t] ? R[B[i + 4*t]] * R[C[i + 4*t]] : R[B[i + 4*t]] + R[C[i + 4*t]];
R[i + 346*t] = Op[i + 5*t] ? R[B[i + 5*t]] * R[C[i + 5*t]] : R[B[i + 5*t]] + R[C[i + 5*t]];
R[i + 347*t] = Op[i + 6*t] ? R[B[i + 6*t]] * R[C[i + 6*t]] : R[B[i + 6*t]] + R[C[i + 6*t]];
R[i + 348*t] = Op[i + 7*t] ? R[B[i + 7*t]] * R[C[i + 7*t]] : R[B[i + 7*t]] + R[C[i + 7*t]];
R[i + 349*t] = Op[i + 8*t] ? R[B[i + 8*t]] * R[C[i + 8*t]] : R[B[i + 8*t]] + R[C[i + 8*t]];
R[i + 350*t] = Op[i + 9*t] ? R[B[i + 9*t]] * R[C[i + 9*t]] : R[B[i + 9*t]] + R[C[i + 9*t]];
R[i + 351*t] = Op[i + 10*t] ? R[B[i + 10*t]] * R[C[i + 10*t]] : R[B[i + 10*t]] + R[C[i + 10*t]];
R[i + 352*t] = Op[i + 11*t] ? R[B[i + 11*t]] * R[C[i + 11*t]] : R[B[i + 11*t]] + R[C[i + 11*t]];
R[i + 353*t] = Op[i + 12*t] ? R[B[i + 12*t]] * R[C[i + 12*t]] : R[B[i + 12*t]] + R[C[i + 12*t]];
R[i + 354*t] = Op[i + 13*t] ? R[B[i + 13*t]] * R[C[i + 13*t]] : R[B[i + 13*t]] + R[C[i + 13*t]];
R[i + 355*t] = Op[i + 14*t] ? R[B[i + 14*t]] * R[C[i + 14*t]] : R[B[i + 14*t]] + R[C[i + 14*t]];
R[i + 356*t] = Op[i + 15*t] ? R[B[i + 15*t]] * R[C[i + 15*t]] : R[B[i + 15*t]] + R[C[i + 15*t]];
R[i + 357*t] = Op[i + 16*t] ? R[B[i + 16*t]] * R[C[i + 16*t]] : R[B[i + 16*t]] + R[C[i + 16*t]];
R[i + 358*t] = Op[i + 17*t] ? R[B[i + 17*t]] * R[C[i + 17*t]] : R[B[i + 17*t]] + R[C[i + 17*t]];
R[i + 359*t] = Op[i + 18*t] ? R[B[i + 18*t]] * R[C[i + 18*t]] : R[B[i + 18*t]] + R[C[i + 18*t]];
R[i + 360*t] = Op[i + 19*t] ? R[B[i + 19*t]] * R[C[i + 19*t]] : R[B[i + 19*t]] + R[C[i + 19*t]];
R[i + 361*t] = Op[i + 20*t] ? R[B[i + 20*t]] * R[C[i + 20*t]] : R[B[i + 20*t]] + R[C[i + 20*t]];
R[i + 362*t] = Op[i + 21*t] ? R[B[i + 21*t]] * R[C[i + 21*t]] : R[B[i + 21*t]] + R[C[i + 21*t]];
R[i + 363*t] = Op[i + 22*t] ? R[B[i + 22*t]] * R[C[i + 22*t]] : R[B[i + 22*t]] + R[C[i + 22*t]];
R[i + 364*t] = Op[i + 23*t] ? R[B[i + 23*t]] * R[C[i + 23*t]] : R[B[i + 23*t]] + R[C[i + 23*t]];
R[i + 365*t] = Op[i + 24*t] ? R[B[i + 24*t]] * R[C[i + 24*t]] : R[B[i + 24*t]] + R[C[i + 24*t]];
R[i + 366*t] = Op[i + 25*t] ? R[B[i + 25*t]] * R[C[i + 25*t]] : R[B[i + 25*t]] + R[C[i + 25*t]];
R[i + 367*t] = Op[i + 26*t] ? R[B[i + 26*t]] * R[C[i + 26*t]] : R[B[i + 26*t]] + R[C[i + 26*t]];
R[i + 368*t] = Op[i + 27*t] ? R[B[i + 27*t]] * R[C[i + 27*t]] : R[B[i + 27*t]] + R[C[i + 27*t]];
R[i + 369*t] = Op[i + 28*t] ? R[B[i + 28*t]] * R[C[i + 28*t]] : R[B[i + 28*t]] + R[C[i + 28*t]];
R[i + 370*t] = Op[i + 29*t] ? R[B[i + 29*t]] * R[C[i + 29*t]] : R[B[i + 29*t]] + R[C[i + 29*t]];
R[i + 371*t] = Op[i + 30*t] ? R[B[i + 30*t]] * R[C[i + 30*t]] : R[B[i + 30*t]] + R[C[i + 30*t]];
R[i + 372*t] = Op[i + 31*t] ? R[B[i + 31*t]] * R[C[i + 31*t]] : R[B[i + 31*t]] + R[C[i + 31*t]];
R[i + 373*t] = Op[i + 32*t] ? R[B[i + 32*t]] * R[C[i + 32*t]] : R[B[i + 32*t]] + R[C[i + 32*t]];
R[i + 374*t] = Op[i + 33*t] ? R[B[i + 33*t]] * R[C[i + 33*t]] : R[B[i + 33*t]] + R[C[i + 33*t]];
R[i + 375*t] = Op[i + 34*t] ? R[B[i + 34*t]] * R[C[i + 34*t]] : R[B[i + 34*t]] + R[C[i + 34*t]];
R[i + 376*t] = Op[i + 35*t] ? R[B[i + 35*t]] * R[C[i + 35*t]] : R[B[i + 35*t]] + R[C[i + 35*t]];
R[i + 377*t] = Op[i + 36*t] ? R[B[i + 36*t]] * R[C[i + 36*t]] : R[B[i + 36*t]] + R[C[i + 36*t]];
R[i + 378*t] = Op[i + 37*t] ? R[B[i + 37*t]] * R[C[i + 37*t]] : R[B[i + 37*t]] + R[C[i + 37*t]];
R[i + 379*t] = Op[i + 38*t] ? R[B[i + 38*t]] * R[C[i + 38*t]] : R[B[i + 38*t]] + R[C[i + 38*t]];
R[i + 380*t] = Op[i + 39*t] ? R[B[i + 39*t]] * R[C[i + 39*t]] : R[B[i + 39*t]] + R[C[i + 39*t]];
R[i + 381*t] = Op[i + 40*t] ? R[B[i + 40*t]] * R[C[i + 40*t]] : R[B[i + 40*t]] + R[C[i + 40*t]];
R[i + 382*t] = Op[i + 41*t] ? R[B[i + 41*t]] * R[C[i + 41*t]] : R[B[i + 41*t]] + R[C[i + 41*t]];
R[i + 383*t] = Op[i + 42*t] ? R[B[i + 42*t]] * R[C[i + 42*t]] : R[B[i + 42*t]] + R[C[i + 42*t]];
R[i + 384*t] = Op[i + 43*t] ? R[B[i + 43*t]] * R[C[i + 43*t]] : R[B[i + 43*t]] + R[C[i + 43*t]];
R[i + 385*t] = Op[i + 44*t] ? R[B[i + 44*t]] * R[C[i + 44*t]] : R[B[i + 44*t]] + R[C[i + 44*t]];
R[i + 386*t] = Op[i + 45*t] ? R[B[i + 45*t]] * R[C[i + 45*t]] : R[B[i + 45*t]] + R[C[i + 45*t]];
R[i + 387*t] = Op[i + 46*t] ? R[B[i + 46*t]] * R[C[i + 46*t]] : R[B[i + 46*t]] + R[C[i + 46*t]];
R[i + 388*t] = Op[i + 47*t] ? R[B[i + 47*t]] * R[C[i + 47*t]] : R[B[i + 47*t]] + R[C[i + 47*t]];
R[i + 389*t] = Op[i + 48*t] ? R[B[i + 48*t]] * R[C[i + 48*t]] : R[B[i + 48*t]] + R[C[i + 48*t]];
R[i + 390*t] = Op[i + 49*t] ? R[B[i + 49*t]] * R[C[i + 49*t]] : R[B[i + 49*t]] + R[C[i + 49*t]];
R[i + 391*t] = Op[i + 50*t] ? R[B[i + 50*t]] * R[C[i + 50*t]] : R[B[i + 50*t]] + R[C[i + 50*t]];
R[i + 392*t] = Op[i + 51*t] ? R[B[i + 51*t]] * R[C[i + 51*t]] : R[B[i + 51*t]] + R[C[i + 51*t]];
R[i + 393*t] = Op[i + 52*t] ? R[B[i + 52*t]] * R[C[i + 52*t]] : R[B[i + 52*t]] + R[C[i + 52*t]];
R[i + 394*t] = Op[i + 53*t] ? R[B[i + 53*t]] * R[C[i + 53*t]] : R[B[i + 53*t]] + R[C[i + 53*t]];
R[i + 395*t] = Op[i + 54*t] ? R[B[i + 54*t]] * R[C[i + 54*t]] : R[B[i + 54*t]] + R[C[i + 54*t]];
R[i + 396*t] = Op[i + 55*t] ? R[B[i + 55*t]] * R[C[i + 55*t]] : R[B[i + 55*t]] + R[C[i + 55*t]];
R[i + 397*t] = Op[i + 56*t] ? R[B[i + 56*t]] * R[C[i + 56*t]] : R[B[i + 56*t]] + R[C[i + 56*t]];
R[i + 398*t] = Op[i + 57*t] ? R[B[i + 57*t]] * R[C[i + 57*t]] : R[B[i + 57*t]] + R[C[i + 57*t]];
R[i + 399*t] = Op[i + 58*t] ? R[B[i + 58*t]] * R[C[i + 58*t]] : R[B[i + 58*t]] + R[C[i + 58*t]];
R[i + 400*t] = Op[i + 59*t] ? R[B[i + 59*t]] * R[C[i + 59*t]] : R[B[i + 59*t]] + R[C[i + 59*t]];
R[i + 401*t] = Op[i + 60*t] ? R[B[i + 60*t]] * R[C[i + 60*t]] : R[B[i + 60*t]] + R[C[i + 60*t]];
R[i + 402*t] = Op[i + 61*t] ? R[B[i + 61*t]] * R[C[i + 61*t]] : R[B[i + 61*t]] + R[C[i + 61*t]];
R[i + 403*t] = Op[i + 62*t] ? R[B[i + 62*t]] * R[C[i + 62*t]] : R[B[i + 62*t]] + R[C[i + 62*t]];
R[i + 404*t] = Op[i + 63*t] ? R[B[i + 63*t]] * R[C[i + 63*t]] : R[B[i + 63*t]] + R[C[i + 63*t]];
R[i + 405*t] = Op[i + 64*t] ? R[B[i + 64*t]] * R[C[i + 64*t]] : R[B[i + 64*t]] + R[C[i + 64*t]];
R[i + 406*t] = Op[i + 65*t] ? R[B[i + 65*t]] * R[C[i + 65*t]] : R[B[i + 65*t]] + R[C[i + 65*t]];
R[i + 407*t] = Op[i + 66*t] ? R[B[i + 66*t]] * R[C[i + 66*t]] : R[B[i + 66*t]] + R[C[i + 66*t]];
R[i + 408*t] = Op[i + 67*t] ? R[B[i + 67*t]] * R[C[i + 67*t]] : R[B[i + 67*t]] + R[C[i + 67*t]];
R[i + 409*t] = Op[i + 68*t] ? R[B[i + 68*t]] * R[C[i + 68*t]] : R[B[i + 68*t]] + R[C[i + 68*t]];
R[i + 410*t] = Op[i + 69*t] ? R[B[i + 69*t]] * R[C[i + 69*t]] : R[B[i + 69*t]] + R[C[i + 69*t]];
R[i + 411*t] = Op[i + 70*t] ? R[B[i + 70*t]] * R[C[i + 70*t]] : R[B[i + 70*t]] + R[C[i + 70*t]];
R[i + 412*t] = Op[i + 71*t] ? R[B[i + 71*t]] * R[C[i + 71*t]] : R[B[i + 71*t]] + R[C[i + 71*t]];
R[i + 413*t] = Op[i + 72*t] ? R[B[i + 72*t]] * R[C[i + 72*t]] : R[B[i + 72*t]] + R[C[i + 72*t]];
R[i + 414*t] = Op[i + 73*t] ? R[B[i + 73*t]] * R[C[i + 73*t]] : R[B[i + 73*t]] + R[C[i + 73*t]];
R[i + 415*t] = Op[i + 74*t] ? R[B[i + 74*t]] * R[C[i + 74*t]] : R[B[i + 74*t]] + R[C[i + 74*t]];
R[i + 416*t] = Op[i + 75*t] ? R[B[i + 75*t]] * R[C[i + 75*t]] : R[B[i + 75*t]] + R[C[i + 75*t]];
R[i + 417*t] = Op[i + 76*t] ? R[B[i + 76*t]] * R[C[i + 76*t]] : R[B[i + 76*t]] + R[C[i + 76*t]];
R[i + 418*t] = Op[i + 77*t] ? R[B[i + 77*t]] * R[C[i + 77*t]] : R[B[i + 77*t]] + R[C[i + 77*t]];
R[i + 419*t] = Op[i + 78*t] ? R[B[i + 78*t]] * R[C[i + 78*t]] : R[B[i + 78*t]] + R[C[i + 78*t]];
R[i + 420*t] = Op[i + 79*t] ? R[B[i + 79*t]] * R[C[i + 79*t]] : R[B[i + 79*t]] + R[C[i + 79*t]];
R[i + 421*t] = Op[i + 80*t] ? R[B[i + 80*t]] * R[C[i + 80*t]] : R[B[i + 80*t]] + R[C[i + 80*t]];
R[i + 422*t] = Op[i + 81*t] ? R[B[i + 81*t]] * R[C[i + 81*t]] : R[B[i + 81*t]] + R[C[i + 81*t]];
R[i + 423*t] = Op[i + 82*t] ? R[B[i + 82*t]] * R[C[i + 82*t]] : R[B[i + 82*t]] + R[C[i + 82*t]];
R[i + 424*t] = Op[i + 83*t] ? R[B[i + 83*t]] * R[C[i + 83*t]] : R[B[i + 83*t]] + R[C[i + 83*t]];
R[i + 425*t] = Op[i + 84*t] ? R[B[i + 84*t]] * R[C[i + 84*t]] : R[B[i + 84*t]] + R[C[i + 84*t]];
R[i + 426*t] = Op[i + 85*t] ? R[B[i + 85*t]] * R[C[i + 85*t]] : R[B[i + 85*t]] + R[C[i + 85*t]];
R[i + 427*t] = Op[i + 86*t] ? R[B[i + 86*t]] * R[C[i + 86*t]] : R[B[i + 86*t]] + R[C[i + 86*t]];
R[i + 428*t] = Op[i + 87*t] ? R[B[i + 87*t]] * R[C[i + 87*t]] : R[B[i + 87*t]] + R[C[i + 87*t]];
R[i + 429*t] = Op[i + 88*t] ? R[B[i + 88*t]] * R[C[i + 88*t]] : R[B[i + 88*t]] + R[C[i + 88*t]];
R[i + 430*t] = Op[i + 89*t] ? R[B[i + 89*t]] * R[C[i + 89*t]] : R[B[i + 89*t]] + R[C[i + 89*t]];
R[i + 431*t] = Op[i + 90*t] ? R[B[i + 90*t]] * R[C[i + 90*t]] : R[B[i + 90*t]] + R[C[i + 90*t]];
R[i + 432*t] = Op[i + 91*t] ? R[B[i + 91*t]] * R[C[i + 91*t]] : R[B[i + 91*t]] + R[C[i + 91*t]];
R[i + 433*t] = Op[i + 92*t] ? R[B[i + 92*t]] * R[C[i + 92*t]] : R[B[i + 92*t]] + R[C[i + 92*t]];
R[i + 434*t] = Op[i + 93*t] ? R[B[i + 93*t]] * R[C[i + 93*t]] : R[B[i + 93*t]] + R[C[i + 93*t]];
R[i + 435*t] = Op[i + 94*t] ? R[B[i + 94*t]] * R[C[i + 94*t]] : R[B[i + 94*t]] + R[C[i + 94*t]];
R[i + 436*t] = Op[i + 95*t] ? R[B[i + 95*t]] * R[C[i + 95*t]] : R[B[i + 95*t]] + R[C[i + 95*t]];
R[i + 437*t] = Op[i + 96*t] ? R[B[i + 96*t]] * R[C[i + 96*t]] : R[B[i + 96*t]] + R[C[i + 96*t]];
R[i + 438*t] = Op[i + 97*t] ? R[B[i + 97*t]] * R[C[i + 97*t]] : R[B[i + 97*t]] + R[C[i + 97*t]];
R[i + 439*t] = Op[i + 98*t] ? R[B[i + 98*t]] * R[C[i + 98*t]] : R[B[i + 98*t]] + R[C[i + 98*t]];
R[i + 440*t] = Op[i + 99*t] ? R[B[i + 99*t]] * R[C[i + 99*t]] : R[B[i + 99*t]] + R[C[i + 99*t]];
R[i + 441*t] = Op[i + 100*t] ? R[B[i + 100*t]] * R[C[i + 100*t]] : R[B[i + 100*t]] + R[C[i + 100*t]];
R[i + 442*t] = Op[i + 101*t] ? R[B[i + 101*t]] * R[C[i + 101*t]] : R[B[i + 101*t]] + R[C[i + 101*t]];
R[i + 443*t] = Op[i + 102*t] ? R[B[i + 102*t]] * R[C[i + 102*t]] : R[B[i + 102*t]] + R[C[i + 102*t]];
R[i + 444*t] = Op[i + 103*t] ? R[B[i + 103*t]] * R[C[i + 103*t]] : R[B[i + 103*t]] + R[C[i + 103*t]];
R[i + 445*t] = Op[i + 104*t] ? R[B[i + 104*t]] * R[C[i + 104*t]] : R[B[i + 104*t]] + R[C[i + 104*t]];
R[i + 446*t] = Op[i + 105*t] ? R[B[i + 105*t]] * R[C[i + 105*t]] : R[B[i + 105*t]] + R[C[i + 105*t]];
R[i + 447*t] = Op[i + 106*t] ? R[B[i + 106*t]] * R[C[i + 106*t]] : R[B[i + 106*t]] + R[C[i + 106*t]];
__syncthreads();
R[i + 448*t] = Op[i + 107*t] ? R[B[i + 107*t]] * R[C[i + 107*t]] : R[B[i + 107*t]] + R[C[i + 107*t]];
R[i + 449*t] = Op[i + 108*t] ? R[B[i + 108*t]] * R[C[i + 108*t]] : R[B[i + 108*t]] + R[C[i + 108*t]];
R[i + 450*t] = Op[i + 109*t] ? R[B[i + 109*t]] * R[C[i + 109*t]] : R[B[i + 109*t]] + R[C[i + 109*t]];
R[i + 451*t] = Op[i + 110*t] ? R[B[i + 110*t]] * R[C[i + 110*t]] : R[B[i + 110*t]] + R[C[i + 110*t]];
R[i + 452*t] = Op[i + 111*t] ? R[B[i + 111*t]] * R[C[i + 111*t]] : R[B[i + 111*t]] + R[C[i + 111*t]];
R[i + 453*t] = Op[i + 112*t] ? R[B[i + 112*t]] * R[C[i + 112*t]] : R[B[i + 112*t]] + R[C[i + 112*t]];
R[i + 454*t] = Op[i + 113*t] ? R[B[i + 113*t]] * R[C[i + 113*t]] : R[B[i + 113*t]] + R[C[i + 113*t]];
R[i + 455*t] = Op[i + 114*t] ? R[B[i + 114*t]] * R[C[i + 114*t]] : R[B[i + 114*t]] + R[C[i + 114*t]];
R[i + 456*t] = Op[i + 115*t] ? R[B[i + 115*t]] * R[C[i + 115*t]] : R[B[i + 115*t]] + R[C[i + 115*t]];
R[i + 457*t] = Op[i + 116*t] ? R[B[i + 116*t]] * R[C[i + 116*t]] : R[B[i + 116*t]] + R[C[i + 116*t]];
R[i + 458*t] = Op[i + 117*t] ? R[B[i + 117*t]] * R[C[i + 117*t]] : R[B[i + 117*t]] + R[C[i + 117*t]];
R[i + 459*t] = Op[i + 118*t] ? R[B[i + 118*t]] * R[C[i + 118*t]] : R[B[i + 118*t]] + R[C[i + 118*t]];
R[i + 460*t] = Op[i + 119*t] ? R[B[i + 119*t]] * R[C[i + 119*t]] : R[B[i + 119*t]] + R[C[i + 119*t]];
R[i + 461*t] = Op[i + 120*t] ? R[B[i + 120*t]] * R[C[i + 120*t]] : R[B[i + 120*t]] + R[C[i + 120*t]];
R[i + 462*t] = Op[i + 121*t] ? R[B[i + 121*t]] * R[C[i + 121*t]] : R[B[i + 121*t]] + R[C[i + 121*t]];
R[i + 463*t] = Op[i + 122*t] ? R[B[i + 122*t]] * R[C[i + 122*t]] : R[B[i + 122*t]] + R[C[i + 122*t]];
R[i + 464*t] = Op[i + 123*t] ? R[B[i + 123*t]] * R[C[i + 123*t]] : R[B[i + 123*t]] + R[C[i + 123*t]];
R[i + 465*t] = Op[i + 124*t] ? R[B[i + 124*t]] * R[C[i + 124*t]] : R[B[i + 124*t]] + R[C[i + 124*t]];
R[i + 466*t] = Op[i + 125*t] ? R[B[i + 125*t]] * R[C[i + 125*t]] : R[B[i + 125*t]] + R[C[i + 125*t]];
R[i + 467*t] = Op[i + 126*t] ? R[B[i + 126*t]] * R[C[i + 126*t]] : R[B[i + 126*t]] + R[C[i + 126*t]];
R[i + 468*t] = Op[i + 127*t] ? R[B[i + 127*t]] * R[C[i + 127*t]] : R[B[i + 127*t]] + R[C[i + 127*t]];
R[i + 469*t] = Op[i + 128*t] ? R[B[i + 128*t]] * R[C[i + 128*t]] : R[B[i + 128*t]] + R[C[i + 128*t]];
R[i + 470*t] = Op[i + 129*t] ? R[B[i + 129*t]] * R[C[i + 129*t]] : R[B[i + 129*t]] + R[C[i + 129*t]];
R[i + 471*t] = Op[i + 130*t] ? R[B[i + 130*t]] * R[C[i + 130*t]] : R[B[i + 130*t]] + R[C[i + 130*t]];
R[i + 472*t] = Op[i + 131*t] ? R[B[i + 131*t]] * R[C[i + 131*t]] : R[B[i + 131*t]] + R[C[i + 131*t]];
R[i + 473*t] = Op[i + 132*t] ? R[B[i + 132*t]] * R[C[i + 132*t]] : R[B[i + 132*t]] + R[C[i + 132*t]];
R[i + 474*t] = Op[i + 133*t] ? R[B[i + 133*t]] * R[C[i + 133*t]] : R[B[i + 133*t]] + R[C[i + 133*t]];
R[i + 475*t] = Op[i + 134*t] ? R[B[i + 134*t]] * R[C[i + 134*t]] : R[B[i + 134*t]] + R[C[i + 134*t]];
R[i + 476*t] = Op[i + 135*t] ? R[B[i + 135*t]] * R[C[i + 135*t]] : R[B[i + 135*t]] + R[C[i + 135*t]];
R[i + 477*t] = Op[i + 136*t] ? R[B[i + 136*t]] * R[C[i + 136*t]] : R[B[i + 136*t]] + R[C[i + 136*t]];
R[i + 478*t] = Op[i + 137*t] ? R[B[i + 137*t]] * R[C[i + 137*t]] : R[B[i + 137*t]] + R[C[i + 137*t]];
R[i + 479*t] = Op[i + 138*t] ? R[B[i + 138*t]] * R[C[i + 138*t]] : R[B[i + 138*t]] + R[C[i + 138*t]];
R[i + 480*t] = Op[i + 139*t] ? R[B[i + 139*t]] * R[C[i + 139*t]] : R[B[i + 139*t]] + R[C[i + 139*t]];
R[i + 481*t] = Op[i + 140*t] ? R[B[i + 140*t]] * R[C[i + 140*t]] : R[B[i + 140*t]] + R[C[i + 140*t]];
R[i + 482*t] = Op[i + 141*t] ? R[B[i + 141*t]] * R[C[i + 141*t]] : R[B[i + 141*t]] + R[C[i + 141*t]];
R[i + 483*t] = Op[i + 142*t] ? R[B[i + 142*t]] * R[C[i + 142*t]] : R[B[i + 142*t]] + R[C[i + 142*t]];
R[i + 484*t] = Op[i + 143*t] ? R[B[i + 143*t]] * R[C[i + 143*t]] : R[B[i + 143*t]] + R[C[i + 143*t]];
R[i + 485*t] = Op[i + 144*t] ? R[B[i + 144*t]] * R[C[i + 144*t]] : R[B[i + 144*t]] + R[C[i + 144*t]];
R[i + 486*t] = Op[i + 145*t] ? R[B[i + 145*t]] * R[C[i + 145*t]] : R[B[i + 145*t]] + R[C[i + 145*t]];
R[i + 487*t] = Op[i + 146*t] ? R[B[i + 146*t]] * R[C[i + 146*t]] : R[B[i + 146*t]] + R[C[i + 146*t]];
R[i + 488*t] = Op[i + 147*t] ? R[B[i + 147*t]] * R[C[i + 147*t]] : R[B[i + 147*t]] + R[C[i + 147*t]];
R[i + 489*t] = Op[i + 148*t] ? R[B[i + 148*t]] * R[C[i + 148*t]] : R[B[i + 148*t]] + R[C[i + 148*t]];
R[i + 490*t] = Op[i + 149*t] ? R[B[i + 149*t]] * R[C[i + 149*t]] : R[B[i + 149*t]] + R[C[i + 149*t]];
R[i + 491*t] = Op[i + 150*t] ? R[B[i + 150*t]] * R[C[i + 150*t]] : R[B[i + 150*t]] + R[C[i + 150*t]];
R[i + 492*t] = Op[i + 151*t] ? R[B[i + 151*t]] * R[C[i + 151*t]] : R[B[i + 151*t]] + R[C[i + 151*t]];
R[i + 493*t] = Op[i + 152*t] ? R[B[i + 152*t]] * R[C[i + 152*t]] : R[B[i + 152*t]] + R[C[i + 152*t]];
R[i + 494*t] = Op[i + 153*t] ? R[B[i + 153*t]] * R[C[i + 153*t]] : R[B[i + 153*t]] + R[C[i + 153*t]];
R[i + 495*t] = Op[i + 154*t] ? R[B[i + 154*t]] * R[C[i + 154*t]] : R[B[i + 154*t]] + R[C[i + 154*t]];
R[i + 496*t] = Op[i + 155*t] ? R[B[i + 155*t]] * R[C[i + 155*t]] : R[B[i + 155*t]] + R[C[i + 155*t]];
R[i + 497*t] = Op[i + 156*t] ? R[B[i + 156*t]] * R[C[i + 156*t]] : R[B[i + 156*t]] + R[C[i + 156*t]];
R[i + 498*t] = Op[i + 157*t] ? R[B[i + 157*t]] * R[C[i + 157*t]] : R[B[i + 157*t]] + R[C[i + 157*t]];
R[i + 499*t] = Op[i + 158*t] ? R[B[i + 158*t]] * R[C[i + 158*t]] : R[B[i + 158*t]] + R[C[i + 158*t]];
R[i + 500*t] = Op[i + 159*t] ? R[B[i + 159*t]] * R[C[i + 159*t]] : R[B[i + 159*t]] + R[C[i + 159*t]];
R[i + 501*t] = Op[i + 160*t] ? R[B[i + 160*t]] * R[C[i + 160*t]] : R[B[i + 160*t]] + R[C[i + 160*t]];
R[i + 502*t] = Op[i + 161*t] ? R[B[i + 161*t]] * R[C[i + 161*t]] : R[B[i + 161*t]] + R[C[i + 161*t]];
R[i + 503*t] = Op[i + 162*t] ? R[B[i + 162*t]] * R[C[i + 162*t]] : R[B[i + 162*t]] + R[C[i + 162*t]];
R[i + 504*t] = Op[i + 163*t] ? R[B[i + 163*t]] * R[C[i + 163*t]] : R[B[i + 163*t]] + R[C[i + 163*t]];
R[i + 505*t] = Op[i + 164*t] ? R[B[i + 164*t]] * R[C[i + 164*t]] : R[B[i + 164*t]] + R[C[i + 164*t]];
R[i + 506*t] = Op[i + 165*t] ? R[B[i + 165*t]] * R[C[i + 165*t]] : R[B[i + 165*t]] + R[C[i + 165*t]];
R[i + 507*t] = Op[i + 166*t] ? R[B[i + 166*t]] * R[C[i + 166*t]] : R[B[i + 166*t]] + R[C[i + 166*t]];
R[i + 508*t] = Op[i + 167*t] ? R[B[i + 167*t]] * R[C[i + 167*t]] : R[B[i + 167*t]] + R[C[i + 167*t]];
R[i + 509*t] = Op[i + 168*t] ? R[B[i + 168*t]] * R[C[i + 168*t]] : R[B[i + 168*t]] + R[C[i + 168*t]];
R[i + 510*t] = Op[i + 169*t] ? R[B[i + 169*t]] * R[C[i + 169*t]] : R[B[i + 169*t]] + R[C[i + 169*t]];
R[i + 511*t] = Op[i + 170*t] ? R[B[i + 170*t]] * R[C[i + 170*t]] : R[B[i + 170*t]] + R[C[i + 170*t]];
R[i + 512*t] = Op[i + 171*t] ? R[B[i + 171*t]] * R[C[i + 171*t]] : R[B[i + 171*t]] + R[C[i + 171*t]];
__syncthreads();
R[i + 513*t] = Op[i + 172*t] ? R[B[i + 172*t]] * R[C[i + 172*t]] : R[B[i + 172*t]] + R[C[i + 172*t]];
R[i + 514*t] = Op[i + 173*t] ? R[B[i + 173*t]] * R[C[i + 173*t]] : R[B[i + 173*t]] + R[C[i + 173*t]];
R[i + 515*t] = Op[i + 174*t] ? R[B[i + 174*t]] * R[C[i + 174*t]] : R[B[i + 174*t]] + R[C[i + 174*t]];
R[i + 516*t] = Op[i + 175*t] ? R[B[i + 175*t]] * R[C[i + 175*t]] : R[B[i + 175*t]] + R[C[i + 175*t]];
R[i + 517*t] = Op[i + 176*t] ? R[B[i + 176*t]] * R[C[i + 176*t]] : R[B[i + 176*t]] + R[C[i + 176*t]];
R[i + 518*t] = Op[i + 177*t] ? R[B[i + 177*t]] * R[C[i + 177*t]] : R[B[i + 177*t]] + R[C[i + 177*t]];
R[i + 519*t] = Op[i + 178*t] ? R[B[i + 178*t]] * R[C[i + 178*t]] : R[B[i + 178*t]] + R[C[i + 178*t]];
R[i + 520*t] = Op[i + 179*t] ? R[B[i + 179*t]] * R[C[i + 179*t]] : R[B[i + 179*t]] + R[C[i + 179*t]];
R[i + 521*t] = Op[i + 180*t] ? R[B[i + 180*t]] * R[C[i + 180*t]] : R[B[i + 180*t]] + R[C[i + 180*t]];
R[i + 522*t] = Op[i + 181*t] ? R[B[i + 181*t]] * R[C[i + 181*t]] : R[B[i + 181*t]] + R[C[i + 181*t]];
R[i + 523*t] = Op[i + 182*t] ? R[B[i + 182*t]] * R[C[i + 182*t]] : R[B[i + 182*t]] + R[C[i + 182*t]];
R[i + 524*t] = Op[i + 183*t] ? R[B[i + 183*t]] * R[C[i + 183*t]] : R[B[i + 183*t]] + R[C[i + 183*t]];
R[i + 525*t] = Op[i + 184*t] ? R[B[i + 184*t]] * R[C[i + 184*t]] : R[B[i + 184*t]] + R[C[i + 184*t]];
R[i + 526*t] = Op[i + 185*t] ? R[B[i + 185*t]] * R[C[i + 185*t]] : R[B[i + 185*t]] + R[C[i + 185*t]];
R[i + 527*t] = Op[i + 186*t] ? R[B[i + 186*t]] * R[C[i + 186*t]] : R[B[i + 186*t]] + R[C[i + 186*t]];
R[i + 528*t] = Op[i + 187*t] ? R[B[i + 187*t]] * R[C[i + 187*t]] : R[B[i + 187*t]] + R[C[i + 187*t]];
R[i + 529*t] = Op[i + 188*t] ? R[B[i + 188*t]] * R[C[i + 188*t]] : R[B[i + 188*t]] + R[C[i + 188*t]];
R[i + 530*t] = Op[i + 189*t] ? R[B[i + 189*t]] * R[C[i + 189*t]] : R[B[i + 189*t]] + R[C[i + 189*t]];
R[i + 531*t] = Op[i + 190*t] ? R[B[i + 190*t]] * R[C[i + 190*t]] : R[B[i + 190*t]] + R[C[i + 190*t]];
R[i + 532*t] = Op[i + 191*t] ? R[B[i + 191*t]] * R[C[i + 191*t]] : R[B[i + 191*t]] + R[C[i + 191*t]];
R[i + 533*t] = Op[i + 192*t] ? R[B[i + 192*t]] * R[C[i + 192*t]] : R[B[i + 192*t]] + R[C[i + 192*t]];
R[i + 534*t] = Op[i + 193*t] ? R[B[i + 193*t]] * R[C[i + 193*t]] : R[B[i + 193*t]] + R[C[i + 193*t]];
R[i + 535*t] = Op[i + 194*t] ? R[B[i + 194*t]] * R[C[i + 194*t]] : R[B[i + 194*t]] + R[C[i + 194*t]];
R[i + 536*t] = Op[i + 195*t] ? R[B[i + 195*t]] * R[C[i + 195*t]] : R[B[i + 195*t]] + R[C[i + 195*t]];
R[i + 537*t] = Op[i + 196*t] ? R[B[i + 196*t]] * R[C[i + 196*t]] : R[B[i + 196*t]] + R[C[i + 196*t]];
R[i + 538*t] = Op[i + 197*t] ? R[B[i + 197*t]] * R[C[i + 197*t]] : R[B[i + 197*t]] + R[C[i + 197*t]];
R[i + 539*t] = Op[i + 198*t] ? R[B[i + 198*t]] * R[C[i + 198*t]] : R[B[i + 198*t]] + R[C[i + 198*t]];
R[i + 540*t] = Op[i + 199*t] ? R[B[i + 199*t]] * R[C[i + 199*t]] : R[B[i + 199*t]] + R[C[i + 199*t]];
R[i + 541*t] = Op[i + 200*t] ? R[B[i + 200*t]] * R[C[i + 200*t]] : R[B[i + 200*t]] + R[C[i + 200*t]];
R[i + 542*t] = Op[i + 201*t] ? R[B[i + 201*t]] * R[C[i + 201*t]] : R[B[i + 201*t]] + R[C[i + 201*t]];
R[i + 543*t] = Op[i + 202*t] ? R[B[i + 202*t]] * R[C[i + 202*t]] : R[B[i + 202*t]] + R[C[i + 202*t]];
R[i + 544*t] = Op[i + 203*t] ? R[B[i + 203*t]] * R[C[i + 203*t]] : R[B[i + 203*t]] + R[C[i + 203*t]];
R[i + 545*t] = Op[i + 204*t] ? R[B[i + 204*t]] * R[C[i + 204*t]] : R[B[i + 204*t]] + R[C[i + 204*t]];
R[i + 546*t] = Op[i + 205*t] ? R[B[i + 205*t]] * R[C[i + 205*t]] : R[B[i + 205*t]] + R[C[i + 205*t]];
R[i + 547*t] = Op[i + 206*t] ? R[B[i + 206*t]] * R[C[i + 206*t]] : R[B[i + 206*t]] + R[C[i + 206*t]];
R[i + 548*t] = Op[i + 207*t] ? R[B[i + 207*t]] * R[C[i + 207*t]] : R[B[i + 207*t]] + R[C[i + 207*t]];
R[i + 549*t] = Op[i + 208*t] ? R[B[i + 208*t]] * R[C[i + 208*t]] : R[B[i + 208*t]] + R[C[i + 208*t]];
R[i + 550*t] = Op[i + 209*t] ? R[B[i + 209*t]] * R[C[i + 209*t]] : R[B[i + 209*t]] + R[C[i + 209*t]];
R[i + 551*t] = Op[i + 210*t] ? R[B[i + 210*t]] * R[C[i + 210*t]] : R[B[i + 210*t]] + R[C[i + 210*t]];
R[i + 552*t] = Op[i + 211*t] ? R[B[i + 211*t]] * R[C[i + 211*t]] : R[B[i + 211*t]] + R[C[i + 211*t]];
R[i + 553*t] = Op[i + 212*t] ? R[B[i + 212*t]] * R[C[i + 212*t]] : R[B[i + 212*t]] + R[C[i + 212*t]];
R[i + 554*t] = Op[i + 213*t] ? R[B[i + 213*t]] * R[C[i + 213*t]] : R[B[i + 213*t]] + R[C[i + 213*t]];
R[i + 555*t] = Op[i + 214*t] ? R[B[i + 214*t]] * R[C[i + 214*t]] : R[B[i + 214*t]] + R[C[i + 214*t]];
R[i + 556*t] = Op[i + 215*t] ? R[B[i + 215*t]] * R[C[i + 215*t]] : R[B[i + 215*t]] + R[C[i + 215*t]];
R[i + 557*t] = Op[i + 216*t] ? R[B[i + 216*t]] * R[C[i + 216*t]] : R[B[i + 216*t]] + R[C[i + 216*t]];
R[i + 558*t] = Op[i + 217*t] ? R[B[i + 217*t]] * R[C[i + 217*t]] : R[B[i + 217*t]] + R[C[i + 217*t]];
R[i + 559*t] = Op[i + 218*t] ? R[B[i + 218*t]] * R[C[i + 218*t]] : R[B[i + 218*t]] + R[C[i + 218*t]];
R[i + 560*t] = Op[i + 219*t] ? R[B[i + 219*t]] * R[C[i + 219*t]] : R[B[i + 219*t]] + R[C[i + 219*t]];
R[i + 561*t] = Op[i + 220*t] ? R[B[i + 220*t]] * R[C[i + 220*t]] : R[B[i + 220*t]] + R[C[i + 220*t]];
R[i + 562*t] = Op[i + 221*t] ? R[B[i + 221*t]] * R[C[i + 221*t]] : R[B[i + 221*t]] + R[C[i + 221*t]];
R[i + 563*t] = Op[i + 222*t] ? R[B[i + 222*t]] * R[C[i + 222*t]] : R[B[i + 222*t]] + R[C[i + 222*t]];
R[i + 564*t] = Op[i + 223*t] ? R[B[i + 223*t]] * R[C[i + 223*t]] : R[B[i + 223*t]] + R[C[i + 223*t]];
R[i + 565*t] = Op[i + 224*t] ? R[B[i + 224*t]] * R[C[i + 224*t]] : R[B[i + 224*t]] + R[C[i + 224*t]];
R[i + 566*t] = Op[i + 225*t] ? R[B[i + 225*t]] * R[C[i + 225*t]] : R[B[i + 225*t]] + R[C[i + 225*t]];
R[i + 567*t] = Op[i + 226*t] ? R[B[i + 226*t]] * R[C[i + 226*t]] : R[B[i + 226*t]] + R[C[i + 226*t]];
R[i + 568*t] = Op[i + 227*t] ? R[B[i + 227*t]] * R[C[i + 227*t]] : R[B[i + 227*t]] + R[C[i + 227*t]];
R[i + 569*t] = Op[i + 228*t] ? R[B[i + 228*t]] * R[C[i + 228*t]] : R[B[i + 228*t]] + R[C[i + 228*t]];
R[i + 570*t] = Op[i + 229*t] ? R[B[i + 229*t]] * R[C[i + 229*t]] : R[B[i + 229*t]] + R[C[i + 229*t]];
R[i + 571*t] = Op[i + 230*t] ? R[B[i + 230*t]] * R[C[i + 230*t]] : R[B[i + 230*t]] + R[C[i + 230*t]];
R[i + 572*t] = Op[i + 231*t] ? R[B[i + 231*t]] * R[C[i + 231*t]] : R[B[i + 231*t]] + R[C[i + 231*t]];
R[i + 573*t] = Op[i + 232*t] ? R[B[i + 232*t]] * R[C[i + 232*t]] : R[B[i + 232*t]] + R[C[i + 232*t]];
R[i + 574*t] = Op[i + 233*t] ? R[B[i + 233*t]] * R[C[i + 233*t]] : R[B[i + 233*t]] + R[C[i + 233*t]];
__syncthreads();
R[i + 575*t] = Op[i + 234*t] ? R[B[i + 234*t]] * R[C[i + 234*t]] : R[B[i + 234*t]] + R[C[i + 234*t]];
R[i + 576*t] = Op[i + 235*t] ? R[B[i + 235*t]] * R[C[i + 235*t]] : R[B[i + 235*t]] + R[C[i + 235*t]];
R[i + 577*t] = Op[i + 236*t] ? R[B[i + 236*t]] * R[C[i + 236*t]] : R[B[i + 236*t]] + R[C[i + 236*t]];
R[i + 578*t] = Op[i + 237*t] ? R[B[i + 237*t]] * R[C[i + 237*t]] : R[B[i + 237*t]] + R[C[i + 237*t]];
R[i + 579*t] = Op[i + 238*t] ? R[B[i + 238*t]] * R[C[i + 238*t]] : R[B[i + 238*t]] + R[C[i + 238*t]];
R[i + 580*t] = Op[i + 239*t] ? R[B[i + 239*t]] * R[C[i + 239*t]] : R[B[i + 239*t]] + R[C[i + 239*t]];
R[i + 581*t] = Op[i + 240*t] ? R[B[i + 240*t]] * R[C[i + 240*t]] : R[B[i + 240*t]] + R[C[i + 240*t]];
R[i + 582*t] = Op[i + 241*t] ? R[B[i + 241*t]] * R[C[i + 241*t]] : R[B[i + 241*t]] + R[C[i + 241*t]];
R[i + 583*t] = Op[i + 242*t] ? R[B[i + 242*t]] * R[C[i + 242*t]] : R[B[i + 242*t]] + R[C[i + 242*t]];
R[i + 584*t] = Op[i + 243*t] ? R[B[i + 243*t]] * R[C[i + 243*t]] : R[B[i + 243*t]] + R[C[i + 243*t]];
R[i + 585*t] = Op[i + 244*t] ? R[B[i + 244*t]] * R[C[i + 244*t]] : R[B[i + 244*t]] + R[C[i + 244*t]];
R[i + 586*t] = Op[i + 245*t] ? R[B[i + 245*t]] * R[C[i + 245*t]] : R[B[i + 245*t]] + R[C[i + 245*t]];
R[i + 587*t] = Op[i + 246*t] ? R[B[i + 246*t]] * R[C[i + 246*t]] : R[B[i + 246*t]] + R[C[i + 246*t]];
R[i + 588*t] = Op[i + 247*t] ? R[B[i + 247*t]] * R[C[i + 247*t]] : R[B[i + 247*t]] + R[C[i + 247*t]];
R[i + 589*t] = Op[i + 248*t] ? R[B[i + 248*t]] * R[C[i + 248*t]] : R[B[i + 248*t]] + R[C[i + 248*t]];
R[i + 590*t] = Op[i + 249*t] ? R[B[i + 249*t]] * R[C[i + 249*t]] : R[B[i + 249*t]] + R[C[i + 249*t]];
R[i + 591*t] = Op[i + 250*t] ? R[B[i + 250*t]] * R[C[i + 250*t]] : R[B[i + 250*t]] + R[C[i + 250*t]];
R[i + 592*t] = Op[i + 251*t] ? R[B[i + 251*t]] * R[C[i + 251*t]] : R[B[i + 251*t]] + R[C[i + 251*t]];
R[i + 593*t] = Op[i + 252*t] ? R[B[i + 252*t]] * R[C[i + 252*t]] : R[B[i + 252*t]] + R[C[i + 252*t]];
R[i + 594*t] = Op[i + 253*t] ? R[B[i + 253*t]] * R[C[i + 253*t]] : R[B[i + 253*t]] + R[C[i + 253*t]];
R[i + 595*t] = Op[i + 254*t] ? R[B[i + 254*t]] * R[C[i + 254*t]] : R[B[i + 254*t]] + R[C[i + 254*t]];
R[i + 596*t] = Op[i + 255*t] ? R[B[i + 255*t]] * R[C[i + 255*t]] : R[B[i + 255*t]] + R[C[i + 255*t]];
R[i + 597*t] = Op[i + 256*t] ? R[B[i + 256*t]] * R[C[i + 256*t]] : R[B[i + 256*t]] + R[C[i + 256*t]];
R[i + 598*t] = Op[i + 257*t] ? R[B[i + 257*t]] * R[C[i + 257*t]] : R[B[i + 257*t]] + R[C[i + 257*t]];
R[i + 599*t] = Op[i + 258*t] ? R[B[i + 258*t]] * R[C[i + 258*t]] : R[B[i + 258*t]] + R[C[i + 258*t]];
R[i + 600*t] = Op[i + 259*t] ? R[B[i + 259*t]] * R[C[i + 259*t]] : R[B[i + 259*t]] + R[C[i + 259*t]];
R[i + 601*t] = Op[i + 260*t] ? R[B[i + 260*t]] * R[C[i + 260*t]] : R[B[i + 260*t]] + R[C[i + 260*t]];
R[i + 602*t] = Op[i + 261*t] ? R[B[i + 261*t]] * R[C[i + 261*t]] : R[B[i + 261*t]] + R[C[i + 261*t]];
R[i + 603*t] = Op[i + 262*t] ? R[B[i + 262*t]] * R[C[i + 262*t]] : R[B[i + 262*t]] + R[C[i + 262*t]];
R[i + 604*t] = Op[i + 263*t] ? R[B[i + 263*t]] * R[C[i + 263*t]] : R[B[i + 263*t]] + R[C[i + 263*t]];
R[i + 605*t] = Op[i + 264*t] ? R[B[i + 264*t]] * R[C[i + 264*t]] : R[B[i + 264*t]] + R[C[i + 264*t]];
R[i + 606*t] = Op[i + 265*t] ? R[B[i + 265*t]] * R[C[i + 265*t]] : R[B[i + 265*t]] + R[C[i + 265*t]];
R[i + 607*t] = Op[i + 266*t] ? R[B[i + 266*t]] * R[C[i + 266*t]] : R[B[i + 266*t]] + R[C[i + 266*t]];
R[i + 608*t] = Op[i + 267*t] ? R[B[i + 267*t]] * R[C[i + 267*t]] : R[B[i + 267*t]] + R[C[i + 267*t]];
R[i + 609*t] = Op[i + 268*t] ? R[B[i + 268*t]] * R[C[i + 268*t]] : R[B[i + 268*t]] + R[C[i + 268*t]];
R[i + 610*t] = Op[i + 269*t] ? R[B[i + 269*t]] * R[C[i + 269*t]] : R[B[i + 269*t]] + R[C[i + 269*t]];
R[i + 611*t] = Op[i + 270*t] ? R[B[i + 270*t]] * R[C[i + 270*t]] : R[B[i + 270*t]] + R[C[i + 270*t]];
R[i + 612*t] = Op[i + 271*t] ? R[B[i + 271*t]] * R[C[i + 271*t]] : R[B[i + 271*t]] + R[C[i + 271*t]];
R[i + 613*t] = Op[i + 272*t] ? R[B[i + 272*t]] * R[C[i + 272*t]] : R[B[i + 272*t]] + R[C[i + 272*t]];
R[i + 614*t] = Op[i + 273*t] ? R[B[i + 273*t]] * R[C[i + 273*t]] : R[B[i + 273*t]] + R[C[i + 273*t]];
R[i + 615*t] = Op[i + 274*t] ? R[B[i + 274*t]] * R[C[i + 274*t]] : R[B[i + 274*t]] + R[C[i + 274*t]];
R[i + 616*t] = Op[i + 275*t] ? R[B[i + 275*t]] * R[C[i + 275*t]] : R[B[i + 275*t]] + R[C[i + 275*t]];
R[i + 617*t] = Op[i + 276*t] ? R[B[i + 276*t]] * R[C[i + 276*t]] : R[B[i + 276*t]] + R[C[i + 276*t]];
R[i + 618*t] = Op[i + 277*t] ? R[B[i + 277*t]] * R[C[i + 277*t]] : R[B[i + 277*t]] + R[C[i + 277*t]];
R[i + 619*t] = Op[i + 278*t] ? R[B[i + 278*t]] * R[C[i + 278*t]] : R[B[i + 278*t]] + R[C[i + 278*t]];
R[i + 620*t] = Op[i + 279*t] ? R[B[i + 279*t]] * R[C[i + 279*t]] : R[B[i + 279*t]] + R[C[i + 279*t]];
R[i + 621*t] = Op[i + 280*t] ? R[B[i + 280*t]] * R[C[i + 280*t]] : R[B[i + 280*t]] + R[C[i + 280*t]];
R[i + 622*t] = Op[i + 281*t] ? R[B[i + 281*t]] * R[C[i + 281*t]] : R[B[i + 281*t]] + R[C[i + 281*t]];
R[i + 623*t] = Op[i + 282*t] ? R[B[i + 282*t]] * R[C[i + 282*t]] : R[B[i + 282*t]] + R[C[i + 282*t]];
R[i + 624*t] = Op[i + 283*t] ? R[B[i + 283*t]] * R[C[i + 283*t]] : R[B[i + 283*t]] + R[C[i + 283*t]];
R[i + 625*t] = Op[i + 284*t] ? R[B[i + 284*t]] * R[C[i + 284*t]] : R[B[i + 284*t]] + R[C[i + 284*t]];
R[i + 626*t] = Op[i + 285*t] ? R[B[i + 285*t]] * R[C[i + 285*t]] : R[B[i + 285*t]] + R[C[i + 285*t]];
R[i + 627*t] = Op[i + 286*t] ? R[B[i + 286*t]] * R[C[i + 286*t]] : R[B[i + 286*t]] + R[C[i + 286*t]];
R[i + 628*t] = Op[i + 287*t] ? R[B[i + 287*t]] * R[C[i + 287*t]] : R[B[i + 287*t]] + R[C[i + 287*t]];
R[i + 629*t] = Op[i + 288*t] ? R[B[i + 288*t]] * R[C[i + 288*t]] : R[B[i + 288*t]] + R[C[i + 288*t]];
__syncthreads();
R[i + 630*t] = Op[i + 289*t] ? R[B[i + 289*t]] * R[C[i + 289*t]] : R[B[i + 289*t]] + R[C[i + 289*t]];
R[i + 631*t] = Op[i + 290*t] ? R[B[i + 290*t]] * R[C[i + 290*t]] : R[B[i + 290*t]] + R[C[i + 290*t]];
R[i + 632*t] = Op[i + 291*t] ? R[B[i + 291*t]] * R[C[i + 291*t]] : R[B[i + 291*t]] + R[C[i + 291*t]];
R[i + 633*t] = Op[i + 292*t] ? R[B[i + 292*t]] * R[C[i + 292*t]] : R[B[i + 292*t]] + R[C[i + 292*t]];
R[i + 634*t] = Op[i + 293*t] ? R[B[i + 293*t]] * R[C[i + 293*t]] : R[B[i + 293*t]] + R[C[i + 293*t]];
R[i + 635*t] = Op[i + 294*t] ? R[B[i + 294*t]] * R[C[i + 294*t]] : R[B[i + 294*t]] + R[C[i + 294*t]];
R[i + 636*t] = Op[i + 295*t] ? R[B[i + 295*t]] * R[C[i + 295*t]] : R[B[i + 295*t]] + R[C[i + 295*t]];
R[i + 637*t] = Op[i + 296*t] ? R[B[i + 296*t]] * R[C[i + 296*t]] : R[B[i + 296*t]] + R[C[i + 296*t]];
R[i + 638*t] = Op[i + 297*t] ? R[B[i + 297*t]] * R[C[i + 297*t]] : R[B[i + 297*t]] + R[C[i + 297*t]];
R[i + 639*t] = Op[i + 298*t] ? R[B[i + 298*t]] * R[C[i + 298*t]] : R[B[i + 298*t]] + R[C[i + 298*t]];
R[i + 640*t] = Op[i + 299*t] ? R[B[i + 299*t]] * R[C[i + 299*t]] : R[B[i + 299*t]] + R[C[i + 299*t]];
R[i + 641*t] = Op[i + 300*t] ? R[B[i + 300*t]] * R[C[i + 300*t]] : R[B[i + 300*t]] + R[C[i + 300*t]];
R[i + 642*t] = Op[i + 301*t] ? R[B[i + 301*t]] * R[C[i + 301*t]] : R[B[i + 301*t]] + R[C[i + 301*t]];
R[i + 643*t] = Op[i + 302*t] ? R[B[i + 302*t]] * R[C[i + 302*t]] : R[B[i + 302*t]] + R[C[i + 302*t]];
R[i + 644*t] = Op[i + 303*t] ? R[B[i + 303*t]] * R[C[i + 303*t]] : R[B[i + 303*t]] + R[C[i + 303*t]];
R[i + 645*t] = Op[i + 304*t] ? R[B[i + 304*t]] * R[C[i + 304*t]] : R[B[i + 304*t]] + R[C[i + 304*t]];
R[i + 646*t] = Op[i + 305*t] ? R[B[i + 305*t]] * R[C[i + 305*t]] : R[B[i + 305*t]] + R[C[i + 305*t]];
R[i + 647*t] = Op[i + 306*t] ? R[B[i + 306*t]] * R[C[i + 306*t]] : R[B[i + 306*t]] + R[C[i + 306*t]];
R[i + 648*t] = Op[i + 307*t] ? R[B[i + 307*t]] * R[C[i + 307*t]] : R[B[i + 307*t]] + R[C[i + 307*t]];
R[i + 649*t] = Op[i + 308*t] ? R[B[i + 308*t]] * R[C[i + 308*t]] : R[B[i + 308*t]] + R[C[i + 308*t]];
R[i + 650*t] = Op[i + 309*t] ? R[B[i + 309*t]] * R[C[i + 309*t]] : R[B[i + 309*t]] + R[C[i + 309*t]];
R[i + 651*t] = Op[i + 310*t] ? R[B[i + 310*t]] * R[C[i + 310*t]] : R[B[i + 310*t]] + R[C[i + 310*t]];
R[i + 652*t] = Op[i + 311*t] ? R[B[i + 311*t]] * R[C[i + 311*t]] : R[B[i + 311*t]] + R[C[i + 311*t]];
R[i + 653*t] = Op[i + 312*t] ? R[B[i + 312*t]] * R[C[i + 312*t]] : R[B[i + 312*t]] + R[C[i + 312*t]];
R[i + 654*t] = Op[i + 313*t] ? R[B[i + 313*t]] * R[C[i + 313*t]] : R[B[i + 313*t]] + R[C[i + 313*t]];
R[i + 655*t] = Op[i + 314*t] ? R[B[i + 314*t]] * R[C[i + 314*t]] : R[B[i + 314*t]] + R[C[i + 314*t]];
R[i + 656*t] = Op[i + 315*t] ? R[B[i + 315*t]] * R[C[i + 315*t]] : R[B[i + 315*t]] + R[C[i + 315*t]];
R[i + 657*t] = Op[i + 316*t] ? R[B[i + 316*t]] * R[C[i + 316*t]] : R[B[i + 316*t]] + R[C[i + 316*t]];
R[i + 658*t] = Op[i + 317*t] ? R[B[i + 317*t]] * R[C[i + 317*t]] : R[B[i + 317*t]] + R[C[i + 317*t]];
R[i + 659*t] = Op[i + 318*t] ? R[B[i + 318*t]] * R[C[i + 318*t]] : R[B[i + 318*t]] + R[C[i + 318*t]];
R[i + 660*t] = Op[i + 319*t] ? R[B[i + 319*t]] * R[C[i + 319*t]] : R[B[i + 319*t]] + R[C[i + 319*t]];
R[i + 661*t] = Op[i + 320*t] ? R[B[i + 320*t]] * R[C[i + 320*t]] : R[B[i + 320*t]] + R[C[i + 320*t]];
R[i + 662*t] = Op[i + 321*t] ? R[B[i + 321*t]] * R[C[i + 321*t]] : R[B[i + 321*t]] + R[C[i + 321*t]];
R[i + 663*t] = Op[i + 322*t] ? R[B[i + 322*t]] * R[C[i + 322*t]] : R[B[i + 322*t]] + R[C[i + 322*t]];
R[i + 664*t] = Op[i + 323*t] ? R[B[i + 323*t]] * R[C[i + 323*t]] : R[B[i + 323*t]] + R[C[i + 323*t]];
R[i + 665*t] = Op[i + 324*t] ? R[B[i + 324*t]] * R[C[i + 324*t]] : R[B[i + 324*t]] + R[C[i + 324*t]];
__syncthreads();
R[i + 666*t] = Op[i + 325*t] ? R[B[i + 325*t]] * R[C[i + 325*t]] : R[B[i + 325*t]] + R[C[i + 325*t]];
R[i + 667*t] = Op[i + 326*t] ? R[B[i + 326*t]] * R[C[i + 326*t]] : R[B[i + 326*t]] + R[C[i + 326*t]];
R[i + 668*t] = Op[i + 327*t] ? R[B[i + 327*t]] * R[C[i + 327*t]] : R[B[i + 327*t]] + R[C[i + 327*t]];
R[i + 669*t] = Op[i + 328*t] ? R[B[i + 328*t]] * R[C[i + 328*t]] : R[B[i + 328*t]] + R[C[i + 328*t]];
R[i + 670*t] = Op[i + 329*t] ? R[B[i + 329*t]] * R[C[i + 329*t]] : R[B[i + 329*t]] + R[C[i + 329*t]];
R[i + 671*t] = Op[i + 330*t] ? R[B[i + 330*t]] * R[C[i + 330*t]] : R[B[i + 330*t]] + R[C[i + 330*t]];
R[i + 672*t] = Op[i + 331*t] ? R[B[i + 331*t]] * R[C[i + 331*t]] : R[B[i + 331*t]] + R[C[i + 331*t]];
R[i + 673*t] = Op[i + 332*t] ? R[B[i + 332*t]] * R[C[i + 332*t]] : R[B[i + 332*t]] + R[C[i + 332*t]];
R[i + 674*t] = Op[i + 333*t] ? R[B[i + 333*t]] * R[C[i + 333*t]] : R[B[i + 333*t]] + R[C[i + 333*t]];
R[i + 675*t] = Op[i + 334*t] ? R[B[i + 334*t]] * R[C[i + 334*t]] : R[B[i + 334*t]] + R[C[i + 334*t]];
R[i + 676*t] = Op[i + 335*t] ? R[B[i + 335*t]] * R[C[i + 335*t]] : R[B[i + 335*t]] + R[C[i + 335*t]];
R[i + 677*t] = Op[i + 336*t] ? R[B[i + 336*t]] * R[C[i + 336*t]] : R[B[i + 336*t]] + R[C[i + 336*t]];
R[i + 678*t] = Op[i + 337*t] ? R[B[i + 337*t]] * R[C[i + 337*t]] : R[B[i + 337*t]] + R[C[i + 337*t]];
R[i + 679*t] = Op[i + 338*t] ? R[B[i + 338*t]] * R[C[i + 338*t]] : R[B[i + 338*t]] + R[C[i + 338*t]];
R[i + 680*t] = Op[i + 339*t] ? R[B[i + 339*t]] * R[C[i + 339*t]] : R[B[i + 339*t]] + R[C[i + 339*t]];
R[i + 681*t] = Op[i + 340*t] ? R[B[i + 340*t]] * R[C[i + 340*t]] : R[B[i + 340*t]] + R[C[i + 340*t]];
R[i + 682*t] = Op[i + 341*t] ? R[B[i + 341*t]] * R[C[i + 341*t]] : R[B[i + 341*t]] + R[C[i + 341*t]];
R[i + 683*t] = Op[i + 342*t] ? R[B[i + 342*t]] * R[C[i + 342*t]] : R[B[i + 342*t]] + R[C[i + 342*t]];
R[i + 684*t] = Op[i + 343*t] ? R[B[i + 343*t]] * R[C[i + 343*t]] : R[B[i + 343*t]] + R[C[i + 343*t]];
R[i + 685*t] = Op[i + 344*t] ? R[B[i + 344*t]] * R[C[i + 344*t]] : R[B[i + 344*t]] + R[C[i + 344*t]];
R[i + 686*t] = Op[i + 345*t] ? R[B[i + 345*t]] * R[C[i + 345*t]] : R[B[i + 345*t]] + R[C[i + 345*t]];
R[i + 687*t] = Op[i + 346*t] ? R[B[i + 346*t]] * R[C[i + 346*t]] : R[B[i + 346*t]] + R[C[i + 346*t]];
R[i + 688*t] = Op[i + 347*t] ? R[B[i + 347*t]] * R[C[i + 347*t]] : R[B[i + 347*t]] + R[C[i + 347*t]];
R[i + 689*t] = Op[i + 348*t] ? R[B[i + 348*t]] * R[C[i + 348*t]] : R[B[i + 348*t]] + R[C[i + 348*t]];
R[i + 690*t] = Op[i + 349*t] ? R[B[i + 349*t]] * R[C[i + 349*t]] : R[B[i + 349*t]] + R[C[i + 349*t]];
R[i + 691*t] = Op[i + 350*t] ? R[B[i + 350*t]] * R[C[i + 350*t]] : R[B[i + 350*t]] + R[C[i + 350*t]];
R[i + 692*t] = Op[i + 351*t] ? R[B[i + 351*t]] * R[C[i + 351*t]] : R[B[i + 351*t]] + R[C[i + 351*t]];
R[i + 693*t] = Op[i + 352*t] ? R[B[i + 352*t]] * R[C[i + 352*t]] : R[B[i + 352*t]] + R[C[i + 352*t]];
R[i + 694*t] = Op[i + 353*t] ? R[B[i + 353*t]] * R[C[i + 353*t]] : R[B[i + 353*t]] + R[C[i + 353*t]];
R[i + 695*t] = Op[i + 354*t] ? R[B[i + 354*t]] * R[C[i + 354*t]] : R[B[i + 354*t]] + R[C[i + 354*t]];
R[i + 696*t] = Op[i + 355*t] ? R[B[i + 355*t]] * R[C[i + 355*t]] : R[B[i + 355*t]] + R[C[i + 355*t]];
R[i + 697*t] = Op[i + 356*t] ? R[B[i + 356*t]] * R[C[i + 356*t]] : R[B[i + 356*t]] + R[C[i + 356*t]];
R[i + 698*t] = Op[i + 357*t] ? R[B[i + 357*t]] * R[C[i + 357*t]] : R[B[i + 357*t]] + R[C[i + 357*t]];
R[i + 699*t] = Op[i + 358*t] ? R[B[i + 358*t]] * R[C[i + 358*t]] : R[B[i + 358*t]] + R[C[i + 358*t]];
R[i + 700*t] = Op[i + 359*t] ? R[B[i + 359*t]] * R[C[i + 359*t]] : R[B[i + 359*t]] + R[C[i + 359*t]];
R[i + 701*t] = Op[i + 360*t] ? R[B[i + 360*t]] * R[C[i + 360*t]] : R[B[i + 360*t]] + R[C[i + 360*t]];
R[i + 702*t] = Op[i + 361*t] ? R[B[i + 361*t]] * R[C[i + 361*t]] : R[B[i + 361*t]] + R[C[i + 361*t]];
R[i + 703*t] = Op[i + 362*t] ? R[B[i + 362*t]] * R[C[i + 362*t]] : R[B[i + 362*t]] + R[C[i + 362*t]];
R[i + 704*t] = Op[i + 363*t] ? R[B[i + 363*t]] * R[C[i + 363*t]] : R[B[i + 363*t]] + R[C[i + 363*t]];
R[i + 705*t] = Op[i + 364*t] ? R[B[i + 364*t]] * R[C[i + 364*t]] : R[B[i + 364*t]] + R[C[i + 364*t]];
R[i + 706*t] = Op[i + 365*t] ? R[B[i + 365*t]] * R[C[i + 365*t]] : R[B[i + 365*t]] + R[C[i + 365*t]];
R[i + 707*t] = Op[i + 366*t] ? R[B[i + 366*t]] * R[C[i + 366*t]] : R[B[i + 366*t]] + R[C[i + 366*t]];
R[i + 708*t] = Op[i + 367*t] ? R[B[i + 367*t]] * R[C[i + 367*t]] : R[B[i + 367*t]] + R[C[i + 367*t]];
R[i + 709*t] = Op[i + 368*t] ? R[B[i + 368*t]] * R[C[i + 368*t]] : R[B[i + 368*t]] + R[C[i + 368*t]];
R[i + 710*t] = Op[i + 369*t] ? R[B[i + 369*t]] * R[C[i + 369*t]] : R[B[i + 369*t]] + R[C[i + 369*t]];
R[i + 711*t] = Op[i + 370*t] ? R[B[i + 370*t]] * R[C[i + 370*t]] : R[B[i + 370*t]] + R[C[i + 370*t]];
R[i + 712*t] = Op[i + 371*t] ? R[B[i + 371*t]] * R[C[i + 371*t]] : R[B[i + 371*t]] + R[C[i + 371*t]];
R[i + 713*t] = Op[i + 372*t] ? R[B[i + 372*t]] * R[C[i + 372*t]] : R[B[i + 372*t]] + R[C[i + 372*t]];
R[i + 714*t] = Op[i + 373*t] ? R[B[i + 373*t]] * R[C[i + 373*t]] : R[B[i + 373*t]] + R[C[i + 373*t]];
R[i + 715*t] = Op[i + 374*t] ? R[B[i + 374*t]] * R[C[i + 374*t]] : R[B[i + 374*t]] + R[C[i + 374*t]];
R[i + 716*t] = Op[i + 375*t] ? R[B[i + 375*t]] * R[C[i + 375*t]] : R[B[i + 375*t]] + R[C[i + 375*t]];
R[i + 717*t] = Op[i + 376*t] ? R[B[i + 376*t]] * R[C[i + 376*t]] : R[B[i + 376*t]] + R[C[i + 376*t]];
R[i + 718*t] = Op[i + 377*t] ? R[B[i + 377*t]] * R[C[i + 377*t]] : R[B[i + 377*t]] + R[C[i + 377*t]];
R[i + 719*t] = Op[i + 378*t] ? R[B[i + 378*t]] * R[C[i + 378*t]] : R[B[i + 378*t]] + R[C[i + 378*t]];
__syncthreads();
R[i + 720*t] = Op[i + 379*t] ? R[B[i + 379*t]] * R[C[i + 379*t]] : R[B[i + 379*t]] + R[C[i + 379*t]];
R[i + 721*t] = Op[i + 380*t] ? R[B[i + 380*t]] * R[C[i + 380*t]] : R[B[i + 380*t]] + R[C[i + 380*t]];
R[i + 722*t] = Op[i + 381*t] ? R[B[i + 381*t]] * R[C[i + 381*t]] : R[B[i + 381*t]] + R[C[i + 381*t]];
R[i + 723*t] = Op[i + 382*t] ? R[B[i + 382*t]] * R[C[i + 382*t]] : R[B[i + 382*t]] + R[C[i + 382*t]];
R[i + 724*t] = Op[i + 383*t] ? R[B[i + 383*t]] * R[C[i + 383*t]] : R[B[i + 383*t]] + R[C[i + 383*t]];
R[i + 725*t] = Op[i + 384*t] ? R[B[i + 384*t]] * R[C[i + 384*t]] : R[B[i + 384*t]] + R[C[i + 384*t]];
R[i + 726*t] = Op[i + 385*t] ? R[B[i + 385*t]] * R[C[i + 385*t]] : R[B[i + 385*t]] + R[C[i + 385*t]];
R[i + 727*t] = Op[i + 386*t] ? R[B[i + 386*t]] * R[C[i + 386*t]] : R[B[i + 386*t]] + R[C[i + 386*t]];
R[i + 728*t] = Op[i + 387*t] ? R[B[i + 387*t]] * R[C[i + 387*t]] : R[B[i + 387*t]] + R[C[i + 387*t]];
R[i + 729*t] = Op[i + 388*t] ? R[B[i + 388*t]] * R[C[i + 388*t]] : R[B[i + 388*t]] + R[C[i + 388*t]];
R[i + 730*t] = Op[i + 389*t] ? R[B[i + 389*t]] * R[C[i + 389*t]] : R[B[i + 389*t]] + R[C[i + 389*t]];
R[i + 731*t] = Op[i + 390*t] ? R[B[i + 390*t]] * R[C[i + 390*t]] : R[B[i + 390*t]] + R[C[i + 390*t]];
R[i + 732*t] = Op[i + 391*t] ? R[B[i + 391*t]] * R[C[i + 391*t]] : R[B[i + 391*t]] + R[C[i + 391*t]];
R[i + 733*t] = Op[i + 392*t] ? R[B[i + 392*t]] * R[C[i + 392*t]] : R[B[i + 392*t]] + R[C[i + 392*t]];
R[i + 734*t] = Op[i + 393*t] ? R[B[i + 393*t]] * R[C[i + 393*t]] : R[B[i + 393*t]] + R[C[i + 393*t]];
R[i + 735*t] = Op[i + 394*t] ? R[B[i + 394*t]] * R[C[i + 394*t]] : R[B[i + 394*t]] + R[C[i + 394*t]];
R[i + 736*t] = Op[i + 395*t] ? R[B[i + 395*t]] * R[C[i + 395*t]] : R[B[i + 395*t]] + R[C[i + 395*t]];
R[i + 737*t] = Op[i + 396*t] ? R[B[i + 396*t]] * R[C[i + 396*t]] : R[B[i + 396*t]] + R[C[i + 396*t]];
R[i + 738*t] = Op[i + 397*t] ? R[B[i + 397*t]] * R[C[i + 397*t]] : R[B[i + 397*t]] + R[C[i + 397*t]];
R[i + 739*t] = Op[i + 398*t] ? R[B[i + 398*t]] * R[C[i + 398*t]] : R[B[i + 398*t]] + R[C[i + 398*t]];
R[i + 740*t] = Op[i + 399*t] ? R[B[i + 399*t]] * R[C[i + 399*t]] : R[B[i + 399*t]] + R[C[i + 399*t]];
R[i + 741*t] = Op[i + 400*t] ? R[B[i + 400*t]] * R[C[i + 400*t]] : R[B[i + 400*t]] + R[C[i + 400*t]];
R[i + 742*t] = Op[i + 401*t] ? R[B[i + 401*t]] * R[C[i + 401*t]] : R[B[i + 401*t]] + R[C[i + 401*t]];
R[i + 743*t] = Op[i + 402*t] ? R[B[i + 402*t]] * R[C[i + 402*t]] : R[B[i + 402*t]] + R[C[i + 402*t]];
R[i + 744*t] = Op[i + 403*t] ? R[B[i + 403*t]] * R[C[i + 403*t]] : R[B[i + 403*t]] + R[C[i + 403*t]];
R[i + 745*t] = Op[i + 404*t] ? R[B[i + 404*t]] * R[C[i + 404*t]] : R[B[i + 404*t]] + R[C[i + 404*t]];
R[i + 746*t] = Op[i + 405*t] ? R[B[i + 405*t]] * R[C[i + 405*t]] : R[B[i + 405*t]] + R[C[i + 405*t]];
R[i + 747*t] = Op[i + 406*t] ? R[B[i + 406*t]] * R[C[i + 406*t]] : R[B[i + 406*t]] + R[C[i + 406*t]];
R[i + 748*t] = Op[i + 407*t] ? R[B[i + 407*t]] * R[C[i + 407*t]] : R[B[i + 407*t]] + R[C[i + 407*t]];
R[i + 749*t] = Op[i + 408*t] ? R[B[i + 408*t]] * R[C[i + 408*t]] : R[B[i + 408*t]] + R[C[i + 408*t]];
R[i + 750*t] = Op[i + 409*t] ? R[B[i + 409*t]] * R[C[i + 409*t]] : R[B[i + 409*t]] + R[C[i + 409*t]];
R[i + 751*t] = Op[i + 410*t] ? R[B[i + 410*t]] * R[C[i + 410*t]] : R[B[i + 410*t]] + R[C[i + 410*t]];
R[i + 752*t] = Op[i + 411*t] ? R[B[i + 411*t]] * R[C[i + 411*t]] : R[B[i + 411*t]] + R[C[i + 411*t]];
R[i + 753*t] = Op[i + 412*t] ? R[B[i + 412*t]] * R[C[i + 412*t]] : R[B[i + 412*t]] + R[C[i + 412*t]];
R[i + 754*t] = Op[i + 413*t] ? R[B[i + 413*t]] * R[C[i + 413*t]] : R[B[i + 413*t]] + R[C[i + 413*t]];
R[i + 755*t] = Op[i + 414*t] ? R[B[i + 414*t]] * R[C[i + 414*t]] : R[B[i + 414*t]] + R[C[i + 414*t]];
R[i + 756*t] = Op[i + 415*t] ? R[B[i + 415*t]] * R[C[i + 415*t]] : R[B[i + 415*t]] + R[C[i + 415*t]];
R[i + 757*t] = Op[i + 416*t] ? R[B[i + 416*t]] * R[C[i + 416*t]] : R[B[i + 416*t]] + R[C[i + 416*t]];
R[i + 758*t] = Op[i + 417*t] ? R[B[i + 417*t]] * R[C[i + 417*t]] : R[B[i + 417*t]] + R[C[i + 417*t]];
R[i + 759*t] = Op[i + 418*t] ? R[B[i + 418*t]] * R[C[i + 418*t]] : R[B[i + 418*t]] + R[C[i + 418*t]];
R[i + 760*t] = Op[i + 419*t] ? R[B[i + 419*t]] * R[C[i + 419*t]] : R[B[i + 419*t]] + R[C[i + 419*t]];
R[i + 761*t] = Op[i + 420*t] ? R[B[i + 420*t]] * R[C[i + 420*t]] : R[B[i + 420*t]] + R[C[i + 420*t]];
R[i + 762*t] = Op[i + 421*t] ? R[B[i + 421*t]] * R[C[i + 421*t]] : R[B[i + 421*t]] + R[C[i + 421*t]];
R[i + 763*t] = Op[i + 422*t] ? R[B[i + 422*t]] * R[C[i + 422*t]] : R[B[i + 422*t]] + R[C[i + 422*t]];
R[i + 764*t] = Op[i + 423*t] ? R[B[i + 423*t]] * R[C[i + 423*t]] : R[B[i + 423*t]] + R[C[i + 423*t]];
__syncthreads();
R[i + 765*t] = Op[i + 424*t] ? R[B[i + 424*t]] * R[C[i + 424*t]] : R[B[i + 424*t]] + R[C[i + 424*t]];
R[i + 766*t] = Op[i + 425*t] ? R[B[i + 425*t]] * R[C[i + 425*t]] : R[B[i + 425*t]] + R[C[i + 425*t]];
R[i + 767*t] = Op[i + 426*t] ? R[B[i + 426*t]] * R[C[i + 426*t]] : R[B[i + 426*t]] + R[C[i + 426*t]];
R[i + 768*t] = Op[i + 427*t] ? R[B[i + 427*t]] * R[C[i + 427*t]] : R[B[i + 427*t]] + R[C[i + 427*t]];
R[i + 769*t] = Op[i + 428*t] ? R[B[i + 428*t]] * R[C[i + 428*t]] : R[B[i + 428*t]] + R[C[i + 428*t]];
R[i + 770*t] = Op[i + 429*t] ? R[B[i + 429*t]] * R[C[i + 429*t]] : R[B[i + 429*t]] + R[C[i + 429*t]];
R[i + 771*t] = Op[i + 430*t] ? R[B[i + 430*t]] * R[C[i + 430*t]] : R[B[i + 430*t]] + R[C[i + 430*t]];
R[i + 772*t] = Op[i + 431*t] ? R[B[i + 431*t]] * R[C[i + 431*t]] : R[B[i + 431*t]] + R[C[i + 431*t]];
R[i + 773*t] = Op[i + 432*t] ? R[B[i + 432*t]] * R[C[i + 432*t]] : R[B[i + 432*t]] + R[C[i + 432*t]];
R[i + 774*t] = Op[i + 433*t] ? R[B[i + 433*t]] * R[C[i + 433*t]] : R[B[i + 433*t]] + R[C[i + 433*t]];
R[i + 775*t] = Op[i + 434*t] ? R[B[i + 434*t]] * R[C[i + 434*t]] : R[B[i + 434*t]] + R[C[i + 434*t]];
R[i + 776*t] = Op[i + 435*t] ? R[B[i + 435*t]] * R[C[i + 435*t]] : R[B[i + 435*t]] + R[C[i + 435*t]];
R[i + 777*t] = Op[i + 436*t] ? R[B[i + 436*t]] * R[C[i + 436*t]] : R[B[i + 436*t]] + R[C[i + 436*t]];
R[i + 778*t] = Op[i + 437*t] ? R[B[i + 437*t]] * R[C[i + 437*t]] : R[B[i + 437*t]] + R[C[i + 437*t]];
R[i + 779*t] = Op[i + 438*t] ? R[B[i + 438*t]] * R[C[i + 438*t]] : R[B[i + 438*t]] + R[C[i + 438*t]];
R[i + 780*t] = Op[i + 439*t] ? R[B[i + 439*t]] * R[C[i + 439*t]] : R[B[i + 439*t]] + R[C[i + 439*t]];
R[i + 781*t] = Op[i + 440*t] ? R[B[i + 440*t]] * R[C[i + 440*t]] : R[B[i + 440*t]] + R[C[i + 440*t]];
R[i + 782*t] = Op[i + 441*t] ? R[B[i + 441*t]] * R[C[i + 441*t]] : R[B[i + 441*t]] + R[C[i + 441*t]];
R[i + 783*t] = Op[i + 442*t] ? R[B[i + 442*t]] * R[C[i + 442*t]] : R[B[i + 442*t]] + R[C[i + 442*t]];
R[i + 784*t] = Op[i + 443*t] ? R[B[i + 443*t]] * R[C[i + 443*t]] : R[B[i + 443*t]] + R[C[i + 443*t]];
__syncthreads();
R[i + 785*t] = Op[i + 444*t] ? R[B[i + 444*t]] * R[C[i + 444*t]] : R[B[i + 444*t]] + R[C[i + 444*t]];
R[i + 786*t] = Op[i + 445*t] ? R[B[i + 445*t]] * R[C[i + 445*t]] : R[B[i + 445*t]] + R[C[i + 445*t]];
R[i + 787*t] = Op[i + 446*t] ? R[B[i + 446*t]] * R[C[i + 446*t]] : R[B[i + 446*t]] + R[C[i + 446*t]];
R[i + 788*t] = Op[i + 447*t] ? R[B[i + 447*t]] * R[C[i + 447*t]] : R[B[i + 447*t]] + R[C[i + 447*t]];
R[i + 789*t] = Op[i + 448*t] ? R[B[i + 448*t]] * R[C[i + 448*t]] : R[B[i + 448*t]] + R[C[i + 448*t]];
R[i + 790*t] = Op[i + 449*t] ? R[B[i + 449*t]] * R[C[i + 449*t]] : R[B[i + 449*t]] + R[C[i + 449*t]];
R[i + 791*t] = Op[i + 450*t] ? R[B[i + 450*t]] * R[C[i + 450*t]] : R[B[i + 450*t]] + R[C[i + 450*t]];
R[i + 792*t] = Op[i + 451*t] ? R[B[i + 451*t]] * R[C[i + 451*t]] : R[B[i + 451*t]] + R[C[i + 451*t]];
R[i + 793*t] = Op[i + 452*t] ? R[B[i + 452*t]] * R[C[i + 452*t]] : R[B[i + 452*t]] + R[C[i + 452*t]];
R[i + 794*t] = Op[i + 453*t] ? R[B[i + 453*t]] * R[C[i + 453*t]] : R[B[i + 453*t]] + R[C[i + 453*t]];
R[i + 795*t] = Op[i + 454*t] ? R[B[i + 454*t]] * R[C[i + 454*t]] : R[B[i + 454*t]] + R[C[i + 454*t]];
R[i + 796*t] = Op[i + 455*t] ? R[B[i + 455*t]] * R[C[i + 455*t]] : R[B[i + 455*t]] + R[C[i + 455*t]];
R[i + 797*t] = Op[i + 456*t] ? R[B[i + 456*t]] * R[C[i + 456*t]] : R[B[i + 456*t]] + R[C[i + 456*t]];
R[i + 798*t] = Op[i + 457*t] ? R[B[i + 457*t]] * R[C[i + 457*t]] : R[B[i + 457*t]] + R[C[i + 457*t]];
R[i + 799*t] = Op[i + 458*t] ? R[B[i + 458*t]] * R[C[i + 458*t]] : R[B[i + 458*t]] + R[C[i + 458*t]];
R[i + 800*t] = Op[i + 459*t] ? R[B[i + 459*t]] * R[C[i + 459*t]] : R[B[i + 459*t]] + R[C[i + 459*t]];
R[i + 801*t] = Op[i + 460*t] ? R[B[i + 460*t]] * R[C[i + 460*t]] : R[B[i + 460*t]] + R[C[i + 460*t]];
__syncthreads();
R[i + 802*t] = Op[i + 461*t] ? R[B[i + 461*t]] * R[C[i + 461*t]] : R[B[i + 461*t]] + R[C[i + 461*t]];
R[i + 803*t] = Op[i + 462*t] ? R[B[i + 462*t]] * R[C[i + 462*t]] : R[B[i + 462*t]] + R[C[i + 462*t]];
R[i + 804*t] = Op[i + 463*t] ? R[B[i + 463*t]] * R[C[i + 463*t]] : R[B[i + 463*t]] + R[C[i + 463*t]];
R[i + 805*t] = Op[i + 464*t] ? R[B[i + 464*t]] * R[C[i + 464*t]] : R[B[i + 464*t]] + R[C[i + 464*t]];
R[i + 806*t] = Op[i + 465*t] ? R[B[i + 465*t]] * R[C[i + 465*t]] : R[B[i + 465*t]] + R[C[i + 465*t]];
R[i + 807*t] = Op[i + 466*t] ? R[B[i + 466*t]] * R[C[i + 466*t]] : R[B[i + 466*t]] + R[C[i + 466*t]];
R[i + 808*t] = Op[i + 467*t] ? R[B[i + 467*t]] * R[C[i + 467*t]] : R[B[i + 467*t]] + R[C[i + 467*t]];
R[i + 809*t] = Op[i + 468*t] ? R[B[i + 468*t]] * R[C[i + 468*t]] : R[B[i + 468*t]] + R[C[i + 468*t]];
R[i + 810*t] = Op[i + 469*t] ? R[B[i + 469*t]] * R[C[i + 469*t]] : R[B[i + 469*t]] + R[C[i + 469*t]];
R[i + 811*t] = Op[i + 470*t] ? R[B[i + 470*t]] * R[C[i + 470*t]] : R[B[i + 470*t]] + R[C[i + 470*t]];
R[i + 812*t] = Op[i + 471*t] ? R[B[i + 471*t]] * R[C[i + 471*t]] : R[B[i + 471*t]] + R[C[i + 471*t]];
R[i + 813*t] = Op[i + 472*t] ? R[B[i + 472*t]] * R[C[i + 472*t]] : R[B[i + 472*t]] + R[C[i + 472*t]];
R[i + 814*t] = Op[i + 473*t] ? R[B[i + 473*t]] * R[C[i + 473*t]] : R[B[i + 473*t]] + R[C[i + 473*t]];
R[i + 815*t] = Op[i + 474*t] ? R[B[i + 474*t]] * R[C[i + 474*t]] : R[B[i + 474*t]] + R[C[i + 474*t]];
__syncthreads();
R[i + 816*t] = Op[i + 475*t] ? R[B[i + 475*t]] * R[C[i + 475*t]] : R[B[i + 475*t]] + R[C[i + 475*t]];
R[i + 817*t] = Op[i + 476*t] ? R[B[i + 476*t]] * R[C[i + 476*t]] : R[B[i + 476*t]] + R[C[i + 476*t]];
R[i + 818*t] = Op[i + 477*t] ? R[B[i + 477*t]] * R[C[i + 477*t]] : R[B[i + 477*t]] + R[C[i + 477*t]];
R[i + 819*t] = Op[i + 478*t] ? R[B[i + 478*t]] * R[C[i + 478*t]] : R[B[i + 478*t]] + R[C[i + 478*t]];
R[i + 820*t] = Op[i + 479*t] ? R[B[i + 479*t]] * R[C[i + 479*t]] : R[B[i + 479*t]] + R[C[i + 479*t]];
R[i + 821*t] = Op[i + 480*t] ? R[B[i + 480*t]] * R[C[i + 480*t]] : R[B[i + 480*t]] + R[C[i + 480*t]];
R[i + 822*t] = Op[i + 481*t] ? R[B[i + 481*t]] * R[C[i + 481*t]] : R[B[i + 481*t]] + R[C[i + 481*t]];
R[i + 823*t] = Op[i + 482*t] ? R[B[i + 482*t]] * R[C[i + 482*t]] : R[B[i + 482*t]] + R[C[i + 482*t]];
R[i + 824*t] = Op[i + 483*t] ? R[B[i + 483*t]] * R[C[i + 483*t]] : R[B[i + 483*t]] + R[C[i + 483*t]];
R[i + 825*t] = Op[i + 484*t] ? R[B[i + 484*t]] * R[C[i + 484*t]] : R[B[i + 484*t]] + R[C[i + 484*t]];
R[i + 826*t] = Op[i + 485*t] ? R[B[i + 485*t]] * R[C[i + 485*t]] : R[B[i + 485*t]] + R[C[i + 485*t]];
__syncthreads();
R[i + 827*t] = Op[i + 486*t] ? R[B[i + 486*t]] * R[C[i + 486*t]] : R[B[i + 486*t]] + R[C[i + 486*t]];
R[i + 828*t] = Op[i + 487*t] ? R[B[i + 487*t]] * R[C[i + 487*t]] : R[B[i + 487*t]] + R[C[i + 487*t]];
R[i + 829*t] = Op[i + 488*t] ? R[B[i + 488*t]] * R[C[i + 488*t]] : R[B[i + 488*t]] + R[C[i + 488*t]];
R[i + 830*t] = Op[i + 489*t] ? R[B[i + 489*t]] * R[C[i + 489*t]] : R[B[i + 489*t]] + R[C[i + 489*t]];
R[i + 831*t] = Op[i + 490*t] ? R[B[i + 490*t]] * R[C[i + 490*t]] : R[B[i + 490*t]] + R[C[i + 490*t]];
R[i + 832*t] = Op[i + 491*t] ? R[B[i + 491*t]] * R[C[i + 491*t]] : R[B[i + 491*t]] + R[C[i + 491*t]];
R[i + 833*t] = Op[i + 492*t] ? R[B[i + 492*t]] * R[C[i + 492*t]] : R[B[i + 492*t]] + R[C[i + 492*t]];
R[i + 834*t] = Op[i + 493*t] ? R[B[i + 493*t]] * R[C[i + 493*t]] : R[B[i + 493*t]] + R[C[i + 493*t]];
R[i + 835*t] = Op[i + 494*t] ? R[B[i + 494*t]] * R[C[i + 494*t]] : R[B[i + 494*t]] + R[C[i + 494*t]];
R[i + 836*t] = Op[i + 495*t] ? R[B[i + 495*t]] * R[C[i + 495*t]] : R[B[i + 495*t]] + R[C[i + 495*t]];
R[i + 837*t] = Op[i + 496*t] ? R[B[i + 496*t]] * R[C[i + 496*t]] : R[B[i + 496*t]] + R[C[i + 496*t]];
R[i + 838*t] = Op[i + 497*t] ? R[B[i + 497*t]] * R[C[i + 497*t]] : R[B[i + 497*t]] + R[C[i + 497*t]];
R[i + 839*t] = Op[i + 498*t] ? R[B[i + 498*t]] * R[C[i + 498*t]] : R[B[i + 498*t]] + R[C[i + 498*t]];
__syncthreads();
R[i + 840*t] = Op[i + 499*t] ? R[B[i + 499*t]] * R[C[i + 499*t]] : R[B[i + 499*t]] + R[C[i + 499*t]];
R[i + 841*t] = Op[i + 500*t] ? R[B[i + 500*t]] * R[C[i + 500*t]] : R[B[i + 500*t]] + R[C[i + 500*t]];
R[i + 842*t] = Op[i + 501*t] ? R[B[i + 501*t]] * R[C[i + 501*t]] : R[B[i + 501*t]] + R[C[i + 501*t]];
R[i + 843*t] = Op[i + 502*t] ? R[B[i + 502*t]] * R[C[i + 502*t]] : R[B[i + 502*t]] + R[C[i + 502*t]];
R[i + 844*t] = Op[i + 503*t] ? R[B[i + 503*t]] * R[C[i + 503*t]] : R[B[i + 503*t]] + R[C[i + 503*t]];
R[i + 845*t] = Op[i + 504*t] ? R[B[i + 504*t]] * R[C[i + 504*t]] : R[B[i + 504*t]] + R[C[i + 504*t]];
R[i + 846*t] = Op[i + 505*t] ? R[B[i + 505*t]] * R[C[i + 505*t]] : R[B[i + 505*t]] + R[C[i + 505*t]];
R[i + 847*t] = Op[i + 506*t] ? R[B[i + 506*t]] * R[C[i + 506*t]] : R[B[i + 506*t]] + R[C[i + 506*t]];
R[i + 848*t] = Op[i + 507*t] ? R[B[i + 507*t]] * R[C[i + 507*t]] : R[B[i + 507*t]] + R[C[i + 507*t]];
R[i + 849*t] = Op[i + 508*t] ? R[B[i + 508*t]] * R[C[i + 508*t]] : R[B[i + 508*t]] + R[C[i + 508*t]];
R[i + 850*t] = Op[i + 509*t] ? R[B[i + 509*t]] * R[C[i + 509*t]] : R[B[i + 509*t]] + R[C[i + 509*t]];
__syncthreads();
R[i + 851*t] = Op[i + 510*t] ? R[B[i + 510*t]] * R[C[i + 510*t]] : R[B[i + 510*t]] + R[C[i + 510*t]];
R[i + 852*t] = Op[i + 511*t] ? R[B[i + 511*t]] * R[C[i + 511*t]] : R[B[i + 511*t]] + R[C[i + 511*t]];
R[i + 853*t] = Op[i + 512*t] ? R[B[i + 512*t]] * R[C[i + 512*t]] : R[B[i + 512*t]] + R[C[i + 512*t]];
R[i + 854*t] = Op[i + 513*t] ? R[B[i + 513*t]] * R[C[i + 513*t]] : R[B[i + 513*t]] + R[C[i + 513*t]];
R[i + 855*t] = Op[i + 514*t] ? R[B[i + 514*t]] * R[C[i + 514*t]] : R[B[i + 514*t]] + R[C[i + 514*t]];
R[i + 856*t] = Op[i + 515*t] ? R[B[i + 515*t]] * R[C[i + 515*t]] : R[B[i + 515*t]] + R[C[i + 515*t]];
R[i + 857*t] = Op[i + 516*t] ? R[B[i + 516*t]] * R[C[i + 516*t]] : R[B[i + 516*t]] + R[C[i + 516*t]];
__syncthreads();
R[i + 858*t] = Op[i + 517*t] ? R[B[i + 517*t]] * R[C[i + 517*t]] : R[B[i + 517*t]] + R[C[i + 517*t]];
R[i + 859*t] = Op[i + 518*t] ? R[B[i + 518*t]] * R[C[i + 518*t]] : R[B[i + 518*t]] + R[C[i + 518*t]];
R[i + 860*t] = Op[i + 519*t] ? R[B[i + 519*t]] * R[C[i + 519*t]] : R[B[i + 519*t]] + R[C[i + 519*t]];
R[i + 861*t] = Op[i + 520*t] ? R[B[i + 520*t]] * R[C[i + 520*t]] : R[B[i + 520*t]] + R[C[i + 520*t]];
R[i + 862*t] = Op[i + 521*t] ? R[B[i + 521*t]] * R[C[i + 521*t]] : R[B[i + 521*t]] + R[C[i + 521*t]];
R[i + 863*t] = Op[i + 522*t] ? R[B[i + 522*t]] * R[C[i + 522*t]] : R[B[i + 522*t]] + R[C[i + 522*t]];
__syncthreads();
R[i + 864*t] = Op[i + 523*t] ? R[B[i + 523*t]] * R[C[i + 523*t]] : R[B[i + 523*t]] + R[C[i + 523*t]];
R[i + 865*t] = Op[i + 524*t] ? R[B[i + 524*t]] * R[C[i + 524*t]] : R[B[i + 524*t]] + R[C[i + 524*t]];
R[i + 866*t] = Op[i + 525*t] ? R[B[i + 525*t]] * R[C[i + 525*t]] : R[B[i + 525*t]] + R[C[i + 525*t]];
R[i + 867*t] = Op[i + 526*t] ? R[B[i + 526*t]] * R[C[i + 526*t]] : R[B[i + 526*t]] + R[C[i + 526*t]];
__syncthreads();
R[i + 868*t] = Op[i + 527*t] ? R[B[i + 527*t]] * R[C[i + 527*t]] : R[B[i + 527*t]] + R[C[i + 527*t]];
R[i + 869*t] = Op[i + 528*t] ? R[B[i + 528*t]] * R[C[i + 528*t]] : R[B[i + 528*t]] + R[C[i + 528*t]];
R[i + 870*t] = Op[i + 529*t] ? R[B[i + 529*t]] * R[C[i + 529*t]] : R[B[i + 529*t]] + R[C[i + 529*t]];
__syncthreads();
R[i + 871*t] = Op[i + 530*t] ? R[B[i + 530*t]] * R[C[i + 530*t]] : R[B[i + 530*t]] + R[C[i + 530*t]];
R[i + 872*t] = Op[i + 531*t] ? R[B[i + 531*t]] * R[C[i + 531*t]] : R[B[i + 531*t]] + R[C[i + 531*t]];
__syncthreads();
R[i + 873*t] = Op[i + 532*t] ? R[B[i + 532*t]] * R[C[i + 532*t]] : R[B[i + 532*t]] + R[C[i + 532*t]];
R[i + 874*t] = Op[i + 533*t] ? R[B[i + 533*t]] * R[C[i + 533*t]] : R[B[i + 533*t]] + R[C[i + 533*t]];
__syncthreads();
R[i + 875*t] = Op[i + 534*t] ? R[B[i + 534*t]] * R[C[i + 534*t]] : R[B[i + 534*t]] + R[C[i + 534*t]];
__syncthreads();
R[i + 876*t] = Op[i + 535*t] ? R[B[i + 535*t]] * R[C[i + 535*t]] : R[B[i + 535*t]] + R[C[i + 535*t]];
__syncthreads();
R[i + 877*t] = Op[i + 536*t] ? R[B[i + 536*t]] * R[C[i + 536*t]] : R[B[i + 536*t]] + R[C[i + 536*t]];
__syncthreads();
R[i + 878*t] = Op[i + 537*t] ? R[B[i + 537*t]] * R[C[i + 537*t]] : R[B[i + 537*t]] + R[C[i + 537*t]];
__syncthreads();
R[i + 879*t] = Op[i + 538*t] ? R[B[i + 538*t]] * R[C[i + 538*t]] : R[B[i + 538*t]] + R[C[i + 538*t]];
__syncthreads();
R[i + 880*t] = Op[i + 539*t] ? R[B[i + 539*t]] * R[C[i + 539*t]] : R[B[i + 539*t]] + R[C[i + 539*t]];
__syncthreads();
R[i + 881*t] = Op[i + 540*t] ? R[B[i + 540*t]] * R[C[i + 540*t]] : R[B[i + 540*t]] + R[C[i + 540*t]];
__syncthreads();
R[i + 882*t] = Op[i + 541*t] ? R[B[i + 541*t]] * R[C[i + 541*t]] : R[B[i + 541*t]] + R[C[i + 541*t]];
__syncthreads();
R[i + 883*t] = Op[i + 542*t] ? R[B[i + 542*t]] * R[C[i + 542*t]] : R[B[i + 542*t]] + R[C[i + 542*t]];
__syncthreads();
R[i + 884*t] = Op[i + 543*t] ? R[B[i + 543*t]] * R[C[i + 543*t]] : R[B[i + 543*t]] + R[C[i + 543*t]];
__syncthreads();
R[i + 885*t] = Op[i + 544*t] ? R[B[i + 544*t]] * R[C[i + 544*t]] : R[B[i + 544*t]] + R[C[i + 544*t]];
if (i==0) { final += R[885*t]; }
__syncthreads();
}
if (i==0) { A[0]= final;}
}
|
7,003 | #include <stdio.h>
#include <stdlib.h>
#include <time.h>
#include <cuda.h>
#include <math.h>
unsigned int getmaxcu(unsigned int *, unsigned int *, unsigned int, unsigned int);
__global__
void foo(unsigned int *, unsigned int *, unsigned int, int);
int main(int argc, char *argv[])
{
unsigned int size = 0; // The size of the array
unsigned int i; // loop index
unsigned int * numbers; //pointer to the array
unsigned int * max;
unsigned int numThreads;
unsigned int numsPerThread;
if(argc !=2)
{
printf("usage: maxseq num\n");
printf("num = size of the array\n");
exit(1);
}
size = atol(argv[1]);
/* Give each thread 1000 numbers */
numsPerThread = 1000;
/* I'm assuming that size is divisible by 1000 since that's the case for all the tests for the writeup */
numThreads = size / numsPerThread;
numbers = (unsigned int *)malloc(size * sizeof(unsigned int));
max = (unsigned int *)malloc(numThreads * sizeof(unsigned int));
if( !numbers )
{
printf("Unable to allocate mem for an array of size %u\n", size);
exit(1);
}
srand(time(NULL)); // setting a seed for the random number generator
// Fill-up the array with random numbers from 0 to size-1
for( i = 0; i < size; i++)
numbers[i] = rand() % size;
printf(" The maximum number in the array is: %u\n",
getmaxcu(numbers, max, size, numThreads));
free(numbers);
exit(0);
}
/*
input: pointer to an array of long int
number of elements in the array
output: the maximum number of the array
*/
unsigned int getmaxcu(unsigned int * num, unsigned int * max, unsigned int size, unsigned int numThreads)
{
unsigned int i;
unsigned int * max_d;
unsigned int * num_d;
unsigned int memSize = size * sizeof(unsigned int);
/* Copy the array from the host to the device */
cudaMalloc((void **) &num_d, memSize);
cudaMemcpy(num_d, num, memSize, cudaMemcpyHostToDevice);
/* Allocate space for the max number */
cudaMalloc((void **) &max_d, numThreads * sizeof(unsigned int));
/**
* Kernel invocation code
*
* Max MP = 15
* Max threads = 2048 * 15 = 30720
* Max threads/block = 1024
*/
//int threads = (30720 > size) ? size : 30720;
/* Find number of threads, rounding up */
//int minNumThreads = (size + numsPerThread - 1) / numsPerThread;
int threadsPerBlock = (numThreads > 1000) ? 1000 : numThreads;
int blocks = numThreads / threadsPerBlock;
int chunk = size / numThreads; // How many numbers each thread will process
/* Invoke the kernel */
foo<<<blocks, threadsPerBlock>>>(num_d, max_d, size, chunk);
/* Copy the max array from device to host and find the max of those */
cudaMemcpy(max, max_d, numThreads * sizeof(unsigned int), cudaMemcpyDeviceToHost);
unsigned int maxNum = 0;
unsigned int cur;
for(i = 0; i < numThreads; i++) {
cur = max[i];
if(cur > maxNum)
maxNum = cur;
}
cudaFree(num_d);
cudaFree(max_d);
return maxNum;
}
/* Each thread will check it's portion of the array and put the max number in the first index */
__global__
void foo(unsigned int * num_d, unsigned int * max_d, unsigned int size, int chunk) {
int i = threadIdx.x;
int j;
int start = i * chunk;
int end = (i+1) * chunk;
/* Local variables to reduce global memory reads */
unsigned int cur;
unsigned int maxNum = 0;
for (j = start; j < end; j++) {
cur = num_d[j];
if (cur > maxNum)
maxNum = cur;
}
max_d[i] = maxNum;
}
|
7,004 | #include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <stdio.h>
__global__ void print_threadIds()
{
printf(" threadIdx.x : %d, threadIdx.y : %d, threadIdx.z : %d \n", threadIdx.x, threadIdx.y, threadIdx.z);
}
int main()
{
int nx, ny;
nx = 16;
ny = 16;
dim3 block(8, 8);
dim3 grid(nx/block.x, ny/block.y);
print_threadIds<<<grid, block>>>();
cudaDeviceSynchronize();
cudaDeviceReset();
return 0;
}
|
7,005 | #include <cuda.h>
#include <stdio.h>
#include <stdlib.h>
__global__ void add(int* d_vec1, int* d_vec2, int* d_vec3)
{
int idx = blockIdx.x * blockDim.x + threadIdx.x;
d_vec3[idx] = d_vec2[idx] + d_vec1[idx];
}
int main()
{
int i ;
int num_blocks = 1000;
int num_threads = 512;
int SIZE = num_threads*num_blocks;
int BYTES = SIZE * sizeof(int);
// declare device and host variables
int h_vec1[SIZE],h_vec2[SIZE],h_vec3[SIZE];
int *d_vec1, *d_vec2, *d_vec3;
// allocate memory on the device
cudaMalloc((void**)&d_vec1,BYTES);
cudaMalloc((void**)&d_vec2,BYTES);
cudaMalloc((void**)&d_vec3,BYTES);
// generate array on host
for(i=0;i<SIZE;i++)
{
h_vec1[i] = rand()%20;
h_vec2[i] = rand()%20;
h_vec3[i] = 0;
}
// move variables from host to device
cudaMemcpy(d_vec1,h_vec1,BYTES,cudaMemcpyHostToDevice);
cudaMemcpy(d_vec2,h_vec2,BYTES,cudaMemcpyHostToDevice);
// lauch kernel
add<<<num_blocks,num_threads>>>(d_vec1,d_vec2,d_vec3);
// move result back to main memory
cudaMemcpy(h_vec3,d_vec3,BYTES,cudaMemcpyDeviceToHost);
//print result
for(i=0;i<SIZE;i++)
printf("%d ", h_vec3[i]);
} |
7,006 | /*
**********************************************
* CS314 Principles of Programming Languages *
* Spring 2020 *
**********************************************
*/
#include <stdio.h>
#include <stdlib.h>
__global__ void exclusive_prefix_sum_gpu(int * oldSum, int * newSum, int distance, int numElements) {
/*YOUR CODE HERE*/
int tid = blockIdx.x * blockDim.x + threadIdx.x;//this is the thread id and will be used as index
while( tid < numElements )
{
if( distance == 0 )//since it's exclusive prefix sum, when distance is 0 shift all values over to the right once
{
if( tid == 0 )
{
newSum[tid] = 0;
}
else
{
newSum[tid] = oldSum[tid - 1];
}
}
else
{
if( tid - distance >= 0 )//if index - stride is still in the array, add those old sums together
{
newSum[tid] = oldSum[tid] + oldSum[tid - distance];
}
else//otherwise just move the element to the same index
{
newSum[tid] = oldSum[tid];
}
}
tid += ( blockDim.x * gridDim.x );
}
return;
}
|
7,007 | #include "includes.h"
__global__ void cudaUpdateActivity_kernel(int * inputs, char * activity, unsigned int * firingRate, unsigned int * exampleFiringRate, int * totalOutput, unsigned long long int * firstEventTime, unsigned long long int * lastEventTime, unsigned int inputsDimX, unsigned int inputsDimY, unsigned int inputsDimZ, unsigned int long long timestamp)
{
const unsigned int inputSize = inputsDimZ * inputsDimX * inputsDimY;
// One batch per block z dimension
const unsigned int batchInputOffset = blockIdx.z * inputSize;
for (unsigned int channel = blockIdx.x; channel < inputsDimZ; channel += gridDim.x) {
for (unsigned int y = threadIdx.y; y < inputsDimY; y += blockDim.y) {
for (unsigned int x = threadIdx.x; x < inputsDimX; x += blockDim.x) {
const unsigned int inputsIdx =
x + y*inputsDimX + channel*inputsDimX*inputsDimY;
int act = inputs[inputsIdx + batchInputOffset];
unsigned int actAbs = abs(act);
char spike = act == 0 ? 0 : act/abs(act);
activity[inputsIdx + batchInputOffset] = spike;
firingRate[inputsIdx + batchInputOffset] += actAbs;
exampleFiringRate[inputsIdx + batchInputOffset] += actAbs;
totalOutput[inputsIdx + batchInputOffset] += act;
}
}
}
} |
7,008 | #include <iostream>
// input: radius (1), nsample (1), xyz1 (b,n,3), xyz2 (b,m,3)
// output: idx (b,m,nsample), pts_cnt (b,m)
__global__ void query_ball_point_gpu(int b, int n, int m, float radius, int nsample, const float *xyz1, const float *xyz2, int *idx, int *pts_cnt) {
int batch_index = blockIdx.x;
xyz1 += n*3*batch_index;
xyz2 += m*3*batch_index;
idx += m*nsample*batch_index;
pts_cnt += m*batch_index; // counting how many unique points selected in local region
int index = threadIdx.x;
int stride = blockDim.x;
for (int j=index;j<m;j+=stride) {
int cnt = 0;
for (int k=0;k<n;++k) {
if (cnt == nsample)
break; // only pick the FIRST nsample points in the ball
float x2=xyz2[j*3+0];
float y2=xyz2[j*3+1];
float z2=xyz2[j*3+2];
float x1=xyz1[k*3+0];
float y1=xyz1[k*3+1];
float z1=xyz1[k*3+2];
float d=max(sqrtf((x2-x1)*(x2-x1)+(y2-y1)*(y2-y1)+(z2-z1)*(z2-z1)),1e-20f);
if (d<radius) {
if (cnt==0) { // set ALL indices to k, s.t. if there are less points in ball than nsample, we still have valid (repeating) indices
for (int l=0;l<nsample;++l)
idx[j*nsample+l] = k;
}
idx[j*nsample+cnt] = k;
cnt+=1;
}
}
pts_cnt[j] = cnt;
}
}
// input: radius (1), nsample (1), xyz1 (b,n,3), xyz2 (b,m,3)
// output: idx1 (b,m,nsample), idx2 (b,m,nsample), idx3 (b,m,nsample), pts_cnt (b,m)
__global__ void query_ball_point_level_gpu(int b, int n, int m, float radius, int nsample, const float *xyz1, const float *xyz2, int *idx1, int *idx2, int *idx3, int *pts_cnt) {
int batch_index = blockIdx.x;
xyz1 += n*3*batch_index;
xyz2 += m*3*batch_index;
idx1 += m*nsample*batch_index;
idx2 += m*nsample*batch_index;
idx3 += m*nsample*batch_index;
pts_cnt += m*batch_index; // counting how many unique points selected in local region
int index = threadIdx.x;
int stride = blockDim.x;
for (int j=index;j<m;j+=stride) {
int cnt1 = 0;
int cnt2 = 0;
int cnt3 = 0;
int idxs = -1;
for (int k=0;k<n;++k) {
float x2=xyz2[j*3+0];
float y2=xyz2[j*3+1];
float z2=xyz2[j*3+2];
float x1=xyz1[k*3+0];
float y1=xyz1[k*3+1];
float z1=xyz1[k*3+2];
float d=max(sqrtf((x2-x1)*(x2-x1)+(y2-y1)*(y2-y1)+(z2-z1)*(z2-z1)),1e-20f);
if (d < 1e-5)
idxs = k;
//0.577
if (d<radius*0.577) {
if (cnt1 < nsample) {
if (cnt1==0) { // set ALL indices to k, s.t. if there are less points in ball than nsample, we still have valid (repeating) indices
for (int l=0;l<nsample;++l)
idx1[j*nsample+l] = k;
}
idx1[j*nsample+cnt1] = k;
cnt1+=1;
}
}
//0.816
if (d<radius*0.816 && d >= radius * 0.577) {
if (cnt2 < nsample) {
if (cnt2==0) { // set ALL indices to k, s.t. if there are less points in ball than nsample, we still have valid (repeating) indices
for (int l=0;l<nsample;++l)
idx2[j*nsample+l] = k;
}
idx2[j*nsample+cnt2] = k;
cnt2+=1;
}
}
if (d<radius && d >= radius * 0.816) {
if (cnt3 < nsample) {
if (cnt3==0) {
for (int l=0;l<nsample;++l)
idx3[j*nsample+l] = k;
}
idx3[j*nsample+cnt3] = k;
cnt3+=1;
}
}
}
idxs = -(idxs + 1);
if (cnt1 == 0) {
for (int l = 0; l < nsample; ++l)
idx1[j*nsample+l] = idxs;
}
if (cnt2 == 0) {
for (int l = 0; l < nsample; ++l)
idx2[j*nsample+l] = idxs;
}
if (cnt3 == 0) {
for (int l = 0; l < nsample; ++l)
idx3[j*nsample+l] = idxs;
}
pts_cnt[j] = cnt1+cnt2+cnt3;
}
}
// input: radius (1), nsample (1), tangent (b,n,2)
// output: idx1 (b,n,nsample), idx2 (b,m,nsample), idx3 (b,n,nsample), pts_cnt (b,n)
__global__ void query_tangent_point_level_gpu(int b, int n, int m, float radius, int nsample, const float *tangent, const int* group, int *idx1, int *idx2, int *idx3, int *pts_cnt) {
int batch_index = blockIdx.x;
tangent += n*m*2*batch_index;
group += n*m*batch_index;
idx1 += n*nsample*batch_index;
idx2 += n*nsample*batch_index;
idx3 += n*nsample*batch_index;
pts_cnt += n*batch_index; // counting how many unique points selected in local region
int index = threadIdx.x;
int stride = blockDim.x;
for (int j=index;j<n;j+=stride) {
int cnt1 = 0;
int cnt2 = 0;
int cnt3 = 0;
int idxs = -1;
for (int k=0;k<m;++k) {
float tx = std::abs(tangent[(j*m+k)*2]);
float ty = std::abs(tangent[(j*m+k)*2+1]);
if (tx < 1e-5 && ty < 1e-5)
idxs = group[j*m+k];
//0.577
tx /= radius;
ty /= radius;
if (tx <= 0.5 && ty <= 0.5) {
if (cnt1 < nsample) {
if (cnt1==0) { // set ALL indices to k, s.t. if there are less points in ball than nsample, we still have valid (repeating) indices
for (int l=0;l<nsample;++l)
idx1[j*nsample+l] = group[j*m+k];
}
idx1[j*nsample+cnt1] = group[j*m+k];
cnt1+=1;
}
}
//0.816
else if (tx > 0.5 && ty > 0.5) {
if (cnt2 < nsample) {
if (cnt2==0) { // set ALL indices to k, s.t. if there are less points in ball than nsample, we still have valid (repeating) indices
for (int l=0;l<nsample;++l)
idx2[j*nsample+l] = group[j*m+k];
}
idx2[j*nsample+cnt2] = group[j*m+k];
cnt2+=1;
}
}
else {
if (cnt3 < nsample) {
if (cnt3==0) {
for (int l=0;l<nsample;++l)
idx3[j*nsample+l] = group[j*m+k];
}
idx3[j*nsample+cnt3] = group[j*m+k];
cnt3+=1;
}
}
}
idxs = -(idxs + 1);
if (cnt1 == 0) {
for (int l = 0; l < nsample; ++l)
idx1[j*nsample+l] = idxs;
}
if (cnt2 == 0) {
for (int l = 0; l < nsample; ++l)
idx2[j*nsample+l] = idxs;
}
if (cnt3 == 0) {
for (int l = 0; l < nsample; ++l)
idx3[j*nsample+l] = idxs;
}
pts_cnt[j] = cnt1+cnt2+cnt3;
}
}
// input: radius (1), nsample (1), tangent (b,n,2)
// output: idx1 (b,n,nsample), idx2 (b,m,nsample), idx3 (b,n,nsample), pts_cnt (b,n)
__global__ void query_radius_point_level_gpu(int b, int n, int m, float radius, int nsample, const float *tangent, const int* group, int *idx1, int *idx2, int *idx3, int *pts_cnt) {
int batch_index = blockIdx.x;
tangent += n*m*2*batch_index;
group += n*m*batch_index;
idx1 += n*nsample*batch_index;
idx2 += n*nsample*batch_index;
idx3 += n*nsample*batch_index;
pts_cnt += n*batch_index; // counting how many unique points selected in local region
int index = threadIdx.x;
int stride = blockDim.x;
for (int j=index;j<n;j+=stride) {
int cnt1 = 0;
int cnt2 = 0;
int cnt3 = 0;
int idxs = -1;
for (int k=0;k<m;++k) {
float tx = std::abs(tangent[(j*m+k)*2]);
float ty = std::abs(tangent[(j*m+k)*2+1]);
if (tx < 1e-5 && ty < 1e-5)
idxs = group[j*m+k];
//0.577
tx /= radius;
ty /= radius;
float sum_r = sqrt(tx * tx + ty * ty);
if (sum_r < 0.577) {
if (cnt1 < nsample) {
if (cnt1==0) { // set ALL indices to k, s.t. if there are less points in ball than nsample, we still have valid (repeating) indices
for (int l=0;l<nsample;++l)
idx1[j*nsample+l] = group[j*m+k];
}
idx1[j*nsample+cnt1] = group[j*m+k];
cnt1+=1;
}
}
//0.816
else if (tx > 0.816) {
if (cnt2 < nsample) {
if (cnt2==0) { // set ALL indices to k, s.t. if there are less points in ball than nsample, we still have valid (repeating) indices
for (int l=0;l<nsample;++l)
idx2[j*nsample+l] = group[j*m+k];
}
idx2[j*nsample+cnt2] = group[j*m+k];
cnt2+=1;
}
}
else {
if (cnt3 < nsample) {
if (cnt3==0) {
for (int l=0;l<nsample;++l)
idx3[j*nsample+l] = group[j*m+k];
}
idx3[j*nsample+cnt3] = group[j*m+k];
cnt3+=1;
}
}
}
idxs = -(idxs + 1);
if (cnt1 == 0) {
for (int l = 0; l < nsample; ++l)
idx1[j*nsample+l] = idxs;
}
if (cnt2 == 0) {
for (int l = 0; l < nsample; ++l)
idx2[j*nsample+l] = idxs;
}
if (cnt3 == 0) {
for (int l = 0; l < nsample; ++l)
idx3[j*nsample+l] = idxs;
}
pts_cnt[j] = cnt1+cnt2+cnt3;
}
}
// input: radius (1), nsample (1), tangent (b,n,2)
// output: idx1 (b,n,nsample), idx2 (b,m,nsample), idx3 (b,n,nsample), pts_cnt (b,n)
__global__ void query_radius_angle_point_level_gpu(int b, int n, int m, float start_angle, float radius, int nsample, const float *tangent, const int* group, int *idx1, int *idx2, int *idx3, int *idx4, int *idx5, int *idx6, int *idx7, int *idx8, int *idx9, int *pts_cnt) {
int batch_index = blockIdx.x;
tangent += n*m*2*batch_index;
group += n*m*batch_index;
idx1 += n*nsample*batch_index;
idx2 += n*nsample*batch_index;
idx3 += n*nsample*batch_index;
idx4 += n*nsample*batch_index;
idx5 += n*nsample*batch_index;
idx6 += n*nsample*batch_index;
idx7 += n*nsample*batch_index;
idx8 += n*nsample*batch_index;
idx9 += n*nsample*batch_index;
pts_cnt += n*batch_index; // counting how many unique points selected in local region
int index = threadIdx.x;
int stride = blockDim.x;
for (int j=index;j<n;j+=stride) {
int cnt1 = 0;
int cnt2 = 0;
int cnt3 = 0;
int cnt4 = 0;
int cnt5 = 0;
int cnt6 = 0;
int cnt7 = 0;
int cnt8 = 0;
int cnt9 = 0;
int idxs = -1;
for (int k=0;k<m;++k) {
float angle = atan2(tangent[(j*m+k)*2],tangent[(j*m+k)*2+1]) / 3.141592654 * 180.0 + start_angle;
int angle_diff = (int)angle % 360 / 120;
float tx = std::abs(tangent[(j*m+k)*2]);
float ty = std::abs(tangent[(j*m+k)*2+1]);
if (tx < 1e-5 && ty < 1e-5)
idxs = group[j*m+k];
//0.577
tx /= radius;
ty /= radius;
float sum_r = sqrt(tx * tx + ty * ty);
if (sum_r < 0.577) {
if (angle_diff == 0) {
if (cnt1 < nsample) {
if (cnt1==0) {
for (int l=0;l<nsample;++l)
idx1[j*nsample+l] = group[j*m+k];
}
idx1[j*nsample+cnt1] = group[j*m+k], cnt1 += 1;
}
}
else if (angle_diff == 1) {
if (cnt2 < nsample) {
if (cnt2==0) {
for (int l=0;l<nsample;++l)
idx2[j*nsample+l] = group[j*m+k];
}
idx2[j*nsample+cnt2] = group[j*m+k], cnt2 += 1;
}
}
else {
if (cnt3 < nsample) {
if (cnt3==0) {
for (int l=0;l<nsample;++l)
idx3[j*nsample+l] = group[j*m+k];
}
idx3[j*nsample+cnt3] = group[j*m+k], cnt3 += 1;
}
}
}
//0.816
else if (tx > 0.816) {
if (angle_diff == 0) {
if (cnt4 < nsample) {
if (cnt4==0) {
for (int l=0;l<nsample;++l)
idx4[j*nsample+l] = group[j*m+k];
}
idx4[j*nsample+cnt4] = group[j*m+k], cnt4 += 1;
}
}
else if (angle_diff == 1) {
if (cnt5 < nsample) {
if (cnt5==0) {
for (int l=0;l<nsample;++l)
idx5[j*nsample+l] = group[j*m+k];
}
idx5[j*nsample+cnt5] = group[j*m+k], cnt5 += 1;
}
}
else {
if (cnt6 < nsample) {
if (cnt6==0) {
for (int l=0;l<nsample;++l)
idx6[j*nsample+l] = group[j*m+k];
}
idx6[j*nsample+cnt6] = group[j*m+k], cnt6 += 1;
}
}
}
else {
if (angle_diff == 0) {
if (cnt7 < nsample) {
if (cnt7==0) {
for (int l=0;l<nsample;++l)
idx7[j*nsample+l] = group[j*m+k];
}
idx7[j*nsample+cnt7] = group[j*m+k], cnt7 += 1;
}
}
else if (angle_diff == 1) {
if (cnt8 < nsample) {
if (cnt8==0) {
for (int l=0;l<nsample;++l)
idx8[j*nsample+l] = group[j*m+k];
}
idx8[j*nsample+cnt8] = group[j*m+k], cnt8 += 1;
}
}
else {
if (cnt9 < nsample) {
if (cnt9==0) {
for (int l=0;l<nsample;++l)
idx9[j*nsample+l] = group[j*m+k];
}
idx9[j*nsample+cnt9] = group[j*m+k], cnt9 += 1;
}
}
}
}
idxs = -(idxs + 1);
if (cnt1 == 0) {
for (int l = 0; l < nsample; ++l)
idx1[j*nsample+l] = idxs;
}
if (cnt2 == 0) {
for (int l = 0; l < nsample; ++l)
idx2[j*nsample+l] = idxs;
}
if (cnt3 == 0) {
for (int l = 0; l < nsample; ++l)
idx3[j*nsample+l] = idxs;
}
if (cnt4 == 0) {
for (int l = 0; l < nsample; ++l)
idx4[j*nsample+l] = idxs;
}
if (cnt5 == 0) {
for (int l = 0; l < nsample; ++l)
idx5[j*nsample+l] = idxs;
}
if (cnt6 == 0) {
for (int l = 0; l < nsample; ++l)
idx6[j*nsample+l] = idxs;
}
if (cnt7 == 0) {
for (int l = 0; l < nsample; ++l)
idx7[j*nsample+l] = idxs;
}
if (cnt8 == 0) {
for (int l = 0; l < nsample; ++l)
idx8[j*nsample+l] = idxs;
}
if (cnt9 == 0) {
for (int l = 0; l < nsample; ++l)
idx9[j*nsample+l] = idxs;
}
pts_cnt[j] = cnt1+cnt2+cnt3+cnt4+cnt5+cnt6+cnt7+cnt8+cnt9;
}
}
// input: radius (1), nsample (1), tangent (b,n,2)
// output: idx1 (b,n,nsample), idx2 (b,m,nsample), idx3 (b,n,nsample), pts_cnt (b,n)
__global__ void query_tangent9_point_level_gpu(int b, int n, int m, float start_angle, float radius, int nsample, const float *tangent, const int* group, int *idx1, int *idx2, int *idx3, int *idx4, int *idx5, int *idx6, int *idx7, int *idx8, int *idx9, int *pts_cnt) {
int batch_index = blockIdx.x;
tangent += n*m*2*batch_index;
group += n*m*batch_index;
idx1 += n*nsample*batch_index;
idx2 += n*nsample*batch_index;
idx3 += n*nsample*batch_index;
idx4 += n*nsample*batch_index;
idx5 += n*nsample*batch_index;
idx6 += n*nsample*batch_index;
idx7 += n*nsample*batch_index;
idx8 += n*nsample*batch_index;
idx9 += n*nsample*batch_index;
pts_cnt += n*batch_index; // counting how many unique points selected in local region
int index = threadIdx.x;
int stride = blockDim.x;
for (int j=index;j<n;j+=stride) {
int cnt1 = 0;
int cnt2 = 0;
int cnt3 = 0;
int cnt4 = 0;
int cnt5 = 0;
int cnt6 = 0;
int cnt7 = 0;
int cnt8 = 0;
int cnt9 = 0;
int idxs = -1;
for (int k=0;k<m;++k) {
float tx = tangent[(j*m+k)*2];
float ty = tangent[(j*m+k)*2+1];
if (abs(tx) < 1e-5 && abs(ty) < 1e-5)
idxs = group[j*m+k];
//0.577
tx /= radius;
ty /= radius;
int angle_diff = 0;
if (tx > -0.5)
angle_diff = 1;
if (tx > 0.5)
angle_diff = 2;
if (ty < -0.5) {
if (angle_diff == 0) {
if (cnt1 < nsample) {
if (cnt1==0) {
for (int l=0;l<nsample;++l)
idx1[j*nsample+l] = group[j*m+k];
}
idx1[j*nsample+cnt1] = group[j*m+k], cnt1 += 1;
}
}
else if (angle_diff == 1) {
if (cnt2 < nsample) {
if (cnt2==0) {
for (int l=0;l<nsample;++l)
idx2[j*nsample+l] = group[j*m+k];
}
idx2[j*nsample+cnt2] = group[j*m+k], cnt2 += 1;
}
}
else {
if (cnt3 < nsample) {
if (cnt3==0) {
for (int l=0;l<nsample;++l)
idx3[j*nsample+l] = group[j*m+k];
}
idx3[j*nsample+cnt3] = group[j*m+k], cnt3 += 1;
}
}
}
//0.816
else if (ty > 0.5) {
if (angle_diff == 0) {
if (cnt4 < nsample) {
if (cnt4==0) {
for (int l=0;l<nsample;++l)
idx4[j*nsample+l] = group[j*m+k];
}
idx4[j*nsample+cnt4] = group[j*m+k], cnt4 += 1;
}
}
else if (angle_diff == 1) {
if (cnt5 < nsample) {
if (cnt5==0) {
for (int l=0;l<nsample;++l)
idx5[j*nsample+l] = group[j*m+k];
}
idx5[j*nsample+cnt5] = group[j*m+k], cnt5 += 1;
}
}
else {
if (cnt6 < nsample) {
if (cnt6==0) {
for (int l=0;l<nsample;++l)
idx6[j*nsample+l] = group[j*m+k];
}
idx6[j*nsample+cnt6] = group[j*m+k], cnt6 += 1;
}
}
}
else {
if (angle_diff == 0) {
if (cnt7 < nsample) {
if (cnt7==0) {
for (int l=0;l<nsample;++l)
idx7[j*nsample+l] = group[j*m+k];
}
idx7[j*nsample+cnt7] = group[j*m+k], cnt7 += 1;
}
}
else if (angle_diff == 1) {
if (cnt8 < nsample) {
if (cnt8==0) {
for (int l=0;l<nsample;++l)
idx8[j*nsample+l] = group[j*m+k];
}
idx8[j*nsample+cnt8] = group[j*m+k], cnt8 += 1;
}
}
else {
if (cnt9 < nsample) {
if (cnt9==0) {
for (int l=0;l<nsample;++l)
idx9[j*nsample+l] = group[j*m+k];
}
idx9[j*nsample+cnt9] = group[j*m+k], cnt9 += 1;
}
}
}
}
idxs = -(idxs + 1);
if (cnt1 == 0) {
for (int l = 0; l < nsample; ++l)
idx1[j*nsample+l] = idxs;
}
if (cnt2 == 0) {
for (int l = 0; l < nsample; ++l)
idx2[j*nsample+l] = idxs;
}
if (cnt3 == 0) {
for (int l = 0; l < nsample; ++l)
idx3[j*nsample+l] = idxs;
}
if (cnt4 == 0) {
for (int l = 0; l < nsample; ++l)
idx4[j*nsample+l] = idxs;
}
if (cnt5 == 0) {
for (int l = 0; l < nsample; ++l)
idx5[j*nsample+l] = idxs;
}
if (cnt6 == 0) {
for (int l = 0; l < nsample; ++l)
idx6[j*nsample+l] = idxs;
}
if (cnt7 == 0) {
for (int l = 0; l < nsample; ++l)
idx7[j*nsample+l] = idxs;
}
if (cnt8 == 0) {
for (int l = 0; l < nsample; ++l)
idx8[j*nsample+l] = idxs;
}
if (cnt9 == 0) {
for (int l = 0; l < nsample; ++l)
idx9[j*nsample+l] = idxs;
}
pts_cnt[j] = cnt1+cnt2+cnt3+cnt4+cnt5+cnt6+cnt7+cnt8+cnt9;
}
}
// input: points (b,n,c), idx (b,m,nsample)
// output: out (b,m,nsample,c)
__global__ void group_point_gpu(int b, int n, int c, int m, int nsample, const float *points, const int *idx, float *out, int relative) {
int batch_index = blockIdx.x;
points += n*c*batch_index;
idx += m*nsample*batch_index;
out += m*nsample*c*batch_index;
int index = threadIdx.x;
int stride = blockDim.x;
for (int j=index;j<m;j+=stride) {
for (int k=0;k<nsample;++k) {
int ii = idx[j*nsample+k];
for (int l=0;l<c;++l) {
if (ii < 0) {
if (relative == 0)
out[j * nsample * c + k * c + l] = 0;
else
out[j * nsample * c + k * c + l] = points[(-ii-1)*c+l];
} else {
out[j*nsample*c+k*c+l] = points[ii*c+l];
}
}
}
}
}
// input: grad_out (b,m,nsample,c), idx (b,m,nsample),
// output: grad_points (b,n,c)
__global__ void group_point_grad_gpu(int b, int n, int c, int m, int nsample, const float *grad_out, const int *idx, float *grad_points, int relative) {
int batch_index = blockIdx.x;
idx += m*nsample*batch_index;
grad_out += m*nsample*c*batch_index;
grad_points += n*c*batch_index;
int index = threadIdx.x;
int stride = blockDim.x;
for (int j=index;j<m;j+=stride) {
for (int k=0;k<nsample;++k) {
int ii = idx[j*nsample+k];
for (int l=0;l<c;++l) {
if (ii < 0) {
if (relative == 1)
atomicAdd(&grad_points[(-ii-1)*c+l], grad_out[j*nsample*c+k*c+l]);
} else {
atomicAdd(&grad_points[ii*c+l], grad_out[j*nsample*c+k*c+l]);
}
}
}
}
}
// input: k (1), distance matrix dist (b,m,n)
// output: idx (b,m,n), dist_out (b,m,n)
// only the top k results within n are useful
__global__ void selection_sort_gpu(int b, int n, int m, int k, const float *dist, int *outi, float *out) {
int batch_index = blockIdx.x;
dist+=m*n*batch_index;
outi+=m*n*batch_index;
out+=m*n*batch_index;
int index = threadIdx.x;
int stride = blockDim.x;
// copy from dist to dist_out
for (int j=index;j<m;j+=stride) {
for (int s=0;s<n;++s) {
out[j*n+s] = dist[j*n+s];
outi[j*n+s] = s;
}
}
float *p_dist;
for (int j=index;j<m;j+=stride) {
p_dist = out+j*n;
// selection sort for the first k elements
for (int s=0;s<k;++s) {
int min=s;
// find the min
for (int t=s+1;t<n;++t) {
if (p_dist[t]<p_dist[min]) {
min = t;
}
}
// swap min-th and i-th element
if (min!=s) {
float tmp = p_dist[min];
p_dist[min] = p_dist[s];
p_dist[s] = tmp;
int tmpi = outi[j*n+min];
outi[j*n+min] = outi[j*n+s];
outi[j*n+s] = tmpi;
}
}
}
}
void queryBallPointLauncher(int b, int n, int m, float radius, int nsample, const float *xyz1, const float *xyz2, int *idx, int *pts_cnt) {
query_ball_point_gpu<<<b,256>>>(b,n,m,radius,nsample,xyz1,xyz2,idx,pts_cnt);
//cudaDeviceSynchronize();
}
void queryBallPointLevelLauncher(int b, int n, int m, float radius, int nsample, const float *xyz1, const float *xyz2, int *idx1, int* idx2, int* idx3, int *pts_cnt) {
query_ball_point_level_gpu<<<b,256>>>(b,n,m,radius,nsample,xyz1,xyz2,idx1,idx2,idx3,pts_cnt);
//cudaDeviceSynchronize();
}
void queryTangentPointLevelLauncher(int b, int n, int m, float radius, int nsample, const float *tangent, const int* group, int *idx1, int* idx2, int* idx3, int *pts_cnt) {
query_tangent_point_level_gpu<<<b,256>>>(b,n,m,radius,nsample,tangent,group,idx1,idx2,idx3,pts_cnt);
//cudaDeviceSynchronize();
}
void queryRadiusPointLevelLauncher(int b, int n, int m, float radius, int nsample, const float *tangent, const int* group, int *idx1, int* idx2, int* idx3, int *pts_cnt) {
query_radius_point_level_gpu<<<b,256>>>(b,n,m,radius,nsample,tangent,group,idx1,idx2,idx3,pts_cnt);
//cudaDeviceSynchronize();
}
void queryRadiusAnglePointLevelLauncher(int b, int n, int m, float start_angle, float radius, int nsample, const float *tangent, const int* group,
int *idx1, int *idx2, int *idx3, int* idx4, int* idx5, int* idx6, int* idx7, int* idx8, int* idx9, int *pts_cnt)
{
query_radius_angle_point_level_gpu<<<b,256>>>(b,n,m,start_angle,radius,nsample,tangent,group,idx1,idx2,idx3,idx4,idx5,idx6,idx7,idx8,idx9,pts_cnt);
}
void queryTangent9PointLevelLauncher(int b, int n, int m, float start_angle, float radius, int nsample, const float *tangent, const int* group,
int *idx1, int *idx2, int *idx3, int* idx4, int* idx5, int* idx6, int* idx7, int* idx8, int* idx9, int *pts_cnt)
{
query_tangent9_point_level_gpu<<<b,256>>>(b,n,m,start_angle,radius,nsample,tangent,group,idx1,idx2,idx3,idx4,idx5,idx6,idx7,idx8,idx9,pts_cnt);
}
void selectionSortLauncher(int b, int n, int m, int k, const float *dist, int *outi, float *out) {
selection_sort_gpu<<<b,256>>>(b,n,m,k,dist,outi,out);
//cudaDeviceSynchronize();
}
void groupPointLauncher(int b, int n, int c, int m, int nsample, const float *points, const int *idx, float *out, int relative){
group_point_gpu<<<b,256>>>(b,n,c,m,nsample,points,idx,out,relative);
//cudaDeviceSynchronize();
}
void groupPointGradLauncher(int b, int n, int c, int m, int nsample, const float *grad_out, const int *idx, float *grad_points, int relative){
group_point_grad_gpu<<<b,256>>>(b,n,c,m,nsample,grad_out,idx,grad_points,relative);
//group_point_grad_gpu<<<1,1>>>(b,n,c,m,nsample,grad_out,idx,grad_points);
//cudaDeviceSynchronize();
}
|
7,009 | #include "includes.h"
#define NUM_THREAD 256 // Number of thread blocks
#define print(x) printf("%d",x)
float *matrixMul_float_serial(float vector1[], float vector2[], int size);
float *matrixMul_float_parallel(float vector1[], float vector2[], int size, int thread_count);
float *matrixMul_float_cuda(float* vector1, float* vector2, int num);
double *matrixMul_double_serial(double vector1[], double vector2[], int size);
double *matrixMul_double_parallel(double vector1[], double vector2[], int size, int thread_count);
double *matrixMul_double_cuda(double* vector1, double* vector2, int num);
double doubleGen();
float floatGen();
void operations(int size, int parallel, int serial, int cuda, int verify, int thread_count);
void print_results_float( int size, double time_spent);
void print_results_double( int size, double time_spent);
double verifyVectord(double *vector1, double *vector2, int size);
float verifyVectorf(float *vector1, float *vector2, int size);
__global__ void matMul_CUDA_float(float *sum, int size, float *vector1, float *vector2){
int idx = blockIdx.x*blockDim.x+threadIdx.x; // Sequential thread index across the blocks
int k;
if(idx < size*size){
for(k=0; k< size; k++){
sum[idx] += (*(vector1+(idx-(idx % size)+k))) * (*(vector2+(k*size+(idx % size))));
}
}
} |
7,010 | #include "socket_reader.cuh"
template<class T>
uint32_t
SocketReader<T>::AcquireFrames(std::vector<T> &buf,
uint64_t frame_offset,
uint32_t n_frames) {
return 0;
};
template<class T>
void SocketReader<T>::Open() {};
template<class T>
void SocketReader<T>::Close() {};
template
class SocketReader<short>;
|
7,011 | #include <stdio.h>
#include <stdlib.h>
#include <cuda.h>
#include <cuda_runtime_api.h>
#include <assert.h>
static const int WORK_SIZE = /*256*/ 2;
/**
* This macro checks return value of the CUDA runtime call and exits
* the application if the call failed.
*/
__device__ unsigned int bitreverse1(unsigned int number) {
number = ((0xf0f0f0f0 & number) >> 4) | ((0x0f0f0f0f & number) << 4);
number = ((0xcccccccc & number) >> 2) | ((0x33333333 & number) << 2);
number = ((0xaaaaaaaa & number) >> 1) | ((0x55555555 & number) << 1);
return number;
}
/**
* CUDA kernel function that reverses the order of bits in each element of the array.
*/
__global__ void bitreverse(void *data) {
unsigned int *idata = (unsigned int*) data;
idata[threadIdx.x] = bitreverse1(idata[threadIdx.x]);
}
/**
* Host function that prepares data array and passes it to the CUDA kernel.
*/
int main() {
void *d = NULL;
int i;
unsigned int idata[WORK_SIZE], odata[WORK_SIZE];
for (i = 0; i < WORK_SIZE; i++){
idata[i] = (unsigned int) i+1;
printf("%u; ", idata[i]);
}
printf("\n");
cudaMalloc((void**) &d, sizeof(int) * WORK_SIZE);
cudaMemcpy(d, idata, sizeof(int) * WORK_SIZE, cudaMemcpyHostToDevice);
bitreverse<<<1, WORK_SIZE, WORK_SIZE * sizeof(int)>>>(d);
//ESBMC_verify_kernel(bitreverse, 1, WORK_SIZE /* *sizeof(int)*/, d);
cudaThreadSynchronize(); // Wait for the GPU launched work to complete
cudaGetLastError();
cudaMemcpy(odata, d, sizeof(int) * WORK_SIZE, cudaMemcpyDeviceToHost);
for (i = 0; i < WORK_SIZE; i++){
printf("Input value: %u, device output: %u\n", idata[i], odata[i]);
assert((idata[i]==1)and(odata[i]==128));
}
cudaFree((void*) d);
cudaDeviceReset();
return 0;
}
|
7,012 | #include "includes.h"
__global__ void callOperationSharedStatic(int *a, int *b, int *c, int n)
{
int tidx = blockDim.x * blockIdx.x + threadIdx.x;
int tidy = blockDim.y * blockIdx.y + threadIdx.y;
if (tidx >= n || tidy >= n)
{
return;
}
int tid = tidx * n + tidy;
__shared__ int s_a[size * size], s_b[size * size], s_c[size * size];
s_a[tid] = a[tid];
s_b[tid] = b[tid];
if (s_a[tid] >= s_b[tid])
{
s_c[tid] = s_a[tid];
}
else
{
s_c[tid] = s_b[tid];
}
c[tid] = s_c[tid];
} |
7,013 | /*
* sarray.cu
*
* Created on: 18/apr/2014
* Author: Giovanni De Cesare
*
* Scopo: somma due vettori
*/
#include <stdio.h>
#define N 10
// Attenzione a questa parola chiave. Definisce un kernel, ovvero un processo che avviene
// sulla GPU
__global__ void dark(void)
{
// Oggi non mi va di fare nulla. E in effetti non faccio niente.
}
// Questo kernel calcola la somma di due vettori "a[N]" e "b[N]"
// I cores della GPU che vanno da 0 a N fanno una somma.
__global__ void add(int *a, int *b, int *c)
{
int id = blockIdx.x;
if (id < N)
{
c[id]= a[id] + b[id];
}
}
int main(void)
{
int a[N], b[N], c[N]; // tre vettori a, b, c allocati sulla CPU
int *dev_a, *dev_b, *dev_c; // tre vettori dev_[a,b,c] da allocare sulla GPU
// Devo allocare la memoria della GPU per i tre vettori a, b, c
cudaMalloc( (void**)&dev_a, N * sizeof(int) );
cudaMalloc( (void**)&dev_b, N * sizeof(int) );
cudaMalloc( (void**)&dev_c, N * sizeof(int) );
// Assegno ai vettori a, b dei valori (arbitrari)
for (int i=0; i < N; i++)
{
a[i] = i;
b[i] = i;
}
// Quindi copio a, b nella GPU
cudaMemcpy(dev_a, a, N * sizeof(int), cudaMemcpyHostToDevice );
cudaMemcpy(dev_b, b, N * sizeof(int), cudaMemcpyHostToDevice );
// lancio il kernel
add<<<N,1>>>(dev_a, dev_b, dev_c);
// L'array somma c sta ancora sulla GPU. Lo devo copiare sulla mamoria del processore
// prima di poterlo usare
cudaMemcpy(c, dev_c, N * sizeof(int), cudaMemcpyDeviceToHost );
// Finalmente scrivo il risultato della somma
for (int i=0; i<N; i++)
{
printf("%d + %d = %d\n", a[i], b[i], c[i]);
}
// E' sempre una buona abitudine liberare la memoria dopo averla usata
cudaFree( dev_a );
cudaFree( dev_b );
cudaFree( dev_c );
printf("Bye.\n");
return 0;
}
|
7,014 | // Colour Sine wave Kernal
// Based on kernal_colour in kernelVBO.cpp by Rob Farber
__global__ void kernel(float4* dVertexArray, uchar4 *dColorArray,
unsigned int width, unsigned int height, float time)
{
unsigned int x = blockIdx.x*blockDim.x + threadIdx.x;
unsigned int y = blockIdx.y*blockDim.y + threadIdx.y;
// Each thread is unique point (u,v) in interval [-1,1],[-1,1]
const float u = 2.0f* (x/(float)width) - 1.0f;
const float v = 2.0f* (y/(float)height) - 1.0f;
const float w = 0.5f * sinf(4.0f*u + time) * cosf(4.0f*v + time);
// Update vertex array for point
dVertexArray[y*width+x] = make_float4(u, w, v, 1.0f);
// Update colour array for point
dColorArray[y*width+x].w = 0.0f;
dColorArray[y*width+x].x = 255.0f *0.5f*(1.f+sinf(w+x));
dColorArray[y*width+x].y = 255.0f *0.5f*(1.f+sinf(x)*cosf(y));
dColorArray[y*width+x].z = 255.0f *0.5f*(1.f+sinf(w+time/10.0f));
}
extern "C" void launch_kernel(float4* dVertexArray, uchar4* dColourArray,
unsigned int width, unsigned int height, float time)
{
dim3 block(8, 8, 1);
dim3 grid(width / block.x, height / block.y, 1);
kernel<<< grid, block>>>(dVertexArray, dColourArray, width, height, time);
}
|
7,015 | #include <stdio.h>
#include <stdlib.h>
#include <time.h>
#include <string.h>
#define MAX_VALUE 2147483647
#define numThreads 32
void printMatrix(int *matrix, int users, int attributes) {
// printf("Matrix:-\n");
for(int i = 0; i < (users * attributes); i++) {
if(i % attributes == 0 && i != 0) {
printf("\n%d ", matrix[i]);
} else {
printf("%d ", matrix[i]);
}
}
printf("\n");
}
int checker(char* input, char* check) {
int i,result=1;
for(i=0; input[i]!='\0' || check[i]!='\0'; i++) {
if(input[i] != check[i]) {
result=0;
break;
}
}
return result;
}
void preliminarySteps(int argc, char** argv, int** dataSetPtr, int** scoresPtr, int* usersPtr, int* attributesPtr, int* kPtr) {
// Check input
if(argc < 5) {
printf("Usage: %s <k> <users> <attributes> <serial/parallel>\n", argv[0]);
exit(0);
}
int * dataSet;
int k = atoi(argv[1]);
int users = atoi(argv[2]);
int attributes = atoi(argv[3]);
*usersPtr = users;
*attributesPtr = attributes;
*kPtr = k;
dataSet = (int*) malloc(sizeof(int) * users * attributes);
if(dataSet != NULL) {
// printf("Allocated an array for %d users and %d attributes\n", users, attributes);
} else {
printf("Couldn't allocate dataSet array, quitting!\n");
exit(0);
}
// Seed the RNG
srand(time(NULL));
// Now fill dataSet with some values
for(int i=0; i < (users * attributes); i++)
//dataSet[i] = rand() % 1000; // Random integers between 0 and 1,000
dataSet[i] = rand() % 15; // Random integers between 0 and 1,000
*dataSetPtr = dataSet;
int *scores;
scores = (int *)malloc(sizeof(int) * users * users);
if(scores != NULL) {
// printf("Allocated a square scores array for %d users\n", users);
} else {
printf("Couldn't allocate scores array, quitting!\n");
exit(0);
}
*scoresPtr = scores;
}
void calculateScore(int* matrix, int* scores, int users, int attributes, int user1, int user2) {
int answer = 0;
int user1Start = attributes*user1;
int user1End = user1Start + attributes - 1;
int user2Start = attributes*user2;
int user2End = user2Start + attributes - 1;
int i; int j; int difference;
for(i = user1Start, j = user2Start; i <= user1End && j <= user2End ; i++, j++) {
difference = matrix[i] - matrix[j];
answer += difference*difference;
}
scores[user1*users + user2] = answer;
}
void calculateScores(int *matrix, int *scores, int users, int attributes) {
int user1; int user2;
for(user1 = 0; user1 < users; user1++) {
for(user2 = 0; user2 < users; user2++) {
calculateScore(matrix, scores, users, attributes, user1, user2);
}
}
}
__global__ void calculateScoreKernel(int *matrix, int *scores, int users, int attributes) {
int user1 = numThreads*blockIdx.x + threadIdx.x;
int user2 = numThreads*blockIdx.y + threadIdx.y;
if(user1 >= 0 && user1 < users && user2 >= 0 && user2 < users) {
int answer = 0;
int user1Start = attributes*user1;
int user1End = user1Start + attributes - 1;
int user2Start = attributes*user2;
int user2End = user2Start + attributes - 1;
int i; int j; int difference;
for(i = user1Start, j = user2Start; i <= user1End && j <= user2End ; i++, j++) {
difference = matrix[i] - matrix[j];
answer += difference*difference;
}
/*
# if __CUDA_ARCH__>=200
printf("%d, %d, %d, %d => %d \n", blockIdx.x, blockIdx.y, threadIdx.x, threadIdx.y, answer);
#endif
*/
scores[user1*users + user2] = answer;
}
}
void launchCalculateScoreKernel(int * dataSet, int * scores, int users, int attributes) {
int * dev_dataSet;
int * dev_scores;
cudaMalloc((void**) &dev_dataSet, users*attributes*sizeof(int));
cudaMalloc((void**) &dev_scores, users*users*sizeof(int));
cudaMemcpy(dev_dataSet, dataSet, users*attributes*sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(dev_scores, scores, users*users*sizeof(int), cudaMemcpyHostToDevice);
int numBlocks = (int) ceil(users*1.0/numThreads);
dim3 grid( numBlocks, numBlocks, 1 );
dim3 block( numThreads, numThreads, 1 );
calculateScoreKernel<<< grid, block >>>(dev_dataSet, dev_scores, users, attributes);
cudaMemcpy(scores, dev_scores, users*users*sizeof(int), cudaMemcpyDeviceToHost);
}
__global__ void calculateKNearestKernel(int * scores, int * kNearest, int users, int K) {
int minValue, minIndex, value, user, k, index;
user = numThreads*blockIdx.x + threadIdx.x;
for(k = 0; k < K; k++) {
minValue = MAX_VALUE;
minIndex = -1;
for(index = 0; index < users; index++) {
value = scores[user*users + index];
if(value < minValue && index != user) {
minValue = value;
minIndex = index;
}
}
if(minIndex != -1) {
// scores[user*users + minIndex] = MAX_VALUE;
}
// kNearest[user*users + k] = minIndex;
}
}
// arguments: scores, kNearest, users, k
void launchCalculateKNearestKernel(int * dataSet, int * scores, int users, int k) {
int * dev_dataSet;
int * dev_scores;
cudaMalloc((void**) &dev_dataSet, users*users*sizeof(int));
cudaMalloc((void**) &dev_scores, users*k*sizeof(int));
cudaMemcpy(dev_dataSet, dataSet, users*users*sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(dev_scores, scores, users*k*sizeof(int), cudaMemcpyHostToDevice);
int numBlocks = (int) ceil(users*1.0/numThreads);
dim3 grid( numBlocks, numBlocks, 1 );
dim3 block( numThreads, numThreads, 1 );
calculateKNearestKernel<<< grid, block >>>(dev_dataSet, dev_scores, users, k);
cudaMemcpy(scores, dev_scores, users*k*sizeof(int), cudaMemcpyDeviceToHost);
}
void writeToFile(clock_t start, clock_t end, char * whichProgramToRun, int users, int attributes, int k, char * fileName) {
FILE * file;
if(checker(whichProgramToRun, (char*) "serial")) {
file = fopen(fileName, "a");
}else {
file = fopen(fileName, "a");
}
long double timeTaken = (long double)(end - start)/CLOCKS_PER_SEC;
fprintf(file, "%s, %d, %d, %d, %Lf\n", whichProgramToRun, users, attributes, k, timeTaken);
fclose(file);
printf("%s, %d, %d, %d, %Lf\n", whichProgramToRun, users, attributes, k, timeTaken);
}
void calculateKNearestSerial(int * scores, int * kNearest, int users, int K) {
int minValue, minIndex, value, user, k, index;
for(user = 0; user < users; user++) {
for(k = 0; k < K; k++) {
minValue = MAX_VALUE;
minIndex = -1;
for(index = 0; index < users; index++) {
value = scores[user*users + index];
if(value < minValue && index != user) {
minValue = value;
minIndex = index;
}
}
if(minIndex != -1) {
// scores[user*users + minIndex] = MAX_VALUE;
}
// kNearest[user*users + k] = minIndex;
}
}
}
int main(int argc, char **argv) {
int * dataSet; int * scores; int users; int attributes; int k;
preliminarySteps(argc, argv, &dataSet, &scores, &users, &attributes, &k);
char* whichProgramToRun = argv[4];
clock_t start = clock();
printf("k = %d\n", k);
// printMatrix(dataSet, users, attributes);
if(checker(whichProgramToRun, (char*) "serial")) {
// serial
calculateScores(dataSet, scores, users, attributes);
free(dataSet);
int * kNearest = (int*) malloc(sizeof(int) * users * k);
calculateKNearestSerial(scores, kNearest, users, k);
free(scores);
free(kNearest);
}else if(checker(whichProgramToRun, (char*) "parallel")) {
// cuda parallel
launchCalculateScoreKernel(dataSet, scores, users, attributes);
free(dataSet);
int * kNearest = (int*) malloc(sizeof(int) * users * k);
launchCalculateKNearestKernel(scores, kNearest, users, k);
free(scores);
free(kNearest);
}else {
printf("Enter correct program to run: serial or parallel.\n");
free(dataSet); free(scores);
exit(0);
}
// printf("Scores:-\n");
// printMatrix(scores, users, users);
clock_t end = clock();
char * fileName = argv[5];
writeToFile(start, end, whichProgramToRun, users, attributes, k, fileName);
return 0;
}
|
7,016 | #include <math.h>
#include <stdint.h>
#include <stdio.h>
__global__ void apply_linear_filter_kernel(uint8_t *src, uint8_t *dst, float *fil, int src_H, int src_W, int src_C, int fil_H, int fil_W) {
int h_idx = threadIdx.x + blockIdx.x*blockDim.x;
int w_idx = threadIdx.y + blockIdx.y*blockDim.y;
int c_idx = threadIdx.z + blockIdx.z*blockDim.z;
if ((h_idx >= (src_H-fil_H+1))|(w_idx >= (src_W-fil_W+1))|(c_idx >= src_C)) return;
extern __shared__ int s[];
uint8_t *shared = (uint8_t*)s;
int src_idx = h_idx*src_W*src_C + w_idx*src_C + c_idx;
for (int i = 0; (h_idx + i*blockDim.x < src_H)&(threadIdx.x + i*blockDim.x < fil_H + blockDim.x - 1); i++) {
for (int j = 0; (w_idx + j*blockDim.y < src_W)&(threadIdx.y + j*blockDim.y < fil_W + blockDim.y - 1); j++) {
int shared_idx = (threadIdx.x + i*blockDim.x)*(blockDim.y + fil_W - 1)*blockDim.z + (threadIdx.y + j*blockDim.y)*blockDim.z + threadIdx.z;
shared[shared_idx] = src[src_idx + i*blockDim.x*src_W*src_C + j*blockDim.y*src_C];
}
}
float new_value = 0.0;
float filter_sum = 0.0;
for (int i = 0; i < fil_H; i++) {
for (int j = 0; j < fil_W; j++) {
int shared_idx = (threadIdx.x + i)*(blockDim.y + fil_W - 1)*blockDim.z + (threadIdx.y + j)*blockDim.z + threadIdx.z;
new_value += (float)shared[shared_idx]*fil[i*fil_W + j];
filter_sum += fil[i*fil_W + j];
}
}
new_value = new_value / filter_sum;
dst[h_idx*(src_W-fil_W+1)*(src_C) + w_idx*src_C + c_idx] = (uint8_t)round(new_value);
}
__host__ void apply_linear_filter(uint8_t *h_src, uint8_t *h_dst, float *h_fil, int src_H, int src_W, int src_C, int fil_H, int fil_W, int block_size) {
uint8_t *d_src;
uint8_t *d_dst;
float *d_fil;
cudaMalloc(&d_src, src_H*src_W*src_C);
cudaMalloc(&d_dst, (src_H-fil_H+1)*(src_W-fil_W+1)*src_C);
cudaMalloc(&d_fil, fil_H*fil_W*sizeof(float));
cudaMemcpy(d_src, h_src, src_H*src_W*src_C, cudaMemcpyHostToDevice);
cudaMemcpy(d_fil, h_fil, fil_H*fil_W*sizeof(float), cudaMemcpyHostToDevice);
int x_blocks = (src_H-fil_H+1);
x_blocks = x_blocks/block_size + (x_blocks%block_size ? 1 : 0);
int y_blocks = (src_W-fil_W+1);
y_blocks = y_blocks/block_size + (y_blocks%block_size ? 1 : 0);
apply_linear_filter_kernel<<<
dim3(x_blocks,y_blocks,1),
dim3(block_size,block_size,src_C),
sizeof(float)*(block_size+fil_H-1)*(block_size+fil_W-1)
>>>(d_src, d_dst, d_fil, src_H, src_W, src_C, fil_H, fil_W);
cudaMemcpy(h_dst, d_dst, (src_H-fil_H+1)*(src_W-fil_W+1)*src_C, cudaMemcpyDeviceToHost);
cudaDeviceSynchronize();
cudaFree(d_src);
cudaFree(d_dst);
cudaFree(d_fil);
}
|
7,017 | #include <unistd.h>
#include <string.h>
int main() {
sync();
exit(0);
}
|
7,018 | /*
* file name: matrix.cu
*
* matrix.cu contains the code that realize some common used matrix operations in CUDA
*
* this is a toy program for learning CUDA, some functions are reusable in other project
*
*/
#include <stdio.h>
#include <stdlib.h>
#include <assert.h>
#ifndef BLOCK_SIZE
#define BLOCK_SIZE 16
#endif
#ifndef BATCH_SIZE
#define BATCH_SIZE 1
#endif
#ifndef NUM_ITERATIONS
#define NUM_ITERATIONS 1024
#endif
/*
*********************************************************************
function name: gpu_matrix_mult
description: dot product of two matrix (not only square)
parameters:
&a GPU device pointer to a m X n matrix (A)
&b GPU device pointer to a n X k matrix (B)
&c GPU device output purpose pointer to a m X k matrix (C)
to store the result
Note:
grid and block should be configured as:
dim3 dimGrid((k + BLOCK_SIZE - 1) / BLOCK_SIZE, (m + BLOCK_SIZE - 1) / BLOCK_SIZE);
dim3 dimBlock(BLOCK_SIZE, BLOCK_SIZE);
further sppedup can be obtained by using shared memory to decrease global memory access times
return: none
*********************************************************************
*/
__global__ void gpu_matrix_mult(int *a,int *b, int *c, int m, int n, int k)
{
int row = blockIdx.y * blockDim.y + threadIdx.y;
int col = blockIdx.x * blockDim.x + threadIdx.x;
int sum = 0;
if( col < k && row < m)
{
for(int i = 0; i < n; i++)
{
sum += a[row * n + i] * b[i * k + col];
}
c[row * k + col] = sum;
}
}
/*
*********************************************************************
function name: main
description: test and compare
parameters:
none
return: none
*********************************************************************
*/
int main(int argc, char const *argv[])
{
int m1, n1, k1, m2, n2, k2, m3, n3, k3;
/* Fixed seed for illustration */
srand(3333);
m1=BATCH_SIZE;
n1=65536;
k1=4096;
m2=BATCH_SIZE;
n2=4096;
k2=1024;
m3=BATCH_SIZE;
n3=4096;
k3=10;
// allocate memory in host RAM
int *h_a, *h_b, *h_c, *h_d, *h_e, *h_f, *h_g;
cudaMallocHost((void **) &h_a, sizeof(int)*m1*n1);
cudaMallocHost((void **) &h_b, sizeof(int)*n1*k1);
cudaMallocHost((void **) &h_c, sizeof(int)*m1*k1);
cudaMallocHost((void **) &h_d, sizeof(int)*n2*k2);
cudaMallocHost((void **) &h_e, sizeof(int)*m2*k2);
cudaMallocHost((void **) &h_f, sizeof(int)*n3*k3);
cudaMallocHost((void **) &h_g, sizeof(int)*m3*k3);
// random initialize matrix B
for (int i = 0; i < n1; ++i) {
for (int j = 0; j < k1; ++j) {
h_b[i * k1 + j] = rand() % 1024;
}
}
// random initialize matrix D
for (int i = 0; i < n2; ++i) {
for (int j = 0; j < k2; ++j) {
h_d[i * k2 + j] = rand() % 1024;
}
}
// random initialize matrix F
for (int i = 0; i < n3; ++i) {
for (int j = 0; j < k3; ++j) {
h_f[i * k3 + j] = rand() % 1024;
}
}
float gpu_elapsed_time_ms;
// some events to count the execution time
cudaEvent_t start, stop;
cudaEventCreate(&start);
cudaEventCreate(&stop);
// Allocate memory space on the device
int *d_a, *d_b, *d_c, *d_d, *d_e, *d_f, *d_g;
cudaMalloc((void **) &d_a, sizeof(int)*m1*n1);
cudaMalloc((void **) &d_b, sizeof(int)*n1*k1);
cudaMalloc((void **) &d_c, sizeof(int)*m1*k1);
cudaMalloc((void **) &d_d, sizeof(int)*n2*k2);
cudaMalloc((void **) &d_e, sizeof(int)*m2*k2);
cudaMalloc((void **) &d_f, sizeof(int)*n3*k3);
cudaMalloc((void **) &d_g, sizeof(int)*m3*k3);
// copy matrix B,D,F from host to device memory - these are weight matrices
cudaMemcpy(d_b, h_b, sizeof(int)*n1*k1, cudaMemcpyHostToDevice);
cudaMemcpy(d_d, h_d, sizeof(int)*n2*k2, cudaMemcpyHostToDevice);
cudaMemcpy(d_f, h_f, sizeof(int)*n3*k3, cudaMemcpyHostToDevice);
int numExamples = 0;
double total_time_ms = 0.0;
for(int i=0;i<NUM_ITERATIONS;i++) {
// random initialize matrix A - this is the input matrix
for (int i = 0; i < m1; ++i) {
for (int j = 0; j < n1; ++j) {
h_a[i * n1 + j] = rand() % 1024;
}
}
cudaEventRecord(start, 0);
// copy from host to device
cudaMemcpy(d_a, h_a, sizeof(int)*m1*n1, cudaMemcpyHostToDevice);
unsigned int grid_rows = (m1 + BLOCK_SIZE - 1) / BLOCK_SIZE;
unsigned int grid_cols = (k1 + BLOCK_SIZE - 1) / BLOCK_SIZE;
dim3 dimGrid(grid_cols, grid_rows);
dim3 dimBlock(BLOCK_SIZE, BLOCK_SIZE);
// Launch kernel for multiplication 1
#ifdef USE_CUDA_STREAMS
gpu_matrix_mult<<<dimGrid, dimBlock, 0, 0>>>(d_a, d_b, d_c, m1, n1, k1); // execute on default stream
#else
gpu_matrix_mult<<<dimGrid, dimBlock>>>(d_a, d_b, d_c, m1, n1, k1);
#endif
cudaDeviceSynchronize();
// Launch kernel for multiplication 2
grid_rows = (m2 + BLOCK_SIZE - 1) / BLOCK_SIZE;
grid_cols = (k2 + BLOCK_SIZE - 1) / BLOCK_SIZE;
dim3 dimGrid2(grid_cols, grid_rows);
dim3 dimBlock2(BLOCK_SIZE, BLOCK_SIZE);
#ifdef USE_CUDA_STREAMS
gpu_matrix_mult<<<dimGrid2, dimBlock2, 0, 0>>>(d_c, d_d, d_e, m2, n2, k2); // execute on default stream
#else
gpu_matrix_mult<<<dimGrid2, dimBlock2>>>(d_c, d_d, d_e, m2, n2, k2);
#endif
// Launch kernel for multiplication 3 - DR model
grid_rows = (m3 + BLOCK_SIZE - 1) / BLOCK_SIZE;
grid_cols = (k3 + BLOCK_SIZE - 1) / BLOCK_SIZE;
dim3 dimGrid3(grid_cols, grid_rows);
dim3 dimBlock3(BLOCK_SIZE, BLOCK_SIZE);
#ifdef USE_CUDA_STREAMS
cudaStream_t streams[1];
cudaStreamCreate(&streams[0]);
gpu_matrix_mult<<<dimGrid3, dimBlock3, 0, streams[0]>>>(d_c, d_f, d_g, m3, n3, k3); // execute on non-default stream
#else
gpu_matrix_mult<<<dimGrid3, dimBlock3>>>(d_c, d_f, d_g, m3, n3, k3);
#endif
// Transfer results from device to host - only DR model result
cudaMemcpy(h_g, d_g, sizeof(int)*m2*k2, cudaMemcpyDeviceToHost);
cudaThreadSynchronize();
// time counting terminate
cudaEventRecord(stop, 0);
cudaEventSynchronize(stop);
// compute time elapse on GPU computing
cudaEventElapsedTime(&gpu_elapsed_time_ms, start, stop);
numExamples += BATCH_SIZE;
total_time_ms += gpu_elapsed_time_ms;
}
printf("Avg. Latency: %g ms :: Avg. Throughput: %g examples/sec\n", total_time_ms/NUM_ITERATIONS, numExamples*1000.0/total_time_ms);
// free memory
cudaFree(d_a);
cudaFree(d_b);
cudaFree(d_c);
cudaFree(d_d);
cudaFree(d_e);
cudaFreeHost(h_a);
cudaFreeHost(h_b);
cudaFreeHost(h_c);
cudaFreeHost(h_d);
cudaFreeHost(h_e);
return 0;
}
|
7,019 | #include<iostream>
int main(){
std::cout << "Hello World from CUDA\n";
return 0;
}
|
7,020 | /*
* HPC Class
* GPU Examples of Indexes and Indexing
*
*/
#include <iostream>
using namespace std;
#define N 20
// GPU kernels:
__global__ void kernel1(int* a)
{
int idx = blockIdx.x * blockDim.x + threadIdx.x;
a[idx] = 7;
}
__global__ void kernel2(int* b)
{
int idx = blockIdx.x * blockDim.x + threadIdx.x;
b[idx] = blockIdx.x;
}
__global__ void kernel3(int* c)
{
int idx = blockIdx.x * blockDim.x + threadIdx.x;
c[idx] = threadIdx.x;
}
int main()
{
int h_a[N], h_b[N],h_c[N]; // h stands for host (stuffs on CPU)
int* d_pa, *d_pb, *d_pc; // d stands for device (stuffs on GPU)
//allocate the memory on the GPU
cudaMalloc( (void**)&d_pa, N*sizeof(int) );
cudaMalloc( (void**)&d_pb, N*sizeof(int) );
cudaMalloc( (void**)&d_pc, N*sizeof(int) );
cudaMemset(d_pa, 0, N);
cudaMemset(d_pb, 0, N);
cudaMemset(d_pc, 0, N);
// call the GPU kernels
kernel1<<<5,4>>>(d_pa);
kernel2<<<5,4>>>(d_pb);
kernel3<<<5,4>>>(d_pc);
// copy the arrays back from the GPU to the CPU
cudaMemcpy(h_a, d_pa, N*sizeof(int), cudaMemcpyDeviceToHost);
cudaMemcpy(h_b, d_pb, N*sizeof(int), cudaMemcpyDeviceToHost);
cudaMemcpy(h_c, d_pc, N*sizeof(int), cudaMemcpyDeviceToHost);
// display the results
cout<< " Results from kernel1:" << endl;
for (int i = 0; i<N; i++)
cout<< h_a[i] << " ";
cout<< endl;
cout<< " Results from kernel2:" << endl;
for (int i = 0; i<N; i++)
cout<< h_b[i] << " ";
cout<< endl;
cout<< " Results from kernel3:" << endl;
for (int i = 0; i<N; i++)
cout<< h_c[i] << " ";
cout<< endl;
//free the memory allocated on the GPU
cudaFree(d_pa);
cudaFree(d_pb);
cudaFree(d_pc);
return 0;
} |
7,021 | #include <stdio.h>
#include "cuda.h"
#define max(x,y) ((x) > (y)? (x) : (y))
#define min(x,y) ((x) < (y)? (x) : (y))
#define ceil(a,b) ((a) % (b) == 0 ? (a) / (b) : ((a) / (b)) + 1)
void check_error (const char* message) {
cudaError_t error = cudaGetLastError ();
if (error != cudaSuccess) {
printf ("CUDA error : %s, %s\n", message, cudaGetErrorString (error));
exit(-1);
}
}
__global__ void j3d125pt (double * __restrict__ t_in, double * __restrict__ t_out, int N) {
//Determing the block's indices
int i0 = (int)(blockIdx.x)*(int)(blockDim.x) + 2;
int i = max(i0,2) + (int)(threadIdx.x);
int j0 = 4*(int)(blockIdx.y)*(int)(blockDim.y) + 2;
int j = max(j0,2) + 4*(int)(threadIdx.y);
int k0 = (int)(blockIdx.z)*(int)(blockDim.z) + 2;
int k = max(k0,2) + (int)(threadIdx.z);
double (*in)[516][516] = (double (*)[516][516])t_in;
double (*out)[516][516] = (double (*)[516][516])t_out;
if (i>=2 && i<=N-3 && j>=2 && j<=N-3 && k>=2 && k<=N-3) {
double _t_2_;
double outkc0jc0ic0;
double _t_4_;
double _t_10_;
double _t_5_;
double _t_18_;
double _t_9_;
double _t_24_;
double _t_19_;
double _t_6_;
double _t_11_;
double _t_17_;
double _t_23_;
double _t_20_;
double _t_3_;
double _t_7_;
double _t_21_;
double _t_8_;
double _t_25_;
double _t_16_;
double _t_12_;
double _t_26_;
double _t_13_;
double _t_14_;
double _t_15_;
double _t_27_;
double _t_22_;
double outkc0jp1ic0;
double outkc0jp2ic0;
double outkc0jp3ic0;
double _t_0_;
double _t_1_;
_t_2_ = in[k-2][j-2][i-1];
_t_2_ += in[k-2][j-2][i-1];
_t_2_ += in[k-2][j-2][i+1];
_t_2_ += in[k-2][j-2][i+1];
_t_2_ += in[k-1][j-2][i-2];
_t_2_ += in[k-1][j-2][i+2];
_t_2_ += in[k][j-2][i];
_t_2_ += in[k+1][j-2][i-2];
_t_2_ += in[k+1][j-2][i+2];
_t_2_ += in[k-2][j+2][i-1];
_t_2_ += in[k-2][j+2][i-1];
_t_2_ += in[k-2][j+2][i+1];
_t_2_ += in[k-2][j+2][i+1];
_t_2_ += in[k-2][j+1][i-2];
_t_2_ += in[k-2][j+1][i-2];
_t_2_ += in[k-2][j+1][i+2];
_t_2_ += in[k-2][j+1][i+2];
_t_2_ += in[k-2][j][i];
_t_2_ += in[k-2][j][i];
_t_2_ += in[k-2][j-1][i-2];
_t_2_ += in[k-2][j-1][i-2];
_t_2_ += in[k-2][j-1][i+2];
_t_2_ += in[k-2][j-1][i+2];
_t_2_ += in[k-1][j+2][i-2];
_t_2_ += in[k-1][j+2][i+2];
_t_2_ += in[k+1][j+2][i-2];
_t_2_ += in[k+1][j+2][i+2];
_t_2_ += in[k][j+2][i];
_t_2_ += in[k][j][i-2];
_t_2_ += in[k][j][i+2];
outkc0jc0ic0 = 1.132 * _t_2_;
_t_4_ = in[k-1][j-2][i];
_t_4_ += in[k][j-2][i-1];
_t_4_ += in[k][j-2][i+1];
_t_4_ += in[k+1][j-2][i];
_t_4_ += in[k-2][j-1][i];
_t_4_ += in[k-2][j-1][i];
_t_10_ = in[k-2][j-1][i];
_t_10_ += in[k-2][j+1][i-2];
_t_10_ += in[k-2][j+1][i+2];
_t_10_ += in[k-2][j-1][i];
_t_10_ += in[k-2][j+1][i-2];
_t_10_ += in[k-2][j+1][i+2];
_t_4_ += in[k][j-1][i-2];
_t_10_ += in[k][j-1][i-2];
_t_4_ += in[k][j-1][i+2];
_t_10_ += in[k][j-1][i+2];
_t_4_ += in[k-2][j+1][i];
_t_4_ += in[k-2][j+1][i];
_t_4_ += in[k-2][j][i-1];
_t_4_ += in[k-2][j][i-1];
_t_4_ += in[k-2][j][i+1];
_t_4_ += in[k-2][j][i+1];
_t_4_ += in[k-1][j+2][i];
_t_10_ += in[k-1][j+2][i];
_t_4_ += in[k+1][j+2][i];
_t_10_ += in[k+1][j+2][i];
_t_4_ += in[k][j+1][i-2];
_t_4_ += in[k][j+1][i+2];
_t_4_ += in[k][j+2][i-1];
_t_4_ += in[k][j+2][i+1];
_t_4_ += in[k-1][j][i-2];
_t_4_ += in[k-1][j][i+2];
_t_4_ += in[k+1][j][i-2];
_t_4_ += in[k+1][j][i+2];
_t_4_ += in[k][j][i];
outkc0jc0ic0 += 2.13 * _t_4_;
_t_5_ = in[k-1][j-2][i-1];
_t_5_ += in[k-1][j-2][i+1];
_t_5_ += in[k+1][j-2][i-1];
_t_5_ += in[k+1][j-2][i+1];
_t_5_ += in[k-1][j][i];
_t_10_ += in[k-1][j][i];
_t_18_ = in[k-2][j+1][i];
_t_18_ += in[k-2][j+2][i-1];
_t_18_ += in[k-2][j+2][i+1];
_t_18_ += in[k-1][j][i];
_t_18_ += in[k-1][j+2][i-2];
_t_18_ += in[k-1][j+2][i+2];
_t_18_ += in[k][j+1][i-2];
_t_18_ += in[k][j+1][i+2];
_t_18_ += in[k][j+2][i];
_t_18_ += in[k+1][j+2][i-2];
_t_18_ += in[k+1][j+2][i+2];
_t_18_ += in[k-2][j+1][i];
_t_18_ += in[k-2][j+2][i-1];
_t_18_ += in[k-2][j+2][i+1];
_t_5_ += in[k+1][j][i];
_t_10_ += in[k+1][j][i];
_t_18_ += in[k+1][j][i];
_t_5_ += in[k-1][j-1][i-2];
_t_9_ = in[k-2][j+1][i];
_t_9_ += in[k-1][j-1][i-2];
_t_9_ += in[k][j+1][i-2];
_t_9_ += in[k][j+1][i+2];
_t_9_ += in[k-2][j+1][i];
_t_24_ = in[k-2][j+1][i];
_t_24_ += in[k-1][j+2][i];
_t_24_ += in[k][j+1][i-2];
_t_24_ += in[k][j+1][i+2];
_t_24_ += in[k+1][j+2][i];
_t_24_ += in[k-2][j+1][i];
_t_19_ = in[k-1][j+2][i];
_t_19_ += in[k+1][j+2][i];
_t_5_ += in[k-1][j-1][i+2];
_t_9_ += in[k-1][j-1][i+2];
_t_5_ += in[k+1][j-1][i-2];
_t_9_ += in[k+1][j-1][i-2];
_t_5_ += in[k+1][j-1][i+2];
_t_9_ += in[k+1][j-1][i+2];
_t_18_ += in[k-1][j+4][i];
_t_24_ += in[k-1][j+4][i];
_t_18_ += in[k+1][j+4][i];
_t_24_ += in[k+1][j+4][i];
_t_24_ += in[k-2][j+5][i];
_t_24_ += in[k-2][j+5][i];
_t_24_ += in[k][j+5][i-2];
_t_24_ += in[k][j+5][i+2];
_t_5_ += in[k-2][j-1][i-1];
_t_9_ += in[k-2][j-1][i-1];
_t_9_ += in[k-2][j-1][i-1];
_t_6_ = in[k-2][j-1][i-1];
_t_5_ += in[k-2][j-1][i+1];
_t_6_ += in[k-2][j-1][i+1];
_t_9_ += in[k-2][j-1][i+1];
_t_9_ += in[k-2][j-1][i+1];
_t_5_ += in[k-2][j+1][i-1];
_t_6_ += in[k-2][j+1][i-1];
_t_19_ += in[k-2][j+1][i-1];
_t_5_ += in[k-2][j+1][i+1];
_t_6_ += in[k-2][j+1][i+1];
outkc0jc0ic0 += 0.332 * _t_6_;
_t_19_ += in[k-2][j+1][i+1];
_t_5_ += in[k][j-1][i-1];
_t_11_ = in[k-2][j][i];
_t_11_ += in[k-2][j+1][i-1];
_t_11_ += in[k-2][j+1][i+1];
_t_11_ += in[k][j-1][i-1];
_t_11_ += in[k][j][i-2];
_t_11_ += in[k][j][i+2];
_t_11_ += in[k-2][j][i];
_t_11_ += in[k-2][j+1][i-1];
_t_11_ += in[k-2][j+1][i+1];
_t_17_ = in[k-2][j][i];
_t_17_ += in[k][j][i-2];
_t_17_ += in[k][j][i+2];
_t_17_ += in[k-2][j][i];
_t_5_ += in[k][j-1][i+1];
_t_11_ += in[k][j-1][i+1];
_t_5_ += in[k-1][j+1][i-2];
_t_11_ += in[k-1][j+1][i-2];
_t_19_ += in[k-1][j+1][i-2];
_t_23_ = in[k-2][j+1][i-1];
_t_23_ += in[k-2][j+1][i+1];
_t_23_ += in[k-1][j+1][i-2];
_t_23_ += in[k-2][j+1][i-1];
_t_23_ += in[k-2][j+1][i+1];
_t_20_ = in[k-2][j+1][i-1];
_t_20_ += in[k-2][j+1][i+1];
_t_5_ += in[k-1][j+1][i+2];
_t_11_ += in[k-1][j+1][i+2];
_t_19_ += in[k-1][j+1][i+2];
_t_23_ += in[k-1][j+1][i+2];
_t_5_ += in[k+1][j+1][i-2];
_t_11_ += in[k+1][j+1][i-2];
_t_19_ += in[k+1][j+1][i-2];
_t_23_ += in[k+1][j+1][i-2];
_t_5_ += in[k+1][j+1][i+2];
_t_11_ += in[k+1][j+1][i+2];
_t_19_ += in[k+1][j+1][i+2];
_t_23_ += in[k+1][j+1][i+2];
_t_10_ += in[k-2][j+3][i];
_t_10_ += in[k-2][j+3][i];
_t_18_ += in[k-2][j+3][i];
_t_18_ += in[k-2][j+3][i];
_t_23_ += in[k-2][j+3][i];
_t_23_ += in[k-2][j+3][i];
_t_10_ += in[k][j+3][i-2];
_t_18_ += in[k][j+3][i-2];
_t_23_ += in[k][j+3][i-2];
_t_10_ += in[k][j+3][i+2];
_t_18_ += in[k][j+3][i+2];
_t_23_ += in[k][j+3][i+2];
_t_23_ += in[k-2][j+5][i-1];
_t_23_ += in[k-2][j+5][i-1];
_t_23_ += in[k-2][j+5][i+1];
_t_23_ += in[k-2][j+5][i+1];
_t_23_ += in[k-1][j+5][i-2];
_t_23_ += in[k-1][j+5][i+2];
_t_23_ += in[k][j+5][i];
_t_23_ += in[k+1][j+5][i-2];
_t_23_ += in[k+1][j+5][i+2];
_t_5_ += in[k-1][j+2][i-1];
_t_17_ += in[k-1][j+2][i-1];
_t_5_ += in[k-1][j+2][i+1];
_t_17_ += in[k-1][j+2][i+1];
_t_5_ += in[k+1][j+2][i-1];
_t_17_ += in[k+1][j+2][i-1];
_t_5_ += in[k+1][j+2][i+1];
_t_17_ += in[k+1][j+2][i+1];
_t_5_ += in[k][j+1][i-1];
_t_19_ += in[k][j+1][i-1];
_t_5_ += in[k][j+1][i+1];
outkc0jc0ic0 += 0.331 * _t_5_;
_t_19_ += in[k][j+1][i+1];
_t_3_ = in[k-2][j-2][i];
_t_3_ += in[k-2][j-2][i];
_t_3_ += in[k-1][j-1][i];
_t_11_ += in[k-1][j-1][i];
_t_3_ += in[k+1][j-1][i];
_t_11_ += in[k+1][j-1][i];
_t_3_ += in[k][j-2][i-2];
_t_3_ += in[k][j-2][i+2];
_t_3_ += in[k-2][j+2][i];
_t_3_ += in[k-2][j+2][i];
_t_11_ += in[k-2][j+2][i];
_t_11_ += in[k-2][j+2][i];
_t_3_ += in[k-2][j][i-2];
_t_3_ += in[k-2][j][i-2];
_t_9_ += in[k-2][j][i-2];
_t_9_ += in[k-2][j][i-2];
_t_3_ += in[k-2][j][i+2];
_t_3_ += in[k-2][j][i+2];
_t_9_ += in[k-2][j][i+2];
_t_9_ += in[k-2][j][i+2];
_t_3_ += in[k-1][j][i-1];
_t_19_ += in[k-1][j][i-1];
_t_7_ = in[k-2][j-1][i-2];
_t_7_ += in[k-2][j-1][i+2];
_t_7_ += in[k-1][j][i-1];
_t_7_ += in[k-1][j+2][i-1];
_t_7_ += in[k-1][j+2][i+1];
_t_7_ += in[k][j][i];
_t_7_ += in[k][j+1][i-1];
_t_7_ += in[k][j+1][i+1];
_t_7_ += in[k][j+2][i];
_t_7_ += in[k+1][j+2][i-1];
_t_7_ += in[k+1][j+2][i+1];
_t_21_ = in[k-2][j+1][i-2];
_t_21_ += in[k-2][j+1][i+2];
_t_21_ += in[k-1][j+2][i-1];
_t_21_ += in[k-1][j+2][i+1];
_t_21_ += in[k][j+2][i];
_t_21_ += in[k+1][j+2][i-1];
_t_21_ += in[k+1][j+2][i+1];
_t_8_ = in[k-2][j-1][i-2];
_t_8_ += in[k-2][j-1][i+2];
_t_25_ = in[k-2][j+2][i];
_t_25_ += in[k][j+1][i-1];
_t_25_ += in[k][j+1][i+1];
_t_25_ += in[k-2][j+2][i];
_t_16_ = in[k-2][j][i-1];
_t_16_ += in[k-2][j][i+1];
_t_16_ += in[k-2][j+1][i-2];
_t_16_ += in[k-2][j+1][i+2];
_t_16_ += in[k-2][j+2][i];
_t_16_ += in[k-1][j][i-2];
_t_16_ += in[k-1][j][i+2];
_t_16_ += in[k][j][i];
_t_16_ += in[k+1][j][i-2];
_t_16_ += in[k+1][j][i+2];
_t_16_ += in[k-2][j][i-1];
_t_16_ += in[k-2][j][i+1];
_t_16_ += in[k-2][j+1][i-2];
_t_16_ += in[k-2][j+1][i+2];
_t_16_ += in[k-2][j+2][i];
_t_12_ = in[k-2][j][i-1];
_t_12_ += in[k-2][j][i+1];
_t_12_ += in[k-2][j+2][i-1];
_t_12_ += in[k-2][j+2][i+1];
_t_12_ += in[k-1][j][i-2];
_t_12_ += in[k-1][j][i+2];
_t_12_ += in[k-1][j+2][i-2];
_t_12_ += in[k-1][j+2][i+2];
_t_12_ += in[k][j+2][i-1];
_t_12_ += in[k][j+2][i+1];
_t_12_ += in[k+1][j][i-2];
_t_12_ += in[k+1][j][i+2];
_t_12_ += in[k+1][j+2][i-2];
_t_12_ += in[k+1][j+2][i+2];
_t_26_ = in[k-2][j+2][i-1];
_t_26_ += in[k-2][j+2][i+1];
_t_26_ += in[k-1][j+2][i-2];
_t_26_ += in[k-1][j+2][i+2];
_t_26_ += in[k][j+2][i-1];
_t_26_ += in[k][j+2][i+1];
_t_26_ += in[k+1][j+2][i-2];
_t_26_ += in[k+1][j+2][i+2];
_t_13_ = in[k-2][j][i-1];
_t_13_ += in[k-2][j][i+1];
_t_13_ += in[k-2][j+2][i-1];
_t_13_ += in[k-2][j+2][i+1];
_t_14_ = in[k-2][j][i-2];
_t_14_ += in[k-2][j][i+2];
_t_14_ += in[k][j+2][i-1];
_t_14_ += in[k][j+2][i+1];
_t_15_ = in[k-2][j][i-2];
_t_15_ += in[k-2][j][i+2];
_t_27_ = in[k-2][j+2][i-1];
_t_27_ += in[k-2][j+2][i+1];
_t_22_ = in[k-2][j+1][i-2];
_t_22_ += in[k-2][j+1][i+2];
_t_3_ += in[k][j+2][i-2];
_t_11_ += in[k][j+2][i-2];
_t_16_ += in[k][j+2][i-2];
_t_25_ += in[k][j+2][i-2];
_t_3_ += in[k][j+2][i+2];
_t_11_ += in[k][j+2][i+2];
_t_16_ += in[k][j+2][i+2];
_t_25_ += in[k][j+2][i+2];
_t_3_ += in[k-1][j+1][i];
_t_12_ += in[k-1][j+1][i];
_t_17_ += in[k-1][j+1][i];
_t_25_ += in[k-1][j+1][i];
_t_3_ += in[k+1][j+1][i];
_t_12_ += in[k+1][j+1][i];
_t_17_ += in[k+1][j+1][i];
_t_25_ += in[k+1][j+1][i];
_t_3_ += in[k-1][j][i+1];
_t_7_ += in[k-1][j][i+1];
_t_19_ += in[k-1][j][i+1];
_t_3_ += in[k+1][j][i-1];
_t_7_ += in[k+1][j][i-1];
_t_19_ += in[k+1][j][i-1];
_t_3_ += in[k+1][j][i+1];
outkc0jc0ic0 += 0.217 * _t_3_;
_t_7_ += in[k+1][j][i+1];
_t_19_ += in[k+1][j][i+1];
_t_7_ += in[k-2][j+3][i-2];
_t_8_ += in[k-2][j+3][i-2];
_t_16_ += in[k-2][j+3][i-2];
_t_16_ += in[k-2][j+3][i-2];
_t_24_ += in[k-2][j+3][i-2];
_t_24_ += in[k-2][j+3][i-2];
_t_7_ += in[k-2][j+3][i+2];
_t_8_ += in[k-2][j+3][i+2];
_t_16_ += in[k-2][j+3][i+2];
_t_16_ += in[k-2][j+3][i+2];
_t_24_ += in[k-2][j+3][i+2];
_t_24_ += in[k-2][j+3][i+2];
outkc0jp1ic0 = 0.332 * _t_13_;
outkc0jp1ic0 += 0.75 * _t_7_;
outkc0jp1ic0 += 0.76 * _t_8_;
_t_9_ += in[k-2][j+3][i-1];
_t_9_ += in[k-2][j+3][i-1];
_t_19_ += in[k-2][j+3][i-1];
_t_20_ += in[k-2][j+3][i-1];
_t_25_ += in[k-2][j+3][i-1];
_t_25_ += in[k-2][j+3][i-1];
_t_9_ += in[k-2][j+3][i+1];
_t_9_ += in[k-2][j+3][i+1];
_t_19_ += in[k-2][j+3][i+1];
_t_20_ += in[k-2][j+3][i+1];
_t_25_ += in[k-2][j+3][i+1];
_t_25_ += in[k-2][j+3][i+1];
_t_11_ += in[k][j+3][i-1];
_t_19_ += in[k][j+3][i-1];
_t_21_ += in[k][j+3][i-1];
_t_11_ += in[k][j+3][i+1];
_t_19_ += in[k][j+3][i+1];
_t_21_ += in[k][j+3][i+1];
_t_9_ += in[k-1][j+3][i-2];
_t_19_ += in[k-1][j+3][i-2];
_t_25_ += in[k-1][j+3][i-2];
_t_9_ += in[k-1][j+3][i+2];
_t_19_ += in[k-1][j+3][i+2];
_t_25_ += in[k-1][j+3][i+2];
_t_9_ += in[k+1][j+3][i-2];
_t_19_ += in[k+1][j+3][i-2];
_t_25_ += in[k+1][j+3][i-2];
_t_9_ += in[k+1][j+3][i+2];
_t_19_ += in[k+1][j+3][i+2];
_t_25_ += in[k+1][j+3][i+2];
_t_11_ += in[k-1][j+3][i];
_t_17_ += in[k-1][j+3][i];
_t_26_ += in[k-1][j+3][i];
_t_11_ += in[k+1][j+3][i];
_t_17_ += in[k+1][j+3][i];
_t_26_ += in[k+1][j+3][i];
_t_9_ += in[k][j+3][i];
_t_14_ += in[k][j+3][i];
_t_25_ += in[k][j+3][i];
_t_12_ += in[k-1][j+3][i-1];
_t_14_ += in[k-1][j+3][i-1];
_t_24_ += in[k-1][j+3][i-1];
_t_12_ += in[k-1][j+3][i+1];
_t_14_ += in[k-1][j+3][i+1];
_t_24_ += in[k-1][j+3][i+1];
_t_12_ += in[k+1][j+3][i-1];
_t_14_ += in[k+1][j+3][i-1];
_t_24_ += in[k+1][j+3][i-1];
_t_12_ += in[k+1][j+3][i+1];
_t_14_ += in[k+1][j+3][i+1];
_t_24_ += in[k+1][j+3][i+1];
_t_14_ += in[k-2][j+4][i-2];
_t_15_ += in[k-2][j+4][i-2];
_t_23_ += in[k-2][j+4][i-2];
_t_23_ += in[k-2][j+4][i-2];
_t_14_ += in[k-2][j+4][i+2];
_t_15_ += in[k-2][j+4][i+2];
_t_23_ += in[k-2][j+4][i+2];
_t_23_ += in[k-2][j+4][i+2];
outkc0jp2ic0 = 0.332 * _t_20_;
outkc0jp2ic0 += 0.76 * _t_15_;
_t_16_ += in[k-2][j+4][i-1];
_t_16_ += in[k-2][j+4][i-1];
_t_26_ += in[k-2][j+4][i-1];
_t_27_ += in[k-2][j+4][i-1];
_t_16_ += in[k-2][j+4][i+1];
_t_16_ += in[k-2][j+4][i+1];
_t_26_ += in[k-2][j+4][i+1];
_t_27_ += in[k-2][j+4][i+1];
outkc0jp3ic0 = 0.217 * _t_24_;
outkc0jp3ic0 += 0.332 * _t_27_;
_t_17_ += in[k-2][j+4][i];
_t_17_ += in[k-2][j+4][i];
_t_25_ += in[k-2][j+4][i];
_t_25_ += in[k-2][j+4][i];
_t_19_ += in[k-1][j+4][i-1];
_t_21_ += in[k-1][j+4][i-1];
_t_19_ += in[k-1][j+4][i+1];
_t_21_ += in[k-1][j+4][i+1];
_t_19_ += in[k+1][j+4][i-1];
_t_21_ += in[k+1][j+4][i-1];
_t_19_ += in[k+1][j+4][i+1];
outkc0jp2ic0 += 0.331 * _t_19_;
_t_21_ += in[k+1][j+4][i+1];
_t_18_ += in[k][j+4][i-1];
_t_26_ += in[k][j+4][i-1];
_t_18_ += in[k][j+4][i+1];
_t_26_ += in[k][j+4][i+1];
_t_16_ += in[k-1][j+4][i-2];
_t_26_ += in[k-1][j+4][i-2];
_t_16_ += in[k-1][j+4][i+2];
_t_26_ += in[k-1][j+4][i+2];
_t_16_ += in[k+1][j+4][i-2];
_t_26_ += in[k+1][j+4][i-2];
_t_16_ += in[k+1][j+4][i+2];
_t_26_ += in[k+1][j+4][i+2];
_t_16_ += in[k][j+4][i];
outkc0jp2ic0 += 1.132 * _t_16_;
_t_21_ += in[k][j+4][i];
_t_17_ += in[k][j+4][i-2];
_t_25_ += in[k][j+4][i-2];
_t_17_ += in[k][j+4][i+2];
_t_25_ += in[k][j+4][i+2];
_t_21_ += in[k-2][j+5][i-2];
_t_22_ += in[k-2][j+5][i-2];
_t_21_ += in[k-2][j+5][i+2];
outkc0jp3ic0 += 0.75 * _t_21_;
_t_22_ += in[k-2][j+5][i+2];
outkc0jp3ic0 += 0.76 * _t_22_;
_t_26_ += in[k-1][j+5][i-1];
_t_26_ += in[k-1][j+5][i+1];
_t_26_ += in[k+1][j+5][i-1];
_t_26_ += in[k+1][j+5][i+1];
_t_25_ += in[k-1][j+5][i];
_t_25_ += in[k][j+5][i-1];
_t_25_ += in[k][j+5][i+1];
_t_25_ += in[k+1][j+5][i];
outkc0jp3ic0 += 2.13 * _t_25_;
_t_9_ += in[k-2][j+2][i-2];
_t_9_ += in[k-2][j+2][i-2];
_t_17_ += in[k-2][j+2][i-2];
_t_17_ += in[k-2][j+2][i-2];
_t_23_ += in[k-2][j+2][i-2];
_t_23_ += in[k-2][j+2][i-2];
_t_9_ += in[k-2][j+2][i+2];
_t_9_ += in[k-2][j+2][i+2];
_t_17_ += in[k-2][j+2][i+2];
_t_17_ += in[k-2][j+2][i+2];
outkc0jp2ic0 += 0.217 * _t_17_;
_t_23_ += in[k-2][j+2][i+2];
_t_23_ += in[k-2][j+2][i+2];
_t_11_ += in[k][j+1][i];
outkc0jp1ic0 += 2.13 * _t_11_;
_t_14_ += in[k][j+1][i];
_t_23_ += in[k][j+1][i];
outkc0jp3ic0 += 1.132 * _t_23_;
_t_0_ = in[k-2][j+2][i-2];
_t_0_ += in[k-2][j+2][i+2];
_t_0_ += in[k][j+1][i];
_t_1_ = in[k-2][j+2][i-2];
_t_1_ += in[k-2][j+2][i+2];
_t_0_ += in[k-1][j+1][i-1];
_t_10_ += in[k-1][j+1][i-1];
_t_14_ += in[k-1][j+1][i-1];
_t_26_ += in[k-1][j+1][i-1];
_t_0_ += in[k-1][j+1][i+1];
_t_10_ += in[k-1][j+1][i+1];
_t_14_ += in[k-1][j+1][i+1];
_t_26_ += in[k-1][j+1][i+1];
_t_0_ += in[k+1][j+1][i-1];
_t_10_ += in[k+1][j+1][i-1];
_t_14_ += in[k+1][j+1][i-1];
_t_26_ += in[k+1][j+1][i-1];
_t_0_ += in[k+1][j+1][i+1];
_t_10_ += in[k+1][j+1][i+1];
outkc0jp1ic0 += 0.217 * _t_10_;
_t_14_ += in[k+1][j+1][i+1];
outkc0jp2ic0 += 0.75 * _t_14_;
_t_26_ += in[k+1][j+1][i+1];
outkc0jp3ic0 += 0.331 * _t_26_;
out[k][j+3][i] = outkc0jp3ic0;
_t_0_ += in[k][j][i-1];
_t_12_ += in[k][j][i-1];
_t_18_ += in[k][j][i-1];
_t_0_ += in[k][j][i+1];
_t_12_ += in[k][j][i+1];
_t_18_ += in[k][j][i+1];
outkc0jp2ic0 += 2.13 * _t_18_;
out[k][j+2][i] = outkc0jp2ic0;
_t_0_ += in[k-1][j-1][i-1];
_t_12_ += in[k-1][j-1][i-1];
_t_0_ += in[k-1][j-1][i+1];
_t_12_ += in[k-1][j-1][i+1];
_t_0_ += in[k+1][j-1][i-1];
_t_12_ += in[k+1][j-1][i-1];
_t_0_ += in[k+1][j-1][i+1];
_t_12_ += in[k+1][j-1][i+1];
outkc0jp1ic0 += 0.331 * _t_12_;
_t_0_ += in[k][j-1][i];
_t_9_ += in[k][j-1][i];
outkc0jp1ic0 += 1.132 * _t_9_;
out[k][j+1][i] = outkc0jp1ic0;
_t_0_ += in[k-2][j-2][i-2];
_t_1_ += in[k-2][j-2][i-2];
_t_0_ += in[k-2][j-2][i+2];
outkc0jc0ic0 += 0.75 * _t_0_;
_t_1_ += in[k-2][j-2][i+2];
outkc0jc0ic0 += 0.76 * _t_1_;
out[k][j][i] = outkc0jc0ic0;
}
}
extern "C" void host_code (double *h_in, double *h_out, int N) {
double *in;
cudaMalloc (&in, sizeof(double)*N*N*N);
check_error ("Failed to allocate device memory for in\n");
cudaMemcpy (in, h_in, sizeof(double)*N*N*N, cudaMemcpyHostToDevice);
double *out;
cudaMalloc (&out, sizeof(double)*N*N*N);
check_error ("Failed to allocate device memory for out\n");
dim3 blockconfig (16, 4, 4);
dim3 gridconfig (ceil(N-4, blockconfig.x), ceil(N-4, 4*blockconfig.y), ceil(N-4, blockconfig.z));
j3d125pt<<<gridconfig, blockconfig>>> (in, out, N);
cudaMemcpy (h_out, out, sizeof(double)*N*N*N, cudaMemcpyDeviceToHost);
cudaFree (in);
cudaFree (out);
}
|
7,022 | //pass
//--blockDim=[17,17] --gridDim=[1,1]
#include <cuda.h>
// code example for blog: Use extent instead of grid class - Sample 1
// created by: Tamer Afify Date:1/1/2012
//This is a sample function for using the grid class to do an image blur
//The pixel blur can be performed by changing every pixel color RBG band to
//the arithmetic average of this pixel value with all its 8 neighbors pixels.
//The grid offset feature can be of great benefit when the compute domain origin
//is different from the data origin. In other words, (0,0) for the data is not
//matching the (0,0) starting point for computation.
//In this sample we will use this feature to blur the inner image box without the
//boarder pixels as they dont have 8 neighbors pixel. So the compute domain origin
//is (1, 1) in the data index. And also compute domain extent is smaller than data
//extent by 2 rows and 2 columns.
// Note: to compile this code you need to use C++ AMP Developer Preview destributed
// During the TAP progrm.
#define width 17
#define height 17
__global__ void boxblur(float* blurimage, float* img)
{
int idxX = blockIdx.x*blockDim.x + threadIdx.x;
int idxY = blockIdx.y*blockDim.y + threadIdx.y;
float r = 0.0f;
int samples = 0;
for (int dy = -1; dy <= 1; dy++)
{
for (int dx = -1; dx <= 1; dx++)
{
r += img[(idxY + dy)*width + idxX + dx];
samples++;
}
}
blurimage[idxY*width + idxX] = r/samples;
#if MUTATION
blurimage[idxY*width + idxX + 1] = blurimage[idxY*width + idxX + 1];
#endif
}
|
7,023 | #include <iostream>
#include <algorithm>
#include <stdio.h>
#include <cstdio>
#include <cstdlib>
#include <vector>
std::vector<float>
read_dr_vec()
{
std::cout << "Reading dr_vec" << std::endl;
char* buffer;
long lSize;
size_t result;
FILE* fp = std::fopen("dr_vec.bin", "r");
if(!fp) {
std::perror("File opening failed");
}
// obtain file size:
fseek (fp , 0 , SEEK_END);
lSize = ftell (fp);
rewind (fp);
// allocate memory to contain the whole file:
buffer = (char*) malloc (sizeof(char)*lSize);
if (buffer == NULL) {fputs ("Memory error",stderr); exit (2);}
// copy the file into the buffer:
result = fread (buffer,1,lSize,fp);
if (result != lSize) {fputs ("Reading error",stderr); exit (3);}
double * buffer_d = reinterpret_cast<double*>(buffer);
size_t num_elems = lSize / sizeof(double);
std::vector<float> dr_vec(buffer_d, buffer_d + num_elems);
// terminate
fclose (fp);
free(buffer);
return dr_vec;
}
std::vector<float>
read_fc_vec()
{
std::cout << "Reading fc_vec" << std::endl;
char* buffer;
long lSize;
size_t result;
FILE* fp = std::fopen("fc_vec.bin", "r");
if(!fp) {
std::perror("File opening failed");
}
// obtain file size:
fseek (fp , 0 , SEEK_END);
lSize = ftell (fp);
rewind (fp);
// allocate memory to contain the whole file:
buffer = (char*) malloc (sizeof(char)*lSize);
if (buffer == NULL) {fputs ("Memory error",stderr); exit (2);}
// copy the file into the buffer:
result = fread (buffer,1,lSize,fp);
if (result != lSize) {fputs ("Reading error",stderr); exit (3);}
double * buffer_d = reinterpret_cast<double*>(buffer);
size_t num_elems = lSize / sizeof(double);
std::vector<float> fc_vec(buffer_d, buffer_d + num_elems);
// terminate
fclose (fp);
free(buffer);
return fc_vec;
}
std::vector<float>
read_rmin()
{
std::cout << "Reading rmin" << std::endl;
char* buffer;
long lSize;
size_t result;
FILE* fp = std::fopen("rmin.bin", "r");
if(!fp) {
std::perror("File opening failed");
}
// obtain file size:
fseek (fp , 0 , SEEK_END);
lSize = ftell (fp);
rewind (fp);
// allocate memory to contain the whole file:
buffer = (char*) malloc (sizeof(char)*lSize);
if (buffer == NULL) {fputs ("Memory error",stderr); exit (2);}
// copy the file into the buffer:
result = fread (buffer,1,lSize,fp);
if (result != lSize) {fputs ("Reading error",stderr); exit (3);}
double * buffer_d = reinterpret_cast<double*>(buffer);
size_t num_elems = lSize / sizeof(double);
std::vector<float> rmin_vec(buffer_d, buffer_d + num_elems);
// terminate
fclose (fp);
free(buffer);
return rmin_vec;
}
std::vector<float3>
read_pos_tx()
{
std::cout << "Reading pos_tx" << std::endl;
char* buffer;
long lSize;
size_t result;
std::vector<float3> pos_tx_vec;
FILE* fp = std::fopen("pos_tx.bin", "r");
if(!fp) {
std::perror("File opening failed");
}
// obtain file size:
fseek (fp , 0 , SEEK_END);
lSize = ftell (fp);
rewind (fp);
// allocate memory to contain the whole file:
buffer = (char*) malloc (sizeof(char)*lSize);
if (buffer == NULL) {fputs ("Memory error",stderr); exit (2);}
// copy the file into the buffer:
result = fread (buffer,1,lSize,fp);
if (result != lSize) {fputs ("Reading error",stderr); exit (3);}
size_t num_elems = lSize / sizeof(double);
double * buffer_d = reinterpret_cast<double*>(buffer);
// pos_tx are x,y,z coordinates, store in the format
// [x0,y0,z0,x1,y1,z1,x2,y2,z2....]. Convert to float3 values instead
pos_tx_vec.resize(num_elems/3);
for( int ii = 0; ii < num_elems/3; ii++ )
{
int i_buff = ii * 3;
pos_tx_vec[ii].x = (float)buffer_d[i_buff];
pos_tx_vec[ii].y = (float)buffer_d[i_buff+1];
pos_tx_vec[ii].z = (float)buffer_d[i_buff+2];
}
// terminate
fclose (fp);
free(buffer);
return pos_tx_vec;
}
std::vector<float3>
read_pos_rx()
{
std::cout << "Reading pos_rx" << std::endl;
char* buffer;
long lSize;
size_t result;
std::vector<float3> pos_rx_vec;
FILE* fp = std::fopen("pos_rx.bin", "r");
if(!fp) {
std::perror("File opening failed");
}
// obtain file size:
fseek (fp , 0 , SEEK_END);
lSize = ftell (fp);
rewind (fp);
// allocate memory to contain the whole file:
buffer = (char*) malloc (sizeof(char)*lSize);
if (buffer == NULL) {fputs ("Memory error",stderr); exit (2);}
// copy the file into the buffer:
result = fread (buffer,1,lSize,fp);
if (result != lSize) {fputs ("Reading error",stderr); exit (3);}
size_t num_elems = lSize / sizeof(double);
double * buffer_d = reinterpret_cast<double*>(buffer);
// pos_tx are x,y,z coordinates, store in the format
// [x0,y0,z0,x1,y1,z1,x2,y2,z2....]. Convert to float3 values instead
pos_rx_vec.resize(num_elems/3);
for( int ii = 0; ii < num_elems/3; ii++ )
{
int i_buff = ii * 3;
pos_rx_vec[ii].x = (float)buffer_d[i_buff];
pos_rx_vec[ii].y = (float)buffer_d[i_buff+1];
pos_rx_vec[ii].z = (float)buffer_d[i_buff+2];
}
// terminate
fclose (fp);
free(buffer);
return pos_rx_vec;
}
std::vector<float3>
read_pos_bp()
{
std::cout << "Reading pos_bp" << std::endl;
char* buffer;
long lSize;
size_t result;
std::vector<float3> pos_bp_vec;
FILE* fp = std::fopen("pos_bp.bin", "r");
if(!fp) {
std::perror("File opening failed");
}
// obtain file size:
fseek (fp , 0 , SEEK_END);
lSize = ftell (fp);
rewind (fp);
// allocate memory to contain the whole file:
buffer = (char*) malloc (sizeof(char)*lSize);
if (buffer == NULL) {fputs ("Memory error",stderr); exit (2);}
// copy the file into the buffer:
result = fread (buffer,1,lSize,fp);
if (result != lSize) {fputs ("Reading error",stderr); exit (3);}
size_t num_elems = lSize / sizeof(double);
double * buffer_d = reinterpret_cast<double*>(buffer);
// pos_tx are x,y,z coordinates, store in the format
// [x0,y0,z0,x1,y1,z1,x2,y2,z2....]. Convert to float3 values instead
pos_bp_vec.resize(num_elems/3);
for( int ii = 0; ii < num_elems/3; ii++ )
{
int i_buff = ii * 3;
pos_bp_vec[ii].x = (float)buffer_d[i_buff];
pos_bp_vec[ii].y = (float)buffer_d[i_buff+1];
pos_bp_vec[ii].z = (float)buffer_d[i_buff+2];
}
// terminate
fclose (fp);
free(buffer);
return pos_bp_vec;
}
std::vector<float3>
read_range_profile_zp()
{
std::cout << "Reading range_profile_zp" << std::endl;
char* buffer;
long lSize;
size_t result;
std::vector<float3> rp_data_vec;
FILE* fp = std::fopen("range_profile_zp.bin", "r");
if(!fp) {
std::perror("File opening failed");
}
// obtain file size:
fseek (fp , 0 , SEEK_END);
lSize = ftell (fp);
rewind (fp);
// allocate memory to contain the whole file:
buffer = (char*) malloc (sizeof(char)*lSize);
if (buffer == NULL) {fputs ("Memory error",stderr); exit (2);}
// copy the file into the buffer:
result = fread (buffer,1,lSize,fp);
if (result != lSize) {fputs ("Reading error",stderr); exit (3);}
size_t num_elems = lSize / sizeof(double);
double * buffer_d = reinterpret_cast<double*>(buffer);
// pos_tx are x,y,z coordinates, store in the format
// [x0,y0,z0,x1,y1,z1,x2,y2,z2....]. Convert to float3 values instead
rp_data_vec.resize(num_elems/2);
const int imag_jump = num_elems/2;
for( int ii = 0; ii < num_elems/2; ii++ )
{
rp_data_vec[ii].y = (float)buffer_d[ii];
rp_data_vec[ii].z = (float)buffer_d[ii+imag_jump];
}
// terminate
fclose (fp);
free(buffer);
return rp_data_vec;
}
int main(int argc, char** argv)
{
std::vector<float> dr_vec = read_dr_vec();
std::vector<float> fc_vec = read_fc_vec();
std::vector<float> rmin_vec = read_rmin();
std::vector<float3> pos_tx_vec = read_pos_tx();
std::vector<float3> pos_rx_vec = read_pos_rx();
std::vector<float3> pos_bp_vec = read_pos_bp();
std::vector<float3> rp_data_vec = read_range_profile_zp();
std::cout << "Buffer values: " << std::endl;
for(int ii = 0; ii < 30; ii++)
{
std::cout << dr_vec[ii] << std::endl;
}
}
|
7,024 | #include "includes.h"
__global__ void sd_t_s1_1_kernel(size_t h1d,size_t h2d,size_t h3d,size_t p4d,size_t p6d,size_t p4ld_t2,size_t h1ld_t2,size_t h3ld_v2,size_t h2ld_v2,size_t p6ld_v2,size_t h3ld_t3,size_t h2ld_t3,size_t h1ld_t3,size_t p6ld_t3,size_t p4ld_t3, double *t2_d, double *v2_d,size_t p4, size_t total_x, double* t3d) {
size_t h1,h2,h3,p6;
__shared__ double t2_shm[T1*4*Tcomm];
for(size_t i=threadIdx.x;i<h1d*p4d;i+=blockDim.x)
if(i<h1d*p4d)
t2_shm[i] = t2_d[i];
size_t rest_x=blockIdx.x;
size_t thread_x = T2*T1 * rest_x + threadIdx.x;
rest_x = thread_x;
__syncthreads();
/* the following computation may need to happen inside the loop */
for(size_t i=0;i<total_x;i+=gridDim.x*blockDim.x)
{
rest_x += i;
h3=rest_x%h3d;
rest_x=rest_x/h3d;
h2=rest_x%h2d;
rest_x=rest_x/h2d;
p6=rest_x%p6d;
if((thread_x+i)<total_x)
for(h1=0;h1<h1d;h1++)
for(p4=0;p4<p4d;p4++)
{
t3d[h3*h3ld_t3+h2*h2ld_t3+h1*h1ld_t3+p6*p6ld_t3+p4*p4ld_t3]+=t2_shm[h1*p4d+p4]*v2_d[h3*h3ld_v2+h2*h2ld_v2+p6*p6ld_v2];
}
}
__syncthreads();
} |
7,025 | #include <cuda_runtime.h>
#include <iostream>
#include <ctime>
using namespace std;
const int N = 1024;
const int THREAD_SIZE = 256;
#define CUDA_CHECK cuda_check(__FILE__, __LINE__)
void cuda_check(string file, int line){
cudaError_t e = cudaGetLastError();
if (e != cudaSuccess){
cout << endl << file << ", line" << line << ": " << cudaGetErrorString(e) << "(" << e << ")" << endl;
exit(1);
}
}
__device__ float add(float a, float b){
return a + b;
}
__global__ void add_arrays(float* a, float* b, float* c, int n){
int ind = threadIdx.x + blockDim.x * blockIdx.x;
if (ind < n) {
c[ind] = add(a[ind], b[ind]);
}
}
int main(int argc, char** argv){
float *a = new float[N];
float *b = new float[N];
float *c = new float[N];
for (int i = 0; i < N; ++ i){
a[i] = rand();
b[i] = rand();
c[i] = 0;
}
for (int i = 0; i < N; ++ i) c[i] = a[i] + b[i];
cout << "CPU:" << endl;
for (int i = 0; i < N; ++ i)
cout << a[i] << "+" << b[i] << "=" << c[i] << endl;
cout << endl;
float *d_a = NULL;
float *d_b = NULL;
float *d_c = NULL;
size_t nbytes = N * sizeof(float);
cudaMalloc(&d_a, nbytes); CUDA_CHECK;
cudaMalloc(&d_b, nbytes); CUDA_CHECK;
cudaMalloc(&d_c, nbytes); CUDA_CHECK;
cudaMemcpy(d_a, a, nbytes, cudaMemcpyHostToDevice);
cudaMemcpy(d_b, b, nbytes, cudaMemcpyHostToDevice);
dim3 block = dim3(THREAD_SIZE, 1, 1);
dim3 grid = dim3((N + block.x - 1) / block.x, 1, 1);
add_arrays <<<grid, block>>> (d_a, d_b, d_c, N);
cudaMemcpy(c, d_c, nbytes, cudaMemcpyDeviceToHost);CUDA_CHECK;
cudaFree(d_a);
cudaFree(d_b);
cudaFree(d_c);
cout << "GPU" << endl;
for (int i = 0; i < N; ++ i)
cout << i << ":" << a[i] << "+" << b[i] << "=" << c[i] << endl;
delete[] a;
delete[] b;
delete[] c;
return 0;
} |
7,026 | #include <stdio.h>
#include "math.h"
__constant__ int perm[] = {151,160,137,91,90,15,
131,13,201,95,96,53,194,233,7,225,140,36,103,30,69,142,8,99,37,240,21,10,23,
190, 6,148,247,120,234,75,0,26,197,62,94,252,219,203,117,35,11,32,57,177,33,
88,237,149,56,87,174,20,125,136,171,168, 68,175,74,165,71,134,139,48,27,166,
77,146,158,231,83,111,229,122,60,211,133,230,220,105,92,41,55,46,245,40,244,
102,143,54, 65,25,63,161, 1,216,80,73,209,76,132,187,208, 89,18,169,200,196,
135,130,116,188,159,86,164,100,109,198,173,186, 3,64,52,217,226,250,124,123,
5,202,38,147,118,126,255,82,85,212,207,206,59,227,47,16,58,17,182,189,28,42,
223,183,170,213,119,248,152, 2,44,154,163, 70,221,153,101,155,167, 43,172,9,
129,22,39,253, 19,98,108,110,79,113,224,232,178,185, 112,104,218,246,97,228,
251,34,242,193,238,210,144,12,191,179,162,241, 81,51,145,235,249,14,239,107,
49,192,214, 31,181,199,106,157,184, 84,204,176,115,121,50,45,127, 4,150,254,
138,236,205,93,222,114,67,29,24,72,243,141,128,195,78,66,215,61,156,180};
__constant__ int grad3[] = {1, 1, 0, -1, 1, 0, 1, -1,
0, -1, -1, 0, 1, 0, 1, -1,
0, 1, 1, 0, -1, -1, 0, -1,
0, 1, 1, 0, -1, 1, 0, 1, -1,
0, -1, -1 };
__device__ double mix(double a, double b, double t) {
return (1 - t) * a + t * b;
}
__device__ double fade(double t) {
return t * t * t * (t * (t * 6 - 15) + 10);
}
__device__ double dot(int * g, double x, double y) {
return g[0] * x + g[1] * y;
}
__device__ double dot(int * g, double x, double y, double z) {
return g[0] * x + g[1] * y + g[2] * z;
}
__device__ int fastFloor(double x) {
return x > 0 ? (int) x : (int) x - 1;
}
__device__ double map(double input, int min1, int max1, int min2, int max2) {
return (((input - min1) / (max1 - min1)) * (max2 - min2)) + min2;
}
extern "C"
__global__ void noise3D(int * output, double x, double y, double z) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
int idy = blockIdx.y * blockDim.y + threadIdx.y;
int pixelIndex = (idy * blockDim.y) + idx;
x += idx;
y += idy;
int X = floor(x);
int Y = floor(y);
int Z = floor(z);
x = x - X;
y = y - Y;
z = z - Z;
X = X & 255;
Y = Y & 255;
Z = Z & 255;
int gi000 = perm[X + perm[Y + perm[Z]]] % 12;
int gi001 = perm[X + perm[Y + perm[Z + 1]]] % 12;
int gi010 = perm[X + perm[Y + 1 + perm[Z]]] % 12;
int gi011 = perm[X + perm[Y + 1 + perm[Z + 1]]] % 12;
int gi100 = perm[X + 1 + perm[Y + perm[Z]]] % 12;
int gi101 = perm[X + 1 + perm[Y + perm[Z + 1]]] % 12;
int gi110 = perm[X + 1 + perm[Y + 1 + perm[Z]]] % 12;
int gi111 = perm[X + 1 + perm[Y + 1 + perm[Z + 1]]] % 12;
double n000 = dot(grad3 + gi000, idx, idy, z);
double n100 = dot(grad3 + gi100, x - 1, y, z);
double n010 = dot(grad3 + gi010, x, y - 1, z);
double n110 = dot(grad3 + gi110, x - 1, y - 1, z);
double n001 = dot(grad3 + gi001, x, y, z - 1);
double n101 = dot(grad3 + gi101, x - 1, y, z - 1);
double n011 = dot(grad3 + gi011, x, y - 1, z - 1);
double n111 = dot(grad3 + gi111, x - 1, y - 1, z - 1);
double u = fade(x);
double v = fade(y);
double w = fade(z);
double nx00 = mix(n000, n100, u);
double nx01 = mix(n001, n101, u);
double nx10 = mix(n010, n110, u);
double nx11 = mix(n011, n111, u);
double nxy0 = mix(nx00, nx10, v);
double nxy1 = mix(nx01, nx11, v);
double nxyz = mix(nxy0, nxy1, w);
output[pixelIndex] = map(nxyz, -1, 1, 0, 255);
} |
7,027 | #include "includes.h"
__global__ void MatrixMulKernelTiles(float* d_M, float* d_N, float* d_P, int Width){
__shared__ float Mds[TILE_WIDTH][TILE_WIDTH];
__shared__ float Nds[TILE_WIDTH][TILE_WIDTH];
int bx = blockIdx.x; int by = blockIdx.y;
int tx = threadIdx.x; int ty = threadIdx.y;
// Identify the row and column of the d_P element to work on
int Row = by * TILE_WIDTH + ty;
int Col = bx * TILE_WIDTH + tx;
float Pvalue = 0;
// Loop over the d_M and d_N tiles required to compute d_P element
for (int m = 0; m < Width/TILE_WIDTH; ++m) {
// Coolaborative loading of d_M and d_N tiles into shared memory
Mds[ty][tx] = d_M[Row*Width + m*TILE_WIDTH + tx];
Nds[ty][tx] = d_N[(m*TILE_WIDTH + ty)*Width + Col];
__syncthreads();
for (int k = 0; k < TILE_WIDTH; ++k) {
Pvalue += Mds[ty][k] * Nds[k][tx];
}
__syncthreads();
}
d_P[Row*Width + Col] = Pvalue;
} |
7,028 |
#include <stdio.h>
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#define N 9
#define threads 3
__global__ void mulKernel(int* A, int* X, int* B)
{
__shared__ int a[threads * threads];
__shared__ int x[threads];
__shared__ int parcial[threads];
int res = 0;
int row = threadIdx.y + blockIdx.y * blockDim.y;
int col = threadIdx.x + blockIdx.x * blockDim.x;
int iA = col + row * N;
int tidA = threadIdx.x + threads * threadIdx.y;
int tidX = threadIdx.y;
if (row >= N || col >= N) return;
a[tidA] = A[iA];
if (threadIdx.x == 0)
x[tidX] = B[row];
__syncthreads();
res = a[tidA] * x[tidX];
atomicAdd(&parcial[tidX], res);
__syncthreads();
if (threadIdx.x == 0)
atomicAdd(&B[col], parcial[tidX]);
}
//lab2-6
__global__ void kernel6(int* A, int* posmin)
{
//__shared__ int sh[threads];
__shared__ int locPosMin;
int loc;
int index = threadIdx.x + blockIdx.x * blockDim.x;
loc = A[index];
if (threadIdx.x == 0)
locPosMin = INT_MAX;
__syncthreads();
if(loc > 0)
atomicMin(&locPosMin, loc);
__syncthreads();
if (threadIdx.x == 0)
atomicMin(posmin, locPosMin);
__syncthreads();
if (threadIdx.x == 0)
locPosMin = *posmin;
__syncthreads();
if (loc <= 0)
loc = locPosMin;
A[index] = loc;
}
int main()
{
return 0;
} |
7,029 | #include "includes.h"
__global__ void add(int *c , int *d){
int tid=threadIdx.x;
d[tid]+=c[tid];
} |
7,030 | //function kernel
__device__ float length(float3 r) {
return r.x*r.x + r.y*r.y + r.z*r.z;
}
__device__ float3 mul_float3(float3 r1, float3 r2) {
return make_float3(r1.x * r2.x, r1.y * r2.y, r1.z * r2.z);
}
__device__ float3 add_float3(float3 r1, float3 r2) {
return make_float3(r1.x + r2.x, r1.y + r2.y, r1.z + r2.z);
}
__device__ float3 dif_float3(float3 r1, float3 r2) {
return make_float3(r1.x - r2.x, r1.y - r2.y, r1.z - r2.z);
}
__device__ float3 scale_float3(float s, float3 r) {
r.x *= s;
r.y *= s;
r.z *= s;
return r;
}
__device__ float Kernel_Poly6(float3 r, float h) {
float PI = 3.14159;
return 315.0f / (64 * PI * pow(h, 9)) * pow(pow(h, 2) - length(r), 3);
}
__device__ float3 Gradient_Kernel_Poly6(float3 r, float h) {
float PI = 3.14159;
return make_float3(
r.x * -945.0f / ( 32.0f * PI * pow(h,9) ) * pow(pow(h, 2) - length(r), 2),
r.y * -945.0f / ( 32.0f * PI * pow(h,9) ) * pow(pow(h, 2) - length(r), 2),
r.z * -945.0f / ( 32.0f * PI * pow(h,9) ) * pow(pow(h, 2) - length(r), 2));
}
__device__ float Lap_Kernel_Poly6(float3 r, float h) {
float PI = 3.14159;
return 945.0f / (8 * PI * pow(h, 9)) * (pow(h, 2) - length(r)) * (length(r) - 3 / 4 * (pow(h, 2) - length(r)));
}
__device__ float3 Gradient_Kernel_Spiky(float3 r, float h) {
float PI = 3.14159;
float _r = sqrt(length(r));
float v = -45.0f / (PI * pow(h, 6) * _r) * pow(h - _r, 2);
return make_float3(r.x*v, r.y*v, r.z*v);
}
__device__ float Lap_Kernel_Viscosity(float3 r, float h) {
float PI = 3.14159;
return 45.0f / (PI * pow(h, 5)) * (1 - sqrt(length(r)) / h);
}
//PBF particle struct
struct pPBF {
float3 pos;
float3 vel;
float m;
float rho;
float lambda;
float col;
};
extern "C" __global__ void
PBD_1(float3 *pos, int *M_index, float *M, const float h, const int N, const int NP)
{
int idx = blockDim.x * blockIdx.x + threadIdx.x;
if (idx > N) return;
float3 _pos = pos[idx];
int i;
int index = 0;
// Policy
// dont use H, and use M for connection
for (i = 0; i < NP; ++i)
{
M_index[i + idx * NP] = 0;
M[i + idx * NP] = 0;
}
// S
for (i = 0; i < N; ++i)
{
if (i == idx) continue;
float3 __pos = pos[i];
float3 r = dif_float3(_pos, __pos);
float len = length(r);
if (len > h*h) continue;
M_index[index + idx * NP] = i;
M[index + idx * NP] = sqrt(len);
++index;
if (index == NP) break;
}
return;
}
|
7,031 | /* Daniel Willen, 2019
*
* Solve the transient heat conduction problem with homogeneous Dirichlet
* boundary conditions:
*
* u(x={0,L}) = u(y={0,L}) = 0
*
* and initial condition:
*
* u(x,y,0) = sin(x) * sin(y)
*
* on the domain 0 <= x,y <= L, with L = pi.
*
* This program solves the above problem on a single GPU with the Jacobi method.
*
*/
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include <math.h>
#include <cuda.h>
#include <thrust/device_ptr.h>
#include <thrust/reduce.h>
#define PI 3.14159265358979323846
#define MAX_THREADS_DIM 16 // Note that this depends on the hardware
/* Note on the structure of this file:
* - Cuda device constant memory declarations are at the top
* - Functions definitions are in the middle. Functions include:
* - - parse_cmdline: Read command-line arguments for domain size
* - - jacobi_solver: Advance the soln to the next time step using Jacobi
* - - check_error: Calculate the error b/t the numeric and analytic solns
* - The `main' function is at the bottom
*
* Note that it is good practice to use header files and break functions out
* into separate files. This has not been done here for simplicity.
*/
/*** Auxiliary Functions ***/
/* Read the command line inputs */
// - argv[0] is the program name
// - argv[1] is the first input (number of points)
int parse_cmdline(int argc, char *argv[]) {
int nx;
if (argc >= 2) {
nx = atoi(argv[1]); // Number of grid points
printf("Grid is %d by %d\n\n", nx, nx);
} else {
printf("Input error. Run like: \n\n");
printf(" $ ./parallel.c n\n\n");
printf(" where n is the number of grid cells in one dimension\n");
exit(EXIT_FAILURE);
}
return nx;
}
/*** GPU Constants ***/
__constant__ int _nx;
__constant__ int _ny;
__constant__ double _Lx;
__constant__ double _Ly;
__constant__ double _dx;
__constant__ double _dy;
__constant__ double _dt;
__constant__ double _D;
__constant__ double _pref;
/*******************************************************************************
* Step IV: Launch the GPU kernel to advance to the next time step with the *
* Jacobi method here. *
******************************************************************************/
__global__ void jacobi_solver(double* u, double* u_new) {
// int ti = blockIdx.x*blockDim.x + threadIdx.x;
// int tj = blockIdx.y*blockDim.y + threadIdx.y;
// if ((ti >= 1 && ti < (_nx-1)) &&
// (tj >= 1 && tj < (_ny-1))) {
// u_new[ti + tj*_nx] =
// u[ti + tj*_nx] + _pref * (
// u[(ti+1) + tj*_nx] +
// u[(ti-1) + tj*_nx] +
// u[ti + (tj+1)*_nx] +
// u[ti + (tj-1)*_nx] +
// u[ti + tj*_nx] * (-4)
// );
// }
__shared__ double s_u[MAX_THREADS_DIM*MAX_THREADS_DIM];
int si = threadIdx.x;
int sj = threadIdx.y;
int ti = blockIdx.x*(blockDim.x-2) + threadIdx.x;
int tj = blockIdx.y*(blockDim.y-2) + threadIdx.y;
if (ti < _nx && tj < _ny) {
s_u[si + sj*blockDim.x] = u[ti + tj*_nx];
}
__syncthreads();
if ((ti >= 1 && ti < (_nx-1)) &&
(tj >= 1 && tj < (_ny-1)) &&
(si > 0 && si < (blockDim.x-1)) &&
(sj > 0 && sj < (blockDim.y-1))) {
u_new[ti + tj*_nx] =
s_u[si + sj*blockDim.x] + _pref * (
s_u[(si+1) + sj*blockDim.x] +
s_u[(si-1) + sj*blockDim.x] +
s_u[si + (sj+1)*blockDim.x] +
s_u[si + (sj-1)*blockDim.x] +
s_u[si + sj*blockDim.x] * (-4)
);
}
return;
}
/******************************************************************************
* Step V: Launch the GPU kernel to calculate the error at each grid point *
* here. *
*****************************************************************************/
__global__ void check_error(double* u, double* error, double time) {
// int ti = blockIdx.x*blockDim.x + threadIdx.x;
// int tj = blockIdx.y*blockDim.y + threadIdx.y;
// if ((ti >= 1 && ti < (_nx-1)) &&
// (tj >= 1 && tj < (_ny-1))) {
// error[ti + tj*_nx] = u[ti + tj*_nx] / (sin(ti*_dx) * sin(tj*_dy) * exp(-2*_D*time)) - 1;
// }
int ti = blockIdx.x*(blockDim.x-2) + threadIdx.x;
int tj = blockIdx.y*(blockDim.y-2) + threadIdx.y;
if ((ti >= 1 && ti < (_nx-1)) &&
(tj >= 1 && tj < (_ny-1)) &&
(threadIdx.x > 0 && threadIdx.x < (blockDim.x-1)) &&
(threadIdx.y > 0 && threadIdx.y < (blockDim.y-1))) {
error[ti + tj*_nx] = u[ti + tj*_nx] / (sin(ti*_dx) * sin(tj*_dy) * exp(-2*_D*time)) - 1;
}
return;
}
/*** Main Function ***/
int main(int argc, char *argv[])
{
/* Variable declaration */
double Lx = PI; // Domain length in x-direction
double Ly = PI; // Domain length in y-direction
double D = 1.; // Diffusion constant
int nx, ny; // Grid points (grid cells + 1)
double dx, dy; // Grid spacing
double dt; // Time step size
double sim_time; // Length of sim time, arbitrary for simplicity
double pref; // Pre-factor in the Jacobi method
double error = 0.; // Mean percent-difference at each grid point
error = error; // To prevent compiler warning
/* Parse command-line for problem size */
nx = parse_cmdline(argc, argv);
ny = nx; // Assume a square grid
/* Initialize variables */
dx = Lx / (nx - 1); // Cell width in x-direction
dy = Ly / (ny - 1); // Cell width in y-direction
dt = 0.25*dx*dy/D; // Limited by diffusive stability
sim_time = Lx*Ly/D; // Arbitrary simulation length
pref = D*dt/(dx*dx); // Jacobi pre-factor
printf("Parameters\n");
printf("---------------------------\n");
printf("Lx = %.5lf\n", Lx);
printf("Lx = %.5lf\n", Ly);
printf("T = %.5lf\n", sim_time);
printf("D = %.5lf\n", D);
printf("nx = %d\n", nx);
printf("ny = %d\n", nx);
printf("dx = %.5lf\n", dx);
printf("dy = %.5lf\n", dy);
printf("dt = %.5lf\n", dt);
printf("\n");
cudaMemcpyToSymbol(_nx, &nx, sizeof(int));
cudaMemcpyToSymbol(_ny, &ny, sizeof(int));
cudaMemcpyToSymbol(_Lx, &Lx, sizeof(double));
cudaMemcpyToSymbol(_Ly, &Ly, sizeof(double));
cudaMemcpyToSymbol(_dx, &dx, sizeof(double));
cudaMemcpyToSymbol(_dy, &dy, sizeof(double));
cudaMemcpyToSymbol(_dt, &dt, sizeof(double));
cudaMemcpyToSymbol(_D, &D, sizeof(double));
cudaMemcpyToSymbol(_pref, &pref, sizeof(double));
/*****************************************************************************
* Step I: Declare, allocate, and initialize memory for the field variable *
* u on the CPU. *
****************************************************************************/
double *u;
u = (double*) malloc(nx*ny * sizeof(double));
for (int i = 0; i < nx; i++) {
for (int j = 0; j < ny; j++) {
u[i+j*nx] = sin(i*dx) * sin(j*dy);
}
}
/*****************************************************************************
* Step II: Declare and allocate GPU memory for _u, _u_new, and _error. Copy *
* the initial condition to the GPU. *
****************************************************************************/
double *_u, *_u_new, *_error;
cudaMalloc(&_u, nx*ny * sizeof(double));
cudaMalloc(&_u_new, nx*ny * sizeof(double));
cudaMalloc(&_error, nx*ny * sizeof(double));
cudaMemcpy(_u, u, nx*ny * sizeof(double), cudaMemcpyHostToDevice);
// Set the new soln and error to 0
cudaMemset(_u_new, 0., nx*ny * sizeof(double));
cudaMemset(_error, 0., nx*ny * sizeof(double));
// Create thrust pointers to device memory for error calculation
thrust::device_ptr<double> t_error(_error);
/*****************************************************************************
* Step III: Set up the kernel execution configuration for the domain based *
* on the input domain size and the MAX_THREADS_DIM variable. *
****************************************************************************/
int threads_x = MAX_THREADS_DIM;
int threads_y = MAX_THREADS_DIM;
// int blocks_x = (int) ceil((double) nx / (double) threads_x);
// int blocks_y = (int) ceil((double) ny / (double) threads_y);
int blocks_x = (int) ceil((double) nx / (double) (threads_x - 2));
int blocks_y = (int) ceil((double) ny / (double) (threads_y - 2));
dim3 dim_blocks(threads_x, threads_y);
dim3 num_blocks(blocks_x, blocks_y);
printf("Parallelization\n");
printf("---------------------------\n");
printf("MAX_THREADS_DIM = %d\n", MAX_THREADS_DIM);
printf("threads_x = %d\n", threads_x);
printf("threads_y = %d\n", threads_y);
printf("blocks_x = %d\n", blocks_x);
printf("blocks_y = %d\n", blocks_y);
printf("\n");
/***************************/
/* Main Time-Stepping Loop */
/***************************/
for (double time = 0.; time <= sim_time; time += dt) {
/***************************************************************************
* Step IV: Launch the GPU kernel to advance to the next time step with *
* the Jacobi method here. *
**************************************************************************/
jacobi_solver<<<num_blocks, dim_blocks>>>(_u, _u_new);
/***************************************************************************
* Step V: Launch the GPU kernel to calculate the error at each grid point *
* here. *
**************************************************************************/
check_error<<<num_blocks, dim_blocks>>>(_u, _error, time);
// Use thrust to do a parallel reduction on the error
error = thrust::reduce(t_error, t_error + nx*ny, 0., thrust::plus<double>());
printf("Error at t* = %.5lf is %e\n", time*D/(Lx*Lx), error/(nx*ny));
// Copy new soln to old. This also blocks to ensure computations are finished.
cudaMemcpy(_u, _u_new, nx*ny * sizeof(double), cudaMemcpyDeviceToDevice);
}
/*****************************************************************************
* Step VI: Copy the memory back to the CPU. *
****************************************************************************/
cudaMemcpy(u, _u, nx*ny * sizeof(double), cudaMemcpyDeviceToHost);
/*****************************************************************************
* Step I and Step II: Free the memory that you declared and allocated *
* earlier in the program. *
****************************************************************************/
cudaFree(_u);
cudaFree(_u_new);
cudaFree(_error);
free(u);
return EXIT_SUCCESS;
}
|
7,032 | #include <fstream>
#include <sstream>
#include <vector>
#include "math.h"
__device__
double calculateStandardDeviation(double *data, int col, int rows, int columns, double mean) {
double standardDeviation = 0;
for (int i = col; i < rows * columns; i += columns) {
auto temp = (data[i] - mean);
standardDeviation += (temp * temp);
}
return sqrt(standardDeviation/rows);
}
__device__
double calculateMean(double *data, int col, int rows, int columns) {
double mean = 0;
for (int i = col; i < rows * columns; i+= columns) {
mean += data[i];
}
return mean/rows;
}
__global__
void runStandardization(double* data, int rows, int columns) {
double *mean = new double[columns - 1];
double *std = new double[columns - 1];
for(int i = 0; i < columns-1; i++) {
mean[i] = calculateMean(data, i, rows, columns);
std[i] = calculateStandardDeviation(data, i, rows, columns, mean[i]);
}
int index = blockIdx.x * blockDim.x + threadIdx.x;
int stride = blockDim.x * gridDim.x;
int columnsCounter = 0;
for(int i = index; i < rows * columns; i+=stride) {
double value = data[i];
if (columnsCounter+1 == columns) {
data[i] = value;
columnsCounter = 0;
}
else {
if(std[columnsCounter] != 0) {
value -= mean[columnsCounter];
value /= std[columnsCounter];
}
data[i] = value;
columnsCounter++;
}
}
}
__host__
int countWords(const std::string& text, char delimiter) {
std::stringstream stream(text);
std::string temp;
int counter = 0;
while(getline(stream, temp, delimiter)) {
counter++;
}
return counter;
}
__host__
void countRowsAndColumns(std::string filename, int* rows, int* columns) {
std::ifstream featuresFile(filename);
std::string line;
std::getline(featuresFile, line);
*columns = countWords(line, ',');
*rows = 1;
while (std::getline(featuresFile, line))
(*rows)++;
featuresFile.close();
}
__host__
void readFeaturesFromCsv(std::string filename, double* result, int rows, int columns) {
std::ifstream featuresFile(filename);
std::string line;
int i = 0;
while(std::getline(featuresFile, line))
{
std::stringstream ss(line);
double value;
while(ss >> value){
result[i] = value;
if(ss.peek() == ',') ss.ignore();
i++;
}
}
featuresFile.close();
}
__host__
void writeFeaturesToCsv(double *output, int rows, int columns){
std::ofstream file("data.csv");
int size = rows * columns;
int columnsCounter = 0;
for(int i = 0; i < size; ++i) {
double value = output[i];
file << value;
if (columnsCounter + 1 != columns) {
file << ",";
columnsCounter++;
} else {
file << "\n";
columnsCounter = 0;
}
}
file.close();
}
int main(int argc, char* argv[]) {
int rows, columns;
std::string fileName("./winequality-white.csv");
countRowsAndColumns(fileName, &rows, &columns);
double *input = new double[rows * columns];
readFeaturesFromCsv(fileName, input, rows, columns);
double *normalized;
cudaMalloc((void **) &normalized, rows * columns * sizeof(double));
cudaMemcpy(normalized, input, rows * columns * sizeof(double), cudaMemcpyHostToDevice);
cudaEvent_t startTime, stopTime;
cudaEventCreate(&startTime);
cudaEventCreate(&stopTime);
cudaEventRecord(startTime);
runStandardization<<<1, 1>>>(normalized, rows, columns);
float resultTime;
cudaEventRecord(stopTime);
cudaEventSynchronize(stopTime);
cudaEventElapsedTime(&resultTime, startTime, stopTime);
printf("Algorithm took: %f (ms)\n", resultTime);
cudaMemcpy(input, normalized, rows * columns * sizeof(double), cudaMemcpyDeviceToHost);
cudaFree(normalized);
writeFeaturesToCsv(input, rows, columns);
} |
7,033 | #include "includes.h"
using namespace std;
#define GRID_SIZE 32
#define SHARED_MEM 16384
__global__ void findY(float *x, float *y, int n, float h, float z, int zLoc, float *returnVal) {
// int col = blockIdx.x * blockDim.x + threadIdx.x;
// int row = blockIdx.y * blockDim.y + threadIdx.y;
__shared__ float sum;
sum = 0;
// float absVal = 0;
int count = 0;
for(int i = 0; i < n; i++) {
// absVal = abs(x[i] - z);
if(abs(x[i] - z) < h) {
//sum = atomicAdd(&sum, y[zLoc]);
sum += y[i];
// cuPrintf("sum = %d\n", sum);
count++;
}
}
*returnVal = sum / count;
// sum = 0;
// count = 0;
} |
7,034 | #include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <algorithm>
#include <cassert>
#include <cstdlib>
#include <iostream>
#include <iterator>
#include <vector>
using std::begin;
using std::copy;
using std::cout;
using std::end;
using std::generate;
using std::vector;
// CUDA kernel for vector addition
// __global__ means this is called from the CPU, and runs on the GPU
__global__ void vectorAdd(int* a, int* b, int* c, int N) {
// Calculate global thread ID
int tid = (blockIdx.x * blockDim.x) + threadIdx.x;
// Boundary check
if (tid < N) {
// Each thread adds a single element
c[tid] = a[tid] + b[tid];
}
}
// Check vector add result
void verify_result(vector<int>& a, vector<int>& b, vector<int>& c) {
for (int i = 0; i < a.size(); i++) {
assert(c[i] == a[i] + b[i]);
}
}
int main() {
// Array size of 2^16 (65536 elements)
constexpr int N = 1 << 26;
size_t bytes = sizeof(int) * N;
// Vectors for holding the host-side (CPU-side) data
vector<int> a(N);
vector<int> b(N);
vector<int> c(N);
// Initialize random numbers in each array
generate(begin(a), end(a), []() { return rand() % 100; });
generate(begin(b), end(b), []() { return rand() % 100; });
// Allocate memory on the device
int* d_a, * d_b, * d_c;
cudaMalloc(&d_a, bytes);
cudaMalloc(&d_b, bytes);
cudaMalloc(&d_c, bytes);
// Copy data from the host to the device (CPU -> GPU)
cudaMemcpy(d_a, a.data(), bytes, cudaMemcpyHostToDevice);
cudaMemcpy(d_b, b.data(), bytes, cudaMemcpyHostToDevice);
// Threads per CTA (1024 threads per CTA)
int NUM_THREADS = 1 << 10;
// CTAs per Grid
// We need to launch at LEAST as many threads as we have elements
// This equation pads an extra CTA to the grid if N cannot evenly be divided
// by NUM_THREADS (e.g. N = 1025, NUM_THREADS = 1024)
int NUM_BLOCKS = (N + NUM_THREADS - 1) / NUM_THREADS;
// Launch the kernel on the GPU
// Kernel calls are asynchronous (the CPU program continues execution after
// call, but no necessarily before the kernel finishes)
vectorAdd <<<NUM_BLOCKS, NUM_THREADS>>> (d_a, d_b, d_c, N);
// Copy sum vector from device to host
// cudaMemcpy is a synchronous operation, and waits for the prior kernel
// launch to complete (both go to the default stream in this case).
// Therefore, this cudaMemcpy acts as both a memcpy and synchronization
// barrier.
cudaMemcpy(c.data(), d_c, bytes, cudaMemcpyDeviceToHost);
// Check result for errors
verify_result(a, b, c);
// Free memory on device
cudaFree(d_a);
cudaFree(d_b);
cudaFree(d_c);
cout << "COMPLETED SUCCESSFULLY\n";
return 0;
} |
7,035 | #include "includes.h"
__global__ void setMultiLHS ( double* dsMulti, double* dlMulti, double* diagMulti, double* duMulti, double* dwMulti, double a, double b, double c, double d, double e, int nx, int batchCount )
{
// Matrix index
int globalIdx = blockDim.x * blockIdx.x + threadIdx.x;
int globalIdy = blockDim.y * blockIdx.y + threadIdx.y;
// Index access
int index = globalIdy * batchCount + globalIdx;
if (globalIdx < batchCount && globalIdy < nx)
{
dsMulti[index] = a;
dlMulti[index] = b;
diagMulti[index] = c;
duMulti[index] = d;
dwMulti[index] = e;
}
} |
7,036 | #include "includes.h"
__global__ void AddToFloat( float *sum, float *out, const float *pIn )
{
(void) atomicAdd( &out[threadIdx.x], pIn[threadIdx.x] );
} |
7,037 | #include "includes.h"
#define SIZE_thread 1024
__global__ void VectorAdd(int *A, int *B, int *C,int n)
{
int i = threadIdx.x + blockIdx.x*blockDim.x;
if(i<n)
C[i]=A[i]+B[i];
} |
7,038 | #include <iostream>
#include <stdlib.h>
#include <chrono>
#include <stdio.h>
__global__
void triad_kernel(int N, double *x, double *y, double *z) {
for(unsigned i = threadIdx.x + blockIdx.x * blockDim.x; i < N; i+=blockDim.x*gridDim.x)
z[i] = x[i] + y[i];
}
int main() {
int N = 100;
double *x, *y, *z;
cudaMallocManaged((void**) &x, N*sizeof(double));
cudaMallocManaged((void**) &y, N*sizeof(double));
cudaMallocManaged((void**) &z, N*sizeof(double));
for(unsigned i = 0; i < N; i++) {
x[i] = 0.001*i; y[i] = 0.03*i;
}
int nthreads = 64;
int nblocks = (N+nthreads-1)/ nthreads;
triad_kernel<<<nblocks, nthreads>>>(N,x,y,z);
cudaDeviceSynchronize();
for(unsigned i = 0; i < N; i++) {
printf("z[%d] = %g; expected z = %g\n|" ,i,z[i],x[i]+y[i]);
}
cudaFree(x); cudaFree(y); cudaFree(z);
return 0;
}
|
7,039 | #include "constants.cuh"
#include "e_field.cuh"
#include "b_field.cuh"
#include "sources.cuh"
#include "update_functions.cuh"
#include "tdma.cuh"
#include "file_io.cuh"
using namespace std;
const int step = 10;
void run_loop(Efield& e, Bfield& b, Source& s) {
Timer update_loop_timer;
update_loop_timer.start();
Tridiagonal td_x_half(nx, dy); // ex n+1/2
Tridiagonal td_y_half(ny, dz); // ey n+1/2
Tridiagonal td_z_half(nz, dx); // ez n+1/2
Tridiagonal td_x_one(nx, dz); // ex n+1
Tridiagonal td_y_one(ny, dx); // ey n+1
Tridiagonal td_z_one(nz, dy); // ez n+1
// Begin time loop
for (int q = 0; q < nt; q++) {
auto t = float(q) * dt;
update_sources(s, t);
/* n -> n + 1/2 */
// Implicit e update
implicit_ex_half(e, b, s);
x_solve(td_x_half, e.ex_rhs, e.ex);
implicit_ey_half(e, b, s);
y_solve(td_y_half, e.ey_rhs, e.ey);
implicit_ez_half(e, b, s);
z_solve(td_z_half, e.ez_rhs, e.ez);
// Explicit E update
explicit_E(e.Ex, e.ex);
explicit_E(e.Ey, e.ey);
explicit_E(e.Ez, e.ez);
// Explicit H update
explicit_Hx_half(b, e);
explicit_Hy_half(b, e);
explicit_Hz_half(b, e);
/* n + 1/2 -> n + 1 */
// Implicit e update
implicit_ex_one(e, b);
x_solve(td_x_one, e.ex_rhs, e.ex);
implicit_ey_one(e, b);
y_solve(td_y_one, e.ey_rhs, e.ey);
implicit_ez_one(e, b);
z_solve(td_z_one, e.ez_rhs, e.ez);
// Explicit E update
explicit_E(e.Ex, e.ex);
explicit_E(e.Ey, e.ey);
explicit_E(e.Ez, e.ez);
// Explicit H update
explicit_Hx_one(b, e);
explicit_Hy_one(b, e);
explicit_Hz_one(b, e);
if (q % step == 0) {
cout << q << "/" << nt << ": " << update_loop_timer.split() << endl;
snapshot(q, e, b);
}
}
cout << "Update loop completed. Processing arrays." << endl;
snapshot(nt, e, b);
update_loop_timer.stop();
cout << "Processing completed.\nTotal time: " << update_loop_timer.total << endl;
}
int main() {
save_params(step);
Efield e;
Bfield b;
Source s;
run_loop(e, b, s);
return 0;
}
|
7,040 | /// ================================================================
///
/// Disclaimer: IMPORTANT: This software was developed at theNT
/// National Institute of Standards and Technology by employees of the
/// Federal Government in the course of their official duties.
/// Pursuant to title 17 Section 105 of the United States Code this
/// software is not subject to copyright protection and is in the
/// public domain. This is an experimental system. NIST assumes no
/// responsibility whatsoever for its use by other parties, and makes
/// no guarantees, expressed or implied, about its quality,
/// reliability, or any other characteristic. We would appreciate
/// acknowledgement if the software is used. This software can be
/// redistributed and/or modified freely provided that any derivative
/// works bear some notice that they are derived from it, and any
/// modified versions bear some notice that they have been modified.
///
/// ================================================================
// ================================================================
//
// Author: Timothy Blattner
// Date: Wed Nov 30 12:36:40 2011 EScufftDoubleComplex
//
// Functions that execute on the graphics card for doing
// Vector computation.
//
// ================================================================
#include <cuda.h>
#include <cufft.h>
#include<float.h>
#define THREADS_PER_BLOCK 256
#define MIN_DISTANCE 1.0
// ================================================================
__device__ double distance(int x1, int x2, int y1, int y2)
{
return ((double(x1-x2))*(double(x1-x2)))+
((double(y1-y2))*(double(y1-y2)));
}
__device__ bool checkDistance(int *maxesRow,
int *maxesCol, int nMax,
int curIdx, int width)
{
int row = curIdx / width;
int col = curIdx % width;
int j;
//double dist;
for (j = 0; j < nMax; j++)
{
if (maxesRow[j] == row && maxesCol[j] == col)
return false;
//dist = distance(maxesRow[j], row, maxesCol[j], col);
//if (dist < MIN_DISTANCE)
// return false;
}
return true;
}
__device__ bool checkDistance(volatile int *maxesRow,
volatile int *maxesCol, int nMax,
int curIdx, int width)
{
int row = curIdx / width;
int col = curIdx % width;
int j;
//double dist;
for (j = 0; j < nMax; j++)
{
if (maxesRow[j] == row && maxesCol[j] == col)
return false;
// dist = distance(maxesRow[j], row, maxesCol[j], col);
// if (dist < MIN_DISTANCE)
// return false;
}
return true;
}
extern "C"
__global__ void
elt_prod_conj(cufftDoubleComplex *fc, cufftDoubleComplex * c1,
cufftDoubleComplex * c2, int size)
{
__shared__ cufftDoubleComplex sfc[THREADS_PER_BLOCK];
__shared__ cufftDoubleComplex sc1[THREADS_PER_BLOCK];
__shared__ cufftDoubleComplex sc2[THREADS_PER_BLOCK];
int idx = threadIdx.x + blockIdx.x * THREADS_PER_BLOCK;
if (idx >= size)
return;
sc1[threadIdx.x] = c1[idx];
sc2[threadIdx.x] = c2[idx];
__syncthreads();
sfc[threadIdx.x] = cuCmul(sc1[threadIdx.x], cuConj(sc2[threadIdx.x]));
double mag = cuCabs(sfc[threadIdx.x]);
if (mag == 0 || isnan(mag))
{
mag = DBL_EPSILON;
sfc[threadIdx.x].x = DBL_EPSILON;
}
fc[idx] = make_cuDoubleComplex(cuCreal(sfc[threadIdx.x]) / mag,
cuCimag(sfc[threadIdx.x]) / mag);
}
extern "C"
__global__ void
elt_prod_conj_v2(cufftDoubleComplex *fc, cufftDoubleComplex * c1,
cufftDoubleComplex * c2, int size)
{
__shared__ cufftDoubleComplex sfc[THREADS_PER_BLOCK];
int idx = threadIdx.x + blockIdx.x * THREADS_PER_BLOCK;
if (idx >= size)
return;
//cufftDoubleComplex fc_res;
sfc[threadIdx.x] = cuCmul(c1[idx], cuConj(c2[idx]));
__syncthreads();
double mag;
// mag = sqrt(fc_res.x * fc_res.x + fc_res.y * fc_res.y);
mag = sqrt(sfc[threadIdx.x].x * sfc[threadIdx.x].x +
sfc[threadIdx.x].y * sfc[threadIdx.x].y);
if (isnan(mag) || mag == 0)
{
mag = DBL_EPSILON; //cuCabs(sfc[threadIdx.x]);
sfc[threadIdx.x].x = DBL_EPSILON;
}
// if (mag == 0)
// mag = DBL_EPSILON;
fc[idx] = make_cuDoubleComplex(sfc[threadIdx.x].x / mag,
sfc[threadIdx.x].y / mag);
}
extern "C"
__global__ void
elt_prod_conj_v3(cufftDoubleComplex *fc, cufftDoubleComplex * c1,
cufftDoubleComplex *c2, int size)
{
int idx = threadIdx.x + blockIdx.x * THREADS_PER_BLOCK;
if (idx >= size)
return;
cufftDoubleComplex _c1 = c1[idx];
cufftDoubleComplex _c2 = c2[idx];
cufftDoubleComplex _fc = cuCmul(_c1, cuConj(_c2));
double mag = sqrt(_fc.x * _fc.x +
_fc.y * _fc.y);
if (isnan(mag) || mag == 0)
mag = cuCabs(_fc);
if (mag == 0)
mag = DBL_EPSILON;
fc[idx] = make_cuDoubleComplex(_fc.x / mag, _fc.y / mag);
}
extern "C"
__global__ void
reduce_max_final(double *g_idata, double *g_odata,
int * max_idx, unsigned int n, int blockSize)
{
__shared__ double sdata[THREADS_PER_BLOCK];
__shared__ int idxData[THREADS_PER_BLOCK];
unsigned int tid = threadIdx.x;
unsigned int i = blockIdx.x*(blockSize*2) + tid;
unsigned int gridSize = blockSize*2*gridDim.x;
double myMax = 0.0;
int myMaxIndex;
while (i < n)
{
if (myMax < g_idata[i])
{
myMax = g_idata[i];
myMaxIndex = max_idx[i];
}
if (i+blockSize < n)
{
if (myMax < g_idata[i+blockSize])
{
myMax = g_idata[i+blockSize];
myMaxIndex = max_idx[i+blockSize];
}
}
i += gridSize;
}
sdata[tid] = myMax;
idxData[tid] = myMaxIndex;
__syncthreads();
if (blockSize >= 512)
{
if (tid < 256)
{
if (myMax < sdata[tid + 256])
{
sdata[tid] = myMax = sdata[tid+256];
idxData[tid] = idxData[tid+256];
}
}
__syncthreads();
}
if (blockSize >= 256)
{
if (tid < 128)
{
if (myMax < sdata[tid + 128])
{
sdata[tid] = myMax = sdata[tid+128];
idxData[tid] = idxData[tid+128];
}
}
__syncthreads();
}
if (blockSize >= 128)
{
if (tid < 64)
{
if(myMax < sdata[tid + 64])
{
sdata[tid] = myMax = sdata[tid+64];
idxData[tid] = idxData[tid+64];
}
}
__syncthreads();
}
volatile double *vdata = sdata;
volatile int *vidxData = idxData;
if (tid < 32)
{
if (blockSize >= 64)
if (myMax < vdata[tid + 32])
{
vdata[tid] = myMax = vdata[tid+32];
vidxData[tid] = vidxData[tid+32];
}
if (blockSize >= 32)
if (myMax < vdata[tid + 16])
{
vdata[tid] = myMax = vdata[tid+16];
vidxData[tid] = vidxData[tid+16];
}
if (blockSize >= 16)
if (myMax < vdata[tid + 8])
{
vdata[tid] = myMax = vdata[tid+8];
vidxData[tid] = vidxData[tid+8];
}
if (blockSize >= 8)
if (myMax < vdata[tid + 4])
{
vdata[tid] = myMax = vdata[tid+4];
vidxData[tid] = vidxData[tid+4];
}
if (blockSize >= 4)
if (myMax < vdata[tid+2])
{
vdata[tid] = myMax = vdata[tid+2];
vidxData[tid] = vidxData[tid+2];
}
if (blockSize >= 2)
if (myMax < vdata[tid + 1])
{
vdata[tid] = myMax = vdata[tid+1];
vidxData[tid] = vidxData[tid+1];
}
__syncthreads();
}
if (tid == 0)
{
g_odata[blockIdx.x] = sdata[0];
max_idx[blockIdx.x] = idxData[0];
}
}
extern "C"
__global__ void
reduce_max_main(double *g_idata, double *g_odata,
int * max_idx, unsigned int n, int blockSize)
{
__shared__ double sdata[THREADS_PER_BLOCK];
__shared__ int idxData[THREADS_PER_BLOCK];
unsigned int tid = threadIdx.x;
unsigned int i = blockIdx.x*(blockSize) + tid;
unsigned int gridSize = blockSize*gridDim.x;
double myMax = 0.0;
int myMaxIndex;
double val;
while (i < n)
{
val = g_idata[i];
if (myMax < val)
{
myMax = val;
myMaxIndex = i;
}
if (i+blockSize < n)
{
val = g_idata[i+blockSize];
if (myMax < val)
{
myMax = val;
myMaxIndex = i+blockSize;
}
}
i += gridSize;
}
sdata[tid] = myMax;
idxData[tid] = myMaxIndex;
__syncthreads();
if (blockSize >= 512)
{
if (tid < 256)
{
if (myMax < sdata[tid + 256])
{
sdata[tid] = myMax = sdata[tid+256];
idxData[tid] = idxData[tid+256];
}
}
__syncthreads();
}
if (blockSize >= 256)
{
if (tid < 128)
{
if (myMax < sdata[tid + 128])
{
sdata[tid] = myMax = sdata[tid+128];
idxData[tid] = idxData[tid+128];
}
}
__syncthreads();
}
if (blockSize >= 128)
{
if (tid < 64)
{
if(myMax < sdata[tid + 64])
{
sdata[tid] = myMax = sdata[tid+64];
idxData[tid] = idxData[tid+64];
}
}
__syncthreads();
}
volatile double *vdata = sdata;
volatile int *vidxData = idxData;
if (tid < 32)
{
if (blockSize >= 64)
if (myMax < vdata[tid + 32])
{
vdata[tid] = myMax = vdata[tid+32];
vidxData[tid] = vidxData[tid+32];
}
if (blockSize >= 32)
if (myMax < vdata[tid + 16])
{
vdata[tid] = myMax = vdata[tid+16];
vidxData[tid] = vidxData[tid+16];
}
if (blockSize >= 16)
if (myMax < vdata[tid + 8])
{
vdata[tid] = myMax = vdata[tid+8];
vidxData[tid] = vidxData[tid+8];
}
if (blockSize >= 8)
if (myMax < vdata[tid + 4])
{
vdata[tid] = myMax = vdata[tid+4];
vidxData[tid] = vidxData[tid+4];
}
if (blockSize >= 4)
if (myMax < vdata[tid+2])
{
vdata[tid] = myMax = vdata[tid+2];
vidxData[tid] = vidxData[tid+2];
}
if (blockSize >= 2)
if (myMax < vdata[tid + 1])
{
vdata[tid] = myMax = vdata[tid+1];
vidxData[tid] = vidxData[tid+1];
}
__syncthreads();
}
if (tid == 0)
{
g_odata[blockIdx.x] = sdata[0];
max_idx[blockIdx.x] = idxData[0];
}
}
extern "C"
__global__ void
reduce_max_filter_final(double *g_idata, double *g_odata,
int * max_idx, unsigned int n, unsigned int width,
int blockSize,
int *maxes, int nMax)
{
__shared__ int smaxesRow[10];
__shared__ int smaxesCol[10];
__shared__ int smaxesVal[10];
__shared__ double sdata[THREADS_PER_BLOCK];
__shared__ int idxData[THREADS_PER_BLOCK];
unsigned int tid = threadIdx.x;
unsigned int i = blockIdx.x*(blockSize*2) + tid;
unsigned int gridSize = blockSize*2*gridDim.x;
if (tid < nMax)
{
smaxesVal[tid] = maxes[tid];
smaxesRow[tid] = smaxesVal[tid] / width;
smaxesCol[tid] = smaxesVal[tid] % width;
}
__syncthreads();
double myMax = 0.0;
int myMaxIndex;
while (i < n)
{
if (myMax < g_idata[i])
{
if (checkDistance(smaxesRow, smaxesCol,
nMax, max_idx[i], width))
{
myMax = g_idata[i];
myMaxIndex = max_idx[i];
}
}
if (i+blockSize < n)
{
if (myMax < g_idata[i+blockSize])
{
if (checkDistance(smaxesRow, smaxesCol,
nMax,
max_idx[i+blockSize],
width))
{
myMax = g_idata[i+blockSize];
myMaxIndex = max_idx[i+blockSize];
}
}
}
i += gridSize;
}
sdata[tid] = myMax;
idxData[tid] = myMaxIndex;
__syncthreads();
if (blockSize >= 512)
{
if (tid < 256)
{
if (myMax < sdata[tid + 256])
{
if (checkDistance(smaxesRow, smaxesCol,
nMax, idxData[tid+256],
width))
{
sdata[tid] = myMax = sdata[tid+256];
idxData[tid] = idxData[tid+256];
}
}
}
__syncthreads();
}
if (blockSize >= 256)
{
if (tid < 128)
{
if (myMax < sdata[tid + 128])
{
if (checkDistance(smaxesRow, smaxesCol,
nMax, idxData[tid+128],
width))
{
sdata[tid] = myMax = sdata[tid+128];
idxData[tid] = idxData[tid+128];
}
}
}
__syncthreads();
}
if (blockSize >= 128)
{
if (tid < 64)
{
if(myMax < sdata[tid + 64])
{
if (checkDistance(smaxesRow, smaxesCol,
nMax, idxData[tid+64],
width))
{
sdata[tid] = myMax = sdata[tid+64];
idxData[tid] = idxData[tid+64];
}
}
}
__syncthreads();
}
volatile double *vdata = sdata;
volatile int *vidxData = idxData;
volatile int *vsmaxesRow = smaxesRow;
volatile int *vsmaxesCol = smaxesCol;
if (tid < 32)
{
if (blockSize >= 64)
if (myMax < vdata[tid + 32])
{
if (checkDistance(vsmaxesRow, vsmaxesCol,
nMax, vidxData[tid+32],
width))
{
vdata[tid] = myMax = vdata[tid+32];
vidxData[tid] = vidxData[tid+32];
}
}
if (blockSize >= 32)
if (myMax < vdata[tid + 16])
{
if (checkDistance(vsmaxesRow, vsmaxesCol,
nMax, vidxData[tid+16],
width))
{
vdata[tid] = myMax = vdata[tid+16];
vidxData[tid] = vidxData[tid+16];
}
}
if (blockSize >= 16)
if (myMax < vdata[tid + 8])
{
if (checkDistance(vsmaxesRow, vsmaxesCol,
nMax, vidxData[tid+8],
width))
{
vdata[tid] = myMax = vdata[tid+8];
vidxData[tid] = vidxData[tid+8];
}
}
if (blockSize >= 8)
if (myMax < vdata[tid + 4])
{
if (checkDistance(vsmaxesRow, vsmaxesCol,
nMax, vidxData[tid+4],
width))
{
vdata[tid] = myMax = vdata[tid+4];
vidxData[tid] = vidxData[tid+4];
}
}
if (blockSize >= 4)
if (myMax < vdata[tid+2])
{
if (checkDistance(vsmaxesRow, vsmaxesCol,
nMax, vidxData[tid+2],
width))
{
vdata[tid] = myMax = vdata[tid+2];
vidxData[tid] = vidxData[tid+2];
}
}
if (blockSize >= 2)
if (myMax < vdata[tid + 1])
{
if (checkDistance(vsmaxesRow, vsmaxesCol,
nMax, vidxData[tid+1],
width))
{
vdata[tid] = myMax = vdata[tid+1];
vidxData[tid] = vidxData[tid+1];
}
}
__syncthreads();
}
if (tid == 0)
{
g_odata[blockIdx.x] = sdata[0];
max_idx[blockIdx.x] = idxData[0];
if (gridDim.x == 1)
maxes[nMax] = idxData[0];
}
}
extern "C"
__global__ void
reduce_max_filter_main(double *g_idata, double *g_odata,
int * max_idx, unsigned int width, unsigned int height,
int blockSize,
int *maxes, int nMax)
{
__shared__ int smaxesRow[10];
__shared__ int smaxesCol[10];
__shared__ int smaxesVal[10];
__shared__ double sdata[THREADS_PER_BLOCK];
__shared__ int idxData[THREADS_PER_BLOCK];
unsigned int tid = threadIdx.x;
unsigned int i = blockIdx.x*(blockSize) + tid;
unsigned int gridSize = blockSize*gridDim.x;
if (tid < nMax)
{
smaxesVal[tid] = maxes[tid];
smaxesRow[tid] = smaxesVal[tid] / width;
smaxesCol[tid] = smaxesVal[tid] % width;
}
__syncthreads();
double myMax = -INFINITY;
int myMaxIndex;
double val;
while (i < width * height)
{
val = g_idata[i];
if (myMax < val)
{
// compute distance . . .
if (checkDistance(smaxesRow, smaxesCol,
nMax, i, width))
{
myMax = val;
myMaxIndex = i;
}
}
if (i+blockSize < width * height)
{
val = g_idata[i+blockSize];
if (myMax < val)
{
if (checkDistance(smaxesRow, smaxesCol,
nMax, i+blockSize, width))
{
myMax = val;
myMaxIndex = i+blockSize;
}
}
}
i += gridSize;
}
sdata[tid] = myMax;
idxData[tid] = myMaxIndex;
__syncthreads();
if (blockSize >= 512)
{
if (tid < 256)
{
if (myMax < sdata[tid + 256])
{
if (checkDistance(smaxesRow, smaxesCol,
nMax, idxData[tid+256],
width))
{
sdata[tid] = myMax = sdata[tid+256];
idxData[tid] = idxData[tid+256];
}
}
}
__syncthreads();
}
if (blockSize >= 256)
{
if (tid < 128)
{
if (myMax < sdata[tid + 128])
{
if (checkDistance(smaxesRow, smaxesCol,
nMax, idxData[tid+128],
width))
{
sdata[tid] = myMax = sdata[tid+128];
idxData[tid] = idxData[tid+128];
}
}
}
__syncthreads();
}
if (blockSize >= 128)
{
if (tid < 64)
{
if(myMax < sdata[tid + 64])
{
if (checkDistance(smaxesRow, smaxesCol,
nMax, idxData[tid+64],
width))
{
sdata[tid] = myMax = sdata[tid+64];
idxData[tid] = idxData[tid+64];
}
}
}
__syncthreads();
}
volatile double *vdata = sdata;
volatile int *vidxData = idxData;
volatile int *vsmaxesRow = smaxesRow;
volatile int *vsmaxesCol = smaxesCol;
if (tid < 32)
{
if (blockSize >= 64)
if (myMax < vdata[tid + 32])
{
if (checkDistance(vsmaxesRow, vsmaxesCol,
nMax, vidxData[tid+32],
width))
{
vdata[tid] = myMax = vdata[tid+32];
vidxData[tid] = vidxData[tid+32];
}
}
if (blockSize >= 32)
if (myMax < vdata[tid + 16])
{
if (checkDistance(vsmaxesRow, vsmaxesCol,
nMax, vidxData[tid+16],
width))
{
vdata[tid] = myMax = vdata[tid+16];
vidxData[tid] = vidxData[tid+16];
}
}
if (blockSize >= 16)
if (myMax < vdata[tid + 8])
{
if (checkDistance(vsmaxesRow, vsmaxesCol,
nMax, vidxData[tid+8],
width))
{
vdata[tid] = myMax = vdata[tid+8];
vidxData[tid] = vidxData[tid+8];
}
}
if (blockSize >= 8)
if (myMax < vdata[tid + 4])
{
if (checkDistance(vsmaxesRow, vsmaxesCol,
nMax, vidxData[tid+4],
width))
{
vdata[tid] = myMax = vdata[tid+4];
vidxData[tid] = vidxData[tid+4];
}
}
if (blockSize >= 4)
if (myMax < vdata[tid+2])
{
if (checkDistance(vsmaxesRow, vsmaxesCol,
nMax, vidxData[tid+2],
width))
{
vdata[tid] = myMax = vdata[tid+2];
vidxData[tid] = vidxData[tid+2];
}
}
if (blockSize >= 2)
if (myMax < vdata[tid + 1])
{
if (checkDistance(vsmaxesRow, vsmaxesCol,
nMax, vidxData[tid+1],
width))
{
vdata[tid] = myMax = vdata[tid+1];
vidxData[tid] = vidxData[tid+1];
}
}
__syncthreads();
}
if (tid == 0)
{
g_odata[blockIdx.x] = sdata[0];
max_idx[blockIdx.x] = idxData[0];
if (gridDim.x == 1)
maxes[nMax] = idxData[0];
}
}
// ================================================================
// ================================================================
// ================================================================
// ================================================================
// ======================= Float versions =========================
// ================================================================
// ================================================================
// ================================================================
// ================================================================
// ================================================================
__device__ float distancef(int x1, int x2, int y1, int y2)
{
return ((float(x1-x2))*(float(x1-x2)))+
((float(y1-y2))*(float(y1-y2)));
}
__device__ bool checkDistancef(int *maxesRow,
int *maxesCol, int nMax,
int curIdx, int width)
{
int row = curIdx / width;
int col = curIdx % width;
int j;
for (j = 0; j < nMax; j++)
{
if (maxesRow[j] == row && maxesCol[j] == col)
return false;
//dist = distance(maxesRow[j], row, maxesCol[j], col);
//if (dist < MIN_DISTANCE)
// return false;
}
return true;
}
__device__ bool checkDistancef(volatile int *maxesRow,
volatile int *maxesCol, int nMax,
int curIdx, int width)
{
int row = curIdx / width;
int col = curIdx % width;
int j;
for (j = 0; j < nMax; j++)
{
if (maxesRow[j] == row && maxesCol[j] == col)
return false;
}
return true;
}
extern "C"
__global__ void
elt_prod_conjf(cufftComplex *fc, cufftComplex * c1,
cufftComplex * c2, int size)
{
__shared__ cufftComplex sfc[THREADS_PER_BLOCK];
__shared__ cufftComplex sc1[THREADS_PER_BLOCK];
__shared__ cufftComplex sc2[THREADS_PER_BLOCK];
int idx = threadIdx.x + blockIdx.x * THREADS_PER_BLOCK;
if (idx >= size)
return;
sc1[threadIdx.x] = c1[idx];
sc2[threadIdx.x] = c2[idx];
__syncthreads();
sfc[threadIdx.x] = cuCmulf(sc1[threadIdx.x], cuConjf(sc2[threadIdx.x]));
float mag = cuCabsf(sfc[threadIdx.x]);
if (mag == 0 || isnan(mag))
{
mag = FLT_EPSILON;
sfc[threadIdx.x].x = FLT_EPSILON;
}
fc[idx] = make_cuComplex(cuCrealf(sfc[threadIdx.x]) / mag,
cuCimagf(sfc[threadIdx.x]) / mag);
}
extern "C"
__global__ void
elt_prod_conj_v2f(cufftComplex *fc, cufftComplex * c1,
cufftComplex * c2, int size)
{
__shared__ cufftComplex sfc[THREADS_PER_BLOCK];
int idx = threadIdx.x + blockIdx.x * THREADS_PER_BLOCK;
if (idx >= size)
return;
//cufftDoubleComplex fc_res;
sfc[threadIdx.x] = cuCmulf(c1[idx], cuConjf(c2[idx]));
__syncthreads();
float mag;
// mag = sqrt(fc_res.x * fc_res.x + fc_res.y * fc_res.y);
mag = sqrtf(sfc[threadIdx.x].x * sfc[threadIdx.x].x +
sfc[threadIdx.x].y * sfc[threadIdx.x].y);
if (isnan(mag) || mag == 0)
{
mag = FLT_EPSILON; //cuCabs(sfc[threadIdx.x]);
sfc[threadIdx.x].x = FLT_EPSILON;
}
// if (mag == 0)
// mag = DBL_EPSILON;
fc[idx] = make_cuComplex(sfc[threadIdx.x].x / mag,
sfc[threadIdx.x].y / mag);
}
extern "C"
__global__ void
elt_prod_conj_v3f(cufftComplex *fc, cufftComplex * c1,
cufftComplex *c2, int size)
{
int idx = threadIdx.x + blockIdx.x * THREADS_PER_BLOCK;
if (idx >= size)
return;
cufftComplex _c1 = c1[idx];
cufftComplex _c2 = c2[idx];
cufftComplex _fc = cuCmulf(_c1, cuConjf(_c2));
float mag = sqrtf(_fc.x * _fc.x +
_fc.y * _fc.y);
if (isnan(mag) || mag == 0)
mag = cuCabsf(_fc);
if (mag == 0)
mag = FLT_EPSILON;
fc[idx] = make_cuComplex(_fc.x / mag, _fc.y / mag);
}
extern "C"
__global__ void
reduce_max_finalf(float *g_idata, float *g_odata,
int * max_idx, unsigned int n, int blockSize)
{
__shared__ float sdata[THREADS_PER_BLOCK];
__shared__ int idxData[THREADS_PER_BLOCK];
unsigned int tid = threadIdx.x;
unsigned int i = blockIdx.x*(blockSize*2) + tid;
unsigned int gridSize = blockSize*2*gridDim.x;
float myMax = 0.0;
int myMaxIndex;
while (i < n)
{
if (myMax < g_idata[i])
{
myMax = g_idata[i];
myMaxIndex = max_idx[i];
}
if (i+blockSize < n)
{
if (myMax < g_idata[i+blockSize])
{
myMax = g_idata[i+blockSize];
myMaxIndex = max_idx[i+blockSize];
}
}
i += gridSize;
}
sdata[tid] = myMax;
idxData[tid] = myMaxIndex;
__syncthreads();
if (blockSize >= 512)
{
if (tid < 256)
{
if (myMax < sdata[tid + 256])
{
sdata[tid] = myMax = sdata[tid+256];
idxData[tid] = idxData[tid+256];
}
}
__syncthreads();
}
if (blockSize >= 256)
{
if (tid < 128)
{
if (myMax < sdata[tid + 128])
{
sdata[tid] = myMax = sdata[tid+128];
idxData[tid] = idxData[tid+128];
}
}
__syncthreads();
}
if (blockSize >= 128)
{
if (tid < 64)
{
if(myMax < sdata[tid + 64])
{
sdata[tid] = myMax = sdata[tid+64];
idxData[tid] = idxData[tid+64];
}
}
__syncthreads();
}
volatile float *vdata = sdata;
volatile int *vidxData = idxData;
if (tid < 32)
{
if (blockSize >= 64)
if (myMax < vdata[tid + 32])
{
vdata[tid] = myMax = vdata[tid+32];
vidxData[tid] = vidxData[tid+32];
}
if (blockSize >= 32)
if (myMax < vdata[tid + 16])
{
vdata[tid] = myMax = vdata[tid+16];
vidxData[tid] = vidxData[tid+16];
}
if (blockSize >= 16)
if (myMax < vdata[tid + 8])
{
vdata[tid] = myMax = vdata[tid+8];
vidxData[tid] = vidxData[tid+8];
}
if (blockSize >= 8)
if (myMax < vdata[tid + 4])
{
vdata[tid] = myMax = vdata[tid+4];
vidxData[tid] = vidxData[tid+4];
}
if (blockSize >= 4)
if (myMax < vdata[tid+2])
{
vdata[tid] = myMax = vdata[tid+2];
vidxData[tid] = vidxData[tid+2];
}
if (blockSize >= 2)
if (myMax < vdata[tid + 1])
{
vdata[tid] = myMax = vdata[tid+1];
vidxData[tid] = vidxData[tid+1];
}
__syncthreads();
}
if (tid == 0)
{
g_odata[blockIdx.x] = sdata[0];
max_idx[blockIdx.x] = idxData[0];
}
}
extern "C"
__global__ void
reduce_max_mainf(float *g_idata, float *g_odata,
int * max_idx, unsigned int n, int blockSize)
{
__shared__ float sdata[THREADS_PER_BLOCK];
__shared__ int idxData[THREADS_PER_BLOCK];
unsigned int tid = threadIdx.x;
unsigned int i = blockIdx.x*(blockSize) + tid;
unsigned int gridSize = blockSize*gridDim.x;
float myMax = 0.0;
int myMaxIndex;
float val;
while (i < n)
{
val = g_idata[i];
if (myMax < val)
{
myMax = val;
myMaxIndex = i;
}
if (i+blockSize < n)
{
val = g_idata[i+blockSize];
if (myMax < val)
{
myMax = val;
myMaxIndex = i+blockSize;
}
}
i += gridSize;
}
sdata[tid] = myMax;
idxData[tid] = myMaxIndex;
__syncthreads();
if (blockSize >= 512)
{
if (tid < 256)
{
if (myMax < sdata[tid + 256])
{
sdata[tid] = myMax = sdata[tid+256];
idxData[tid] = idxData[tid+256];
}
}
__syncthreads();
}
if (blockSize >= 256)
{
if (tid < 128)
{
if (myMax < sdata[tid + 128])
{
sdata[tid] = myMax = sdata[tid+128];
idxData[tid] = idxData[tid+128];
}
}
__syncthreads();
}
if (blockSize >= 128)
{
if (tid < 64)
{
if(myMax < sdata[tid + 64])
{
sdata[tid] = myMax = sdata[tid+64];
idxData[tid] = idxData[tid+64];
}
}
__syncthreads();
}
volatile float *vdata = sdata;
volatile int *vidxData = idxData;
if (tid < 32)
{
if (blockSize >= 64)
if (myMax < vdata[tid + 32])
{
vdata[tid] = myMax = vdata[tid+32];
vidxData[tid] = vidxData[tid+32];
}
if (blockSize >= 32)
if (myMax < vdata[tid + 16])
{
vdata[tid] = myMax = vdata[tid+16];
vidxData[tid] = vidxData[tid+16];
}
if (blockSize >= 16)
if (myMax < vdata[tid + 8])
{
vdata[tid] = myMax = vdata[tid+8];
vidxData[tid] = vidxData[tid+8];
}
if (blockSize >= 8)
if (myMax < vdata[tid + 4])
{
vdata[tid] = myMax = vdata[tid+4];
vidxData[tid] = vidxData[tid+4];
}
if (blockSize >= 4)
if (myMax < vdata[tid+2])
{
vdata[tid] = myMax = vdata[tid+2];
vidxData[tid] = vidxData[tid+2];
}
if (blockSize >= 2)
if (myMax < vdata[tid + 1])
{
vdata[tid] = myMax = vdata[tid+1];
vidxData[tid] = vidxData[tid+1];
}
__syncthreads();
}
if (tid == 0)
{
g_odata[blockIdx.x] = sdata[0];
max_idx[blockIdx.x] = idxData[0];
}
}
extern "C"
__global__ void
reduce_max_filter_finalf(float *g_idata, float *g_odata,
int * max_idx, unsigned int n, unsigned int width,
int blockSize,
int *maxes, int nMax)
{
__shared__ int smaxesRow[10];
__shared__ int smaxesCol[10];
__shared__ int smaxesVal[10];
__shared__ float sdata[THREADS_PER_BLOCK];
__shared__ int idxData[THREADS_PER_BLOCK];
unsigned int tid = threadIdx.x;
unsigned int i = blockIdx.x*(blockSize*2) + tid;
unsigned int gridSize = blockSize*2*gridDim.x;
if (tid < nMax)
{
smaxesVal[tid] = maxes[tid];
smaxesRow[tid] = smaxesVal[tid] / width;
smaxesCol[tid] = smaxesVal[tid] % width;
}
__syncthreads();
float myMax = 0.0;
int myMaxIndex;
while (i < n)
{
if (myMax < g_idata[i])
{
if (checkDistancef(smaxesRow, smaxesCol,
nMax, max_idx[i], width))
{
myMax = g_idata[i];
myMaxIndex = max_idx[i];
}
}
if (i+blockSize < n)
{
if (myMax < g_idata[i+blockSize])
{
if (checkDistancef(smaxesRow, smaxesCol,
nMax,
max_idx[i+blockSize],
width))
{
myMax = g_idata[i+blockSize];
myMaxIndex = max_idx[i+blockSize];
}
}
}
i += gridSize;
}
sdata[tid] = myMax;
idxData[tid] = myMaxIndex;
__syncthreads();
if (blockSize >= 512)
{
if (tid < 256)
{
if (myMax < sdata[tid + 256])
{
if (checkDistancef(smaxesRow, smaxesCol,
nMax, idxData[tid+256],
width))
{
sdata[tid] = myMax = sdata[tid+256];
idxData[tid] = idxData[tid+256];
}
}
}
__syncthreads();
}
if (blockSize >= 256)
{
if (tid < 128)
{
if (myMax < sdata[tid + 128])
{
if (checkDistancef(smaxesRow, smaxesCol,
nMax, idxData[tid+128],
width))
{
sdata[tid] = myMax = sdata[tid+128];
idxData[tid] = idxData[tid+128];
}
}
}
__syncthreads();
}
if (blockSize >= 128)
{
if (tid < 64)
{
if(myMax < sdata[tid + 64])
{
if (checkDistancef(smaxesRow, smaxesCol,
nMax, idxData[tid+64],
width))
{
sdata[tid] = myMax = sdata[tid+64];
idxData[tid] = idxData[tid+64];
}
}
}
__syncthreads();
}
volatile float *vdata = sdata;
volatile int *vidxData = idxData;
volatile int *vsmaxesRow = smaxesRow;
volatile int *vsmaxesCol = smaxesCol;
if (tid < 32)
{
if (blockSize >= 64)
if (myMax < vdata[tid + 32])
{
if (checkDistancef(vsmaxesRow, vsmaxesCol,
nMax, vidxData[tid+32],
width))
{
vdata[tid] = myMax = vdata[tid+32];
vidxData[tid] = vidxData[tid+32];
}
}
if (blockSize >= 32)
if (myMax < vdata[tid + 16])
{
if (checkDistancef(vsmaxesRow, vsmaxesCol,
nMax, vidxData[tid+16],
width))
{
vdata[tid] = myMax = vdata[tid+16];
vidxData[tid] = vidxData[tid+16];
}
}
if (blockSize >= 16)
if (myMax < vdata[tid + 8])
{
if (checkDistancef(vsmaxesRow, vsmaxesCol,
nMax, vidxData[tid+8],
width))
{
vdata[tid] = myMax = vdata[tid+8];
vidxData[tid] = vidxData[tid+8];
}
}
if (blockSize >= 8)
if (myMax < vdata[tid + 4])
{
if (checkDistancef(vsmaxesRow, vsmaxesCol,
nMax, vidxData[tid+4],
width))
{
vdata[tid] = myMax = vdata[tid+4];
vidxData[tid] = vidxData[tid+4];
}
}
if (blockSize >= 4)
if (myMax < vdata[tid+2])
{
if (checkDistancef(vsmaxesRow, vsmaxesCol,
nMax, vidxData[tid+2],
width))
{
vdata[tid] = myMax = vdata[tid+2];
vidxData[tid] = vidxData[tid+2];
}
}
if (blockSize >= 2)
if (myMax < vdata[tid + 1])
{
if (checkDistancef(vsmaxesRow, vsmaxesCol,
nMax, vidxData[tid+1],
width))
{
vdata[tid] = myMax = vdata[tid+1];
vidxData[tid] = vidxData[tid+1];
}
}
__syncthreads();
}
if (tid == 0)
{
g_odata[blockIdx.x] = sdata[0];
max_idx[blockIdx.x] = idxData[0];
if (gridDim.x == 1)
maxes[nMax] = idxData[0];
}
}
extern "C"
__global__ void
reduce_max_filter_mainf(float *g_idata, float *g_odata,
int * max_idx, unsigned int width, unsigned int height,
int blockSize,
int *maxes, int nMax)
{
__shared__ int smaxesRow[10];
__shared__ int smaxesCol[10];
__shared__ int smaxesVal[10];
__shared__ float sdata[THREADS_PER_BLOCK];
__shared__ int idxData[THREADS_PER_BLOCK];
unsigned int tid = threadIdx.x;
unsigned int i = blockIdx.x*(blockSize) + tid;
unsigned int gridSize = blockSize*gridDim.x;
if (tid < nMax)
{
smaxesVal[tid] = maxes[tid];
smaxesRow[tid] = smaxesVal[tid] / width;
smaxesCol[tid] = smaxesVal[tid] % width;
}
__syncthreads();
float myMax = -INFINITY;
int myMaxIndex;
float val;
while (i < width * height)
{
val = g_idata[i];
if (myMax < val)
{
// compute distance . . .
if (checkDistancef(smaxesRow, smaxesCol,
nMax, i, width))
{
myMax = val;
myMaxIndex = i;
}
}
if (i+blockSize < width * height)
{
val = g_idata[i+blockSize];
if (myMax < val)
{
if (checkDistancef(smaxesRow, smaxesCol,
nMax, i+blockSize, width))
{
myMax = val;
myMaxIndex = i+blockSize;
}
}
}
i += gridSize;
}
sdata[tid] = myMax;
idxData[tid] = myMaxIndex;
__syncthreads();
if (blockSize >= 512)
{
if (tid < 256)
{
if (myMax < sdata[tid + 256])
{
if (checkDistancef(smaxesRow, smaxesCol,
nMax, idxData[tid+256],
width))
{
sdata[tid] = myMax = sdata[tid+256];
idxData[tid] = idxData[tid+256];
}
}
}
__syncthreads();
}
if (blockSize >= 256)
{
if (tid < 128)
{
if (myMax < sdata[tid + 128])
{
if (checkDistancef(smaxesRow, smaxesCol,
nMax, idxData[tid+128],
width))
{
sdata[tid] = myMax = sdata[tid+128];
idxData[tid] = idxData[tid+128];
}
}
}
__syncthreads();
}
if (blockSize >= 128)
{
if (tid < 64)
{
if(myMax < sdata[tid + 64])
{
if (checkDistancef(smaxesRow, smaxesCol,
nMax, idxData[tid+64],
width))
{
sdata[tid] = myMax = sdata[tid+64];
idxData[tid] = idxData[tid+64];
}
}
}
__syncthreads();
}
volatile float *vdata = sdata;
volatile int *vidxData = idxData;
volatile int *vsmaxesRow = smaxesRow;
volatile int *vsmaxesCol = smaxesCol;
if (tid < 32)
{
if (blockSize >= 64)
if (myMax < vdata[tid + 32])
{
if (checkDistancef(vsmaxesRow, vsmaxesCol,
nMax, vidxData[tid+32],
width))
{
vdata[tid] = myMax = vdata[tid+32];
vidxData[tid] = vidxData[tid+32];
}
}
if (blockSize >= 32)
if (myMax < vdata[tid + 16])
{
if (checkDistancef(vsmaxesRow, vsmaxesCol,
nMax, vidxData[tid+16],
width))
{
vdata[tid] = myMax = vdata[tid+16];
vidxData[tid] = vidxData[tid+16];
}
}
if (blockSize >= 16)
if (myMax < vdata[tid + 8])
{
if (checkDistancef(vsmaxesRow, vsmaxesCol,
nMax, vidxData[tid+8],
width))
{
vdata[tid] = myMax = vdata[tid+8];
vidxData[tid] = vidxData[tid+8];
}
}
if (blockSize >= 8)
if (myMax < vdata[tid + 4])
{
if (checkDistancef(vsmaxesRow, vsmaxesCol,
nMax, vidxData[tid+4],
width))
{
vdata[tid] = myMax = vdata[tid+4];
vidxData[tid] = vidxData[tid+4];
}
}
if (blockSize >= 4)
if (myMax < vdata[tid+2])
{
if (checkDistancef(vsmaxesRow, vsmaxesCol,
nMax, vidxData[tid+2],
width))
{
vdata[tid] = myMax = vdata[tid+2];
vidxData[tid] = vidxData[tid+2];
}
}
if (blockSize >= 2)
if (myMax < vdata[tid + 1])
{
if (checkDistancef(vsmaxesRow, vsmaxesCol,
nMax, vidxData[tid+1],
width))
{
vdata[tid] = myMax = vdata[tid+1];
vidxData[tid] = vidxData[tid+1];
}
}
__syncthreads();
}
if (tid == 0)
{
g_odata[blockIdx.x] = sdata[0];
max_idx[blockIdx.x] = idxData[0];
if (gridDim.x == 1)
maxes[nMax] = idxData[0];
}
}
// ================================================================
// Local Variables:
// time-stamp-line-limit: 30
// End:
|
7,041 | #include "includes.h"
__global__ void calculateMatrixFormula(int *a, int *b, int *res, int n)
{
int tidx = blockDim.x * blockIdx.x + threadIdx.x;
int tidy = blockDim.y * blockIdx.y + threadIdx.y;
if (tidx >= n || tidy >= n) {
return;
}
int tid = tidx * n + tidy;
res[tid] = a[tid] - b[tid];
} |
7,042 | #include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <stdio.h>
#include <iostream>
#include <stdlib.h>
#include <time.h>
#include <iomanip>
#include <math.h>
using namespace std;
typedef double myfloat;
const double pi_const = 3.1415926535897932384626433832795;
void generate_components(float* pi_comp, int n);
float add_components(float* pi_comp, int n);
void print_components(float* pi_comp, int n);
void generate_components(double* pi_comp, int n);
double add_components(double* pi_comp, int n);
void print_components(double* pi_comp, int n);
__global__ void add_components_GPU_2(myfloat* pi_components, myfloat* pi_components_2, int thr_adds)
{
int id_i = (blockIdx.x * blockDim.x + threadIdx.x) * thr_adds;
for (int i = id_i; i < id_i + thr_adds; i++)
{
*(pi_components_2 + id_i / thr_adds) += *(pi_components + i);
}
}
__global__ void generate_GPU(myfloat* pi_components, int n)
{
int id_i = (blockIdx.x * blockDim.x + threadIdx.x);
for (int i = id_i; i < n; i += blockDim.x + gridDim.x)
{
*(pi_components + i) = 4 * 1.0 / (2 * i + 1) * ((2 * i) % 4 ? -1.0 : 1.0);
}
}
int main()
{
int n = 1;
//std::cout << "Give n: ";
//std::cin >> n;
n = 600000000;
std::cout << "n: " << n << "\n\n";
myfloat* pi_comp_CPU = (myfloat*)malloc(n * sizeof(myfloat));
cudaError_t cudaStatus;
//Obliczenia CPU
//++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
generate_components(pi_comp_CPU, n);
clock_t start_CPU = clock();
myfloat pi_no = add_components(pi_comp_CPU, n);
clock_t stop_CPU = clock();
std::cout << "pi_constant: " << std::setprecision(50) << pi_const << "\n\n";
std::cout << "\n\nCPU\n";
std::cout << "pi CPU: " << std::setprecision(50) << pi_no << "\n";
std::cout << "Czas_CPU: " << 1000 * (stop_CPU - start_CPU) / ((double)CLOCKS_PER_SEC) << " ms" << std::endl;
std::cout << "pi_error: " << std::setprecision(50) << pi_const - pi_no << "\n\n";
//++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
//GPU
myfloat* pi_components;
myfloat* pi_components_2;
int BLOCK_SIZE = 256;
int GRID_SIZE = 16;
myfloat* pi_sum_h = (myfloat*)malloc(sizeof(myfloat));
cudaStatus = cudaMalloc(&pi_components, (n + BLOCK_SIZE * GRID_SIZE) * sizeof(myfloat));
if (cudaStatus != cudaSuccess) {
fprintf(stderr, "cudaMalloc failed!\n");
}
cudaStatus = cudaMalloc(&pi_components_2, BLOCK_SIZE * GRID_SIZE * sizeof(myfloat));//(n+1)/2
if (cudaStatus != cudaSuccess) {
fprintf(stderr, "cudaMalloc failed!\n");
}
cudaMemcpy(pi_components, pi_comp_CPU, n * sizeof(myfloat), cudaMemcpyHostToDevice);
clock_t start_GPU = clock();
// Obliczenia GPU
//--------------------------------------------------------------
int thr_adds = (n + BLOCK_SIZE * GRID_SIZE - 1) / (BLOCK_SIZE * GRID_SIZE);//ile jeden watek ma wykonac dodawan
add_components_GPU_2 <<<GRID_SIZE, BLOCK_SIZE >>> (pi_components, pi_components_2, thr_adds);
cudaStatus = cudaGetLastError();
if (cudaStatus != cudaSuccess) {
fprintf(stderr, "add_components_GPU_2 launch failed: %s\n", cudaGetErrorString(cudaStatus));
}
cudaMemcpy(pi_comp_CPU, pi_components_2, (BLOCK_SIZE * GRID_SIZE) * sizeof(myfloat), cudaMemcpyDeviceToHost);
*pi_sum_h = add_components(pi_comp_CPU, (BLOCK_SIZE * GRID_SIZE));
//--------------------------------------------------------------
clock_t stop_GPU = clock();
std::cout << "\n\nGPU\n";
std::cout << "pi GPU: " << std::setprecision(50) << *pi_sum_h << "\n";
std::cout << "Czas_GPU: " << 1000 * (stop_GPU - start_GPU) / ((double)CLOCKS_PER_SEC) << " ms" << std::endl;
std::cout << "pi_error: " << std::setprecision(50) << pi_const - *pi_sum_h << "\n\n";
std::cout << "Speedup: " << (double)(stop_CPU - start_CPU) / (stop_GPU - start_GPU) << "\n\n";
cudaFree(pi_components);
cudaFree(pi_components_2);
free(pi_comp_CPU);
free(pi_sum_h);
return 0;
}
//##################################################################################
void generate_components(myfloat* pi_comp, int n)
{
int sign = 1;
for (int i = 1; i < 2 * n; i += 2)
{
*(pi_comp + i / 2) = 4 * 1.0 / i * sign;
sign = -sign;
}
}
myfloat add_components(myfloat* pi_comp, int n)
{
myfloat pi_no = 0.0;
for (int i = 0; i < n; i++)
{
pi_no += pi_comp[i];
}
return pi_no;
}
void print_components(myfloat* pi_comp, int n)
{
for (int i = 0; i < n; i++)
{
std::cout << "comp " << i << ": " << pi_comp[i] << "\n";
}
}
|
7,043 | #include <stdio.h>
#define CHECK(call) \
{ \
const cudaError_t error = call; \
if (error != cudaSuccess) \
{ \
fprintf(stderr, "Error: %s:%d, ", __FILE__, __LINE__); \
fprintf(stderr, "code: %d, reason: %s\n", error, \
cudaGetErrorString(error)); \
exit(EXIT_FAILURE); \
} \
}
struct GpuTimer
{
cudaEvent_t start;
cudaEvent_t stop;
GpuTimer()
{
cudaEventCreate(&start);
cudaEventCreate(&stop);
}
~GpuTimer()
{
cudaEventDestroy(start);
cudaEventDestroy(stop);
}
void Start()
{
cudaEventRecord(start, 0);
}
void Stop()
{
cudaEventRecord(stop, 0);
}
float Elapsed()
{
float elapsed;
cudaEventSynchronize(stop);
cudaEventElapsedTime(&elapsed, start, stop);
return elapsed;
}
};
int reduceByHost(int * in, int n)
{
int s = in[0];
for (int i = 1; i < n; i++)
s += in[i];
return s;
}
// Kernel 1 - warp divergence
__global__ void reduceByDevice1(int * in, int * out, int n)
{
// TODO
int i = (blockIdx.x*blockDim.x + threadIdx.x)*2;
int stride;
int sum = 0;
for(stride = 0; stride < 2*threadIdx.x; stride *= 2){
if(threadIdx.x%stride == 0){
if(i+stride < n){
sum += in[i+stride];
}
}
__syncthreads();
}
out[i] = sum;
if(threadIdx.x == 0){
out[blockIdx.x*blockDim.x] = out[blockIdx.x*blockDim.x*2];
}
}
// Kernel 2 - less warp divergence
__global__ void reduceByDevice2(int * in, int * out, int n)
{
// TODO
int num = blockIdx.x*blockDim.x*2;
for(int stride = 1; stride < blockDim.x*2; stride *= 2){
int i = num + threadIdx.x*2*stride;
if(threadIdx.x < blockDim.x/stride){
if(i + stride < n){
in[i] += in[i+stride];
}
__syncthreads();
}
}
if(threadIdx.x == 0){
out[blockIdx.x] = in[num];
}
}
// Kernel 3 - less warp divergence + efficient memory access
__global__ void reduceByDevice3(int * in, int * out, int n)
{
// TODO
int num = blockIdx.x*blockDim.x*2;
for(int stride = blockDim.x; stride > 0; stride /= 2){
int i = num + threadIdx.x*2*stride;
if(threadIdx.x < stride){
if(i + stride < n){
in[i] += in[i + stride];
}
__syncthreads();
}
}
if(threadIdx.x == 0){
out[blockIdx.x] = in[num];
}
}
/*
// Kernel 4 - use shared memmory
__global__ void reduceByDevice4(int * in, int * out, int n){
// todo
// mỗi block load dữ liệu từ GMEM(ram) lên SMEM
// vì khai báo cần 1 kích thước tĩnh nên ở đây giả sử kích thước mỗi block là 256
__shared__ int blkData[2*256];
// số phần tử trước block hiện tại
int num = blockIdx.x*blockDim*2;
blkData[threadIdx.x]=in[num + threadIdx.x];
blkData[blockDim.x + threadIdx.x]=in[num + blockDim.x + threadIdx.x];
__syncthreads();
// tinh toan voi du lieu luu tai SMem
for(int stride = blockDim.x; stride > 0; stride/=2){
if(threadIdx.x < stride){
blkData[threadIdx.x]+=blkData[threadIdx.x + stride];
}
__syncthreads();
}
// chep du lieu ve lai GMem
if(threadIdx.x == 0){
out[blockIdx.x] = blkData[0];
}
}
*/
int main(int argc, char ** argv)
{
// Print out device info
cudaDeviceProp devProv;
CHECK(cudaGetDeviceProperties(&devProv, 0));
printf("**********GPU info**********\n");
printf("Name: %s\n", devProv.name); // TODO
printf("Compute capability: %d\n", devProv.major); // TODO
printf("Num SMs: %d\n", devProv.multiProcessorCount); // TODO
printf("Max num threads per SM: %d\n", devProv.maxThreadsPerMultiProcessor); // TODO
printf("Max num warps per SM: %d\n", devProv.maxThreadsPerMultiProcessor/32 ); // TODO
printf("****************************\n\n");
// Set up input size
int n = (1 << 24) + 1;
printf("Input size: %d\n", n);
// Set up execution configuration
dim3 blockSize(256); // Default
if (argc == 2) // Get block size from cmd argument
blockSize.x = atoi(argv[1]);
dim3 gridSize((n-1)/(2*blockSize.x) + 1); // TODO
printf("Grid size: %d, block size: %d\n", gridSize.x, blockSize.x);
// Allocate memories
size_t bytes = n * sizeof(int);
int * in = (int *) malloc(bytes);
int * out = (int *) malloc(gridSize.x * sizeof(int));
// Set up input data
for (int i = 0; i < n; i++)
{
// Generate a random integer in [0, 255]
in[i] = (int)(rand() & 0xFF);
}
// Reduce on host
int host_sum = reduceByHost(in, n);
printf("\n%15s%12s%16s%21s%16s\n",
"Function", "Result", "KernelTime(ms)", "Post-kernelTime(ms)", "TotalTime(ms)");
printf("%15s%12d%16s%21s%16s\n",
"reduceByHost", host_sum, "-", "-", "-");
//========================================================
// Allocate device memories
int *d_in, *d_out;
CHECK(cudaMalloc(&d_in, bytes));
CHECK(cudaMalloc(&d_out, gridSize.x * sizeof(int)));
// Copy data to device memories
CHECK(cudaMemcpy(d_in, in, bytes, cudaMemcpyHostToDevice));
// Kernel 1 - warp divergence
CHECK(cudaMemcpy(d_in, in, bytes, cudaMemcpyHostToDevice));
GpuTimer timer;
timer.Start();
reduceByDevice1<<<gridSize, blockSize>>>(d_in, d_out, n);
cudaDeviceSynchronize();
CHECK(cudaGetLastError());
timer.Stop();
float kernelTime = timer.Elapsed();
CHECK(cudaMemcpy(out, d_out, gridSize.x * sizeof(int), cudaMemcpyDeviceToHost));
timer.Start();
int device_sum = 0;
for (int i = 0; i < gridSize.x; i++)
device_sum += out[i];
timer.Stop();
float postKernelTime = timer.Elapsed();
printf("%15s%12d%16.3f%21.3f%16.3f\n",
"reduceByDevice1", device_sum, kernelTime, postKernelTime, kernelTime + postKernelTime);
bool correct1 = (host_sum == device_sum); // Check result
// Reset d_in and d_out
CHECK(cudaMemcpy(d_in, in, bytes, cudaMemcpyHostToDevice)); // Re-copy input data to d_in
CHECK(cudaMemset(d_out, 0, gridSize.x * sizeof(int))); // Reset d_out
// Kernel 2 - less warp divergence
timer.Start();
reduceByDevice2<<<gridSize, blockSize>>>(d_in, d_out, n);
cudaDeviceSynchronize();
CHECK(cudaGetLastError());
timer.Stop();
kernelTime = timer.Elapsed();
CHECK(cudaMemcpy(out, d_out, gridSize.x * sizeof(int), cudaMemcpyDeviceToHost));
timer.Start();
device_sum = 0;
for (int i = 0; i < gridSize.x; i++)
device_sum += out[i];
timer.Stop();
postKernelTime = timer.Elapsed();
printf("%15s%12d%16.3f%21.3f%16.3f\n",
"reduceByDevice2", device_sum, kernelTime, postKernelTime, kernelTime + postKernelTime);
bool correct2 = (host_sum == device_sum); // Check result
// Reset d_in and d_out
CHECK(cudaMemcpy(d_in, in, bytes, cudaMemcpyHostToDevice)); // Re-copy input data to d_in
CHECK(cudaMemset(d_out, 0, gridSize.x * sizeof(int))); // Reset d_out
// Kernel 3 - less warp divergence + efficient memory access
CHECK(cudaMemcpy(d_in, in, bytes, cudaMemcpyHostToDevice)); // Re-copy input data to d_in
CHECK(cudaMemset(d_out, 0, gridSize.x * sizeof(int))); // Reset d_out
timer.Start();
reduceByDevice3<<<gridSize, blockSize>>>(d_in, d_out, n);
cudaDeviceSynchronize();
CHECK(cudaGetLastError());
timer.Stop();
kernelTime = timer.Elapsed();
CHECK(cudaMemcpy(out, d_out, gridSize.x * sizeof(int), cudaMemcpyDeviceToHost));
timer.Start();
device_sum = 0;
for (int i = 0; i < gridSize.x; i++)
device_sum += out[i];
timer.Stop();
postKernelTime = timer.Elapsed();
printf("%15s%12d%16.3f%21.3f%16.3f\n",
"reduceByDevice3", device_sum, kernelTime, postKernelTime, kernelTime + postKernelTime);
bool correct3 = (host_sum == device_sum); // Check result
/*
// Reset d_in and d_out
CHECK(cudaMemcpy(d_in, in, bytes, cudaMemcpyHostToDevice)); // Re-copy input data to d_in
CHECK(cudaMemset(d_out, 0, gridSize.x * sizeof(int))); // Reset d_out
// Kernel 4 - use shared memory
CHECK(cudaMemcpy(d_in, in, bytes, cudaMemcpyHostToDevice)); // Re-copy input data to d_in
CHECK(cudaMemset(d_out, 0, gridSize.x * sizeof(int))); // Reset d_out
timer.Start();
reduceByDevice4<<<gridSize, blockSize>>>(d_in, d_out, n);
cudaDeviceSynchronize();
CHECK(cudaGetLastError());
timer.Stop();
kernelTime = timer.Elapsed();
CHECK(cudaMemcpy(out, d_out, gridSize.x * sizeof(int), cudaMemcpyDeviceToHost));
timer.Start();
device_sum = 0;
for (int i = 0; i < gridSize.x; i++)
device_sum += out[i];
timer.Stop();
postKernelTime = timer.Elapsed();
printf("%15s%12d%16.3f%21.3f%16.3f\n",
"reduceByDevice4", device_sum, kernelTime, postKernelTime, kernelTime + postKernelTime);
bool correct4 = (host_sum == device_sum); // Check result
*/
// Print out errors
printf("\n");
if (correct1 == false)
fprintf(stderr, "Error: reduceByDevice1 is incorrect!\n");
if (correct2 == false)
fprintf(stderr, "Error: reduceByDevice2 is incorrect!\n");
if (correct3 == false)
fprintf(stderr, "Error: reduceByDevice3 is incorrect!\n");
//if (correct4 == false)
// fprintf(stderr, "Error: reduceByDevice3 is incorrect!\n");
// Free device memories
CHECK(cudaFree(d_in));
CHECK(cudaFree(d_out));
//========================================================
// Free memories
free(in);
free(out);
return EXIT_SUCCESS;
} |
7,044 | __global__ void test(float *A){
int i = threadIdx.x;
if (i % 2 == 0){
float x = A[i];
}
if (i % 3 == 0){
A[i] = i;
}
} |
7,045 | #include "includes.h"
__global__ void transponer(float* entrada, float* salida, int ANCHO){
int tx = blockIdx.x*blockDim.x + threadIdx.x;
int ty = blockIdx.y*blockDim.y + threadIdx.y;
salida[tx*ANCHO + ty] = entrada[ty*ANCHO + tx];
} |
7,046 | /*
My very first program in c++
square a vector
*/
#include <iostream>
#define ARR_SIZE 64
/* Kernel - square the array */
__global__
void square(float *d_in, float *d_out)
{
int ndx = threadIdx.x;
d_out[ndx] = d_in[ndx] * d_in[ndx];
}
int main()
{
// Allocate array @CPU RAM
float h_vec_a[ARR_SIZE];
float h_vec_res[ARR_SIZE];
// Initialize array @CPU
for (int i=0; i<ARR_SIZE; i++) {
h_vec_a[i] = (float)i;
h_vec_res[i] = 0;
}
// Allocate arrays @GPU
float *d_vec_a;
float *d_vec_res;
const long arr_bytes = ARR_SIZE * sizeof(float);
if (cudaMalloc((void **) &d_vec_a, arr_bytes) != cudaSuccess) {
std::cout << "Failed at cudaMalloc vec_a\n";
}
if (cudaMalloc((void **) &d_vec_res, arr_bytes) != cudaSuccess) {
std::cout << "Failed at cudaMalloc vec_res\n";
}
// Transfer data from CPU to GPU
cudaError_t err;
err = cudaMemcpy(d_vec_a, h_vec_a, arr_bytes, cudaMemcpyHostToDevice);
if (err != cudaSuccess) {
std::cout << cudaGetErrorString(err) << "\n";
std::cout << "Failed at cudaMemcpy D2H at" << __FILE__ << " line: " << __LINE__ << "\n";
}
// Kernel launch
square<<<1, ARR_SIZE>>>(d_vec_a, d_vec_res);
// Transfer data from GPU to CPU
err = cudaMemcpy(h_vec_res, d_vec_res, arr_bytes, cudaMemcpyDeviceToHost);
if ( err != cudaSuccess) {
std::cout << cudaGetErrorString(err) << "\n";
std::cout << "Failed at cudaMemcpy D2H at" << __FILE__ << " line: " << __LINE__ << "\n";
}
std::cout << "h_vec_res:\n";
for (int i=0; i< ARR_SIZE; i++) {
std::cout << h_vec_res[i] << "\n";
}
// Free GPU memory
cudaFree(d_vec_a);
cudaFree(d_vec_res);
// Freeing host memory produces some weird crap -> investigate and/or do c++ style
return 0;
} |
7,047 | #include "includes.h"
#define ROUND_OFF 50000
#define CUDA_NUM_THREADS 1024
#define WARPS_PER_BLOCK 1
#define THREADS_PER_WARP 32
#define CUDA_KERNEL_LOOP(i, n) for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); i += blockDim.x * gridDim.x)
#define GET_BLOCKS(n, t) (n+t-1) / t
// == Dimension rearrangement Kernel
__global__ void CorrelateDataSubtract_1d(const int nthreads, int num, int item, int topwidth, int topheight, int topchannels, int topcount, int max_displacement, int x_shift, int neighborhood_grid_width, int kernel_radius, int stride1, int stride2, int bottomwidth, int bottomheight, int bottomchannels, const float *bottom0, const float *bottom1, float *top)
{
CUDA_KERNEL_LOOP(index, nthreads) {
int x = index % topwidth; //w-pos
int y = (index / topwidth) % topheight; //h-pos
int c = (index / topwidth / topheight) % topchannels; //channels
// Offset of patch in image 2
int s2o = (c % neighborhood_grid_width + x_shift) * stride2;
// First (upper left) position of kernel center in current neighborhood in image 1
int x1 = x*stride1 + kernel_radius + max_displacement;
int y1 = y*stride1 + kernel_radius + 0;
// Iterate through 3D patch
float sum = 0;
for(int j = -kernel_radius; j <= kernel_radius; j++) { // HEIGHT
for(int i = -kernel_radius; i <= kernel_radius; i++) { // WIDTH
for(int l = 0; l < bottomchannels; l++) { // CHANNELS
// Calculate position in image 2
int x2 = x1 + s2o;
int y2 = y1;
// Indices in bottom data: (CH=l,W=x2,H=y2,N)
int idx1 = ((item * bottomheight + y1+j) * bottomwidth + x1+i) * bottomchannels + l;
int idx2 = ((item * bottomheight + y2+j) * bottomwidth + x2+i) * bottomchannels + l;
// Do the correlation:
sum += fabsf(bottom0[idx1] - bottom1[idx2]);
}
}
}
const int sumelems = (kernel_radius*2+1)*(kernel_radius*2+1)*bottomchannels;
top[index + item*topcount] = sum / (float)sumelems;
}
} |
7,048 | //
// Created by smallflyfly on 2021/5/17.
//
#include <stdio.h>
__global__ void hello_world() {
printf("GPU HELLO WORLD!\n");
}
int main(int argc, char** argv) {
hello_world<<<1, 10>>>();
cudaDeviceReset();
return 0;
} |
7,049 | #include <cuda_runtime.h>
//#define NUMS 64
//#define num_size 8
//#define NUM 49
#define local_1(NUMS) \
__global__ void local_1_##NUMS(float *a) \
{\
float tmp[NUMS];\
int i;\
for(i=0;i<NUMS;i++)\
{\
tmp[i]=a[i];\
}\
for(i=0;i<NUMS;i++)\
{\
a[i]+=tmp[i];\
}\
}
//local_1(29) //sm_10
//local_1(30)
//local_1(73) //sm_35
//local_1(74)
#define local_2(num_size) \
__global__ void local_2_##num_size(float *a,float *b,float *c)\
{\
float tmp_a[num_size*num_size];\
float temp;\
int i,j,k;\
for (i=0;i<num_size*num_size;i++)\
{\
tmp_a[i]=a[i];\
}\
for (i=0;i<num_size;i++)\
{\
for (j=0;j<num_size;j++)\
{\
temp=0.0;\
for (k=0;k<num_size;k++)\
{\
temp+=tmp_a[i*num_size+k]*b[k*num_size+j];\
}\
c[i*num_size+j]=temp;\
}\
}\
}
//local_2(2) //sm_10
//local_2(3)
//local_2(4)
//local_2(5)
//local_2(6) //sm_35
//local_2(7)
//local_2(8)
//local_2(9)
#define local_3(NUM) \
__global__ void local_3_##NUM(float *a)\
{\
float tmp[NUM];\
float minf=0.0,temp;\
int mind;\
int i,j;\
for(i=0;i<NUM;i++)\
{\
tmp[i]=a[i];\
}\
for(i=0;i<NUM;i++)\
{\
minf=tmp[i];\
mind=i;\
for (j=i;j<NUM;j++)\
{\
if (minf>tmp[j])\
{\
minf=tmp[j];\
mind=i;\
} \
}\
if (mind!=i)\
{\
temp=tmp[i];\
tmp[i]=tmp[mind];\
tmp[mind]=temp;\
}\
}\
a[0]=tmp[NUM-1];\
}
//sm_10
local_3(2)
local_3(4)
local_3(8)
local_3(16)
local_3(32)
local_3(64)
local_3(128)
|
7,050 | extern "C" {
#define RED_WEIGHT 0.2989f
#define GREEN_WEIGHT 0.5870f
#define BLUE_WEIGHT 0.1140f
#define MAX_PIXEL(r, g, b) fmaxf(r, fmaxf(g, b))
#define MIN_PIXEL(r, g, b) fminf(r, fminf(g, b))
#define BLOCK_DIM 32
__device__ uchar3 hsv(float3 *rgb)
{
float h, s, v;
float r = rgb->x, g = rgb->y, b = rgb->z;
v = MAX_PIXEL(r, g, b);
float min = MIN_PIXEL(r, g, b);
min = v - min;
s = v != 0.0f ? 255.0f*min / v : 0.0f;
float tmp = 60.0f / min;
if (v == r)
h = tmp*(g - b);
if (v == g)
h = 120.0f + tmp*(b - r);
if (v == b)
h = 240.0f + tmp*(r - b);
h = h < 0.0f ? 360.0f + h : h;
//h = h > 180.0f ? h : 180.0f;
return make_uchar3((unsigned char)h, (unsigned char)s, (unsigned char)v);
}
__global__ void rgb2hsv(unsigned char *d_input, unsigned char *d_output,int height, int width)
{
unsigned int row = blockDim.y*blockIdx.y + threadIdx.y;
unsigned int col = blockDim.x*blockIdx.x;
__shared__ float smem[6][32 * 3];
__shared__ unsigned char hsv_result[6][32*3];
for (int i = row; i < height; i += blockDim.y*gridDim.y)
for (int j = col; j + threadIdx.x < width; j += blockDim.x*gridDim.x)
{
int index = 3 * (i*width + j) / 4 + threadIdx.x;
uchar4 p0;
if (threadIdx.x < 24) // 24 * 4 = 32 * 3
{
p0 = reinterpret_cast<uchar4*>(d_input)[index];
reinterpret_cast<float4*>(smem)[24 * threadIdx.y + threadIdx.x] = make_float4((float)p0.x, (float)p0.y, (float)p0.z, (float)p0.w);
}
__syncthreads();
float3 gray = make_float3(smem[threadIdx.y][3 * threadIdx.x], smem[threadIdx.y][3 * threadIdx.x + 1], smem[threadIdx.y][3 * threadIdx.x + 2]);
reinterpret_cast<uchar3*>(hsv_result)[32 * threadIdx.y + threadIdx.x] = hsv(&gray);
__syncthreads();
if (threadIdx.x < 24) // 24 * 4 = 32 * 3
{
uchar4 p1 = reinterpret_cast<uchar4*>(hsv_result)[24 * threadIdx.y + threadIdx.x];
reinterpret_cast<uchar4*>(d_output)[index] = p1;
}
}
}
__device__ uchar3 hsv_(uchar3 *rgb)
{
float h, s, v;
float r = rgb->x, g = rgb->y, b = rgb->z;
v = MAX_PIXEL(r, g, b);
float min = MIN_PIXEL(r, g, b);
min = v - min;
s = v != 0.0f ? 255.0f*min / v : 0.0f;
float tmp = 60.0f / min;
if (v == r)
h = tmp*(g - b);
if (v == g)
h = 120.0f + tmp*(b - r);
if (v == b)
h = 240.0f + tmp*(r - b);
h = h < 0.0f ? 360.0f + h : h;
//h = h > 180.0f ? h : 180.0f;
return make_uchar3((unsigned char)h, (unsigned char)s, (unsigned char)v);
}
__global__ void rgb2hsv_(unsigned char *d_input, unsigned char *d_output,int height, int width)
{
int row = blockDim.y*blockIdx.y + threadIdx.y;
int col = blockDim.x*blockIdx.x + threadIdx.x;
for (int i = row; i < height; i += blockDim.y*gridDim.y)
for (int j = col; j < width; j += blockDim.x*gridDim.x)
{
uchar3 p0 = reinterpret_cast<uchar3*>(d_input)[i*width + j];
reinterpret_cast<uchar3*>(d_output)[i*width + j] = hsv_(&p0);
}
}
} |
7,051 | #include "includes.h"
__device__ inline int getTransArrayIndex(unsigned int width, unsigned int height, unsigned int i) {
return height * (i % width) + i / width;
}
__global__ void kAddTransSlow(float* a, float* b, float* dest, unsigned int width, unsigned int height, unsigned int numEls, float scaleA, float scaleB) {
const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
const unsigned int numThreads = blockDim.x * gridDim.x;
// const unsigned int idx = blockIdx.y * height + blockIdx.x * blockDim.x + threadIdx.y*blockDim.x + threadIdx.x;
for (unsigned int i = idx; i < numEls; i += numThreads) {
dest[i] = scaleA * a[i] + scaleB * b[getTransArrayIndex(width, height, i)];
}
} |
7,052 | #include "Tensor.cuh"
#include <cstring>
#include <numeric>
using namespace std;
Tensor::Tensor(std::initializer_list<unsigned> l)
: shape(l) {
const unsigned len = std::accumulate(
shape.begin(), shape.end(), 1, std::multiplies<unsigned>());
cudaMallocManaged(&m_data, len * sizeof(float));
/* memset(m_data, 0, len); */
for (unsigned i = 0; i < len; ++i) m_data[i] = 0;
cudaDeviceSynchronize();
}
Tensor::Tensor(Tensor const& other)
: shape(other.shape) {
cudaMallocManaged(&m_data, other.size() * sizeof(float));
for (unsigned i = 0; i < other.size(); ++i) m_data[i] = other.m_data[i];
cudaDeviceSynchronize();
}
Tensor& Tensor::operator=(const Tensor& other) {
if (this == &other) return *this;
if (this->size() != other.size()) {
delete[] m_data;
cudaMallocManaged(&m_data, other.size() * sizeof(float));
}
for (unsigned i = 0; i < other.size(); ++i) m_data[i] = other.m_data[i];
cudaDeviceSynchronize();
return *this;
}
Tensor::~Tensor() {
cudaDeviceSynchronize();
cudaFree(m_data);
}
size_t Tensor::size() const {
return std::accumulate(
shape.begin(), shape.end(), 1, std::multiplies<unsigned>());
}
|
7,053 | #include "includes.h"
__global__ void InterpolateSymbolsKernel( float *symbolVectors, int symbolOneId, int symbolTwoId, float weightOne, float weightTwo, float *resultSymbol, int symbolSize )
{
int threadId = blockDim.x*blockIdx.y*gridDim.x //rows preceeding current row in grid
+ blockDim.x*blockIdx.x //blocks preceeding current block
+ threadIdx.x;
if(threadId < symbolSize)
{
int symbolOneCellId = symbolOneId * symbolSize + threadId;
int symbolTwoCellId = symbolTwoId * symbolSize + threadId;
resultSymbol[threadId] = weightOne * symbolVectors[symbolOneCellId] + weightTwo * symbolVectors[symbolTwoCellId];
}
} |
7,054 | #include "cuda_field.cu"
extern "C" {
__global__
void field_init(int m){
cuda_field_init(m);
}
__global__
void make_unit(cuda_field_element* B, int n){
int thidX = (blockIdx.x * blockDim.x) + threadIdx.x;
int thidY = (blockIdx.y * blockDim.y) + threadIdx.y;
if(thidX < n && thidY < n){
B[thidX*n+thidY] = (thidX == thidY);
}
}
__global__
void find_nonzero(cuda_field_element* A, int n, int i, int* k){
int thidX = (blockIdx.x * blockDim.x) + threadIdx.x;
if(i < thidX && thidX < n)
if(A[thidX*n+i] != 0)
*k = thidX;
}
__global__
void swap(cuda_field_element* M, int n, int i, int k){
int thidX = (blockIdx.x * blockDim.x) + threadIdx.x;
if(thidX < n){
cuda_field_element v = M[i*n+thidX];
M[i*n+thidX] = M[k*n+thidX];
M[k*n+thidX] = v;
}
}
__global__
void fix_row(cuda_field_element* M, int n, int i, cuda_field_element mul){
int thidX = (blockIdx.x * blockDim.x) + threadIdx.x;
if(thidX < n){
M[i*n+thidX] = M[i*n+thidX] * mul;
}
}
__global__
void update_column(cuda_field_element* A, cuda_field_element* i_th_column, int n, int i){
int thidX = (blockIdx.x * blockDim.x) + threadIdx.x;
if(thidX < n){
i_th_column[thidX] = A[thidX*n + i];
}
}
__global__
void fix_column(cuda_field_element* M, cuda_field_element* i_th_column, int n, int i){
int thidX = (blockIdx.x * blockDim.x) + threadIdx.x;
int thidY = (blockIdx.y * blockDim.y) + threadIdx.y;
//__shared__ cuda_field_element P[32];
if(thidY != i && thidX < n && thidY < n){
//P[threadIdx.x] = M[i*n+thidX];
M[thidY*n+thidX] -= i_th_column[thidY]*M[i*n+thidX];//P[threadIdx.x];
}
}
}
|
7,055 | #include "includes.h"
__global__ void scale_dropblock_kernel(float *output, int size, int outputs, float *drop_blocks_scale)
{
const int index = blockIdx.x*blockDim.x + threadIdx.x;
if (index >= size) return;
const int b = index / outputs;
output[index] *= drop_blocks_scale[b];
} |
7,056 | #include "includes.h"
__device__ float gamma_correction(float f_stop, float gamma, float val)
{
return powf((val*powf(2.0,f_stop)),(1.0/gamma));
}
__device__ float rgb2Lum(float B, float G, float R)
{
return B * 0.0722 + G * 0.7152 + R * 0.2126;
}
__global__ void gamma_tonemap_kernel(float* imageIn, float* imageOut, int width, int height, int channels, float f_stop, float gamma)
{
int Row = blockDim.y * blockIdx.y + threadIdx.y;
int Col = blockDim.x * blockIdx.x + threadIdx.x;
if(Row < height && Col < width) {
float B, G, R, L, nL, scale;
B = imageIn[(Row*width+Col)*3+BLUE];
G = imageIn[(Row*width+Col)*3+GREEN];
R = imageIn[(Row*width+Col)*3+RED];
L = rgb2Lum(B, G, R);
nL = gamma_correction(f_stop, gamma, L);
scale = nL / L;
imageOut[(Row*width+Col)*3+BLUE] = B * scale;
imageOut[(Row*width+Col)*3+GREEN] = G * scale;
imageOut[(Row*width+Col)*3+RED] = R * scale;
}
} |
7,057 | #include <iostream>
#include <stdio.h>
class fixedFunction{
public:
__host__ fixedFunction() {}
__host__ __device__ double operator()(double x) {
return x*x;
}
};
__host__ __device__ double f1(double x){
return x*x;
}
typedef double (*pf) (double var);
__device__ pf f1_d = f1;
class genericFunction{
public:
__host__ genericFunction(double (*h_infunc)(double), double (*d_infunc)(double)) : h_func(h_infunc),d_func(d_infunc){}
__host__ __device__ double operator()(double x) {
#ifdef __CUDA_ARCH__
return d_func(x);
#else
return h_func(x);
#endif
}
private:
pf h_func;
pf d_func;
};
__global__ void kernel1(fixedFunction* g1){
unsigned int tid = blockIdx.x *blockDim.x + threadIdx.x;
printf("Func val is: %f\n", (*g1)(tid));
}
__global__ void kernel2(genericFunction* g1){
unsigned int tid = blockIdx.x *blockDim.x + threadIdx.x;
printf("Func val is: %f\n", (*g1)(tid));
}
int main(){
fixedFunction h_g1;
fixedFunction* d_g1;
cudaMallocManaged(&d_g1, sizeof(h_g1));
//Host call
std::cout << h_g1(2.0) << "\n";
//device call
kernel1<<<1,32>>>(d_g1);
cudaDeviceSynchronize();
pf d_f1;
cudaMemcpyFromSymbol(&d_f1, f1_d, sizeof(void*));
genericFunction h_g2(f1, d_f1);
genericFunction* d_g2;
cudaMallocManaged(&d_g2, sizeof(h_g2));
cudaMemcpy(d_g2, &h_g2, sizeof(h_g2), cudaMemcpyDefault);
//Host call
std::cout << h_g2(3.0) << "\n";
//device call
kernel2<<<1,32>>>(d_g2);
cudaDeviceSynchronize();
}
|
7,058 | #include <cuda.h>
#include <device_launch_parameters.h>
/*
Inspired by the implementation of CustomPong.cu + GridWorld.cu
@author mp
*/
extern "C"
{
__device__ __inline__ unsigned int AsUint(float *sourceImage, int pixelId)
{
return *(((unsigned int*)sourceImage) + pixelId);
}
__device__ __inline__ unsigned int GetComponent(unsigned int pixel, int comp)
{
return (pixel >> (comp * 8)) & 0xFF;
}
/*
Draws a background color into a 3-component image.
inputWidth & inputHeight: map dimensions in pixels
gridDim.y = 3, one for each color component
*/
__global__ void DrawRgbBackgroundKernel(float *target, int inputWidth, int inputHeight,
float r, float g, float b)
{
int column = threadIdx.x + blockDim.x * blockIdx.z;
if (column >= inputWidth)
return;
int id = inputWidth * ( blockIdx.y * gridDim.x + blockIdx.x) // blockIdx.x == row, blockIdx.y == color channel
+ column;
int imagePixels = inputWidth * inputHeight;
if (id < 3*imagePixels) // 3 for RGB
{
float color = 0.0f;
switch (blockIdx.y)
{
case 0:
color = r;
break;
case 1:
color = g;
break;
case 2:
color = b;
break;
}
target[id] = color;
}
}
/*
Adds noise into a 3-component image.
inputWidth & inputHeight: map dimensions in pixels
*/
__global__ void AddRgbNoiseKernel(float *target, int inputWidth, int inputHeight, float *randoms, int isBlackAndWhiteNoise)
{
int id = blockDim.x * blockIdx.y * gridDim.x
+ blockDim.x * blockIdx.x
+ threadIdx.x;
int imagePixels = inputWidth * inputHeight;
if (id < imagePixels)
{
unsigned int tg = *((unsigned int*)(&target[id]));
int random = randoms[id];
int blue = (tg >> 0) & (0xFF);
blue += random;
blue = blue < 255 ? blue : 255;
blue = blue > 0 ? blue : 0;
int green = ((tg >> 8) & (0xFF));
green += (isBlackAndWhiteNoise ? random : (int)(randoms[id + imagePixels]));
green = green < 255 ? green : 255;
green = green > 0 ? green : 0;
int red = ((tg >> 16) & (0xFF));
red += (isBlackAndWhiteNoise ? random : (int)(randoms[id + imagePixels * 2]));
red = red < 255 ? red : 255;
red = red > 0 ? red : 0;
// alpha is the last channel (<< 24)
unsigned int tmp = (*((unsigned int *)(&blue)) << 0)
| (*((unsigned int *)(&green)) << 8)
| (*((unsigned int *)(&red)) << 16);
target[id] = *((float *)(&tmp));
}
}
/* Fill specified rectangle with color */
__global__ void DrawRgbaColorKernel(float *target, int targetWidth, int targetHeight, int inputX, int inputY,
int areaWidth, int areaHeight, float r, float g, float b)
{
int id = blockDim.x * blockIdx.y * gridDim.x
+ blockDim.x * blockIdx.x
+ threadIdx.x;
int targetPixels = targetWidth * targetHeight;
int texturePixels = areaWidth * areaHeight;
int idTextureRgb = id / texturePixels;
int idTexturePixel = (id - idTextureRgb * texturePixels); // same as (id % texturePixels), but the kernel runs 10% faster
int idTextureY = idTexturePixel / areaWidth;
int idTextureX = (idTexturePixel - idTextureY * areaWidth); // same as (id % textureWidth), but the kernel runs another 10% faster
if (idTextureRgb < 3) // 3 channels that we will write to
{
// if the texture pixel offset by inputX, inputY, lies inside the target
if (idTextureX + inputX < targetWidth &&
idTextureX + inputX >= 0 &&
idTextureY + inputY < targetHeight &&
idTextureY + inputY >= 0)
{
float color = 0.0f;
switch (idTextureRgb)
{
case 0:
color = r;
break;
case 1:
color = g;
break;
case 2:
color = b;
break;
}
int tIndex = targetPixels * idTextureRgb + targetWidth * (idTextureY + inputY) + (idTextureX + inputX);
target[tIndex] = color;
}
}
}
/*
Draws a texture into a 3-component target. RGBA. Checks bounds.
*/
__global__ void DrawRgbaTextureKernel(float *target, int targetWidth, int targetHeight, int inputX, int inputY,
float *texture, int textureWidth, int textureHeight)
{
int id = blockDim.x * blockIdx.y * gridDim.x
+ blockDim.x * blockIdx.x
+ threadIdx.x;
int targetPixels = targetWidth * targetHeight;
int texturePixels = textureWidth * textureHeight;
int idTextureRgb = id / texturePixels;
int idTexturePixel = (id - idTextureRgb * texturePixels); // same as (id % texturePixels), but the kernel runs 10% faster
int idTextureY = idTexturePixel / textureWidth;
int idTextureX = (idTexturePixel - idTextureY * textureWidth); // same as (id % textureWidth), but the kernel runs another 10% faster
if (idTextureRgb < 3) // 3 channels that we will write to
{
// the texture is in BGR format, we want RGB
switch (idTextureRgb)
{
case 0: // R
idTextureRgb = 2; // B
break;
case 2: // B
idTextureRgb = 0; // R
break;
}
// if the texture pixel offset by inputX, inputY, lies inside the target
if (idTextureX + inputX < targetWidth &&
idTextureX + inputX >= 0 &&
idTextureY + inputY < targetHeight &&
idTextureY + inputY >= 0)
{
int tIndex = targetPixels * idTextureRgb + targetWidth * (idTextureY + inputY) + (idTextureX + inputX);
int aIndex = idTexturePixel + 3 * texturePixels; // the A component of the texture
float a = texture[aIndex];
target[tIndex] = target[tIndex] * (1.0f - a) + a * texture[id];
}
}
}
/*
Draws a texture into a 3-component target. RGBA. Checks bounds. Stretches the texture.
*/
__global__ void DrawRgbaTextureKernelNearestNeighbor(float *target, int targetWidth, int targetHeight, int inputX, int inputY,
float *texture, int textureWidth, int textureHeight, int objectWidth, int objectHeight)
{
int id = blockDim.x * blockIdx.y * gridDim.x
+ blockDim.x * blockIdx.x
+ threadIdx.x;
int targetPixels = targetWidth * targetHeight;
int texturePixels = textureWidth * textureHeight;
int objectPixels = objectWidth * objectHeight;
int idObjectRgb = id / objectPixels;
int idObjectPixel = (id - idObjectRgb * objectPixels); // same as (id % objectPixels), but the kernel runs 10% faster
int idObjectY = idObjectPixel / objectWidth;
int idObjectX = (idObjectPixel - idObjectY * objectWidth); // same as (id % textureWidth), but the kernel runs another 10% faster
if (idObjectRgb < 3) // 3 channels that we will write to
{
int targetRgb = idObjectRgb;
// the texture is in BGR format, we want RGB
switch (idObjectRgb)
{
case 0: // R
targetRgb = 2; // B
break;
case 2: // B
targetRgb = 0; // R
break;
}
// if the object pixel offset by inputX, inputY, lies inside the target
if (idObjectX + inputX < targetWidth &&
idObjectX + inputX >= 0 &&
idObjectY + inputY < targetHeight &&
idObjectY + inputY >= 0)
{
// nearest neighbor texture X,Y:
int textureX = textureWidth * idObjectX / objectWidth;
int textureY = textureHeight * idObjectY / objectHeight;
int textureId = textureY * textureWidth + textureX;
int rgbIndex = textureId + idObjectRgb * texturePixels;
float textureValue = texture[rgbIndex];
int tIndex = targetPixels * targetRgb + targetWidth * (idObjectY + inputY) + (idObjectX + inputX);
int aIndex = textureId + 3 * texturePixels; // the A component of the texture
float a = texture[aIndex];
target[tIndex] = target[tIndex] * (1.0f - a) + a * textureValue;
}
}
}
/*
Same as DrawRgbaTextureKernelNearestNeighbor, but texture = mask and texture's pixel values are replaced by a single color
*/
__global__ void DrawMaskedColorKernelNearestNeighbor(float *target, int targetWidth, int targetHeight, int inputX, int inputY,
float *texture, int textureWidth, int textureHeight, int objectWidth, int objectHeight,
float r, float g, float b ) // texture = mask
{
int id = blockDim.x * blockIdx.y * gridDim.x
+ blockDim.x * blockIdx.x
+ threadIdx.x;
int targetPixels = targetWidth * targetHeight;
int texturePixels = textureWidth * textureHeight;
int objectPixels = objectWidth * objectHeight;
int idObjectRgb = id / objectPixels;
int idObjectPixel = (id - idObjectRgb * objectPixels); // same as (id % objectPixels), but the kernel runs 10% faster
int idObjectY = idObjectPixel / objectWidth;
int idObjectX = (idObjectPixel - idObjectY * objectWidth); // same as (id % textureWidth), but the kernel runs another 10% faster
if (idObjectRgb < 3) // 3 channels that we will write to
{
int targetRgb = idObjectRgb;
// the texture is in BGR format, we want RGB
switch (idObjectRgb)
{
case 0: // R
targetRgb = 2; // B
break;
case 2: // B
targetRgb = 0; // R
break;
}
// if the object pixel offset by inputX, inputY, lies inside the target
if (idObjectX + inputX < targetWidth &&
idObjectX + inputX >= 0 &&
idObjectY + inputY < targetHeight &&
idObjectY + inputY >= 0)
{
// nearest neighbor texture X,Y:
int textureX = textureWidth * idObjectX / objectWidth;
int textureY = textureHeight * idObjectY / objectHeight;
int textureId = textureY * textureWidth + textureX;
int tIndex = targetPixels * targetRgb + targetWidth * (idObjectY + inputY) + (idObjectX + inputX);
int aIndex = textureId + 3 * texturePixels; // the A component of the texture
float a = texture[aIndex];
if (a > 0) // mask allows color here
{
// apply this: target[tIndex] = target[tIndex] * (1.0f - a) + a * color;
target[tIndex] = target[tIndex] * (1.0f - a);
switch (idObjectRgb)
{
case 0:
target[tIndex] += a*r;
break;
case 1:
target[tIndex] += a*g;
break;
case 2:
default:
target[tIndex] += a*b;
break;
}
}
}
}
}
/*
Optimized version of DrawRgbaTextureKernel : avoids division operations (~30% speedup)
The width of the texture is in blockDim.x
The height of the texture is distributed between blockDim.y and gridDim.x
*/
__global__ void DrawRgbaTextureKernel2DBlock(float *target, int targetWidth, int targetHeight, int inputX, int inputY,
float *texture, int textureWidth, int textureHeight)
{
int id = blockDim.x * blockDim.y * (blockIdx.y * gridDim.x + blockIdx.x)
+ blockDim.x * threadIdx.y
+ threadIdx.x; // 2D grid of 2D blocks; block dimension x = texture width;
// grid dimension x + block dimension y = texture height
int targetPixels = targetWidth * targetHeight;
int texturePixels = textureWidth * textureHeight;
int idTextureRgb = blockIdx.y;
int idTexturePixel = (id - idTextureRgb * texturePixels);
int idTextureY = blockIdx.x * blockDim.y + threadIdx.y;
int idTextureX = threadIdx.x;
if (idTextureRgb < 3) // 3 channels that we will write to
{
// the texture is in BGR format, we want RGB
switch (idTextureRgb)
{
case 0: // R
idTextureRgb = 2; // B
break;
case 2: // B
idTextureRgb = 0; // R
break;
}
// if the texture pixel offset by inputX, inputY, lies inside the target
if (idTextureX + inputX < targetWidth &&
idTextureX + inputX >= 0 &&
idTextureY + inputY < targetHeight &&
idTextureY + inputY >= 0)
{
int tIndex = targetPixels * idTextureRgb + targetWidth * (idTextureY + inputY) + (idTextureX + inputX);
int aIndex = idTexturePixel + 3 * texturePixels; // the A component of the texture
float a = texture[aIndex];
target[tIndex] = target[tIndex] * (1.0f - a) + a * texture[id];
}
}
}
/*
Draws an RGB color into the masked area. The color is drawn in each pixel that has non-0 alpha.
*/
__global__ void DrawMaskedColorKernel(float *target, int targetWidth, int targetHeight, int inputX, int inputY,
float *textureMask, int textureWidth, int textureHeight, float r, float g, float b)
{
int id = blockDim.x * blockIdx.y * gridDim.x
+ blockDim.x * blockIdx.x
+ threadIdx.x;
int targetPixels = targetWidth * targetHeight;
int texturePixels = textureWidth * textureHeight;
int idTextureRgb = id / texturePixels;
int idTexturePixel = (id - idTextureRgb * texturePixels); // same as (id % texturePixels), but the kernel runs 10% faster
int idTextureY = idTexturePixel / textureWidth;
int idTextureX = (idTexturePixel - idTextureY * textureWidth); // same as (id % textureWidth), but the kernel runs another 10% faster
if (idTextureRgb < 3) // only RGB channels are interesting
{
// if the texture pixel offset by inputX, inputY, lies inside the target
if (idTextureX + inputX < targetWidth &&
idTextureX + inputX >= 0 &&
idTextureY + inputY < targetHeight &&
idTextureY + inputY >= 0)
{
int tIndex = targetPixels * idTextureRgb + targetWidth * (idTextureY + inputY) + (idTextureX + inputX);
int aIndex = idTexturePixel + 3 * texturePixels; // the A component of the texture
float a = textureMask[aIndex];
if (a > 0) // mask allows color here
{
switch (idTextureRgb)
{
case 0:
target[tIndex] = r;
break;
case 1:
target[tIndex] = g;
break;
case 2:
default:
target[tIndex] = b;
break;
}
}
}
}
}
/*
Optimized version of DrawMaskedColorKernel : avoids division operations (~30% speedup)
The width of the texture is in blockDim.x
The height of the texture is distributed between blockDim.y and gridDim.x
*/
__global__ void DrawMaskedColorKernel2DBlock(float *target, int targetWidth, int targetHeight, int inputX, int inputY,
float *textureMask, int textureWidth, int textureHeight, float r, float g, float b)
{
int id = blockDim.x * blockDim.y * (blockIdx.y * gridDim.x + blockIdx.x)
+ blockDim.x * threadIdx.y
+ threadIdx.x; // 2D grid of 2D blocks; block dimension x = texture width;
// grid dimension x + block dimension y = texture height
int targetPixels = targetWidth * targetHeight;
int texturePixels = textureWidth * textureHeight;
int idTextureRgb = blockIdx.y;
int idTexturePixel = (id - idTextureRgb * texturePixels);
int idTextureY = blockIdx.x * blockDim.y + threadIdx.y;
int idTextureX = threadIdx.x;
if (idTextureRgb < 3) // only RGB channels are interesting
{
// if the texture pixel offset by inputX, inputY, lies inside the target
if (idTextureX + inputX < targetWidth &&
idTextureX + inputX >= 0 &&
idTextureY + inputY < targetHeight &&
idTextureY + inputY >= 0)
{
int tIndex = targetPixels * idTextureRgb + targetWidth * (idTextureY + inputY) + (idTextureX + inputX);
int aIndex = idTexturePixel + 3 * texturePixels; // the A component of the texture
float a = textureMask[aIndex];
if (a > 0) // mask allows color here
{
switch (idTextureRgb)
{
case 0:
target[tIndex] = r;
break;
case 1:
target[tIndex] = g;
break;
case 2:
default:
target[tIndex] = b;
break;
}
}
}
}
}
/*
Convert Raw to RGB
*/
__global__ void ExtractRawComponentsToRgbKernel(float *target, int inputWidth, int inputHeight)
{
int pixelId = blockDim.x*blockIdx.y*gridDim.x
+ blockDim.x*blockIdx.x
+ threadIdx.x;
int imagePixels = inputWidth * inputHeight;
if (pixelId >= imagePixels)
return;
unsigned int* uTarget = (unsigned int*)target;
unsigned int pixel = uTarget[pixelId];
for (int i = 2; i >= 0; i--)
{
unsigned int component = pixel;
component = component >> (8 * (2-i)); // 2-i == RGB -> BGR
component = component & 0xFF;
target[imagePixels * i + pixelId] = ((float)component)/255.0f;
__syncthreads();
}
}
/*
Convert Raw to RGB
*/
__global__ void RawToRgbKernel(float *source, float *target, int pixelCount)
{
int pixelId = blockDim.x*blockIdx.x
+ threadIdx.x;
if (pixelId >= pixelCount)
return;
unsigned int pixel = AsUint(source, pixelId);
for (int i = 0; i < 3; i++) // 3: don't care about alpha
{
target[pixelCount * i + pixelId] = GetComponent(pixel,2-i) / 255.0f; // /255.0f to re-scale from 0 to 1,
// 2-i to convert between RGB and BGR
}
}
/*
Convert Raw to Raw grayscale
http://stackoverflow.com/questions/687261/converting-rgb-to-grayscale-intensity
*/
__global__ void RawToRawGrayscaleKernel(float *source, float *target, int pixelCount)
{
int pixelId = blockDim.x*blockIdx.x
+ threadIdx.x;
if (pixelId >= pixelCount)
return;
unsigned int pixel = AsUint(source, pixelId);
unsigned int luminance = (unsigned int)
(.2126f * GetComponent(pixel, 2) + .7152f * GetComponent(pixel, 1) + .0722f * GetComponent(pixel, 0));
unsigned int alpha = GetComponent(pixel, 3);
*((unsigned int*)&target[pixelId]) = luminance | (luminance << 8) | (luminance << 16) | (alpha << 24);
}
/*
Convert Raw to Grayscale
http://stackoverflow.com/questions/687261/converting-rgb-to-grayscale-intensity
*/
__global__ void RawToGrayscaleKernel(float *source, float *target, int pixelCount)
{
int pixelId = blockDim.x*blockIdx.x
+ threadIdx.x;
if (pixelId >= pixelCount)
return;
unsigned int pixel = AsUint(source, pixelId);
float luminance =
(.2126f * GetComponent(pixel, 2) + .7152f * GetComponent(pixel, 1) + .0722f * GetComponent(pixel, 0));
target[pixelId] = luminance / 255.0f; // to re-scale from 0 to 1
}
}
|
7,059 | #include "includes.h"
__global__ void cudaSinv_kernel(unsigned int size, const float *x, float *y)
{
const unsigned int index = blockIdx.x * blockDim.x + threadIdx.x;
const unsigned int stride = blockDim.x * gridDim.x;
for (unsigned int i = index; i < size; i += stride) {
y[i] = 1.0f / x[i];
}
} |
7,060 | #include "includes.h"
__global__ void windowBlackman2d(float* idata, int length, int height)
{
int tidx = threadIdx.x + blockIdx.x*blockDim.x;
int tidy = threadIdx.y + blockIdx.y*blockDim.y;
if (tidx < length && tidy < height)
{
idata[tidy * length + tidx] = (0.74 / 2 * -0.5 * cos(2 * PI_F*tidy / (height - 1)) + 0.16 / 2 * sin(4 * PI_F*tidy / (height - 1)))
* (0.74 / 2 * -0.5 * cos(2 * PI_F*tidx / (length - 1)) + 0.16 / 2 * sin(4 * PI_F*tidx / (length - 1)));
}
} |
7,061 | #include <stdio.h>
#define CSC(call) { \
cudaError err = call; \
if(err != cudaSuccess) { \
fprintf(stderr, "CUDA error in file '%s' in line %i: %s.\n", \
__FILE__, __LINE__, cudaGetErrorString(err)); \
exit(1); \
} \
} while (0)
__global__ void kernel(int *a, int *b, int *c, int n) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
int offset = gridDim.x * blockDim.x;
for(; idx < n; idx += offset)
c[idx] = a[idx] + b[idx];
}
int main() {
int i, n = 2000000;
int *a = (int *)malloc(sizeof(int) * n);
int *b = (int *)malloc(sizeof(int) * n);
int *c = (int *)malloc(sizeof(int) * n);
for(i = 0; i < n; i++)
a[i] = b[i] = i;
int *dev_a;
int *dev_b;
int *dev_c;
cudaEvent_t start, stop;
CSC(cudaEventCreate(&start));
CSC(cudaEventCreate(&stop));
CSC(cudaMalloc(&dev_a, sizeof(int) * n));
CSC(cudaMalloc(&dev_b, sizeof(int) * n));
CSC(cudaMalloc(&dev_c, sizeof(int) * n));
CSC(cudaMemcpy(dev_a, a, sizeof(int) * n, cudaMemcpyHostToDevice));
CSC(cudaMemcpy(dev_b, b, sizeof(int) * n, cudaMemcpyHostToDevice));
CSC(cudaEventRecord(start, 0));
//for(i = 0; i < n; i++)
// c[i] = a[i] + b[i];
kernel<<<6, 256>>>(dev_a, dev_b, dev_c, n);
CSC(cudaGetLastError());
CSC(cudaEventRecord(stop, 0));
CSC(cudaEventSynchronize(stop));
float t;
CSC(cudaEventElapsedTime(&t, start, stop));
printf("time = %f\n", t);
CSC(cudaEventDestroy(start));
CSC(cudaEventDestroy(stop));
CSC(cudaMemcpy(c, dev_c, sizeof(int) * n, cudaMemcpyDeviceToHost));
//for(i = 0; i < n; i++)
// printf("%d ", c[i]);
//printf("\n");
CSC(cudaFree(dev_a));
CSC(cudaFree(dev_b));
CSC(cudaFree(dev_c));
free(a);
free(b);
free(c);
return 0;
}
|
7,062 | #include <stdio.h>
//#include <cuda.h>
#include <stdlib.h>
#define N 64
#define R 3
// Kernel definition
//__global__ void MatMul(int A[N][N], int B[N][N], int C[N][N])
//{
// //finish matrix multiplication, each thread calculate one element of C
// int mi = threadIdx.x + blockIdx.x*blockDim.x;
// int mj = threadIdx.y + blockIdx.y*blockDim.y;
// C[mi][mj] = 0;
// for(int i = 0; i < N; i++){
// C[mi][mj] += A[mi][i] * B[i][mj];
// }
//}
int main() {
int a_h[N], b_h[N];
int (*a_d)[N], (*b_d)[N];
int size = N*N*sizeof(int);
////allocate the memory on the device
//cudaMalloc((void*)&a_d, size);
//cudaMalloc((void*)&b_d, size);
//assign values to matrixes
for(int i=0; i<N; i++)
{
a_h[i] = i;
b_h[i] = 0;
}
//sequential implementation
for(int i = 0; i < N; i++){
for(int j = -R; j < R+1; j++){
if(!(i+j < 0 || i+j >= N)){
b_h[i] += a_h[i+j];
}
}
}
for(int i = 0; i < N; i++){
printf("%d\n", b_h[i]);
}
//copy matrixes to the device
//cudaMemcpy(a_d, a_h, size, cudaMemcpyHostToDevice);
//cudaMemcpy(b_d, b_h, size, cudaMemcpyHostToDevice);
//cudaMemcpy(c_d, c_h, size, cudaMemcpyHostToDevice);
//// launch kernel on the device by combining two-dimensional blocks with two-dimensional threads
////define numBlocks and threadsPerBlock
//int nThreads = 4;
//dim3 numBlocks(N/nThreads,N/nThreads);
//dim3 numThreads(nThreads,nThreads);
//MatMul<<<numBlocks, numThreads>>>(a_d, b_d, c_d);
////copy results from the device to the host
//cudaMemcpy(c_h, c_d, size, cudaMemcpyDeviceToHost);
////print the results
//for(int i=0;i<N;i++){
// for(int j=0;j<N;j++){
// printf("%d ", c_h[i][j]);
// }
// printf("\n");
//}
////free the memory
//cudaFree(a_d);
//cudaFree(b_d);
//cudaFree(c_d);
return 0;
}
|
7,063 | #include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#define HEIGHT 960
#define WIDTH 1280
#define MASK_WIDTH 53
#define MASK_RADIUS (MASK_WIDTH/2)
#define BLOCK_SIZE 32
// gabor filter.
__global__ void convolution_kernel(float * input, float * output, int height,
int width, const float * __restrict__ Mask)
{
int row = blockIdx.y * blockDim.y + threadIdx.y;
int col = blockIdx.x * blockDim.x + threadIdx.x;
float accum = 0.0;
if (row < height && col < width)
{
for (int y = -MASK_RADIUS; y <= MASK_RADIUS;++y)
{
for (int x = -MASK_RADIUS; x <= MASK_RADIUS; ++x)
{
int xoffset = col + x;
int yoffset = row + y;
if (xoffset >= 0 && xoffset < WIDTH && yoffset >= 0 && yoffset < HEIGHT)
accum += input[yoffset*WIDTH + xoffset]*Mask[(y+MASK_RADIUS)*MASK_WIDTH+x+MASK_RADIUS];
}
}
output[row*WIDTH + col] = accum;
}
}
// »ñÈ¡½Ø¶ÏÏàλ²î
__global__ void get_phase(float * ref, float * sce, const float * __restrict__ maskReal,
const float * __restrict__ maskImage, float * phase)
{
int row = blockIdx.y * blockDim.y + threadIdx.y;
int col = blockIdx.x * blockDim.x + threadIdx.x;
float ref_real = 0.0;
float ref_image = 0.0;
float sce_real = 0.0;
float sce_image = 0.0;
if (row < HEIGHT && col < WIDTH)
{
for (int y = -MASK_RADIUS; y <= MASK_RADIUS;++y)
{
for (int x = -MASK_RADIUS; x <= MASK_RADIUS; ++x)
{
int xoffset = col + x;
int yoffset = row + y;
if (xoffset >= 0 && xoffset < WIDTH && yoffset >= 0 && yoffset < HEIGHT)
{
ref_real += ref[yoffset*WIDTH + xoffset]*maskReal[(y+MASK_RADIUS)*MASK_WIDTH+x+MASK_RADIUS];
ref_image += ref[yoffset*WIDTH + xoffset]*maskImage[(y+MASK_RADIUS)*MASK_WIDTH+x+MASK_RADIUS];
sce_real += sce[yoffset*WIDTH + xoffset]*maskReal[(y+MASK_RADIUS)*MASK_WIDTH+x+MASK_RADIUS];
sce_image += sce[yoffset*WIDTH + xoffset]*maskImage[(y+MASK_RADIUS)*MASK_WIDTH+x+MASK_RADIUS];
}
}
}
// sce - ref
phase[row*WIDTH + col] = atan2(sce_image, sce_real) - atan2(ref_image, ref_real);
}
}
// ´ÓÎļþÖе¼ÈëͼÏñÊý¾Ý
void import_data(const char * inputFile, float * hostInput,
int height, int width)
{
FILE *input = fopen(inputFile, "r");
for (int i = 0; i < height; ++i)
{
for (int j = 0; j < width; ++j)
{
fscanf(input, "%f ",&hostInput[i*width+j]);
}
}
fclose(input);
}
int main()
{
float * hostRef; // ²Î¿¼Æ½Ãæ
float * hostSce; // ³¡¾°
float * hostPhase; // ½Ø¶ÏÏàλ
float * hostMaskReal;// GaborºËʵ²¿
float * hostMaskImage;// GaborºËÐ鲿
float * deviceRef;
float * deviceSce;
float * devicePhase;
float * deviceMaskReal;
float * deviceMaskImage;
float time_elapsed = 0; //¼Ç¼GPU¼ÆËãµÄʱ¼ä
cudaError_t err = cudaSuccess; //GPU ״̬(¼Ç¼ÄÚ´æ·ÖÅäÊÇ·ñ³É¹¦µÈµÈ)
cudaEvent_t start, stop;
//@@ allocate memory on CPU
hostRef = (float *)malloc(HEIGHT*WIDTH*sizeof(float));
hostSce = (float *)malloc(HEIGHT*WIDTH*sizeof(float));
hostPhase = (float *)malloc(HEIGHT*WIDTH*sizeof(float));
hostMaskReal = (float *)malloc(MASK_WIDTH*MASK_WIDTH*sizeof(float));
hostMaskImage = (float *)malloc(MASK_WIDTH*MASK_WIDTH*sizeof(float));
//@@ load image data and Gabor mask.
import_data("ref.txt", hostRef, HEIGHT, WIDTH);
import_data("sce.txt", hostSce, HEIGHT, WIDTH);
import_data("gabor_kernel_re.txt", hostMaskReal, MASK_WIDTH, MASK_WIDTH);
import_data("gabor_kernel_im.txt", hostMaskImage, MASK_WIDTH, MASK_WIDTH);
//@@ allocate memory on gpu
err = cudaMalloc((void **)&deviceRef, HEIGHT*WIDTH*sizeof(float));
if(err!=cudaSuccess)
{
perror("the cudaMalloc on GPU is failed");
return 1;
}
err = cudaMalloc((void **)&deviceSce, HEIGHT*WIDTH*sizeof(float));
if(err!=cudaSuccess)
{
perror("the cudaMalloc on GPU is failed");
return 1;
}
err = cudaMalloc((void **)&devicePhase, HEIGHT*WIDTH*sizeof(float));
if(err!=cudaSuccess)
{
perror("the cudaMalloc on GPU is failed");
return 1;
}
err = cudaMalloc((void **)&deviceMaskReal, MASK_WIDTH*MASK_WIDTH*sizeof(float));
if(err!=cudaSuccess)
{
perror("the cudaMalloc on GPU is failed");
return 1;
}
err = cudaMalloc((void **)&deviceMaskImage, MASK_WIDTH*MASK_WIDTH*sizeof(float));
if(err!=cudaSuccess)
{
perror("the cudaMalloc on GPU is failed");
return 1;
}
//@@ copy data to device
cudaMemcpy(deviceRef, hostRef, HEIGHT*WIDTH*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(deviceSce, hostSce, HEIGHT*WIDTH*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(deviceMaskReal, hostMaskReal, MASK_WIDTH*MASK_WIDTH*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(deviceMaskImage, hostMaskImage, MASK_WIDTH*MASK_WIDTH*sizeof(float), cudaMemcpyHostToDevice);
cudaEventCreate(&start); //´´½¨Event
cudaEventCreate(&stop);
//@@ launch the kernel
dim3 dimBlock(BLOCK_SIZE, BLOCK_SIZE, 1);
dim3 dimGrid((WIDTH-1)/BLOCK_SIZE+1, (HEIGHT-1)/BLOCK_SIZE+1,1);
cudaEventRecord(start, 0);
/*convolution_kernel<<<dimGrid, dimBlock>>>(deviceInputImageData, deviceOutputImageData, HEIGHT,
WIDTH, deviceMaskData);*/
get_phase<<<dimGrid, dimBlock>>>(deviceRef, deviceSce, deviceMaskReal,
deviceMaskImage, devicePhase);
cudaDeviceSynchronize();
cudaEventRecord(stop, 0);
cudaEventSynchronize(start); //Waits for an event to complete.
cudaEventSynchronize(stop); //Waits for an event to complete.Record֮ǰµÄÈÎÎñ
cudaEventElapsedTime(&time_elapsed,start,stop); //¼ÆËãʱ¼ä²î
printf("Ö´ÐÐʱ¼ä£º%f(ms)\n",time_elapsed);
cudaMemcpy(hostPhase, devicePhase, HEIGHT*WIDTH*sizeof(float), cudaMemcpyDeviceToHost);
FILE *outputImage = fopen("phase.txt", "w");
for (int i = 0; i < HEIGHT; ++i)
{
for (int j = 0; j < WIDTH; ++j)
{
fprintf(outputImage, j < WIDTH -1 ? "%f ":"%f\n", hostPhase[i*WIDTH + j]);
}
}
fclose(outputImage);
cudaEventDestroy(start); //destory the event
cudaEventDestroy(stop);
// free the memory on CPU and GPU.
cudaFree(deviceRef);
cudaFree(deviceSce);
cudaFree(devicePhase);
cudaFree(deviceMaskReal);
cudaFree(deviceMaskImage);
free(hostRef);
free(hostSce);
free(hostPhase);
free(hostMaskReal);
free(hostMaskImage);
getchar();
return 0;
}
|
7,064 | #include <iostream>
#include <math.h>
#include <time.h>
#include <stdlib.h>
#include <random>
#include <vector>
#include <chrono>
#define TILE_DIM 32
__global__ void convolve(const int *mat_1, const int *mat_2, int *mat_conv, const int n, const int m, const int p, const int q, const int stride, const int u, const int v) {
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
int start_row = stride*y;
int start_col = stride*x;
if (y < u && x < v && start_row+p <= n && start_col+q <= m) {
int sum = 0;
for (int i = start_row; i < start_row+p; i++) {
int i1 = i-start_row;
for (int j = start_col; j < start_col+q; j++) {
int j1 = j-start_col;
sum += mat_1[i*m+j]*mat_2[i1*q+j1];
}
}
mat_conv[y*v+x] = sum;
}
}
std::vector<std::vector<int>> random_matrix(const int num_rows, const int num_cols, const int min_val=0.0, const int max_val=1000.0) {
std::vector<std::vector<int>> my_arr;
static std::random_device rd;
static std::mt19937 mte(rd());
std::uniform_int_distribution<int> dist(min_val, max_val);
for (int i = 0; i < num_rows; i++) {
std::vector<int> my_arr_col;
for (int j = 0; j < num_cols; j++) {
my_arr_col.push_back(dist(mte));
}
my_arr.push_back(my_arr_col);
}
return my_arr;
}
bool check_correctness(const int *mat_1, const int *mat_2, int *mat_conv, const int n, const int m, const int p, const int q, const int stride, const int u, const int v) {
for (int i = 0; i < u*v; i++) {
int r = i/v;
int c = i % v;
int start_row = stride*r;
int start_col = stride*c;
if (start_row+p <= n && start_col+q <= m) {
int sum = 0;
for (int i1 = start_row; i1 < start_row+p; i1++) {
int i2 = i1-start_row;
for (int j1 = start_col; j1 < start_col+q; j1++) {
int j2 = j1-start_col;
sum += mat_1[i1*m+j1]*mat_2[i2*q+j2];
}
}
if (sum != mat_conv[i]) {
return false;
}
}
else {
return false;
}
}
return true;
}
int main(void) {
int n = 5000;
int m = 8000;
int p = 7;
int q = 11;
int stride = 1;
int u = (n-p)/stride + 1;
int v = (m-q)/stride + 1;
dim3 dimGrid((v + TILE_DIM - 1)/TILE_DIM, (u + TILE_DIM - 1)/TILE_DIM, 1);
dim3 dimBlock(TILE_DIM, TILE_DIM, 1);
int *mat_1, *mat_2, *mat_conv;
cudaMallocManaged(&mat_1, n*m*sizeof(int));
cudaMallocManaged(&mat_2, p*q*sizeof(int));
cudaMallocManaged(&mat_conv, u*v*sizeof(int));
std::vector<std::vector<int>> my_arr_1 = random_matrix(n, m, 0, 10);
std::vector<std::vector<int>> my_arr_2 = random_matrix(p, q, 0, 10);
for (int i = 0; i < n; i++) {
for (int j = 0; j < m; j++) {
mat_1[i*m + j] = my_arr_1[i][j];
}
}
for (int i = 0; i < p; i++) {
for (int j = 0; j < q; j++) {
mat_2[i*q + j] = my_arr_2[i][j];
}
}
auto t1 = std::chrono::high_resolution_clock::now();
convolve<<<dimGrid, dimBlock>>>(mat_1, mat_2, mat_conv, n, m, p, q, stride, u, v);
cudaDeviceSynchronize();
auto t2 = std::chrono::high_resolution_clock::now();
auto duration = std::chrono::duration_cast<std::chrono::milliseconds>( t2 - t1 ).count();
std::cout << duration << std::endl;
std::cout << check_correctness(mat_1, mat_2, mat_conv, n, m, p, q, stride, u, v) << std::endl;
return 0;
}
|
7,065 | /*
* Overdamped Brownian particle in symmetric piecewise linear potential
*
* \dot{x} = -V'(x) + Gaussian, Poissonian and dichotomous noise
*
*/
#include <stdio.h>
#include <getopt.h>
#include <stdlib.h>
#include <math.h>
#include <time.h>
#include <cuda.h>
#include <curand.h>
#include <curand_kernel.h>
#define PI 3.14159265358979f
//model
__constant__ float d_Dg, d_Dp, d_lambda, d_mean, d_fa, d_fb, d_mua, d_mub;
__constant__ int d_comp;
float h_lambda, h_fa, h_fb, h_mua, h_mub, h_mean;
int h_comp;
//simulation
float h_trans;
int h_dev, h_block, h_grid, h_spp, h_samples;
long h_paths, h_periods, h_threads, h_steps, h_trigger;
__constant__ int d_spp, d_samples;
__constant__ long d_paths;
//output
char *h_domain;
char h_domainx;
float h_beginx, h_endx;
int h_logx, h_points, h_moments;
__constant__ char d_domainx;
__constant__ int d_points;
//vector
float *h_x, *h_xb, *h_fx, *h_dx;
float *d_x, *d_fx, *d_dx;
int *d_pcd, *d_dcd, *d_dst;
unsigned int *h_seeds, *d_seeds;
curandState *d_states;
size_t size_f, size_i, size_ui, size_p;
curandGenerator_t gen;
static struct option options[] = {
{"Dg", required_argument, NULL, 'a'},
{"Dp", required_argument, NULL, 'b'},
{"lambda", required_argument, NULL, 'c'},
{"fa", required_argument, NULL, 'd'},
{"fb", required_argument, NULL, 'e'},
{"mua", required_argument, NULL, 'f'},
{"mub", required_argument, NULL, 'g'},
{"comp", required_argument, NULL, 'h'},
{"mean", required_argument, NULL, 'i'},
{"dev", required_argument, NULL, 'j'},
{"block", required_argument, NULL, 'k'},
{"paths", required_argument, NULL, 'l'},
{"periods", required_argument, NULL, 'm'},
{"trans", required_argument, NULL, 'n'},
{"spp", required_argument, NULL, 'o'},
{"samples", required_argument, NULL, 'p'},
{"mode", required_argument, NULL, 'q'},
{"domain", required_argument, NULL, 'r'},
{"domainx", required_argument, NULL, 's'},
{"logx", required_argument, NULL, 't'},
{"points", required_argument, NULL, 'u'},
{"beginx", required_argument, NULL, 'v'},
{"endx", required_argument, NULL, 'w'}
};
void usage(char **argv)
{
printf("Usage: %s <params> \n\n", argv[0]);
printf("Model params:\n");
printf(" -a, --Dg=FLOAT set the Gaussian noise intensity 'D_G' to FLOAT\n");
printf(" -b, --Dp=FLOAT set the Poissonian noise intensity 'D_P' to FLOAT\n");
printf(" -c, --lambda=FLOAT set the Poissonian kicks frequency '\\lambda' to FLOAT\n");
printf(" -d, --fa=FLOAT set the first state of the dichotomous noise 'F_a' to FLOAT\n");
printf(" -e, --fb=FLOAT set the second state of the dichotomous noise 'F_b' to FLOAT\n");
printf(" -f, --mua=FLOAT set the transition rate of the first state of dichotomous noise '\\mu_a' to FLOAT\n");
printf(" -g, --mub=FLOAT set the transition rate of the second state of dichotomous noise '\\mu_b' to FLOAT\n");
printf(" -h, --comp=INT choose between biased and unbiased Poissonian or dichotomous noise. INT can be one of:\n");
printf(" 0: biased; 1: unbiased\n");
printf(" -i, --mean=FLOAT if is nonzero, fix the mean value of Poissonian noise or dichotomous noise to FLOAT, matters only for domains p, l, a, b, m or n\n");
printf("Simulation params:\n");
printf(" -j, --dev=INT set the gpu device to INT\n");
printf(" -k, --block=INT set the gpu block size to INT\n");
printf(" -l, --paths=LONG set the number of paths to LONG\n");
printf(" -m, --periods=LONG set the number of periods to LONG\n");
printf(" -n, --trans=FLOAT specify fraction FLOAT of periods which stands for transients\n");
printf(" -o, --spp=INT specify how many integration steps should be calculated for the smallest characteristic time scale\n");
printf(" -p, --samples=INT specify how many integration steps should be calculated for a single kernel call\n");
printf("Output params:\n");
printf(" -q, --mode=STRING sets the output mode. STRING can be one of:\n");
printf(" moments: the first moment <<v>>\n");
printf(" -r, --domain=STRING simultaneously scan over one or two model params. STRING can be one of:\n");
printf(" 1d: only one parameter\n");
printf(" -s, --domainx=CHAR sets the first domain of the moments. CHAR can be one of:\n");
printf(" D: Dg; p: Dp; l: lambda; a: fa; b: fb; m: mua; n: mub\n");
printf(" -t, --logx=INT choose between linear and logarithmic scale of the domainx\n");
printf(" 0: linear; 1: logarithmic\n");
printf(" -u, --points=INT set the number of samples to generate between begin and end\n");
printf(" -v, --beginx=FLOAT set the starting value of the domainx to FLOAT\n");
printf(" -w, --endx=FLOAT set the end value of the domainx to FLOAT\n");
printf("\n");
}
void parse_cla(int argc, char **argv)
{
float ftmp;
int c, itmp;
while( (c = getopt_long(argc, argv, "a:b:c:d:e:f:g:h:i:j:k:l:m:n:o:p:q:r:s:t:u:v:w", options, NULL)) != EOF) {
switch (c) {
case 'a':
ftmp = atof(optarg);
cudaMemcpyToSymbol(d_Dg, &ftmp, sizeof(float));
break;
case 'b':
ftmp = atof(optarg);
cudaMemcpyToSymbol(d_Dp, &ftmp, sizeof(float));
break;
case 'c':
h_lambda = atof(optarg);
cudaMemcpyToSymbol(d_lambda, &h_lambda, sizeof(float));
break;
case 'd':
h_fa = atof(optarg);
cudaMemcpyToSymbol(d_fa, &h_fa, sizeof(float));
break;
case 'e':
h_fb = atof(optarg);
cudaMemcpyToSymbol(d_fb, &h_fb, sizeof(float));
break;
case 'f':
h_mua = atof(optarg);
cudaMemcpyToSymbol(d_mua, &h_mua, sizeof(float));
break;
case 'g':
h_mub = atof(optarg);
cudaMemcpyToSymbol(d_mub, &h_mub, sizeof(float));
break;
case 'h':
h_comp = atoi(optarg);
cudaMemcpyToSymbol(d_comp, &h_comp, sizeof(int));
break;
case 'i':
h_mean = atof(optarg);
cudaMemcpyToSymbol(d_mean, &h_mean, sizeof(float));
break;
case 'j':
itmp = atoi(optarg);
cudaSetDevice(itmp);
break;
case 'k':
h_block = atoi(optarg);
break;
case 'l':
h_paths = atol(optarg);
cudaMemcpyToSymbol(d_paths, &h_paths, sizeof(long));
break;
case 'm':
h_periods = atol(optarg);
break;
case 'n':
h_trans = atof(optarg);
break;
case 'o':
h_spp = atoi(optarg);
cudaMemcpyToSymbol(d_spp, &h_spp, sizeof(int));
break;
case 'p':
h_samples = atoi(optarg);
cudaMemcpyToSymbol(d_samples, &h_samples, sizeof(int));
break;
case 'q':
if ( !strcmp(optarg, "moments") ) {
h_moments = 1;
}
break;
case 'r':
h_domain = optarg;
break;
case 's':
h_domainx = optarg[0];
cudaMemcpyToSymbol(d_domainx, &h_domainx, sizeof(char));
break;
case 't':
h_logx = atoi(optarg);
break;
case 'u':
h_points = atoi(optarg);
cudaMemcpyToSymbol(d_points, &h_points, sizeof(int));
break;
case 'v':
h_beginx = atof(optarg);
break;
case 'w':
h_endx = atof(optarg);
break;
}
}
}
__global__ void init_dev_rng(unsigned int *d_seeds, curandState *d_states)
{
long idx = blockIdx.x * blockDim.x + threadIdx.x;
curand_init(d_seeds[idx], 0, 0, &d_states[idx]);
}
__device__ float drift(float l_x)
{
if (-sinf(PI*l_x) < 0.0f) {
return -1.0f;
} else {
return 1.0f;
}
}
__device__ float diffusion(float l_Dg, float l_dt, curandState *l_state)
{
if (l_Dg != 0.0f) {
float r = curand_uniform(l_state);
float g = sqrtf(2.0f*l_Dg);
if ( r <= 1.0f/6.0f ) {
return -g*sqrtf(3.0f*l_dt);
} else if ( r > 1.0f/6.0f && r <= 1.0f/3.0f ) {
return g*sqrtf(3.0f*l_dt);
} else {
return 0.0f;
}
} else {
return 0.0f;
}
}
__global__ void init_noise(float *d_dx, int *d_pcd, int *d_dcd, int *d_dst, curandState *d_states)
//init noise
{
long idx = blockIdx.x * blockDim.x + threadIdx.x;
float l_dx;
curandState l_state;
//cache model parameters in local variables
l_state = d_states[idx];
float l_Dp, l_lambda, l_mean, l_fa, l_fb, l_mua, l_mub;
int l_comp;
l_Dp = d_Dp;
l_lambda = d_lambda;
l_comp = d_comp;
l_mean = d_mean;
l_fa = d_fa;
l_fb = d_fb;
l_mua = d_mua;
l_mub = d_mub;
long ridx = (idx/d_paths) % d_points;
l_dx = d_dx[ridx];
switch(d_domainx) {
case 'p':
l_Dp = l_dx;
if (l_mean != 0.0f) l_lambda = (l_mean*l_mean)/l_Dp;
break;
case 'l':
l_lambda = l_dx;
if (l_mean != 0.0f) l_Dp = (l_mean*l_mean)/l_lambda;
break;
case 'a':
l_fa = l_dx;
if (l_comp == 1) {
l_fb = -l_fa*l_mub/l_mua;
} else if (l_mean != 0.0f) {
l_fb = (l_mean*(l_mua + l_mub) - l_fa*l_mub)/l_mua;
}
break;
case 'b':
l_fb = l_dx;
if (l_comp == 1) {
l_fa = -l_fb*l_mua/l_mub;
} else if (l_mean != 0.0f) {
l_fa = (l_mean*(l_mua + l_mub) - l_fb*l_mua)/l_mub;
}
break;
case 'm':
l_mua = l_dx;
if (l_comp == 1) {
l_mub = -l_fb*l_mua/l_fa;
} else if (l_mean != 0.0f) {
l_mub = (l_fb - l_mean)*l_mua/(l_mean - l_fa);
}
break;
case 'n':
l_mub = l_dx;
if (l_comp == 1) {
l_mua = -l_fa*l_mub/l_fb;
} else if (l_mean != 0.0f) {
l_mua = (l_fa - l_mean)*l_mub/(l_mean - l_fb);
}
break;
}
//step size
float l_dt;
int l_spp;
l_spp = d_spp;
if (l_lambda != 0.0f) {
l_dt = 1.0f/l_lambda/l_spp;
}
if (l_mua != 0.0f) {
float taua, taub;
taua = 1.0f/l_mua;
taub = 1.0f/l_mub;
if (taua < taub) {
l_dt = taua/l_spp;
} else {
l_dt = taub/l_spp;
}
}
//jump countdowns
int l_pcd, l_dcd, l_dst;
if (l_lambda != 0.0f) l_pcd = (int) floorf( -logf( curand_uniform(&l_state) )/l_lambda/l_dt + 0.5f );
if (l_mua != 0.0f) {
float rn;
rn = curand_uniform(&l_state);
if (rn < 0.5f) {
l_dst = 0;
l_dcd = (int) floorf( -logf( curand_uniform(&l_state) )/l_mua/l_dt + 0.5f);
} else {
l_dst = 1;
l_dcd = (int) floorf( -logf( curand_uniform(&l_state) )/l_mub/l_dt + 0.5f);
}
}
//write back noise states to the global memory
d_pcd[idx] = l_pcd;
d_dcd[idx] = l_dcd;
d_dst[idx] = l_dst;
d_states[idx] = l_state;
}
__device__ float adapted_jump_poisson(int &npcd, int pcd, float l_lambda, float l_Dp, int l_comp, float l_dt, curandState *l_state)
{
if (l_lambda != 0.0f) {
if (pcd <= 0) {
float ampmean = sqrtf(l_lambda/l_Dp);
npcd = (int) floorf( -logf( curand_uniform(l_state) )/l_lambda/l_dt + 0.5f );
if (l_comp) {
float comp = sqrtf(l_Dp*l_lambda)*l_dt;
return -logf( curand_uniform(l_state) )/ampmean - comp;
} else {
return -logf( curand_uniform(l_state) )/ampmean;
}
} else {
npcd = pcd - 1;
if (l_comp) {
float comp = sqrtf(l_Dp*l_lambda)*l_dt;
return -comp;
} else {
return 0.0f;
}
}
} else {
return 0.0f;
}
}
__device__ float adapted_jump_dich(int &ndcd, int dcd, int &ndst, int dst, float l_fa, float l_fb, float l_mua, float l_mub, float l_dt, curandState *l_state)
{
if (l_mua != 0.0f) {
if (dcd <= 0) {
if (dst == 0) {
ndst = 1;
ndcd = (int) floorf( -logf( curand_uniform(l_state) )/l_mub/l_dt + 0.5f );
return l_fb*l_dt;
} else {
ndst = 0;
ndcd = (int) floorf( -logf( curand_uniform(l_state) )/l_mua/l_dt + 0.5f );
return l_fa*l_dt;
}
} else {
ndcd = dcd - 1;
if (dst == 0) {
return l_fa*l_dt;
} else {
return l_fb*l_dt;
}
}
} else {
return 0.0f;
}
}
__device__ void predcorr(float &corrl_x, float l_x, int &npcd, int pcd, curandState *l_state, \
float l_Dg, float l_Dp, float l_lambda, int l_comp, \
int &ndcd, int dcd, int &ndst, int dst, float l_fa, float l_fb, float l_mua, float l_mub, float l_dt)
/* simplified weak order 2.0 adapted predictor-corrector scheme
( see E. Platen, N. Bruti-Liberati; Numerical Solution of Stochastic Differential Equations with Jumps in Finance; Springer 2010; p. 503, p. 532 )
*/
{
float l_xt, l_xtt, predl_x;
l_xt = drift(l_x);
predl_x = l_x + l_xt*l_dt + diffusion(l_Dg, l_dt, l_state);
l_xtt = drift(predl_x);
predl_x = l_x + 0.5f*(l_xt + l_xtt)*l_dt + diffusion(l_Dg, l_dt, l_state);
l_xtt = drift(predl_x);
corrl_x = l_x + 0.5f*(l_xt + l_xtt)*l_dt + adapted_jump_dich(ndcd, dcd, ndst, dst, l_fa, l_fb, l_mua, l_mub, l_dt, l_state) + diffusion(l_Dg, l_dt, l_state) + adapted_jump_poisson(npcd, pcd, l_lambda, l_Dp, l_comp, l_dt, l_state);
}
__global__ void fold(float *d_x, float *d_fx)
{
long idx = blockIdx.x * blockDim.x + threadIdx.x;
float l_x, l_fx, f;
l_x = d_x[idx];
l_fx = d_fx[idx];
f = floorf(l_x/2.0f)*2.0f;
l_x = l_x - f;
l_fx = l_fx + f;
d_x[idx] = l_x;
d_fx[idx] = l_fx;
}
void unfold(float *x, float *fx)
{
int i;
for (i = 0; i < h_threads; i++) {
x[i] = x[i] + fx[i];
}
}
__global__ void run_moments(float *d_x, float *d_dx, int *d_pcd, int *d_dcd, int *d_dst, curandState *d_states)
//actual moments kernel
{
long idx = blockIdx.x * blockDim.x + threadIdx.x;
float l_x, l_dx;
curandState l_state;
//cache path and model parameters in local variables
l_x = d_x[idx];
l_state = d_states[idx];
float l_Dg, l_Dp, l_lambda, l_mean, l_fa, l_fb, l_mua, l_mub;
int l_comp;
l_Dg = d_Dg;
l_Dp = d_Dp;
l_lambda = d_lambda;
l_comp = d_comp;
l_mean = d_mean;
l_fa = d_fa;
l_fb = d_fb;
l_mua = d_mua;
l_mub = d_mub;
//run simulation for multiple values of the system parameters
long ridx = (idx/d_paths) % d_points;
l_dx = d_dx[ridx];
switch(d_domainx) {
case 'D':
l_Dg = l_dx;
break;
case 'p':
l_Dp = l_dx;
if (l_mean != 0.0f) l_lambda = (l_mean*l_mean)/l_Dp;
break;
case 'l':
l_lambda = l_dx;
if (l_mean != 0.0f) l_Dp = (l_mean*l_mean)/l_lambda;
break;
case 'a':
l_fa = l_dx;
if (l_comp == 1) {
l_fb = -l_fa*l_mub/l_mua;
} else if (l_mean != 0.0f) {
l_fb = (l_mean*(l_mua + l_mub) - l_fa*l_mub)/l_mua;
}
break;
case 'b':
l_fb = l_dx;
if (l_comp == 1) {
l_fa = -l_fb*l_mua/l_mub;
} else if (l_mean != 0.0f) {
l_fa = (l_mean*(l_mua + l_mub) - l_fb*l_mua)/l_mub;
}
break;
case 'm':
l_mua = l_dx;
if (l_comp == 1) {
l_mub = -l_fb*l_mua/l_fa;
} else if (l_mean != 0.0f) {
l_mub = (l_fb - l_mean)*l_mua/(l_mean - l_fa);
}
break;
case 'n':
l_mub = l_dx;
if (l_comp == 1) {
l_mua = -l_fa*l_mub/l_fb;
} else if (l_mean != 0.0f) {
l_mua = (l_fa - l_mean)*l_mub/(l_mean - l_fb);
}
break;
}
//step size & number of steps
float l_dt;
int i, l_spp, l_samples;
l_spp = d_spp;
if (l_lambda != 0.0f) {
l_dt = 1.0f/l_lambda/l_spp;
}
if (l_mua != 0.0f) {
float taua, taub;
taua = 1.0f/l_mua;
taub = 1.0f/l_mub;
if (taua < taub) {
l_dt = taua/l_spp;
} else {
l_dt = taub/l_spp;
}
}
l_samples = d_samples;
//jump countdowns
int l_pcd, l_dcd, l_dst;
l_pcd = d_pcd[idx];
l_dcd = d_dcd[idx];
l_dst = d_dst[idx];
for (i = 0; i < l_samples; i++) {
predcorr(l_x, l_x, l_pcd, l_pcd, &l_state, l_Dg, l_Dp, l_lambda, l_comp, \
l_dcd, l_dcd, l_dst, l_dst, l_fa, l_fb, l_mua, l_mub, l_dt);
}
//write back path parameters to the global memory
d_x[idx] = l_x;
d_pcd[idx] = l_pcd;
d_dcd[idx] = l_dcd;
d_dst[idx] = l_dst;
d_states[idx] = l_state;
}
void prepare()
//prepare simulation
{
//grid size
h_paths = (h_paths/h_block)*h_block;
h_threads = h_paths;
if (h_moments) h_threads *= h_points;
h_grid = h_threads/h_block;
//number of steps
if (h_moments) h_steps = h_periods*h_spp;
//host memory allocation
size_f = h_threads*sizeof(float);
size_i = h_threads*sizeof(int);
size_ui = h_threads*sizeof(unsigned int);
size_p = h_points*sizeof(float);
h_x = (float*)malloc(size_f);
h_fx = (float*)malloc(size_f);
h_seeds = (unsigned int*)malloc(size_ui);
//create & initialize host rng
curandCreateGeneratorHost(&gen, CURAND_RNG_PSEUDO_DEFAULT);
curandSetPseudoRandomGeneratorSeed(gen, time(NULL));
curandGenerate(gen, h_seeds, h_threads);
//device memory allocation
cudaMalloc((void**)&d_x, size_f);
cudaMalloc((void**)&d_fx, size_f);
cudaMalloc((void**)&d_seeds, size_ui);
cudaMalloc((void**)&d_pcd, size_i);
cudaMalloc((void**)&d_dcd, size_i);
cudaMalloc((void**)&d_dst, size_i);
cudaMalloc((void**)&d_states, h_threads*sizeof(curandState));
//copy seeds from host to device
cudaMemcpy(d_seeds, h_seeds, size_ui, cudaMemcpyHostToDevice);
//initialization of device rng
init_dev_rng<<<h_grid, h_block>>>(d_seeds, d_states);
free(h_seeds);
cudaFree(d_seeds);
//moments specific requirements
if (h_moments) {
h_trigger = h_steps*h_trans;
h_xb = (float*)malloc(size_f);
h_dx = (float*)malloc(size_p);
float dxtmp = h_beginx;
float dxstep = (h_endx - h_beginx)/h_points;
int i;
//set domainx
for (i = 0; i < h_points; i++) {
if (h_logx) {
h_dx[i] = exp10f(dxtmp);
} else {
h_dx[i] = dxtmp;
}
dxtmp += dxstep;
}
cudaMalloc((void**)&d_dx, size_p);
cudaMemcpy(d_dx, h_dx, size_p, cudaMemcpyHostToDevice);
}
}
void copy_to_dev()
{
cudaMemcpy(d_x, h_x, size_f, cudaMemcpyHostToDevice);
cudaMemcpy(d_fx, h_fx, size_f, cudaMemcpyHostToDevice);
}
void copy_from_dev()
{
cudaMemcpy(h_x, d_x, size_f, cudaMemcpyDeviceToHost);
cudaMemcpy(h_fx, d_fx, size_f, cudaMemcpyDeviceToHost);
}
void initial_conditions()
//set initial conditions for path parameters
{
int i;
curandGenerateUniform(gen, h_x, h_threads); //x in (0,1]
for (i = 0; i < h_threads; i++) {
h_x[i] = 2.0f*h_x[i] - 1.0f; //x in (-1,1]
}
memset(h_fx, 0.0f, size_f);
copy_to_dev();
}
void moments(float *av)
//calculate the first moment of v
{
float sx, sxb, tmp, taua, taub, dt;
int i, j;
copy_from_dev();
unfold(h_x, h_fx);
for (j = 0; j < h_points; j++) {
sx = 0.0f;
sxb = 0.0f;
for (i = 0; i < h_paths; i++) {
sx += h_x[j*h_paths + i];
sxb += h_xb[j*h_paths + i];
}
//Poissonian
if (h_domainx == 'l') {
dt = 1.0f/h_dx[j]/h_spp;
} else if (h_domainx == 'p' && h_mean != 0.0f) {
dt = 1.0f/(h_mean*h_mean/h_dx[j])/h_spp;
} else if (h_lambda != 0.0f) {
dt = 1.0f/h_lambda/h_spp;
}
//Dichotomous
if (h_domainx == 'm') {
taua = 1.0f/h_dx[j];
taub = 1.0f/h_mub;
if (h_comp) {
tmp = 1.0f/(-h_fb*h_dx[j]/h_fa);
} else if (h_mean != 0.0f) {
tmp = 1.0f/((h_fb - h_mean)*h_dx[j]/(h_mean - h_fa));
} else {
tmp = taub;
}
if (taua <= tmp) {
dt = taua/h_spp;
} else {
dt = tmp/h_spp;
}
} else if (h_domainx == 'n') {
taua = 1.0f/h_mua;
taub = 1.0f/h_dx[j];
if (h_comp) {
tmp = 1.0f/(-h_fa*h_dx[j]/h_fb);
} else if (h_mean != 0.0f) {
tmp = 1.0f/((h_fa - h_mean)*h_dx[j]/(h_mean - h_fb));
} else {
tmp = taua;
}
if (taub <= tmp) {
dt = taub/h_spp;
} else {
dt = tmp/h_spp;
}
} else if (h_mua != 0.0f || h_mub != 0.0f) {
taua = 1.0f/h_mua;
taub = 1.0f/h_mub;
if (taua < taub) {
dt = taua/h_spp;
} else {
dt = taub/h_spp;
}
}
av[j] = (sx - sxb)/( (1.0f - h_trans)*h_steps*dt )/h_paths;
}
}
void finish()
//free memory
{
free(h_x);
free(h_fx);
curandDestroyGenerator(gen);
cudaFree(d_x);
cudaFree(d_fx);
cudaFree(d_pcd);
cudaFree(d_dcd);
cudaFree(d_dst);
cudaFree(d_states);
if (h_moments) {
free(h_xb);
free(h_dx);
cudaFree(d_dx);
}
}
int main(int argc, char **argv)
{
parse_cla(argc, argv);
if (!h_moments) {
usage(argv);
return -1;
}
prepare();
initial_conditions();
//asymptotic long time average velocity <<v>>
if (h_moments) {
float *av;
int i;
av = (float*)malloc(size_p);
if ( !strcmp(h_domain, "1d") ) {
init_noise<<<h_grid, h_block>>>(d_dx, d_pcd, d_dcd, d_dst, d_states);
for (i = 0; i < h_steps; i += h_samples) {
run_moments<<<h_grid, h_block>>>(d_x, d_dx, d_pcd, d_dcd, d_dst, d_states);
fold<<<h_grid, h_block>>>(d_x, d_fx);
if (i == h_trigger) {
cudaMemcpy(h_xb, d_x, size_f, cudaMemcpyDeviceToHost);
cudaMemcpy(h_fx, d_fx, size_f, cudaMemcpyDeviceToHost);
unfold(h_xb, h_fx);
}
}
moments(av);
printf("#%c <<v>>\n", h_domainx);
for (i = 0; i < h_points; i++) {
printf("%e %e\n", h_dx[i], av[i]);
}
}
free(av);
}
finish();
return 0;
}
|
7,066 | #pragma once
#include <algorithm>
#include <cmath>
#include "Vector3.cuh.cu"
#include "Ray.cuh.cu"
#include <iostream>
namespace RayTracing
{
class aabb
{
private:
Point3 m_min, m_max;
public:
aabb() {}
aabb(const Point3& min, const Point3& max)
: m_min(min), m_max(max)
{}
Point3 min() const { return m_min; }
Point3 max() const { return m_max; }
__host__ __device__
bool Hit(const Ray& r, double t_min, double t_max) const
{
for (int a = 0; a < 3; a++)
{
auto t0 = fminf((m_min[a] - r.origin[a]) / r.direction[a],
(m_max[a] - r.origin[a]) / r.direction[a]);
auto t1 = fmaxf((m_min[a] - r.origin[a]) / r.direction[a],
(m_max[a] - r.origin[a]) / r.direction[a]);
t_min = fmaxf(t0, t_min);
t_max = fminf(t1, t_max);
if (t_max < t_min)
return false;
}
return true;
}
static aabb SurroundingBox(const aabb &a, const aabb &b)
{
return aabb{
Point3{
std::min(a.m_min.d.x, b.m_min.d.x),
std::min(a.m_min.d.y, b.m_min.d.y),
std::min(a.m_min.d.z, b.m_min.d.z),
},
Point3{
std::max(a.m_max.d.x, b.m_max.d.x),
std::max(a.m_max.d.y, b.m_max.d.y),
std::max(a.m_max.d.z, b.m_max.d.z),
}
};
}
static bool Compare(const aabb& left, const aabb& right, int axis)
{
return left.m_min[axis] < right.m_min[axis];
}
};
} // RayTracing
|
7,067 | #include "includes.h"
__global__ void gpu_rBRIEF_Loop(int N, float4* patches, int4* pattern)
{
// // 1) Shared memory management
// extern __shared__ float4 shared[];
// int4* sharedPattern = (int4*) shared;
// float4* sharedPatches0 = (float4*) &shared[256];
// float4* sharedPatches1 = (float4*) &shared[N*blockDim.x*24 + 256];
// float4* thisPatches;
// float4* nextPatches;
// float4* tmp;
//
// // 2) Load pattern into shared memory (static part of kernel)
// int id = threadIdx.x;
// int stride = blockDim.x;
// for (int i = id; i < 256; i+= stride) {
// sharedPattern[i] = pattern[i];
// }
//
// // 3) Preload patches 0 into shared memory
// int start = blockIdx.x * (N*24) + id;
// int end = blockIdx.x * (N*24) + N*24;
// for (int i = start; i < end; i+=stride) {
// sharedPatches0[i] = patches[i];
// }
// thisPatches = sharedPatches0;
// Kernel Loop begin:
//for (int i = blockIdx.x; i < (P - 1) * N * blockDim.x*24; i+= )
}; |
7,068 | #include "math.h"
#include "float.h"
__device__ void normalize_vector(float *a, float *b, float *c)
{
float len;
float ta, tb, tc;
ta = *a;
tb = *b;
tc = *c;
len = ta*ta + tb*tb + tc*tc;
if(len == 0.0)
return;
len = sqrt(len);
*a = ta/len;
*b = tb/len;
*c = tc/len;
}
__device__ void intersectGPU(float *ray_array, float *scene, float *output, int *hit_obj_index, int scene_size)
{
/*
input: ray_array, scene, scene_size
output: [hit_point v3, hit_normal v3]
*/
int i;
int scene_index;
int flag;
int current_obj_i = -1;
float l1, l2, l3;
float t_ca;
float d_squared;
float radius_squared;
float t_hc;
float t;
float hit_point1;
float hit_point2;
float hit_point3;
float hit_normal1;
float hit_normal2;
float hit_normal3;
float current_t = FLT_MAX;
for(i = 0; i < scene_size; i++){
flag = 0;
scene_index = i*16;
if (scene[scene_index] < 0.5){ /* means sphere */
/* calculate intersect */
l1 = scene[scene_index+1] - ray_array[0];
l2 = scene[scene_index+2] - ray_array[1];
l3 = scene[scene_index+3] - ray_array[2];
t_ca = l1*ray_array[3] +
l2*ray_array[4] +
l3*ray_array[5];
if(t_ca < 0)
flag = 1;
d_squared = l1*l1 + l2*l2 +l3*l3 - t_ca*t_ca;
radius_squared = scene[scene_index+4]*scene[scene_index+4];
if(d_squared > radius_squared)
flag = 1;
t_hc = sqrt(radius_squared - d_squared);
t = t_ca - t_hc;
if(t < 0)
t = t_ca + t_hc;
hit_point1 = ray_array[0] + t * ray_array[3];
hit_point2 = ray_array[1] + t * ray_array[4];
hit_point3 = ray_array[2] + t * ray_array[5];
hit_normal1 = hit_point1 - scene[scene_index+1];
hit_normal2 = hit_point2 - scene[scene_index+2];
hit_normal3 = hit_point3 - scene[scene_index+3];
normalize_vector(&hit_normal1, &hit_normal2, &hit_normal3);
}
if(flag == 0){
if(t < current_t){
current_t = t;
current_obj_i = i;
output[0] = hit_point1;
output[1] = hit_point2;
output[2] = hit_point3;
output[3] = hit_normal1;
output[4] = hit_normal2;
output[5] = hit_normal3;
}
}
}
*hit_obj_index = current_obj_i;
}
__device__ void trace_non_diffuseGPU (float *ray, int hit_obj_index, float *hit_point, float *hit_normal,
float *scene, int scene_size, int depth, int max_depth, float *output);
__device__ void trace_diffuseGPU(int obj_index, float *hit_point, float *hit_normal, float *scene, int scene_size, float *output);
__device__ void trace_recursivelyGPU(float *ray, float *scene, int scene_size, float *output, int depth, int max_depth){
/*
hit_object, hit_point, hit_normal = self.__intersect(ray)
if hit_object is None:
return Vector3(0.3, 0.3, 0.3) # horizon
traced_color = Vector3()
if not hit_object.material.is_diffuse and depth < self.__max_recursion_depth:
traced_color = self.__trace_non_diffuse(ray, hit_object, hit_point, hit_normal, depth)
else:
traced_color = self.__trace_diffuse(hit_object, hit_point, hit_normal)
return traced_color + hit_object.material.emission_color
*/
int hit_obj_index;
float output_intersect[6];
intersectGPU(ray, scene, output_intersect, &hit_obj_index, scene_size);
if (hit_obj_index < 0) {
output[0] = 0.3;
output[1] = 0.3;
output[2] = 0.3;
return;
}
float traced_color[3];
if (depth < max_depth && scene[16*hit_obj_index+14] < 0.5)
/* trace_non_diffuseGPU (ray, hit_obj_index, output_intersect, output_intersect + 3,
scene, scene_size, depth, max_depth, traced_color);*/
trace_diffuseGPU (hit_obj_index, output_intersect, output_intersect + 3, scene, scene_size, traced_color);
else
trace_diffuseGPU (hit_obj_index, output_intersect, output_intersect + 3, scene, scene_size, traced_color);
traced_color[0] += scene[16*hit_obj_index + 8];
traced_color[1] += scene[16*hit_obj_index + 9];
traced_color[2] += scene[16*hit_obj_index + 10];
output[0] = traced_color[0];
output[1] = traced_color[1];
output[2] = traced_color[2];
}
__device__ void trace_diffuseGPU(int obj_index, float *hit_point, float *hit_normal, float *scene, int scene_size, float *output){
/*
summed_color = Vector3()
for light in filter(lambda obj: obj.is_light, self.__scene):
transmission = Vector3(1, 1, 1)
light_direction = (light.primitive.position - hit_point).normalize()
for other in filter(lambda obj: obj != light, self.__scene):
if other.primitive.intersect(Ray(hit_point + self.__bias * hit_normal,
light_direction)):
transmission = Vector3()
break
summed_color = summed_color + (
hit_object.material.surface_color
.mul_comp(transmission)
.mul_comp(light.material.emission_color) *
max(0, hit_normal.dot(light_direction)))
return summed_color
*/
int i;
float summed_color[3] = {0, 0, 0};
float transmission[3] = {1, 1, 1};
int hit_obj_index;
float output_intersect[6];
float current_ray[7];
float temp;
for(i = 0; i < scene_size; i++){
if(scene[i*16 + 15] > 0.5){ /* is light */
current_ray[0] = hit_point[0] + 0.0001*hit_normal[0];
current_ray[1] = hit_point[1] + 0.0001*hit_normal[1];
current_ray[2] = hit_point[2] + 0.0001*hit_normal[2];
current_ray[3] = scene[i*16 + 1] - hit_point[0];
current_ray[4] = scene[i*16 + 2] - hit_point[1];
current_ray[5] = scene[i*16 + 3] - hit_point[2];
current_ray[6] = 1.0;
normalize_vector(current_ray+3, current_ray+4, current_ray+5);
for(i = 0; i < scene_size; i++){
if(scene[i*16 + 15] < 0.5)
intersectGPU(current_ray, scene+i, output_intersect, &hit_obj_index, 1);
if(hit_obj_index < 0){
transmission[0] = 0;
transmission[1] = 0;
transmission[2] = 0;
break;
}
}
temp = hit_normal[0]*current_ray[3] + hit_normal[1]*current_ray[4] + hit_normal[2]*current_ray[5];
temp = 0 > temp ? 0 : temp;
summed_color[0] += scene[obj_index*16 + 5] * transmission[0] * scene[i*16 + 8] * temp;
summed_color[1] += scene[obj_index*16 + 6] * transmission[1] * scene[i*16 + 9] * temp;
summed_color[2] += scene[obj_index*16 + 7] * transmission[2] * scene[i*16 + 10] * temp;
}
}
output[0] = summed_color[0];
output[1] = summed_color[1];
output[2] = summed_color[2];
}
__device__ void trace_non_diffuseGPU (float *ray, int hit_obj_index, float *hit_point, float *hit_normal,
float *scene, int scene_size, int depth, int max_depth, float *output)
{
/*
inside = ray.direction.dot(hit_normal) > 0
if inside:
hit_normal = -hit_normal
facing_ratio = -ray.direction.dot(hit_normal)
fresnel = self.__mix((1 - facing_ratio) ** 2, 1, 0.1)
reflection_ray = Ray(hit_point + self.__bias * hit_normal,
ray.direction.reflect(hit_normal).normalize())
reflection = self.__trace_recursively(reflection_ray, depth + 1)
refraction = Vector3()
# transparent?
if hit_object.material.transparency > 0:
from_ior = ray.current_ior if inside else hit_object.material.ior
to_ior = hit_object.material.ior if inside else ray.current_ior
refraction_ray = Ray(hit_point - self.__bias * hit_normal,
ray.direction.refract(from_ior, to_ior, hit_normal)
.normalize())
refraction = self.__trace_recursively(refraction_ray, depth + 1)
# mix according to fresnel
return ((reflection * fresnel +
refraction * (1 - fresnel) * hit_object.material.transparency)
.mul_comp(hit_object.material.surface_color))
*/
if (ray[3]*hit_normal[0] + ray[4]*hit_normal[1] + ray[5]*hit_normal[2] > 0){
hit_normal[0] *= -1;
hit_normal[1] *= -1;
hit_normal[2] *= -1;
}
float facing_ratio = - (ray[3]*hit_normal[0] + ray[4]*hit_normal[1] + ray[5]*hit_normal[2]);
float fresnel = (1 - facing_ratio) * (1 - facing_ratio) * 0.9 + 0.1;
float reflection_ray[7];
reflection_ray[0] = hit_point[0] - 0.0001*hit_normal[0];
reflection_ray[1] = hit_point[1] - 0.0001*hit_normal[1];
reflection_ray[2] = hit_point[2] - 0.0001*hit_normal[2];
float temp;
temp = ray[0]*hit_normal[0] + ray[1]*hit_normal[1] + ray[2]*hit_normal[2];
temp *= 2;
reflection_ray[3] = ray[0] - temp * hit_normal[0];
reflection_ray[4] = ray[1] - temp * hit_normal[1];
reflection_ray[5] = ray[2] - temp * hit_normal[2];
normalize_vector(reflection_ray+3, reflection_ray+4, reflection_ray+5);
reflection_ray[6] = 1.0;
float reflection[3];
float refraction[3] = {0, 0, 0};
trace_recursivelyGPU(reflection_ray, scene, scene_size, reflection, depth+1, max_depth);
/*
def refract(self, from_ior, to_ior, normal):
# Refracts the vector with regard to material change and normal
eta = to_ior / from_ior
cos_i = -normal.dot(self)
k = 1 - eta ** 2 * (1 - cos_i ** 2)
return self * eta + normal * (eta * cos_i - math.sqrt(k))
*/
if (scene[hit_obj_index*16 + 12] > 0){
float from_ior, to_ior;
if (ray[3]*hit_normal[0] + ray[4]*hit_normal[1] + ray[5]*hit_normal[2] > 0) {
from_ior = ray[6];
to_ior = scene[hit_obj_index*16 + 13];
}
else {
from_ior = scene[hit_obj_index*16 + 13];
to_ior = ray[6];
}
float refraction_ray[7];
refraction_ray[0] = hit_point[0] - 0.0001*hit_normal[0];
refraction_ray[1] = hit_point[1] - 0.0001*hit_normal[1];
refraction_ray[2] = hit_point[2] - 0.0001*hit_normal[2];
float eta = to_ior / from_ior;
float cos_i = -(ray[3]*hit_normal[0] + ray[4]*hit_normal[1] + ray[5]*hit_normal[2]);
float k = 1 - eta*eta * (1 - cos_i*cos_i);
temp = eta*cos_i - sqrt(k);
refraction_ray[3] = ray[3] * eta + hit_normal[0] * temp;
refraction_ray[4] = ray[4] * eta + hit_normal[1] * temp;
refraction_ray[5] = ray[5] * eta + hit_normal[2] * temp;
normalize_vector(refraction_ray+3, refraction_ray+4, refraction_ray+5);
refraction_ray[6] = 1;
trace_recursivelyGPU(refraction_ray, scene, scene_size, refraction, depth+1, max_depth);
}
output[0] = (reflection[0] * fresnel + refraction[0] * (1-fresnel) * scene[hit_obj_index*16 + 12]) * scene[hit_obj_index*16 + 5];
output[1] = (reflection[1] * fresnel + refraction[1] * (1-fresnel) * scene[hit_obj_index*16 + 12]) * scene[hit_obj_index*16 + 6];
output[2] = (reflection[2] * fresnel + refraction[2] * (1-fresnel) * scene[hit_obj_index*16 + 12]) * scene[hit_obj_index*16 + 7];
}
__global__ void traceGPU(float *ray_array, float *scene, int width, int scene_size, float *output, int max_depth){
int tx;
int ray_array_index;
tx = blockIdx.x*blockDim.x + threadIdx.x;
if (tx >= width)
return;
ray_array_index = tx;
int hit_obj_index;
float output_c[3];
trace_recursivelyGPU(ray_array + ray_array_index*7, scene, scene_size, output_c, 0, max_depth);
output[0] = output_c[0];
output[1] = output_c[1];
output[2] = output_c[2];
}
|
7,069 |
#include "random.hpp"
namespace util {
namespace random {
std::random_device device; // Note this is a callable object.
std::default_random_engine engine(device());
} // namespace random
} // namespace util
|
7,070 | #include <stdio.h>
#include <stdlib.h>
#include <fcntl.h>
#include "string.h"
#include <sys/time.h>
#include <time.h>
#define DEFAULT_THRESHOLD 4000
#define DEFAULT_FILENAME "ansel3.ppm"
unsigned int* read_ppm(char* filename, int* image_width, int* image_height, int* maxval);
void write_ppm(char* filename, int image_width, int image_height, int maxval, int* pic);
__global__ void sobel(int* input_image, int* output_image, int image_width, int image_height, int thresh);
int main( int argc, char **argv )
{
int thresh = DEFAULT_THRESHOLD;
char *filename;
filename = strdup( DEFAULT_FILENAME);
if (argc > 1)
{
if (argc == 3)
{ // filename AND threshold
filename = strdup( argv[1]);
thresh = atoi( argv[2] );
}
if (argc == 2)
{ // default file but specified threshhold
thresh = atoi( argv[1] );
}
fprintf(stderr, "file %s threshold %d\n", filename, thresh);
}
int image_width, image_height, maxval;
unsigned int *host_input_image = read_ppm( filename, &image_width, &image_height, &maxval );
int numbytes = image_width * image_height * sizeof(int);
int *host_result = (int *) malloc(numbytes);
if (!host_result)
{
fprintf(stderr, "sobel() unable to malloc %d bytes\n", numbytes);
exit(-1); // fail
}
int* device_input_image;
int* device_result;
cudaMalloc(&device_input_image, numbytes);
cudaMalloc(&device_result, numbytes);
cudaMemcpy(device_input_image, host_input_image, numbytes, cudaMemcpyHostToDevice);
dim3 threads_per_block(32, 32);
dim3 blocks_per_grid(ceil(image_width/32.0), ceil(image_height/32.0));
float elapsed_time;
cudaEvent_t start_event, stop_event;
cudaEventCreate(&start_event);
cudaEventCreate(&stop_event);
cudaEventRecord(start_event, 0);
sobel<<<blocks_per_grid, threads_per_block>>>(device_input_image, device_result, image_width, image_height, thresh);
cudaEventRecord(stop_event, 0);
cudaEventSynchronize(stop_event);
cudaEventElapsedTime(&elapsed_time, start_event, stop_event);
cudaMemcpy(host_result, device_result, numbytes, cudaMemcpyDeviceToHost);
cudaFree(device_input_image);
cudaFree(device_result);
write_ppm( "result.ppm", image_width, image_height, 255, host_result);
printf("Elapsed time: %f milliseconds\n", elapsed_time);
return 0;
}
unsigned int *read_ppm( char *filename, int * image_width, int * image_height, int *maxval ){
if ( !filename || filename[0] == '\0') {
fprintf(stderr, "read_ppm but no file name\n");
return NULL; // fail
}
FILE *fp;
fprintf(stderr, "read_ppm( %s )\n", filename);
fp = fopen( filename, "rb");
if (!fp)
{
fprintf(stderr, "read_ppm() ERROR file '%s' cannot be opened for reading\n", filename);
return NULL; // fail
}
char chars[1024];
//int num = read(fd, chars, 1000);
int num = fread(chars, sizeof(char), 1000, fp);
if (chars[0] != 'P' || chars[1] != '6')
{
fprintf(stderr, "Texture::Texture() ERROR file '%s' does not start with \"P6\" I am expecting a binary PPM file\n", filename);
return NULL;
}
unsigned int width, height, maxvalue;
char *ptr = chars+3; // P 6 newline
if (*ptr == '#') // comment line!
{
ptr = 1 + strstr(ptr, "\n");
}
num = sscanf(ptr, "%d\n%d\n%d", &width, &height, &maxvalue);
fprintf(stderr, "read %d things width %d height %d maxval %d\n", num, width, height, maxvalue);
*image_width = width;
*image_height = height;
*maxval = maxvalue;
unsigned int *pic = (unsigned int *)malloc( width * height * sizeof(unsigned int));
if (!pic) {
fprintf(stderr, "read_ppm() unable to allocate %d x %d unsigned ints for the picture\n", width, height);
return NULL; // fail but return
}
// allocate buffer to read the rest of the file into
int bufsize = 3 * width * height * sizeof(unsigned char);
if ((*maxval) > 255) bufsize *= 2;
unsigned char *buf = (unsigned char *)malloc( bufsize );
if (!buf) {
fprintf(stderr, "read_ppm() unable to allocate %d bytes of read buffer\n", bufsize);
return NULL; // fail but return
}
// TODO really read
char duh[80];
char *line = chars;
// find the start of the pixel data. no doubt stupid
sprintf(duh, "%d\0", *image_width);
line = strstr(line, duh);
//fprintf(stderr, "%s found at offset %d\n", duh, line-chars);
line += strlen(duh) + 1;
sprintf(duh, "%d\0", *image_height);
line = strstr(line, duh);
//fprintf(stderr, "%s found at offset %d\n", duh, line-chars);
line += strlen(duh) + 1;
sprintf(duh, "%d\0", *maxval);
line = strstr(line, duh);
fprintf(stderr, "%s found at offset %d\n", duh, line - chars);
line += strlen(duh) + 1;
long offset = line - chars;
//lseek(fd, offset, SEEK_SET); // move to the correct offset
fseek(fp, offset, SEEK_SET); // move to the correct offset
//long numread = read(fd, buf, bufsize);
long numread = fread(buf, sizeof(char), bufsize, fp);
fprintf(stderr, "Texture %s read %ld of %ld bytes\n", filename, numread, bufsize);
fclose(fp);
int pixels = (*image_width) * (*image_height);
int i;
for (i=0; i<pixels; i++) pic[i] = (int) buf[3*i]; // red channel
return pic; // success
}
void write_ppm( char *filename, int image_width, int image_height, int maxval, int *pic)
{
FILE *fp;
fp = fopen(filename, "w");
if (!fp)
{
fprintf(stderr, "FAILED TO OPEN FILE '%s' for writing\n");
exit(-1);
}
fprintf(fp, "P6\n");
fprintf(fp,"%d %d\n%d\n", image_width, image_height, maxval);
int numpix = image_width * image_height;
int i;
for (i=0; i<numpix; i++) {
unsigned char uc = (unsigned char) pic[i];
fprintf(fp, "%c%c%c", uc, uc, uc);
}
fclose(fp);
}
__global__ void sobel(int* input_image, int* output_image, int image_width, int image_height, int thresh)
{
int j = blockIdx.x * blockDim.x + threadIdx.x + 1;
int i = blockIdx.y * blockDim.y + threadIdx.y + 1;
if( (i < (image_height-1)) && (j < (image_width-1)) )
{
int sum1, sum2, magnitude;
int offset = i*image_width + j;
sum1 = input_image[ image_width * (i-1) + j+1 ] - input_image[ image_width*(i-1) + j-1 ]
+ 2 * input_image[ image_width * (i) + j+1 ] - 2 * input_image[ image_width*(i) + j-1 ]
+ input_image[ image_width * (i+1) + j+1 ] - input_image[ image_width*(i+1) + j-1 ];
sum2 = input_image[ image_width * (i-1) + j-1 ] + 2 * input_image[ image_width * (i-1) + j ] + input_image[ image_width * (i-1) + j+1 ]
- input_image[image_width * (i+1) + j-1 ] - 2 * input_image[ image_width * (i+1) + j ] - input_image[ image_width * (i+1) + j+1 ];
magnitude = sum1*sum1 + sum2*sum2;
if (magnitude > thresh)
output_image[offset] = 255;
else
output_image[offset] = 0;
}
}
|
7,071 |
__global__ void summup(double* h, double* e, int N)
{
int idx = blockIdx.x*blockDim.x + threadIdx.x;
if (idx<N)
h[idx] += e[idx];
// __syncthreads();
}
|
7,072 |
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <stdio.h>
#include <math.h>
#include <float.h>
#define IDX2C(i,j,rows) (((j)*(rows))+(i))
__global__ void matrixPlusVector(float* input, float* bias, float * output, int rows, int columns)
{
int i = blockDim.x * blockIdx.x + threadIdx.x;
int j = blockDim.y * blockIdx.y + threadIdx.y;
if (i < rows && j < columns)
{
int ij = IDX2C(i, j, rows);
output[ij] = input[ij] + bias[i];
}
}
__global__ void matrixTanh(float* input, float* output, int rows, int columns)
{
int i = blockDim.x * blockIdx.x + threadIdx.x;
int j = blockDim.y * blockIdx.y + threadIdx.y;
if (i < rows && j < columns)
{
int ij = IDX2C(i, j, rows);
output[ij] = tanh(input[ij]);
}
}
__global__ void matrixIncorporateTanhDeriv(float* base, float* activation, float* output, int rows, int columns)
{
int i = blockDim.x * blockIdx.x + threadIdx.x;
int j = blockDim.y * blockIdx.y + threadIdx.y;
if (i < rows && j < columns)
{
int ij = IDX2C(i, j, rows);
output[ij] = base[ij] * (1 + activation[ij])*(1 - activation[ij]);
}
}
__global__ void matrixReLu(float* input, float* output, int rows, int columns)
{
int i = blockDim.x * blockIdx.x + threadIdx.x;
int j = blockDim.y * blockIdx.y + threadIdx.y;
if (i < rows && j < columns)
{
int ij = IDX2C(i, j, rows);
output[ij] = fmaxf(input[ij], 0);
}
}
__global__ void matrixIncorporateReLuDeriv(float* base, float* activation, float* output, int rows, int columns)
{
int j = blockDim.x * blockIdx.x + threadIdx.x;
int i = blockDim.y * blockIdx.y + threadIdx.y;
if (i < rows && j < columns)
{
int ij = IDX2C(i, j, rows);
output[ij] = activation[ij] <= 0 ? 0 : base[ij];
}
}
__global__ void matrixSigmoid(float* input, float* output, int rows, int columns)
{
int i = blockDim.x * blockIdx.x + threadIdx.x;
int j = blockDim.y * blockIdx.y + threadIdx.y;
if (i < rows && j < columns)
{
int ij = IDX2C(i, j, rows);
// how to refactor the sigmoid calculation???
output[ij] = (tanhf((input[ij]) / 2) + 1) / 2.0f;
}
}
__global__ void matrixIncorporateSigmoidDeriv(float* base, float* activation, float* output, int rows, int columns)
{
int i = blockDim.x * blockIdx.x + threadIdx.x;
int j = blockDim.y * blockIdx.y + threadIdx.y;
if (i < rows && j < columns)
{
int ij = IDX2C(i, j, rows);
output[ij] = base[ij] * activation[ij] * (1 - activation[ij]);
}
}
__global__ void matrixCrossEntropyError(float* sigmoidScores, float* trueLabels, float* output, int rows, int columns)
{
int i = blockDim.x * blockIdx.x + threadIdx.x;
int j = blockDim.y * blockIdx.y + threadIdx.y;
if (i < rows && j < columns)
{
int ij = IDX2C(i, j, rows);
output[ij] = trueLabels[ij] > 0 ? logf(sigmoidScores[ij] + FLT_EPSILON) : logf(1 - sigmoidScores[ij] + FLT_EPSILON);
output[ij] *= -1;
}
}
__global__ void matrixBellmanErrorAndDeriv(float* predictedQValues, float* maxQHatValues, float* chosenActionIndices, float* currentRewards, float* error, float* errorDerivative,
float discount, float* isLastEpisode, int rows, int columns)
{
int i = blockDim.x * blockIdx.x + threadIdx.x;
int j = blockDim.y * blockIdx.y + threadIdx.y;
if (i < rows && j < columns)
{
int ij = IDX2C(i, j, rows);
float y = isLastEpisode[j] > 0 ? currentRewards[j] : currentRewards[j] + (discount*maxQHatValues[j]);
errorDerivative[ij] = 0;
// Calculating error and errorDerivative
if (i == chosenActionIndices[j])
{
float tmp = predictedQValues[i] - y;
errorDerivative[ij] = tmp;
error[j] = 0.5*tmp*tmp;
}
}
}
__global__ void DqnStanfordEvaluation(float* predictedactionIndices, float* chosenActionIndices, float* currentRewards, float* matchPredictRewards, float* nonMatchPredictRewards, int rows)
{
int i = blockDim.x * blockIdx.x + threadIdx.x;
if (i < rows)
{
if (predictedactionIndices[i] == chosenActionIndices[i])
{
matchPredictRewards[i] = currentRewards[i];
}
else
{
nonMatchPredictRewards[i] = currentRewards[i];
}
}
}
__global__ void matrixHadamard(float* input1, float* input2, float alpha, float* output, float beta, int rows, int columns)
{
int i = blockDim.x * blockIdx.x + threadIdx.x;
int j = blockDim.y * blockIdx.y + threadIdx.y;
if (i < rows && j < columns)
{
int ij = IDX2C(i, j, rows);
output[ij] = alpha*input1[ij] * input2[ij] + beta*output[ij];
}
}
__global__ void columnwiseMax(float* input, float* output, int rows, int columns)
{
int j = blockDim.x * blockIdx.x + threadIdx.x;
if (j < columns)
{
float maxInColumn = input[IDX2C(0, j, rows)];
for (int i = 0; i < rows; i++)
{
int ij = IDX2C(i, j, rows);
if (input[ij] > maxInColumn)
{
maxInColumn = input[ij];
}
}
output[j] = maxInColumn;
}
}
__global__ void columnwiseMaxIndex(float* input, float* output, int rows, int columns)
{
int j = blockDim.x * blockIdx.x + threadIdx.x;
if (j < columns)
{
int maxInColumnIndex = 0;
float maxInColumn = input[IDX2C(maxInColumnIndex, j, rows)];
for (int i = 0; i < rows; i++)
{
int ij = IDX2C(i, j, rows);
if (input[ij] > maxInColumn)
{
maxInColumn = input[ij];
maxInColumnIndex = i;
}
}
output[j] = (float)maxInColumnIndex;
}
}
int main()
{
return 0;
} |
7,073 | #include "includes.h"
__global__ void update(float* original, float* newTE, float* current, int nhalf) {
int i = threadIdx.x + blockDim.x*blockIdx.x;
if (i < nhalf) {
current[i] /= nhalf;
newTE[i] = (original[i] < current[i]) ? current[i] : original[i]; // LIKELY, THERE IS A PERFORMANCE LOSS HERE
}
} |
7,074 | #include<stdio.h>
__global__ void serial()
{
printf("%d\n",threadIdx.x);
}
int main()
{
serial<<<1,10>>>();
cudaDeviceSynchronize();
}
|
7,075 | /*
* This program uses the host CURAND API to generate 100
* pseudorandom floats.
*/
#include <stdio.h>
#include <stdlib.h>
#include <cuda.h>
#include <curand.h>
#define CUDA_CALL(x) do { if((x)!=cudaSuccess) { \
printf("Error at %s:%d\n",__FILE__,__LINE__);\
return EXIT_FAILURE;}} while(0)
#define CURAND_CALL(x) do { if((x)!=CURAND_STATUS_SUCCESS) { \
printf("Error at %s:%d\n",__FILE__,__LINE__);\
return EXIT_FAILURE;}} while(0)
int main(int argc, char *argv[])
{
size_t n = 100;
size_t i;
curandGenerator_t gen;
float *devData, *hostData;
/* Allocate n floats on host */
hostData = (float *)calloc(n, sizeof(float));
/* Allocate n floats on device */
CUDA_CALL(cudaMalloc((void **)&devData, n*sizeof(float)));
/* Create pseudo-random number generator */
CURAND_CALL(curandCreateGenerator(&gen,
CURAND_RNG_PSEUDO_DEFAULT));
/* Set seed */
CURAND_CALL(curandSetPseudoRandomGeneratorSeed(gen,
1234ULL));
/* Generate n floats on device */
CURAND_CALL(curandGenerateUniform(gen, devData, n));
/* Copy device memory to host */
CUDA_CALL(cudaMemcpy(hostData, devData, n * sizeof(float),
cudaMemcpyDeviceToHost));
/* Show result */
for(i = 0; i < n; i++) {
printf("%1.4f ", hostData[i]);
}
printf("\n");
/* Cleanup */
CURAND_CALL(curandDestroyGenerator(gen));
CUDA_CALL(cudaFree(devData));
free(hostData);
return EXIT_SUCCESS;
} |
7,076 | //
///**
// * Copyright 1993-2012 NVIDIA Corporation. All rights reserved.
// *
// * Please refer to the NVIDIA end user license agreement (EULA) associated
// * with this source code for terms and conditions that govern your use of
// * this software. Any use, reproduction, disclosure, or distribution of
// * this software and related documentation outside the terms of the EULA
// * is strictly prohibited.
// */
//#include <stdio.h>
//#include <stdlib.h>
//
//#include <cuda.h>
//
//static const int WORK_SIZE = 256;
//
///**
// * This macro checks return value of the CUDA runtime call and exits
// * the application if the call failed.
// *
// * See cuda.h for error code descriptions.
// */
//#define CHECK_CUDA_RESULT(N) { \
// CUresult result = N; \
// if (result != 0) { \
// printf("CUDA call on line %d returned error %d\n", __LINE__, \
// result); \
// exit(1); \
// } }
//
//int main(int argc, char **argv)
//{
// CUmodule module;
// CUcontext context;
// CUdevice device;
// CUdeviceptr deviceArray;
// CUfunction process;
//
// void *kernelArguments[] = { &deviceArray };
// int deviceCount;
// unsigned int idata[WORK_SIZE], odata[WORK_SIZE];
//
// for (int i = 0; i < WORK_SIZE; ++i) {
// idata[i] = i;
// }
//
// CHECK_CUDA_RESULT(cuInit(0));
// CHECK_CUDA_RESULT(cuDeviceGetCount(&deviceCount));
// if (deviceCount == 0) {
// printf("No CUDA-compatible devices found\n");
// exit(1);
// }
// CHECK_CUDA_RESULT(cuDeviceGet(&device, 0));
// CHECK_CUDA_RESULT(cuCtxCreate(&context, 0, device));
//
// CHECK_CUDA_RESULT(cuModuleLoad(&module, "bitreverse.fatbin"));
// CHECK_CUDA_RESULT(cuModuleGetFunction(&process, module, "bitreverse"));
//
// CHECK_CUDA_RESULT(cuMemAlloc(&deviceArray, sizeof(int) * WORK_SIZE));
// CHECK_CUDA_RESULT(
// cuMemcpyHtoD(deviceArray, idata, sizeof(int) * WORK_SIZE));
//
// CHECK_CUDA_RESULT(
// cuLaunchKernel(process, 1, 1, 1, WORK_SIZE, 1, 1, 0, NULL, kernelArguments, NULL));
//
// CHECK_CUDA_RESULT(
// cuMemcpyDtoH(odata, deviceArray, sizeof(int) * WORK_SIZE));
//
// for (int i = 0; i < WORK_SIZE; ++i) {
// printf("Input value: %u, output value: %u\n", idata[i], odata[i]);
// }
//
// CHECK_CUDA_RESULT(cuMemFree(deviceArray));
// CHECK_CUDA_RESULT(cuCtxDestroy(context));
//
// return 0;
//}
|
7,077 | /* $Id: print.c,v 1.1 2019/12/12 15:03:38 gbarbd Exp gbarbd $ */
#include <stdio.h>
#include <inttypes.h>
static int
is_little_endian(void) {
int num = 1;
return (*((char *)&num) == 1);
}
void
print_binary(char *fname, int num, double *u) {
FILE *f_ptr;
size_t written;
size_t items = num*num*num;
if ( (f_ptr = fopen(fname, "w")) == NULL ) {
perror("No output! fopen()");
return;
}
written = fwrite(u, sizeof(double), items, f_ptr);
if ( written != items ) {
fprintf(stderr, "Writing failed: only %lu of %lu items saved!\n",
written, items);
}
fclose(f_ptr);
}
void
print_vtk(const char *fname, int n, double *u) {
FILE *f_ptr;
size_t written;
size_t items = n * n * n;
size_t i;
int b;
unsigned char tmp;
if ( (f_ptr = fopen(fname, "w")) == NULL ) {
perror("No output! fopen()");
return;
}
// Write VTK file header
fprintf(f_ptr, "# vtk DataFile Version 3.0\n");
fprintf(f_ptr, "saved from function print_vtk.\n");
fprintf(f_ptr, "BINARY\n");
fprintf(f_ptr, "DATASET STRUCTURED_POINTS\n");
fprintf(f_ptr, "DIMENSIONS %d %d %d\n", n, n, n);
fprintf(f_ptr, "ORIGIN %d %d %d\n", 0, 0, 0);
fprintf(f_ptr, "SPACING %d %d %d\n", 1, 1, 1);
fprintf(f_ptr, "POINT_DATA %lu\n", items);
fprintf(f_ptr, "SCALARS %s %s 1\n", "gray", "double");
fprintf(f_ptr, "LOOKUP_TABLE default\n");
if ( is_little_endian() ) {
// System is little endian, so we need to reverse the byte order.
written = 0;
for (i = 0; i < items; ++i) {
uint64_t crnt = *(uint64_t *)(u + i); // Get double as int
// Reverse byte order and write to file
crnt = (crnt & 0x00000000FFFFFFFF) << 32 | (crnt & 0xFFFFFFFF00000000) >> 32;
crnt = (crnt & 0x0000FFFF0000FFFF) << 16 | (crnt & 0xFFFF0000FFFF0000) >> 16;
crnt = (crnt & 0x00FF00FF00FF00FF) << 8 | (crnt & 0xFF00FF00FF00FF00) >> 8;
written += fwrite(&crnt, sizeof(uint64_t), 1, f_ptr);
}
} else {
// System is big endian, so just dump the data.
written = fwrite(u, sizeof(double), items, f_ptr);
}
if ( written != items ) {
fprintf(stderr, "Writing failed: only %lu of %lu items saved!\n",
written, items);
}
fclose(f_ptr);
}
|
7,078 | #include <iostream>
#include <fstream>
#include <chrono>
__global__
void sumOne(int n,int *m,int *partialSum,int *sum){
int index = blockIdx.x * blockDim.x + threadIdx.x;
int stride = blockDim.x * gridDim.x;
int threadSum =0;
for (int i = index; i < n; i += stride){
if(m[i]==1){
threadSum++;
}
}
// Block Sum
atomicAdd(&partialSum[blockIdx.x],threadSum);
__syncthreads();
if(threadIdx.x==0){
// Global Sum;
atomicAdd(&sum[0],partialSum[blockIdx.x]);
}
}
int main(int argc,char **argv){
//Read input matrix
std::ifstream infile;
infile.open(argv[1]);
if (!infile.is_open()){
std::cerr << "Couldn't read " << argv[1] << std::endl;
return 0;
}
int w,h;
infile >> w >> h;
int N = w*h;
int *m;
//Unified memory allocation
cudaMallocManaged(&m, N*sizeof(int));
for(int i=0;i<N;i++){
infile >> m[i];
}
infile.close();
auto start = std::chrono::system_clock::now();
//Block,Grid parameters
int blockSize = 256;
int numBlocks = (N + blockSize - 1) / blockSize;
int *partialSum;
int *sum;
cudaMallocManaged(&partialSum,numBlocks*sizeof(int));
cudaMallocManaged(&sum, sizeof(int));
//prefetch input matrix
int device = -1;
cudaGetDevice(&device);
cudaMemPrefetchAsync(m, N*sizeof(int), device, NULL);
cudaMemPrefetchAsync(partialSum, numBlocks*sizeof(int), device, NULL);
cudaMemPrefetchAsync(sum, sizeof(int), device, NULL);
//Sum ones
sumOne<<<numBlocks, blockSize>>>(N, m, partialSum,sum);
cudaDeviceSynchronize();
std::cout << sum[0] << std::endl;
cudaFree(m);
cudaFree(partialSum);
cudaFree(sum);
return 0;
} |
7,079 | #include <stdio.h>
#define MAX_THREADS 1024
__global__ void Reduce(double *srcImage,
double *smpImage,
int srcwidth,
int srcheight,
int smpwidth,
int smpheight,
double *sigmaST) {
unsigned int srcx = blockIdx.x;
unsigned int srcy = threadIdx.x;
for(unsigned int next = 0; srcy + next < srcwidth - smpwidth; next += MAX_THREADS) {
double sigmast = 0.0;
for(unsigned int i = 0; i < smpheight; ++ i) {
for(unsigned int j = 0; j < smpwidth; ++ j) {
sigmast += srcImage[(srcx + i) * srcwidth + srcy + next + j] * smpImage[i * smpwidth + j];
}
}
sigmaST[srcx * srcwidth + srcy + next] = sigmast;
}
}
void kernel2(double *srcImage,
double *smpImage,
int srcwidth,
int srcheight,
int smpwidth,
int smpheight,
double *sigmaST) {
dim3 blockD(srcheight - smpheight, 1, 1);
dim3 threadD(MAX_THREADS, 1, 1);
Reduce<<<blockD, threadD>>>(srcImage, smpImage, srcwidth, srcheight, smpwidth, smpheight, sigmaST);
cudaThreadSynchronize();
}
|
7,080 | #include <stdio.h>
#include <stdlib.h>
#include <fcntl.h>
#include "string.h"
// #include<time.h>
#include<float.h>
__constant__ int FIL[32*5*5];
__global__ void conv1(unsigned int *picd, int *resultd){
int i,j,k,l;
int sum, offset;
i = threadIdx.y;
j = threadIdx.x;
l = blockIdx.x;
offset = l*25;
int xsize = 28;
int filterdim = 5;
k=0;
sum =0;
if(i<(xsize -filterdim +1)&& j<(xsize -filterdim +1)){
sum = FIL[offset + k]*picd[ xsize * (i) + j ] + FIL[offset+ k+1]*picd[ xsize*(i) + (j+1) ]
+ FIL[offset+ k+2]*picd[ xsize * (i)+(j+2)] + FIL[offset+k+3]*picd[xsize * (i)+(j+3)]
+ FIL[offset+k+4]*picd[ xsize * (i)+(j+4)]+ FIL[offset+ k+5]*picd[ xsize*(i+1)+(j) ]
+ FIL[offset+k+6]*picd[ xsize * (i+1) + (j+1) ] + FIL[offset+ k+7]*picd[ xsize*(i+1) + (j+2) ] +
FIL[offset+k+8]*picd[ xsize*(i+1) + (j+3) ] + FIL[offset+k+9]*picd[ xsize*(i+1) + (j+4) ] +
FIL[offset+k+10]*picd[ xsize*(i+2) + (j) ] + FIL[offset+k+11]*picd[ xsize * (i+2) + (j+1) ] +
FIL[offset+k+12]*picd[ xsize*(i+2) + (j+2)] + FIL[offset+k+13]*picd[ xsize*(i+2) + (j+3)]
+FIL[offset+k+14]*picd[ xsize*(i+2) + (j+4)] + FIL[offset +k+15]*picd[ xsize*(i+3) + (j)]
+ FIL[offset+k+16]*picd[ xsize*(i+3) + (j+1)] + FIL[offset+k+17]*picd[ xsize*(i+3) + (j+2)]
+ FIL[offset+k+18]*picd[ xsize*(i+3) + (j+3)] + FIL[offset+k+19]*picd[ xsize*(i+3) + (j+4)]
+ FIL[offset+k+20]*picd[ xsize*(i+4) + (j)] +FIL[offset+k+21]*picd[ xsize*(i+3) + (j+1)]
+ FIL[offset+k+22]*picd[ xsize*(i+4) + (j+2)] + FIL[offset+k+23]*picd[ xsize*(i+4) + (j+3)]
+ FIL[offset+ k+24]*picd[ xsize*(i+4) + (j+4)];
resultd[l*(xsize -filterdim +1)*(xsize -filterdim +1) + i*(xsize - filterdim +1)+j] = sum;
//printf("resultgpu[%d][%d]=%d\n",l,i*(xsize - filterdim +1)+j,resulth[l*(xsize -filterdim +1)*(xsize -filterdim +1) + i*(xsize - filterdim +1)+j]);
}
}
__global__ void maxpooling(int *maxip1d, int *maxop1d){
int i,j,l;
i = threadIdx.y;
j = threadIdx.x;
l = blockIdx.x;
int xsize = 28;
int filterdim = 5;
if(i<((xsize-filterdim+1)/2)&&(j<((xsize-filterdim+1)/2))){
int a,b,c,d,index, max1, max2;
index = l*((xsize -filterdim +1)*(xsize -filterdim +1))+ threadIdx.x*2 + threadIdx.y*2*(xsize -filterdim +1);
a = maxip1d[index];
b = maxip1d[index +1];
c = maxip1d[index+(xsize-filterdim+1)];
d = maxip1d[index + (xsize-filterdim+2)];
if(a>b){
max1 = a;
}
else{
max1 = b;
}
if(c>d){
max2 = c;
}
else{
max2 = d;
}
if(max1>max2){
maxop1d[l*(xsize -filterdim +1)*(xsize -filterdim +1)/4 + i*(xsize - filterdim +1)/2+j]=max1;
}
else{
maxop1d[l*(xsize -filterdim +1)*(xsize -filterdim +1)/4 + i*(xsize - filterdim +1)/2+j] = max2;
}
}
}
__global__ void conv2(int *cip2d, int *filter2d, int *cop2d){
int i,j,l,sum;
i = threadIdx.y;
j = threadIdx.x;
l = blockIdx.x;
int lstar;
lstar = l*800;
sum = 0;
int k =0;
int di = 12;
int disquare = di*di;
int m;
if(i<8 && j<8){
for(m = 0; m<32; m++){
sum = sum + filter2d[lstar + k]*cip2d[(m*disquare)+ (di*i) + j] + filter2d[lstar + k+1]*cip2d[(m*disquare)+ di*(i) + (j+1)]
+ filter2d[lstar+ k+2]*cip2d[(m*disquare)+ di*(i)+(j+2)] + filter2d[lstar +k+3]*cip2d[(m*disquare)+ di*(i)+(j+3)]
+ filter2d[lstar+k+4]*cip2d[(m*disquare)+ di*(i)+(j+4)]+ filter2d[lstar+ k+5]*cip2d[(m*disquare)+ di*(i+1)+(j)]
+ filter2d[lstar +k+6]*cip2d[(m*disquare)+ di* (i+1) + (j+1) ] + filter2d[lstar+ k+7]*cip2d[(m*disquare)+ di*(i+1)+(j+2)]
+ filter2d[lstar+k+8]*cip2d[(m*disquare)+ di*(i+1) + (j+3) ] + filter2d[lstar +k+9]*cip2d[(m*disquare)+ di*(i+1) +(j+4)]
+ filter2d[lstar+k+10]*cip2d[(m*disquare)+ di*(i+2) +(j)] + filter2d[lstar+k+11]*cip2d[(m*disquare)+ di* (i+2) + (j+1)]
+ filter2d[lstar+k+12]*cip2d[(m*disquare)+ di*(i+2) + (j+2)] +filter2d[lstar+k+13]*cip2d[(m*disquare)+ di*(i+2)+(j+3)]
+ filter2d[lstar+k+14]*cip2d[(m*disquare)+ di*(i+2)+(j+4)]+filter2d[lstar+k+15]*cip2d[(m*disquare)+ di*(i+3)+(j)]
+ filter2d[lstar+k+16]*cip2d[(m*disquare)+ di*(i+3)+(j+1)]+filter2d[lstar+k+17]*cip2d[(m*disquare)+ di*(i+3)+(j+2)]
+ filter2d[lstar+k+18]*cip2d[(m*disquare)+ di*(i+3)+(j+3)] + filter2d[lstar+k+19]*cip2d[(m*disquare)+di*(i+3)+(j+4)]
+ filter2d[lstar+k+20]*cip2d[(m*disquare)+ di*(i+4)+(j)] +filter2d[lstar+k+21]*cip2d[(m*disquare)+ di*(i+3)+(j+1)]
+ filter2d[lstar +k+22]*cip2d[(m*disquare)+ di*(i+4)+(j+2)] + filter2d[lstar+k+23]*cip2d[(m*disquare)+ di*(i+4)+(j+3)]
+ filter2d[lstar+ k+24]*cip2d[(m*disquare)+ di*(i+4) + (j+4)];
k+=25;
}
cop2d[l*64+i*8+j] = sum;
// printf("resultdevice[%d][%d]:%d\n",l,i*8+j,cop2d[l*64+i*8+j]);
}
}
__global__ void maxpool(int *maxip2d, int *maxop2d){
int i,j,l;
i = threadIdx.y;
j = threadIdx.x;
l = blockIdx.x;
int xsize = 12;
int filterdim = 5;
if(i<((xsize-filterdim+1)/2)&&(j<((xsize-filterdim+1)/2))){
int a,b,c,d,index, max1, max2;
index = l*((xsize -filterdim +1)*(xsize -filterdim +1))+ threadIdx.x*2 + threadIdx.y*2*(xsize -filterdim +1);
a = maxip2d[index];
b = maxip2d[index +1];
c = maxip2d[index+(xsize-filterdim+1)];
d = maxip2d[index + (xsize-filterdim+2)];
if(a>b){
max1 = a;
}
else{
max1 = b;
}
if(c>d){
max2 = c;
}
else{
max2 = d;
}
if(max1>max2){
maxop2d[l*(xsize -filterdim +1)*(xsize -filterdim +1)/4 + i*(xsize - filterdim +1)/2+j]=max1;
}
else{
maxop2d[l*(xsize -filterdim +1)*(xsize -filterdim +1)/4 + i*(xsize - filterdim +1)/2+j] = max2;
}
}
}
int main(int argc, char **argv){
int xsize;
int filterdim;
int numfilters;
int numfilters1;
xsize = 28;
filterdim = 5;
numfilters = 32;
numfilters1 = 64;
/*Numbytes required for initial image*/
int numbytes = xsize*xsize*sizeof(int);
/*Numbytes require for the output of first convolution layer*/
int numbytes2 = (xsize-filterdim+1)*(xsize-filterdim+1)*sizeof(int); //24x24
/**Numbytes required for output of first maxpool layer**/
int numbytes3 = ((xsize-filterdim+1)*(xsize-filterdim+1)/4)*sizeof(int); //12x12
/*Numbytes required for the output of second convolution layer*/
int numbytes4 = ((xsize-filterdim+1)/2 - filterdim + 1)*((xsize-filterdim+1)/2 - filterdim + 1)*sizeof(int);//8x8
/*Numbytes required for the output of second maxpool layer*/
int numbytes5 = (numbytes4/4)*sizeof(int);//4x4
/*Image on host side*/
/*Ip and op to first conv layer*/
unsigned int *pic = (unsigned int *)malloc(numbytes);
int *result;
int filter[numfilters*filterdim*filterdim];
/*Ip and op to first maxpool layer*/
int *maxip1;
int *maxop1;
/*Ip and op of second conv layer*/
int *cip2;
int *cop2;
int *filter2;
/*ip and op to second maxpool layer*/
int *maxip2;
int *maxop2;
/*Device side variables*/
unsigned int *picd;
int *resultd;
/*Ip and op to first maxpool layer*/
int *maxip1d;
int *maxop1d;
/*Ip and op of second conv layer*/
int *cip2d;
int *cop2d;
int *filter2d;
/*ip and op to second maxpool layer*/
int *maxip2d;
int *maxop2d;
result = (int *)malloc(numfilters*numbytes2);
maxip1 = (int *)malloc(numfilters*numbytes2);
maxop1 = (int *)malloc(numfilters*numbytes3);
cip2 = (int *)malloc(numfilters*numbytes3);
cop2 = (int *)malloc(numfilters1*numbytes4);
filter2 = (int *)malloc(numfilters1*numfilters*filterdim*filterdim*sizeof(int));
maxip2 = (int *)malloc(numfilters1*numbytes4);
maxop2 = (int *)malloc(numfilters1*numbytes5);
cudaMalloc(&picd, numbytes);
cudaMalloc(&resultd, numfilters*numbytes2);
cudaMalloc(&maxip1d, numfilters*numbytes2);
cudaMalloc(&maxop1d, numfilters*numbytes3);
cudaMalloc(&cip2d, numfilters*numbytes3);
cudaMalloc(&cop2d, numfilters1*numbytes4);
cudaMalloc(&filter2d, numfilters1*numfilters*filterdim*filterdim*sizeof(int));
cudaMalloc(&maxip2d, numfilters1*numbytes4);
cudaMalloc(&maxop2d, numfilters1*numbytes5);
/*Initializing the image on host side*/
/*Should modify to later on read in image*/
int i,j,k,l,count,dimx;
for (i=0; i<xsize; i++) {
for (j=0; j<xsize; j++) {
pic[i*xsize + j] = 1;
//printf("pic[%d][%d] : %d\t",i,j,pic[i*xsize + j]);
}
// printf("\n");
}
/*Initializing the filter for first conv layer to a value*/
/*TO DO : Read in filter from a file */
for(int k=0;k<numfilters;k++){
for (int i=0; i<filterdim; i++) {
for (int j=0; j<filterdim; j++){
filter[k*(filterdim*filterdim) + i*filterdim + j] = 1;
// printf("filter[%d][%d]: %d\n",k, i*filterdim + j, filter[k*(filterdim*filterdim) + i*filterdim + j]);
}
}
}
/*Initializing the filter for second conv layer to a value*/
/*TO DO : Read in filter from a file */
for(int k=0;k<numfilters1;k++){
for(int m= 0; m<numfilters;m++){
for (int i=0; i<filterdim; i++) {
for (int j=0; j<filterdim; j++){
filter2[k*(numfilters*filterdim*filterdim)+ m*filterdim*filterdim + i*filterdim + j] = 1;
// printf("filter2[%d][%d]: %d\t",k, m*filterdim*filterdim+i*filterdim + j, filter2[k*(numfilters*filterdim*filterdim)+ m*filterdim*filterdim + i*filterdim + j]);
}
}
}
// printf("\n");
}
dim3 dimGrid (32);
dim3 dimBlock (32,32);
cudaMemcpy(picd,pic,numbytes, cudaMemcpyHostToDevice);
cudaMemcpyToSymbol(FIL, filter, numfilters*filterdim*filterdim*sizeof(int));
conv1<<<dimGrid, dimBlock>>>(picd,resultd);
cudaMemcpy(result,resultd,numfilters*numbytes2,cudaMemcpyDeviceToHost);
dim3 dimBlock1 (16,16);
cudaMemcpy(maxip1d, result,numfilters*numbytes2, cudaMemcpyHostToDevice);
maxpooling<<<dimGrid, dimBlock1>>>(maxip1d, maxop1d);
cudaMemcpy(maxop1, maxop1d, numfilters*numbytes3, cudaMemcpyDeviceToHost);
cudaMemcpy(cip2d, maxop1,numfilters*numbytes3,cudaMemcpyHostToDevice);
cudaMemcpy(filter2d, filter2,numfilters1*numfilters*filterdim*filterdim*sizeof(int), cudaMemcpyHostToDevice);
dim3 dimGrid2(64);
dim3 dimBlock2(8,8);
conv2<<<dimGrid2, dimBlock2>>>(cip2d, filter2d, cop2d);
cudaMemcpy(cop2, cop2d,numfilters1*numbytes4,cudaMemcpyDeviceToHost);
cudaMemcpy(maxip2d, cop2,numfilters1*numbytes4,cudaMemcpyHostToDevice);
maxpool<<<dimGrid2, dimBlock2>>>(maxip2d, maxop2d);
cudaMemcpy(maxop2, maxop2d, numfilters*numbytes5, cudaMemcpyDeviceToHost);
for(k=0;k<numfilters1;k++){
for(i=0;i<4;i++){
for(j=0;j<4;j++){
printf("maxpool[%d][%d]:%d\t",k,i*4+j, maxop2[k*16+i*4+j]);
}
printf("\n");
}
printf("\n\n");
}
}
|
7,081 | #include <stdio.h>
#include <numeric>
#include <stdlib.h>
#include <cuda.h>
#include <iostream>
#include <fstream>
using namespace std;
#define BLOCK_SIZE 32
__global__ void reduceKernel(float *device_output, float *device_input, float *final_output);
//function to call reduce kernel for array size
string reduceInvoker(int arraySize) {
int N = arraySize;
printf("%i\n", N);
//Create variables for input and output array sizes
size_t size = N * sizeof(float);
size_t size_output = size / BLOCK_SIZE;
size_t size_final_output = sizeof(float);
//initialising host input and output arrays
float host_input[N];
float host_output[1];
//initialising pointers for device arrays
float *device_input, *device_output, *final_output;
//initialising variables and events for cuda timer
cudaEvent_t start, stop;
cudaEventCreate(&start);
cudaEventCreate(&stop);
cudaError_t err;
//filling host input array with values
for (int i = 0; i < N; i++) {
host_input[i] = 1.0f;
}
//allocating space for device input array
cudaMalloc((void**) &device_input, size);
//copying host input array to device
cudaMemcpy(device_input, host_input, size, cudaMemcpyHostToDevice);
//allocating space for device and final output arrays
cudaMalloc((void**) &device_output, size_output);
cudaMalloc((void**) &final_output, size_final_output);
//calculating grid size
int grid_size = N / BLOCK_SIZE;
printf("Grid Size is: %d\n", grid_size);
printf("Block Size is: %d\n", BLOCK_SIZE);
dim3 threadsPerBlock(BLOCK_SIZE);
dim3 blocks(grid_size);
//start timer
cudaEventRecord(start);
//call kernel
reduceKernel<<<blocks, threadsPerBlock>>>(device_output, device_input, final_output);
//stop timer
cudaEventRecord(stop);
// Wait for GPU to finish before accessing on host
err = cudaDeviceSynchronize();
printf("Run kernel: %s\n", cudaGetErrorString(err));
printf("\n");
//copy final output array to host
err = cudaMemcpy(host_output, final_output, size_final_output, cudaMemcpyDeviceToHost);
printf("Copy host_output off device: %s\n", cudaGetErrorString(err));
printf("\n");
//calculate elapsed time
float milliseconds = 0;
cudaEventElapsedTime(&milliseconds, start, stop);
printf("Elapsed time was: %f\n", milliseconds);
printf("And the final reduction is: %f\n", host_output[0]);
//free device memory
cudaFree(device_input);
cudaFree(device_output);
cudaFree(final_output);
//format string for csv file
string csv = to_string(arraySize) + "," + to_string(milliseconds) + ","
+ to_string(BLOCK_SIZE) + "," + to_string(grid_size) + "\n";
return csv;
}
int main(void) {
//Open file
std::ofstream myFile;
myFile.open("times.csv", std::ofstream::trunc);
//Write headers
myFile << "Array Size,Elapsed Time,Block Size,Grid Size\n";
//Call reduce function invoker for different array sizes and write result to file
for (unsigned int i = 1; i <= (1 << 20); i <<= 1) {
printf("%i\n", i);
string csv = reduceInvoker(i);
myFile << csv;
}
//close file
myFile.close();
}
__global__ void reduceKernel(float* device_output, float* device_input, float *final_output) {
//work out global thread id
int myId = threadIdx.x + blockDim.x * blockIdx.x; // ID relative
//work out local thread id
int tid = threadIdx.x; // Local ID
//initialise static shared memory
__shared__ float temp[BLOCK_SIZE];
//set value in shared memory
temp[tid] = device_input[myId];
//make sure threads are synced before adding values
__syncthreads();
// do reduction in shared memory
for (unsigned int s = blockDim.x / 2; s >= 1; s >>= 1) {
if (tid < s) {
temp[tid] += temp[tid + s];
}
__syncthreads(); // make sure all adds at one stage are
}
// only thread 0 writes result for this block back to global memory
if (tid == 0) {
device_output[blockIdx.x] = temp[tid];
//add partial reductions to get final reduction
atomicAdd(&final_output[0], device_output[blockIdx.x]);
}
}
|
7,082 | #include <iostream>
#include <numeric>
#include <cstdlib>
// Kolaylık olsun
using namespace std;
// Tipik bir C++ fonksiyonu
void carp(int n, float *x, float *y, float *z)
{
for (int i = 0; i < n; i++)
{
z[i] = x[i] * y[i];
}
}
// Ustteki fonksiyonun CUDA versiyonu
__global__
void carp_cuda(int n, float *x, float *y, float *z)
{
for (int i = threadIdx.x; i < n; i += blockDim.x)
{
z[i] = x[i] * y[i];
}
}
int main(int argc, char *argv[])
{
// Çok büyük bir sayı belirleyelim
int N = 10000;
float *x_gpu, *y_gpu, *z_gpu, *x_cpu, *y_cpu, *z_cpu;
// GPU ve CPU tarafindan ulasilabilen memory ayirtalim
cudaMallocManaged(&x_gpu, N * sizeof(float));
cudaMallocManaged(&y_gpu, N * sizeof(float));
cudaMallocManaged(&z_gpu, N * sizeof(float));
// Sadece CPU tarafindan ulasilabilen memory ayirtalim
x_cpu = new float[N];
y_cpu = new float[N];
z_cpu = new float[N];
//
for (int i = 0; i < N; ++i) {
x_gpu[i] = 1.0f;
y_gpu[i] = 2.0f;
x_cpu[i] = 1.0f;
y_cpu[i] = 2.0f;
}
// Fonksiyonu GPU'da argv[1] blokta ve her blokta argv[2] thread
// olacak sekilde çagiralım
int blok_sayisi = atoi(argv[1]);
int thread_sayisi = atoi(argv[2]);
carp_cuda<<<blok_sayisi, thread_sayisi>>>(N, x_gpu, y_gpu, z_gpu);
// GPU'yu bekleyelim de isini bitirsin, yoksa ortam karisir.
cudaDeviceSynchronize();
// Normal CPU fonlsiyonunu çagiralım
carp(N, x_cpu, y_cpu, z_cpu);
// Bakalim dogru mu yaptik?
// z_gpu ve z_cpu ayni degerlere sahip olması lazim
for(int i = 0; i < N; ++i)
cout << z_cpu[i] << " " << z_gpu[i] << endl;
// Release the Kraken - Kraken'i saliverin gelsin.
cudaFree(x_gpu);
cudaFree(y_gpu);
cudaFree(z_gpu);
delete [] x_cpu;
delete [] y_cpu;
delete [] z_cpu;
return 0;
}
|
7,083 | #include "includes.h"
#define BLOCK_SIZE 16
__global__ void Evolve(bool* field, float* scores, double b, int size, bool* next_field)
{
int row = blockIdx.y * blockDim.y + threadIdx.y;
int col = blockIdx.x * blockDim.x + threadIdx.x;
int memberIndex;
// Score
if (col >= size || row >= size)
return;
//printf("(%i, %i)\n", col, row);
float score = 0;
for (int i = -1; i <= 1; i++) //Row
{
for (int j = -1; j <= 1; j++) //Col
{
memberIndex = (col + i + size) % size + size * ((row + j + size) % size);
if (field[memberIndex] == true)
score++;
}
}
if (!field[row*size + col])
scores[row*size + col] = score * b;
else
scores[row*size + col] = score;
__syncthreads();
// Strategy
int bestStrategyIndex = row*size + col;
for (int i = -1; i <= 1; i++) //Row
{
for (int j = -1; j <= 1; j++) //Col
{
memberIndex = (col + i + size) % size + size * ((row + j + size) % size);
if (scores[bestStrategyIndex] < scores[memberIndex])
{
bestStrategyIndex = memberIndex;
}
}
}
next_field[row*size + col] = field[bestStrategyIndex];
__syncthreads();
} |
7,084 | // Berat Postalcioglu
/* OUTPUT
minimum element of the array (minCPU): -9649.35
minimum element of the array (minGPU): -9649.35
*/
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <cmath>
#include <cstdio>
#include <ctime>
const int ArrSize = 60000;
const int ThreadsPerBlock = 512;
const int BlocksPerGrid = 32;
// generates a random array
void generateArray(double *data, int count) {
//generate a random data set
for (int i = 0; i < count; i++) {
data[i] = rand() / (rand() + 1.1) * (rand() % 2 ? 1 : -1);
}
}
double minCPU(double *data, int count)
{
int minIndex = 0;
for (int i = 0; i < count; i++)
{
if (std::isgreater(data[minIndex], data[i]))
{
minIndex = i;
}
}
return data[minIndex];
}
__global__ void minGPU(double *data, int count, double *res)
{
__shared__ double cache[ThreadsPerBlock];
int tid = threadIdx.x + blockIdx.x * blockDim.x;
int cacheIndex = threadIdx.x;
double temp = 0;
while (tid < count)
{
temp += data[tid];
//cache[cacheIndex] = data[tid];
tid += blockDim.x * gridDim.x;
}
cache[cacheIndex] = temp;
__syncthreads();
int i = blockDim.x / 2;
while (i != 0) {
if (cacheIndex < i)
{
if (cache[cacheIndex] > cache[cacheIndex + i])
{
cache[cacheIndex] = cache[cacheIndex + i];
}
}
__syncthreads();
i /= 2;
}
if (cacheIndex == 0)
res[blockIdx.x] = cache[0];
}
int main()
{
srand(time(NULL));
// cpu
double data[ArrSize];
generateArray(data, ArrSize);
double minElementCpu = minCPU(data, ArrSize);
printf("minimum element of the array (minCPU): %.2f\n", minElementCpu);
// gpu
double *gpuData, *gpuRes;
cudaMalloc((void**)&gpuData, ArrSize * sizeof(double));
cudaMalloc((void**)&gpuRes, BlocksPerGrid * sizeof(double));
cudaMemcpy((void*)gpuData, (const void*) data, ArrSize * sizeof(double), cudaMemcpyHostToDevice);
minGPU <<<BlocksPerGrid, ThreadsPerBlock>>> (gpuData, ArrSize, gpuRes);
double blockResults[BlocksPerGrid];
cudaMemcpy((void*)blockResults, (const void *)gpuRes, BlocksPerGrid * sizeof(double), cudaMemcpyDeviceToHost);
double minElementGpu = minCPU(blockResults, BlocksPerGrid);
printf("minimum element of the array (minGPU): %.2f\n", minElementGpu);
return 0;
} |
7,085 | #include "includes.h"
__global__ void vectFill( int * data1, int * data2, int * restult, unsigned long sizeOfArray )
{
unsigned long i = blockDim.x * blockIdx.x + threadIdx.x;
if( i < sizeOfArray )
{
restult[ i ] = data1[i] + data2[i];
}
} |
7,086 | # include <stdio.h>
# include <stdlib.h> // To use the exit function and malloc
# include <string.h>
/*
* ============================================
* Find a word in a given string (CUDA version)
* ============================================
*
* Usage: find_word <word> <input_file>
*
* Given a word, load the first line of the input file and
* search the word in it. This version uses a CUDA-enabled
* graphics card.
*/
// Global constant
# define NOT_FOUND (-1)
# define THREADS_PER_BLOCK (128)
// Function declaration
int find_word_in_gpu(char *word, char *search_here);
// ----------------------------------------------------------------------------
// Kernel definition
void __global__ find_word_kernel(char *word, char *search_here, int *found_here, int ref_length) {
/*
* Search for the given word in the search_here string.
* At first occurrence, returns the starting position.
* If the word was not found, return -1.
*/
// The starting position of each thread
int start = (blockDim.x * blockIdx.x) + threadIdx.x;
if (start < ref_length-1) { // Check for a valid position
int found = 1; // Pretend you found it
int letters_coincide;
// ---> Check if the word is found from here
for (int j=0; word[j] != '\0'; j++) {
// Check if the letters coincide
letters_coincide = (search_here[start+j] == word[j]);
found = (found && letters_coincide);
}
// Place your mark
found_here[start] = found;
}
return;
} // --- find_word_kernel
// ----------------------------------------------------------------------------
/* --- << Main function >> --- */
int main(int argc, char *argv[]) {
// 1. ---> Find the input file and the word to search
char *search_here = argv[1];
char *word = argv[2];
// 2. ---> Search the word in the reference string
int found_here = find_word_in_gpu(word, search_here);
// 3. ---> Display the results
if( found_here == NOT_FOUND ) {
// The word was not found
printf("Sorry, the word was not found in the reference string\n");
printf("Word: %s\nReference string: %s\n\n", word, search_here);
} else {
// The word was found
printf("The word was found at position: %d\n", found_here);
// Signal the position
printf("Word: %s\nReference string: %s\n", word, search_here);
printf(" ");
for (int i=0; i < found_here-1; i++)
printf(" ");
printf("^\n\n");
}
// 4. ---> Finish!
return 0;
} // --- main
// ----------------------------------------------------------------------------
/* --- << Functions >> --- */
// --- --- - --- --- - --- --- - --- --- - --- --- - --- --- - --- --
int find_word_in_gpu(char *word, char *search_here) {
/*
* Search for the given word in the search_here string.
*
* At first occurrence, returns the starting position. If the word was not
* found, return NOT_FOUND. Uses a CUDA-enabled graphics card.
*/
// 1. --- > Prepare the data in the CPU
// Lookup the lengths of the words
int word_length = strlen(word);
int str_length = strlen(search_here);
int found_here = NOT_FOUND;
// Copy the word to the GPU
char *word_tmp;
cudaMallocManaged(&word_tmp, word_length * sizeof(char));
strcpy(word_tmp, word);
// Copy the search_string to the GPU
char *str_tmp;
cudaMallocManaged(&str_tmp, str_length * sizeof(char));
strcpy(str_tmp, search_here);
// Prepare for the arrival of the result
int *found_here_tmp;
cudaMallocManaged(&found_here_tmp, str_length * sizeof(int));
for (int i=0; i < str_length; i++) {
found_here_tmp[i] = 0;
}
// 2. --- > Prepare and launch the Kernel
// Calculate the total threads to use (one per window)
int total_threads = (str_length - word_length) + 1;
// Calculate the blocks needed for that
int blocks = (total_threads + THREADS_PER_BLOCK-1) / THREADS_PER_BLOCK;
printf("Launching %d threads in %d blocks\n", THREADS_PER_BLOCK, blocks);
// Launch the kernel
find_word_kernel<<<blocks, THREADS_PER_BLOCK>>>(word_tmp, str_tmp, found_here_tmp, str_length);
cudaDeviceSynchronize();
// 3. --- > Analyze the result
for (int i=0; i<str_length; i++) {
if ( found_here_tmp[i] ) {
found_here = i;
break;
}
}
// 4. ---> Cleanup and return
// Free unneeded memory
cudaFree(found_here_tmp);
cudaFree(word_tmp);
cudaFree(str_tmp);
return found_here;
} // --- find_word_in_gpu
|
7,087 | #include <stdio.h>
#define N 64 // Specify a constant value for array length.
#define TPB 32
// A scaling function to convert integers 0,1,...,N-1
// to evenly spaced floats ranging from 0 to 1.
__device__ float scale(int i, int n)
{
return ((float)i) / (n - 1);
}
// Compute the distance between 2 points on a line.
__device__ float distance(float x1, float x2)
{
return sqrt((x2 - x1)*(x2 - x1));
}
__global__ void distanceKernel(float *d_out, float ref, int len){
const int i = blockIdx.x * blockDim.x + threadIdx.x;
const float x = scale(i, N);
d_out[i] = distance(x, ref);
printf("i = %2d: dist from %f to %f is %f.\n", i, ref, x, d_out[i]);
}
int main()
{
// Create an array of N floats (initialized to 0.0).
// We will overwrite these values to store our results.
float *d_out;
cudaMalloc(&d_out, N*sizeof(float));
// Choose a reference value from which distances are measured.
const float ref = 0.5f;
/* for loop to scale the index to obtain coordinate value,
*compute the distance from the reference point,
*and store the result in the corresponding entry in out. */
distanceKernel<<<N/TPB, TPB>>>(d_out, ref, N);
cudaFree(d_out);
return 0;
}
|
7,088 | #include<stdio.h>
#include<cuda.h>
__global__ void update(int *matrix,int *query,int *row,int *row_ele,int *no_query,int *prev_array,int n,int m,int q)
{
int id=blockIdx.x*blockDim.x+threadIdx.x;
if(id<(m*q))
{
int query_no = id/m;
int row_no = id%m;
if(matrix[row_no*n+row[query_no]-1]==row_ele[query_no]) // Updating if the row element matches
{
for(int i=0;i<no_query[query_no];i++)
{
if(query[prev_array[query_no]+i*3+2]==0)
{
atomicSub(&matrix[row_no*n+query[prev_array[query_no]+i*3]-1] , query[prev_array[query_no]+i*3+1]);
}
else
{
atomicAdd(&matrix[row_no*n+query[prev_array[query_no]+i*3]-1] , query[prev_array[query_no]+i*3+1]);
}
}
}
}
}
int main(int argc,char **argv)
{
int M,N,q;
FILE *fpi,*fpo;
fpi=fopen(argv[1],"r");
fpo=fopen(argv[2],"w");
fscanf(fpi,"%d %d", &M,&N);
int *matrix, *hmatrix;
char character;int *row,*row_ele,*drow,*drow_ele;
int *query,*no_query,*prev_array,*dquery,*dno_query,*dprev_array;
cudaMalloc(&matrix, (M) * (N) * sizeof(int));
hmatrix = (int *)malloc(M * N * sizeof(int));
for (int ii = 0; ii < M; ++ii)
{
for (int jj = 0; jj < N; ++jj)
{
fscanf(fpi,"%d",&hmatrix[ii*N+jj]);
}
}
cudaMemcpy(matrix, hmatrix, M * N * sizeof(int), cudaMemcpyHostToDevice);
fscanf(fpi,"%d", &q);
cudaMalloc(&dquery, 90 * q * sizeof(int));
cudaMalloc(&drow, q * sizeof(int));
cudaMalloc(&drow_ele, q * sizeof(int));
cudaMalloc(&dno_query, q * sizeof(int));
cudaMalloc(&dprev_array, q * sizeof(int));
query = (int *)malloc(90 * q * sizeof(int));
row = (int *)malloc(q * sizeof(int));
row_ele = (int *)malloc(q * sizeof(int));
no_query = (int *)malloc(q * sizeof(int));
prev_array = (int *)malloc(q * sizeof(int));
int prev=0;char c1[50];
// Parsing Queries
for (int i = 0; i < q; i++)
{
fscanf(fpi,"%[^U]s",c1);
fscanf(fpi,"%c",&character);
fscanf(fpi," %c",&character);
fscanf(fpi,"%d %d %d",&row[i],&row_ele[i],&no_query[i]);
for(int j=0;j<no_query[i];j++)
{
fscanf(fpi," %c %d %d %c",&character,&query[prev+(j*3)],&query[prev+(j*3)+1],&character);
if(character=='+')
query[prev+(j*3)+2]=1;
else
query[prev+(j*3)+2]=0;
}
prev_array[i]=prev;
prev += no_query[i]*3;
}
cudaMemcpy(dquery, query, 90 * q * sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(drow, row, q * sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(drow_ele, row_ele, q * sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(dno_query, no_query, q * sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(dprev_array, prev_array, q * sizeof(int), cudaMemcpyHostToDevice);
update<<<3000,1024>>>(matrix,dquery,drow,drow_ele,dno_query,dprev_array,N,M,q);
cudaMemcpy(hmatrix, matrix, M * N * sizeof(int), cudaMemcpyDeviceToHost);
cudaDeviceSynchronize();
for (int ii = 0; ii < M; ++ii)
{
for (int jj = 0; jj < N; ++jj)
{
if(jj==N-1)
fprintf(fpo,"%d ",hmatrix[ii*N+jj]);
else
fprintf(fpo,"%d ",hmatrix[ii*N+jj]);
}
fprintf(fpo,"\n");
}
return 0;
} |
7,089 | #include <iostream>
#include <sys/time.h>
#define min(a, b) (a < b ? a : b)
__device__ unsigned int getIdx(dim3* threads, dim3* blocks) {
int x;
return threadIdx.x +
threadIdx.y * (x = threads->x) +
threadIdx.z * (x *= threads->y) +
blockIdx.x * (x *= threads->z) +
blockIdx.y * (x *= blocks->z) +
blockIdx.z * (x *= blocks->y);
}
__device__ void gpu_bottomUpMerge(double* source, double* dest, long start, long middle, long end) {
long i = start;
long j = middle;
for (long k = start; k < end; k++) {
if (i < middle && (j >= end || source[i] < source[j])) {
dest[k] = source[i];
i++;
} else {
dest[k] = source[j];
j++;
}
}
}
__global__ void gpu_mergesort(double* source, double* dest, long size, long width, long slices, dim3* threads, dim3* blocks) {
unsigned int idx = getIdx(threads, blocks);
long start = width*idx*slices,
middle,
end;
for (long slice = 0; slice < slices; slice++) {
if (start >= size)
break;
middle = min(start + (width >> 1), size);
end = min(start + width, size);
gpu_bottomUpMerge(source, dest, start, middle, end);
start += width;
}
}
void mergesort_gpu(double* data, long size, int xThreadPerBlock, int xBlocksPerGrid) {
dim3 threadsPerBlock;
dim3 blocksPerGrid;
threadsPerBlock.x = xThreadPerBlock;
threadsPerBlock.y = 1;
threadsPerBlock.z = 1;
blocksPerGrid.x = xBlocksPerGrid;
blocksPerGrid.y = 1;
blocksPerGrid.z = 1;
double* D_data;
double* D_swp;
dim3* D_threads;
dim3* D_blocks;
cudaMalloc((void**) &D_data, size * sizeof(double));
cudaMalloc((void**) &D_swp, size * sizeof(double));
cudaMemcpy(D_data, data, size * sizeof(long), cudaMemcpyHostToDevice);
cudaMalloc((void**) &D_threads, sizeof(dim3));
cudaMalloc((void**) &D_blocks, sizeof(dim3));
cudaMemcpy(D_threads, &threadsPerBlock, sizeof(dim3), cudaMemcpyHostToDevice);
cudaMemcpy(D_blocks, &blocksPerGrid, sizeof(dim3), cudaMemcpyHostToDevice);
double* A = D_data;
double* B = D_swp;
long nThreads = threadsPerBlock.x * threadsPerBlock.y * threadsPerBlock.z *
blocksPerGrid.x * blocksPerGrid.y * blocksPerGrid.z;
for (int width = 2; width < (size << 1); width <<= 1) {
long slices = size / ((nThreads) * width) + 1;
gpu_mergesort<<<blocksPerGrid, threadsPerBlock>>>(A, B, size, width, slices, D_threads, D_blocks);
A = A == D_data ? D_swp : D_data;
B = B == D_data ? D_swp : D_data;
}
cudaMemcpy(data, A, size * sizeof(double), cudaMemcpyDeviceToHost);
cudaFree(A);
cudaFree(B);
}
|
7,090 | #include <iostream>
#include <cmath>
#include <cstdio>
__constant__ int device_n;
__global__
void add(int n, float* x, float* y) {
int index = blockIdx.x * blockDim.x + threadIdx.x;
int stride = blockDim.x * gridDim.x;
// if (threadIdx.x == 0) {
// printf("%d %d %d\n", blockIdx.x, gridDim.x, blockDim.x);
// }
for (int i = index; i < n; i += stride) {
y[i] = x[i] + y[i];
}
}
int main() {
int N = 1 << 28;
size_t size = N * sizeof(float);
float *h_x = (float*)malloc(size);
float *h_y = (float*)malloc(size);
float *d_x, *d_y;
cudaMalloc(&d_x, size);
cudaMalloc(&d_y, size);
for (int i = 0; i < N; ++i) {
h_x[i] = 1.0f;
h_y[i] = 2.0f;
}
cudaMemcpy(d_x, h_x, size, cudaMemcpyHostToDevice);
cudaMemcpy(d_y, h_y, size, cudaMemcpyHostToDevice);
int blockSize = 256;
int numBlocks = (N + blockSize - 1) / blockSize;
add<<<numBlocks, blockSize>>>(N, d_x, d_y);
// cudaDeviceSynchronize();
cudaMemcpy(h_y, d_y, size, cudaMemcpyDeviceToHost);
float maxError = 0.0f;
for (int i = 0; i < N; i++) {
maxError = fmax(maxError, fabs(h_y[i]-3.0f));
}
std::cout << "Max error: " << maxError << std::endl;
cudaFree(d_x);
cudaFree(d_y);
free(h_x);
free(h_y);
return 0;
}
|
7,091 | #include "includes.h"
__global__ void mult2Matrix(float *M, float *N, float *P) {
__shared__ int shared_m_tile[TILE_WIDTH][TILE_WIDTH];
__shared__ int shared_n_tile[TILE_WIDTH][TILE_WIDTH];
int tx = threadIdx.x;
int ty = threadIdx.y;
int col = blockIdx.x * blockDim.x + threadIdx.x;
int row = blockIdx.y * blockDim.y + threadIdx.y;
//check if thread directly maps to the dimensions of the resulting matrix
if (row < WIDTH && col < WIDTH)
{
float result = 0;
int k;
int phase;
//calculate P matrix indexes in phases. Each phase shares
//TILE_SIZE * TILE_SIZE data copied to the shared matrix M
//and matrix N.
for (phase = 0; phase <= WIDTH / TILE_WIDTH; phase++)
{
shared_m_tile[ty][tx] = M[row * WIDTH + phase * TILE_WIDTH + tx];
shared_n_tile[ty][tx] = N[(phase * TILE_WIDTH + ty) * WIDTH + col];
__syncthreads();
for (k = 0; k < TILE_WIDTH; k++)
{
if (k + (phase * TILE_WIDTH) < WIDTH)
{
result += (shared_m_tile[ty][k] * shared_n_tile[k][tx]);
}
}
__syncthreads();
}
P[row * WIDTH + col] = result;
}
} |
7,092 | // example of using CUDA streams
#include <stdio.h>
#include <stdlib.h>
#include <iostream>
#include <chrono>
#include <math.h>
#include <fstream>
using namespace std::chrono;
#define BLOCK_SIZE 256
void initWithNoStream(float num, float *a, int N)
{
for(int i = 0; i < N; ++i)
{
a[i] = num;
}
}
__global__
void reduceVector(float *g_data, int N) {
__shared__ float sdata[2*BLOCK_SIZE];
unsigned int tid = threadIdx.x;
unsigned int i = blockIdx.x * 2 * blockDim.x + threadIdx.x;
unsigned int gridStride = 2 * blockDim.x * gridDim.x;
sdata[tid] = 0;
while (i < N) {
sdata[tid] += g_data[i] + g_data[i + blockDim.x];
i += gridStride;
}
for(unsigned j = blockDim.x / 2; j > 32; j >>= 1) {
__syncthreads();
if (tid < j) {
sdata[tid] += sdata[tid + j];
}
}
//printf("tid %d i %d blockIdx.x %d sdata[%d] %f\n", tid, i, blockIdx.x, tid, sdata[tid]);
__syncthreads();
//
if(tid < 32) {
sdata[tid] += sdata[tid + 32];
sdata[tid] += sdata[tid + 16];
sdata[tid] += sdata[tid + 8];
sdata[tid] += sdata[tid + 4];
sdata[tid] += sdata[tid + 2];
sdata[tid] += sdata[tid + 1];
}
if (tid == 0) g_data[blockIdx.x] = sdata[0];
}
__global__
void reduceVector_naive(float *g_data, int N) {
__shared__ float sdata[2*BLOCK_SIZE];
unsigned int tid = threadIdx.x;
unsigned int i = blockIdx.x * 2 * blockDim.x + threadIdx.x;
sdata[tid] = g_data[i];
sdata[blockDim.x+tid] = g_data[i + blockDim.x];
for(unsigned int j = 1; j <= blockDim.x; j *= 2) {
__syncthreads();
if (tid % j == 0) {
sdata[2*tid] += sdata[2*tid + j];
}
}
__syncthreads();
if (tid == 0) g_data[blockIdx.x] = sdata[tid];
}
int main(int argc, char** argv)
{
int in_s = 14;
char* pEnd;
if (!in_s && argc < 2) {
printf("Podaj liczbe która będzie przesunięciem 2 które jest rozmiarem\n");
return 1;
} else if(!in_s) {
in_s = strtol(argv[1], &pEnd, 10);
}
int deviceId;
int numberOfSMs;
cudaGetDevice(&deviceId);
cudaDeviceGetAttribute(&numberOfSMs, cudaDevAttrMultiProcessorCount, deviceId);
std::ofstream host_file, device_file, device_naive_file;
host_file.open ("reduction_host.txt");
device_file.open ("reduction_device.txt");
device_naive_file.open ("reduction__device_naive.txt");
for(int in_s = 14; in_s < 27; in_s++) {
const int N = 2<<in_s;
host_file << N;
device_file << N;
device_naive_file << N;
size_t size = N * sizeof(float);
size_t threads;
size_t blocks;
threads = BLOCK_SIZE;
blocks = N / threads / 2;
printf("threads %d blocks %d\n", threads, blocks);
cudaError_t addVectorsErr;
cudaError_t asyncErr;
for(int k = 0; k < 10; k++) {
float *a;
float *b;
float *c;
cudaMallocManaged(&b, size);
cudaMallocManaged(&a, size);
cudaMallocManaged(&c, size);
initWithNoStream(1, a, N);
initWithNoStream(1, b, N);
initWithNoStream(1, c, N);
cudaMemPrefetchAsync(a, size, deviceId);
cudaMemPrefetchAsync(b, size, deviceId);
asyncErr = cudaDeviceSynchronize();
if(asyncErr != cudaSuccess) printf("Device sync Error: %s\n", cudaGetErrorString(asyncErr));
auto start = high_resolution_clock::now();
reduceVector<<<32 * numberOfSMs, threads>>>(a, N);
cudaMemPrefetchAsync(a, size, cudaCpuDeviceId);
asyncErr = cudaDeviceSynchronize();
if(asyncErr != cudaSuccess) printf("Device sync Error: %s\n", cudaGetErrorString(asyncErr));
for(int i = 1; i < 32 * numberOfSMs; i++) {
a[0] += a[i];
}
auto stop = high_resolution_clock::now();
auto duration = duration_cast<microseconds>(stop - start);
std::cout<< "Reduction time in us: " << duration.count()<< std::endl;
device_file << "\t" << duration.count();
addVectorsErr = cudaGetLastError();
if(addVectorsErr != cudaSuccess) printf("Reduction Error: %s\n", cudaGetErrorString(addVectorsErr));
start = high_resolution_clock::now();
reduceVector_naive<<<blocks, threads>>>(b, N);
cudaMemPrefetchAsync(b, size, cudaCpuDeviceId);
asyncErr = cudaDeviceSynchronize();
if(asyncErr != cudaSuccess) printf("Device sync Error: %s\n", cudaGetErrorString(asyncErr));
for(int i = 1; i < blocks; i++) {
b[0] += b[i];
}
stop = high_resolution_clock::now();
duration = duration_cast<microseconds>(stop - start);
std::cout<< "Reduction (naive) time in us: " << duration.count()<< std::endl;
device_naive_file << "\t" << duration.count();
addVectorsErr = cudaGetLastError();
if(addVectorsErr != cudaSuccess) printf("Reduction Error: %s\n", cudaGetErrorString(addVectorsErr));
asyncErr = cudaDeviceSynchronize();
if(asyncErr != cudaSuccess) printf("Device sync Error: %s\n", cudaGetErrorString(asyncErr));
double result = 0;
start = high_resolution_clock::now();
for(long i = 0; i < N; i++) {
result += c[i];
}
stop = high_resolution_clock::now();
duration = duration_cast<microseconds>(stop - start);
std::cout<< "Cpu add rest time in us: " << duration.count() << std::endl;
host_file << "\t" << duration.count();
printf("%f %f %f %d\n", a[0], b[0], result, N);
cudaFree(a);
cudaFree(b);
cudaFree(c);
}
}
}
|
7,093 | #include "includes.h"
namespace ann {
// CUDA2
}
__global__ void kernel(int n, float *arr){
volatile int idx = threadIdx.x + blockDim.x*blockIdx.x;
if(idx >= n) return;
arr[idx] *= 2.0f;
} |
7,094 | // Template file for the OpenCL Assignment 4
#include <stdio.h>
#include <stdlib.h>
#include <CL/cl.h>
#include <math.h>
#include <sys/time.h>
#define NUM_PARTICLES 10000
#define NUM_ITERATIONS 1000
#define BLOCK_SIZE 64
#define error 1e-6
// This is a macro for checking the error variable:>.
#define CHK_ERROR(err) if (err != CL_SUCCESS) fprintf(stderr,"Error: %s\n",clGetErrorString(err));
// A errorCode to string converter (forward declaration)
const char* clGetErrorString(int);
const char *mykernel =
"typedef struct{ \n"
"float position[3]; \n"
"float velocity[3]; \n"
"}particle; \n"
"__kernel \n"
"void kernel_upload( \n"
" const int iter, \n"
" const int nump, \n"
" __global particle* X) \n"
"{int index = get_global_id(0); \n"
"if(index < nump) { \n"
" X[index].velocity[0] = (3*index + iter) % nump; \n"
" X[index].velocity[1] = (4*index + iter) % nump; \n"
" X[index].velocity[2] = (5*index + iter) % nump; \n"
" X[index].position[0] = X[index].position[0] + X[index].velocity[0]; \n"
" X[index].position[1] = X[index].position[1] + X[index].velocity[1]; \n"
" X[index].position[2] = X[index].position[2] + X[index].velocity[2]; \n"
"} \n"
"} \n";
typedef struct{
float position[3];
float velocity[3];
}particle;
double cpuSecond() {
struct timeval tp;
gettimeofday(&tp,NULL);
return ((double)tp.tv_sec + (double)tp.tv_usec*1.e-6);
}
void init_Array(particle *particulas){
for(int i = 0; i < NUM_PARTICLES; i++){
particulas[i].position[0] = rand() % 1000;
particulas[i].position[1] = rand() % 1000;
particulas[i].position[2] = rand() % 1000;
particulas[i].velocity[0] = rand() % 1000;
particulas[i].velocity[1] = rand() % 1000;
particulas[i].velocity[2] = rand() % 1000;
}
}
int main(int argc, char **argv) {
//All the variables used
particle X[NUM_PARTICLES], results[NUM_PARTICLES];
float array = NUM_PARTICLES * sizeof(particle);
int count = NUM_PARTICLES;
bool correct = true;
int i, j;
cl_platform_id * platforms; cl_uint n_platform;
// Find OpenCL Platforms
cl_int err = clGetPlatformIDs(0, NULL, &n_platform); CHK_ERROR(err);
platforms = (cl_platform_id *) malloc(sizeof(cl_platform_id)*n_platform);
err = clGetPlatformIDs(n_platform, platforms, NULL); CHK_ERROR(err);
// Find and sort devices
cl_device_id *device_list; cl_uint n_devices;
err = clGetDeviceIDs( platforms[0], CL_DEVICE_TYPE_GPU, 0,NULL, &n_devices);CHK_ERROR(err);
device_list = (cl_device_id *) malloc(sizeof(cl_device_id)*n_devices);
err = clGetDeviceIDs( platforms[0],CL_DEVICE_TYPE_GPU, n_devices, device_list, NULL);CHK_ERROR(err);
// Create and initialize an OpenCL context
cl_context context = clCreateContext( NULL, n_devices, device_list, NULL, NULL, &err);CHK_ERROR(err);
if (!context) {
printf("Error: Failed to create a compute context!\n");
return EXIT_FAILURE;
}
// Create a command queue
cl_command_queue cmd_queue = clCreateCommandQueue(context, device_list[0], 0, &err);CHK_ERROR(err);
if (!cmd_queue) {
printf("Error: Failed to create a command queue!\n");
return EXIT_FAILURE;
}
/* Insert your own code here */
//program
cl_program program = clCreateProgramWithSource(context, 1, (const char **)&mykernel, NULL, &err);CHK_ERROR(err);
//Initialize values of the arrays
init_Array(X);
//Array on GPU part
cl_mem dx = clCreateBuffer(context, CL_MEM_READ_WRITE, array, NULL, &err);CHK_ERROR(err);
//Copy the values of X in dx
err = clEnqueueWriteBuffer(cmd_queue, dx, CL_TRUE, 0, array, X, 0, NULL, NULL);CHK_ERROR(err);
//Cpu part
double start = cpuSecond();
for(j = 0; j < NUM_ITERATIONS; j++) {
for(i = 0; i < NUM_PARTICLES; i++){
X[i].velocity[0] = (3*i + j) % NUM_PARTICLES;
X[i].velocity[1] = (4*i + j) % NUM_PARTICLES;
X[i].velocity[2] = (5*i + j) % NUM_PARTICLES;
X[i].position[0] = X[i].position[0] + X[i].velocity[0];
X[i].position[1] = X[i].position[1] + X[i].velocity[1];
X[i].position[2] = X[i].position[2] + X[i].velocity[2];
}
}
double totalCpu = cpuSecond() - start;
printf("Computing SAXPY on the CPU... Done\n");
//build the program
err = clBuildProgram(program, 1, device_list, NULL, NULL, NULL);
if (err != CL_SUCCESS){
size_t len;
char buffer[2048];
clGetProgramBuildInfo(program, device_list[0], CL_PROGRAM_BUILD_LOG, sizeof(buffer), buffer, &len);
fprintf(stderr, "Build error: %s\n", buffer);
return 0;
}
//Create the kernel function
cl_kernel kernel = clCreateKernel(program, "kernel_upload", &err);CHK_ERROR(err);
//More argument, number of workitems we are going to use
unsigned int mult = BLOCK_SIZE;
unsigned int size = BLOCK_SIZE;
while(mult < NUM_PARTICLES){
mult = mult * 2;
}
size_t n_workitem[1] = {mult};
size_t workgroup_size[1] = {size};
err = clSetKernelArg(kernel, 1, sizeof(int), (void*) &count);CHK_ERROR(err);
err = clSetKernelArg(kernel, 2, sizeof(cl_mem), (void*) &dx);CHK_ERROR(err);
double st = cpuSecond();
for(i = 0; i < NUM_ITERATIONS; i++){
//Arguments for the Kernel function
err = clSetKernelArg(kernel, 0, sizeof(int), (void*) &i);CHK_ERROR(err);
//Launch kernel
err = clEnqueueNDRangeKernel(cmd_queue, kernel, 1, NULL, n_workitem, workgroup_size, 0, NULL, NULL);CHK_ERROR(err);
//Wait for everything to finish
err = clFlush(cmd_queue);
err = clFinish(cmd_queue);
}
err = clEnqueueReadBuffer(cmd_queue, dx, CL_TRUE, 0, array, results, 0, NULL, NULL);CHK_ERROR(err);
double timeGpu = cpuSecond() - st;
printf("Computing SACY on the GPU... Done\n");
for(int i = 0; i < NUM_PARTICLES; i++){
for(int dim = 0; dim < 3; dim++){
if(abs(results[i].position[dim] - X[i].position[dim]) > error ){
correct = false;
printf("Error en %d i en dim %d, resultado %f i %f\n",i,dim,results[i].position[1],X[i].position[1]);
break;
}
}
if(!correct) {break;}
}
if (correct) {
printf("Comparing the output for each implementation... Correct!\n");
}
else {
printf("Comparing the output for each implementation... Incorrect!\n");
}
printf("NUM_PARTICLES: %d\n", NUM_PARTICLES);
printf("BLOCK_SIZE: %d\n", BLOCK_SIZE);
printf("NUM_ITERATIONS: %d\n", NUM_ITERATIONS);
printf("Execution time on CPU: %f\n", totalCpu);
printf("Execution time on GPU: %f\n", timeGpu);
// Finally, release all that we have allocated.
clReleaseMemObject(dx);CHK_ERROR(err);
clReleaseProgram(program);CHK_ERROR(err);
clReleaseKernel(kernel);CHK_ERROR(err);
err = clReleaseCommandQueue(cmd_queue);CHK_ERROR(err);
err = clReleaseContext(context);CHK_ERROR(err);
free(platforms);
free(device_list);
return 0;
}
// The source for this particular version is from: https://stackoverflow.com/questions/24326432/convenient-way-to-show-opencl-error-codes
const char* clGetErrorString(int errorCode) {
switch (errorCode) {
case 0: return "CL_SUCCESS";
case -1: return "CL_DEVICE_NOT_FOUND";
case -2: return "CL_DEVICE_NOT_AVAILABLE";
case -3: return "CL_COMPILER_NOT_AVAILABLE";
case -4: return "CL_MEM_OBJECT_ALLOCATION_FAILURE";
case -5: return "CL_OUT_OF_RESOURCES";
case -6: return "CL_OUT_OF_HOST_MEMORY";
case -7: return "CL_PROFILING_INFO_NOT_AVAILABLE";
case -8: return "CL_MEM_COPY_OVERLAP";
case -9: return "CL_IMAGE_FORMAT_MISMATCH";
case -10: return "CL_IMAGE_FORMAT_NOT_SUPPORTED";
case -12: return "CL_MAP_FAILURE";
case -13: return "CL_MISALIGNED_SUB_BUFFER_OFFSET";
case -14: return "CL_EXEC_STATUS_ERROR_FOR_EVENTS_IN_WAIT_LIST";
case -15: return "CL_COMPILE_PROGRAM_FAILURE";
case -16: return "CL_LINKER_NOT_AVAILABLE";
case -17: return "CL_LINK_PROGRAM_FAILURE";
case -18: return "CL_DEVICE_PARTITION_FAILED";
case -19: return "CL_KERNEL_ARG_INFO_NOT_AVAILABLE";
case -30: return "CL_INVALID_VALUE";
case -31: return "CL_INVALID_DEVICE_TYPE";
case -32: return "CL_INVALID_PLATFORM";
case -33: return "CL_INVALID_DEVICE";
case -34: return "CL_INVALID_CONTEXT";
case -35: return "CL_INVALID_QUEUE_PROPERTIES";
case -36: return "CL_INVALID_COMMAND_QUEUE";
case -37: return "CL_INVALID_HOST_PTR";
case -38: return "CL_INVALID_MEM_OBJECT";
case -39: return "CL_INVALID_IMAGE_FORMAT_DESCRIPTOR";
case -40: return "CL_INVALID_IMAGE_SIZE";
case -41: return "CL_INVALID_SAMPLER";
case -42: return "CL_INVALID_BINARY";
case -43: return "CL_INVALID_BUILD_OPTIONS";
case -44: return "CL_INVALID_PROGRAM";
case -45: return "CL_INVALID_PROGRAM_EXECUTABLE";
case -46: return "CL_INVALID_KERNEL_NAME";
case -47: return "CL_INVALID_KERNEL_DEFINITION";
case -48: return "CL_INVALID_KERNEL";
case -49: return "CL_INVALID_ARG_INDEX";
case -50: return "CL_INVALID_ARG_VALUE";
case -51: return "CL_INVALID_ARG_SIZE";
case -52: return "CL_INVALID_KERNEL_ARGS";
case -53: return "CL_INVALID_WORK_DIMENSION";
case -54: return "CL_INVALID_WORK_GROUP_SIZE";
case -55: return "CL_INVALID_WORK_ITEM_SIZE";
case -56: return "CL_INVALID_GLOBAL_OFFSET";
case -57: return "CL_INVALID_EVENT_WAIT_LIST";
case -58: return "CL_INVALID_EVENT";
case -59: return "CL_INVALID_OPERATION";
case -60: return "CL_INVALID_GL_OBJECT";
case -61: return "CL_INVALID_BUFFER_SIZE";
case -62: return "CL_INVALID_MIP_LEVEL";
case -63: return "CL_INVALID_GLOBAL_WORK_SIZE";
case -64: return "CL_INVALID_PROPERTY";
case -65: return "CL_INVALID_IMAGE_DESCRIPTOR";
case -66: return "CL_INVALID_COMPILER_OPTIONS";
case -67: return "CL_INVALID_LINKER_OPTIONS";
case -68: return "CL_INVALID_DEVICE_PARTITION_COUNT";
case -69: return "CL_INVALID_PIPE_SIZE";
case -70: return "CL_INVALID_DEVICE_QUEUE";
case -71: return "CL_INVALID_SPEC_ID";
case -72: return "CL_MAX_SIZE_RESTRICTION_EXCEEDED";
case -1002: return "CL_INVALID_D3D10_DEVICE_KHR";
case -1003: return "CL_INVALID_D3D10_RESOURCE_KHR";
case -1004: return "CL_D3D10_RESOURCE_ALREADY_ACQUIRED_KHR";
case -1005: return "CL_D3D10_RESOURCE_NOT_ACQUIRED_KHR";
case -1006: return "CL_INVALID_D3D11_DEVICE_KHR";
case -1007: return "CL_INVALID_D3D11_RESOURCE_KHR";
case -1008: return "CL_D3D11_RESOURCE_ALREADY_ACQUIRED_KHR";
case -1009: return "CL_D3D11_RESOURCE_NOT_ACQUIRED_KHR";
case -1010: return "CL_INVALID_DX9_MEDIA_ADAPTER_KHR";
case -1011: return "CL_INVALID_DX9_MEDIA_SURFACE_KHR";
case -1012: return "CL_DX9_MEDIA_SURFACE_ALREADY_ACQUIRED_KHR";
case -1013: return "CL_DX9_MEDIA_SURFACE_NOT_ACQUIRED_KHR";
case -1093: return "CL_INVALID_EGL_OBJECT_KHR";
case -1092: return "CL_EGL_RESOURCE_NOT_ACQUIRED_KHR";
case -1001: return "CL_PLATFORM_NOT_FOUND_KHR";
case -1057: return "CL_DEVICE_PARTITION_FAILED_EXT";
case -1058: return "CL_INVALID_PARTITION_COUNT_EXT";
case -1059: return "CL_INVALID_PARTITION_NAME_EXT";
case -1094: return "CL_INVALID_ACCELERATOR_INTEL";
case -1095: return "CL_INVALID_ACCELERATOR_TYPE_INTEL";
case -1096: return "CL_INVALID_ACCELERATOR_DESCRIPTOR_INTEL";
case -1097: return "CL_ACCELERATOR_TYPE_NOT_SUPPORTED_INTEL";
case -1000: return "CL_INVALID_GL_SHAREGROUP_REFERENCE_KHR";
case -1098: return "CL_INVALID_VA_API_MEDIA_ADAPTER_INTEL";
case -1099: return "CL_INVALID_VA_API_MEDIA_SURFACE_INTEL";
case -1100: return "CL_VA_API_MEDIA_SURFACE_ALREADY_ACQUIRED_INTEL";
case -1101: return "CL_VA_API_MEDIA_SURFACE_NOT_ACQUIRED_INTEL";
default: return "CL_UNKNOWN_ERROR";
}
}
|
7,095 | #include <cstdlib>
#include <cstdio>
#include <cmath>
#include <fstream>
#include <vector>
#include <iostream>
#include <cassert>
#define size 6
#define MAX_RAY_DEPTH 3
const unsigned int window_width = 512;
const unsigned int window_height = 512;
struct Sphere{
float3 color;
float radius;
float3 center;
float3 emissionColor;
float transparency;
float reflection;
__device__ bool hit(float3 origin, float3 direct, float *t0, float *t1) {
float3 l = make_float3(center.x - origin.x, center.y-origin.y, center.z-origin.z);
float tca = l.x*direct.x + l.y*direct.y+ l.z*direct.z;
if(tca <0) return false;
float d2 = l.x*l.x + l.y*l.y + l.z*l.z - tca*tca;
if(d2 > radius*radius) return false;
float thc = sqrtf(radius*radius - d2);
*t0 = tca -thc;
*t1 = tca +thc;
return true;
}
};
__constant__ Sphere s[size];
__device__ void normalize(float3 &f)
{
float temp = sqrtf(f.x*f.x + f.y*f.y + f.z*f.z);
f.x = f.x/temp;
f.y = f.y/temp;
f.z = f.z/temp;
}
__device__ float mix(float a, float b, float mix)
{
return b * mix + a * (1 - mix);
}
__device__ float3 trace(float3 origin, float3 direct, int depth)
{
float dist = INFINITY;
int intersect_object = -1;
for(unsigned i=0; i<size; i++){
float t0 = INFINITY, t1 = INFINITY;
if(s[i].hit(origin, direct, &t0, &t1))
{
if(t0<0) t0 = t1;
if(t0< dist){
dist = t0;
intersect_object = i;
}
}
}
if(intersect_object == -1){
float3 result = make_float3(2, 2, 2);
return result;
}
float3 surfaceColor = make_float3(0,0,0);
float3 phit = make_float3(origin.x + direct.x*dist,origin.y + direct.y*dist,origin.z + direct.z*dist);
float3 nhit = make_float3(phit.x-s[intersect_object].center.x, phit.y-s[intersect_object].center.y, phit.z-s[intersect_object].center.z);
normalize(nhit);
float bias = 1e-4;
bool inside = false;
if( (direct.x*nhit.x+direct.y*nhit.y+direct.z*nhit.z) > 0){
nhit.x = (-1)*nhit.x;
nhit.y = (-1)*nhit.y;
nhit.z = (-1)*nhit.z;
inside = true;
}
if((s[intersect_object].transparency > 0 || s[intersect_object].reflection > 0 ) && depth < MAX_RAY_DEPTH)
{
float tmp = direct.x*nhit.x + direct.y*nhit.y + direct.z*nhit.z;
float facingratio = (-1)*tmp;
float fresneleffect = mix(pow(1-facingratio, 3), 1, 0.1);
float3 refldir = make_float3(direct.x - 2*tmp*nhit.x, direct.y - 2*tmp*nhit.y, direct.z - 2*tmp*nhit.z);
normalize(refldir);
float3 newReflRay = make_float3(phit.x + nhit.x*bias, phit.y + nhit.y*bias, phit.z + nhit.z*bias);
float3 reflection = trace(newReflRay, refldir, depth+1);
float3 refraction = make_float3(0,0,0);
if(s[intersect_object].transparency){
float ior = 1.1, eta;
if(inside) eta = ior; else eta = 1/ior;
float cosi = (-1)*(nhit.x*direct.x + nhit.y*direct.y + nhit.z*direct.z);
float k = 1 -eta*eta*(1-cosi*cosi);
float3 refrdir = make_float3(direct.x*eta+nhit.x*(eta*cosi-sqrt(k)), direct.y*eta+nhit.y*(eta*cosi-sqrt(k)), direct.z*eta+nhit.z*(eta*cosi-sqrt(k)));
normalize(refrdir);
float3 newRefrRay = make_float3(phit.x-bias*nhit.x, phit.y-bias*nhit.y, phit.z-bias*nhit.z);
refraction = trace(newRefrRay, refrdir, depth+1);
}
surfaceColor.x = (reflection.x * fresneleffect + refraction.x * (1 - fresneleffect) * s[intersect_object].transparency )*s[intersect_object].color.x;
surfaceColor.y = (reflection.y * fresneleffect + refraction.y * (1 - fresneleffect) * s[intersect_object].transparency )*s[intersect_object].color.y;
surfaceColor.z = (reflection.z * fresneleffect + refraction.z * (1 - fresneleffect) * s[intersect_object].transparency )*s[intersect_object].color.z;
}
else{
// diffuse
for(unsigned i=0; i<size; i++){
if(s[i].emissionColor.x>0){
float3 transmission = make_float3(1,1,1);
float3 lightDirection = make_float3(s[i].center.x-phit.x, s[i].center.y-phit.y, s[i].center.z-phit.z);
normalize(lightDirection);
for(unsigned j=0; j< size; j++){
if(i!=j){
float t0, t1;
float3 newRay = make_float3(phit.x+bias*nhit.x, phit.y+bias*nhit.y, phit.z+bias*nhit.z);
if(s[j].hit(newRay, lightDirection, &t0, &t1)){
transmission.x = 0; transmission.y=0; transmission.z=0;
break;
}
}
}
float tmp = nhit.x*lightDirection.x+nhit.y*lightDirection.y+nhit.z*lightDirection.z;
if(tmp<0) tmp = 0;
surfaceColor.x = surfaceColor.x+ s[intersect_object].color.x*transmission.x*tmp*s[i].emissionColor.x;
surfaceColor.y = surfaceColor.y+ s[intersect_object].color.y*transmission.y*tmp*s[i].emissionColor.y;
surfaceColor.z = surfaceColor.z+ s[intersect_object].color.z*transmission.z*tmp*s[i].emissionColor.z;
}
}
}
float3 result2 = make_float3(surfaceColor.x+s[intersect_object].emissionColor.x,
surfaceColor.y+s[intersect_object].emissionColor.y,
surfaceColor.z+s[intersect_object].emissionColor.z);
return result2;
}
__global__ void tracer_kernel(float3 *image)
{
int x = blockIdx.x*blockDim.x + threadIdx.x;
int y = blockIdx.y*blockDim.y + threadIdx.y;
if(x < window_width && y < window_height)
{
//----------compute ray----------------/
float window_ratio = window_width / float(window_height);
float fov = 90;
float angle = tan(fov * 0.5 * M_PI / 180.0);
float u = (2.0 * (x+0.5)/(float)window_width -1) * angle * window_ratio;
float v = (1 - 2.0 * (y+0.5) / (float) window_height) * angle;
float3 origin = make_float3(0,0,0);
float3 direct = make_float3(u,v,-1);
normalize(direct);
//----------compute ray--------------//
image[x+y*window_height] = trace(origin, direct, 0);
}
}
void init_kernel()
{
Sphere *temp_s = (Sphere*)malloc(sizeof(Sphere)*size);
temp_s[0].center = make_float3(0, 0, -20);
temp_s[0].radius = 4;
temp_s[0].color = make_float3(1.00, 0.32, 0.36);
temp_s[0].emissionColor = make_float3(0,0,0);
temp_s[0].transparency = 0.5;
temp_s[0].reflection = 1;
temp_s[1].center = make_float3(0, 20, -30);
temp_s[1].radius = 3;
temp_s[1].color = make_float3(0, 0, 0);
temp_s[1].emissionColor = make_float3(3, 3, 3);
temp_s[1].transparency = 0;
temp_s[1].reflection = 0;
temp_s[2].center = make_float3(0, -10004, -20);
temp_s[2].radius = 10000;
temp_s[2].color = make_float3(0.2, 0.2, 0.2);
temp_s[2].emissionColor = make_float3(0, 0, 0);
temp_s[2].transparency = 0;
temp_s[2].reflection = 0;
temp_s[3].center = make_float3(5, -1, -15);
temp_s[3].radius = 2;
temp_s[3].color = make_float3(0.9, 0.76, 0.46);
temp_s[3].emissionColor = make_float3(0, 0, 0);
temp_s[3].transparency = 0;
temp_s[3].reflection = 1;
temp_s[4].center = make_float3(5, 0, -25);
temp_s[4].radius = 3;
temp_s[4].color = make_float3(0.65, 0.77, 0.97);
temp_s[4].emissionColor = make_float3(0, 0, 0);
temp_s[4].transparency = 0;
temp_s[4].reflection = 1;
temp_s[5].center = make_float3(-5.5, -0, -15);
temp_s[5].radius = 3;
temp_s[5].color = make_float3(0.9, 0.9, 0.9);
temp_s[5].emissionColor = make_float3(0, 0, 0);
temp_s[5].transparency = 0;
temp_s[5].reflection = 1;
for(int i=0; i<size; i++){
printf("x,y,z: %f, %f, %f, radius: %f\n", temp_s[i].center.x, temp_s[i].center.y,temp_s[i].center.z,temp_s[i].radius);
}
size_t sz = size*sizeof(Sphere);
cudaMemcpyToSymbol(s, temp_s, sz, size_t(0), cudaMemcpyHostToDevice);
free(temp_s);
}
int main(void)
{
init_kernel();
float3 *image;
cudaMalloc((void **)&image, sizeof(float3)*window_width*window_height);
dim3 block(8,8,1);
dim3 grid(window_width / block.x, window_height / block.y, 1);
tracer_kernel<<<grid, block>>>(image);
cudaDeviceSynchronize();
float3 h_image[window_width*window_height];
cudaMemcpy(&h_image[0], image, sizeof(float3)*window_width*window_height, cudaMemcpyDeviceToHost);
std::ofstream ofs("./yuxin.ppm", std::ios::out | std::ios::binary);
ofs << "P6\n" << window_width << " " << window_height << "\n255\n";
for (unsigned i = 0; i < window_width * window_height; ++i) {
ofs << (unsigned char)(std::min(float(1), h_image[i].x) *255) <<
(unsigned char)(std::min(float(1), h_image[i].y) *255) <<
(unsigned char)(std::min(float(1), h_image[i].z) *255);
}
ofs.close();
}
|
7,096 | #include "test.cuh"
template <class T>
Test<T>::Test()
{
this->a = 1;
this->b = 1;
}
template <class T>
int Test<T>::sum(int a, int b)
{
return a + b;
}
|
7,097 | #include "device_launch_parameters.h"
#include <iostream>
#include <stdio.h>
#include <cuda_runtime.h>
#include <time.h>
using namespace std;
#define eps 1e-4
__global__ void accumulate(float *da, float* ans_device, int N){
int bx = blockIdx.x;
int tx = threadIdx.x;
int idx = bx * blockDim.x + tx;
//printf("%d\n", idx);
for(int stride = N / 2; stride > 0; stride >>= 1){
if(idx < stride){
da[idx] = da[idx] + da[idx + stride];
}
__syncthreads();
}
if(idx == 0){
ans_device[0] = da[idx];
//printf("ans 0: %f\n", ans_device[0]);
}
}
float accumulate_cpu(float *da, int size){
if(size == 1)
return da[0];
int newsize = size / 2;
int stride = newsize;
for(int i = 0; i < newsize; i++){
da[i] = da[i] + da[i + stride];
}
if(size % 2 == 1){
da[0] = da[0] + da[size - 1];
}
else{
;
}
return accumulate_cpu(da, newsize);
}
void check(float *ha, float *ans_host, int N){
float sum = 0;
//cout<<sum<<' '<<ans_host[0]<<endl;
for(int i = 0; i < N; i++){
sum += ha[i];
}
if(sum == ans_host[0]){
cout<<"Nice ! Equal !!!"<<endl;
}
else{
cout<<"Bad ! Not Equal !"<<endl;
}
}
int main(){
int N = 1<<8;
size_t size = N * sizeof(float);
float *ha = (float*)malloc(size);
float *ans_host = (float*)malloc(1*sizeof(float));
for(int i = 0; i < N; i++)
ha[i] = 1;
//float ans = accumulate_cpu(ha, N);
//cout<<ans<<endl;
float *da = NULL;
float *ans_device = NULL;
cudaMalloc((void**)&da, size);
cudaMalloc((void**)&ans_device, 1*sizeof(float));
cudaMemcpy(da, ha, size, cudaMemcpyHostToDevice);
//dim3 threadPerBlock(N);
//dim3 blockPerGrid(1);
dim3 threadPerBlock(32);
dim3 blockPerGrid((N + threadPerBlock.x - 1) / threadPerBlock.x);
accumulate<<<blockPerGrid, threadPerBlock>>> (da, ans_device, N);
cudaDeviceSynchronize();
cudaMemcpy(ans_host, ans_device, 1*sizeof(float), cudaMemcpyDeviceToHost);
check(ha, ans_host, N);
free(ans_host);
free(ha);
cudaFree(ans_device);
cudaFree(da);
return 0;
} |
7,098 |
#ifdef _INTELLISENSE_
void __syncthreads();
#endif
#include <stdio.h>
#include <iostream>
#include "cuda.h"
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <device_functions.h>
#define DIMBLOCKX 32
template<class T>
void print_vector(T* v, unsigned s) {
for (unsigned i = 0; i < s; i++) {
std::cout << v[i] << " ";
}
std::cout << "\n";
}
__global__ void SumaColMatrizKernel(int M, float* Md, float* Nd) {
__shared__ float Nds[DIMBLOCKX];
float Pvalue = 0;
int columna = blockIdx.x;
int pasos = M / blockDim.x;
int posIni = columna * M + threadIdx.x * pasos;
for (int k = 0; k < pasos; ++k) {
Pvalue = Pvalue + Md[posIni + k];
}
Nds[threadIdx.x] = Pvalue;
__syncthreads();
if (threadIdx.x == 0) {
for (int i = 1; i < blockDim.x; ++i) {
Nds[0] = Nds[0] + Nds[i];
}
Nd[blockIdx.x] = Nds[0];
}
}
void SumaColMatriz(int M, int N, int* Mh, int* Nh) {
int size = M * N * sizeof(float), size2 = N * sizeof(float);
float* Md, * Nd;
// Allocate en device
cudaMalloc(&Md, size);
cudaMalloc(&Nd, size2);
// Inicializo matrices en el device
// Inicializo matrices en el device
cudaMemcpy(Md, Mh, size, cudaMemcpyHostToDevice);
cudaMemset(Nd, 0, size2);
// Invocar el kernel que suma en GPU
// configuracin de la ejecucin
int chunk = 32;
dim3 tamGrid(N, 1); //Grid dimensin
dim3 tamBlock(M / chunk,1, 1); //Block dimensin
// lanzamiento del kernel
SumaColMatrizKernel <<<tamGrid, tamBlock >>> (M, Md, Nd);
// Traer resultado;
cudaMemcpy(Nh, Nd, size2, cudaMemcpyDeviceToHost);
// Free matrices en device
}
int main()
{
int M = 32;
int N = 64;
int* Mh = new int[M * N];
int* Rh = new int[N];
for (int i = 0; i < M; i++) {
for (int j = 0; j < N; j++) {
Mh[(i * N) + j] = j;
}
}
SumaColMatriz(M, N, Mh, Rh);
print_vector(Rh, N);
delete[] Mh;
delete[] Rh;
return 0;
}
|
7,099 | #include <stdio.h>
#ifndef UNABLE_LOG
#define elog(...) fprintf(stderr, __VA_ARGS__)
#else
#define elog(...) ;
#endif
#define exit_failure(...) \
do \
{ \
printf(__VA_ARGS__); \
exit(EXIT_FAILURE); \
} while (0)
#define CUDA_CHECK(call) \
do \
{ \
const cudaError_t e = call; \
if (e != cudaSuccess) \
exit_failure("Error: %s:%d code:%d, reason: %s\n", __FILE__, \
__LINE__, e, cudaGetErrorString(e)); \
} while (0)
__global__ void kernel(int *ptr, int *plan)
{
plan[threadIdx.x] = ptr[threadIdx.x] * 10;
}
__host__ static void *
cudaPalloc(size_t size)
{
void *ptr;
elog("palloc size=%zu\n", size);
CUDA_CHECK(cudaMalloc(&ptr, size));
return ptr;
}
__host__ static void
cudaPfree(void *ptr)
{
CUDA_CHECK(cudaFree(ptr));
}
#define SIZE (n * sizeof(int))
int main(void)
{
int n = 16;
int ar[123456];
int plan[124356];
for (int i = 0; i < 123456; ++i)
ar[i] = i;
int * d_ptr = (int *) cudaPalloc(SIZE);
int * d_plan = (int *) cudaPalloc(SIZE);
for (int i = 0; i < 10; ++i)
{
CUDA_CHECK(cudaMemset(d_ptr, 0, SIZE));
CUDA_CHECK(cudaMemcpy(d_ptr, ar, SIZE, cudaMemcpyHostToDevice));
kernel<<<1, 1>>>(d_ptr, d_plan);
CUDA_CHECK(cudaMemcpy(plan, d_plan, SIZE, cudaMemcpyDeviceToHost));
n *= 2;
cudaPfree(d_ptr);
d_ptr = (int *) cudaPalloc(SIZE);
cudaPfree(d_plan);
d_plan = (int *) cudaPalloc(SIZE);
}
cudaPfree(d_ptr);
cudaPfree(d_plan);
return 0;
}
|
7,100 | #include <stdio.h>
extern "C" __global__ void matSum(int *a, int i)
{
int tid = blockIdx.x;
if (threadIdx.x == 0) printf("my block id is %d, a is %d\n", tid, *a);
clock_t start = clock();
clock_t now;
printf("i is %d\n", i);
return;
for (;;) {
now = clock();
clock_t cycles = now > start ? now - start : now + (0xffffffff - start);
if (cycles >= 100000) {
printf("A is %d\n", *a);
start = clock();
//break;
}
}
}
|
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