serial_no
int64 1
24.2k
| cuda_source
stringlengths 11
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1,001
|
#include "includes.h"
__device__ int d(void) { return 8; }
__global__ void g(void) {}
|
1,002
|
__global__ void step(
int n,
float *xy,
int *num_edges,
int *first,
int *num,
int *map,
int *potential,
float stp,
float reject_stp,
float attract_stp,
float spring_stp,
float spring_reject_rad,
float spring_attract_rad,
float node_rad
){
const int i = blockIdx.x*512 + threadIdx.x;
if (i>=n) {
return;
}
float sx = 0;
float sy = 0;
float dx = 0;
float dy = 0;
float dd = 0;
int j;
int jj;
int aa;
int count = 0;
bool linked;
float ja;
float ia;
const int ii = 2*i;
for (int k=0;k<num[i];k++){
j = map[first[i]+k];
jj = 2*j;
dx = xy[ii] - xy[jj];
dy = xy[ii+1] - xy[jj+1];
dd = sqrt(dx*dx + dy*dy);
linked = true;
for (int l=0;l<num[i];l++){
aa = 2*map[first[i]+l];
ia = sqrt(powf(xy[ii] - xy[aa],2.0) + powf(xy[ii+1] - xy[aa+1],2.0));
ja = sqrt(powf(xy[jj] - xy[aa],2.0) + powf(xy[jj+1] - xy[aa+1],2.0));
if (dd>max(ia,ja)){
linked = false;
break;
}
}
if (dd>0.0){
dx /= dd;
dy /= dd;
if (linked){
/*if (dd<2*spring_attract_rad && linked){*/
count += 1;
if (dd>spring_attract_rad){
sx += -dx*spring_stp;
sy += -dy*spring_stp;
}
else if(dd<spring_reject_rad){
sx += dx*spring_stp;
sy += dy*spring_stp;
}
}
else{ // unlinked
if (potential[i]>0 && potential[j]>0){
sx += -dx*attract_stp;
sy += -dy*attract_stp;
}
else{
sx += dx*reject_stp;
sy += dy*reject_stp;
}
}
}
}
__syncthreads();
xy[ii] = xy[ii] + sx*stp;
xy[ii+1] = xy[ii+1] + sy*stp;
num_edges[i] = count;
}
|
1,003
|
// SPDX-FileCopyrightText: 2020 CERN
// SPDX-License-Identifier: Apache-2.0
#include <curand.h>
int main()
{
// What are the ways to transform the CPU `fisher_price` to CUDA/GPU
// An obvious branch/divergence (eloss vs pair), but good exercise
// As first steps in microkernel workflow.
return 0;
}
|
1,004
|
#include <stdio.h>
#include <cmath>
#include <math.h>
#include <ctime>
__global__ void findwindow (bool* mask_img,
int* scores) {
int wh_p = blockIdx.x * blockDim.x + threadIdx.x; // threadIdx.x;
int ui = blockIdx.y * blockDim.y + threadIdx.y; // 60
int vi = blockIdx.z * blockDim.z + threadIdx.z; // 80
int wh = 16;
int cwhp = wh_p;
while(cwhp > 0) {
wh*=2;
cwhp--;
}
if (ui == 0 && vi == 0) {
//printf("wh: %d; wh_p: %d \n", wh, wh_p);
}
int score_id = wh_p * 60 * 80 + ui * 80 + vi;
int start_u = ui*8; //60 480
int start_v = vi*8; //80 640
int sc = 0;
if (start_u + wh >= 480 || start_v + wh >= 640) {
scores[score_id] = 0;
return;
}
for (int cu = ui*8; cu < ui*8+wh; cu++) {
for (int cv = vi*8; cv < vi*8+wh; cv++) {
int mask_id = cu * 640 + cv;
if (mask_img[mask_id]) {
sc = sc+1;
//printf("cu: %d; cv: %d; mask_id: %d\n" , cu, cv, mask_id);
}
}
}
scores[score_id] = sc;
if (sc > 0 && ui == 0 && vi == 0) {
//printf("sc: %d wh: %d ui: %d vi: %d \n", sc, wh, ui, vi);
}
}
__global__ void loop_2d_bbox (int* scores,
int* uvl) {
//float mul = 1.3*1.3*1.3*1.3*1.3;
float mul = 3*3*3*3*3;
float final_c_best = 0.;
//printf("start! \n");
for (int idx = 0; idx < 4; idx++) {
//mul /= 3.5;
mul /= 3;
int icbest = 0;
int csu = 0, csv = 0, cwh = 0;
for (int i = 0; i < 60; i++) {
for (int j = 0; j < 80; j++) {
if (scores[idx*4800+i*80+j] > icbest) {
icbest = scores[idx*4800+i*80+j];
csu = i*8;
csv = j*8;
cwh = idx;
//printf("icbest: %d su: %d, sv: %d, wh: %d\n", icbest, csu, csv, cwh);
}
}
}
if (float(icbest)*mul > final_c_best) {
final_c_best = float(icbest)*mul;
uvl[0] = csu;
uvl[1] = csv;
uvl[2] = cwh;
//printf("su: %d, sv: %d, wh: %d\n", csu, csv, cwh);
}
}
}
void rgb_window(bool* mask_img, int* scores, int* uvl) {
//dim3 grid(4, 1, 1);
//dim3 block(1, 60, 80);
dim3 grid(4, 60, 80);
dim3 block(1, 1, 1);
findwindow<<<grid, block>>>(mask_img, scores);
dim3 loop_grid(1, 1, 1);
dim3 loop_block(1, 1, 1);
loop_2d_bbox<<<loop_grid, loop_block>>>(scores, uvl);
cudaDeviceSynchronize();
}
__global__ void para_find_loc (float* pts,
int ptnum,
int* scores,
float* xyz_limits) {
int d_ix = blockIdx.x * blockDim.x + threadIdx.x;
int d_iy = blockIdx.y * blockDim.y + threadIdx.y;
int d_iz = blockIdx.z * blockDim.z + threadIdx.z;
//printf("d_ix: %d d_iy: %d d_iz: %d\n", d_ix, d_iy, d_iz);
float start_x = xyz_limits[0];
float start_y = xyz_limits[2];
float start_z = xyz_limits[4];
//printf("start_x: %.0f start_y: %.0f start_z: %.0f\n", start_x, start_y, start_z);
float end_x = xyz_limits[1];
float end_y = xyz_limits[3];
float end_z = xyz_limits[5];
float cx = start_x + d_ix*10;
float cy = start_y + d_iy*10;
float cz = start_z + d_iz*10;
if (cx > end_x || cy > end_y || cz > end_z) {
//printf("cx: %.0f cy: %.0f cz: %.0f end_x: %.0f end_y: %.0f end_z: %.0f ", cx, cy, cz, end_x, end_y, end_z);
scores[d_ix*100*400+d_iy*400+d_iz] = 0;
return;
}
//printf("cx: %.0f cy: %.0f cz: %.0f end_x: %.0f end_y: %.0f end_z: %.0f \n", cx, cy, cz, end_x, end_y, end_z);
int cnt = 0;
for(int i = 0; i < ptnum; i++) {
float tx = pts[i*3];
float ty = pts[i*3+1];
float tz = pts[i*3+2];
if (tz > cz) continue;
float d2c = sqrt((tx-cx)*(tx-cx) + (ty-cy)*(ty-cy) + (tz-cz)*(tz-cz));
//printf("tx: %.0f ty: %.0f tz: %.0f d2c: %.0f\n", tx, ty, tz, d2c);
/*
if (d2c < 1000) {
printf("tx: %.0f ty: %.0f tz: %.0f d2c: %.0f\n", tx, ty, tz, d2c);
}
*/
//printf("tx: %.0f ty: %.0f tz: %.0f cx: %.0f cy: %.0f cz: %.0f \n", tx, ty, tz, cx, cy, cz);
//printf("tx: %.0f ty: %.0f tz: %.0f d2c: %.0f\n", tx, ty, tz, d2c);
if (d2c >= 50 && d2c <= 53 ) {
cnt += 1;
}
}
scores[d_ix*100*400+d_iy*400+d_iz] = cnt;
}
__global__ void find_best_score (int* scores,
float* xyz_limits,
float* device_pred_xyz) {
int c_best = 0;
device_pred_xyz[0] = -10000;
device_pred_xyz[1] = -10000;
device_pred_xyz[2] = -10000;
int ixmax = int((xyz_limits[1] - xyz_limits[0])/10);
if (ixmax > 100) ixmax = 100;
int iymax = int((xyz_limits[3] - xyz_limits[2])/10);
if (iymax > 100) iymax = 100;
int izmax = int((xyz_limits[5] - xyz_limits[4])/10);
//if (izmax > 400) izmax = 400;
if (izmax > 100) izmax = 100;
printf("ixmax : %d; iymax : %d; izmax : %d\n", ixmax, iymax, izmax);
for (int ix = 0; ix < ixmax; ix++) {
for (int iy = 0; iy < iymax; iy++) {
for (int iz = 0; iz < izmax; iz++) {
//c_best = c_best > scores[ix*100*400+iy*400+iz] ? c_best : scores[ix*100*400+iy*400+iz];
if (c_best < scores[ix*100*400+iy*400+iz]) {
c_best = scores[ix*100*400+iy*400+iz];
device_pred_xyz[0] = xyz_limits[0] + 10*ix;
device_pred_xyz[1] = xyz_limits[2] + 10*iy;
device_pred_xyz[2] = xyz_limits[4] + 10*iz;
//printf("Score: %d x: %.0f y: %.0f z:%.0f \n", c_best, device_pred_xyz[0], device_pred_xyz[1], device_pred_xyz[2]);
}
}
}
}
}
void find_loc(float* pts, int ptnum, int* scores, float* xyz_limits, float* device_pred_xyz) {
//dim3 grid(10, 100, 1);
//dim3 block(10, 1, 400);
dim3 grid(100, 100, 2);
dim3 block(1, 1, 50);
std::clock_t start, end;
start = std::clock();
para_find_loc<<<grid, block>>>(pts, ptnum, scores, xyz_limits);
end = std::clock();
printf("para_find_loc: %.3f ms\n", 1000. * (end - start)/CLOCKS_PER_SEC);
start = std::clock();
find_best_score<<<1, 1>>>(scores, xyz_limits, device_pred_xyz);
end = std::clock();
printf("find_best_score: %.3f ms\n", 1000. * (end - start)/CLOCKS_PER_SEC);
cudaDeviceSynchronize();
}
|
1,005
|
#include <thrust/device_vector.h>
#include <thrust/fill.h>
#include <thrust/sequence.h>
#include <iostream>
struct soma_impares {
__device__ __host__
int operator()(const double &x, const double &y) {
return x + y;
}
};
int main() {
thrust::device_vector<double> v(100);
thrust::sequence(v.begin(), v.end());
double d = thrust::reduce(v.begin(), v.end(), 0.0, soma_impares());
std::cout << v[99] << " " << d << "\n";
return 0;
}
|
1,006
|
// ref https://devblogs.nvidia.com/parallelforall/even-easier-introduction-cuda/
// ver 20170219 by jian
#include <iostream>
#include <math.h>
// ``kernel'' to add the elements of two arrays
__global__ void add(int n, float *x, float *y)
{
int index = blockIdx.x * blockDim.x + threadIdx.x;
int stride = blockDim.x * gridDim.x;
for (int i = index; i < n; i += stride)
y[i] = x[i] + y[i];
}
int main(void)
{
int N = 1<<20; // 1M elements
//float *x = new float[N];
//float *y = new float[N];
float *x,*y;
cudaMallocManaged(&x,N*sizeof(float));
cudaMallocManaged(&y,N*sizeof(float));
// initialize x and y arrays on the host
for (int i = 0; i < N; i++) {
x[i] = 1.0f;
y[i] = 2.0f;
}
// Run kernel on 1M elements on the CPU
int blockSize = 256;
int numBlocks = (N + blockSize - 1) / blockSize;
add<<<numBlocks,blockSize>>>(N, x, y);
// Wait for GPU to finish before accessing on host
cudaDeviceSynchronize();
// Check for errors (all values should be 3.0f)
float maxError = 0.0f;
for (int i = 0; i < N; i++)
maxError = fmax(maxError, fabs(y[i]-3.0f));
std::cout << "Max error: " << maxError << std::endl;
// Free memory
//delete [] x;
//delete [] y;
cudaFree(x);cudaFree(y);
return 0;
}
|
1,007
|
__global__ void k(int *input, int *output, int count)
{
int tid = threadIdx.x;
int nid = blockDim.x;
#pragma unroll 3
for(int i =tid; i<count; i+=nid)
{
output[i]=input[i]*16;
}
}
|
1,008
|
#include <stdio.h>
class CudaClass
{
public:
int* data;
CudaClass(int x) {
data = new int[1]; data[0] = x;
}
};
__global__ void useClass(CudaClass *cudaClass)
{
printf("%d\n", cudaClass->data[0]);
};
int main()
{
CudaClass c(1);
// create class storage on device and copy top level class
CudaClass *d_c;
cudaMalloc((void **)&d_c, sizeof(CudaClass));
cudaMemcpy(d_c, &c, sizeof(CudaClass), cudaMemcpyHostToDevice);
// make an allocated region on device for use by pointer in class
int *hostdata;
cudaMalloc((void **)&hostdata, sizeof(int));
cudaMemcpy(hostdata, c.data, sizeof(int), cudaMemcpyHostToDevice);
// copy pointer to allocated device storage to device class
cudaMemcpy(&(d_c->data), &hostdata, sizeof(int *), cudaMemcpyHostToDevice);
useClass<<<1,1>>>(d_c);
cudaDeviceSynchronize();
return 0;
}
|
1,009
|
//Just your regular Hello World file
// to be compiled with nvcc rather than gcc
#include <stdio.h>
int main(void) {
printf("Hello World from CPU!\n");
return 0;
}
|
1,010
|
#include <stdio.h>
#include <stdlib.h>
#define SIZE (1024*1024)
__global__ void addVector(float* left, float* right, float* result)
{
int idx = threadIdx.x;
result[idx] = left[idx] + right[idx];
}
__host__ int main()
{
float* vec1 = new float[SIZE];
float* vec2 = new float[SIZE];
float* vec3 = new float[SIZE];
for (int i = 0; i < SIZE; i++)
{
vec1[i] = i;
vec2[i] = i;
}
float* devVec1;
float* devVec2;
float* devVec3;
cudaEvent_t start, stop;
cudaMalloc((void**)&devVec1, sizeof(float) * SIZE);
cudaMalloc((void**)&devVec2, sizeof(float) * SIZE);
cudaMalloc((void**)&devVec3, sizeof(float) * SIZE);
cudaMemcpy(devVec1, vec1, sizeof(float) * SIZE, cudaMemcpyHostToDevice);
cudaMemcpy(devVec2, vec2, sizeof(float) * SIZE, cudaMemcpyHostToDevice);
int block = 512;
cudaEventCreate(&start);
cudaEventCreate(&stop);
cudaEventRecord(start);
addVector<<<SIZE/512, block>>>(devVec1, devVec2, devVec3);
cudaEventRecord(stop);
cudaEvent_t syncEvent;
cudaEventCreate(&syncEvent);
cudaEventRecord(syncEvent, 0);
cudaEventSynchronize(syncEvent);
cudaMemcpy(vec3, devVec3, sizeof(float) * SIZE, cudaMemcpyDeviceToHost);
float time = 0;
// for (int i = 0; i < SIZE; i++)
// printf("Element #%i: %.1f\n", i , vec3[i]);
cudaEventElapsedTime(&time, start, stop);
printf("Elapsed time: %f\n", time);
FILE *f = fopen("time.txt", "a+");
if (f == NULL) {
fprintf(stderr, "FILE ERROR!\n");
} else {
fprintf(f, "%f 512\n", time);
}
fclose(f);
cudaEventDestroy(syncEvent);
cudaEventDestroy(start);
cudaEventDestroy(stop);
cudaFree(devVec1);
cudaFree(devVec2);
cudaFree(devVec3);
return EXIT_SUCCESS;
}
|
1,011
|
#include <stdio.h>
#include <fstream>
#include <iostream>
#include <string.h>
#include <vector>
#include <stdlib.h>
#include <unistd.h>
#include <sys/time.h>
#include <time.h>
#include <cuda.h>
//#include "sha256.h"
#define uchar unsigned char // 8-bit byte
#define uint unsigned int // 32-bit word
//define for sha256
#define DBL_INT_ADD(a,b,c) if (a > 0xffffffff - (c)) ++b; a += c;
#define ROTLEFT(a,b) (((a) << (b)) | ((a) >> (32-(b))))
#define ROTRIGHT(a,b) (((a) >> (b)) | ((a) << (32-(b))))
#define CH(x,y,z) (((x) & (y)) ^ (~(x) & (z)))
#define MAJ(x,y,z) (((x) & (y)) ^ ((x) & (z)) ^ ((y) & (z)))
#define EP0(x) (ROTRIGHT(x,2) ^ ROTRIGHT(x,13) ^ ROTRIGHT(x,22))
#define EP1(x) (ROTRIGHT(x,6) ^ ROTRIGHT(x,11) ^ ROTRIGHT(x,25))
#define SIG0(x) (ROTRIGHT(x,7) ^ ROTRIGHT(x,18) ^ ((x) >> 3))
#define SIG1(x) (ROTRIGHT(x,17) ^ ROTRIGHT(x,19) ^ ((x) >> 10))
#define GPUerrchk(ans) { GPUassert((ans), __FILE__, __LINE__); }
//#define numThread 1024*22
typedef struct {
uchar data[64];
uint datalen;
uint bitlen[2];
uint state[8];
} SHA256_CTX;
__constant__ uint k[64] = {
0x428a2f98,0x71374491,0xb5c0fbcf,0xe9b5dba5,0x3956c25b,0x59f111f1,0x923f82a4,0xab1c5ed5,
0xd807aa98,0x12835b01,0x243185be,0x550c7dc3,0x72be5d74,0x80deb1fe,0x9bdc06a7,0xc19bf174,
0xe49b69c1,0xefbe4786,0x0fc19dc6,0x240ca1cc,0x2de92c6f,0x4a7484aa,0x5cb0a9dc,0x76f988da,
0x983e5152,0xa831c66d,0xb00327c8,0xbf597fc7,0xc6e00bf3,0xd5a79147,0x06ca6351,0x14292967,
0x27b70a85,0x2e1b2138,0x4d2c6dfc,0x53380d13,0x650a7354,0x766a0abb,0x81c2c92e,0x92722c85,
0xa2bfe8a1,0xa81a664b,0xc24b8b70,0xc76c51a3,0xd192e819,0xd6990624,0xf40e3585,0x106aa070,
0x19a4c116,0x1e376c08,0x2748774c,0x34b0bcb5,0x391c0cb3,0x4ed8aa4a,0x5b9cca4f,0x682e6ff3,
0x748f82ee,0x78a5636f,0x84c87814,0x8cc70208,0x90befffa,0xa4506ceb,0xbef9a3f7,0xc67178f2
};
//__constant__ uchar answer[32];
//==============cuda kernel=====================================
__device__ void sha256_transform(SHA256_CTX *ctx, uchar *data){
uint a,b,c,d,e,f,g,h,i,j,t1,t2,m[64];
for (i=0,j=0; i < 16; ++i, j += 4)
m[i] = (data[j] << 24) | (data[j+1] << 16) | (data[j+2] << 8) | (data[j+3]);
for ( ; i < 64; ++i)
m[i] = SIG1(m[i-2]) + m[i-7] + SIG0(m[i-15]) + m[i-16];
a = ctx->state[0];
b = ctx->state[1];
c = ctx->state[2];
d = ctx->state[3];
e = ctx->state[4];
f = ctx->state[5];
g = ctx->state[6];
h = ctx->state[7];
for (i = 0; i < 64; ++i) {
t1 = h + EP1(e) + CH(e,f,g) + k[i] + m[i];
t2 = EP0(a) + MAJ(a,b,c);
h = g;
g = f;
f = e;
e = d + t1;
d = c;
c = b;
b = a;
a = t1 + t2;
}
ctx->state[0] += a;
ctx->state[1] += b;
ctx->state[2] += c;
ctx->state[3] += d;
ctx->state[4] += e;
ctx->state[5] += f;
ctx->state[6] += g;
ctx->state[7] += h;
}
__device__ void sha256(SHA256_CTX *ctx, uchar *data, uchar *hash, int len){
//init sha256 data structure
ctx->datalen = 0;
ctx->bitlen[0] = 0;
ctx->bitlen[1] = 0;
ctx->state[0] = 0x6a09e667;
ctx->state[1] = 0xbb67ae85;
ctx->state[2] = 0x3c6ef372;
ctx->state[3] = 0xa54ff53a;
ctx->state[4] = 0x510e527f;
ctx->state[5] = 0x9b05688c;
ctx->state[6] = 0x1f83d9ab;
ctx->state[7] = 0x5be0cd19;
//update
uint i;
//uint len = 5; //need to fix!!
for (i=0; i < len; ++i) {
ctx->data[ctx->datalen] = data[i];
ctx->datalen++;
if (ctx->datalen == 64) {
sha256_transform(ctx,ctx->data);
DBL_INT_ADD(ctx->bitlen[0],ctx->bitlen[1],512);
ctx->datalen = 0;
}
}
//final
i = ctx->datalen;
if (ctx->datalen < 56) {
ctx->data[i++] = 0x80;
while (i < 56)
ctx->data[i++] = 0x00;
}
else {
ctx->data[i++] = 0x80;
while (i < 64)
ctx->data[i++] = 0x00;
sha256_transform(ctx,ctx->data);
memset(ctx->data,0,56);
}
//par here
DBL_INT_ADD(ctx->bitlen[0],ctx->bitlen[1],ctx->datalen * 8);
ctx->data[63] = ctx->bitlen[0];
ctx->data[62] = ctx->bitlen[0] >> 8;
ctx->data[61] = ctx->bitlen[0] >> 16;
ctx->data[60] = ctx->bitlen[0] >> 24;
ctx->data[59] = ctx->bitlen[1];
ctx->data[58] = ctx->bitlen[1] >> 8;
ctx->data[57] = ctx->bitlen[1] >> 16;
ctx->data[56] = ctx->bitlen[1] >> 24;
sha256_transform(ctx,ctx->data);
//we can paralized at here
for (i=0; i < 4; ++i) {
hash[i] = (ctx->state[0] >> (24-i*8)) & 0x000000ff;
hash[i+4] = (ctx->state[1] >> (24-i*8)) & 0x000000ff;
hash[i+8] = (ctx->state[2] >> (24-i*8)) & 0x000000ff;
hash[i+12] = (ctx->state[3] >> (24-i*8)) & 0x000000ff;
hash[i+16] = (ctx->state[4] >> (24-i*8)) & 0x000000ff;
hash[i+20] = (ctx->state[5] >> (24-i*8)) & 0x000000ff;
hash[i+24] = (ctx->state[6] >> (24-i*8)) & 0x000000ff;
hash[i+28] = (ctx->state[7] >> (24-i*8)) & 0x000000ff;
}
}
__device__ void sha256_hash(SHA256_CTX *ctx, uchar *data, uchar *hash, int len, int round){
sha256(ctx, data, hash, len);
while(round > 1){
sha256(ctx, hash, hash, 32);
round --;
}
}
__device__ int my_strcmp(uchar *str_a, uchar *str_b, uint len){
for(int i=0; i<len; i++){
if(str_a[i] != str_b[i])
return false;
}
return true;
}
__global__ void sha256_wrap(uchar *pwarray, uchar *target, int* pwcount, uchar *result){
int idx = threadIdx.x + blockDim.x * blockIdx.x;
uchar* data = (uchar*)malloc(pwcount[idx]*sizeof(uchar));
SHA256_CTX ctx;// = new SHA256_CTX;
uchar hash[32];
int round = 10000, count = 0;
for(int i=0; i<idx; i++){
count += pwcount[i];
}
memcpy(data,&pwarray[count],pwcount[idx]*sizeof(uchar));
sha256_hash(&ctx,data,hash,pwcount[idx],round);
for (int i=0; i<5;i++){
if(my_strcmp(hash,&target[32*i],32))
memcpy(result,data,pwcount[idx]*sizeof(uchar));
}
}
//====================================================================
void print_hash(unsigned char hash[]){
int idx;
for (idx=0; idx < 32; idx++)
printf("%02x",hash[idx]);
printf("\n");
}
void read_hash(char filename[],unsigned char target[5][32]){
FILE *rhash = fopen(filename,"r+");
fread(target[0],1,32,rhash);
fread(target[1],1,32,rhash);
fread(target[2],1,32,rhash);
fread(target[3],1,32,rhash);
fread(target[4],1,32,rhash);
fclose(rhash);
}
void cudasafe(cudaError_t error, char* message){
if(error!=cudaSuccess) { fprintf(stderr,"ERROR: %s : %i\n",message,error); exit(-1);}
}
//============================================================================
int main(int argc, char **argv){
std::string password;
std::vector<std::string> pwarray;
int tot_dict_size = 0;
int dict_size = 55*1024;
int numThread = 55*1024;
uchar target[5][32];
uchar *result = new uchar[32];
//variable for GPU
// uchar *dev_result;
uchar *dev_target;
// uchar *dev_password;
// int *dev_pwcount;
if(argc < 2){
std::cerr << "need load dictionary!! \n";
return 1;
}
//timing program
struct timeval starttime, endtime;
double runTime = 0.0;
gettimeofday(&starttime,NULL);
//read target hash
read_hash(argv[2],target);
//copy hash into cuda (maybe into constant memory?)
// cudaMalloc((void**)&dev_target,32*5*sizeof(uchar));
// for(int i=0;i<5;i++){
// cudaMemcpy((void*)&dev_target[32*i],target[i],32*sizeof(uchar),cudaMemcpyHostToDevice);
// }
//read from dictionary
std::ifstream dict(argv[1]);
if(!dict){
std::cerr << "No such file!! \n";
return 1;
}
while(std::getline(dict, password)){
pwarray.push_back(password);
tot_dict_size++;
}
uchar *pwstring = (uchar*)malloc(dict_size*32*sizeof(uchar));
int *pw_count = (int*)malloc(dict_size*sizeof(int));
//============================================================
for(int lp = 0; lp < tot_dict_size/numThread + 1; lp++){
//devpassword
//variable for GPU
uchar *dev_result;
uchar *dev_password;
int *dev_pwcount;
if(lp == tot_dict_size/numThread && tot_dict_size%numThread != 0){
dict_size = tot_dict_size % numThread;
}
//int *pw_count = (int*)malloc(dict_size*sizeof(int));
int temp_count = 0;
//uchar *pwstring = (uchar*)malloc(dict_size*32*sizeof(uchar));
for(int i=0; i<dict_size; i++){
pw_count[i] = pwarray.at(i+lp*numThread).length();
strcpy((char*)&pwstring[temp_count],pwarray.at(i+lp*numThread).c_str());
temp_count += pw_count[i];
}
cudasafe( cudaMalloc((void**)&dev_password,32*dict_size*sizeof(uchar)), "cudaMalloc");
cudasafe( cudaMemcpy((void*)dev_password,pwstring,32*dict_size*sizeof(uchar),cudaMemcpyHostToDevice), "cudaMemcpy");
cudaMalloc((void**)&dev_pwcount,dict_size*sizeof(int));
cudaMemcpy((void*)dev_pwcount,pw_count,dict_size*sizeof(int),cudaMemcpyHostToDevice);
cudaMalloc((void**)&dev_target,32*5*sizeof(uchar));
for(int i=0;i<5;i++){
cudaMemcpy((void*)&dev_target[32*i],target[i],32*sizeof(uchar),cudaMemcpyHostToDevice);
}
cudaMalloc((void**)&dev_result,32*sizeof(uchar));
cudaMemset(dev_result,0,32*sizeof(uchar));
cudaDeviceSynchronize();
dim3 DimBlock(1024,1);
dim3 DimGrid(55,1);
sha256_wrap <<< DimGrid, DimBlock >>> (dev_password, dev_target, dev_pwcount, dev_result);
cudaDeviceSynchronize();
cudaMemcpy(result,dev_result,32*sizeof(uchar),cudaMemcpyDeviceToHost);
if(strlen((const char*)result)!= 0)
printf("password: %s \n", result);
memset(result, 0, strlen((const char*)result));
cudaDeviceReset();
// cudaFree(dev_result); cudaFree(dev_password); cudaFree(dev_pwcount);
}
gettimeofday(&endtime,NULL);
runTime=1000000*(endtime.tv_sec-starttime.tv_sec)+endtime.tv_usec-starttime.tv_usec;
runTime=runTime/1000;
printf("timing: %f ms \n",runTime);
cudaFree(dev_target);
return 0;
}
|
1,012
|
#include <stdio.h>
__device__ void MatrixSquare(void *input)
{
float *matrix = (float *) input;
int warp_size=32;
int thread = threadIdx.x % warp_size;
int matrixWidth = 32;
for (unsigned int i = thread; i < matrixWidth; i=i+32)
{
for (unsigned int j = 0; j < matrixWidth; j++) {
float sum = 0;
for (unsigned int k = 0; k < matrixWidth; k++) {
float a = matrix[i * matrixWidth + k];
float b = matrix[k * matrixWidth + j];
sum += a * b;
}
matrix[i * matrixWidth + j + (matrixWidth * matrixWidth)] = sum;
}
}
}
|
1,013
|
#include <cuda.h>
#include <stdio.h>
#include <iostream>
#include <time.h>
#define T 4
using namespace std;
//prod matrice matrice terza versione(vedi slide)
//input: l,m,n, size blocco (blocchi bidimensionali, quadrati, l m ed n devono essere multipli interi di sizeblocco)
__host__ void allocaEInizializzaMatrice(int **res,int m,int n){
*res=new int[m*n];
for(int i=0;i<m;i++)
for(int j=0;j<n;j++)
*((*res)+i*n+j)=1+rand()%10;
}
__host__ void stampaMatrice(int *a,int m,int n){
cout<<"--------------------------------------"<<endl;
for(int i=0;i<m;i++){
for(int j=0;j<n;j++)
cout<<a[i*n+j]<<" ";
cout<<endl;
}
}
__host__ void matMatCPU(int *a,int *b,int *res,int l,int m,int n){
for(int i=0;i<l;i++)
for(int j=0;j<n;j++){
int v=0;
for(int k=0;k<m;k++)
v+=a[i*m+k] * b[k*n+j];
res[i*n+j]=v;
}
}
__global__
void matMatGPUv3(int *a,int *b,int *c,int l,int m,int n){
//blockDim.x e blockDim.y sono uguali tra loro per modello !
__shared__ int buffa[T][T], buffb[T][T];
int globx=blockIdx.x * blockDim.x + threadIdx.x;
int globy=blockIdx.y * blockDim.y + threadIdx.y;
int astart=blockIdx.x * T * m;
int bstart=blockIdx.y * T;
int cumsum=0;
for(int as = astart, bs= bstart; as<=astart+m-1; as+= blockDim.y, bs+=blockDim.y*n){
buffa[threadIdx.x][threadIdx.y] = a[as+ threadIdx.x * m + threadIdx.y];
buffb[threadIdx.x][threadIdx.y] = b[bs+threadIdx.x * n +threadIdx.y];
__syncthreads();
for(int i=0;i<blockDim.x;i++)
cumsum+=buffa[threadIdx.x][i]*buffb[i][threadIdx.y];
__syncthreads();
}
c[globx * n + globy] = cumsum;
}
int main(int argc,char *argv[]){
int l,m,n;
dim3 sizeGriglia,sizeBlocco;
if(argc!=5){
l=16;
m=12;
n=8;
sizeBlocco.x = T;
sizeBlocco.y = T;
}
else{
sscanf(argv[1],"%d",&l);
sscanf(argv[2],"%d",&m);
sscanf(argv[3],"%d",&n);
sscanf(argv[4],"%d",&sizeBlocco.x);
sizeBlocco.y = T;
sizeBlocco.y = sizeBlocco.x;
}
sizeGriglia.x = l / sizeBlocco.x;
sizeGriglia.y = n / sizeBlocco.y;
int *ha,*hb,*hc;
allocaEInizializzaMatrice(&ha,l,m);
allocaEInizializzaMatrice(&hb,m,n);
stampaMatrice(ha,l,m);
stampaMatrice(hb,m,n);
hc=new int[l*n];
matMatCPU(ha,hb,hc,l,m,n);
stampaMatrice(hc,l,n);
int *da,*db,*dc;
cudaMalloc(&da,l*m*sizeof(int));
cudaMalloc(&db,m*n*sizeof(int));
cudaMalloc(&dc,l*n*sizeof(int));
cudaMemset(dc,0,l*n*sizeof(int));
cudaMemcpy(da,ha,l*m*sizeof(int),cudaMemcpyHostToDevice);
cudaMemcpy(db,hb,m*n*sizeof(int),cudaMemcpyHostToDevice);
matMatGPUv3<<<sizeGriglia,sizeBlocco,sizeBlocco.x>>>(da,db,dc,l,m,n);
int *copy=new int[l*n];
cudaMemcpy(copy,dc,l*n*sizeof(int),cudaMemcpyDeviceToHost);
stampaMatrice(copy,l,n);
}
|
1,014
|
#include <stdio.h>
#define GREENWICH_LON (0.0f)
#define PI (3.141592653589793f)
#define DEG2RAD (PI / 180.0f)
#define RAD2DEG (180.0f / PI)
#define i_dt (threadIdx.z)
#define i_dxy (blockIdx.x + (blockIdx.y * gridDim.x))
#define i_dxyt (i_dxy + gridDim.x * gridDim.y * i_dt)
__device__ void getdeclination(float *declination, float *gamma)
{
const float g = gamma[i_dt] * DEG2RAD;
declination[i_dt] = round((0.006918f - 0.399912f * cos(g) +
0.070257f * sin(g) -
0.006758f * cos(2.0f * g) +
0.000907f * sin(2.0f * g) -
0.002697f * cos(3.0f * g) +
0.00148f * sin(3.0f * g)) * RAD2DEG);
}
__device__ float gethourlyangle(float *lat, float *lon,
float *decimalhour, float *gamma)
{
const float g = gamma[i_dt] * DEG2RAD;
float timeequation = (0.000075f + 0.001868f * cos(g) -
0.032077f * sin(g) -
0.014615f * cos(2.0f * g) -
0.04089f * sin(2.0f * g)) * (12.0f / PI);
float lon_diff = (GREENWICH_LON - lon[i_dxy]) * DEG2RAD;
float tst_hour = decimalhour[i_dt] - lon_diff * (12.0f / PI) +
timeequation;
float lat_sign = lat[i_dxy] / abs(lat[i_dxy]);
return ((tst_hour - 12.0f) * lat_sign * PI / 12.0f) * RAD2DEG;
}
__device__ float getzenithangle(float *declination,
float *lat, float *lon, float *decimalhour, float *gamma)
{
float hourlyangle;
hourlyangle = gethourlyangle(lat, lon, decimalhour, gamma) * DEG2RAD;
float lat_r = lat[i_dxy] * DEG2RAD;
float dec_r = declination[i_dt] * DEG2RAD;
return (acos(sin(dec_r) * sin(lat_r) + cos(dec_r) *
cos(lat_r) * cos(hourlyangle)) * RAD2DEG);
}
__device__ float getelevation(float zenithangle)
{
float za = zenithangle * DEG2RAD;
return ((PI / 2.0f) - za) * RAD2DEG;
}
__device__ float getexcentricity(float *gamma)
{
const float g = gamma[i_dt] * DEG2RAD;
return (1.000110f + 0.034221f * cos(g) +
0.001280f * sin(g) +
0.000719f * cos(2.0f * g) +
0.000077f * sin(2.0f * g));
}
__device__ float getcorrectedelevation(float elevation)
{
float e = elevation * DEG2RAD;
float p = pow(e, 2.0f);
return (e +
0.061359f * ((0.1594f + 1.1230f * e +
0.065656f * p) /
(1.0f + 28.9344f * e +
277.3971f * p))) * RAD2DEG;
}
__device__ float getopticalpath(float correctedelevation,
float *dem, float *HEIGHT)
{
float ce = correctedelevation;
if (ce < 0) { ce = 0.0f; }
// In the next line the correctedelevation is used over a degree base.
float p = pow(ce + 6.07995f, -1.6364f);
return(exp(-dem[i_dxy]/HEIGHT[0]) /
(sin(ce * DEG2RAD) + 0.50572f * p));
}
__device__ float getopticaldepth(float opticalpath)
{
float tmp = 1.0f;
if (opticalpath <= 20.0f){
tmp = (6.6296f + 1.7513f * opticalpath -
0.1202f * pow(opticalpath, 2.0f) +
0.0065f * pow(opticalpath, 3.0f) -
0.00013f * pow(opticalpath, 4.0f));
} else {
tmp = (10.4f + 0.718f * opticalpath);
}
tmp = 1.0f / tmp;
return tmp;
}
__device__ float getbeamtransmission(float *linke,
float opticalpath, float opticaldepth)
{
return exp(-0.8662f * linke[i_dxyt] * opticalpath *
opticaldepth);
}
__device__ float gethorizontalirradiance(float *EXT_RAD,
float *excentricity, float *zenithangle)
{
float radzenith = zenithangle[i_dxyt] * DEG2RAD;
return EXT_RAD[0] * excentricity[i_dt] *
cos(radzenith);
}
__device__ float getbeamirradiance(float *EXT_RAD,
float *excentricity, float *zenithangle, float solarelevation,
float *linke, float *dem, float *HEIGHT)
{
float corrected = getcorrectedelevation(solarelevation);
float opticalpath = getopticalpath(corrected, dem,
HEIGHT);
float opticaldepth = getopticaldepth(opticalpath);
return gethorizontalirradiance(EXT_RAD,
excentricity, zenithangle) *
getbeamtransmission(linke, opticalpath,
opticaldepth);
}
__device__ float getzenithdiffusetransmitance(float *linke)
{
return -0.015843f + 0.030543f * linke[i_dxyt] +
0.0003797f * pow(linke[i_dxyt], 2.0f);
}
__device__ float getangularcorrection(float solarelevation,
float *linke)
{
float sin_se = sin(solarelevation * DEG2RAD);
float squared_linke = pow(linke[i_dxyt], 2.0f);
float a0 = 0.264631f - 0.061581f * linke[i_dxyt] +
0.0031408f * squared_linke;
float a1 = 2.0402f + 0.018945f * linke[i_dxyt] -
0.011161f * squared_linke;
float a2 = -1.3025f + 0.039231f * linke[i_dxyt] +
0.0085079f * squared_linke;
float ztdifftr = getzenithdiffusetransmitance(linke);
if (a0 * ztdifftr < 0.002f){
a0 = 0.002f / ztdifftr;
}
return a0 + a1 * sin_se + a2 * pow(sin_se, 2.0f);
}
__device__ float getdiffusetransmitance(float *linke,
float solarelevation)
{
return getzenithdiffusetransmitance(linke) *
getangularcorrection(solarelevation, linke);
}
__device__ void gettransmitance(float *transmitance, float *linke,
float opticalpath, float opticaldepth, float solarelevation)
{
transmitance[i_dxyt] = getbeamtransmission(linke, opticalpath,
opticaldepth) +
getdiffusetransmitance(linke, solarelevation);
}
__device__ float getdiffuseirradiance(float *EXT_RAD,
float *excentricity, float solarelevation, float *linketurbidity)
{
return EXT_RAD[0] * excentricity[i_dt] *
getdiffusetransmitance(linketurbidity, solarelevation);
}
__device__ void getglobalirradiance(float *gc, float beamirradiance,
float diffuseirradiance)
{
gc[i_dxyt] = beamirradiance + diffuseirradiance;
}
#define rpol 6356.5838f
#define req 6378.1690f
#define h 42166.55637f
//define h 42164.0f
__device__ float getsatellitalzenithangle(float *lat,
float *lon, float *sub_lon)
{
float la = lat[i_dxy] * DEG2RAD;
float lon_diff = (lon[i_dxy] - sub_lon[0]) * DEG2RAD;
float lat_cos_only = cos(la);
float re = rpol / (sqrt(1 - (pow(req, 2.0f) - pow(rpol, 2.0f)) /
(pow(req, 2.0f)) * pow(lat_cos_only, 2.0f)));
float lat_cos = re * lat_cos_only;
float r1 = h - lat_cos * cos(lon_diff);
float r2 = - lat_cos * sin(lon_diff);
float r3 = re * sin(la);
float rs = sqrt(pow(r1, 2.0f) + pow(r2, 2.0f) + pow(r3, 2.0f));
return (PI - acos((pow(h, 2.0f) -
pow(re, 2.0f) - pow(rs, 2.0f)) / (-2.0f * re * rs))) * RAD2DEG;
}
__device__ float getatmosphericradiance(float *EXT_RAD,
float *i0met, float diffuseclearsky, float satellitalzenithangle)
{
float anglerelation = pow(0.5f / cos(satellitalzenithangle * DEG2RAD),
0.8f);
return ((i0met[0] * diffuseclearsky * anglerelation) /
(PI * EXT_RAD[0]));
}
__device__ float getdifferentialalbedo(float firstalbedo,
float secondalbedo, float t_earth, float t_sat)
{
return (firstalbedo - secondalbedo) / (t_earth * t_sat);
}
__device__ void getalbedo(float *albedo, float radiance,
float *i0met, float *excentricity, float zenithangle)
{
albedo[i_dxyt] = ((PI * radiance) /
(i0met[0] * excentricity[i_dt] *
cos(zenithangle * DEG2RAD)));
}
__device__ float geteffectivealbedo(float solarangle)
{
return 0.78f - 0.13f * (1.0f -
exp(-4.0f * pow(cos(solarangle * DEG2RAD), 5.0f)));
}
__device__ void getcloudalbedo(float *result, float effectivealbedo,
float atmosphericalbedo, float t_earth, float t_sat)
{
float ca = getdifferentialalbedo(effectivealbedo, atmosphericalbedo,
t_earth, t_sat);
if (ca < 0.2f) { ca = 0.2f; }
float effectiveproportion = 2.24f * effectivealbedo;
if ( ca > effectiveproportion) { ca = effectiveproportion; }
result[i_dxyt] = ca;
}
__global__ void update_temporalcache(float *declination,
float *solarangle, float *solarelevation, float *excentricity,
float *gc, float *atmosphericalbedo, float *t_sat, float *t_earth,
float *cloudalbedo, float *lat, float *lon, float *decimalhour,
float *gamma, float *dem, float *linke, float *SAT_LON,
float *i0met, float *EXT_RAD, float *HEIGHT)
{
float bc, dc, satellitalzenithangle, atmosphericradiance,
satellitalelevation, satellital_opticalpath,
satellital_opticaldepth, solar_opticalpath,
solar_opticaldepth, effectivealbedo;
getdeclination(declination, gamma);
solarangle[i_dxyt] = getzenithangle(declination, lat, lon,
decimalhour, gamma);
solarelevation[i_dxyt] = getelevation(solarangle[i_dxyt]);
excentricity[i_dt] = getexcentricity(gamma);
bc = getbeamirradiance(EXT_RAD, excentricity, solarangle,
solarelevation[i_dxyt], linke, dem, HEIGHT);
dc = getdiffuseirradiance(EXT_RAD, excentricity,
solarelevation[i_dxyt], linke);
getglobalirradiance(gc, bc, dc);
satellitalzenithangle = getsatellitalzenithangle(lat, lon, SAT_LON);
atmosphericradiance = getatmosphericradiance(EXT_RAD, i0met,
dc,
satellitalzenithangle);
getalbedo(atmosphericalbedo, atmosphericradiance, i0met,
excentricity, satellitalzenithangle);
satellitalelevation = getelevation(satellitalzenithangle);
satellital_opticalpath = getopticalpath(
getcorrectedelevation(satellitalelevation), dem, HEIGHT);
satellital_opticaldepth = getopticaldepth(satellital_opticalpath);
gettransmitance(t_sat, linke, satellital_opticalpath,
satellital_opticaldepth, satellitalelevation);
solar_opticalpath = getopticalpath(
getcorrectedelevation(solarelevation[i_dxyt]), dem,
HEIGHT);
solar_opticaldepth = getopticaldepth(solar_opticalpath);
gettransmitance(t_earth, linke, solar_opticalpath, solar_opticaldepth,
solarelevation[i_dxyt]);
effectivealbedo = geteffectivealbedo(solarangle[i_dxyt]);
getcloudalbedo(cloudalbedo, effectivealbedo,
atmosphericalbedo[i_dxyt], t_earth[i_dxyt],
t_sat[i_dxyt]);
}
|
1,015
|
/******************************************************************************/
/* */
/* (C) 2010 Texas Advanced Computing Center. All rights reserved. */
/* For information, contact Frank Willmore: willmore@tacc.utexas.edu */
/* */
/******************************************************************************/
#include <stdio.h>
__device__ char d_string[65536][256];
extern "C" void cmain();
__global__ void toUpper()
{
if ((d_string[blockIdx.x][threadIdx.x] <= 122) && (d_string[blockIdx.x][threadIdx.x]) >=97)
d_string[blockIdx.x][threadIdx.x] -= 32;
}
void cmain()
{
char line[65536][256];
int n_lines;
for (n_lines=0; !feof(stdin); n_lines++) fgets(&line[n_lines][0], 256, stdin);
cudaMemcpyToSymbol(d_string, line, sizeof(line), 0, cudaMemcpyHostToDevice);
toUpper<<< n_lines, 256 >>>();
cudaMemcpyFromSymbol(line, d_string, sizeof(line), 0, cudaMemcpyDeviceToHost);
for (int i=0; i<n_lines; i++) printf("%s", line[i]);
}
|
1,016
|
#ifdef _WIN32
# define NOMINMAX
#endif
// includes, system
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include <math.h>
__global__ void MatMult(double *dA, double *dB, double *dC, int nRows, int nInnerDimension, int nCols, int TileSize)
{
int bx = blockIdx.x;
int by = blockIdx.y;
int tx = threadIdx.x;
int ty = threadIdx.y;
int aBegin = nRows * TileSize * by;
int aEnd = aBegin + nRows - 1;
int aStep = TileSize;
int bBegin = bx * TileSize;
int bStep = TileSize * nInnerDimension;
double Csub = 0.0;
volatile __shared__ double As[32][32];
volatile __shared__ double Bs[32][32];
for (int a = aBegin, b = bBegin; a <= aEnd; a += aStep, b +=bStep)
{
As[ty][tx] = dA[a + nRows * ty + tx];
Bs[ty][tx] = dB[b + nInnerDimension * ty + tx];
__syncthreads();
for (int k = 0; k < TileSize; ++k)
{
Csub += As[ty][k] * Bs[k][tx];
}
__syncthreads();
//__threadfence_block();
}
int c = nInnerDimension * TileSize * by + TileSize * bx;
dC[c + nInnerDimension * ty + tx] = Csub;
}
double* read_array(const char* filename, int len) {
double *x = (double*) malloc(len * sizeof(double));
FILE *fp = fopen(filename, "r");
for (int i = 0; i < len; i++) {
fscanf(fp, "%lf", &x[i]);
}
fclose(fp);
return x;
}
void computeOnDevice(double* hA,double* hB, double* hC, int nRows,
int nInnerDimension,int nCols, int tileSize, float* incTime );
////////////////////////////////////////////////////////////////////////////////
// Program main
////////////////////////////////////////////////////////////////////////////////
int main( int argc, char** argv)
{
if(argc!=2)
{
printf("Usage: ./problem2 N\n");
return 0;
}
int nRows = 1024;
int nInnerDimension = 1024;
int nCols = 1024;
int num_elementsA= nRows*nInnerDimension;
int num_elementsB=nInnerDimension*nCols;
int num_elementsC= nRows*nCols;
int tileSize = atoi(argv[1]); //change this for scaling analysis
float incTime=0; // Time for GPU
double* hA = read_array("inputA.inp",num_elementsA);
double* hB = read_array("inputB.inp",num_elementsB);
double* hC = (double*) malloc(num_elementsC * sizeof(double));
// **===-------- Modify the body of this function -----------===**
computeOnDevice( hA, hB,hC, nRows, nInnerDimension, nCols, tileSize, &incTime);
// **===-----------------------------------------------------------===**
printf("%f\n%f\n%d\n",hC[num_elementsC-1],incTime,tileSize);
// cleanup memory
free(hA);
free(hB);
free(hC);
return 0;
}
void computeOnDevice(double* hA,double* hB, double* hC, int nRows, int nInnerDimension, int nCols, int TileSize, float* incTime)
{
cudaEvent_t startEvent_inc, stopEvent_inc;
float elapsedTime_inc;
cudaEventCreate(&startEvent_inc);
cudaEventCreate(&stopEvent_inc);
cudaEventRecord(startEvent_inc,0);
double* Ad;
cudaMalloc((void**)&Ad, nRows * nInnerDimension * sizeof(double));
cudaMemcpy(Ad, hA, nRows * nInnerDimension * sizeof(double), cudaMemcpyHostToDevice);
double* Bd;
cudaMalloc((void **)&Bd, nInnerDimension * nCols * sizeof(double));
cudaMemcpy(Bd, hB, nInnerDimension * nCols * sizeof(double), cudaMemcpyHostToDevice);
double* Cd;
cudaMalloc((void **)&Cd, nRows * nCols * sizeof(double));
dim3 dimBlock(TileSize, TileSize);
int tempx = nRows;
if (nInnerDimension > nRows) tempx = nInnerDimension;
tempx = (tempx + TileSize - 1)/TileSize;
int tempy = nCols;
if (nInnerDimension > nCols) tempy = nInnerDimension;
tempy = (tempy + TileSize - 1)/TileSize;
dim3 dimGrid(tempx, tempy);
MatMult<<<dimGrid, dimBlock, sizeof(double) * TileSize * TileSize>>>(Ad, Bd, Cd, nRows, nInnerDimension, nCols, TileSize); //, sizeof(double) * TileSize * TileSize
cudaMemcpy(hC, Cd, nRows * nCols * sizeof(double), cudaMemcpyDeviceToHost);
cudaFree(Ad);
cudaFree(Bd);
cudaFree(Cd);
cudaEventRecord(stopEvent_inc,0);
cudaEventSynchronize(stopEvent_inc);
cudaEventElapsedTime(&elapsedTime_inc, startEvent_inc, stopEvent_inc);
*incTime = elapsedTime_inc;
return;
}
|
1,017
|
/*#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <stdio.h>
#include <opencv2/core.hpp>
#include <opencv2/imgcodecs.hpp>
#include <opencv2/highgui.hpp>
#include<opencv2\imgproc.hpp>
#include <iostream>
#define totalThreads 16
__global__ void kernel(unsigned char* d_img_in, unsigned char* d_img_out, int channels,int totalSizeImg)
{
int x = threadIdx.x + blockIdx.x*blockDim.x;
int y = threadIdx.y + blockIdx.y*blockDim.y;
int offset = x + y*gridDim.x*blockDim.x;
while (offset<totalSizeImg)
{
float grey = 0.0f;
float blue = d_img_in[offset*channels + 0];
float green = d_img_in[offset*channels+ 1];
float red = d_img_in[offset*channels+ 2];
grey = blue*0.11f + green*0.59f + red*0.3f;
d_img_out[offset + 0] = (grey);
offset+= (gridDim.x*blockDim.x)*(gridDim.y*blockDim.y);//offsetting the threads accessing the grey image
}
}
int main()
{
unsigned char* d_img_in;
unsigned char* d_img_out;
std::string fileName = "Resources/Highway.jpg";
cv::Mat h_img_in = cv::imread(fileName, cv::IMREAD_COLOR);
int channels = h_img_in.channels();
int imgSizeColor = h_img_in.rows*h_img_in.cols*channels;//total Size of the colored Image
int totalSize = h_img_in.rows*h_img_in.cols;
cv::Mat h_img_out(h_img_in.rows, h_img_in.cols, CV_8UC1, cv::Scalar(0));//create the grey image
int imgSizeGrey = h_img_in.rows*h_img_in.cols;//total Size of the colored Image
cudaMalloc((void**)&d_img_in, imgSizeColor);//assign memory to GPU variables
cudaMalloc((void**)&d_img_out, imgSizeGrey);
dim3 threads(totalThreads, totalThreads);
dim3 blocks((h_img_in.cols+ totalThreads-1) / totalThreads, (h_img_in.rows+ totalThreads-1) / totalThreads);
cudaMemcpy(d_img_in, h_img_in.ptr(), imgSizeColor, cudaMemcpyHostToDevice);
cudaMemcpy(d_img_out, h_img_out.ptr(), imgSizeGrey, cudaMemcpyHostToDevice);
kernel << <blocks, threads >> >(d_img_in, d_img_out, channels,totalSize);//need the dimesnsions of the image to stop thread overwriting
cudaMemcpy(h_img_out.ptr(), d_img_out, imgSizeGrey, cudaMemcpyDeviceToHost);
cv::imshow("img",h_img_out);
cv::waitKey();
system("pause");
cv::destroyAllWindows();
cudaFree(d_img_in);
cudaFree(d_img_out);
return 911;
}*/
|
1,018
|
/* Compute the sum of two vectors using CUDA
* Vishwas S
*/
#include <stdio.h>
#include <stdlib.h>
__global__ void add(int *a, int *b, int *c, int n)
{
int id = blockIdx.x*blockDim.x + threadIdx.x;
if(id<n)
c[id] = a[id] + b[id];
}
int main()
{
int N;
int *a, *b, *c, *da, *db, *dc;
scanf("%d",&N);
cudaEvent_t start, stop;
cudaEventCreate(&start);
cudaEventCreate(&stop);
a = (int *)calloc(N,sizeof(int));
b = (int *)calloc(N,sizeof(int));
c = (int *)calloc(N,sizeof(int));
for(int i = 0; i < N; i++)
{
a[i] = rand()%48;
b[i] = rand()%50;
}
int size = N*sizeof(int);
cudaMalloc(&da,size);
cudaMalloc(&db,size);
cudaMalloc(&dc,size);
cudaMemcpy(da,a,size,cudaMemcpyHostToDevice);
cudaMemcpy(db,b,size,cudaMemcpyHostToDevice);
cudaEventRecord(start);
add<<<(N+511)/512,512>>>(da,db,dc,N); //block count, threads per block
cudaEventRecord(stop);
cudaMemcpy(c,dc,size,cudaMemcpyDeviceToHost);
cudaEventSynchronize(stop);
float ms;
cudaEventElapsedTime(&ms,start,stop);
printf("%f\n",ms);
}
|
1,019
|
#include "includes.h"
__global__ void vc(float *dA, float *dB, int N) {
int id = blockIdx.x * blockDim.x + threadIdx.x;
if (id < N) {
dA[id] = dB[id];
}
}
|
1,020
|
#include "includes.h"
__global__ void sinewave(float *heightMap, unsigned int width, unsigned int height, float time)
{
const float freq = 4.0f;
const size_t stride = gridDim.x * blockDim.x;
// Iterate through the entire array in a way that is
// independent of the grid configuration
for (size_t tid = blockIdx.x * blockDim.x + threadIdx.x; tid < width * height; tid += stride) {
// Calculate the x, y coordinates
const size_t y = tid / width;
const size_t x = tid - y * width;
// Normalize x, y to [0,1]
const float u = ((2.0f * x) / width) - 1.0f;
const float v = ((2.0f * y) / height) - 1.0f;
// Calculate the new height value
const float w = 0.5f * sinf(u * freq + time) * cosf(v * freq + time);
// Store this new height value
heightMap[tid] = w;
}
}
|
1,021
|
#include "includes.h"
__global__ void LinearPolynomProbsImpl( const float* features, int batchSize, const int* splits, const float* conditions, const int* polynomOffsets, int polynomCount, float lambda, float* probs, const int* origFIds) {
if (threadIdx.x < batchSize) {
int polynomId = blockIdx.x;
features += threadIdx.x;
probs += threadIdx.x;
while (polynomId < polynomCount) {
int offset = polynomOffsets[polynomId];
int nextOffset = polynomOffsets[polynomId + 1];
const int depth = nextOffset - offset;
const int origFId = origFIds[polynomId];
bool zeroProb = false;
for (int i = 0; i < depth; ++i) {
if (zeroProb) {
continue;
}
const float c = __ldg(conditions + offset + i);
const int f = __ldg(splits + offset + i);
const float x = __ldg(features + f * batchSize);
if (x <= c) {
zeroProb = true;
}
}
float prob = 0.0f;
if (!zeroProb) {
// TODO we store fID = -1 as our bias column, but it's a hack and we need to get rid of this
if (origFId != -1) {
prob = __ldg(features + origFId * batchSize);
} else {
prob = 1.0f;
}
}
probs[polynomId * batchSize] = prob;
polynomId += gridDim.x;
}
}
}
|
1,022
|
#include "job.cuh"
#include "common.cuh"
int calc_jobs(int real_job_num)
{
return (real_job_num / THREADS_PER_BLOCK) * THREADS_PER_BLOCK + ((real_job_num % THREADS_PER_BLOCK) ? THREADS_PER_BLOCK : 0);
}
job_t allocate_host_job(job_t job)
{
job_t host_job = job;
int jobs_num = calc_jobs(host_job.image_width * host_job.image_height);
safeMalloc((void **) &host_job.gather_arr, sizeof(int) * jobs_num);
safeMalloc((void **)&host_job.target_idx, sizeof(int) * jobs_num);
safeMalloc((void **)&host_job.image_dest, sizeof(float) * jobs_num * 3);
safeMalloc((void **)&host_job.ray_pos, sizeof(float) * jobs_num * 3);
safeMalloc((void **)&host_job.ray_dir, sizeof(float) * jobs_num * 3);
return host_job;
}
void free_host_job(job_t *host_job)
{
safeFree(host_job->gather_arr);
host_job->gather_arr = NULL;
safeFree(host_job->target_idx);
host_job->target_idx = NULL;
safeFree(host_job->image_dest);
host_job->image_dest = NULL;
safeFree(host_job->ray_pos);
host_job->ray_pos = NULL;
safeFree(host_job->ray_dir);
host_job->ray_dir = NULL;
}
job_t allocate_device_job(job_t job)
{
job_t dev_job = job;
int jobs_num = calc_jobs(dev_job.image_width * dev_job.image_height);
cudaSafeMalloc((void **)&dev_job.gather_arr, sizeof(int) * jobs_num);
cudaSafeMalloc((void **)&dev_job.target_idx, sizeof(int) * jobs_num);
cudaSafeMalloc((void **)&dev_job.image_dest, sizeof(float) * jobs_num * 3);
cudaSafeMalloc((void **)&dev_job.ray_pos, sizeof(float) * jobs_num * 3);
cudaSafeMalloc((void **)&dev_job.ray_dir, sizeof(float) * jobs_num * 3);
return dev_job;
}
void free_device_job(job_t *dev_job)
{
cudaSafeFree(dev_job->gather_arr);
dev_job->gather_arr = NULL;
cudaSafeFree(dev_job->target_idx);
dev_job->target_idx = NULL;
cudaSafeFree(dev_job->image_dest);
dev_job->image_dest = NULL;
cudaSafeFree(dev_job->ray_pos);
dev_job->ray_pos = NULL;
cudaSafeFree(dev_job->ray_dir);
dev_job->ray_dir = NULL;
}
void copy_job_to_dev(job_t *dev_dest, job_t *host_src)
{
dev_dest->image_width = host_src->image_width;
dev_dest->image_height = host_src->image_height;
dev_dest->pass_count = host_src->pass_count;
int hc = calc_jobs(host_src->image_width * host_src->image_height);
cudaMemcpy(dev_dest->gather_arr, host_src->gather_arr, hc * sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(dev_dest->target_idx, host_src->target_idx, hc * sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(dev_dest->image_dest, host_src->image_dest, hc * sizeof(float) * 3, cudaMemcpyHostToDevice);
cudaMemcpy(dev_dest->ray_pos, host_src->ray_pos, hc * sizeof(float) * 3, cudaMemcpyHostToDevice);
cudaMemcpy(dev_dest->ray_dir, host_src->ray_dir, hc * sizeof(float) * 3, cudaMemcpyHostToDevice);
}
void copy_job_to_host(job_t *host_dest, job_t *dev_src)
{
host_dest->image_width = dev_src->image_width;
host_dest->image_height = dev_src->image_height;
host_dest->pass_count = dev_src->pass_count;
int hc = calc_jobs(dev_src->image_width * dev_src->image_height);
cudaMemcpy(host_dest->gather_arr, dev_src->gather_arr, hc * sizeof(int), cudaMemcpyDeviceToHost);
cudaMemcpy(host_dest->target_idx, dev_src->target_idx, hc * sizeof(int), cudaMemcpyDeviceToHost);
cudaMemcpy(host_dest->image_dest, dev_src->image_dest, hc * sizeof(float) * 3, cudaMemcpyDeviceToHost);
cudaMemcpy(host_dest->ray_pos, dev_src->ray_pos, hc * sizeof(float) * 3, cudaMemcpyDeviceToHost);
cudaMemcpy(host_dest->ray_dir, dev_src->ray_dir, hc * sizeof(float) * 3, cudaMemcpyDeviceToHost);
}
|
1,023
|
#include<iostream>
#include<chrono>
#include<cuda.h>
#include<cuda_runtime.h>
#define N 1024
using namespace std;
using namespace std::chrono;
static const int wholeArraySize = 100000000;
static const int blockSize = 1024;
static const int gridSize = 24; //this number is hardware-dependent; usually #SM*2 is a good number.
__global__ void maxPerBlock(const int *gArr, int arraySize, int *gOut) {
int thIdx = threadIdx.x;
int gthIdx = thIdx + blockIdx.x*blockSize;
const int gridSize = blockSize*gridDim.x;
int max = gArr[0];
for (int i = gthIdx; i < arraySize; i += gridSize)
if(max < gArr[i])
max = gArr[i];
__shared__ int shArr[blockSize];
shArr[thIdx] = max;
__syncthreads();
/*for (int size = blockSize/2; size>0; size/=2) { //uniform
if (thIdx<size)
shArr[thIdx] += shArr[thIdx+size];
__syncthreads();
}*/
if (thIdx == 0)
{
max = shArr[0];
for(int i = 0 ; i < blockSize ; i++)
{
if(max < shArr[i])
{
max = shArr[i];
}
}
}
if (thIdx == 0)
gOut[blockIdx.x] = max;
}
int main() {
int *arr = new int[wholeArraySize];
for(int i = 0; i < wholeArraySize ; i++)
{
arr[i] = (i+1)%10;
}
int* dev_arr;
cudaMalloc((void**)&dev_arr, wholeArraySize * sizeof(int));
cudaMemcpy(dev_arr, arr, wholeArraySize * sizeof(int), cudaMemcpyHostToDevice);
int out;
int* dev_out;
cudaMalloc((void**)&dev_out, sizeof(int)*gridSize);
maxPerBlock<<<gridSize, blockSize>>>(dev_arr, wholeArraySize, dev_out);
//dev_out now holds the partial result
maxPerBlock<<<1, blockSize>>>(dev_out, gridSize, dev_out);
//dev_out[0] now holds the final result
cudaDeviceSynchronize();
cudaMemcpy(&out, dev_out, sizeof(int), cudaMemcpyDeviceToHost);
cudaFree(dev_arr);
cudaFree(dev_out);
cout<<"Max is : "<<out;
}
|
1,024
|
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <stdio.h>
__global__ void swap(int *a, int n)
{
int temp;
int i = blockIdx.x * blockDim.x + 0;
int j = blockIdx.x * blockDim.x + 1;
temp = a[i];
a[i]= a[j];
a[j]=temp;
}
int main(void)
{
int n,a[100],i;
printf("Enter no of elements in the array\n");
scanf("%d", &n);
printf("Enter the array elements\n");
for(i=0;i<n;i++)
{
scanf("%d", &a[i]);
}
int *d_a,size;
size = sizeof(int);
cudaMalloc((void **)&d_a,n*size);
cudaMemcpy(d_a,a,n*size,cudaMemcpyHostToDevice);
dim3 dimgrid(n/2,1,1);
dim3 dimblock(2,1,1);
swap<<<dimgrid,dimblock>>>(d_a,n);
cudaMemcpy(a,d_a,n*size,cudaMemcpyDeviceToHost);
for(i=0;i<n;i++)
{
printf("%d\n", a[i]);
}
cudaFree(d_a);
return 0;
}
|
1,025
|
#include <cstdlib>
#include <iostream>
//#include <cuda_runtime.h>
#include <cuda.h>
#include <stdio.h>
#define CUDA_CHECK_RETURN(value) {\
cudaError_t _m_cudaStat = value;\
if (_m_cudaStat != cudaSuccess) {\
fprintf(stderr, "Error %s at line %d in file %s\n",\
cudaGetErrorString(_m_cudaStat), __LINE__, __FILE__);\
exit(1);\
} }
__global__ void vecAdd_kernel(float * a, float * b, float * result, int n)
{
int i = threadIdx.x + blockIdx.x * blockDim.x;
a[i] = b[i] = i;
if (i < n)
result[i] = a[i] + b[i];
}
int main() {
int n = 1000000;
int threads_per_block = 8;
// while(threads_per_block <= 1024){
int num_of_blocks = n / threads_per_block;
float elapsedTime;
cudaEvent_t start,stop; // встроенный тип данных – структура, для фиксации контрольных
//точек
cudaEventCreate(&start); // инициализация
cudaEventCreate(&stop); // событий
//float * a = new float[n],
float * a_gpu, * b_gpu, *result_gpu;
CUDA_CHECK_RETURN(cudaMalloc((void**)&a_gpu, n * sizeof(float)));
//float * b = new float[n], * b_gpu;
CUDA_CHECK_RETURN(cudaMalloc((void**)&b_gpu, n * sizeof(float)));
float * result = new float[n];//, * result_gpu;
CUDA_CHECK_RETURN(cudaMalloc((void**)&result_gpu, n * sizeof(float)));
//for (int i = 0; i < n; i++)
// a[i] = b[i] = i;
//CUDA_CHECK_RETURN(cudaMemcpy(a_gpu, a, n * sizeof(float), cudaMemcpyHostToDevice));
//CUDA_CHECK_RETURN(cudaMemcpy(b_gpu, b, n * sizeof(float), cudaMemcpyHostToDevice));
cudaEventRecord(start,0); // привязка события
//const int block_size = 256;
//int num_blocks = (n + block_size - 1) / block_size;
//vecAdd_kernel <<< num_blocks, block_size >>> (a_gpu, b_gpu, result_gpu, n);
vecAdd_kernel <<< dim3(num_of_blocks), dim3(threads_per_block) >>> (a_gpu, b_gpu, result_gpu, n);
cudaEventRecord(stop,0); // привязка события
cudaEventSynchronize(stop); // синхронизация по событию
//cudaDeviceSynchronize();
CUDA_CHECK_RETURN(cudaGetLastError());
cudaEventElapsedTime(&elapsedTime,start,stop); // вычисление затраченного времени
fprintf(stderr,"gTest took %g \t\t num_of_blocks = %d \t\t threads_per_block = %d\n", elapsedTime, num_of_blocks, threads_per_block);
cudaEventDestroy(start); // освобождение
cudaEventDestroy(stop); // памяти
CUDA_CHECK_RETURN(cudaMemcpy(result, result_gpu, n * sizeof(float), cudaMemcpyDeviceToHost));
//for (int i = 0; i < n; i ++)
//printf("%g\n", result[i]);
//delete [] a;
//delete [] b;
delete [] result;
cudaFree(a_gpu);
cudaFree(b_gpu);
cudaFree(result_gpu);
threads_per_block *= 2;
// }
return 0;
}
|
1,026
|
#include<iostream>
using namespace std;
#define size 256
#define ssize size*4
__global__ void max_reduction(int *v,int *v_r)
{
__shared__ int partial_sum[ssize];
int tid=blockIdx.x*blockDim.x+threadIdx.x;
partial_sum[threadIdx.x]=v[tid];
__syncthreads();
for(int i=blockDim.x/2;i>0;i=i/2)
{
if(threadIdx.x<i)
{
partial_sum[threadIdx.x]=max(partial_sum[threadIdx.x],partial_sum[threadIdx.x+i]);
}
__syncthreads();
}
if(threadIdx.x==0)
{
v_r[blockIdx.x]=partial_sum[0];
}
}
__global__ void min_reduction(int *v,int *v_r)
{
__shared__ int partial_sum[ssize];
int tid=blockIdx.x*blockDim.x+threadIdx.x;
partial_sum[threadIdx.x]=v[tid];
__syncthreads();
for(int i=blockDim.x/2;i>0;i=i/2)
{
if(threadIdx.x<i)
{
partial_sum[threadIdx.x]=min(partial_sum[threadIdx.x],partial_sum[threadIdx.x+i]);
}
__syncthreads();
}
if(threadIdx.x==0)
{
v_r[blockIdx.x]=partial_sum[0];
}
}
__global__ void sum_reduction(int *v,int *v_r)
{
__shared__ int partial_sum[ssize];
int tid=blockIdx.x*blockDim.x+threadIdx.x;
partial_sum[threadIdx.x]=v[tid];
__syncthreads();
for(int i=blockDim.x/2;i>0;i=i/2)
{
if(threadIdx.x<i)
{
partial_sum[threadIdx.x]+=partial_sum[threadIdx.x+i];
}
__syncthreads();
}
if(threadIdx.x==0)
{
v_r[blockIdx.x]=partial_sum[0];
}
}
__global__ void variance_reduction(int *v,int *v_r,float *mean)
{
__shared__ int partial_sum[ssize];
int tid=blockIdx.x*blockDim.x+threadIdx.x;
partial_sum[threadIdx.x]=v[tid];
__syncthreads();
partial_sum[threadIdx.x]=(partial_sum[threadIdx.x]-*mean)*(partial_sum[threadIdx.x]-*mean);
__syncthreads();
for(int i=blockDim.x/2;i>0;i=i/2)
{
if(threadIdx.x<i)
{
partial_sum[threadIdx.x]+=partial_sum[threadIdx.x+i];
}
__syncthreads();
}
if(threadIdx.x==0)
{
v_r[blockIdx.x]=partial_sum[0];
}
}
int main()
{
int n = size*size;
int blockthread=size;
int no_block=n/blockthread;
int *a;
int *a_gpu;
int *b_gpu;
int *b;
float time;
cudaMalloc(&a_gpu,n*sizeof(int));
cudaMalloc(&b_gpu,no_block*sizeof(int));
a=(int*)malloc(n*sizeof(int));
b=(int*)malloc(no_block*sizeof(int));
for(int i =0;i<n;i++){
a[i]= rand()%1000;
}
cudaMemcpy(a_gpu,a,n*sizeof(int),cudaMemcpyHostToDevice);
cudaEvent_t start,stop;
cudaEventCreate(&start);
cudaEventCreate(&stop);
cudaEventRecord(start);
max_reduction<<<no_block,blockthread>>>(a_gpu,b_gpu);
max_reduction<<<1,blockthread>>>(b_gpu,b_gpu);
cudaMemcpy(b,b_gpu,blockthread*sizeof(int),cudaMemcpyDeviceToHost);
cudaEventRecord(stop);
cudaEventElapsedTime(&time,start,stop);
cout<<b[0]<<"\n";
cout<<time;
}
|
1,027
|
#include <stdio.h>
#include <stdlib.h>
#include <cuda.h>
#include <cuda_runtime.h>
#define CHECK_STATUS(status) \
if (status != cudaSuccess) \
fprintf(stderr, "File: %s\nLine:%d Function:%s>>>%s\n", __FILE__, __LINE__, __FUNCTION__,\
cudaGetErrorString(status))
// Device code
__global__ void MyKernel(cudaPitchedPtr devPitchedPtr,
int width, int height, int depth)
{
char* devPtr = (char*)devPitchedPtr.ptr; //获取数据指针
size_t pitch = devPitchedPtr.pitch; //获取一行所占的字节数
size_t slicePitch = pitch * height; //获取一层的大小，单位为字节
for (int z = 0; z < depth; ++z) {
char* slice = devPtr + z * slicePitch; // 得到第z层的起始地址
for (int y = 0; y < height; ++y) {
float* row = (float*)(slice + y * pitch); //得到第z层中，第y行的起始地址
for (int x = 0; x < width; ++x) {
float element = row[x]; //得到第y行的第x个元素
}
}
}
}
int main(int argc, char **argv) {
CHECK_STATUS(cudaSetDevice(0));
size_t width = 64, height = 64, depth = 64;
// 定义三维数组大小
cudaExtent extent = make_cudaExtent(width * sizeof(float), height, depth);
// 三维数组的一个数据结构
cudaPitchedPtr devPitchedPtr;
// 分配三维数组
CHECK_STATUS(cudaMalloc3D(&devPitchedPtr,extent));
// 调用内核
MyKernel<<<100, 512>>>(devPitchedPtr, width, height, depth);
// 检查错误
CHECK_STATUS(cudaGetLastError());
// 释放内存
CHECK_STATUS(cudaFree(devPitchedPtr.ptr));
return 0;
}
|
1,028
|
#include <cstdio>
const int NUM_THREADS = 256;
const int NUM_CATEGORIES = 5;
template <typename T, int NUM_ARRS>
__device__ void multiReduce(volatile T arr[NUM_ARRS][NUM_THREADS])
{
if (threadIdx.x < 128)
{
for (int i = 0; i < NUM_ARRS; ++i) arr[i][threadIdx.x] += arr[i][threadIdx.x + 128];
}
__syncthreads();
if (threadIdx.x < 64)
{
for (int i = 0; i < NUM_ARRS; ++i) arr[i][threadIdx.x] += arr[i][threadIdx.x + 64];
}
__syncthreads();
if (threadIdx.x < 32)
{
for (int i = 0; i < NUM_ARRS; ++i)
{
arr[i][threadIdx.x] = arr[i][threadIdx.x] + arr[i][threadIdx.x + 32];
arr[i][threadIdx.x] = arr[i][threadIdx.x] + arr[i][threadIdx.x + 16];
arr[i][threadIdx.x] = arr[i][threadIdx.x] + arr[i][threadIdx.x + 8];
arr[i][threadIdx.x] = arr[i][threadIdx.x] + arr[i][threadIdx.x + 4];
arr[i][threadIdx.x] = arr[i][threadIdx.x] + arr[i][threadIdx.x + 2];
arr[i][threadIdx.x] = arr[i][threadIdx.x] + arr[i][threadIdx.x + 1];
}
}
__syncthreads();
}
__global__ void linearRegressionMapperZero(float * const valueSpace)
{
valueSpace[threadIdx.x] = 0.0f;
}
__global__ void linearRegressionMapperKernel(const float2 * const points,
const int numPoints,
float * const valueSpace)
{
__shared__ volatile float vals[NUM_CATEGORIES][NUM_THREADS];
float x = 0.0f, y = 0.0f, xx = 0.0f, yy = 0.0f, xy = 0.0f;
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < numPoints; i += gridDim.x * blockDim.x)
{
float2 myPoint = points[i];
x += myPoint.x;
y += myPoint.y;
xx += myPoint.x * myPoint.x;
yy += myPoint.y * myPoint.y;
xy += myPoint.x * myPoint.y;
}
vals[0][threadIdx.x] = x;
vals[1][threadIdx.x] = y;
vals[2][threadIdx.x] = xx;
vals[3][threadIdx.x] = yy;
vals[4][threadIdx.x] = xy;
__syncthreads();
multiReduce<float, NUM_CATEGORIES>(vals);
if (threadIdx.x == 0)
{
for (int i = 0; i < NUM_CATEGORIES; ++i) valueSpace[blockIdx.x * NUM_CATEGORIES + i] = vals[i][0];
}
}
__global__ void linearRegressionMapperCombineKernel(const int numElems,
const int numBlocks,
int * const keySpace,
float * const valSpace,
const float * const globalValueSpace)
{
float val = globalValueSpace[threadIdx.x];
for (int i = 1; i < numBlocks; ++i)
{
val += globalValueSpace[threadIdx.x + i * blockDim.x];
}
keySpace[threadIdx.x] = threadIdx.x;
valSpace[threadIdx.x] += val;
keySpace[NUM_CATEGORIES] = NUM_CATEGORIES;
valSpace[NUM_CATEGORIES] += static_cast<float>(numElems);
}
void linearRegressionMapperExecute(const void * const points,
const int numPoints,
const int numBlocks,
const int numThreads,
void * const keySpace,
void * const valueSpace,
void * const globalValueSpace,
const bool firstMapping,
cudaStream_t & stream)
{
if (firstMapping) linearRegressionMapperZero<<<1, numBlocks, 0, stream>>>(reinterpret_cast<float * >(valueSpace));
linearRegressionMapperKernel<<<numBlocks, numThreads, 0, stream>>>(reinterpret_cast<const float2 * >(points),
numPoints,
reinterpret_cast<float * >(globalValueSpace));
linearRegressionMapperCombineKernel<<<1, NUM_CATEGORIES, 0, stream>>>(numPoints,
numBlocks,
reinterpret_cast<int * >(keySpace),
reinterpret_cast<float * >(valueSpace),
reinterpret_cast<float * >(globalValueSpace));
}
|
1,029
|
#include <cuda.h>
//#include <cutil_inline.h>
#include <stdio.h>
#include <stdlib.h>
#include <string>
#include <iostream>
#include <fstream>
#define ASIZE 256
#define DATA_SIZE 1024
__device__ int shifts[ASIZE];
__device__ int results[DATA_SIZE];
__global__ void processPattern(char* x ,int m, int shifts[])
{
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if ( idx >= m ) return;
char c = x[idx];
for( int i = m - 1; i >= idx; --i )
{
if ( x[i] == c )
{// match is found
shifts[c] = m - i;
return;
}
}
}
__global__ void search(char *x, int m, char* y, int n, int shifts[], int indx[], int results[])
{
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if ( idx > (n - m) )
return;
if ( indx[idx] != idx )
return;
unsigned int yes = 1;
for( int i = 0; i < m; ++i )
{
// try to match the string
if ( x[i] != y[idx + i] )
{
yes = 0;
break;
}
}
results[idx] = yes;
}
void precomputeShiftIndx(char* y, int n, int m, int shifts[], int indx[])
{
int j = 0;
int limit = n - m;
while (j <= limit ) {
j += shifts[ y[j + m] ];
indx[j] = j;
}
}
void display_results(int n, int results[]) {
int j = 0;
int flag =0;
for( int i =0; i < n; ++i )
if ( results[i] == 1 )
{
printf("%d. Found match at %d\n",j++, i);
flag=1;
}
if(flag==0)
printf("Not found\n");
}
int main(int argc, char* argv[])
{
srand(time(NULL));
char values[] = "ACGT";
int cuda_device = 0;
int n = 10000000; // length of main string
int m = 100; // length of substring
char* mainString = (char*)malloc(n * sizeof(char));
char* subString = (char*)malloc(m * sizeof(char));
for(int i=0;i < n;i++)
{
mainString[i] = values[rand()%4];
}
for(int i=0;i < m;i++)
{
subString[i] = values[rand()%4];
}
//
// Initialize the shift and index array
//
int* l_shifts = (int*)malloc( ASIZE * sizeof(int) );
for( int i = 0; i < ASIZE; ++i )
l_shifts[i] = m + 1;
int* l_indx = (int*) malloc( n * sizeof(int) );
for( int i = 0; i < n; ++i )
l_indx[i] = -1;
cudaError_t error;
cudaEvent_t start_event, stop_event;
float time;
float time2;
// initializing the GPU timers
cudaEventCreate(&start_event);
cudaEventCreate(&stop_event);
//
// Allocate global memory to host the pattern, text and other supporting data
// structures
//
char* d_substr = 0;
int* d_shifts = 0;
int* d_indx = 0;
char* d_text = 0;
int *d_results = 0,*l_results=(int*) malloc( n * sizeof(int) );
for( int i = 0; i < n; ++i )
l_results[i] = 0;
//cudaGetSymbolAddress((void**)&d_shifts, "shifts");
cudaMalloc((void**)&d_results, n * sizeof(int)) ;
cudaMalloc((void**)&d_shifts, sizeof(int) * ASIZE) ;
//error = cudaGetLastError();
//printf("Error1: %s\n", cudaGetErrorString(error));
cudaMalloc((void**)&d_indx, n * sizeof(int)) ;
//error = cudaGetLastError();
//printf("Error2: %s\n", cudaGetErrorString(error));
cudaMalloc((void**)&d_substr, (m + 1)*sizeof(char)) ;
//error = cudaGetLastError();
//printf("Error3: %s\n", cudaGetErrorString(error));
cudaMalloc((void**)&d_text, (strlen(mainString)+1)*sizeof(char)) ;
//error = cudaGetLastError();
//printf("Error4: %s\n", cudaGetErrorString(error));
cudaMemcpy(d_shifts, l_shifts, sizeof(int) * ASIZE, cudaMemcpyHostToDevice ) ;
cudaMemcpy(d_results, l_results, sizeof(int) * n, cudaMemcpyHostToDevice ) ;
//error = cudaGetLastError();
//printf("Error5: %s\n", cudaGetErrorString(error));
cudaMemcpy(d_text, mainString, sizeof(char)*(strlen(mainString)+1), cudaMemcpyHostToDevice ) ;
//error = cudaGetLastError();
//printf("Error6: %s\n", cudaGetErrorString(error));
cudaMemcpy(d_substr, subString, sizeof(char)*(strlen(subString)+1), cudaMemcpyHostToDevice) ;
//error = cudaGetLastError();
//printf("Error7: %s\n", cudaGetErrorString(error));
//
// Pre-process the pattern to be matched
//
dim3 threadsPerBlocks(ASIZE, 1);
int t = m / threadsPerBlocks.x;
int t1 = m % threadsPerBlocks.x;
if ( t1 != 0 ) t += 1;
dim3 numBlocks(t, 1);
printf("Launching kernel with blocks=%d, threadsperblock=%d\n", numBlocks.x, threadsPerBlocks.x);
cudaEventRecord(start_event, 0);
processPattern<<<numBlocks,threadsPerBlocks>>>(d_substr, m, d_shifts);
cudaThreadSynchronize();
cudaEventRecord(stop_event, 0);
cudaEventSynchronize( stop_event );
cudaEventElapsedTime( &time, start_event, stop_event );
cudaMemcpy(l_shifts, d_shifts, sizeof(int) * ASIZE, cudaMemcpyDeviceToHost ) ;
//error = cudaGetLastError();
//printf("Error8: %s\n", cudaGetErrorString(error));
//
// Transfer the pre-computed shift indexes from host to device memory
//
cudaMemcpy(l_shifts, d_shifts, ASIZE * sizeof(int), cudaMemcpyDeviceToHost) ;
precomputeShiftIndx(mainString , n, m, l_shifts, l_indx);
cudaMemcpy(d_shifts, l_shifts, ASIZE * sizeof(int), cudaMemcpyHostToDevice) ;
cudaMemcpy(d_indx, l_indx, n * sizeof(int), cudaMemcpyHostToDevice) ;
//error = cudaGetLastError();
//printf("Error9: %s\n", cudaGetErrorString(error));
//
// Perform the actual search
//
t = n / threadsPerBlocks.x;
t1 = n % threadsPerBlocks.x;
if ( t1 != 0 ) t += 1;
dim3 numBlocks2(t, 1);
printf("Launching kernel with blocks=%d, threadsperblock=%d\n", numBlocks2.x, threadsPerBlocks.x);
cudaEventRecord(start_event, 0);
search<<<numBlocks2,threadsPerBlocks>>>(d_substr, m, d_text, n, d_shifts, d_indx,d_results);
cudaThreadSynchronize();
cudaEventRecord(stop_event, 0);
cudaEventSynchronize( stop_event );
cudaEventElapsedTime( &time2, start_event, stop_event );
cudaEventDestroy( start_event ); // cleanup
cudaEventDestroy( stop_event ); // cleanup
printf("done and it took: %f+%f=%f milliseconds\n",time, time2, time+time2);
//cudaGetSymbolAddress((void**)&d_results, "results");
//cudaMalloc((void**)&d_results, n * sizeof(int)) ;
//int* l_results = (int*) malloc( n * sizeof(int) );
cudaMemcpy(l_results, d_results, n * sizeof(int), cudaMemcpyDeviceToHost) ;
display_results(n, l_results);
//error = cudaGetLastError();
//printf("Error10: %s\n", cudaGetErrorString(error));
cudaFree(d_substr);
cudaFree(d_shifts);
cudaFree(d_indx);
cudaFree(d_text);
free(mainString);
free(subString);
free(l_indx);
free(l_shifts);
free(l_results);
cudaThreadExit();
return 0;
}
|
1,030
|
#include "includes.h"
__global__ void izhikevich_update_membrane_potentials_kernel(float *d_membrane_potentials_v, float *d_states_u, float *d_param_a, float *d_param_b, float* d_current_injections, float* thresholds_for_action_potentials, float* last_spike_time_of_each_neuron, float* resting_potentials, float current_time_in_seconds, float timestep, size_t total_number_of_neurons) {
// We require the equation timestep in ms:
float eqtimestep = timestep*1000.0f;
// Get thread IDs
int idx = threadIdx.x + blockIdx.x * blockDim.x;
while (idx < total_number_of_neurons) {
// Update the neuron states according to the Izhikevich equations
float v_update = 0.04f*d_membrane_potentials_v[idx]*d_membrane_potentials_v[idx]
+ 5.0f*d_membrane_potentials_v[idx]
+ 140
- d_states_u[idx]
+ d_current_injections[idx];
d_membrane_potentials_v[idx] += eqtimestep*v_update;
d_states_u[idx] += eqtimestep*(d_param_a[idx] * (d_param_b[idx] * d_membrane_potentials_v[idx] -
d_states_u[idx]));
if (d_membrane_potentials_v[idx] >= thresholds_for_action_potentials[idx]){
d_membrane_potentials_v[idx] = resting_potentials[idx];
last_spike_time_of_each_neuron[idx] = current_time_in_seconds;
}
idx += blockDim.x * gridDim.x;
}
__syncthreads();
}
|
1,031
|
//每个线程同时加密64块即1024B数据
//将数据预处理，事先翻转
//在远端服务器块内线程256：21.483 Gbps
//远端服务器块内线程512：22.75 Gbps
#include<stdio.h>
#include<string.h>
#include <stdint.h>
#include <stdlib.h>
#include <iostream>
#include <fstream>
#include <cstring>
#include <cuda.h>
#include <iomanip>
#include <time.h>
typedef uint64_t word_t;
#define BYTE unsigned char
#define BLOCK_SIZE 64
#define KEY_SCHEDULE_SIZE 176
#define WORD_SIZE 64
#define BS_BLOCK_SIZE (BLOCK_SIZE * WORD_SIZE / 8)
#define WORDS_PER_BLOCK (BLOCK_SIZE / WORD_SIZE)
#define BLOCK_LEN BLOCK_SIZE*8
#define ONE 1ULL
#define MUL_SHIFT 6
using namespace std;
class aes_block
{
public:
BYTE block[BLOCK_LEN];
};
class trans_aes_block
{
public:
word_t block[BLOCK_SIZE];
};
void printBytes(uint8_t b[],int len){
for(int i=0; i<len; i++)
printf("%2x ",b[i]);
printf("\n");
}
__device__ void printByte(uint8_t b[],int len){
for(int i=0; i<len; i++)
printf("%2x ",b[i]);
printf("\n");
}
void f1printBytes(BYTE b[], int len, FILE* fp) {
int i;
for (i=0; i<len; i++)
fprintf(fp, "%02x ", b[i]);
fprintf(fp, "\n");
}
static const uint8_t sbox[256] = {
//0 1 2 3 4 5 6 7 8 9 A B C D E F
0x63, 0x7c, 0x77, 0x7b, 0xf2, 0x6b, 0x6f, 0xc5, 0x30, 0x01, 0x67, 0x2b, 0xfe, 0xd7, 0xab, 0x76,
0xca, 0x82, 0xc9, 0x7d, 0xfa, 0x59, 0x47, 0xf0, 0xad, 0xd4, 0xa2, 0xaf, 0x9c, 0xa4, 0x72, 0xc0,
0xb7, 0xfd, 0x93, 0x26, 0x36, 0x3f, 0xf7, 0xcc, 0x34, 0xa5, 0xe5, 0xf1, 0x71, 0xd8, 0x31, 0x15,
0x04, 0xc7, 0x23, 0xc3, 0x18, 0x96, 0x05, 0x9a, 0x07, 0x12, 0x80, 0xe2, 0xeb, 0x27, 0xb2, 0x75,
0x09, 0x83, 0x2c, 0x1a, 0x1b, 0x6e, 0x5a, 0xa0, 0x52, 0x3b, 0xd6, 0xb3, 0x29, 0xe3, 0x2f, 0x84,
0x53, 0xd1, 0x00, 0xed, 0x20, 0xfc, 0xb1, 0x5b, 0x6a, 0xcb, 0xbe, 0x39, 0x4a, 0x4c, 0x58, 0xcf,
0xd0, 0xef, 0xaa, 0xfb, 0x43, 0x4d, 0x33, 0x85, 0x45, 0xf9, 0x02, 0x7f, 0x50, 0x3c, 0x9f, 0xa8,
0x51, 0xa3, 0x40, 0x8f, 0x92, 0x9d, 0x38, 0xf5, 0xbc, 0xb6, 0xda, 0x21, 0x10, 0xff, 0xf3, 0xd2,
0xcd, 0x0c, 0x13, 0xec, 0x5f, 0x97, 0x44, 0x17, 0xc4, 0xa7, 0x7e, 0x3d, 0x64, 0x5d, 0x19, 0x73,
0x60, 0x81, 0x4f, 0xdc, 0x22, 0x2a, 0x90, 0x88, 0x46, 0xee, 0xb8, 0x14, 0xde, 0x5e, 0x0b, 0xdb,
0xe0, 0x32, 0x3a, 0x0a, 0x49, 0x06, 0x24, 0x5c, 0xc2, 0xd3, 0xac, 0x62, 0x91, 0x95, 0xe4, 0x79,
0xe7, 0xc8, 0x37, 0x6d, 0x8d, 0xd5, 0x4e, 0xa9, 0x6c, 0x56, 0xf4, 0xea, 0x65, 0x7a, 0xae, 0x08,
0xba, 0x78, 0x25, 0x2e, 0x1c, 0xa6, 0xb4, 0xc6, 0xe8, 0xdd, 0x74, 0x1f, 0x4b, 0xbd, 0x8b, 0x8a,
0x70, 0x3e, 0xb5, 0x66, 0x48, 0x03, 0xf6, 0x0e, 0x61, 0x35, 0x57, 0xb9, 0x86, 0xc1, 0x1d, 0x9e,
0xe1, 0xf8, 0x98, 0x11, 0x69, 0xd9, 0x8e, 0x94, 0x9b, 0x1e, 0x87, 0xe9, 0xce, 0x55, 0x28, 0xdf,
0x8c, 0xa1, 0x89, 0x0d, 0xbf, 0xe6, 0x42, 0x68, 0x41, 0x99, 0x2d, 0x0f, 0xb0, 0x54, 0xbb, 0x16 };
static void rotate(unsigned char *in) {
unsigned char a,c;
a = in[0];
for(c=0;c<3;c++)
in[c] = in[c + 1];
in[3] = a;
return;
}
/* Calculate the rcon used in key expansion */
static unsigned char rcon(unsigned char in) {
unsigned char c=1;
if(in == 0)
return 0;
while(in != 1) {
unsigned char b;
b = c & 0x80;
c <<= 1;
if(b == 0x80) {
c ^= 0x1b;
}
in--;
}
return c;
}
static void schedule_core(unsigned char *in, unsigned char i) {
char a;
/* Rotate the input 8 bits to the left */
rotate(in);
/* Apply Rijndael's s-box on all 4 bytes */
for(a = 0; a < 4; a++)
in[a] = sbox[in[a]];
/* On just the first byte, add 2^i to the byte */
in[0] ^= rcon(i);
}
void expand_key(unsigned char *in) {
unsigned char t[4];
/* c is 16 because the first sub-key is the user-supplied key */
unsigned char c = 16;
unsigned char i = 1;
unsigned char a;
/* We need 11 sets of sixteen bytes each for 128-bit mode */
while(c < 176) {
/* Copy the temporary variable over from the last 4-byte
* block */
for(a = 0; a < 4; a++)
t[a] = in[a + c - 4];
/* Every four blocks (of four bytes),
* do a complex calculation */
if(c % 16 == 0) {
schedule_core(t,i);
i++;
}
for(a = 0; a < 4; a++) {
in[c] = in[c - 16] ^ t[a];
c++;
}
}
}
void key_transpose_dst(word_t * transpose, word_t * blocks)
{
int i,k;
word_t w;
for(k=0; k < WORD_SIZE; k++)
{
int bitpos = ONE << k;
for (i=0; i < WORDS_PER_BLOCK; i++)
{
w = blocks[k * WORDS_PER_BLOCK + i];
int offset = i << MUL_SHIFT;
transpose[(offset)+ 0 ] |= (w & (ONE << 0 )) ? (bitpos) : 0;
transpose[(offset)+ 1 ] |= (w & (ONE << 1 )) ? (bitpos) : 0;
transpose[(offset)+ 2 ] |= (w & (ONE << 2 )) ? (bitpos) : 0;
transpose[(offset)+ 3 ] |= (w & (ONE << 3 )) ? (bitpos) : 0;
transpose[(offset)+ 4 ] |= (w & (ONE << 4 )) ? (bitpos) : 0;
transpose[(offset)+ 5 ] |= (w & (ONE << 5 )) ? (bitpos) : 0;
transpose[(offset)+ 6 ] |= (w & (ONE << 6 )) ? (bitpos) : 0;
transpose[(offset)+ 7 ] |= (w & (ONE << 7 )) ? (bitpos) : 0;
transpose[(offset)+ 8 ] |= (w & (ONE << 8 )) ? (bitpos) : 0;
transpose[(offset)+ 9 ] |= (w & (ONE << 9 )) ? (bitpos) : 0;
transpose[(offset)+ 10] |= (w & (ONE << 10)) ? (bitpos) : 0;
transpose[(offset)+ 11] |= (w & (ONE << 11)) ? (bitpos) : 0;
transpose[(offset)+ 12] |= (w & (ONE << 12)) ? (bitpos) : 0;
transpose[(offset)+ 13] |= (w & (ONE << 13)) ? (bitpos) : 0;
transpose[(offset)+ 14] |= (w & (ONE << 14)) ? (bitpos) : 0;
transpose[(offset)+ 15] |= (w & (ONE << 15)) ? (bitpos) : 0;
transpose[(offset)+ 16] |= (w & (ONE << 16)) ? (bitpos) : 0;
transpose[(offset)+ 17] |= (w & (ONE << 17)) ? (bitpos) : 0;
transpose[(offset)+ 18] |= (w & (ONE << 18)) ? (bitpos) : 0;
transpose[(offset)+ 19] |= (w & (ONE << 19)) ? (bitpos) : 0;
transpose[(offset)+ 20] |= (w & (ONE << 20)) ? (bitpos) : 0;
transpose[(offset)+ 21] |= (w & (ONE << 21)) ? (bitpos) : 0;
transpose[(offset)+ 22] |= (w & (ONE << 22)) ? (bitpos) : 0;
transpose[(offset)+ 23] |= (w & (ONE << 23)) ? (bitpos) : 0;
transpose[(offset)+ 24] |= (w & (ONE << 24)) ? (bitpos) : 0;
transpose[(offset)+ 25] |= (w & (ONE << 25)) ? (bitpos) : 0;
transpose[(offset)+ 26] |= (w & (ONE << 26)) ? (bitpos) : 0;
transpose[(offset)+ 27] |= (w & (ONE << 27)) ? (bitpos) : 0;
transpose[(offset)+ 28] |= (w & (ONE << 28)) ? (bitpos) : 0;
transpose[(offset)+ 29] |= (w & (ONE << 29)) ? (bitpos) : 0;
transpose[(offset)+ 30] |= (w & (ONE << 30)) ? (bitpos) : 0;
transpose[(offset)+ 31] |= (w & (ONE << 31)) ? (bitpos) : 0;
transpose[(offset)+ 32] |= (w & (ONE << 32)) ? (bitpos) : 0;
transpose[(offset)+ 33] |= (w & (ONE << 33)) ? (bitpos) : 0;
transpose[(offset)+ 34] |= (w & (ONE << 34)) ? (bitpos) : 0;
transpose[(offset)+ 35] |= (w & (ONE << 35)) ? (bitpos) : 0;
transpose[(offset)+ 36] |= (w & (ONE << 36)) ? (bitpos) : 0;
transpose[(offset)+ 37] |= (w & (ONE << 37)) ? (bitpos) : 0;
transpose[(offset)+ 38] |= (w & (ONE << 38)) ? (bitpos) : 0;
transpose[(offset)+ 39] |= (w & (ONE << 39)) ? (bitpos) : 0;
transpose[(offset)+ 40] |= (w & (ONE << 40)) ? (bitpos) : 0;
transpose[(offset)+ 41] |= (w & (ONE << 41)) ? (bitpos) : 0;
transpose[(offset)+ 42] |= (w & (ONE << 42)) ? (bitpos) : 0;
transpose[(offset)+ 43] |= (w & (ONE << 43)) ? (bitpos) : 0;
transpose[(offset)+ 44] |= (w & (ONE << 44)) ? (bitpos) : 0;
transpose[(offset)+ 45] |= (w & (ONE << 45)) ? (bitpos) : 0;
transpose[(offset)+ 46] |= (w & (ONE << 46)) ? (bitpos) : 0;
transpose[(offset)+ 47] |= (w & (ONE << 47)) ? (bitpos) : 0;
transpose[(offset)+ 48] |= (w & (ONE << 48)) ? (bitpos) : 0;
transpose[(offset)+ 49] |= (w & (ONE << 49)) ? (bitpos) : 0;
transpose[(offset)+ 50] |= (w & (ONE << 50)) ? (bitpos) : 0;
transpose[(offset)+ 51] |= (w & (ONE << 51)) ? (bitpos) : 0;
transpose[(offset)+ 52] |= (w & (ONE << 52)) ? (bitpos) : 0;
transpose[(offset)+ 53] |= (w & (ONE << 53)) ? (bitpos) : 0;
transpose[(offset)+ 54] |= (w & (ONE << 54)) ? (bitpos) : 0;
transpose[(offset)+ 55] |= (w & (ONE << 55)) ? (bitpos) : 0;
transpose[(offset)+ 56] |= (w & (ONE << 56)) ? (bitpos) : 0;
transpose[(offset)+ 57] |= (w & (ONE << 57)) ? (bitpos) : 0;
transpose[(offset)+ 58] |= (w & (ONE << 58)) ? (bitpos) : 0;
transpose[(offset)+ 59] |= (w & (ONE << 59)) ? (bitpos) : 0;
transpose[(offset)+ 60] |= (w & (ONE << 60)) ? (bitpos) : 0;
transpose[(offset)+ 61] |= (w & (ONE << 61)) ? (bitpos) : 0;
transpose[(offset)+ 62] |= (w & (ONE << 62)) ? (bitpos) : 0;
transpose[(offset)+ 63] |= (w & (ONE << 63)) ? (bitpos) : 0;
}
}
}
void bs_transpose_dst(word_t * transpose, word_t * blocks)
{
int i,k;
word_t w;
for(k=0; k < WORD_SIZE; k++)
{
int bitpos = ONE << k;
for (i=0; i < WORDS_PER_BLOCK; i++)
{
w = blocks[k * WORDS_PER_BLOCK + i];
int offset = i << MUL_SHIFT;
transpose[(offset)+ 0 ] |= (w & (ONE << 0 )) ? (bitpos) : 0;
transpose[(offset)+ 1 ] |= (w & (ONE << 1 )) ? (bitpos) : 0;
transpose[(offset)+ 2 ] |= (w & (ONE << 2 )) ? (bitpos) : 0;
transpose[(offset)+ 3 ] |= (w & (ONE << 3 )) ? (bitpos) : 0;
transpose[(offset)+ 4 ] |= (w & (ONE << 4 )) ? (bitpos) : 0;
transpose[(offset)+ 5 ] |= (w & (ONE << 5 )) ? (bitpos) : 0;
transpose[(offset)+ 6 ] |= (w & (ONE << 6 )) ? (bitpos) : 0;
transpose[(offset)+ 7 ] |= (w & (ONE << 7 )) ? (bitpos) : 0;
transpose[(offset)+ 8 ] |= (w & (ONE << 8 )) ? (bitpos) : 0;
transpose[(offset)+ 9 ] |= (w & (ONE << 9 )) ? (bitpos) : 0;
transpose[(offset)+ 10] |= (w & (ONE << 10)) ? (bitpos) : 0;
transpose[(offset)+ 11] |= (w & (ONE << 11)) ? (bitpos) : 0;
transpose[(offset)+ 12] |= (w & (ONE << 12)) ? (bitpos) : 0;
transpose[(offset)+ 13] |= (w & (ONE << 13)) ? (bitpos) : 0;
transpose[(offset)+ 14] |= (w & (ONE << 14)) ? (bitpos) : 0;
transpose[(offset)+ 15] |= (w & (ONE << 15)) ? (bitpos) : 0;
transpose[(offset)+ 16] |= (w & (ONE << 16)) ? (bitpos) : 0;
transpose[(offset)+ 17] |= (w & (ONE << 17)) ? (bitpos) : 0;
transpose[(offset)+ 18] |= (w & (ONE << 18)) ? (bitpos) : 0;
transpose[(offset)+ 19] |= (w & (ONE << 19)) ? (bitpos) : 0;
transpose[(offset)+ 20] |= (w & (ONE << 20)) ? (bitpos) : 0;
transpose[(offset)+ 21] |= (w & (ONE << 21)) ? (bitpos) : 0;
transpose[(offset)+ 22] |= (w & (ONE << 22)) ? (bitpos) : 0;
transpose[(offset)+ 23] |= (w & (ONE << 23)) ? (bitpos) : 0;
transpose[(offset)+ 24] |= (w & (ONE << 24)) ? (bitpos) : 0;
transpose[(offset)+ 25] |= (w & (ONE << 25)) ? (bitpos) : 0;
transpose[(offset)+ 26] |= (w & (ONE << 26)) ? (bitpos) : 0;
transpose[(offset)+ 27] |= (w & (ONE << 27)) ? (bitpos) : 0;
transpose[(offset)+ 28] |= (w & (ONE << 28)) ? (bitpos) : 0;
transpose[(offset)+ 29] |= (w & (ONE << 29)) ? (bitpos) : 0;
transpose[(offset)+ 30] |= (w & (ONE << 30)) ? (bitpos) : 0;
transpose[(offset)+ 31] |= (w & (ONE << 31)) ? (bitpos) : 0;
transpose[(offset)+ 32] |= (w & (ONE << 32)) ? (bitpos) : 0;
transpose[(offset)+ 33] |= (w & (ONE << 33)) ? (bitpos) : 0;
transpose[(offset)+ 34] |= (w & (ONE << 34)) ? (bitpos) : 0;
transpose[(offset)+ 35] |= (w & (ONE << 35)) ? (bitpos) : 0;
transpose[(offset)+ 36] |= (w & (ONE << 36)) ? (bitpos) : 0;
transpose[(offset)+ 37] |= (w & (ONE << 37)) ? (bitpos) : 0;
transpose[(offset)+ 38] |= (w & (ONE << 38)) ? (bitpos) : 0;
transpose[(offset)+ 39] |= (w & (ONE << 39)) ? (bitpos) : 0;
transpose[(offset)+ 40] |= (w & (ONE << 40)) ? (bitpos) : 0;
transpose[(offset)+ 41] |= (w & (ONE << 41)) ? (bitpos) : 0;
transpose[(offset)+ 42] |= (w & (ONE << 42)) ? (bitpos) : 0;
transpose[(offset)+ 43] |= (w & (ONE << 43)) ? (bitpos) : 0;
transpose[(offset)+ 44] |= (w & (ONE << 44)) ? (bitpos) : 0;
transpose[(offset)+ 45] |= (w & (ONE << 45)) ? (bitpos) : 0;
transpose[(offset)+ 46] |= (w & (ONE << 46)) ? (bitpos) : 0;
transpose[(offset)+ 47] |= (w & (ONE << 47)) ? (bitpos) : 0;
transpose[(offset)+ 48] |= (w & (ONE << 48)) ? (bitpos) : 0;
transpose[(offset)+ 49] |= (w & (ONE << 49)) ? (bitpos) : 0;
transpose[(offset)+ 50] |= (w & (ONE << 50)) ? (bitpos) : 0;
transpose[(offset)+ 51] |= (w & (ONE << 51)) ? (bitpos) : 0;
transpose[(offset)+ 52] |= (w & (ONE << 52)) ? (bitpos) : 0;
transpose[(offset)+ 53] |= (w & (ONE << 53)) ? (bitpos) : 0;
transpose[(offset)+ 54] |= (w & (ONE << 54)) ? (bitpos) : 0;
transpose[(offset)+ 55] |= (w & (ONE << 55)) ? (bitpos) : 0;
transpose[(offset)+ 56] |= (w & (ONE << 56)) ? (bitpos) : 0;
transpose[(offset)+ 57] |= (w & (ONE << 57)) ? (bitpos) : 0;
transpose[(offset)+ 58] |= (w & (ONE << 58)) ? (bitpos) : 0;
transpose[(offset)+ 59] |= (w & (ONE << 59)) ? (bitpos) : 0;
transpose[(offset)+ 60] |= (w & (ONE << 60)) ? (bitpos) : 0;
transpose[(offset)+ 61] |= (w & (ONE << 61)) ? (bitpos) : 0;
transpose[(offset)+ 62] |= (w & (ONE << 62)) ? (bitpos) : 0;
transpose[(offset)+ 63] |= (w & (ONE << 63)) ? (bitpos) : 0;
}
}
}
void bs_transpose_rev(word_t * blocks)
{
int k;
word_t w;
word_t transpose[BLOCK_SIZE];
memset(transpose, 0, sizeof(transpose));
for(k=0; k < BLOCK_SIZE; k++)
{
w = blocks[k];
word_t bitpos = ONE << (k % WORD_SIZE);
word_t offset = k / WORD_SIZE;
transpose[0 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 0 )) ? bitpos : 0;
transpose[1 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 1 )) ? bitpos : 0;
transpose[2 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 2 )) ? bitpos : 0;
transpose[3 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 3 )) ? bitpos : 0;
transpose[4 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 4 )) ? bitpos : 0;
transpose[5 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 5 )) ? bitpos : 0;
transpose[6 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 6 )) ? bitpos : 0;
transpose[7 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 7 )) ? bitpos : 0;
transpose[8 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 8 )) ? bitpos : 0;
transpose[9 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 9 )) ? bitpos : 0;
transpose[10 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 10)) ? bitpos : 0;
transpose[11 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 11)) ? bitpos : 0;
transpose[12 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 12)) ? bitpos : 0;
transpose[13 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 13)) ? bitpos : 0;
transpose[14 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 14)) ? bitpos : 0;
transpose[15 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 15)) ? bitpos : 0;
transpose[16 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 16)) ? bitpos : 0;
transpose[17 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 17)) ? bitpos : 0;
transpose[18 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 18)) ? bitpos : 0;
transpose[19 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 19)) ? bitpos : 0;
transpose[20 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 20)) ? bitpos : 0;
transpose[21 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 21)) ? bitpos : 0;
transpose[22 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 22)) ? bitpos : 0;
transpose[23 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 23)) ? bitpos : 0;
transpose[24 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 24)) ? bitpos : 0;
transpose[25 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 25)) ? bitpos : 0;
transpose[26 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 26)) ? bitpos : 0;
transpose[27 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 27)) ? bitpos : 0;
transpose[28 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 28)) ? bitpos : 0;
transpose[29 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 29)) ? bitpos : 0;
transpose[30 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 30)) ? bitpos : 0;
transpose[31 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 31)) ? bitpos : 0;
transpose[32 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 32)) ? bitpos : 0;
transpose[33 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 33)) ? bitpos : 0;
transpose[34 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 34)) ? bitpos : 0;
transpose[35 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 35)) ? bitpos : 0;
transpose[36 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 36)) ? bitpos : 0;
transpose[37 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 37)) ? bitpos : 0;
transpose[38 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 38)) ? bitpos : 0;
transpose[39 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 39)) ? bitpos : 0;
transpose[40 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 40)) ? bitpos : 0;
transpose[41 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 41)) ? bitpos : 0;
transpose[42 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 42)) ? bitpos : 0;
transpose[43 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 43)) ? bitpos : 0;
transpose[44 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 44)) ? bitpos : 0;
transpose[45 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 45)) ? bitpos : 0;
transpose[46 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 46)) ? bitpos : 0;
transpose[47 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 47)) ? bitpos : 0;
transpose[48 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 48)) ? bitpos : 0;
transpose[49 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 49)) ? bitpos : 0;
transpose[50 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 50)) ? bitpos : 0;
transpose[51 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 51)) ? bitpos : 0;
transpose[52 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 52)) ? bitpos : 0;
transpose[53 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 53)) ? bitpos : 0;
transpose[54 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 54)) ? bitpos : 0;
transpose[55 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 55)) ? bitpos : 0;
transpose[56 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 56)) ? bitpos : 0;
transpose[57 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 57)) ? bitpos : 0;
transpose[58 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 58)) ? bitpos : 0;
transpose[59 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 59)) ? bitpos : 0;
transpose[60 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 60)) ? bitpos : 0;
transpose[61 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 61)) ? bitpos : 0;
transpose[62 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 62)) ? bitpos : 0;
transpose[63 * WORDS_PER_BLOCK + (offset )] |= (w & (ONE << 63)) ? bitpos : 0;
}
memcpy(blocks,transpose,sizeof(transpose));
}
void key_transpose(word_t * blocks)
{
word_t transpose[BLOCK_SIZE];
memset(transpose, 0, sizeof(transpose));
key_transpose_dst(transpose,blocks);
memcpy(blocks,transpose,sizeof(transpose));
}
void bs_transpose(word_t * blocks)
{
word_t transpose[BLOCK_SIZE];
memset(transpose, 0, sizeof(transpose));
bs_transpose_dst(transpose,blocks);
memcpy(blocks,transpose,sizeof(transpose));
}
void bs_expand_key(word_t (* rk)[BLOCK_SIZE], uint8_t * _key)
{
// TODO integrate this better
uint8_t key[KEY_SCHEDULE_SIZE];
memmove(key,_key,BLOCK_SIZE/8);
expand_key(key);
int i, j = 0, k, l;
for (i = 0; i < KEY_SCHEDULE_SIZE; i += (BLOCK_SIZE/8))
{
memmove(rk[j], key + i, BLOCK_SIZE / 8);
for (k = WORDS_PER_BLOCK; k < 128; k += WORDS_PER_BLOCK)
{
for (l = 0; l < WORDS_PER_BLOCK; l++)
{
rk[j][k + l] = rk[j][l];
}
}
key_transpose(rk[j]);
j++;
}
}
__device__ void bs_addroundkey(word_t * B, word_t * rk)
{
int i;
for (i = 0; i < BLOCK_SIZE; i++)
B[i] ^= rk[i];
}
__device__ void bs_sbox(word_t U[8])
{
word_t S[8];
word_t
T1,T2,T3,T4,T5,T6,T7,T8,
T9,T10,T11,T12,T13,T14,T15,T16,
T17,T18,T19,T20,T21,T22,T23,T24,
T25, T26, T27;
word_t
M1,M2,M3,M4,M5,M6,M7,M8,
M9,M10,M11,M12,M13,M14,M15,
M16,M17,M18,M19,M20,M21,M22,
M23,M24,M25,M26,M27,M28,M29,
M30,M31,M32,M33,M34,M35,M36,
M37,M38,M39,M40,M41,M42,M43,
M44,M45,M46,M47,M48,M49,M50,
M51,M52,M53,M54,M55,M56,M57,
M58,M59,M60,M61,M62,M63;
word_t
L0,L1,L2,L3,L4,L5,L6,L7,L8,
L9,L10,L11,L12,L13,L14,
L15,L16,L17,L18,L19,L20,
L21,L22,L23,L24,L25,L26,
L27,L28,L29;
T1 = U[7] ^ U[4];
T2 = U[7] ^ U[2];
T3 = U[7] ^ U[1];
T4 = U[4] ^ U[2];
T5 = U[3] ^ U[1];
T6 = T1 ^ T5;
T7 = U[6] ^ U[5];
T8 = U[0] ^ T6;
T9 = U[0] ^ T7;
T10 = T6 ^ T7;
T11 = U[6] ^ U[2];
T12 = U[5] ^ U[2];
T13 = T3 ^ T4;
T14 = T6 ^ T11;
T15 = T5 ^ T11;
T16 = T5 ^ T12;
T17 = T9 ^ T16;
T18 = U[4] ^ U[0];
T19 = T7 ^ T18;
T20 = T1 ^ T19;
T21 = U[1] ^ U[0];
T22 = T7 ^ T21;
T23 = T2 ^ T22;
T24 = T2 ^ T10;
T25 = T20 ^ T17;
T26 = T3 ^ T16;
T27 = T1 ^ T12;
M1 = T13 & T6;
M2 = T23 & T8;
M3 = T14 ^ M1;
M4 = T19 & U[0];
M5 = M4 ^ M1;
M6 = T3 & T16;
M7 = T22 & T9;
M8 = T26 ^ M6;
M9 = T20 & T17;
M10 = M9 ^ M6;
M11 = T1 & T15;
M12 = T4 & T27;
M13 = M12 ^ M11;
M14 = T2 & T10;
M15 = M14 ^ M11;
M16 = M3 ^ M2;
M17 = M5 ^ T24;
M18 = M8 ^ M7;
M19 = M10 ^ M15;
M20 = M16 ^ M13;
M21 = M17 ^ M15;
M22 = M18 ^ M13;
M23 = M19 ^ T25;
M24 = M22 ^ M23;
M25 = M22 & M20;
M26 = M21 ^ M25;
M27 = M20 ^ M21;
M28 = M23 ^ M25;
M29 = M28 & M27;
M30 = M26 & M24;
M31 = M20 & M23;
M32 = M27 & M31;
M33 = M27 ^ M25;
M34 = M21 & M22;
M35 = M24 & M34;
M36 = M24 ^ M25;
M37 = M21 ^ M29;
M38 = M32 ^ M33;
M39 = M23 ^ M30;
M40 = M35 ^ M36;
M41 = M38 ^ M40;
M42 = M37 ^ M39;
M43 = M37 ^ M38;
M44 = M39 ^ M40;
M45 = M42 ^ M41;
M46 = M44 & T6;
M47 = M40 & T8;
M48 = M39 & U[0];
M49 = M43 & T16;
M50 = M38 & T9;
M51 = M37 & T17;
M52 = M42 & T15;
M53 = M45 & T27;
M54 = M41 & T10;
M55 = M44 & T13;
M56 = M40 & T23;
M57 = M39 & T19;
M58 = M43 & T3;
M59 = M38 & T22;
M60 = M37 & T20;
M61 = M42 & T1;
M62 = M45 & T4;
M63 = M41 & T2;
L0 = M61 ^ M62;
L1 = M50 ^ M56;
L2 = M46 ^ M48;
L3 = M47 ^ M55;
L4 = M54 ^ M58;
L5 = M49 ^ M61;
L6 = M62 ^ L5;
L7 = M46 ^ L3;
L8 = M51 ^ M59;
L9 = M52 ^ M53;
L10 = M53 ^ L4;
L11 = M60 ^ L2;
L12 = M48 ^ M51;
L13 = M50 ^ L0;
L14 = M52 ^ M61;
L15 = M55 ^ L1;
L16 = M56 ^ L0;
L17 = M57 ^ L1;
L18 = M58 ^ L8;
L19 = M63 ^ L4;
L20 = L0 ^ L1;
L21 = L1 ^ L7;
L22 = L3 ^ L12;
L23 = L18 ^ L2;
L24 = L15 ^ L9;
L25 = L6 ^ L10;
L26 = L7 ^ L9;
L27 = L8 ^ L10;
L28 = L11 ^ L14;
L29 = L11 ^ L17;
S[7] = L6 ^ L24;
S[6] = ~(L16 ^ L26);
S[5] = ~(L19 ^ L28);
S[4] = L6 ^ L21;
S[3] = L20 ^ L22;
S[2] = L25 ^ L29;
S[1] = ~(L13 ^ L27);
S[0] = ~(L6 ^ L23);
memcpy(U,S,sizeof(S));
}
__device__ void bs_apply_sbox(word_t * input)
{
int i;
for(i=0; i < BLOCK_SIZE; i+=8)
{
bs_sbox(input+i);
}
}
#define A0 0
#define A1 8
#define A2 16
#define A3 24
#define R0 0
#define R1 8
#define R2 16
#define R3 24
#define B0 0
#define B1 32
#define B2 64
#define B3 96
// Does shift rows and mix columns in same step
__device__ void bs_shiftmix(word_t * B)
{
word_t Bp_space[BLOCK_SIZE];
word_t * Bp = Bp_space;
word_t * Br0 = B + 0;
word_t * Br1 = B + 32;
word_t * Br2 = B + 64;
word_t * Br3 = B + 96;
uint8_t offsetr0 = 0;
uint8_t offsetr1 = 32;
uint8_t offsetr2 = 64;
uint8_t offsetr3 = 96;
Br0 = B + offsetr0;
Br1 = B + offsetr1;
Br2 = B + offsetr2;
Br3 = B + offsetr3;
int i;
for (i = 0; i < 4; i++)
{
// B0
// 2*A0 2*A1 A1 A2 A3
word_t of =Br0[R0+7]^ Br1[R1+7];
Bp[A0+0] = Br1[R1+0] ^ Br2[R2+0] ^ Br3[R3+0] ^ of;
Bp[A0+1] = Br0[R0+0] ^ Br1[R1+0] ^ Br1[R1+1] ^ Br2[R2+1] ^ Br3[R3+1] ^ of;
Bp[A0+2] = Br0[R0+1] ^ Br1[R1+1] ^ Br1[R1+2] ^ Br2[R2+2] ^ Br3[R3+2];
Bp[A0+3] = Br0[R0+2] ^ Br1[R1+2] ^ Br1[R1+3] ^ Br2[R2+3] ^ Br3[R3+3] ^ of;
Bp[A0+4] = Br0[R0+3] ^ Br1[R1+3] ^ Br1[R1+4] ^ Br2[R2+4] ^ Br3[R3+4] ^ of;
Bp[A0+5] = Br0[R0+4] ^ Br1[R1+4] ^ Br1[R1+5] ^ Br2[R2+5] ^ Br3[R3+5];
Bp[A0+6] = Br0[R0+5] ^ Br1[R1+5] ^ Br1[R1+6] ^ Br2[R2+6] ^ Br3[R3+6];
Bp[A0+7] = Br0[R0+6] ^ Br1[R1+6] ^ Br1[R1+7] ^ Br2[R2+7] ^ Br3[R3+7];
// A0 2*A1 2*A2 A2 A3
of = Br1[R1+7] ^ Br2[R2+7];
Bp[A1+0] = Br0[R0+0] ^ Br2[R2+0] ^ Br3[R3+0] ^ of;
Bp[A1+1] = Br0[R0+1] ^ Br1[R1+0] ^ Br2[R2+0] ^ Br2[R2+1] ^ Br3[R3+1] ^ of;
Bp[A1+2] = Br0[R0+2] ^ Br1[R1+1] ^ Br2[R2+1] ^ Br2[R2+2] ^ Br3[R3+2];
Bp[A1+3] = Br0[R0+3] ^ Br1[R1+2] ^ Br2[R2+2] ^ Br2[R2+3] ^ Br3[R3+3] ^ of;
Bp[A1+4] = Br0[R0+4] ^ Br1[R1+3] ^ Br2[R2+3] ^ Br2[R2+4] ^ Br3[R3+4] ^ of;
Bp[A1+5] = Br0[R0+5] ^ Br1[R1+4] ^ Br2[R2+4] ^ Br2[R2+5] ^ Br3[R3+5];
Bp[A1+6] = Br0[R0+6] ^ Br1[R1+5] ^ Br2[R2+5] ^ Br2[R2+6] ^ Br3[R3+6];
Bp[A1+7] = Br0[R0+7] ^ Br1[R1+6] ^ Br2[R2+6] ^ Br2[R2+7] ^ Br3[R3+7];
// A0 A1 2*A2 2*A3 A3
of = Br2[R2+7] ^ Br3[R3+7];
Bp[A2+0] = Br0[R0+0] ^ Br1[R1+0] ^ Br3[R3+0] ^ of;
Bp[A2+1] = Br0[R0+1] ^ Br1[R1+1] ^ Br2[R2+0] ^ Br3[R3+0] ^ Br3[R3+1] ^ of;
Bp[A2+2] = Br0[R0+2] ^ Br1[R1+2] ^ Br2[R2+1] ^ Br3[R3+1] ^ Br3[R3+2];
Bp[A2+3] = Br0[R0+3] ^ Br1[R1+3] ^ Br2[R2+2] ^ Br3[R3+2] ^ Br3[R3+3] ^ of;
Bp[A2+4] = Br0[R0+4] ^ Br1[R1+4] ^ Br2[R2+3] ^ Br3[R3+3] ^ Br3[R3+4] ^ of;
Bp[A2+5] = Br0[R0+5] ^ Br1[R1+5] ^ Br2[R2+4] ^ Br3[R3+4] ^ Br3[R3+5];
Bp[A2+6] = Br0[R0+6] ^ Br1[R1+6] ^ Br2[R2+5] ^ Br3[R3+5] ^ Br3[R3+6];
Bp[A2+7] = Br0[R0+7] ^ Br1[R1+7] ^ Br2[R2+6] ^ Br3[R3+6] ^ Br3[R3+7];
// A0 2*A0 A1 A2 2*A3
of = Br0[R0+7] ^ Br3[R3+7];
Bp[A3+0] = Br0[R0+0] ^ Br1[R1+0] ^ Br2[R2+0] ^ of;
Bp[A3+1] = Br0[R0+1] ^ Br0[R0+0] ^ Br1[R1+1] ^ Br2[R2+1] ^ Br3[R3+0] ^ of;
Bp[A3+2] = Br0[R0+2] ^ Br0[R0+1] ^ Br1[R1+2] ^ Br2[R2+2] ^ Br3[R3+1];
Bp[A3+3] = Br0[R0+3] ^ Br0[R0+2] ^ Br1[R1+3] ^ Br2[R2+3] ^ Br3[R3+2] ^ of;
Bp[A3+4] = Br0[R0+4] ^ Br0[R0+3] ^ Br1[R1+4] ^ Br2[R2+4] ^ Br3[R3+3] ^ of;
Bp[A3+5] = Br0[R0+5] ^ Br0[R0+4] ^ Br1[R1+5] ^ Br2[R2+5] ^ Br3[R3+4];
Bp[A3+6] = Br0[R0+6] ^ Br0[R0+5] ^ Br1[R1+6] ^ Br2[R2+6] ^ Br3[R3+5];
Bp[A3+7] = Br0[R0+7] ^ Br0[R0+6] ^ Br1[R1+7] ^ Br2[R2+7] ^ Br3[R3+6];
Bp += BLOCK_SIZE/4;
offsetr0 = (offsetr0 + BLOCK_SIZE/4) & 0x7f;
offsetr1 = (offsetr1 + BLOCK_SIZE/4) & 0x7f;
offsetr2 = (offsetr2 + BLOCK_SIZE/4) & 0x7f;
offsetr3 = (offsetr3 + BLOCK_SIZE/4) & 0x7f;
Br0 = B + offsetr0;
Br1 = B + offsetr1;
Br2 = B + offsetr2;
Br3 = B + offsetr3;
}
memcpy(B,Bp_space,sizeof(Bp_space));
}
__device__ void bs_shiftrows(word_t * B)
{
word_t Bp_space[BLOCK_SIZE];
word_t * Bp = Bp_space;
word_t * Br0 = B + 0;
word_t * Br1 = B + 32;
word_t * Br2 = B + 64;
word_t * Br3 = B + 96;
uint8_t offsetr0 = 0;
uint8_t offsetr1 = 32;
uint8_t offsetr2 = 64;
uint8_t offsetr3 = 96;
int i;
for(i=0; i<4; i++)
{
Bp[B0 + 0] = Br0[0];
Bp[B0 + 1] = Br0[1];
Bp[B0 + 2] = Br0[2];
Bp[B0 + 3] = Br0[3];
Bp[B0 + 4] = Br0[4];
Bp[B0 + 5] = Br0[5];
Bp[B0 + 6] = Br0[6];
Bp[B0 + 7] = Br0[7];
Bp[B1 + 0] = Br1[0];
Bp[B1 + 1] = Br1[1];
Bp[B1 + 2] = Br1[2];
Bp[B1 + 3] = Br1[3];
Bp[B1 + 4] = Br1[4];
Bp[B1 + 5] = Br1[5];
Bp[B1 + 6] = Br1[6];
Bp[B1 + 7] = Br1[7];
Bp[B2 + 0] = Br2[0];
Bp[B2 + 1] = Br2[1];
Bp[B2 + 2] = Br2[2];
Bp[B2 + 3] = Br2[3];
Bp[B2 + 4] = Br2[4];
Bp[B2 + 5] = Br2[5];
Bp[B2 + 6] = Br2[6];
Bp[B2 + 7] = Br2[7];
Bp[B3 + 0] = Br3[0];
Bp[B3 + 1] = Br3[1];
Bp[B3 + 2] = Br3[2];
Bp[B3 + 3] = Br3[3];
Bp[B3 + 4] = Br3[4];
Bp[B3 + 5] = Br3[5];
Bp[B3 + 6] = Br3[6];
Bp[B3 + 7] = Br3[7];
offsetr0 = (offsetr0 + BLOCK_SIZE/16 + BLOCK_SIZE/4) & 0x7f;
offsetr1 = (offsetr1 + BLOCK_SIZE/16 + BLOCK_SIZE/4) & 0x7f;
offsetr2 = (offsetr2 + BLOCK_SIZE/16 + BLOCK_SIZE/4) & 0x7f;
offsetr3 = (offsetr3 + BLOCK_SIZE/16 + BLOCK_SIZE/4) & 0x7f;
Br0 = B + offsetr0;
Br1 = B + offsetr1;
Br2 = B + offsetr2;
Br3 = B + offsetr3;
Bp += 8;
}
memcpy(B,Bp_space,sizeof(Bp_space));
}
__global__ void AES_Encrypt(trans_aes_block trans_aes_block_array[],word_t (* rk)[BLOCK_SIZE],int block_number){
int global_thread_index = blockDim.x*blockIdx.x + threadIdx.x;
int stride=blockDim.x*gridDim.x;
__shared__ word_t key[11][BLOCK_SIZE];
for(int real_thread=global_thread_index;real_thread < block_number;real_thread+=stride){
if(threadIdx.x==0){
memcpy(key,rk,11*BLOCK_SIZE*sizeof(word_t));
}
word_t block[BLOCK_SIZE];
memcpy(block,trans_aes_block_array[real_thread].block,BLOCK_LEN);
int round;
bs_addroundkey(block,key[0]);
for (round = 1; round < 10; round++)
{
bs_apply_sbox(block);
bs_shiftmix(block);
bs_addroundkey(block,key[round]);
}
bs_apply_sbox(block);
bs_shiftrows(block);
bs_addroundkey(block,key[10]);
memcpy(trans_aes_block_array[real_thread].block,block,BLOCK_LEN);
}
}
int main(int argc, char* argv[]){
ifstream ifs;
ifs.open(argv[1], ios::binary);
if(!ifs){
cerr<<"错误：无法打开加密文件"<<endl;
exit(1);
}
ifs.seekg(0, ios::end);
int infileLength = ifs.tellg();
infileLength-=1;
ifs.seekg(0, ios::beg);
cout<<"输入文件长度为(字节)： "<<infileLength<<endl<<"文件块个数为： "<<infileLength/BLOCK_LEN<<endl;
int block_number = infileLength/BLOCK_LEN ;
int number_of_zero_pending = infileLength%BLOCK_LEN;
aes_block* aes_block_array;
trans_aes_block* trans_aes_block_array;
BYTE key[16]; //定义AES中需要的最大的key
int keyLen = 0;
ifstream key_fp;
key_fp.open(argv[2]);
while(key_fp.peek()!=EOF)
{
key_fp>>key[keyLen];
if(key_fp.eof())
break;
keyLen++;
}
cout<<"密码长度为（字节）:"<<keyLen<<endl;
word_t rk[11][BLOCK_SIZE];
bs_expand_key(rk, key);
printf("hello\n");
if(number_of_zero_pending != 0){
aes_block_array = new aes_block [ block_number + 1];
trans_aes_block_array=new trans_aes_block[block_number+1];
}
else{
aes_block_array = new aes_block[ block_number ];
trans_aes_block_array=new trans_aes_block[block_number];
}
char temp[BLOCK_LEN];
FILE* en_fp; //定义加密文件
en_fp = fopen(argv[3], "wb");
for(int i=0; i<block_number; i++){
ifs.read(temp, BLOCK_LEN);
for(int j=0; j<BLOCK_LEN; j++){
aes_block_array[i].block[j] = (unsigned char)temp[j];
}
}
if(number_of_zero_pending != 0)
{
ifs.read(temp, number_of_zero_pending);
for(int j=0; j<BLOCK_LEN; j++){
aes_block_array[block_number].block[j] = (unsigned char)temp[j];
}
for(int j=1; j<=BLOCK_LEN-number_of_zero_pending; j++)
aes_block_array[block_number].block[BLOCK_LEN-j] = '\0';
block_number++;
}
// cudaSetDevice(0); //选择设备
// cudaDeviceProp prop;
// cudaGetDeviceProperties(&prop, 0);
// int num_sm = prop.multiProcessorCount;
printf("数据预处理开始\n");
word_t block[BLOCK_SIZE];
for(int i=0;i<block_number;i++){
memcpy(block,&aes_block_array[i],BLOCK_LEN);
bs_transpose(block);
memcpy(&trans_aes_block_array[i],block,BLOCK_LEN);
}
printf("数据预处理结束\n");
word_t block2[BLOCK_SIZE];
for(int i=0;i<block_number;i++){
memcpy(block2,trans_aes_block_array[i].block,BLOCK_LEN);
bs_transpose_rev(block2);
memcpy(aes_block_array[i].block,block2,BLOCK_LEN);
f1printBytes(aes_block_array[i].block, BLOCK_LEN, en_fp);
}
// trans_aes_block *cuda_trans_aes_block_array;
// word_t (*cuda_key)[BLOCK_SIZE];
// // int thrdperblock = block_number/num_sm;
// // if(block_number%num_sm>0)
// // thrdperblock++;
// // //设备线程快内线程数最多为1024
// // if(thrdperblock>1024){
// // thrdperblock = 1024;
// // num_sm = block_number/1024;
// // if(block_number%1024>0){
// // num_sm++;
// // }
// // }
// dim3 ThreadperBlock(256);
// dim3 BlockperGrid(num_sm);
// cudaMalloc(&cuda_trans_aes_block_array, block_number*sizeof(class trans_aes_block));
// cudaMalloc(&cuda_key,11*BLOCK_SIZE*sizeof(word_t));
// cudaMemcpy(cuda_trans_aes_block_array, trans_aes_block_array, block_number*sizeof(class trans_aes_block), cudaMemcpyHostToDevice);
// cudaMemcpy(cuda_key,rk,11*BLOCK_SIZE*sizeof(word_t),cudaMemcpyHostToDevice);
// printf("加密数据块数: %d\n", block_number);
// cudaEvent_t start1;
// cudaEventCreate(&start1);
// cudaEvent_t stop1;
// cudaEventCreate(&stop1);
// cudaEventRecord(start1, NULL);
// AES_Encrypt <<<BlockperGrid,ThreadperBlock>>>(cuda_trans_aes_block_array,cuda_key,block_number);
// cudaEventRecord(stop1, NULL);
// cudaEventSynchronize(stop1);
// float msecTotal1 = 0.0f,total;
// cudaEventElapsedTime(&msecTotal1, start1, stop1);
// total=msecTotal1/1000;
// cout<<"加密时间："<<total<<endl;
// long r=1<<27; //单位换算常数
// cout<<"吞吐量为："<<block_number*BLOCK_LEN/total/r<<" Gbps"<<endl;
// cudaMemcpy(trans_aes_block_array, cuda_trans_aes_block_array, block_number*sizeof(class aes_block), cudaMemcpyDeviceToHost);
// word_t block2[BLOCK_SIZE];
// for(int i=0;i<block_number;i++){
// memcpy(block2,trans_aes_block_array[i].block,BLOCK_LEN);
// bs_transpose_rev(block2);
// memcpy(aes_block_array[i].block,block2,BLOCK_LEN);
// f1printBytes(aes_block_array[i].block, BLOCK_LEN, en_fp);
// }
return 0;
}
|
1,032
|
__device__
int countBits(unsigned int v) {
int c; // c accumulates the total bits set in v
for (c = 0; v; v >>= 1) {
c += v & 1;
}
return c;
}
__device__
bool checkerFunc (unsigned int* queensList,int width, int numQueens){
////input exceptions
// if (numQueens > width){
// printf("The Number of Queens is greater than width of the board\n");
// }
bool ifCheck = true;
////max we can do is 32 x 32
unsigned int in_checkArr[32] ={0};
for (int q = 0; q < numQueens; q++){
int posqueen = queensList[q];
// if (posqueen < 0 || posqueen >= width * width){
// printf("The position of Queen is invalid\n");
// }
int row = posqueen/width;
int col = posqueen % width;
////row easy!
in_checkArr[row] |= 0xffffffff;
for (int r = 0; r < width; r++){
////column in loop
in_checkArr[r] |= 1 << col;
////main diagon
if (row + col - r < width && row + col -r >= 0)
in_checkArr[r] |= 1 << row + col - r;
////other diagon
if (col - row + r >= 0 && col - row + r < width)
in_checkArr[r] |=1 << col - row + r;
if (countBits(in_checkArr[r]) < width && q == numQueens - 1)
ifCheck = false;
}
}
return ifCheck;
}
__device__
int addtoSolution (unsigned int* queensList, int numQueens, unsigned int* d_solution, unsigned int* count, int pitch){
// claim one of the valid solutions
int solution_id = atomicAdd(count, 1);
// // the below line sets solution = d_solution[solution_id]
// solution is of the form [a,b] where a<b and each number
// is an index of a queen into the 1-dimensional n*n-element chessboard
unsigned int * solution =
(unsigned int *) ((char *) d_solution + solution_id * pitch);
for (int q = 0 ; q < numQueens; q++){
solution[q] = queensList[q];
}
return solution_id;
}
|
1,033
|
#include <cuda.h>
#include <stdio.h>
#include <stdlib.h>
/*inline void CUDA_ERROR_CHECK(const cudaError_t &err){
if(err != cudaSuccess){
fprintf(stderr, "CUDA error: %s\n", cudaGetErrorString(err));
exit(EXIT_FAILURE);
}
}*/
__device__ int mandel(float c_re, float c_im, int maxIteration)
{
float z_re = c_re, z_im = c_im;
int i;
for (i = 0; i < maxIteration; ++i)
{
if (z_re * z_re + z_im * z_im > 4.f)
break;
float new_re = z_re * z_re - z_im * z_im;
float new_im = 2.f * z_re * z_im;
z_re = c_re + new_re;
z_im = c_im + new_im;
}
return i;
}
__global__ void mandelKernel(float lowerX, float lowerY, float stepX, float stepY, int *d_res, int resX, int resY, int maxIterations){
// To avoid error caused by the floating number, use the following pseudo code
//
// float x = lowerX + thisX * stepX;
// float y = lowerY + thisY * stepY;
int now_x, now_y;
now_x = blockIdx.x * blockDim.x + threadIdx.x;
now_y = blockIdx.y * blockDim.y + threadIdx.y;
if(now_x >= resX || now_y >= resY) return;
float x, y;
x = lowerX + now_x * stepX;
y = lowerY + now_y * stepY;
int idx;
idx = now_y*resX+now_x;
d_res[idx] = mandel(x, y, maxIterations);
}
// Host front-end function that allocates the memory and launches the GPU kernel
void hostFE (float upperX, float upperY, float lowerX, float lowerY, int* img, int resX, int resY, int maxIterations)
{
float stepX = (upperX - lowerX) / resX;
float stepY = (upperY - lowerY) / resY;
int blocksX = (int) ceil(resX/16.0);
int blocksY = (int) ceil(resY/16.0);
dim3 block(16, 16);
dim3 grid(blocksX, blocksY);
int *d_res, *h;
int size;
size = resX*resY*sizeof(int);
size_t pitch;
cudaMallocPitch(&d_res, &pitch, resX*sizeof(int), resY);
cudaHostAlloc(&h, size, cudaHostAllocMapped);
mandelKernel <<< grid, block >>> (lowerX, lowerY, stepX, stepY, d_res, resX, resY, maxIterations);
cudaDeviceSynchronize();
cudaMemcpy(h, d_res, size, cudaMemcpyDeviceToHost);
memcpy(img, h, size);
cudaFreeHost(h);
cudaFree(d_res);
}
|
1,034
|
/*!
* \brief Matrix multi with shared memory
*/
#include <stdio.h>
#include <cuda_runtime.h>
#include <device_launch_parameters.h>
#include <time.h>
#include <stdlib.h>
__global__ void MatrixMuiOnDevice(int *M,int *N, int *P, int width)
{
int x = threadIdx.x;
int y = threadIdx.y;
float Pervalue = 0;
for (int i = 0; i < width; i++)
{
float Mdlement = M[y * width + i];
float Ndlement = N[width * i + x];
Pervalue += Mdlement * Ndlement;
}
P[y * width + x] = Pervalue;
}
int main()
{
int a[30][30],b[30][30],c[30][30];
int *M, *N, *P;
int width = 30;
int NUM = 900;
dim3 dimBlock(30,30);
cudaEvent_t start,stop;
float elapsedTime;
cudaEventCreate(&start);
cudaEventCreate(&stop);
cudaMalloc((void**)&M, 900*sizeof(int));
cudaMalloc((void**)&N, 900*sizeof(int));
cudaMalloc((void**)&P, 900*sizeof(int));
for(int i = 0; i < 30; i++)
for(int j = 0; j < 30; j++)
{
a[i][j] = 2;
b[i][j] = 3;
}
cudaMemcpy(M,a,NUM*sizeof(int),cudaMemcpyHostToDevice);
cudaMemcpy(N,b,NUM*sizeof(int),cudaMemcpyHostToDevice);
cudaMemcpy(c,P,NUM*sizeof(int),cudaMemcpyDeviceToHost);
cudaEventRecord(start,0);
MatrixMuiOnDevice<<<1,dimBlock>>>(M,N,P,width);
cudaThreadSynchronize();
cudaEventRecord(stop,0);
cudaEventSynchronize(stop);
cudaEventElapsedTime(&elapsedTime,start,stop);
printf("%f\n",elapsedTime);
for(int i = 0; i < 30; i++)
for(int j = 0; j < 30; j++)
{
printf("%d \n",c[i][j]);
}
cudaFree(M);
cudaFree(N);
cudaFree(P);
return 0;
}
|
1,035
|
#include <iostream>
#include <math.h>
using namespace std;
__global__ void kernel_sum (int *A, int *B, int *C, int n);
void sum (int *A, int *B, int *C, int n);
int main()
{
int n;
cout<<"Enter n:";
cin>>n;
int size=n*sizeof(int);
int *deviceA,*deviceB,*deviceC;
int *hostA = (int*)malloc(size);
int *hostB = (int*)malloc(size);
int *hostC = (int*)malloc(size);
cudaEvent_t start,end,start1,end1;
cudaEventCreate(&start);
cudaEventCreate(&end);
cudaEventCreate(&start1);
cudaEventCreate(&end1);
for(int i=0;i<n;i++)
{
hostA[i]=rand()%n;
hostB[i]=rand()%n;
}
cout<<"\nHost A:\n";
for(int i=0;i<n;i++)
{
cout<<hostA[i]<<"\t";
}
cout<<"\nHost B:\n";
for(int i=0;i<n;i++)
{
cout<<hostB[i]<<"\t";
}
float t=0,t1=0;
cudaEventRecord(start);
cout<<"\nSequential processing result:\n";
for(int i=0;i<n;i++)
{
cout<<hostA[i]+hostB[i]<<"\t";
}
cudaEventRecord(end);
cudaEventSynchronize(end);
cudaEventElapsedTime(&t,start,end);
cudaMalloc(&deviceA,size);
cudaMalloc(&deviceB,size);
cudaMalloc(&deviceC,size);
cudaMemcpy(deviceA,hostA,size,cudaMemcpyHostToDevice);
cudaMemcpy(deviceB,hostB,size,cudaMemcpyHostToDevice);
cudaEventRecord(start1);
sum(deviceA,deviceB,deviceC,n);
cudaEventRecord(end1);
cudaEventSynchronize(end1);
cudaEventElapsedTime(&t1,start1,end1);
cudaMemcpy(hostC,deviceC,size,cudaMemcpyDeviceToHost);
cout<<"\nParallel Execution:\nExpected\tActual\n\n";
for(int i=0;i<n;i++)
{
cout<<hostA[i]+hostB[i]<<"\t\t"<<hostC[i]<<"\n";
}
cout<<"\n";
cout<<"\nSequential time:"<<t;
cout<<"\nParallel time:"<<t1;
cudaFree(deviceA);
cudaFree(deviceB);
cudaFree(deviceC);
return cudaDeviceSynchronize();
}
void sum(int *A, int *B, int *C, int n)
{
int threadsPerBlock, blocksPerGrid;
if(n<512)
{
threadsPerBlock = n;
blocksPerGrid = 1;
}
else
{
threadsPerBlock = 512;
blocksPerGrid = ceil(double(n)/double(threadsPerBlock));
}
kernel_sum<<<blocksPerGrid,threadsPerBlock>>>(A,B,C,n);
}
__global__ void kernel_sum (int *A, int *B, int *C, int n)
{
int index=blockDim.x * blockIdx.x + threadIdx.x;
if(index<n)
C[index] = A[index] + B[index];
}
|
1,036
|
/*
============================================================================
Name : structoverflow.cu
Author :
Version :
Copyright : Your copyright notice
Description : CUDA compute reciprocals
============================================================================
*/
#include <stdio.h>
#include <iostream>
#include <numeric>
#include <stdlib.h>
using namespace std;
#define BUF_LEN 6
static void CheckCudaErrorAux (const char *, unsigned, const char *, cudaError_t);
#define CUDA_CHECK_RETURN(value) CheckCudaErrorAux(__FILE__,__LINE__, #value, value)
__device__ __noinline__ void normal()
{
printf("normal!\n");
}
__device__ __noinline__ void secret()
{
printf("Hello Admin!\n");
}
struct unsafe
{
unsigned long buf[BUF_LEN];
void (*normal)();
};
__device__ __noinline__ void init(struct unsafe *data)
{
data->normal=normal;
}
__global__ void test_kernel(unsigned long *input,int len,int admin)
{
struct unsafe cu;
init(&cu);
for(int i=0;i<len;i++)
cu.buf[i]=input[i];
cu.normal();
secret();
printf("%p",secret);
}
int main(void)
{
unsigned long input[10];
unsigned long *dev_input;
int len=6;
int admin=0;
for(int i=0;i<10;i++)
{
input[i]=0xb2140;//this is secret（） address
}
CUDA_CHECK_RETURN(cudaMalloc((void**)&dev_input,10*sizeof(unsigned long)));
CUDA_CHECK_RETURN(cudaMemcpy(dev_input,input,10*sizeof(unsigned long),cudaMemcpyHostToDevice));
test_kernel<<<1,1>>>(dev_input,len,admin);
cudaFree(dev_input);
return 0;
}
/**
* Check the return value of the CUDA runtime API call and exit
* the application if the call has failed.
*/
static void CheckCudaErrorAux (const char *file, unsigned line, const char *statement, cudaError_t err)
{
if (err == cudaSuccess)
return;
std::cerr << statement<<" returned " << cudaGetErrorString(err) << "("<<err<< ") at "<<file<<":"<<line << std::endl;
exit (1);
}
|
1,037
|
#include <stdio.h>
#include <math.h>
int main(){
// GetGPUProperties
cudaDeviceProp props;
cudaGetDeviceProperties(&props,0);
// Get maximum threads, blocks and grids
printf("GPU Info\n");
printf("Name: %s\n",props.name);
printf("Max Threads Per Block %d\n",props.maxThreadsPerBlock);
printf("Max Threads Size %d %d %d\n",
props.maxThreadsDim[0],
props.maxThreadsDim[1],
props.maxThreadsDim[2]);
printf("Max Grid Size %d %d %d\n",
props.maxGridSize[0],
props.maxGridSize[1],
props.maxGridSize[2]);
printf("Compute Capability %d\n",props.major);
}
|
1,038
|
/*
* main.cpp
*
* Created on: Jul 4, 2015
* Author: markus
*/
#include <iostream>
#include <iomanip>
#include <sstream>
#include <fstream>
#include <numeric>
#include <stdlib.h>
#include <chrono>
#include <vector>
#include <algorithm>
using namespace std::chrono;
using namespace std;
struct Mat {
Mat() : data(NULL), w(0), h(0) {}
~Mat() { if (data) delete[] data; }
float *data;
int w;
int h;
};
inline static float getValue(Mat *mat, int x, int y)
{
if (x > mat->w || y > mat->h) {
throw runtime_error("invalid access");
}
return mat->data[y * mat->w + x];
}
inline static void setValue(Mat *mat, int x, int y, float val)
{
if (x > mat->w || y > mat->h) {
throw runtime_error("invalid access");
}
mat->data[y * mat->w + x] = val;
}
static void initMat(Mat *mat, int height, int width)
{
//std::cout << "make matrix (w/h): " << width << "/" << height << std::endl;
mat->data = new float[height * width];
mat->w = width;
mat->h = height;
for (int i = 0; i < mat->w; i++) {
for (int j = 0; j < mat->h; j++) {
setValue(mat, i, j, 0.0f);
}
}
}
static void printMat(Mat &mat, bool force = false)
{
std::cout << "Dim: " << mat.h << ", " << mat.w << "\n";
if ((mat.w < 10 && mat.h < 10) || force)
{
for (int j = 0; j < mat.h; j++) {
for (int i = 0; i < mat.w; i++) {
std::cout << getValue(&mat, i, j) << "\t";
}
std::cout << "\n";
}
}
std::cout << std::endl;
}
static bool read_csv(string file, Mat *xs, Mat *ys)
{
ifstream s(file);
if (!s.is_open()) {
throw runtime_error(file + " doesn't exist");
}
int rows = 0;
int cols = 0;
string line;
while (getline(s, line)) {
// if we read first line, check how many columns
if (rows++ == 0) {
stringstream ss(line);
while (ss.good()) {
string substr;
getline(ss, substr, ',');
cols++;
}
}
}
std::cout << "found " << rows << " rows with " << cols << " columns." << std::endl;
s.clear() ;
s.seekg(0, ios::beg);
initMat(xs, rows - 1, cols - 2);
initMat(ys, rows - 1, 1);
// go to second line
getline(s, line);
int y = 0;
while (getline(s, line)) {
stringstream ss(line);
int x = 0;
while (ss.good()) {
string substr;
getline(ss, substr, ',');
// first column is uninteresting
// second column is target values
if (x == 1) {
float val = atof(substr.c_str());
setValue(ys, 0, y, val);
} else if (x > 1) {
float val = atof(substr.c_str());
setValue(xs, (x - 2), y, val);
}
x++;
}
y++;
}
return true;
}
static void min_max_normalize(Mat *m)
{
for (int x = 0; x < m->w; ++x) {
// calculate std for each column
float min = getValue(m, x, 0);
float max = getValue(m, x, 0);
for (int y = 1; y < m->h; ++y) {
float val = getValue(m, x, y);
if (val < min) {
min = val;
} else if (val > max) {
max = val;
}
}
for (int y = 0; y < m->h; ++y) {
float val = getValue(m, x, y);
setValue(m, x, y, (val - min) / max);
}
}
}
static void fillRandom(Mat *mat, float LO, float HI)
{
for (int i = 0; i < mat->w; ++i) {
for (int j = 0; j < mat->h; ++j) {
float r = LO + static_cast <float> (rand()) /( static_cast <float> (RAND_MAX/(HI-LO)));
setValue(mat, i, j, r);
}
}
}
static void CheckCudaErrorAux (const char *file, unsigned line, const char *statement, cudaError_t err)
{
if (err == cudaSuccess)
return;
std::cerr << statement<<" returned " << cudaGetErrorString(err) << "("<<err<< ") at "<<file<<":"<<line << std::endl;
exit (1);
}
#define SAFE_CALL(value) CheckCudaErrorAux(__FILE__,__LINE__, #value, value)
__global__ void matrixMulKernel(float *m1, float *m2, float *r, int m1w, int m2w, int rw, int rh)
{
int row = blockIdx.y * blockDim.y + threadIdx.y;
int col = blockIdx.x * blockDim.x + threadIdx.x;
if ((row < rh) && (col < rw)) {
// dot product
float accum = 0.0f;
for (int c = 0; c < m1w; c++)
{
float v1 = m1[row * m1w + c];
float v2 = m2[c * m2w + col];
accum += (v1 * v2);
}
r[row * rw + col] = accum;
}
}
__global__ void sigmoidKernel(float *r, int m)
{
int index = blockIdx.x * blockDim.x + threadIdx.x;
if (index < m) {
float val = r[index];
r[index] = 1.0 / (1.0 + expf(-val));
}
}
__global__ void matrixAbsErrorKernel(float *p, float *ys, float *r, int rw, int rh)
{
int row = blockIdx.y * blockDim.y + threadIdx.y;
int col = blockIdx.x * blockDim.x + threadIdx.x;
if ((row < rh) && (col < rw)) {
float pval = p[row * rw + col];
float ysval = ys[row * rw + col];
float v = pval - ysval;
r[row * rw + col] = v * v;
}
}
__global__ void absErrorKernel(float *p, float *ys, float *r, int m)
{
int index = blockIdx.x * blockDim.x + threadIdx.x;
if (index < m) {
float pval = p[index];
float ysval = ys[index];
float v = pval - ysval;
r[index] = v * v;
}
}
__global__ void updateParamsAbsErrorKernel(float *p, float *ys, float *th, float *xs, int m, float alpha)
{
int index = blockIdx.x * blockDim.x + threadIdx.x;
if (index < m) {
float h = *p;
float y = *ys;
float x = xs[index];
th[index] = th[index] - alpha * (h - y) * x;
}
}
__global__ void crossEntropyKernel(float *p, float *ys, float *r, int m)
{
int index = blockIdx.x * blockDim.x + threadIdx.x;
if (index < m) {
float pval = p[index];
float ysval = ys[index];
float ex = log1pf(expf(-ysval * pval));
r[index] = ex;
}
}
#define REDUCE_BLOCK_SIZE 128
__global__ void reduceKernel(float * input, float * output, int len) {
//@@ Load a segment of the input vector into shared memory
__shared__ float partialSum[2 * REDUCE_BLOCK_SIZE];
unsigned int t = threadIdx.x, start = 2 * blockIdx.x * REDUCE_BLOCK_SIZE;
if (start + t < len)
partialSum[t] = input[start + t];
else
partialSum[t] = 0;
if (start + REDUCE_BLOCK_SIZE + t < len)
partialSum[REDUCE_BLOCK_SIZE + t] = input[start + REDUCE_BLOCK_SIZE + t];
else
partialSum[REDUCE_BLOCK_SIZE + t] = 0;
//@@ Traverse the reduction tree
for (unsigned int stride = REDUCE_BLOCK_SIZE; stride >= 1; stride >>= 1) {
__syncthreads();
if (t < stride)
partialSum[t] += partialSum[t+stride];
}
//@@ Write the computed sum of the block to the output vector at the
//@@ correct index
if (t == 0)
output[blockIdx.x] = partialSum[0];
}
static void train_LogRegGpu2(Mat *xs, Mat *ys, Mat *params, Mat *trainedParams, int maxIterations, float alpha, vector<float> &costs)
{
// put stuff into gpu
float *gpu_xs;
float *gpu_ys;
float *gpu_prediction;
float *gpu_params;
float *gpu_abs_error;
float *gpu_err_cost;
float *gpu_predictions;
Mat predictions;
initMat(&predictions, ys->h, ys->w);
Mat absErrors;
initMat(&absErrors, ys->h, ys->w);
int m = ys->h;
int numOutputElements;
numOutputElements = m / (REDUCE_BLOCK_SIZE<<1);
if (m % (REDUCE_BLOCK_SIZE<<1)) {
numOutputElements++;
}
SAFE_CALL(cudaMalloc((void**)&gpu_xs, sizeof(float) * xs->w * xs->h));
SAFE_CALL(cudaMalloc((void**)&gpu_ys, sizeof(float) * ys->w * ys->h));
SAFE_CALL(cudaMalloc((void**)&gpu_prediction, sizeof(float)));
SAFE_CALL(cudaMalloc((void**)&gpu_predictions, sizeof(float) * ys->w * ys->h));
SAFE_CALL(cudaMalloc((void**)&gpu_abs_error, sizeof(float) * ys->w * ys->h));
SAFE_CALL(cudaMalloc((void**)&gpu_params, sizeof(float) * params->w * params->h));
SAFE_CALL(cudaMalloc((void**)&gpu_err_cost, sizeof(float) * numOutputElements));
SAFE_CALL(cudaMemcpy(gpu_xs, xs->data, sizeof(float) * xs->w * xs->h, cudaMemcpyHostToDevice));
SAFE_CALL(cudaMemcpy(gpu_ys, ys->data, sizeof(float) * ys->w * ys->h, cudaMemcpyHostToDevice));
SAFE_CALL(cudaMemcpy(gpu_params, params->data, sizeof(float) * params->w * params->h, cudaMemcpyHostToDevice));
// invoke kernel
static const int blockWidth = 16;
static const int blockHeight = blockWidth;
int numBlocksW = xs->w / blockWidth;
int numBlocksH = xs->h / blockHeight;
if (xs->w % blockWidth) numBlocksW++;
if (xs->h % blockHeight) numBlocksH++;
dim3 dimGrid(numBlocksW, numBlocksH);
dim3 dimBlock(blockWidth, blockHeight);
dim3 dimReduce((m - 1) / REDUCE_BLOCK_SIZE + 1);
dim3 dimReduceBlock(REDUCE_BLOCK_SIZE);
dim3 dimVectorGrid(((m - 1) / blockWidth * blockWidth) + 1);
dim3 dimVectorBlock(blockWidth * blockWidth);
float* error_accum = new float[numOutputElements];
for (int iter = 0; iter < maxIterations; ++iter) {
for (int i = 0; i < m; ++i) {
matrixMulKernel<<<dimGrid, dimBlock>>>(&gpu_xs[i * xs->w], gpu_params, gpu_prediction, xs->w, params->w, 1, 1);
sigmoidKernel<<<dimVectorGrid, dimVectorBlock>>>(gpu_prediction, 1);
updateParamsAbsErrorKernel<<<dimVectorGrid, dimVectorBlock>>>(gpu_prediction, &gpu_ys[i], gpu_params, &gpu_xs[i * xs->w], params->h, alpha);
}
matrixMulKernel<<<dimGrid, dimBlock>>>(gpu_xs, gpu_params, gpu_predictions, xs->w, params->w, predictions.w, predictions.h);
sigmoidKernel<<<dimVectorGrid, dimVectorBlock>>>(gpu_predictions, m);
// calculate error
absErrorKernel<<<dimVectorGrid, dimVectorBlock>>>(gpu_predictions, gpu_ys, gpu_abs_error, m);
reduceKernel<<<dimReduce, dimReduceBlock>>>(gpu_abs_error, gpu_err_cost, m);
SAFE_CALL(cudaMemcpy(error_accum, gpu_err_cost, sizeof(float) * numOutputElements, cudaMemcpyDeviceToHost));
float g_sum = 0;
for (int i = 0; i < numOutputElements; ++i)
{
g_sum += error_accum[i];
}
g_sum /= (2*m);
costs.push_back(g_sum);
cout << g_sum << "\n";
}
cout << endl;
delete[] error_accum;
SAFE_CALL(cudaFree(gpu_xs));
SAFE_CALL(cudaFree(gpu_ys));
SAFE_CALL(cudaFree(gpu_abs_error));
SAFE_CALL(cudaFree(gpu_prediction));
SAFE_CALL(cudaFree(gpu_predictions));
SAFE_CALL(cudaFree(gpu_params));
SAFE_CALL(cudaFree(gpu_err_cost));
}
int main(int argc, char **argv)
{
string csv_file("./houses.csv");
Mat xs;
Mat ys;
Mat params;
Mat trainedParams;
read_csv(csv_file, &xs, &ys);
//printMat(xs, true);
//printMat(ys);
// width of features + 1 for bias
initMat(¶ms, xs.w, 1);
initMat(&trainedParams, xs.w, 1);
// fill parameter with random initializations from -1 to 1
fillRandom(¶ms, -1.0, 1.0);
//printMat(xs, true);
//printMat(params, true);
min_max_normalize(&xs);
vector<float> costs;
train_LogRegGpu2(&xs, &ys, ¶ms, &trainedParams, 150, 0.03, costs);
//for (int i = 0; i < costs.size(); ++i) {
// cout << costs[i] << "\n";
//}
std::cout << "done" << std::endl;
//printMat(trainedParams, true);
return 0;
}
|
1,039
|
float h_A[]= {
0.9437638001242697, 0.9136534788427546, 0.7464430964435773, 0.6148443668541146, 0.8639953234778698, 0.6252542682698676, 0.8583020500593208, 0.6776662703078024, 0.7384280592300332, 0.8195537928976389, 0.7435505093785533, 0.626861660657713, 0.7911855482725979, 0.5991974690271062, 0.9075270446196592, 0.9953466037408092, 0.7690805306881561, 0.8956796109458764, 0.8679365531773078, 0.6207529048132827, 0.9881191343977853, 0.7160215600620699, 0.6898866524238874, 0.8984419376329755, 0.6982142733261427, 0.6175516071114158, 0.5954772424429844, 0.5195265415072572, 0.8642915458915976, 0.5456453095843659, 0.7607629080840101, 0.8986394597161068, 0.7310209642322841, 0.9316646045376935, 0.700730965188278, 0.7829585378801782, 0.9997843585210642, 0.6602843411221028, 0.7344865438600596, 0.7702229750143608, 0.6225312625636665, 0.664412526816109, 0.8161840272201812, 0.5099063137720985, 0.8411401375821985, 0.7548786170940878, 0.7146915851252265, 0.5193652591038629, 0.509164448242808, 0.6729334426717801, 0.5688354113525872, 0.9893275530040507, 0.866981816101055, 0.5933158282839729, 0.7021598168653651, 0.5629874904781926, 0.7387052269547849, 0.5400936232047899, 0.6910968001713653, 0.6042842097607337, 0.8224552715726414, 0.6979565482301533, 0.8236828597539724, 0.5260142884415993, 0.7334252382450093, 0.5431310901620576, 0.7555461125515529, 0.76590071083447, 0.9991961719925445, 0.825339125906722, 0.9069167883095026, 0.7893603622403365, 0.9950853475888857, 0.6023211918146771, 0.7212427684962115, 0.6135613913759453, 0.7952959051054316, 0.5868005561561551, 0.8995574398042061, 0.605282571688311, 0.9857073335049454, 0.787160449796415, 0.9156960803973602, 0.771326985676333, 0.7347869412117394, 0.6326728985685577, 0.6107049356269396, 0.8962574702956194, 0.8564869760219143, 0.9648170084996757, 0.7209115870640668, 0.7257706555621033, 0.976996495461115, 0.8435016743358197, 0.6546182760402428, 0.8979966588606674, 0.768685728603324, 0.778777887377107, 0.9541199105492656, 0.6078300525087843, 0.6961409334251973, 0.6624604759335513, 0.5098959745834715, 0.5976341940567567, 0.7684621010254741, 0.9790707661035631, 0.529123325238831, 0.5176505158694402, 0.5977606602136671, 0.7965763840609504, 0.545735377932274, 0.8871545567907942, 0.6393617142785202, 0.741500399011366, 0.8344423217132115, 0.7848423231570492, 0.595804005571705, 0.9525112620805685, 0.7476541091021371, 0.7739551669105634, 0.8896144786319382, 0.8916376682558098, 0.8353158195258044, 0.5542474538164386, 0.730727132613423, 0.8563212135551371, 0.8290039880086851, 0.7187021881771032, 0.5240176971172685, 0.6647905093324363, 0.567208044403199, 0.8425445572117389, 0.9300701976370851, 0.5490561250774649, 0.8618724158416808, 0.5219668576686931, 0.9567148475387469, 0.6628384394441089, 0.9590228221028786, 0.9448239175202995, 0.5442930701331588, 0.5421407967112558, 0.7884239261238779, 0.9421159943644575, 0.5628298117185805, 0.5499192460778579, 0.5873378150160697, 0.9344316458791363, 0.5014274590151426, 0.7746652282330098, 0.6885224253467468, 0.9609374585010406, 0.8456642239506211, 0.6174798913053248, 0.6750616880013336, 0.8588536395471469, 0.9413504395093819, 0.8149002671625153, 0.7935598423485811, 0.9899280504071317, 0.7413347757446336, 0.8333514856167346, 0.7152672637437314, 0.6446612659174646, 0.7246778624883614, 0.8290205453846132, 0.5624146297320605, 0.9420529510653175, 0.8154715612172581, 0.6574399235566206, 0.9058964304269566, 0.8041056190481959, 0.9171849366316169, 0.6279748935429875, 0.8230759552767787, 0.522731672857302, 0.5824762862384749, 0.7702370658471693, 0.5389622879170841, 0.8042465026223342, 0.5656596861373859, 0.6863019686941283, 0.9602473527014213, 0.5695043715891839, 0.6733218514397348, 0.8664271444852394, 0.8452404261203261, 0.500361828718097, 0.9714952802129933, 0.888736792996724, 0.6717118478177577, 0.8378617708453333, 0.7555222523141785, 0.5084005612977673, 0.5770561148966646, 0.8351691906261276, 0.7260579815338486, 0.8232528265555628, 0.6614539053911919, 0.8648272462788469, 0.6320064412897639, 0.7196774351681128, 0.5270665210394285, 0.9795908425965754, 0.8112883969509049, 0.70251065934103, 0.5019149576172377, 0.529224048841562, 0.7915958040390659, 0.6020446665581576, 0.8147663753146129, 0.5276349456714455, 0.8445373409038721, 0.5319007428088716, 0.785350163967663, 0.8284933710775563, 0.7756063978950825, 0.6726218787056555, 0.9752868849686924, 0.7511761170472515, 0.9340003660677756, 0.5441683910120719, 0.9303647417198754, 0.5821390164380809, 0.5100228410278362, 0.8456772020835446, 0.970166638069326, 0.8745009653426457, 0.8561576001866963, 0.7471631906939193, 0.8205958160894717, 0.8609256501290254, 0.632175484334655, 0.9897050639120469, 0.7035028800527443, 0.9404705874023953, 0.6106254587921265, 0.5411719449246459, 0.9889783095920899, 0.5945744770967668, 0.8919277013058089, 0.9795110832720622, 0.6176348679839674, 0.6723558214231024, 0.8389779283022676, 0.5378664857480422, 0.7986930695673617, 0.8439215790305269, 0.5464524065750207, 0.6888468328060438, 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0.907090391162962, 0.5676790174665052, 0.6120036942166539, 0.9478547596916702, 0.5172315112638632, 0.7149298153964949, 0.9082557491898772, 0.504545034427017, 0.5944031433332819, 0.744234584835761, 0.6132205431902907, 0.9219303504052316, 0.550117819960019, 0.540662179548945, 0.6766803771908134, 0.9119298957371735, 0.8221668079607214, 0.9995314132748407, 0.9068057255704131, 0.7928631392320696, 0.8558223326648905, 0.7815596174787691, 0.5133600734854753, 0.6689039565214208, 0.6204966887230503, 0.5739918087669038, 0.9764086463878647, 0.730022279532323, 0.8753220949803231, 0.8199585439735078, 0.9877375173527647, 0.7025616707840021, 0.807254120831879, 0.5011719187695816, 0.7060376923295951, 0.7883821319380473, 0.9315988804585363, 0.6887329649002014, 0.7646349459806314, 0.9339413098597349, 0.7653808791983658, 0.5422859131847989, 0.7677819296766357, 0.9759560247643743, 0.9670070720083355, 0.7401289442490817, 0.7030384546641857, 0.5885458672030053, 0.6451798909455242, 0.5343982294811411, 0.8484692515254887, 0.6296743701707439, 0.5089552260837424, 0.818603450397551, 0.8810689222330362, 0.6628542809114827, 0.8329602784958927, 0.9630744053361713, 0.6090338489601512, 0.8508246444262506, 0.8211315082521512, 0.6914041393290065, 0.8278685990260456, 0.7930862192739919, 0.6382926065896841, 0.6670464901213543, 0.956681282941071, 0.8211010336292157, 0.6066367407747779, 0.7996017899466656, 0.5772303444733358, 0.5430658509421236, 0.8632619632338991, 0.5733406127912585, 0.599797949627568, 0.9956779019327762, 0.5513303046765539, 0.5503091092725034, 0.5437747084213098, 0.6658428589952403, 0.6162651244281143, 0.9542076020932273, 0.965326127955136, 0.6195440265970085, 0.591782310606978, 0.546277980185109, 0.5011782416782136, 0.7597392713805817, 0.6396473739032135, 0.8284586892257457, 0.9680734055667755, 0.955200596977792, 0.6363440003320653, 0.705324545382169, 0.7231856588760424, 0.9484553942390288, 0.6408092865905755, 0.9833052000937681, 0.661451533304007, 0.6303535437538184, 0.9593226623243705, 0.5560224052859633, 0.9017400799258051, 0.8467644099175289, 0.6452080593618297, 0.8423830517630218, 0.6483854503145707, 0.7252785345471022, 0.734217822932462, 0.9522968772603314, 0.8414200679259953, 0.8007888036897925, 0.6022888026187172, 0.5162141304098677, 0.6185928451653224, 0.6873334622321479, 0.5822124979751645, 0.7882425625074674, 0.6309769889269891, 0.5517661679567873, 0.5904048907639301, 0.7521448951000032, 0.8459130926233138, 0.8071534203137143, 0.5072132976680259, 0.5606378632680092, 0.6621611669514342, 0.701390943931907, 0.7093655640007124, 0.8324158546833145, 0.928569796292585, 0.9753046799347412, 0.6823572855227305, 0.8010033900003468, 0.5101701710580537, 0.8892611029121166, 0.8015795215328505, 0.5682681929331839, 0.7679410189866867, 0.7613691466300038, 0.9709750292837644, 0.675556804344938, 0.7032823338892944, 0.818239006756112, 0.5105404368545893, 0.583841573059903, 0.7355495382412469, 0.5602736114462306, 0.6551443099100813, 0.9366811897419859, 0.7376133171968169, 0.9318352301311026, 0.9896614934278813, 0.8458398515860409, 0.9280243400781666, 0.7069553323801784, 0.8788874951718468, 0.9335865235288078, 0.9377887002237184, 0.5911395128118404, 0.5088155611415133, 0.9719574407381915, 0.965444431990313, 0.7919961166159799, 0.6626982644000181, 0.750926872351807, 0.5576510350719559, 0.8322162096162988, 0.5578719392542243, 0.9709501544105874, 0.937338149578568, 0.881228529507494, 0.5208791370930117, 0.7537809355920868, 0.966114849384114, 0.6475075458073822, 0.9382226230498443, 0.860617004579081, 0.5328402852575218, 0.7705120073041767, 0.5650204856991419, 0.9989032669474088, 0.8290936151877804, 0.5520061623176241, 0.5769871361708215, 0.9633849294650696, 0.9829894437685722, 0.9261188162063646, 0.8336187135790815, 0.9710693848022706, 0.7716023887328682, 0.6174300311504305, 0.9163997344363359, 0.9096212447020768, 0.8662906372927123, 0.8579236540503054, 0.651528632618303, 0.8129193017726193, 0.7855710519127238, 0.7026523104277449, 0.7530938387846714, 0.7185074110436485, 0.5067352101139302, 0.6190770515683792, 0.5062464723154955, 0.5036768624617991, 0.8927007146246873, 0.5980109378544991, 0.6706755112079084, 0.9389381146494242, 0.9019423424465018, 0.8929642312755155, 0.9144487287606827, 0.708129795336551, 0.8793599766257116, 0.7640625433512208, 0.8139468899886884, 0.9250747249208935, 0.8750433218398932, 0.6739854261657947, 0.854608948259894, 0.9338857889403743, 0.7359143674455987, 0.6457212054675274, 0.8888928988201631, 0.6547449700573256, 0.7700483593541558, 0.6514957753939681, 0.6816475349184153, 0.8955997945500973, 0.5391127087902867, 0.8070464649318072, 0.9286861799610502, 0.735341474072071, 0.535245215947264, 0.9158701007511396, 0.6226390504118184, 0.8445555620717553, 0.6104328842428649, 0.9138329216094059, 0.7571927154489317, 0.565922743016428, 0.5584694370836367, 0.8550478662806975, 0.9494386869895721, 0.9974406067080592, 0.8218766752875497, 0.8567937434801425, 0.8212001284602997, 0.5381838174975132, 0.9910799382026672, 0.6547370826422116, 0.6003031785356546, 0.5013489701808465, 0.7884091460074029, 0.7793283504492365, 0.7374517045752601, 0.7753076342818125, 0.5954098583559944, 0.7839816175061802, 0.8197166311549373, 0.5865190315845569, 0.898113371250731, 0.9289770020989843, 0.9572511501304852, 0.6621351516459016, 0.7548992300431248, 0.8671821373166507, 0.7211281764256077, 0.6772857584781913, 0.8362427879167211, 0.5612891772090318, 0.9319730839364381, 0.669474578443991, 0.9180304562954924, 0.8132431743634589, 0.6945672071041992, 0.6591407659514723, 0.8080806633984752, 0.5862920812777273, 0.9603857178224963, 0.9578859953637835, 0.5485196188561199, 0.8829339606380209, 0.9859136790254118, 0.8385572697931596, 0.7536856685261748, 0.6861174127793341, 0.7562805244812606, 0.9917858378274629, 0.8584322679221258, 0.7131770110137083, 0.8078001683923812, 0.5671327788471383, 0.9505258186899148, 0.9063248209242003, 0.8411398043076522, 0.5535992585908924, 0.8451812326709389, 0.9090382351139671, 0.8572806113282925, 0.8183591712436376, 0.9703760296660329, 0.8554404702413866, 0.8830141925454524, 0.8681198607929866, 0.5891836294113584, 0.5908343046087592, 0.988665673300943, 0.7959823267420159, 0.6671062474205296, 0.7187052025728075, 0.8611827524107729, 0.7850899101470508, 0.9085567337621585, 0.5548257375051653, 0.7486448989832304, 0.6973783921411779, 0.8108011774458534, 0.7652914672134805, 0.9810345859231777, 0.7129119363170571, 0.6263587493686755, 0.9609633103520479, 0.7302320824376145, 0.5906542711778762, 0.6594373719654336, 0.6874931059114572, 0.6625038944396836, 0.7027152810186198, 0.5914604378647245, 0.5076103444175765, 0.6503931701941279, 0.8322942110386937, 0.522597830367973, 0.6068136346889433, 0.5685133661346558, 0.8506817440263218, 0.7434194206755094, 0.5256792282595903, 0.7912304104381499, 0.5990442624089457, 0.7867294421749865, 0.9898465984095794, 0.8897820208768161, 0.9419351161681496, 0.7137756798848707, 0.7644817618136792, 0.9678751069749629, 0.5290828253471078, 0.6038118969795616, 0.8421019118488928, 0.6200495319043269, 0.8936159503447667, 0.7378544321477996, 0.9628615611966019, 0.7919817196275775, 0.5842398673084275, 0.9667895715376702, 0.7887599305104056, 0.6145218876888774, 0.5439966290941043, 0.911621508259305, 0.5619934712336297, 0.7644993221771881, 0.9362973681772488, 0.7035922369576051, 0.8900797953363555, 0.5021741844568955, 0.6912000513728093, 0.9115617643392333, 0.8893513794853316, 0.6242025422455728, 0.6023228592171466, 0.72917153669702, 0.6871251374490336, 0.5408860040109806, 0.7092697812222301, 0.5111342325302803, 0.5404283962884329, 0.8272732440224361, 0.8565348485272114, 0.5512511643003243, 0.8775708820139455, 0.8303874472825732, 0.6038671131475831, 0.6839778714261637, 0.9397136621104598, 0.8405825608377919, 0.9457305505564735, 0.583868240988075, 0.999097674544726, 0.5352764699348231, 0.7512216387824513, 0.7149121343711944, 0.9002407789675727, 0.6320034569546248, 0.8772591575914274, 0.6874388274396159, 0.9278353198756368, 0.6740363662476672, 0.6026996457844405, 0.5600970122328621, 0.6768207609531055, 0.712174144357408, 0.6642843587510836, 0.6528029920299576, 0.7664195094301316, 0.5409897331175069, 0.7476322941204219, 0.8993184856285522, 0.5626927749838089, 0.7011484926866982, 0.8134051065324722, 0.6818214203355226, 0.7268977747810996, 0.9309231535100735, 0.8027245582439184, 0.7588973554718246, 0.7624612687525569, 0.5800564878743909, 0.9759271720833806, 0.5180699972921698, 0.5258842906447305, 0.9432057214781973, 0.7484654029506014, 0.5247092977954045, 0.9930421916549903, 0.636384999057161, 0.8963954679150613, 0.6439019309355242, 0.9313568475003078, 0.5390681535566744, 0.6964793288350023, 0.5067168593321194, 0.550101308989807, 0.6360890833603643, 0.6261976479548197, 0.9218489835232601, 0.6517564633886108, 0.8246909233794547, 0.975338635032885, 0.6673652120608042, 0.5009331357651277, 0.8265945448106173, 0.5612864724768973, 0.9515482439010126, 0.8776195681394316, 0.9235797182226247, 0.6302151467648409, 0.969415309350409, 0.9637544480742661, 0.9787940471112141, 0.5207277239272818, 0.7093375874130374, 0.5872501065222535, 0.8919730263019954, 0.554690361002617, 0.6023567900176674, 0.8131148024000696, 0.8218244519406261, 0.7977986168403353, 0.7408120687531559, 0.7426850911566645, 0.8549503532185975, 0.6130831746694817, 0.6655660409855475, 0.7357782596183712, 0.6491107531313414, 0.8571008271761095, 0.9264330629173503, 0.630833509148574, 0.7906465668876831, 0.9895656084735204, 0.8849637153398959, 0.6429888374280966, 0.79484144764847, 0.6758851919154573, 0.6262198774827239, 0.8082330052378583, 0.7713254791749233, 0.7263544752346058, 0.5759386212691396, 0.9833040852989765, 0.9538799531746075, 0.7143930404311644, 0.8067556757775904, 0.9627872759968426, 0.6199561990951921, 0.7470216017396014, 0.7156990685407925, 0.8028404742811909, 0.9607423797691577, 0.8215278791375046, 0.9892092233243652, 0.769025707514525, 0.855502311473046, 0.6687622886308856, 0.9763697485269032, 0.88684573748802, 0.6580487911161645, 0.7195372622408683, 0.738401812336406, 0.6311784159267234, 0.5281915988545423, 0.9405592961468808, 0.5072776565209812, 0.8357131715579456, 0.7443801634196727, 0.928555128301523, 0.9260904212276141, 0.9179710041655729, 0.9883364065038653, 0.8438688945241228, 0.7561011479949842, 0.5788574411479626, 0.8136719594519888, 0.6109569198948916, 0.6432203330583918, 0.5147463198652305, 0.7612444602329652, 0.9744542633082717, 0.6399355108557485, 0.6789014321036522, 0.8646050617394969, 0.6040740071931814, 0.9348147363800403, 0.9037144352713466, 0.5512117207548726, 0.6635243322304363, 0.8868581783394927, 0.6195849698487113, 0.6530839684276373, 0.8044611076011177, 0.7015817671510505, 0.5923664357220224, 0.5687840112395737, 0.7505054572905991, 0.9582107801355725, 0.6987696947835438, 0.5002426805043048, 0.5524076205705062, 0.9830108485040747, 0.5535225137947115, 0.8006586249006125, 0.6728872746147158, 0.7064273666883654, 0.8238892106039547, 0.5962503231925704, 0.6314665442171143, 0.642053259025088, 0.5515882168725317, 0.9117510497667569, 0.7021796192031169, 0.6944048474292166, 0.5514040796275814, 0.7808872699409906, 0.7431371494399184, 0.6245706905532493, 0.6432108677831007, 0.5244617715582214, 0.9263754542880139, 0.9872221743477854, 0.8208081602015038, 0.681097679820994, 0.6707505550742792, 0.5703469525570559, 0.5003653236332906, 0.5972345462120223, 0.9184405830098981, 0.7296363273906042, 0.5145931008169833, 0.6150408352593708, 0.6775795818024277, 0.8937268220472093, 0.750402788310405, 0.6221215397506177, 0.7393348542461873, 0.760058981147353, 0.8531519152151028, 0.5881015136584782, 0.7994231160843022, 0.8394578637995898, 0.6700901202950402, 0.5201518516356143, 0.5645481016222125, 0.5689526332497611, 0.7938965578065369, 0.836955337325017, 0.6908876439556462, 0.8535354154617655, 0.8125681336139001, 0.847656330400889, 0.8458593222709514, 0.5704767632473948, 0.9638669146419272, 0.6412557887835226, 0.627553348155425, 0.5719288392461426, 0.6090953374869549, 0.870432173632548, 0.60336731125558, 0.8040102000582298, 0.7664710669627735, 0.9769065806920914, 0.7324906663630382, 0.6587351729334394, 0.6508305054220578, 0.8764361355366046, 0.6026774500640278, 0.8648656848597582, 0.6072638131496888, 0.5731179700507707, 0.691217891277892, 0.848034240900015, 0.9685578611372898, 0.7798431274889199, 0.6674475657914016, 0.8307908884543241, 0.9405096912529766, 0.8700496482311286, 0.7862730715084445, 0.7277103162100964, 0.8490187590197735, 0.7231104472394944, 0.6531857460882184, 0.8746443229915195, 0.7312863999068249, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0};
int h_B[]= {
1, 3, 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, 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, 373, 375, 377, 379, 381, 383, 385, 387, 389, 391, 393, 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, 493, 495, 497, 499, 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, 559, 561, 563, 565, 567, 569, 572, 574, 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, 699, 701, 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, 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24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 18241, 18242, 18244, 18245, 18247, 18248, 18250, 18251, 18253, 18256, 18258, 18260, 18264, 18266, 18268, 18270, 18272, 18275, 18278, 18282, 18285, 18287, 18289, 18291, 18292, 18296, 18298, 18300, 18302, 18305, 18306, 18308, 18309, 17319, 18314, 18315, 18317, 18319, 17347, 18322, 18324, 18326, 18328, 18330, 18334, 18336, 18338, 18340, 18342, 18344, 18345, 17074, 18348, 18350, 18352, 18353, 18354, 18357, 18359, 18361, 18363, 18090, 18367, 18368, 18097, 18372, 18374, 18376, 17157, 18380, 17248, 17187, 18385, 18387, 18389, 18391, 18393, 18395, 17248, 18400, 18402, 18405, 18407, 17294, 18412, 18415, 18417, 18419, 18422, 18425, 18427, 18429, 18432, 18435, 18436, 15870, 18440, 18444, 18448, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 18496, 18498, 18500, 18502, 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18679, 18643, 18680, 18641, 18681, 18643, 18682, 18642, 18683, 18643, 18185, 18644, 18685, 18686, 18647, 18687, 18688, 18689, 18442, 18691, 18692, 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, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 18755, 18651, 18757, 18653, 18759, 18332, 18752, 18762, 18764, 18765, 18766, 18767, 18768, 18769, 18770, 18772, 18773, 18776, 18777, 18778, 18780, 18781, 18782, 18113, 18784, 18785, 18786, 18787, 18788, 18130, 18792, 18794, 18795, 18796, 18797, 18798, 18399, 18800, 18802, 18176, 18804, 18806, 18807, 18808, 18411, 18753, 18811, 18812, 18754, 18592, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 18539, 18886, 18890, 18901, 18903, 18905, 18907, 18909, 18912, 18914, 18916, 18404, 18922, 18924, 18925, 18927, 18928, 18881, 18883, 18793, 18763, 18779, 18801, 18805, 18561, 18657, 18662, 18898, 18775, 18790, 18791, 18671, 18774, 18659, 18894, 18549, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 18944, 18948, 18951, 18952, 18953, 18955, 18957, 18959, 18961, 18962, 18761, 18963, 18964, 18965, 18949, 18966, 18967, 18968, 18969, 18946, 18956, 18970, 18971, 18972, 18947, 18954, 18973, 18974, 18950, 18975, 18976, 18977, 18978, 18979, 18810, 18813, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 19008, 19018, 19022, 19027, 19012, 19028, 19032, 19010, 19011, 19013, 19033, 19036, 19009, 19019, 19023, 19025, 19030, 19037, 19040, 19014, 19042, 19015, 19043, 18434, 18588, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 19072, 19076, 19079, 19080, 19081, 19084, 19021, 19077, 19082, 19035, 19086, 19091, 19093, 18817, 19095, 19096, 18816, 18814, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 19075, 19078, 19139, 19141, 19085, 19145, 19149, 18431, 18439, 18443, 18447, 19152, 18589, 19153, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 19200, 19088, 19202, 19203, 19204, 19207, 19208, 19209, 19210, 19212, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 19264, 19266, 19206, 19270, 19151, 19272, 19273, 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, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 19268, 19329, 19330, 19332, 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, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 19392, 19394, 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, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 18929, 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, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 19520, 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, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 19334, 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, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 19457, 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, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63};
int h_C[]= {
2, 4, 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, 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, 372, 374, 376, 378, 380, 382, 384, 386, 388, 390, 392, 394, 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, 494, 496, 498, 500, 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, 560, 562, 564, 566, 568, 570, 573, 575, 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, 700, 702, 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, 846, 848, 850, 852, 854, 856, 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, 916, 918, 920, 922, 924, 926, 928, 930, 932, 934, 936, 938, 940, 942, 944, 946, 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, 1023, 1025, 1027, 1029, 1031, 1033, 1035, 1037, 1041, 1043, 1045, 1047, 1049, 1051, 1053, 1055, 1057, 1059, 1063, 1065, 1070, 1072, 1074, 1076, 1078, 1080, 1082, 1084, 1086, 1088, 1091, 1093, 1095, 1097, 1099, 1101, 1103, 1105, 1107, 1109, 1112, 1114, 1116, 1118, 1120, 1122, 1124, 1126, 1128, 1130, 1133, 1135, 1137, 1139, 1142, 1144, 1146, 1148, 1151, 1153, 1157, 1159, 1162, 1164, 1168, 1170, 1172, 1174, 1176, 1178, 1180, 1182, 1185, 1187, 1190, 1192, 1194, 1196, 1198, 1200, 1202, 1204, 1206, 1208, 1211, 1213, 1216, 1218, 1221, 1223, 1226, 1228, 1231, 1233, 1239, 1241, 1244, 1246, 1249, 1251, 1253, 1255, 1257, 1259, 1261, 1263, 1265, 1267, 1269, 1271, 1273, 1275, 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, 1335, 1337, 1339, 1341, 1343, 1345, 1347, 1349, 1351, 1353, 1358, 1360, 1362, 1364, 1368, 1370, 1373, 1375, 1377, 1379, 1381, 1383, 1385, 1387, 1389, 1391, 1394, 1396, 1400, 1402, 1405, 1407, 1410, 1412, 1415, 1417, 1420, 1422, 1425, 1427, 1430, 1432, 1435, 1437, 1440, 1442, 1444, 1446, 1448, 1450, 1453, 1455, 1459, 1461, 1463, 1465, 1470, 1472, 1474, 1476, 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, 1557, 1559, 1561, 1563, 1565, 1567, 1569, 1571, 1573, 1575, 1577, 1579, 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, 1645, 1647, 1649, 1651, 1653, 1655, 1657, 1659, 1662, 1664, 1666, 1668, 1670, 1672, 1674, 1676, 1678, 1680, 1682, 1684, 1686, 1688, 1690, 1692, 1696, 1698, 1702, 1704, 1706, 1708, 1710, 1712, 1714, 1716, 1719, 1721, 1723, 1725, 1727, 1729, 1733, 1735, 1741, 1743, 1745, 1747, 1749, 1751, 1754, 1756, 1759, 1761, 1763, 1765, 1767, 1769, 1772, 1774, 1777, 1779, 1782, 1784, 1787, 1789, 1792, 1794, 1797, 1799, 1801, 1803, 1805, 1807, 1811, 1813, 1815, 1817, 1819, 1821, 1823, 1825, 1827, 1829, 1831, 1833, 1835, 1837, 1839, 1841, 1843, 1845, 1847, 1849, 1851, 1853, 1856, 1858, 1860, 1862, 1866, 1868, 1870, 1872, 1874, 1876, 1863, 1863, 1854, 1854, 1863, 1863, 1922, 1924, 1926, 1928, 1930, 1932, 286, 1477, 1660, 1236, 1236, 1477, 1660, 1730, 1276, 1276, 492, 492, 1236, 1236, 948, 1236, 1236, 1730, 1730, 1738, 1738, 286, 571, 571, 571, 571, 571, 571, 1738, 577, 1236, 1236, 1642, 558, 1236, 1236, 1236, 1236, 1038, 395, 571, 1236, 1236, 1068, 1068, 1131, 1131, 1140, 1140, 1021, 1021, 1642, 1863, 492, 1236, 1236, 492, 1236, 1236, 501, 501, 1236, 1236, 1068, 1068, 1131, 1131, 492, 492, 1236, 1236, 1068, 1068, 1131, 1131, 501, 501, 1236, 1236, 1089, 1089, 558, 1642, 1642, 1809, 947, 571, 577, 1809, 1236, 1236, 1038, 703, 2286, 2288, 2290, 2292, 2295, 2297, 2299, 2301, 2304, 2306, 2308, 2310, 2313, 2315, 2317, 2319, 2321, 2323, 2325, 2327, 2329, 2331, 2333, 2335, 2337, 2339, 2341, 2343, 2345, 2347, 2350, 2352, 2354, 2356, 2358, 2360, 1365, 1365, 2365, 2367, 2369, 2371, 2373, 2375, 2377, 2379, 2381, 2383, 2385, 2387, 2389, 2391, 2393, 2395, 1236, 1236, 1068, 1068, 1131, 1131, 1236, 1236, 1140, 1140, 1089, 1089, 1140, 1140, 1276, 1276, 1365, 1242, 914, 1700, 1021, 1021, 1038, 1038, 947, 948, 1021, 1021, 1038, 1038, 1693, 1021, 1021, 1730, 1738, 1038, 1038, 1039, 2616, 2618, 2620, 2622, 2624, 2626, 2628, 2630, 2632, 2634, 2636, 2638, 2640, 2642, 2645, 2647, 2650, 2652, 2654, 2656, 1089, 1089, 1066, 1066, 1068, 1068, 1140, 1140, 1067, 1067, 1068, 1068, 1166, 1131, 1131, 1140, 1140, 1154, 1154, 1236, 1236, 1236, 1236, 1242, 1242, 1247, 1365, 1365, 1366, 1354, 1276, 1276, 1333, 1354, 1355, 1356, 1365, 1365, 1366, 1392, 1397, 1466, 1466, 1854, 1642, 1642, 1693, 1700, 1730, 1730, 1738, 1738, 1854, 1854, 1863, 1863, 1854, 1854, 1863, 1863, 1854, 1863, 2980, 2982, 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, 3066, 3068, 3071, 3073, 3075, 3077, 3079, 3081, 3084, 3086, 3090, 3092, 3095, 3097, 3101, 3103, 3105, 3107, 3109, 3111, 3114, 3116, 3120, 3122, 3125, 3127, 3131, 3133, 3135, 3137, 3140, 3142, 2643, 2643, 3145, 3145, 2643, 2643, 2302, 2302, 2302, 2302, 3145, 3145, 2964, 2971, 2348, 2348, 2293, 3145, 3145, 2348, 2348, 2293, 2348, 2348, 2348, 2348, 2348, 2348, 2348, 2348, 2302, 2302, 2302, 2302, 2302, 2302, 2348, 2348, 2348, 2348, 2348, 2348, 2311, 2311, 2311, 2311, 3145, 3145, 2348, 2348, 2348, 2348, 2348, 2348, 2348, 2348, 2348, 2348, 2348, 2348, 2657, 3681, 3683, 3689, 3691, 3148, 3148, 3069, 3069, 3148, 3148, 3150, 3708, 3710, 2643, 2643, 2648, 2643, 2643, 2643, 2643, 2648, 2657, 2964, 2971, 4019, 4021, 3145, 3145, 4054, 4056, 4058, 4060, 4063, 4065, 3145, 3145, 3145, 3145, 3148, 3148, 3087, 3087, 3117, 3117, 3138, 3138, 3145, 3145, 3148, 3148, 3150, 4141, 4143, 4146, 4148, 4153, 4155, 4158, 4160, 4163, 4165, 4167, 4169, 4172, 4174, 4176, 4178, 3846, 3676, 3846, 3846, 4180, 4180, 3846, 3846, 4180, 4180, 4180, 4180, 3676, 3846, 4150, 4180, 4180, 4150, 4170, 4170, 4180, 4180, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 6593, 6595, 6597, 6599, 6601, 6603, 6605, 6607, 6609, 6611, 6613, 6615, 6617, 6619, 6621, 6623, 6625, 6627, 6629, 6631, 6633, 6635, 6637, 6639, 6641, 6643, 6645, 6647, 6649, 6651, 6653, 6655, 6657, 6659, 6661, 6663, 6665, 6667, 6669, 6671, 6673, 6675, 6677, 6679, 6681, 6683, 6685, 6687, 6689, 6691, 6693, 6695, 6697, 6699, 6701, 6703, 6705, 6707, 6709, 6711, 6713, 6715, 6717, 6719, 6721, 6723, 6725, 6727, 6729, 6731, 6733, 6735, 6737, 6739, 6741, 6743, 6745, 6747, 6749, 6751, 6753, 6755, 6757, 6759, 6761, 6763, 6765, 6767, 6769, 6771, 6773, 6775, 6777, 6779, 6781, 6783, 6785, 6787, 6789, 6791, 6793, 6795, 6797, 6799, 6801, 6803, 6805, 6807, 6809, 6811, 6813, 6815, 6817, 6819, 6821, 6823, 6825, 6827, 6829, 6831, 6833, 6835, 6837, 6839, 6841, 6843, 6845, 6847, 6849, 6851, 6853, 6855, 6857, 6859, 6861, 6863, 6865, 6867, 6869, 6871, 6873, 6875, 6877, 6879, 6881, 6883, 6885, 6887, 6889, 6891, 6893, 6895, 6897, 6899, 6901, 6903, 6905, 6907, 6909, 6911, 6913, 6915, 6917, 6919, 6921, 6923, 6925, 6927, 6929, 6931, 6933, 6935, 6937, 6939, 6941, 6943, 6945, 6947, 6949, 6951, 6953, 6955, 6957, 6959, 6961, 6963, 6965, 6967, 6969, 6971, 6973, 6975, 6977, 6979, 6981, 6983, 6985, 6987, 6989, 6991, 6993, 6995, 6997, 6999, 7001, 7003, 7005, 7007, 7009, 7011, 7013, 7015, 7017, 7019, 7021, 7023, 7025, 7027, 7029, 7031, 7033, 7035, 7037, 7039, 7041, 7043, 7045, 7047, 7049, 7051, 7053, 7055, 7057, 7059, 7061, 7063, 7065, 7067, 7069, 7071, 7073, 7075, 7077, 7079, 7081, 7083, 7085, 7087, 7089, 7091, 7093, 7095, 7097, 7099, 7101, 7103, 7105, 7107, 7109, 7111, 7113, 7115, 7117, 7119, 7121, 7123, 7125, 7127, 7129, 7131, 7133, 7135, 7137, 7139, 7141, 7143, 7145, 7147, 7149, 7151, 7153, 7155, 7157, 7159, 7161, 7163, 7165, 7167, 7169, 7171, 7173, 7175, 7177, 7179, 7181, 7183, 7185, 7187, 7189, 7191, 7193, 7195, 7197, 7199, 7201, 7203, 7205, 7207, 7209, 7211, 7213, 7215, 7217, 7219, 7221, 7223, 7225, 7227, 7229, 7231, 7233, 7235, 7237, 7239, 7241, 7243, 7245, 7247, 7249, 7251, 7253, 7255, 7257, 7259, 7261, 7263, 7265, 7267, 7269, 7271, 7273, 7275, 7277, 7279, 7281, 7283, 7285, 7287, 7289, 7291, 7293, 7295, 7297, 7299, 7301, 7303, 7305, 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16344, 6266, 17087, 16346, 6269, 6271, 16351, 6274, 16705, 16351, 16354, 16355, 17112, 16355, 16353, 15727, 6287, 16356, 15727, 16358, 16359, 15723, 16356, 6294, 6295, 6296, 16360, 16363, 16363, 6305, 16365, 16368, 6309, 16368, 6314, 17144, 16371, 6317, 16374, 16373, 6320, 15745, 15748, 15748, 16375, 6325, 16374, 16378, 16376, 16378, 16377, 16379, 6336, 6337, 16379, 16382, 16385, 16384, 17170, 16386, 16385, 16389, 16388, 16387, 16389, 16390, 16392, 16767, 16392, 17192, 17200, 17200, 17199, 6363, 17202, 16397, 17203, 16396, 17201, 16397, 6370, 17202, 16406, 6373, 16404, 15772, 16405, 15772, 6378, 16406, 15776, 6382, 15786, 15785, 16409, 16408, 16407, 6388, 16409, 15786, 17355, 17224, 17355, 17270, 17239, 17231, 17239, 17238, 16419, 16418, 16420, 16418, 16423, 16424, 15797, 16421, 6414, 15802, 6416, 16424, 15802, 17271, 17355, 17355, 17270, 16431, 15809, 16433, 15806, 16433, 6431, 6432, 16429, 15809, 16434, 16435, 16435, 16436, 17403, 17405, 17408, 17410, 17412, 17414, 17416, 17418, 17420, 17422, 17424, 17426, 17428, 17430, 17432, 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, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 17491, 17487, 17493, 17495, 17496, 17501, 17487, 17506, 17507, 17508, 17512, 17487, 17516, 17026, 17028, 17521, 17523, 17489, 17489, 17488, 17490, 17489, 17527, 17472, 17530, 17473, 17059, 17534, 17474, 17071, 17538, 17540, 17543, 17475, 17546, 17476, 17549, 17551, 17553, 17555, 17477, 17558, 17478, 17562, 17479, 17568, 17571, 17480, 17154, 17575, 17577, 5791, 17583, 17173, 17587, 17589, 17481, 17592, 17595, 17194, 17599, 17601, 17482, 17604, 17606, 17607, 17609, 17611, 17483, 17613, 17615, 17617, 17245, 17621, 17623, 17484, 17626, 17628, 17266, 17631, 17633, 17485, 17636, 17486, 17639, 17642, 17487, 17645, 17646, 17648, 17651, 17653, 17489, 17490, 17489, 17489, 17488, 17659, 17487, 17337, 17665, 17666, 17668, 17670, 17488, 17489, 17489, 17489, 17490, 16995, 17355, 16993, 17355, 17355, 17013, 17355, 17004, 17021, 17355, 17030, 17355, 17367, 17355, 17312, 17355, 17303, 17355, 17524, 17683, 6238, 6239, 6240, 6241, 6242, 6245, 6246, 6248, 6249, 6250, 6251, 6254, 6256, 6257, 6258, 6260, 6261, 6262, 6263, 6264, 6267, 6268, 6272, 6275, 6276, 6280, 6281, 6282, 6283, 6284, 6286, 6288, 6289, 6290, 6291, 6292, 6293, 17560, 17560, 6299, 6300, 6301, 17560, 17731, 17565, 17566, 6307, 6308, 6310, 17564, 17565, 6315, 6316, 6318, 6319, 6321, 6322, 6323, 6324, 6326, 6328, 6330, 6331, 6332, 6334, 6338, 6339, 17759, 6341, 6342, 6343, 6344, 6345, 6346, 6348, 6349, 6350, 6352, 6354, 6355, 6356, 6358, 6360, 6361, 6362, 6364, 6365, 6366, 6367, 6368, 6369, 6371, 6372, 6374, 6375, 6376, 6377, 6379, 6380, 6383, 6384, 6385, 6386, 6387, 6389, 6390, 6393, 6394, 6395, 6396, 6399, 6400, 6401, 6402, 6404, 6405, 6407, 6408, 6410, 6411, 6412, 6413, 6415, 6417, 6418, 6420, 6421, 6422, 6424, 6426, 6427, 6428, 6429, 6430, 6433, 6434, 6436, 6438, 6439, 6440, 17845, 17355, 17312, 17355, 17303, 17322, 17355, 17355, 17320, 17656, 17355, 17853, 17355, 17355, 17334, 17332, 17350, 17355, 17355, 17355, 17676, 17353, 17858, 17849, 17679, 17678, 17680, 17856, 17855, 17684, 17848, 17847, 17846, 17849, 17851, 17850, 17854, 17856, 17855, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 17492, 5482, 17923, 17502, 5508, 17928, 17513, 5535, 17933, 17936, 5564, 5566, 5567, 5568, 5570, 17528, 5597, 17531, 5618, 17535, 5633, 17950, 17541, 17544, 5658, 17547, 5670, 17556, 5691, 17559, 5705, 17563, 5732, 17569, 17572, 5773, 17969, 17578, 17971, 17584, 17974, 17590, 5819, 17593, 17596, 17980, 17602, 5869, 17605, 17985, 17612, 5905, 17990, 17618, 17993, 17624, 5938, 17627, 17998, 17634, 5976, 17637, 6000, 17640, 17643, 6055, 18008, 18011, 6081, 6083, 6084, 6086, 6087, 17660, 6120, 18020, 18023, 6146, 6147, 6148, 6149, 6151, 6191, 6192, 6193, 6194, 6202, 6203, 6204, 6205, 6211, 6212, 6213, 6215, 6221, 6222, 6223, 6224, 6225, 6226, 18050, 18052, 17690, 18054, 17946, 18057, 18059, 18062, 18065, 18067, 18069, 17710, 17957, 17956, 17958, 18071, 18073, 18075, 18077, 17722, 18081, 18083, 18085, 6297, 6298, 6302, 18089, 6304, 6306, 6311, 6312, 17738, 18100, 17743, 18103, 18104, 18106, 17751, 18111, 18115, 18118, 18120, 18124, 18128, 18132, 18134, 18136, 18138, 17786, 17789, 18143, 18145, 18146, 18148, 18150, 18152, 18153, 18157, 18161, 18164, 18168, 18170, 18171, 18172, 18175, 18179, 18181, 17836, 18183, 18187, 17406, 6457, 6459, 6460, 6461, 6466, 6467, 6468, 6469, 6470, 6471, 6485, 6486, 6487, 6488, 6493, 6494, 6495, 6496, 6497, 6498, 6510, 6512, 6513, 6515, 18041, 18048, 6520, 6521, 6522, 6551, 6552, 6553, 6556, 18200, 6559, 6560, 6562, 18211, 6565, 6566, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 16983, 17924, 17001, 17929, 17019, 17934, 17036, 18252, 17037, 17044, 17056, 17067, 17085, 17093, 17108, 17118, 17129, 17150, 17581, 17180, 17197, 17208, 17986, 17229, 17236, 17254, 17999, 17276, 17287, 17301, 18009, 17318, 18310, 18311, 17330, 18021, 18318, 18320, 18321, 18323, 18325, 18327, 18329, 18331, 18335, 18337, 18339, 18341, 18343, 6252, 18346, 18261, 18349, 18351, 6273, 6277, 6278, 18358, 18360, 18362, 18364, 18365, 17735, 18095, 18369, 18373, 18375, 18377, 18276, 18381, 18280, 18283, 18386, 18388, 18390, 18392, 18394, 18396, 18294, 18401, 18403, 18406, 18408, 18303, 18413, 18416, 18418, 18420, 18423, 18426, 18428, 18430, 18433, 6517, 6519, 18410, 18441, 6558, 6564, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 16984, 17503, 17020, 18503, 18254, 17045, 17057, 17068, 17086, 17094, 17109, 17119, 17130, 17151, 17181, 17209, 17230, 17255, 17277, 17288, 17302, 18528, 18312, 17331, 18533, 17348, 18497, 18536, 18499, 18538, 18501, 18541, 18544, 18545, 6255, 18068, 17713, 18552, 18078, 18555, 18556, 18557, 18559, 18560, 18563, 6329, 18514, 18121, 6347, 6353, 18516, 18569, 18571, 18573, 18518, 18520, 6406, 18576, 18522, 18578, 6437, 18526, 18582, 18531, 18586, 18437, 6554, 18445, 18449, 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, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 18628, 18646, 18649, 18240, 6195, 18243, 6206, 18246, 6214, 18542, 18255, 18055, 18257, 18259, 18658, 18262, 18263, 18265, 18660, 18661, 18267, 18269, 17729, 18665, 18271, 18667, 18273, 18108, 18274, 18669, 18277, 6335, 18279, 18672, 18281, 18673, 18284, 6359, 18140, 18147, 18286, 18154, 18288, 6392, 18290, 6398, 18293, 18165, 18295, 18173, 18297, 6423, 18299, 18184, 18301, 18684, 18304, 6458, 18583, 18313, 6484, 18587, 18438, 18690, 18446, 18450, 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, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 6190, 18756, 6201, 18758, 6210, 18760, 18249, 6237, 6247, 6253, 18347, 6259, 6265, 6270, 18771, 6279, 6285, 6303, 18370, 6313, 6327, 18378, 6333, 18783, 6340, 18382, 6351, 18383, 6357, 18789, 6381, 6391, 18155, 6397, 18159, 6403, 18799, 6409, 6419, 18803, 6425, 6435, 18409, 6456, 18809, 18307, 6483, 18421, 18316, 18815, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 18885, 6220, 18063, 18112, 18116, 18125, 18129, 18384, 18397, 18398, 18166, 18919, 18188, 18580, 6465, 18584, 6492, 18880, 18882, 18910, 18887, 18910, 18917, 18920, 18899, 18888, 18895, 18897, 18902, 18910, 18910, 18904, 18896, 18891, 18893, 18892, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 18333, 18379, 18133, 18158, 18162, 18177, 18414, 18424, 6511, 6514, 18945, 6523, 6524, 6525, 18915, 6527, 6528, 6529, 6530, 18889, 18921, 6534, 6535, 6536, 18900, 18915, 6542, 6543, 18906, 6545, 6546, 6548, 6549, 6550, 18958, 18960, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 18884, 6518, 6526, 6531, 18913, 6533, 6537, 18908, 18911, 18918, 6541, 6544, 18902, 19020, 19024, 19026, 19031, 19038, 19041, 18923, 6557, 18926, 6563, 19017, 19016, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 6516, 6532, 6538, 6539, 6540, 6547, 19074, 19029, 19034, 19083, 19087, 6555, 6561, 19094, 6570, 6571, 19092, 19073, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 19137, 19138, 19140, 19039, 19142, 19089, 6567, 19147, 19147, 19147, 19148, 6574, 19136, 6576, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 19143, 19201, 19144, 19090, 19146, 6568, 6569, 6572, 6573, 6575, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 19265, 19205, 19269, 19150, 19271, 19211, 19213, 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, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 19328, 19267, 19331, 19333, 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, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 19393, 19395, 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, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 19456, 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, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 6577, 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, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 19584, 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, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 19648, 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, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63};
bool h_Op[]= {
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#define THREADS_PER_BLOCK 64
#define BLOCKS_PER_GRID 1
#define SIZE_OF_IN 6592
#define SIZE_OF_AC 13184
__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[309*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];
__syncthreads();
for (int iter=0; iter< n_iter; iter++) {
R[i + 103*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 + 104*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 + 105*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 + 106*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 + 107*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 + 108*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 + 109*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 + 110*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 + 111*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 + 112*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 + 113*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 + 114*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 + 115*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 + 116*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 + 117*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 + 118*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 + 119*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 + 120*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 + 121*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 + 122*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 + 123*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 + 124*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]];
__syncthreads();
R[i + 125*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 + 126*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 + 127*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 + 128*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 + 129*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 + 130*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 + 131*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 + 132*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 + 133*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 + 134*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 + 135*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 + 136*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 + 137*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 + 138*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]];
__syncthreads();
R[i + 139*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 + 140*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 + 141*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 + 142*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 + 143*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 + 144*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 + 145*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 + 146*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 + 147*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 + 148*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 + 149*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 + 150*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 + 151*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 + 152*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 + 153*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 + 154*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 + 155*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 + 156*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]];
__syncthreads();
R[i + 157*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 + 158*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 + 159*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 + 160*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 + 161*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 + 162*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 + 163*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 + 164*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 + 165*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 + 166*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 + 167*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 + 168*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 + 169*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 + 170*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 + 171*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 + 172*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 + 173*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 + 174*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]];
__syncthreads();
R[i + 175*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 + 176*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 + 177*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 + 178*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 + 179*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 + 180*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 + 181*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 + 182*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 + 183*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 + 184*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 + 185*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 + 186*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 + 187*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 + 188*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]];
__syncthreads();
R[i + 189*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 + 190*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 + 191*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 + 192*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 + 193*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 + 194*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 + 195*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 + 196*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 + 197*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 + 198*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 + 199*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 + 200*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 + 201*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 + 202*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]];
__syncthreads();
R[i + 203*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 + 204*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 + 205*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 + 206*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 + 207*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 + 208*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 + 209*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]];
R[i + 210*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 + 211*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 + 212*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 + 213*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 + 214*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]];
__syncthreads();
R[i + 215*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 + 216*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 + 217*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 + 218*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 + 219*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 + 220*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 + 221*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 + 222*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 + 223*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 + 224*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 + 225*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 + 226*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 + 227*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]];
__syncthreads();
R[i + 228*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 + 229*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 + 230*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 + 231*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 + 232*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 + 233*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 + 234*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 + 235*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 + 236*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 + 237*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]];
__syncthreads();
R[i + 238*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 + 239*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 + 240*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 + 241*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 + 242*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 + 243*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 + 244*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 + 245*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 + 246*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 + 247*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 + 248*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]];
__syncthreads();
R[i + 249*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 + 250*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 + 251*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 + 252*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 + 253*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 + 254*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 + 255*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 + 256*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 + 257*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]];
__syncthreads();
R[i + 258*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 + 259*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 + 260*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 + 261*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 + 262*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 + 263*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 + 264*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]];
__syncthreads();
R[i + 265*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 + 266*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 + 267*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 + 268*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 + 269*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 + 270*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 + 271*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 + 272*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]];
__syncthreads();
R[i + 273*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 + 274*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]];
R[i + 275*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 + 276*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 + 277*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 + 278*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 + 279*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]];
__syncthreads();
R[i + 280*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 + 281*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 + 282*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 + 283*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 + 284*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]];
__syncthreads();
R[i + 285*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 + 286*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 + 287*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 + 288*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]];
__syncthreads();
R[i + 289*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 + 290*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]];
__syncthreads();
R[i + 291*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 + 292*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]];
__syncthreads();
R[i + 293*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 + 294*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]];
__syncthreads();
R[i + 295*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]];
__syncthreads();
R[i + 296*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]];
__syncthreads();
R[i + 297*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]];
__syncthreads();
R[i + 298*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]];
__syncthreads();
R[i + 299*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]];
__syncthreads();
R[i + 300*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]];
__syncthreads();
R[i + 301*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]];
__syncthreads();
R[i + 302*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]];
__syncthreads();
R[i + 303*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]];
__syncthreads();
R[i + 304*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]];
__syncthreads();
R[i + 305*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]];
__syncthreads();
R[i + 306*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]];
__syncthreads();
R[i + 307*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]];
__syncthreads();
R[i + 308*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]];
if (i==0) { final += R[308*t]; }
__syncthreads();
}
if (i==0) { A[0]= final;}
}
|
1,040
|
#include <iostream>
#include <set>
#include <algorithm>
#include <assert.h>
#include "cuda_runtime.h"
using namespace std;
#define ITERATION_FINEGRAINED (1)
#define KB (1024/sizeof(int))
#define MB (KB*1024)
#define MAX_NUM_THREADS (1024) // a block has maximal thread size
//kernel function
__global__ void strided_access_onepass(unsigned *arr, int length, int stride, bool record, unsigned *duration, double *help); //used to benchmark the TLB structure
__global__ void strided_access_finegrained(unsigned *arr, int length, bool record, unsigned *duration, unsigned *index); //obsolete: use to attain average cycle and pages visited
void TLB_latency(int N, int stride);
void TLB_benchmarking(int beginSize, int endSize, int stride);
void generate_strided(unsigned *arr, int length, int stride);
void generate_strided_onepass(unsigned *arr, int length, int stride);
//global variables
int numThreadsGlobal;
int numBlocksGlobal;
int dataSizeGlobal; //in MB
int pageSizeGlobal; //in KB
/*
* TLB benchmarking: ./tlb_GPU pageSize_KB dataSize_begin_MB dataSize_end_MB
*
* blockSize=1 and gridSize=1 for TLB benchmarking;
*/
int main(int argc, char* argv[]){
if (argc < 4) {
cerr<<"Shall provide the blockSize, gridSize used and page size."<<endl;
cerr<<"Eg.: ./tlb_GPU bSize gSize dataSize_MB pageSize_KB"<<endl;
exit(0);
}
numThreadsGlobal = 1;
numBlocksGlobal = 1;
pageSizeGlobal = atoi(argv[1]) * KB;
int dataSize_begin = atoi(argv[2]) * MB;
int dataSize_end = atoi(argv[3]) * MB;
cudaSetDevice(0);
TLB_benchmarking(dataSize_begin, dataSize_end,pageSizeGlobal);
cudaDeviceReset();
return 0;
}
void TLB_benchmarking(int beginSize, int endSize, int stride) {
for (int ds = beginSize; ds <= endSize; ds += stride) {
cout << "Struc: Data size: " << (float)ds / MB << "MB\t" << "Stride: " << stride / MB << "MB\t";
cudaDeviceReset();
cudaError_t error_id;
unsigned *h_a, *d_a;
unsigned *h_timeinfo, *d_timeinfo;
double *help;
h_a = (unsigned*)malloc(sizeof(unsigned)*ds);
error_id = cudaMalloc ((void **) &d_a, sizeof(unsigned)*ds);
if (error_id != cudaSuccess) cerr<<"Error 1.0 is "<<cudaGetErrorString(error_id)<<endl;
/* initialize array elements on CPU with pointers into d_a. */
generate_strided_onepass(h_a,ds,stride);
/* copy array elements from CPU to GPU */
error_id = cudaMemcpy(d_a, h_a, sizeof(unsigned)*ds, cudaMemcpyHostToDevice);
if (error_id != cudaSuccess) cerr<<"Error 1.1 is "<<cudaGetErrorString(error_id)<<endl;
h_timeinfo = (unsigned *) malloc(sizeof(unsigned) * numThreadsGlobal * numBlocksGlobal);
error_id = cudaMalloc((void **) &d_timeinfo, sizeof(unsigned) * numThreadsGlobal * numBlocksGlobal);
if (error_id != cudaSuccess) cerr << "Error 1.2 is " << cudaGetErrorString(error_id) << endl;
error_id = cudaMalloc((void **) &help, sizeof(double) * numThreadsGlobal * numBlocksGlobal);
if (error_id != cudaSuccess) cerr << "Error 1.3 is " << cudaGetErrorString(error_id) << endl;
cudaThreadSynchronize();
/* launch kernel*/
dim3 Db = dim3(numThreadsGlobal);
dim3 Dg = dim3(numBlocksGlobal);
strided_access_onepass<<< Dg, Db >>> (d_a, ds, stride, false, NULL, NULL); //warp up
strided_access_onepass<<< Dg, Db >>> (d_a, ds, stride, true, d_timeinfo, help); //formal
cudaThreadSynchronize();
error_id = cudaGetLastError();
if (error_id != cudaSuccess) {
cerr << "Error kernel is " << cudaGetErrorString(error_id) << endl;
}
/* copy results from GPU to CPU */
cudaThreadSynchronize();
error_id = cudaMemcpy((void *) h_timeinfo, (void *) d_timeinfo, sizeof(unsigned) * numThreadsGlobal * numBlocksGlobal, cudaMemcpyDeviceToHost);
if (error_id != cudaSuccess) cerr << "Error 2.2 is " << cudaGetErrorString(error_id) << endl;
double total = 0; //here we use double, otherwise it will overflow
for (int i = 0; i < numThreadsGlobal * numBlocksGlobal; i++) {
total += h_timeinfo[i];
}
total /= (numThreadsGlobal * numBlocksGlobal);
cout << "cycle: " << total << endl;
cudaThreadSynchronize();
/* free memory on GPU */
cudaFree(help);
cudaFree(d_a);
cudaFree(d_timeinfo);
/*free memory on CPU */
free(h_a);
free(h_timeinfo);
cudaDeviceReset();
}
}
//used for TLB benchmarking
__global__ void strided_access_onepass(unsigned *arr, int length, int stride, bool record, unsigned *duration, double *help) {
unsigned long start, end;
unsigned gid = blockDim.x * blockIdx.x + threadIdx.x;
unsigned curIdx = 0;
double anc = 0;
double total = 0;
int myIteration = 0;
//traverse the data array once
while (curIdx < length) {
start = clock64();
curIdx = arr[curIdx];
anc += curIdx; //to ensure the curIdx has been read, this instruction is 16-cycle long on K40m
end = clock64();
total += (end-start-16);
myIteration++;
}
if (record) {
duration[gid] = (total/myIteration);
help[gid] = anc;
}
}
void generate_strided_onepass(unsigned *arr, int length, int stride) {
for (int i = 0 ; i < length; i++) {
arr[i] = i+stride;
}
}
//void measure_global() {
//
// int stride = pageSizeGlobal*KB; //2MB stride
// set<int> missPages; //recording the overall missing pages in each case
//
// //begin and end size in MBs
// /* To test the TLB structures the beginSize and endSize is different;
// * To test the latency of multi-thread, beginSize and endSize should set as the data size tested */
// int beginSize = dataSizeGlobal * MB;
// int endSize = dataSizeGlobal * MB;
//
// //1. The L1 TLB has 16 entries. Test with N_min=28 *1024*256, N_max>32*1024*256
// //2. The L2 TLB has 65 entries. Test with N_min=128*1024*256, N_max=160*1024*256
// for (int dataSize = beginSize; dataSize <= endSize; dataSize += (128*KB)) {
//// cout<<"Data size: "<<(float)dataSize/MB<<"MB\t"<<"Stride: "<< stride/MB <<"MB"<<endl;
// cout<<"Data size: "<<(float)dataSize/MB<<"MB\tbsize: "<<numThreadsGlobal<<"\tgsize: "<<numBlocksGlobal<<'\t';
// parametric_measure_global(dataSize, false, stride, missPages); //not finegrained
// }
//}
//void TLB_finegrained(int N, bool finegrained, int stride, set<int> & lastMissPages) {
// cudaDeviceReset();
// cudaError_t error_id;
// int i;
// unsigned *h_a, *d_a;
// h_a = (unsigned*)malloc(sizeof(unsigned)*N);
// error_id = cudaMalloc ((void **) &d_a, sizeof(unsigned)*N);
//
// if (error_id != cudaSuccess)
// cerr<<"Error 1.0 is "<<cudaGetErrorString(error_id)<<endl;
//
// /* initialize array elements on CPU with pointers into d_a. */
// generate_strided(h_a,N,stride);
// //generate_strided_onepass(h_a,N,(mul)*stride);
//
// /* copy array elements from CPU to GPU */
// error_id = cudaMemcpy(d_a, h_a, sizeof(unsigned)*N, cudaMemcpyHostToDevice);
// if (error_id != cudaSuccess) {
// cerr<<"Error 1.1 is "<<cudaGetErrorString(error_id)<<endl;
// }
//
// unsigned *h_index, *h_timeinfo, *d_timeinfo, *d_index;
// double *help;
//
// if (finegrained) {
// h_index = (unsigned *) malloc(sizeof(unsigned) * numThreadsGlobal * numBlocksGlobal * ITERATION);
// h_timeinfo = (unsigned *) malloc(sizeof(unsigned) * numThreadsGlobal * numBlocksGlobal * ITERATION);
//
// //recording time and visited locations
// error_id = cudaMalloc((void **) &d_timeinfo, sizeof(unsigned) * numThreadsGlobal * numBlocksGlobal * ITERATION);
// if (error_id != cudaSuccess) {
// cerr << "Error 1.2 is " << cudaGetErrorString(error_id) << endl;
// }
//
// error_id = cudaMalloc((void **) &d_index, sizeof(unsigned) * numThreadsGlobal * numBlocksGlobal *ITERATION);
// if (error_id != cudaSuccess) {
// cerr << "Error 1.3 is " << cudaGetErrorString(error_id) << endl;
// }
// }
// else {
// h_timeinfo = (unsigned *) malloc(sizeof(unsigned) * numThreadsGlobal * numBlocksGlobal);
// error_id = cudaMalloc((void **) &d_timeinfo, sizeof(unsigned) * numThreadsGlobal * numBlocksGlobal);
// if (error_id != cudaSuccess) {
// cerr << "Error 1.4 is " << cudaGetErrorString(error_id) << endl;
// }
// error_id = cudaMalloc((void **) &help, sizeof(double) * numThreadsGlobal * numBlocksGlobal);
// if (error_id != cudaSuccess) {
// cerr << "Error 1.5 is " << cudaGetErrorString(error_id) << endl;
// }
// }
//
// cudaThreadSynchronize ();
// /* launch kernel*/
// dim3 Db = dim3(numThreadsGlobal);
// dim3 Dg = dim3(numBlocksGlobal);
// if (finegrained) {
// strided_access_finegrained<<<Dg, Db>>>(d_a, N, false, NULL, NULL);
// strided_access_finegrained<<<Dg, Db>>>(d_a, N, false, d_timeinfo, d_index);
// }
// else {
// strided_access<<<Dg, Db>>>(d_a, N, stride, false, NULL, NULL); //warp up
// strided_access<<<Dg, Db>>>(d_a, N, stride, true, d_timeinfo, help); //formal
// }
//
// cudaThreadSynchronize();
//
// error_id = cudaGetLastError();
// if (error_id != cudaSuccess) {
// cerr<<"Error kernel is "<<cudaGetErrorString(error_id)<<endl;
// }
//
// /* copy results from GPU to CPU */
// cudaThreadSynchronize ();
//
// if (finegrained) {
// error_id = cudaMemcpy((void *)h_timeinfo, (void *)d_timeinfo, sizeof(unsigned)*ITERATION*numThreadsGlobal * numBlocksGlobal, cudaMemcpyDeviceToHost);
// if (error_id != cudaSuccess) {
// cerr<<"Error 2.0 is "<<cudaGetErrorString(error_id)<<endl;
// }
// error_id = cudaMemcpy((void *)h_index, (void *)d_index, sizeof(unsigned)*ITERATION*numThreadsGlobal * numBlocksGlobal, cudaMemcpyDeviceToHost);
// if (error_id != cudaSuccess) {
// cerr<<"Error 2.1 is "<<cudaGetErrorString(error_id)<<endl;
// }
//
// //statistics
// int count_less_300 = 0, count_300_400 = 0, count_400_500 = 0, count_500_600 = 0, count_larger_600 = 0;
// double total = 0;
//
// int loop = 0; //how many times the array is looped
//
// set<int> curMissPages;
// for(i=0 ;i<ITERATION;i++) {
// int curPage = h_index[i]/stride;
// if ( (h_timeinfo[i] > 400) && (h_timeinfo[i] < 510)) {
// curMissPages.insert(curPage);
// }
// cout<<curPage<<'\t'<<h_index[i]<<'\t'<<h_timeinfo[i]<<endl;
//
// if (h_index[i]<stride) loop ++;
// if (h_timeinfo[i] < 300) count_less_300++;
// else if (h_timeinfo[i] < 400) count_300_400 ++;
// else if (h_timeinfo[i] < 500) count_400_500 ++;
// else if (h_timeinfo[i] < 600) count_500_600++;
// else count_larger_600++;
// total += h_timeinfo[i];
// }
// set<int> diffSet;
// set_difference(curMissPages.begin(), curMissPages.end(),lastMissPages.begin(), lastMissPages.end(), inserter(diffSet,diffSet.end()));
//
// //to check that pages missed in last dataset will be hit in this dataset
// set<int> checkSet;
// set_difference(lastMissPages.begin(), lastMissPages.end(), curMissPages.begin(), curMissPages.end(), inserter(checkSet,checkSet.end()));
// assert(checkSet.size() == 0);
//
// int totalPages = N /512 / 1024;
// cout<<"Pages: "<<totalPages<<", misses: "<<count_400_500<<", loops: "<<loop<<", new miss pages: ";
// for (set<int>::iterator it = diffSet.begin(); it != diffSet.end(); ++it) {
// cout<<*it<<' ';
// }
// cout<<endl;
//
// // lastMissPages = curMissPages;
//
// total = total / ITERATION;
// cout<<"Average: "<<total<<endl;
// // cout<<"Statistics:"<<endl;
// // cout<<"Data size: "<<N / 1024 / 256<<" MB."<<endl;
//
// // cout<<"less than 300: "<<count_less_300<<endl;
// // cout<<"300 - 400: "<<count_300_400<<endl;
// // cout<<"400 - 500: "<<count_400_500<<endl;
// // cout<<"500 - 600: "<<count_500_600<<endl;
// // cout<<"larger than 600: "<<count_larger_600<<endl;
// // cout<<"Average cycles: "<<total<<" in "<<ITERATION<<" iterations."<<endl;
// }
// else {
// error_id = cudaMemcpy((void *)h_timeinfo, (void *)d_timeinfo, sizeof(unsigned)*numThreadsGlobal * numBlocksGlobal, cudaMemcpyDeviceToHost);
// if (error_id != cudaSuccess) {
// cerr<<"Error 2.2 is "<<cudaGetErrorString(error_id)<<endl;
// }
//
// double total = 0; //here we use double, otherwise it will overflow
// for(int i = 0; i < numThreadsGlobal*numBlocksGlobal; i++) {
// total += h_timeinfo[i];
// }
// total /= (numThreadsGlobal*numBlocksGlobal);
// cout<<"cycle: "<<total<<endl;
// }
// cudaThreadSynchronize();
//
// /* free memory on GPU */
// if (finegrained) {
// cudaFree(d_index);
// free(h_index);
// }
// else {
// cudaFree(help);
// }
//
// cudaFree(d_a);
// cudaFree(d_timeinfo);
//
// /*free memory on CPU */
// free(h_a);
// free(h_timeinfo);
//
// cudaDeviceReset();
//}
//obsolete: to record the page number and study the cache replacement policy
//__global__ void strided_access_finegrained(unsigned *arr, int length, bool record, unsigned *duration, unsigned *index) {
//
// unsigned timestamp;
// unsigned gid = blockDim.x * blockIdx.x + threadIdx.x;
// unsigned gsize = blockDim.x * gridDim.x;
// unsigned curIdx = (blockDim.x * threadIdx.x + blockIdx.x) % length;
//
// __shared__ unsigned int s_tvalue[ITERATION_FINEGRAINED*MAX_NUM_THREADS];
// __shared__ unsigned int s_index[ITERATION_FINEGRAINED*MAX_NUM_THREADS];
//
// unsigned it = gid;
// while (it < ITERATION_FINEGRAINED * MAX_NUM_THREADS) {
// s_index[it] = 0;
// s_tvalue[it] = 0;
// it += gsize;
// }
// __syncthreads();
//
// it = gid;
// for (int k = 0; k < ITERATION_FINEGRAINED; k++) {
// timestamp = clock();
// curIdx = arr[curIdx];
// s_index[it]= curIdx;
// timestamp = clock() - timestamp;
// s_tvalue[it] = timestamp;
// it += ITERATION_FINEGRAINED;
// }
//
// if (record) {
// it = threadIdx.x;
// while (it < blockDim.x * ITERATION_FINEGRAINED) {
// duration[it + blockIdx.x*blockDim.x*ITERATION_FINEGRAINED] = s_tvalue[it];
// index[it + blockIdx.x*blockDim.x*ITERATION_FINEGRAINED] = s_index[it];
// it += blockDim.x;
// }
// }
//}
|
1,041
|
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#define PI 3.14159265359
__global__ void solve(const int N, float * u, float *newu, float *f, float *res2v){
float w = 0.5;
int id = blockIdx.x*blockDim.x + threadIdx.x;
if (id>N+2 && id<(N+2)*(N+2)-(N+2) && id%(N+2) !=N+1 && id%(N+2) != 0){
const float Ru = -u[id-(N+2)] -u[id+(N+2)] - u[id-1] - u[id+1];
const float rhs = (1./4)*(f[id]-Ru);
const float unew = w*rhs + (1-w)*u[id];
newu[id] = unew;
res2v[id] = (unew-u[id])*(unew-u[id]);
}
}
__global__ void res2fred(int Nblocks, int blockdim, float* res2, float* res2_small){
int bid = blockIdx.x;
int I = blockDim.x*blockIdx.x+threadIdx.x;
float extra_sum = 0;
if (bid < Nblocks-1){
for (int s = blockdim*(Nblocks-1)/2; s>1; s/=2){
if (I<s){
res2_small[I] += res2[I+s];
}
__syncthreads();
}
}
else{
extra_sum += res2[I];
}
__syncthreads();
res2_small[Nblocks-1] = extra_sum;
}
int main(void)
{
int N = 1000;
double tol = 1e-6;
int Nthreads = 128;
const int Nblocks = (N+Nthreads-1)/Nthreads+1;
dim3 threadsPerblock(Nthreads, 1,1);
dim3 blocks(Nblocks,1,1);
int blockdim = Nthreads;
float *f_c, *u_c, *unew_c, *res2v_c, *res2vsmall_c;
cudaMalloc(&u_c,(N+2)*(N+2)*sizeof(float));
cudaMalloc(&f_c,(N+2)*(N+2)*sizeof(float));
cudaMalloc(&unew_c,(N+2)*(N+2)*sizeof(float));
cudaMalloc(&res2v_c, (N+2)*(N+2)*sizeof(float));
cudaMalloc(&res2vsmall_c, (N+2)*(N+2)*sizeof(float));
float *u = (float*) calloc((N+2)*(N+2), sizeof(float));
float *f = (float*) calloc((N+2)*(N+2),sizeof(float));
float *unew = (float*) calloc((N+2)*(N+2), sizeof(float));
float *res2v = (float*) calloc((N+2)*(N+2), sizeof(float));
float *res2vsmall = (float*) calloc(100, sizeof(float));
float h = 2.0/(N+1);
for (int i = 0; i<N+2; i++){
for (int j = 0; j<N+2; j++){
const float x = -1 + i*h;
const float y = -1 + j*h;
f[i+j*(N+2)] = sin(PI*x)*sin(PI*y)*h*h;
}
}
int iter = 0;
float res2 = 1;
float res2sum = 0;
while (res2 > tol*tol){
cudaMemcpy(f_c, f, (N+2)*(N+2)*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(u_c, u, (N+2)*(N+2)*sizeof(float), cudaMemcpyHostToDevice);
solve <<<blocks, threadsPerblock>>> (N,u_c,unew_c, f_c, res2v_c);
res2fred <<<blocks, threadsPerblock >>> (Nblocks, blockdim, res2v_c, res2vsmall_c);
cudaMemcpy(unew, unew_c, (N+2)*(N+2)*sizeof(float), cudaMemcpyDeviceToHost);
cudaMemcpy(res2vsmall, res2vsmall_c, 100*sizeof(float), cudaMemcpyDeviceToHost);
cudaMemcpy(res2v, res2v_c, (N+2)*(N+2)*sizeof(float), cudaMemcpyDeviceToHost);
// for (int i = 0; i<Nblocks; i++){
// printf("res2vsmall[%d] = %f\n",i, res2vsmall[i]);
// res2sum += res2vsmall[i];
// }
for (int i = 0; i<(N+2)*(N+2); i++){
u[i] = unew[i];
res2sum +=res2v[i];
}
res2 = res2sum;
res2sum = 0;
// printf("%d", iter);
iter++;
}
float err = fabs(u[0]-f[0]/(h*h*2.0*PI*PI));
for (int i = 0; i<(N+2)*(N+2)-1; i++){
if (err <=fabs(u[i+1]-f[i+1]/(h*h*2.0*PI*PI))){
err = fabs(u[i+1]-f[i+1]/(h*h*2.0*PI*PI));
}
}
printf("iter %d", iter);
cudaFree(u_c);
cudaFree(f_c);
cudaFree(unew_c);
cudaFree(res2v_c);
free(u);
free(f);
free(unew);
free(res2v);
return 0;
}
|
1,042
|
#include <stdio.h>
// 1. Note the convention d_* is used for device and h_* is used for host allocations.
// 2. __global__ tells cuda that what follows is a kernel implementation
// Cuda kernel that returns a cube of a given array
// Mostly written in a serial manner
__global__ void cube(float *d_out, float *d_in) {
int idx = threadIdx.x;
float f = d_in[idx];
d_out[idx] = f * f * f;
}
int main(int argc, char **argv) {
const int ARRAY_SIZE = 1000;
const int ARRAY_BYTES = ARRAY_SIZE * sizeof(float);
// Create input array on the host.
float h_in[ARRAY_SIZE];
for (int i=0; i < ARRAY_SIZE; i++) {
h_in[i] = i;
}
float h_out[ARRAY_SIZE];
// Declare pointers for GPU memory
float *d_in;
float *d_out;
// Allocate memory on GPU.
cudaMalloc((void **) &d_in, ARRAY_BYTES);
cudaMalloc((void **) &d_out, ARRAY_BYTES);
// Transfer array to GPU.
cudaMemcpy(d_in, h_in, ARRAY_BYTES, cudaMemcpyHostToDevice);
// Launch the kernel
cube<<<1, ARRAY_SIZE>>>(d_out, d_in);
// Copy back the result
cudaMemcpy(h_out, d_out, ARRAY_BYTES, cudaMemcpyDeviceToHost);
// Print the resulting array
for(int i=0; i < ARRAY_SIZE; i++) {
printf("%f", h_out[i]);
printf(((i % 4) != 3) ? "\t" : "\n");
}
cudaFree(d_in);
cudaFree(d_out);
return 0;
}
|
1,043
|
/*
* SpaceTime Simulator
* Curso Deep Learning y Cuda - 2020
* Autor: Oscar Noel Amaya Garcia
* email: dbanshee@gmail.com
*/
#include <cuda_runtime.h>
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
#include <math.h>
#include <unistd.h>
#include <string.h>
#include <sys/time.h>
#define RUN_MODE_SIM 0
#define RUN_MODE_BENCH 1
#define SP_FILENAME "sp.json"
#define SP_FILENAME_BUFF1 "sp_0.json"
#define SP_FILENAME_BUFF2 "sp_1.json"
#define SP_FILENAME_BENCH "sp_bench.json"
#define MAX_BLACK_HOLES 10
#define SOFTENING 1e-9f
#define DT 0.05f
#define MAX_ASTEROIDS 30
#define AST_FILENAME "ast.json"
#define AST_FILENAME_BUFF1 "ast_0.json"
#define AST_FILENAME_BUFF2 "ast_1.json"
#define AST_FILENAME_BENCH "ast_bench.json"
#define MAX_BENCHMARKS 128
#define BENCH_FILENAME "benchmark.json"
#define BENCH_TIME_SECS 10
#define BENCH_CPU 0
#define BENCH_GPU 1
#define BENCH_REGEN_BH_STEPS 5
#define BENCH_FILE_ACCESS_STEPS 3
#define CUDA_OPT_NLEVELS 4
#define CUDA_OPT_LEVEL_0 0
#define CUDA_OPT_LEVEL_1 1
#define CUDA_OPT_LEVEL_2 2
#define CUDA_OPT_LEVEL_3 3
#define MAX_TIME_SIMULATION_SEC 360
#define REGEN_BLACK_HOLES_SEC 20
#define NUM_BECHMARKS 10
typedef struct blackHole {
float x, y, g;
} blackHole;
typedef struct spacePoint {
float x, y, g;
} spacePoint;
typedef struct asteroid {
float x, y, vx, vy;
} asteroid;
typedef struct benchmark {
char name[1024];
int number;
int config;
int type; // CPU = 0, GPU = 1
long time; // millis
int steps;
} benchmark;
///////////////
// Global Vars
///////////////
// Runtime
int runMode = RUN_MODE_SIM;
int spCurrentBuff = 0;
int astCurrentBuff = 0;
int nBlackHoles = 0;
int nAsteroids = MAX_ASTEROIDS;
blackHole* blackHoles = NULL;
int bhSize;
asteroid* asteroids = NULL;
int astSize;
spacePoint* SPBox = NULL;
int spSize;
float top = 2, left = -2, bottom = -2, right = 2;
float spStep = 0.1;
int nelems;
int rows, cols;
int cudaOptLevel = CUDA_OPT_LEVEL_3;
// BenchMark
int nBenchmark;
int bechmarkRegenBHSteps = MAX_TIME_SIMULATION_SEC;
int bechmarkRegenWriteFileSteps = REGEN_BLACK_HOLES_SEC;
benchmark BENCHS[MAX_BENCHMARKS];
char benchName[1024];
int benchNum;
int benchConfig;
int benchType;
//////////////////
// Error Handling
//////////////////
void checkCudaError(cudaError_t err) {
if (err != cudaSuccess) {
printf("Error: %s", cudaGetErrorString(err));
exit(-1);
}
}
/////////////////
// Miscellaneous
/////////////////
long currentTimeMillis() {
struct timeval time;
gettimeofday(&time, NULL);
long s1 = (int64_t)(time.tv_sec) * 1000;
long s2 = (time.tv_usec / 1000);
return s1 + s2;
}
void initRandom() {
srand(time(NULL));
}
float getRandom() {
return (double) rand() / (double)RAND_MAX ;
}
float getRandonLimits(float min, float max) {
float scale = rand() / (float) RAND_MAX; /* [0, 1.0] */
return min + scale * ( max - min ); /* [min, max] */
}
int nsleep(long miliseconds) {
struct timespec req, rem;
if (miliseconds > 999) {
req.tv_sec = (int)(miliseconds / 1000);
req.tv_nsec = (miliseconds - ((long)req.tv_sec * 1000)) * 1000000;
} else {
req.tv_sec = 0;
req.tv_nsec = miliseconds * 1000000;
}
return nanosleep(&req , &rem);
}
/////////////
// File Dump
/////////////
void spaceTimeToFile(spacePoint* SPBox, int nRows, int nCols) {
char filename[16];
if (runMode != RUN_MODE_SIM) {
strcpy(filename, SP_FILENAME_BENCH);
} else {
if (spCurrentBuff == 0) {
strcpy(filename, SP_FILENAME_BUFF1);
spCurrentBuff = 1;
} else {
strcpy(filename, SP_FILENAME_BUFF2);
spCurrentBuff = 0;
}
}
FILE *fp;
fp = fopen(filename, "w+");
fputs("{\"space\":[", fp);
for (int i = 0; i < nRows; i++) {
for (int j = 0; j < nCols; j++) {
int idx = i*nRows+j;
fprintf(fp, "[%0.2f,%0.2f,%0.2f]", SPBox[idx].x, SPBox[idx].y, SPBox[idx].g);
if (i+1 < nRows || j+1 < nCols) {
fprintf(fp, ",");
}
}
}
fputs("]", fp);
fputs(",\"blackHoles\":[", fp);
for (int i = 0; i < nBlackHoles; i++) {
fprintf(fp, "[%0.2f,%0.2f,%0.2f]", blackHoles[i].x, blackHoles[i].y, blackHoles[i].g);
if (i+1 < nBlackHoles) {
fprintf(fp, ",");
}
}
fputs("]}", fp);
fclose(fp);
if (runMode == RUN_MODE_SIM) {
if (access(SP_FILENAME, F_OK) != -1) {
remove(SP_FILENAME);
}
symlink(filename, SP_FILENAME);
}
}
void asteroidsToFile(asteroid* AST, int nAsteroids) {
char filename[16];
if (runMode != RUN_MODE_SIM) {
strcpy(filename, AST_FILENAME_BENCH);
} else {
if (astCurrentBuff == 0) {
strcpy(filename, AST_FILENAME_BUFF1);
astCurrentBuff = 1;
} else {
strcpy(filename, AST_FILENAME_BUFF2);
astCurrentBuff = 0;
}
}
FILE *fp;
fp = fopen(filename, "w+");
fputs("{\"asteroids\":[", fp);
for (int i = 0; i < nAsteroids; i++) {
fprintf(fp, "[%0.2f,%0.2f]", AST[i].x, AST[i].y);
if (i+1 < nAsteroids) {
fprintf(fp, ",");
}
}
fputs("]}", fp);
fclose(fp);
if (runMode == RUN_MODE_SIM) {
if (access(AST_FILENAME, F_OK) != -1) {
remove(AST_FILENAME);
}
symlink(filename, AST_FILENAME);
}
}
void benchMarksToFile(benchmark* BENCHS, int nBenchMarks) {
printf("Writing benchmark to File: %s\n", BENCH_FILENAME);
fflush(stdout);
FILE *fp;
fp = fopen(BENCH_FILENAME, "w+");
fputs("{\"benchmarks\":[", fp);
for (int i = 0; i < nBenchMarks; i++) {
fputs("{", fp);
fprintf(fp, "\"name\": \"%s\",", BENCHS[i].name);
fprintf(fp, "\"number\": %d,", BENCHS[i].number);
fprintf(fp, "\"type\": %d,", BENCHS[i].type);
fprintf(fp, "\"config\": %d,", BENCHS[i].config);
fprintf(fp, "\"time\": %ld,", BENCHS[i].time);
fprintf(fp, "\"steps\": %d", BENCHS[i].steps);
fputs("}", fp);
if (i+1 < nBenchMarks) {
fprintf(fp, ",");
}
}
fputs("]}", fp);
fclose(fp);
}
///////////////
// CPU Runtime
///////////////
void freeBlackHoles() {
if (blackHoles != NULL) {
cudaFree(blackHoles);
blackHoles = NULL;
}
}
void initBlackHoles() {
freeBlackHoles();
bhSize = nBlackHoles*sizeof(blackHole);
checkCudaError(cudaMallocManaged(&blackHoles, bhSize));
for (int i = 0 ; i < nBlackHoles ; i++) {
blackHoles[i].x = getRandonLimits(left, right);
blackHoles[i].y = getRandonLimits(bottom, top);
blackHoles[i].g = 500.0f;
}
}
void generateBlackHoles() {
nBlackHoles = (int) getRandonLimits(1, MAX_BLACK_HOLES) % MAX_BLACK_HOLES;
initBlackHoles();
}
void initSpaceTime() {
rows = abs(right-left) / spStep;
cols = abs(top-bottom) / spStep;
nelems = cols*rows;
spSize = sizeof(spacePoint) * nelems;
checkCudaError(cudaMallocManaged(&SPBox, spSize));
memset(SPBox, 0, spSize);
}
void freeSpaceTime() {
if (SPBox != NULL) {
cudaFree(SPBox);
SPBox = NULL;
}
}
void calculateSpaceTime(spacePoint* SPBox, int nRows, int nCols) {
for (int i = 0; i < nRows; i++) {
for (int j = 0; j < nCols; j++) {
float x = (i / (float) nRows * (right-left)) + left;
float y = (j / (float) nCols * (top-bottom)) + bottom;
int idx = i*nRows+j;
float g = 0.0f;
for (int b = 0; b < nBlackHoles; b++){
float dx = x - blackHoles[b].x;
float dy = y - blackHoles[b].y;
float distSqr = sqrt(dx*dx + dy*dy);
if (distSqr == 0.0f) {
distSqr = 0.000000001;
}
float invDist = 1 / (pow(distSqr, 0.05));
g += (blackHoles[b].g * invDist);
}
SPBox[idx].x = x;
SPBox[idx].y = y;
SPBox[idx].g = g;
}
}
}
void freeAsteroids() {
if (asteroids != NULL) {
cudaFree(asteroids);
asteroids = NULL;
}
}
void initAsteroids() {
freeAsteroids();
astSize = nAsteroids*sizeof(asteroid);
checkCudaError(cudaMallocManaged(&asteroids, astSize));
memset(asteroids, 0, astSize);
for (int i = 0; i < nAsteroids; i++) {
asteroids[i].x = getRandonLimits(left, right);
asteroids[i].y = getRandonLimits(bottom, top);
}
}
void calculateAsteroids(asteroid* AST, int nAsteroids, blackHole* BH, int nBlackHoles, float dt) {
for (int i = 0; i < nAsteroids; i++) {
float Fx = 0.0f; float Fy = 0.0f;
for (int j = 0; j < nBlackHoles; j++) {
float dx = AST[i].x - BH[j].x;
float dy = AST[i].y - BH[j].y;
if (dx == 0) break;
if (dy == 0) break;
float distSqr = dx*dx + dy*dy;
//float invDist = rsqrtf(distSqr);
float invDist = 1 / sqrt(distSqr*distSqr);
Fx += dx * invDist; Fy += dy * invDist;
}
float modF = sqrt(Fx*Fx + Fy*Fy);
Fx = Fx / modF;
Fy = Fy / modF;
AST[i].vx += dt*-Fx;
AST[i].vy += dt*-Fy;
AST[i].x += AST[i].vx*0.01;
AST[i].y += AST[i].vy*0.01;
}
}
void runSimulationCPU() {
printf("Running Simulation CPU ...\n");
printf("SpaceTime Params: rows: %d, cols: %d, size: %d\n", rows, cols, nelems);
long initSimulation = currentTimeMillis();
int elapsedSimulation = 0;
long lastBlackHoleRegen = 0;
initSpaceTime();
initAsteroids();
while (elapsedSimulation < MAX_TIME_SIMULATION_SEC) {
long current = currentTimeMillis();
if (blackHoles == NULL || (current - lastBlackHoleRegen) / 1000 > REGEN_BLACK_HOLES_SEC) {
generateBlackHoles();
calculateSpaceTime(SPBox, rows, cols);
spaceTimeToFile(SPBox, rows, cols);
lastBlackHoleRegen = current;
}
calculateAsteroids(asteroids, nAsteroids, blackHoles, nBlackHoles, DT);
asteroidsToFile(asteroids, nAsteroids);
nsleep(250);
elapsedSimulation = (currentTimeMillis() - initSimulation) / 1000;
}
freeAsteroids();
}
////////////////////
// GPU CUDA Runtime
////////////////////
__global__ void calculateSpaceTimeK(spacePoint* SPBox, int nRows, int nCols, float left, float right, float top, float bottom, blackHole* BH, int nBlackHoles) {
int i = threadIdx.x + blockIdx.x * blockDim.x;
int j = threadIdx.y + blockIdx.y * blockDim.y;
if (i < nRows && j < nCols) {
float x = (i / (float) nRows * (right-left)) + left;
float y = (j / (float) nCols * (top-bottom)) + bottom;
int idx = i*nRows+j;
float g = 0.0f;
for (int b = 0; b < nBlackHoles; b++){
float dx = x - BH[b].x;
float dy = y - BH[b].y;
float distSqr = sqrt(dx*dx + dy*dy);
if (distSqr == 0.0f) {
distSqr = 0.000000001;
}
float invDist = 1 / (pow((float)distSqr, (float)0.05));
g += (BH[b].g * invDist);
}
SPBox[idx].x = x;
SPBox[idx].y = y;
SPBox[idx].g = g;
}
}
__global__ void calculateSpaceTimeKSHM(spacePoint* SPBox, int nRows, int nCols, float left, float right, float top, float bottom, blackHole* BH, int nBlackHoles, int bhSize) {
extern __shared__ float s[];
blackHole* bhCache = (blackHole*) s;
int i = threadIdx.x + blockIdx.x * blockDim.x;
int j = threadIdx.y + blockIdx.y * blockDim.y;
if (i == 0 && j == 0)
memcpy(bhCache, BH, bhSize);
__syncthreads();
if (i < nRows && j < nCols) {
float x = (i / (float) nRows * (right-left)) + left;
float y = (j / (float) nCols * (top-bottom)) + bottom;
int idx = i*nRows+j;
float g = 0.0f;
for (int b = 0; b < nBlackHoles; b++){
float dx = x - bhCache[b].x;
float dy = y - bhCache[b].y;
float distSqr = sqrt(dx*dx + dy*dy);
if (distSqr == 0.0f) {
distSqr = 0.000000001;
}
float invDist = 1 / (pow((float)distSqr, (float)0.05));
g += (bhCache[b].g * invDist);
}
SPBox[idx].x = x;
SPBox[idx].y = y;
SPBox[idx].g = g;
}
}
__global__ void calculateAsteroidsK(asteroid* AST, int nAsteroids, blackHole* BH, int nBlackHoles, float dt) {
int idx = (blockIdx.x * blockDim.x) + threadIdx.x;
int stride = gridDim.x * blockDim.x;
for (int i = idx; i < nAsteroids; i += stride) {
float Fx = 0.0f; float Fy = 0.0f;
for (int j = 0; j < nBlackHoles; j++) {
float dx = AST[i].x - BH[j].x;
float dy = AST[i].y - BH[j].y;
if (dx == 0) break;
if (dy == 0) break;
float distSqr = dx*dx + dy*dy; //+ SOFTENING;
//float invDist = rsqrtf(distSqr);
float invDist = 1 / sqrt(distSqr*distSqr);
//float invDist3 = invDist * invDist * invDist;
Fx += dx * invDist; Fy += dy * invDist;
}
float modF = sqrt(Fx*Fx + Fy*Fy);
Fx = Fx / modF;
Fy = Fy / modF;
AST[i].vx += dt*-Fx;
AST[i].vy += dt*-Fy;
AST[i].x += AST[i].vx*0.01;
AST[i].y += AST[i].vy*0.01;
}
}
__global__ void calculateAsteroidsKSHM(asteroid* AST, int nAsteroids, blackHole* BH, int nBlackHoles, float dt, int bhSize) {
extern __shared__ float s[];
blackHole* bhCache = (blackHole*) s;
int idx = (blockIdx.x * blockDim.x) + threadIdx.x;
int stride = gridDim.x * blockDim.x;
if (idx == 0)
memcpy(bhCache, BH, bhSize);
__syncthreads();
for (int i = idx; i < nAsteroids; i += stride) {
float Fx = 0.0f; float Fy = 0.0f;
for (int j = 0; j < nBlackHoles; j++) {
float dx = AST[i].x - bhCache[j].x;
float dy = AST[i].y - bhCache[j].y;
if (dx == 0) break;
if (dy == 0) break;
float distSqr = dx*dx + dy*dy;
float invDist = 1 / sqrt(distSqr*distSqr);
Fx += dx * invDist; Fy += dy * invDist;
}
float modF = sqrt(Fx*Fx + Fy*Fy);
Fx = Fx / modF;
Fy = Fy / modF;
AST[i].vx += dt*-Fx;
AST[i].vy += dt*-Fy;
AST[i].x += AST[i].vx*0.01;
AST[i].y += AST[i].vy*0.01;
}
}
void runSimulationGPU() {
printf("Running Simulation GPU ...\n");
long initSimulation = currentTimeMillis();
int elapsedSimulation = 0;
long lastBlackHoleRegen = 0;
int multiProcessorCount = 32;
initSpaceTime();
initAsteroids();
printf("SpaceTime Params: rows: %d, cols: %d, size: %d\n", rows, cols, nelems);
dim3 threads_per_block_K1(32, 32, 1);
dim3 total_blocks_K1((rows / threads_per_block_K1.x) + 1, (cols / threads_per_block_K1.y) + 1, 1);
int threads_per_block_K2 = 1024;
int number_of_blocks_K2 = ceil(((nAsteroids / (float) threads_per_block_K2) + 1) / multiProcessorCount) * multiProcessorCount;
while (elapsedSimulation < MAX_TIME_SIMULATION_SEC) {
long current = currentTimeMillis();
if (blackHoles == NULL || (current - lastBlackHoleRegen) / 1000 > REGEN_BLACK_HOLES_SEC) {
generateBlackHoles();
calculateSpaceTimeK<<<total_blocks_K1, threads_per_block_K1>>>(SPBox, rows, cols, left, right, top, bottom, blackHoles, nBlackHoles);
checkCudaError(cudaGetLastError());
calculateAsteroidsK<<<number_of_blocks_K2, threads_per_block_K2>>>(asteroids, nAsteroids, blackHoles, nBlackHoles, DT);
checkCudaError(cudaGetLastError());
checkCudaError(cudaDeviceSynchronize());
spaceTimeToFile(SPBox, rows, cols);
lastBlackHoleRegen = current;
}
calculateAsteroidsK<<<number_of_blocks_K2, threads_per_block_K2>>>(asteroids, nAsteroids, blackHoles, nBlackHoles, DT);
checkCudaError(cudaGetLastError());
checkCudaError(cudaDeviceSynchronize());
asteroidsToFile(asteroids, nAsteroids);
nsleep(250);
elapsedSimulation = (currentTimeMillis() - initSimulation) / 1000;
}
freeAsteroids();
}
//////////////
// Benchmarks
//////////////
void recordBenchmark(int i, char* name, int num, int type, int config, long time, int steps) {
if (i < MAX_BENCHMARKS) {
strcpy(BENCHS[i].name, name);
BENCHS[i].number = num;
BENCHS[i].type = type;
BENCHS[i].config = config;
BENCHS[i].time = time;
BENCHS[i].steps = steps;
}
}
void accessSPBenchCPU(spacePoint* SPBox, int nRows, int nCols) {
float x, y, g;
for (int i = 0; i < nRows; i++) {
for (int j = 0; j < nCols; j++) {
int idx = i*nRows+j;
x = SPBox[idx].x; y = SPBox[idx].y; g = SPBox[idx].g;
x++; y++; g++;
}
}
}
void accessBHBenchCPU(){
float x, y, g;
for (int i = 0; i < nBlackHoles; i++) {
x = blackHoles[i].x; y = blackHoles[i].y; g = blackHoles[i].g;
x++; y++; g++;
}
}
void accessAstBenchCPU(asteroid* AST, int nAsteroids){
float x, y;
for (int i = 0; i < nAsteroids; i++) {
x = AST[i].x; y = AST[i].y;
x++; y++;
}
}
void runBenchMarkCPU() {
printf("\nBenchmark CPU %d_%d - Config %d\n", benchNum, benchConfig, benchConfig);
printf(" top: %0.2f, bottom: %0.2f, left: %0.2f, right: %0.2f\n", top, bottom, left, right);
printf(" spStep : %f, nBlackHoles :%d, nAsteroids: %d\n", spStep, nBlackHoles, nAsteroids);
printf(" Matrix Space Resolution [%d, %d]\n", (int) (abs(right-left) / spStep), (int) (abs(top-bottom) / spStep));
fflush(stdout);
initSpaceTime();
initAsteroids();
initBlackHoles();
long startTime = currentTimeMillis();
int step = 0;
while (((currentTimeMillis() - startTime) / 1000) < BENCH_TIME_SECS) {
if (step % bechmarkRegenBHSteps == 0) {
initBlackHoles();
}
if (step % bechmarkRegenWriteFileSteps == 0) {
accessSPBenchCPU(SPBox, rows, cols);
accessAstBenchCPU(asteroids, nAsteroids);
}
calculateSpaceTime(SPBox, rows, cols);
calculateAsteroids(asteroids, nAsteroids, blackHoles, nBlackHoles, DT);
step++;
}
accessSPBenchCPU(SPBox, rows, cols);
accessAstBenchCPU(asteroids, nAsteroids);
long elapsedTime = currentTimeMillis() - startTime;
freeSpaceTime();
freeAsteroids();
freeBlackHoles();
sprintf(benchName, "Benchmark %d_%d", benchNum, benchConfig);
recordBenchmark(nBenchmark, benchName, benchNum, benchType, benchConfig, elapsedTime, step);
printf("\n Benchmark %d - Time: %ld millis, steps : %d\n", nBenchmark, elapsedTime, step);
printf(" Steps Per Second : %ld\n\n", step / (elapsedTime / 1000));
}
void runBenchMarkGPU() {
printf("\n Benchmark GPU %d_%d - Config %d\n", benchNum, benchConfig, benchConfig);
printf(" top: %0.2f, bottom: %0.2f, left: %0.2f, right: %0.2f\n", top, bottom, left, right);
printf(" spStep: %f, nBlackHoles: %d, nAsteroids: %d\n", spStep, nBlackHoles, nAsteroids);
printf(" Matrix Space Resolution [%d, %d]\n", (int) (abs(right-left) / spStep), (int) (abs(top-bottom) / spStep));
fflush(stdout);
int deviceId;
int multiProcessorCount = 32;
int computePreemptionSupported;
cudaDeviceProp deviceProp;
cudaGetDevice(&deviceId);
cudaGetDeviceProperties(&deviceProp, deviceId);
multiProcessorCount = deviceProp.multiProcessorCount;
computePreemptionSupported = deviceProp.computePreemptionSupported;
printf(" Device supports Compute Preemption: %s\n", computePreemptionSupported ? "Yes" : "No");
initSpaceTime();
initAsteroids();
initBlackHoles();
dim3 threads_per_block_K1(32, 32, 1);
dim3 total_blocks_K1((rows / threads_per_block_K1.x) + 1, (cols / threads_per_block_K1.y) + 1, 1);
//printf(" K1 -> Threads Per Block: [%d,%d], Total BLocks: [%d, %d]\n", threads_per_block_K1.x, threads_per_block_K1.y, total_blocks_K1.x, total_blocks_K1.y);
//int threads_per_block_K2 = 64;
int threads_per_block_K2 = 1024;
int number_of_blocks_K2 = ceil(((nAsteroids / (float) threads_per_block_K2) + 1) / multiProcessorCount) * multiProcessorCount;
//printf(" K2 -> Threads Per Block: [%d], Total BLocks: [%d]\n", threads_per_block_K2, number_of_blocks_K2);
long startTime = currentTimeMillis();
int step = 0;
while (((currentTimeMillis() - startTime) / 1000) < BENCH_TIME_SECS) {
if (step % bechmarkRegenBHSteps == 0) {
checkCudaError(cudaDeviceSynchronize());
if (cudaOptLevel >= CUDA_OPT_LEVEL_2 && computePreemptionSupported)
cudaMemPrefetchAsync(blackHoles, bhSize, cudaCpuDeviceId);
initBlackHoles();
if (cudaOptLevel >= CUDA_OPT_LEVEL_2 && computePreemptionSupported)
cudaMemPrefetchAsync(blackHoles, bhSize, deviceId);
}
if (step % bechmarkRegenWriteFileSteps == 0) {
if (cudaOptLevel >= CUDA_OPT_LEVEL_2 && computePreemptionSupported) {
cudaMemPrefetchAsync(SPBox, spSize, cudaCpuDeviceId);
cudaMemPrefetchAsync(asteroids, astSize, cudaCpuDeviceId);
}
//nsleep(150L); // CPU Ocupancy
checkCudaError(cudaDeviceSynchronize());
accessSPBenchCPU(SPBox, rows, cols);
accessAstBenchCPU(asteroids, nAsteroids);
if (cudaOptLevel >= CUDA_OPT_LEVEL_2 && computePreemptionSupported) {
cudaMemPrefetchAsync(SPBox, spSize, deviceId);
cudaMemPrefetchAsync(asteroids, astSize, deviceId);
}
}
if (cudaOptLevel >= CUDA_OPT_LEVEL_1) {
cudaStream_t stream1, stream2;
checkCudaError(cudaStreamCreate(&stream1));
checkCudaError(cudaStreamCreate(&stream2));
if (cudaOptLevel >= CUDA_OPT_LEVEL_3) {
calculateSpaceTimeKSHM<<<total_blocks_K1, threads_per_block_K1, bhSize, stream1>>>(SPBox, rows, cols, left, right, top, bottom, blackHoles, nBlackHoles, bhSize);
calculateAsteroidsKSHM<<<number_of_blocks_K2, threads_per_block_K2, bhSize, stream2>>>(asteroids, nAsteroids, blackHoles, nBlackHoles, DT, bhSize);
} else {
calculateSpaceTimeK<<<total_blocks_K1, threads_per_block_K1, 0, stream1>>>(SPBox, rows, cols, left, right, top, bottom, blackHoles, nBlackHoles);
calculateAsteroidsK<<<number_of_blocks_K2, threads_per_block_K2, 0, stream2>>>(asteroids, nAsteroids, blackHoles, nBlackHoles, DT);
}
checkCudaError(cudaGetLastError());
cudaStreamDestroy(stream1);
cudaStreamDestroy(stream2);
} else {
calculateSpaceTimeK<<<total_blocks_K1, threads_per_block_K1>>>(SPBox, rows, cols, left, right, top, bottom, blackHoles, nBlackHoles);
checkCudaError(cudaGetLastError());
calculateAsteroidsK<<<number_of_blocks_K2, threads_per_block_K2>>>(asteroids, nAsteroids, blackHoles, nBlackHoles, DT);
checkCudaError(cudaGetLastError());
}
step++;
}
if (cudaOptLevel >= CUDA_OPT_LEVEL_2 && computePreemptionSupported) {
cudaMemPrefetchAsync(SPBox, spSize, cudaCpuDeviceId);
cudaMemPrefetchAsync(asteroids, astSize, cudaCpuDeviceId);
}
checkCudaError(cudaDeviceSynchronize());
accessSPBenchCPU(SPBox, rows, cols);
accessAstBenchCPU(asteroids, nAsteroids);
long elapsedTime = currentTimeMillis() - startTime + 1;
freeSpaceTime();
freeAsteroids();
freeBlackHoles();
sprintf(benchName, "Benchmark %d_%d", benchNum, benchConfig);
recordBenchmark(nBenchmark, benchName, benchNum, benchType, benchConfig, elapsedTime, step);
printf("\n Benchmark %d - Time: %ld millis, steps : %d\n", nBenchmark, elapsedTime, step);
printf(" Steps Per Second : %ld\n\n", step / (elapsedTime / 1000));
}
void runBenchmarks() {
benchType = BENCH_GPU;
for (int opLevel = 0; opLevel < CUDA_OPT_NLEVELS; opLevel++) {
// Start on BENCH_CPU for include CPU execution.
for (int cp = BENCH_GPU ; cp <= BENCH_GPU; cp++) {
benchType = cp;
benchConfig = cudaOptLevel = opLevel;
// Benchmark 0
benchNum = 0;
top = 2, left = -2, bottom = -2, right = 2; spStep = 0.01;
nBlackHoles = 100; nAsteroids = 100;
if(cp == BENCH_CPU) runBenchMarkCPU();
else runBenchMarkGPU();
nBenchmark++;
// Benchmark 1
benchNum = 1;
top = 2, left = -2, bottom = -2, right = 2; spStep = 0.01;
nBlackHoles = 500; nAsteroids = 500;
if(cp == BENCH_CPU) runBenchMarkCPU();
else runBenchMarkGPU();
nBenchmark++;
// Benchmark 2
benchNum = 2;
top = 2, left = -2, bottom = -2, right = 2; spStep = 0.01;
nBlackHoles = 1000; nAsteroids = 1000;
if(cp == BENCH_CPU) runBenchMarkCPU();
else runBenchMarkGPU();
nBenchmark++;
// Benchmark 3
benchNum = 3;
top = 2, left = -2, bottom = -2, right = 2; spStep = 0.01;
nBlackHoles = 1000; nAsteroids = 5000;
if(cp == BENCH_CPU) runBenchMarkCPU();
else runBenchMarkGPU();
nBenchmark++;
// Benchmark 4
benchNum = 4;
top = 2, left = -2, bottom = -2, right = 2; spStep = 0.001;
nBlackHoles = 100; nAsteroids = 100;
if(cp == BENCH_CPU) runBenchMarkCPU();
else runBenchMarkGPU();
nBenchmark++;
// Benchmark 5
benchNum = 5;
top = 2, left = -2, bottom = -2, right = 2; spStep = 0.001;
nBlackHoles = 500; nAsteroids = 500;
if(cp == BENCH_CPU) runBenchMarkCPU();
else runBenchMarkGPU();
nBenchmark++;
// Benchmark 6
benchNum = 6;
top = 2, left = -2, bottom = -2, right = 2; spStep = 0.001;
nBlackHoles = 1000; nAsteroids = 1000;
if(cp == BENCH_CPU) runBenchMarkCPU();
else runBenchMarkGPU();
nBenchmark++;
// Benchmark 7
/*
benchNum = 7;
top = 2, left = -2, bottom = -2, right = 2; spStep = 0.001;
nBlackHoles = 5000; nAsteroids = 5000;
if(cp == BENCH_CPU) runBenchMarkCPU();
else runBenchMarkGPU();
nBenchmark++;
*/
// Benchmark 8
benchNum = 8;
top = 2, left = -2, bottom = -2, right = 2; spStep = 0.0005;
nBlackHoles = 100; nAsteroids = 100;
if(cp == BENCH_CPU) runBenchMarkCPU();
else runBenchMarkGPU();
nBenchmark++;
/*
// Benchmark 9
benchNum = 9;
top = 2, left = -2, bottom = -2, right = 2; spStep = 0.0005;
nBlackHoles = 500; nAsteroids = 500;
if(cp == BENCH_CPU) runBenchMarkCPU();
else runBenchMarkGPU();
nBenchmark++;
*/
/*
// Benchmark 10
benchNum = 10;
top = 2, left = -2, bottom = -2, right = 2; spStep = 0.0005;
nBlackHoles = 1000; nAsteroids = 1000;
if(cp == BENCH_CPU) runBenchMarkCPU();
else runBenchMarkGPU();
nBenchmark++;
// Benchmark 11
benchNum = 11;
top = 2, left = -2, bottom = -2, right = 2; spStep = 0.0005;
nBlackHoles = 1000; nAsteroids = 1000;
if(cp == BENCH_CPU) runBenchMarkCPU();
else runBenchMarkGPU();
nBenchmark++;
*/
}
}
fflush(stdout);
}
void runAllBechmarks() {
printf("Running All Benchmarks ...\n");
long startTime = currentTimeMillis();
nBenchmark = 0;
runBenchmarks();
benchMarksToFile(BENCHS, nBenchmark);
printf("Benchmarks in : %ld seconds\n", (currentTimeMillis() - startTime) / 1000);
}
// Device Query code extracted from https://github.com/NVIDIA/cuda-samples
inline int _ConvertSMVer2Cores(int major, int minor) {
// Defines for GPU Architecture types (using the SM version to determine
// the # of cores per SM
typedef struct {
int SM; // 0xMm (hexidecimal notation), M = SM Major version,
// and m = SM minor version
int Cores;
} sSMtoCores;
sSMtoCores nGpuArchCoresPerSM[] = {
{0x30, 192},
{0x32, 192},
{0x35, 192},
{0x37, 192},
{0x50, 128},
{0x52, 128},
{0x53, 128},
{0x60, 64},
{0x61, 128},
{0x62, 128},
{0x70, 64},
{0x72, 64},
{0x75, 64},
{-1, -1}};
int index = 0;
while (nGpuArchCoresPerSM[index].SM != -1) {
if (nGpuArchCoresPerSM[index].SM == ((major << 4) + minor)) {
return nGpuArchCoresPerSM[index].Cores;
}
index++;
}
// If we don't find the values, we default use the previous one
// to run properly
printf(
"MapSMtoCores for SM %d.%d is undefined."
" Default to use %d Cores/SM\n",
major, minor, nGpuArchCoresPerSM[index - 1].Cores);
return nGpuArchCoresPerSM[index - 1].Cores;
}
void printfGPUDeviceInfo(){
int deviceId;
cudaDeviceProp deviceProp;
int driverVersion = 0, runtimeVersion = 0;
char msg[256];
cudaGetDevice(&deviceId);
cudaGetDeviceProperties(&deviceProp, deviceId);
printf("GPU Info\n");
printf(" Device: \"%s\"\n", deviceProp.name);
cudaDriverGetVersion(&driverVersion);
cudaRuntimeGetVersion(&runtimeVersion);
printf(" CUDA Driver Version / Runtime Version %d.%d / %d.%d\n",
driverVersion / 1000, (driverVersion % 100) / 10,
runtimeVersion / 1000, (runtimeVersion % 100) / 10);
printf(" CUDA Capability Major/Minor version number: %d.%d\n",
deviceProp.major, deviceProp.minor);
snprintf(msg, sizeof(msg),
" Total amount of global memory: %.0f MBytes "
"(%llu bytes)\n",
static_cast<float>(deviceProp.totalGlobalMem / 1048576.0f),
(unsigned long long)deviceProp.totalGlobalMem);
printf("%s", msg);
printf(" (%2d) Multiprocessors, (%3d) CUDA Cores/MP: %d CUDA Cores\n",
deviceProp.multiProcessorCount,
_ConvertSMVer2Cores(deviceProp.major, deviceProp.minor),
_ConvertSMVer2Cores(deviceProp.major, deviceProp.minor) *
deviceProp.multiProcessorCount);
printf(
" GPU Max Clock rate: %.0f MHz (%0.2f "
"GHz)\n",
deviceProp.clockRate * 1e-3f, deviceProp.clockRate * 1e-6f);
printf(
" Maximum Texture Dimension Size (x,y,z) 1D=(%d), 2D=(%d, "
"%d), 3D=(%d, %d, %d)\n",
deviceProp.maxTexture1D, deviceProp.maxTexture2D[0],
deviceProp.maxTexture2D[1], deviceProp.maxTexture3D[0],
deviceProp.maxTexture3D[1], deviceProp.maxTexture3D[2]);
printf(
" Maximum Layered 1D Texture Size, (num) layers 1D=(%d), %d layers\n",
deviceProp.maxTexture1DLayered[0], deviceProp.maxTexture1DLayered[1]);
printf(
" Maximum Layered 2D Texture Size, (num) layers 2D=(%d, %d), %d "
"layers\n",
deviceProp.maxTexture2DLayered[0], deviceProp.maxTexture2DLayered[1],
deviceProp.maxTexture2DLayered[2]);
printf(" Total amount of constant memory: %zu bytes\n",
deviceProp.totalConstMem);
printf(" Total amount of shared memory per block: %zu bytes\n",
deviceProp.sharedMemPerBlock);
printf(" Total number of registers available per block: %d\n",
deviceProp.regsPerBlock);
printf(" Warp size: %d\n",
deviceProp.warpSize);
printf(" Maximum number of threads per multiprocessor: %d\n",
deviceProp.maxThreadsPerMultiProcessor);
printf(" Maximum number of threads per block: %d\n",
deviceProp.maxThreadsPerBlock);
printf(" Max dimension size of a thread block (x,y,z): (%d, %d, %d)\n",
deviceProp.maxThreadsDim[0], deviceProp.maxThreadsDim[1],
deviceProp.maxThreadsDim[2]);
printf(" Max dimension size of a grid size (x,y,z): (%d, %d, %d)\n",
deviceProp.maxGridSize[0], deviceProp.maxGridSize[1],
deviceProp.maxGridSize[2]);
printf(" Maximum memory pitch: %zu bytes\n",
deviceProp.memPitch);
printf(" Texture alignment: %zu bytes\n",
deviceProp.textureAlignment);
printf(
" Concurrent copy and kernel execution: %s with %d copy "
"engine(s)\n",
(deviceProp.deviceOverlap ? "Yes" : "No"), deviceProp.asyncEngineCount);
printf(" Run time limit on kernels: %s\n",
deviceProp.kernelExecTimeoutEnabled ? "Yes" : "No");
printf(" Integrated GPU sharing Host Memory: %s\n",
deviceProp.integrated ? "Yes" : "No");
printf(" Support host page-locked memory mapping: %s\n",
deviceProp.canMapHostMemory ? "Yes" : "No");
printf(" Alignment requirement for Surfaces: %s\n",
deviceProp.surfaceAlignment ? "Yes" : "No");
printf(" Device has ECC support: %s\n",
deviceProp.ECCEnabled ? "Enabled" : "Disabled");
printf(" Device supports Unified Addressing (UVA): %s\n",
deviceProp.unifiedAddressing ? "Yes" : "No");
printf(" Device supports Compute Preemption: %s\n",
deviceProp.computePreemptionSupported ? "Yes" : "No");
printf(" Supports Cooperative Kernel Launch: %s\n",
deviceProp.cooperativeLaunch ? "Yes" : "No");
printf(" Supports MultiDevice Co-op Kernel Launch: %s\n",
deviceProp.cooperativeMultiDeviceLaunch ? "Yes" : "No");
printf(" Device PCI Domain ID / Bus ID / location ID: %d / %d / %d\n",
deviceProp.pciDomainID, deviceProp.pciBusID, deviceProp.pciDeviceID);
}
int main(const int argc, const char** argv) {
runMode = RUN_MODE_SIM; // RUN_MODE_SIM | RUN_MODE_BENCH
printf("Cuda Space Time Simulator - 2020\n\n");
printf(" Author: Oscar Noel Amaya Garcia. email: dbanshee@gmail.com\n");
printf(" nVidia DLI - Volunteer practice\n");
printf("\n\n");
printfGPUDeviceInfo();
printf("\n\n");
if (argc > 2) {
printf("Invalid args.\n");
printf("cudaspsim [benchmark]\n");
exit(-1);
} else if(argc == 2 && strcmp(argv[1], "benchmark") == 0) {
runMode = RUN_MODE_BENCH;
}
if (runMode == RUN_MODE_SIM) {
runSimulationGPU();
} else {
runAllBechmarks();
printf("\n");
}
}
|
1,044
|
#include <iostream>
#include <stdio.h>
using namespace std;
#define imin(a,b) (a<b?a:b)
const int N = 33*1024;
const int threadsPerBlock = 256;
const int blocksPerGrid = imin(32, (N+threadsPerBlock-1)/threadsPerBlock);
// dot on the kernel
__global__ void dot(float *a, float *b, float *c)
{
__shared__ float cache[threadsPerBlock];
int cacheIndex = threadIdx.x;
float temp = 0.0;
for (int tid = threadIdx.x + blockIdx.x*blockDim.x; tid<N; tid += blockDim.x*gridDim.x)
{
temp += a[tid]*b[tid];
}
cache[cacheIndex] = temp;
__syncthreads();
// reduction
for (int i = blockDim.x/2; i>0; i /= 2)
{
if (cacheIndex < i) cache[cacheIndex] += cache[cacheIndex + i];
__syncthreads();
}
if (threadIdx.x == 0) c[blockIdx.x] = cache[0];
}
// main fn
int main(void)
{
float *a, *b, c, *partial_c;
float *dev_a, *dev_b, *dev_partial_c;
a = (float*)malloc(N*sizeof(float));
b = (float*)malloc(N*sizeof(float));
partial_c = (float*)malloc(blocksPerGrid*sizeof(float));
cudaMalloc((void**)&dev_a, N*sizeof(float));
cudaMalloc((void**)&dev_b, N*sizeof(float));
cudaMalloc((void**)&dev_partial_c, blocksPerGrid*sizeof(float));
for (int i=0; i<N; i++)
{
a[i] = i;
b[i] = 2*i;
}
cudaMemcpy(dev_a, a, N*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(dev_b, b, N*sizeof(float), cudaMemcpyHostToDevice);
dot<<<blocksPerGrid,threadsPerBlock>>>(dev_a, dev_b, dev_partial_c);
cudaMemcpy(partial_c, dev_partial_c, blocksPerGrid*sizeof(float), cudaMemcpyDeviceToHost);
c = 0.0;
for (int i=0; i<blocksPerGrid; i++)
{
c += partial_c[i];
}
#define sum_squares(x) (x*(x+1)*(2*x+1)/6)
cout<< "GPU value = "<<c<<" analytical value = "<<2*sum_squares((float)(N-1))<<endl;
cudaFree(dev_a);
cudaFree(dev_b);
cudaFree(partial_c);
free(a);
free(b);
free(partial_c);
}
|
1,045
|
/*
* File: HelloCuda.cu
*
* Created on June 24, 2012
*
* Purpose: Demonstrate 2D Blocks and Threads with Hello World
*
* If it works, it was written by Brian Swenson.
* Otherwise, I have no idea who wrote it.
*/
#include <iostream>
#include <cuda_runtime.h>
#include <stdio.h>
using namespace std;
dim3 blocks(4, 4);
dim3 threads(8, 8);
__global__ void sayHelloCuda1D()
{
int threadIdxX = threadIdx.x;
int blockIdxX = blockIdx.x;
//dimension of the block in threads
int blockDimX = blockDim.x;
//dimension of the grid in blocks
int gridDimX = gridDim.x;
//calculate a unique thread id, useful for things like accessing arrays
int id = threadIdx.x + blockIdx.x * blockDim.x;
//printf("Hello thread %d! (threadIdx.x=%d, blockIdx.x=%d, blockDim.x=%d, gridDim.x=%d\n", id, threadIdxX, blockIdxX, blockDimX, gridDimX);
}
__global__ void sayHelloCuda2D()
{
int threadIdxX = threadIdx.x;
int threadIdxY = threadIdx.y;
int blockIdxX = blockIdx.x;
int blockIdxY = blockIdx.y;
//dimension of the block in threads
int blockDimX = blockDim.x;
int blockDimY = blockDim.y;
//dimension of the grid in blocks
int gridDimX = gridDim.x;
int gridDimY = gridDim.y;
//calculate a unique thread id, useful for things like accessing arrays
int x = threadIdx.x + blockIdx.x * blockDim.x;
int y = threadIdx.y + blockIdx.y * blockDim.y;
int id = x + y * blockDim.x * gridDim.x;
//printf("Hello thread %d! (threadIdx.x=%d threadIdx.y=%d, blockIdx.x=%d, blockIdx.y=%d, blockDim.x=%d, blockDim.y=%d, gridDim.x=%d, gridDim.y=%d)\n", id, threadIdxX, threadIdxY, blockIdxX, blockIdxY, blockDimX, blockDimY, gridDimX, gridDimY);
}
int main(int argc, char** argv)
{
printf("1 block with 10 threads\n");
sayHelloCuda1D<<<1, 10>>>();
cudaDeviceSynchronize();
printf("\n");
printf("10 blocks each with 1 thread\n");
sayHelloCuda1D<<<10, 1>>>();
cudaDeviceSynchronize();
printf("\n");
printf("3 blocks each with 3 threads\n");
sayHelloCuda1D<<<3, 3>>>();
cudaDeviceSynchronize();
printf("\n");
dim3 blocks(3, 3);
dim3 threads(2, 2);
printf("3x3 blocks each with 2x2 threads\n");
sayHelloCuda2D<<<blocks, threads>>>();
cudaDeviceSynchronize();
return 0;
}
|
1,046
|
/* Copyright 2015 Stanford University, NVIDIA Corporation
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include <thrust/device_ptr.h>
#include <thrust/device_vector.h>
#include <thrust/random.h>
#include <thrust/transform.h>
#include <thrust/iterator/counting_iterator.h>
// Create a random number generator functor
struct RandomGenerator {
double start, stop;
__host__ __device__
RandomGenerator(double a, double b) : start(a), stop(b) { };
__host__ __device__
double operator()(const unsigned int n) const
{
thrust::default_random_engine rng;
thrust::uniform_real_distribution<double> dist(start, stop);
rng.discard(n);
return dist(rng);
}
};
__host__
void initialize_gpu_array(double *ptr, size_t size)
{
// Make our device pointers
thrust::device_ptr<double> device_ptr(ptr);
// Fill the vector with random numbers between 0.0 and 1.0
thrust::counting_iterator<unsigned int> index_sequence(0);
thrust::transform(index_sequence, index_sequence + size,
device_ptr, RandomGenerator(0.0, 1.0));
}
// Make the saxpy functor
struct SaxpyFunctor : public thrust::binary_function<double,double,double> {
const double alpha;
__host__ __device__
SaxpyFunctor(double a) : alpha(a) { }
__host__ __device__
double operator()(const double &x, const double &y) const
{
return alpha * x + y;
}
};
__host__
void gpu_saxpy(double alpha, double *x_ptr, double *y_ptr, double *z_ptr, size_t size)
{
// Make our device pointers
thrust::device_ptr<double> x_vec(x_ptr);
thrust::device_ptr<double> y_vec(y_ptr);
thrust::device_ptr<double> z_vec(z_ptr);
thrust::transform(x_vec, x_vec + size, y_vec, z_vec, SaxpyFunctor(alpha));
}
|
1,047
|
#include <time.h>
#include <stdlib.h>
#include <stdint.h>
#include <stdio.h>
#include <iostream>
// Required to include CUDA vector types
#include <cuda_runtime.h>
#include <vector_types.h>
#define REPETITIONS 1000
#define NS_PER_SECOND 1000000000L
#define VECTOR_SIZE 1000*1024
// CUDA kernel. Each thread takes care of one element of c
// From https://www.olcf.ornl.gov/tutorials/cuda-vector-addition
__global__
void kernel_vector_sum(float *dst, float *v, int n)
{
// Get our global thread ID
int id = blockIdx.x*blockDim.x+threadIdx.x;
// Make sure we do not go out of bounds
if (id < n)
dst[id] += v[id];
}
void cpu_vector_sum(float *dst, float *v, int n)
{
for (int i=0; i<n; i++)
dst[n] += v[n];
}
void vectorSumTest()
{
struct timespec t0, t1;
uint64_t dt;
float *dst_cpu = new float[VECTOR_SIZE];
float *v_cpu = new float[VECTOR_SIZE];
std::cout << "CPU Vector Sum test. ";
dt = 0;
for (int r=0; r<REPETITIONS; r++)
{
clock_gettime(CLOCK_MONOTONIC, &t0);
cpu_vector_sum(dst_cpu, v_cpu, VECTOR_SIZE);
clock_gettime(CLOCK_MONOTONIC, &t1);
dt += NS_PER_SECOND * (t1.tv_sec - t0.tv_sec) + t1.tv_nsec - t0.tv_nsec;
}
std::cout << "Time: " << ((double)dt/(double)(REPETITIONS*NS_PER_SECOND)) << " s (" << (dt/REPETITIONS) << " ns)" << std::endl;
float *dst_gpu;
float *v_gpu;
int numCudaThreads = 1;
cudaMalloc(&dst_gpu, VECTOR_SIZE*sizeof(float));
cudaMalloc(&v_gpu, VECTOR_SIZE*sizeof(float));
dt = 0;
int blockSize = 1024;
int gridSize = (int)ceil((float)numCudaThreads/blockSize);
std::cout << "GPU Vector Sum test. ";
for (int r=0; r<REPETITIONS; r++)
{
clock_gettime(CLOCK_MONOTONIC, &t0);
kernel_vector_sum <<< gridSize, blockSize >>> (dst_gpu, v_gpu, VECTOR_SIZE);
clock_gettime(CLOCK_MONOTONIC, &t1);
dt += NS_PER_SECOND * (t1.tv_sec - t0.tv_sec) + t1.tv_nsec - t0.tv_nsec;
}
std::cout << "Time for "<< numCudaThreads << " threads: " << ( (double)dt/(double)(REPETITIONS*NS_PER_SECOND)) << " s (" << (dt/REPETITIONS) << " ns)" << std::endl;
numCudaThreads = 64;
dt = 0;
blockSize = 1024;
gridSize = (int)ceil((float)numCudaThreads/blockSize);
std::cout << "GPU Vector Sum test. ";
for (int r=0; r<REPETITIONS; r++)
{
clock_gettime(CLOCK_MONOTONIC, &t0);
kernel_vector_sum <<< gridSize, blockSize >>> (dst_gpu, v_gpu, VECTOR_SIZE);
clock_gettime(CLOCK_MONOTONIC, &t1);
dt += NS_PER_SECOND * (t1.tv_sec - t0.tv_sec) + t1.tv_nsec - t0.tv_nsec;
}
std::cout << "Time for "<< numCudaThreads << " threads: " << ( (double)dt/(double)(REPETITIONS*NS_PER_SECOND)) << " s (" << (dt/REPETITIONS) << " ns)" << std::endl;
numCudaThreads = 128;
dt = 0;
blockSize = 1024;
gridSize = (int)ceil((float)numCudaThreads/blockSize);
std::cout << "GPU Vector Sum test. ";
for (int r=0; r<REPETITIONS; r++)
{
clock_gettime(CLOCK_MONOTONIC, &t0);
kernel_vector_sum <<< gridSize, blockSize >>> (dst_gpu, v_gpu, VECTOR_SIZE);
clock_gettime(CLOCK_MONOTONIC, &t1);
dt += NS_PER_SECOND * (t1.tv_sec - t0.tv_sec) + t1.tv_nsec - t0.tv_nsec;
}
std::cout << "Time for "<< numCudaThreads << " threads: " << ( (double)dt/(double)(REPETITIONS*NS_PER_SECOND)) << " s (" << (dt/REPETITIONS) << " ns)" << std::endl;
numCudaThreads = 128;
dt = 0;
blockSize = 1024;
gridSize = (int)ceil((float)numCudaThreads/blockSize);
std::cout << "GPU Vector Sum test. ";
const int vsize = VECTOR_SIZE*sizeof(float);
for (int r=0; r<REPETITIONS; r++)
{
clock_gettime(CLOCK_MONOTONIC, &t0);
cudaMemcpy( dst_gpu, dst_cpu, vsize, cudaMemcpyHostToDevice );
cudaMemcpy( v_gpu, v_cpu, vsize, cudaMemcpyHostToDevice );
kernel_vector_sum <<< gridSize, blockSize >>> (dst_gpu, v_gpu, VECTOR_SIZE);
cudaMemcpy( dst_cpu, dst_gpu, vsize, cudaMemcpyDeviceToHost );
clock_gettime(CLOCK_MONOTONIC, &t1);
dt += NS_PER_SECOND * (t1.tv_sec - t0.tv_sec) + t1.tv_nsec - t0.tv_nsec;
}
std::cout << "Time for "<< numCudaThreads << " threads (with memcpy): " << ( (double)dt/(double)(REPETITIONS*NS_PER_SECOND)) << " s (" << (dt/REPETITIONS) << " ns)" << std::endl;
cudaFree(&dst_gpu);
cudaFree(&v_gpu);
delete[] v_cpu;
delete[] dst_cpu;
}
#define LOOP_COUNT 10000000
__global__
void gpu_loopTest()
{
long tmp=0;
for (int i=0; i<LOOP_COUNT; i++) {
tmp++;
}
}
void cpu_loopTest()
{
long tmp=0;
for (int i=0; i<LOOP_COUNT; i++) {
tmp++;
}
}
// Loop
void loopTest()
{
struct timespec t0, t1;
uint64_t dt;
std::cout << "CPU Loop test. ";
dt = 0;
for (int r=0; r<REPETITIONS; r++)
{
clock_gettime(CLOCK_MONOTONIC, &t0);
cpu_loopTest();
clock_gettime(CLOCK_MONOTONIC, &t1);
dt += NS_PER_SECOND * (t1.tv_sec - t0.tv_sec) + t1.tv_nsec - t0.tv_nsec;
}
std::cout << "Time: " << ((double)dt/(double)(REPETITIONS*NS_PER_SECOND)) << " s (" << (dt/REPETITIONS) << " ns)" << std::endl;
dt = 0;
dim3 dimBlock( 1, 1 );
dim3 dimGrid( 1, 1 );
std::cout << "GPU Loop test. ";
for (int r=0; r<REPETITIONS; r++)
{
clock_gettime(CLOCK_MONOTONIC, &t0);
gpu_loopTest <<< dimGrid, dimBlock >>> ();
clock_gettime(CLOCK_MONOTONIC, &t1);
dt += NS_PER_SECOND * (t1.tv_sec - t0.tv_sec) + t1.tv_nsec - t0.tv_nsec;
}
std::cout << "Time: " << ( (double)dt/(double)(REPETITIONS*NS_PER_SECOND)) << " s (" << (dt/REPETITIONS) << " ns)" << std::endl;
}
int main()
{
std::cout.imbue( std::locale("") );
std::cout << "CUDA test" << std::endl;
std::cout << "Averages for " << REPETITIONS << " repetitions." << std::endl;
loopTest();
vectorSumTest();
return 0;
}
|
1,048
|
#include "includes.h"
__global__ void convdx_gpu_kernel(float *dx, float *dy, float *weights, const int S,const int outSize, const int inSize){
int row = blockIdx.y*blockDim.y+threadIdx.y;
int col = blockIdx.x*blockDim.x+threadIdx.x;
if(row < inSize && col < outSize){
// printf("row %d, col %d, bias[col] %.2f\n", row, col,bias[col]);
for(int i = 0; i < S; ++i){
dx[row*outSize+col] +=dy[row* S + i ]*weights[col*S+i];
// printf("dy[%d] is %.1f,weight[%d] is %.1f\n", row*S+i,dy[row*S+i],col*S+i,weights[col*S+i]);
}
// printf("conv dx %d is %3f\n",row*outSize+col, dx[row*outSize+col] );
}
}
|
1,049
|
#include "includes.h"
__global__ void vectorAddGPU(float *a, float *b, float *c, int N)
{
int idx = blockIdx.x*blockDim.x + threadIdx.x;
if (idx < N)
{
c[idx] = a[idx] + b[idx];
}
}
|
1,050
|
#include <cuComplex.h>
#include <cuda.h>
#include <cuda_runtime.h>
__host__ __device__ double carg(const cuFloatComplex &z) {
return atan2(cuCimagf(z), cuCrealf(z));
}
__host__ __device__ cuFloatComplex conj(const cuFloatComplex &z) {
return make_cuFloatComplex(z.x, -z.y);
}
__global__ void calc_beta_err_kernel(cuFloatComplex *in, float *polarity,
int current_symbol_index,
cuFloatComplex *last_symbol, float bw,
float freq, float *beta, float *err,
int n) {
int i = blockIdx.x * blockDim.x + threadIdx.x;
if (i < n) {
cuFloatComplex *current_symbol = &in[i * 64];
cuFloatComplex pp0, pp1, pp2, pp3;
if (i > 0) {
pp0 = in[(i - 1) * 64 + 11];
pp1 = in[(i - 1) * 64 + 25];
pp2 = in[(i - 1) * 64 + 39];
pp3 = in[(i - 1) * 64 + 53];
} else {
pp0 = last_symbol[11];
pp1 = last_symbol[25];
pp2 = last_symbol[39];
pp3 = last_symbol[53];
}
float p = polarity[(current_symbol_index + i - 2) % 127];
if (current_symbol_index + i <= 2) {
pp1 = cuCmulf(pp1, make_cuFloatComplex(-1.0, 0.0));
} else {
float last_p = polarity[(current_symbol_index + i - 2 - 1) % 127];
pp0 = cuCmulf(pp0, make_cuFloatComplex(last_p, 0.0));
pp1 = cuCmulf(pp1, make_cuFloatComplex(last_p, 0.0));
pp2 = cuCmulf(pp2, make_cuFloatComplex(last_p, 0.0));
pp3 = cuCmulf(pp3, make_cuFloatComplex(-last_p, 0.0));
}
if ((current_symbol_index + i) < 2) {
beta[i] = carg(
make_cuFloatComplex(current_symbol[11].x - current_symbol[25].x +
current_symbol[39].x + current_symbol[53].x,
current_symbol[11].y - current_symbol[25].y +
current_symbol[39].y + current_symbol[53].y));
} else {
beta[i] = carg(make_cuFloatComplex(
(current_symbol[11].x * p) + (current_symbol[39].x * p) +
(current_symbol[25].x * p) + (current_symbol[53].x * -p),
(current_symbol[11].y * p) + (current_symbol[39].y * p) +
(current_symbol[25].y * p) + (current_symbol[53].y * -p)));
}
err[i] = carg(cuCaddf(
cuCaddf((make_cuFloatComplex(cuCmulf(pp0, current_symbol[11]).x * p,
-cuCmulf(pp0, current_symbol[11]).y * p)),
(make_cuFloatComplex(cuCmulf(pp1, current_symbol[25]).x * p,
-cuCmulf(pp1, current_symbol[25]).y * p))),
cuCaddf(
(make_cuFloatComplex(cuCmulf(pp2, current_symbol[39]).x * p,
-cuCmulf(pp2, current_symbol[39]).y * p)),
(make_cuFloatComplex(cuCmulf(pp3, current_symbol[53]).x * -p,
-cuCmulf(pp3, current_symbol[53]).y * -p)))));
err[i] *= (bw / (2 * M_PI * freq * 80));
}
}
__global__ void correct_sampling_offset_kernel(cuFloatComplex *in,
cuFloatComplex *out,
int start_idx, float freq_offset,
int n) {
int i = blockIdx.x * blockDim.x + threadIdx.x;
if (i < n) {
int symbol_index = i / 64;
int sample_index = i % 64;
// // compensate sampling offset
// for(int i = 0; i < 64; i++) {
// current_symbol[i] *= exp(gr_complex(0,
// 2*M_PI*d_current_symbol*80*(d_epsilon0 + d_er)*(i-32)/64));
// }
float x = -freq_offset * (float)(start_idx + symbol_index) *
(float)(sample_index - 32) / 64;
out[i] = cuCmulf(in[i], make_cuFloatComplex(cos(x), sin(x)));
}
}
__global__ void multiply_phase_kernel(cuFloatComplex *in, cuFloatComplex *out,
float *beta, int n) {
// beta applies for an entire symbol
int i = blockIdx.x * blockDim.x + threadIdx.x;
if (i < n) {
// e ix = cos x + i sin x
out[i] = cuCmulf(in[i], make_cuFloatComplex(cos(beta[i/64]), sin(beta[i/64])));
}
}
void exec_calc_beta_err(cuFloatComplex *in, float *polarity,
int current_symbol_index, cuFloatComplex *last_symbol,
float bw, float freq, float *beta, float *err, int n,
int grid_size, int block_size, cudaStream_t stream) {
calc_beta_err_kernel<<<grid_size, block_size, 0, stream>>>(
in, polarity, current_symbol_index, last_symbol, bw, freq, beta, err, n);
}
void get_block_and_grid_calc_beta_err(int *minGrid, int *minBlock) {
cudaOccupancyMaxPotentialBlockSize(minGrid, minBlock, calc_beta_err_kernel, 0,
0);
}
void exec_correct_sampling_offset(cuFloatComplex *in, cuFloatComplex *out,
int start_idx, float freq_offset, int n,
int grid_size, int block_size,
cudaStream_t stream) {
correct_sampling_offset_kernel<<<grid_size, block_size, 0, stream>>>(
in, out, start_idx, freq_offset, n);
}
void exec_multiply_phase(cuFloatComplex *in, cuFloatComplex *out, float *beta,
int n, int grid_size, int block_size,
cudaStream_t stream) {
multiply_phase_kernel<<<grid_size, block_size, 0, stream>>>(in, out, beta, n);
}
|
1,051
|
////////////////////////////////////////////////////////////////
// 2D FDTD solution for Mur's Absorbing Boundary Condition
// Using GPU acceleration (CUDA implementation)
// Simple harmonic excitation source
//////////////////////////////////////////////////////////////
// Set your code generation to "compute_20,sm_20"
// when you compile this code.
//////////////////////////////////////////////////////////////
#include <iostream>
#include <stdlib.h>
#include <time.h>
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
using namespace std;
#define BLOCK_LENGTH 32
#define PI 3.1415926535897
#define SQRT2 1.414213562373
double mu = 12.56637e-7;
double epsilon = 8.8542e-12;
double cv = 3e8; //Velocity of light
double sigma = 0; //Electrical conductivity in vacuity
//Coefficients for media
inline double ca(double t)
{
double tmp = sigma*t / (2 * epsilon);
return (1 - tmp) / (1 + tmp);
}
inline double cb(double t)
{
double tmp = t / epsilon;
return tmp / (1 + tmp*(sigma / 2));
}
inline double cp(double t)
{
double tmp = sigma*t / (2 * mu);
return (1 - tmp) / (1 + tmp);
}
inline double cq(double t)
{
double tmp = t / mu;
return tmp / (1 + tmp*(sigma / 2));
}
//ezf[i][j] is the electrical field to be calculated, ezfm1[i][j] is electrical field in the previous time step;
//hxfm1[i][j] and hyfm1[i][j] are the magnetic field in the previous time step;
//(width, height) is the total size of the field;
//(s_x, s_y) is the point of source. 't' is the current time. 'dl' is the size of cell. 'dt' is time interval;
//'a' and 'b' are the coeffiecients of the media.
__global__ void calcEz(double *ezf, double *ezfm1, double *hxfm1, double *hyfm1, int width, int height, int s_x, int s_y, int t, double dl, double dt, double a, double b)
{
int tx = threadIdx.x; int ty = threadIdx.y;
int thx = blockIdx.x*blockDim.x + tx;
int thy = blockIdx.y*blockDim.y + ty;
if (thx < 0 || thy < 0 || thx >= (width - 1) || thy >= (height - 1)) return;
//All index of elements of H add 1, and 0 is the index for halo elements
__shared__ double hx[BLOCK_LENGTH + 1][BLOCK_LENGTH + 1];
__shared__ double hy[BLOCK_LENGTH + 1][BLOCK_LENGTH + 1];
hx[tx + 1][ty + 1] = hxfm1[width*thy + thx];
hy[tx + 1][ty + 1] = hyfm1[width*thy + thx];
//Copy halo elements
if (tx == 0 && thx != 0) hy[0][ty + 1] = hyfm1[width*thy + thx - 1];
if (ty == 0 && thy != 0) hx[tx + 1][0] = hxfm1[width*(thy - 1) + thx];
__syncthreads();
if (thx == 0 || thy == 0) return;
if (thx == s_x && thy == s_y)
{
//Source
double frq = 1.5e13;
ezf[width*thy + thx] = sin(t * dt * 2 * PI * frq);
}
else //Recursion
ezf[width*thy + thx] = a*ezfm1[width*thy + thx] + b*((hy[tx + 1][ty + 1] - hy[tx][ty + 1]) / dl - (hx[tx + 1][ty + 1] - hx[tx + 1][ty]) / dl);
}
__global__ void calcH(double *ezf, double *hxf, double *hxfm1, double *hyf, double *hyfm1, int width, int height, double dl, double p, double q)
{
int tx = threadIdx.x; int ty = threadIdx.y;
int thx = blockIdx.x*blockDim.x + tx;
int thy = blockIdx.y*blockDim.y + ty;
if (thx < 0 || thy < 0 || thx >= (width - 1) || thy >= (height - 1)) return;
__shared__ double ez[BLOCK_LENGTH + 1][BLOCK_LENGTH + 1];
if (tx == 0 && ty == 0)
ez[0][0] = ezf[width*thy + thx];
ez[tx + 1][ty] = ezf[width*thy + thx + 1];
ez[tx][ty + 1] = ezf[width*(thy + 1) + thx];
__syncthreads();
hxf[width*thy + thx] = p*hxfm1[width*thy + thx] - q*(ez[tx][ty + 1] - ez[tx][ty]) / dl;
hyf[width*thy + thx] = p*hyfm1[width*thy + thx] + q*(ez[tx + 1][ty] - ez[tx][ty]) / dl;
}
__global__ void calcVerticalBoundaries(double *ezf, double *ezfm1, double *ezfm2, int width, int height, double coe1, double coe2, double coe3)
{
int tx = threadIdx.x;
int thx = blockIdx.x*blockDim.x + tx;
__shared__ double left[BLOCK_LENGTH + 2];
__shared__ double l_inner[BLOCK_LENGTH + 2];
left[tx + 1] = ezfm1[width*thx];
l_inner[tx + 1] = ezfm1[width*thx + 1];
__shared__ double right[BLOCK_LENGTH + 2];
__shared__ double r_inner[BLOCK_LENGTH + 2];
right[tx + 1] = ezfm1[width*(thx + 1) - 1];
r_inner[tx + 1] = ezfm1[width*(thx + 1) - 2];
if (tx == 0 && thx != 0)
{
left[0] = ezfm1[width*(thx - 1)];
l_inner[0] = ezfm1[width*(thx - 1) + 1];
right[0] = ezfm1[width*thx - 1];
r_inner[0] = ezfm1[width*thx - 2];
}
if (tx == BLOCK_LENGTH - 1 && thx != height - 1)
{
left[BLOCK_LENGTH + 1] = ezfm1[width*(thx + 1)];
l_inner[BLOCK_LENGTH + 1] = ezfm1[width*(thx + 1) + 1];
right[BLOCK_LENGTH + 1] = ezfm1[width*(thx + 2) - 1];
r_inner[BLOCK_LENGTH + 1] = ezfm1[width*(thx + 2) - 2];
}
__syncthreads();
if (!(thx == 0 || thx == height - 1))
{
ezf[width*thx] = 0 - ezfm2[width*thx + 1] + coe1*(ezf[width*thx + 1] + ezfm2[width*thx]) + coe2*(left[tx + 1] + l_inner[tx + 1]) + coe3*(left[tx + 2] - 2 * left[tx + 1] + left[tx] + l_inner[tx + 2] - 2 * l_inner[tx + 1] + l_inner[tx]);
ezf[width*thx + width - 1] = 0 - ezfm2[width*(thx + 1) - 2] + coe1*(ezf[width*(thx + 1) - 2] + ezfm2[width*(thx + 1) - 1]) + coe2*(right[tx + 1] + r_inner[tx + 1]) + coe3*(right[tx + 2] - 2 * right[tx + 1] + right[tx] + r_inner[tx + 2] - 2 * r_inner[tx + 1] + r_inner[tx]);
}
}
__global__ void calcHorizontalBoundaries(double *ezf, double *ezfm1, double *ezfm2, int width, int height, double coe1, double coe2, double coe3)
{
int tx = threadIdx.x;
int thx = blockIdx.x*blockDim.x + tx;
__shared__ double down[BLOCK_LENGTH + 2];
__shared__ double d_inner[BLOCK_LENGTH + 2];
down[tx + 1] = ezfm1[thx];
d_inner[tx + 1] = ezfm1[width + thx];
__shared__ double up[BLOCK_LENGTH + 2];
__shared__ double u_inner[BLOCK_LENGTH + 2];
up[tx + 1] = ezfm1[width*(height - 1) + thx];
u_inner[tx + 1] = ezfm1[width*(height - 2) + thx];
if (tx == 0 && thx != 0)
{
down[0] = ezfm1[thx - 1];
d_inner[0] = ezfm1[width + thx - 1];
up[0] = ezfm1[width*(height - 1) + thx - 1];
u_inner[0] = ezfm1[width*(height - 2) + thx - 1];
}
if (tx == BLOCK_LENGTH - 1 && thx != width - 1)
{
down[BLOCK_LENGTH + 1] = ezfm1[thx + 1];
d_inner[BLOCK_LENGTH + 1] = ezfm1[width + thx + 1];
up[BLOCK_LENGTH + 1] = ezfm1[width*(height - 1) + thx + 1];
u_inner[BLOCK_LENGTH + 1] = ezfm1[width*(height - 2) + thx + 1];
}
__syncthreads();
if (!(thx == 0 || thx == width - 1))
{
ezf[thx] = 0 - ezfm2[width + thx] + coe1*(ezf[width + thx] + ezfm2[thx]) + coe2*(down[tx + 1] + d_inner[tx + 1]) + coe3*(down[tx + 2] - 2 * down[tx + 1] + down[tx] + d_inner[tx + 2] - 2 * d_inner[tx + 1] + d_inner[tx]);
ezf[width*(height - 1) + thx] = 0 - ezfm2[width*(height - 2) + thx] + coe1*(ezf[width*(height - 2) + thx] + ezfm2[width*(height - 1) + thx]) + coe2*(up[tx + 1] + u_inner[tx + 1]) + coe3*(up[tx + 2] - 2 * up[tx + 1] + up[tx] + u_inner[tx + 2] - 2 * u_inner[tx + 1] + u_inner[tx]);
}
}
__global__ void calcCorner(double *ezf, double *ezfm1, int width, int height, double coe)
{
int thx = blockIdx.x*blockDim.x + threadIdx.x;
if (thx == 0) //left-down
ezf[0] = ezfm1[width + 1] + coe*(ezf[width + 1] - ezfm1[0]);
else if (thx == 1) //right-down
ezf[width - 1] = ezfm1[width * 2 - 2] + coe*(ezf[width * 2 - 2] - ezfm1[width - 1]);
else if (thx == 2) //left-up
ezf[width*(height - 1)] = ezfm1[width*(height - 2) + 1] + coe*(ezf[width*(height - 2) + 1] - ezfm1[width*(height - 1)]);
else //right-up
ezf[width*height - 1] = ezfm1[width*height - width - 2] + coe*(ezf[width*height - width - 2] - ezfm1[width*height - 1]);
}
int main()
{
int width = 1024;
int height = 1024;
int time = 2000;
int cx = 32; int cy = 32;
double dl = 1e-6;
double st = 1 / SQRT2;
double dt = st*dl / cv;
dt = dt / 2;
cout << "Input width and height of the field: ";
cin >> width >> height;
cout << "Input time steps to be calculated: ";
cin >> time;
cout << "Input x and y coordinate of the wave source: ";
cin >> cx >> cy;
int size = sizeof(double)*width*height;
double *ezf, *ezfm1, *ezfm2, *hxf, *hxfm1, *hyf, *hyfm1;
cudaMalloc((void**)&ezf, size); cudaMemset(ezf, 0, size);
cudaMalloc((void**)&ezfm1, size); cudaMemset(ezfm1, 0, size);
cudaMalloc((void**)&hxf, size); cudaMemset(hxf, 0, size);
cudaMalloc((void**)&hyf, size); cudaMemset(hyf, 0, size);
//Coefficients of media
double a = ca(dt); double b = cb(dt);
double p = cp(dt); double q = cq(dt);
//Coefficients for boundary conditions
double coe1 = (cv*dt - dl) / (cv*dt + dl);
double coe2 = (2 * dl) / (cv*dt + dl);
double coe3 = (cv*cv*dt*dt) / (2 * dl*(cv*dt + dl));
//Coefficient for the corner
double coe_cor = (cv*dt - SQRT2*dl) / (cv*dt + SQRT2*dl);
double t_start, t_end, duration;
t_start = clock();
dim3 DimBlock(BLOCK_LENGTH, BLOCK_LENGTH, 1);
dim3 DimGrid((width - 1) / BLOCK_LENGTH + 1, (height - 1) / BLOCK_LENGTH + 1, 1);
dim3 db_h(BLOCK_LENGTH, 1, 1);
dim3 dg_h((height - 1) / BLOCK_LENGTH + 1, 1, 1);
dim3 db_w(BLOCK_LENGTH, 1, 1);
dim3 dg_w((width - 1) / BLOCK_LENGTH + 1, 1, 1);
dim3 db_cor(4, 1, 1); dim3 dg_cor(1, 1, 1);
for (int t = 0; t < time; t++)
{
ezfm2 = ezfm1;
ezfm1 = ezf;
cudaMalloc((void**)&ezf, size); cudaMemset(ezf, 0, size);
hxfm1 = hxf;
cudaMalloc((void**)&hxf, size); cudaMemset(hxf, 0, size);
hyfm1 = hyf;
cudaMalloc((void**)&hyf, size); cudaMemset(hyf, 0, size);
calcEz<<<DimGrid, DimBlock>>>(ezf, ezfm1, hxfm1, hyfm1, width, height, cx, cy, t, dl, dt, a, b);
//Boundary conditions
calcVerticalBoundaries<<<dg_h, db_h>>>(ezf, ezfm1, ezfm2, width, height, coe1, coe2, coe3);
calcHorizontalBoundaries<<<dg_w, db_w>>>(ezf, ezfm1, ezfm2, width, height, coe1, coe2, coe3);
//Corner conditions
calcCorner<<<dg_cor, db_cor>>>(ezf, ezfm1, width, height, coe_cor);
calcH<<<DimGrid, DimBlock>>>(ezf, hxf, hxfm1, hyf, hyfm1, width, height, dl, p, q);
//Output -------
//You can add code here to copy data in array 'ezf', 'hxf' and 'hyf' out to output.
cudaFree(ezfm2); cudaFree(hxfm1); cudaFree(hyfm1);
}
double *output = (double*)malloc(size);
cudaMemcpy(output, ezf, size, cudaMemcpyDeviceToHost);
t_end = clock();
duration = t_end - t_start;
cout << "Time using: " << duration << " ms." << endl << endl;
for(int i=0;i<height;i++)
{
for(int j=0;j<width;j++)
cout<<output[i*width+j]<<" ";
cout<<endl;
}
free(output);
cudaFree(ezf); cudaFree(hxf); cudaFree(hyf);
cudaFree(ezfm1);
return 0;
}
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1,052
|
#include <stdio.h>
#include <time.h>
const long int M = 10;
const long int N = 10;
const long int O = 10;
#define BLOCK_WIDTH 16
void fillMatrix(float *A, long long int sizeA);
void printMatrix(float *A, long long int sizeA, long long int N);
float getValue(float *A, long long int i, long long int j, long long int cols);
void setValue(float *A, long long int i, long long int j, float value);
void multiMatricesCPU(float *A, float *B, float *C);
void multiMatricesGPU(float *A, float *B, float *C);
int main(int argc, char const *argv[])
{
//Creting the sizes
long long int sizeA = M * N;
long long int sizeB = M * O;
long long int sizeC = M * O;
//Creating the matrices
float A[sizeA];
float B[sizeB];
float C[sizeC];
//Filling the matrices with secuencial numbers
fillMatrix(A, sizeA);
fillMatrix(B, sizeB);
fillMatrix(C, sizeC);
// Multiplying A and B
//multiMatricesCPU(A, B, C);
multiMatricesGPU(A, B, C);
//printMatrix(A, sizeA, N);
printMatrix(C, sizeC, O);
return 0;
}
__global__ void matrixMultKernel (float *d_A, float *d_B, float *d_C, int N)
{
// Calculate the row index of the d_C element and d_A
int row = blockIdx.y * blockDim.y + threadIdx.y;
// Calculate the column index of d_C and d_B
int col = blockIdx.x * blockDim.x + threadIdx.x;
if ((row < N) && (col < N))
{
float Cvalue = 0;
for (int k = 0; k < N; k++)
Cvalue += d_A[row * N + k] * d_B[k * N + col];
d_C[row * N + col] = Cvalue;
}
}
void fillMatrix(float *A, long long int sizeA)
{
for (long long int i = 0; i < sizeA; i++)
A[i] = i+1;
}
// Get the value in the position (i, j) in matrix A
float getValue(float *A, long long int i, long long int j, long long int cols)
{
return A[cols * i + j];
}
// Set the value on the position (i, j) of matrix A
void setValue(float *A, long long int i, long long int j, float value)
{
A[O * i + j] = value;
}
// Print the entire matrix A
void printMatrix(float *A, long long int sizeA, long long int N)
{
for (long long int i = 0; i < sizeA; i++)
{
if (i % N == 0 && i != 0)
printf("\n");
printf("%d ", (int) A[i]);
}
printf("\n");
}
// Computes the multiplications between matrices A and B and stores the result on Matrix C
void multiMatricesCPU(float *A, float *B, float *C)
{
long long int temp = 0;
for (long long int i = 0; i < M; i++)
{
for (long long int j = 0; j < O; j++)
{
for (long long int k = 0; k < N; k++)
{
temp = temp + getValue(A, i, k, N) * getValue(B, k, j, O);
}
setValue(C, i, j, temp);
temp = 0;
}
}
}
void multiMatricesGPU(float *h_A, float *h_B, float *h_C)
{
// Define sizes of matrices in device memory
int A_size = N * N * sizeof(float);
int B_size = A_size;
int C_size = A_size;
// Create device matrices
float *d_A, *d_B, *d_C;
// Allocate device memory for matrices
// copy host matrices to device matrices
cudaError_t err = cudaMalloc((void **) &d_A, A_size);
if (err != cudaSuccess)
{
printf("%s in %s at line %d\n", cudaGetErrorString(err), __FILE__, __LINE__);
exit(EXIT_FAILURE);
}
cudaMemcpy(d_A, h_A, A_size, cudaMemcpyHostToDevice);
err = cudaMalloc((void **) &d_B, B_size);
if (err != cudaSuccess)
{
printf("%s in %s at line %d\n", cudaGetErrorString(err), __FILE__, __LINE__);
exit(EXIT_FAILURE);
}
cudaMemcpy(d_B, h_B, B_size, cudaMemcpyHostToDevice);
err = cudaMalloc((void **) &d_C, C_size);
if (err != cudaSuccess)
{
printf("%s in %s at line %d\n", cudaGetErrorString(err), __FILE__, __LINE__);
exit(EXIT_FAILURE);
}
// Launch kernel
int NumBlocks = N / BLOCK_WIDTH;
if (N % BLOCK_WIDTH)
NumBlocks++;
dim3 dimGrid(NumBlocks, NumBlocks);
dim3 dimBlock(BLOCK_WIDTH, BLOCK_WIDTH);
matrixMultKernel<<<dimGrid, dimBlock>>>(d_A, d_B, d_C, N);
// Put results again on host conrainers
cudaMemcpy(h_C, d_C, C_size, cudaMemcpyDeviceToHost);
cudaFree(d_A);
cudaFree(d_B);
cudaFree(d_C);
}
|
1,053
|
#define TILE_DIM 32
template<typename T, typename R>
__device__ void common_std(const T* matrix, R* result,
const int numRows, const int numColumns) {
__shared__ R sumTile[TILE_DIM][TILE_DIM];
__shared__ R squareSumTile[TILE_DIM][TILE_DIM];
int tx = threadIdx.x;
int ty = threadIdx.y;
sumTile[ty][tx] = 0;
squareSumTile[ty][tx] = 0;
#pragma unroll
for (int tr = 0; tr < (numRows - 1) / TILE_DIM + 1; tr++) {
for (int tc = 0; tc < (numColumns - 1) / TILE_DIM + 1; tc++) {
int r = tr * TILE_DIM + ty;
int c = tc * TILE_DIM + tx;
if (r < numRows && c < numColumns) {
T value = matrix[r * numColumns + c];
sumTile[ty][tx] += value;
squareSumTile[ty][tx] += value * value;
}
__syncthreads();
}
}
if (tx == 0 && ty == 0) {
R sum = 0;
R squareSum = 0;
#pragma unroll
for (int i = 0; i < TILE_DIM; i++) {
#pragma unroll
for (int j = 0; j < TILE_DIM; j++) {
sum += sumTile[i][j];
squareSum += squareSumTile[i][j];
}
}
int length = numRows * numColumns;
result[0] = sqrt((squareSum - (sum * sum) / length) / length);
}
}
template<typename T>
__device__ void math_std(const T* matrix, float* result,
const int numRows, const int numColumns) {
common_std<T, float>(matrix, result, numRows, numColumns);
}
template<typename T>
__device__ void math_stdd(const T* matrix, double* result,
const int numRows, const int numColumns) {
common_std<T, double>(matrix, result, numRows, numColumns);
}
template<typename T, typename R>
__device__ void common_rowsStd(const T* matrix, R* result,
const int numRows, const int numColumns) {
__shared__ T tile[TILE_DIM][TILE_DIM];
int by = blockIdx.y;
int ty = threadIdx.y;
int row = by * blockDim.y + ty;
R sum = 0;
R squareSum = 0;
#pragma unroll
for (int t = 0; t < (numColumns - 1) / TILE_DIM + 1; t++) {
#pragma unroll
for (int i = 0; i < TILE_DIM; i++) {
int r = by * TILE_DIM + i;
int c = t * TILE_DIM + ty;
if (r < numRows && c < numColumns) {
tile[i][ty] = matrix[r * numColumns + c];
} else {
tile[i][ty] = 0;
}
}
__syncthreads();
#pragma unroll
for (int j = 0; j < TILE_DIM; j++) {
T value = tile[ty][j];
sum += value;
squareSum += value * value;
}
__syncthreads();
}
if (row < numRows) {
result[row] = sqrt((squareSum - (sum * sum) / numColumns) / numColumns);
}
}
template<typename T>
__device__ void rowsStd(const T* matrix, float* result,
const int numRows, const int numColumns) {
common_rowsStd<T, float>(matrix, result, numRows, numColumns);
}
template<typename T>
__device__ void rowsStdd(const T* matrix, double* result,
const int numRows, const int numColumns) {
common_rowsStd<T, double>(matrix, result, numRows, numColumns);
}
template<typename T, typename R>
__device__ void common_columnsStd(const T* matrix, R* result,
const int numRows, const int numColumns) {
int bx = blockIdx.x;
int tx = threadIdx.x;
int col = bx * blockDim.x + tx;
if (col < numColumns) {
R sum = 0;
R squareSum = 0;
#pragma unroll
for (int i = 0; i < numRows; i++) {
int index = i * numColumns + col;
T value = matrix[index];
sum += value;
squareSum += value * value;
}
result[col] = sqrt((squareSum - (sum * sum) / numRows) / numRows);
}
}
template<typename T>
__device__ void columnsStd(const T* matrix, float* result,
const int numRows, const int numColumns) {
common_columnsStd<T, float>(matrix, result, numRows, numColumns);
}
template<typename T>
__device__ void columnsStdd(const T* matrix, double* result,
const int numRows, const int numColumns) {
common_columnsStd<T, double>(matrix, result, numRows, numColumns);
}
|
1,054
|
#include <stdio.h>
__global__ void printid(){
int id = blockIdx.x;
printf("I'm a thread in block %d!\n", id);
__syncthreads();
}
int main(int argc, char **argv){
printid<<<16, 1>>>();
cudaDeviceSynchronize();
printf("That's all!\n");
return 0;
}
|
1,055
|
// runSim.cu
#include <stdio.h>
#include <stdlib.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <math.h>
#include <thrust/reduce.h>
#include <thrust/execution_policy.h>
// Executes the A1 operator optimized
__global__ void A1_kernel(double* r, double* v, double dt) {
size_t id = blockIdx.x * blockDim.x + threadIdx.x;
r[id] += v[id] * dt;
}
// Executes the A2 operator
__global__ void A2_kernel(double *r, double *v, double *m, double dt, double *varr, double *status, int numParticles) {
size_t id = blockIdx.x * blockDim.x + threadIdx.x + 1;
double invdist;
double dirvec[3];
if (id < numParticles) {
dirvec[0] = r[0] - r[3*id];
dirvec[1] = r[1] - r[3*id+1];
dirvec[2] = r[2] - r[3*id+2];
// Distance between particle 0 and i
invdist = dt * rnorm3d(dirvec[0], dirvec[1], dirvec[2])*\
rnorm3d(dirvec[0], dirvec[1], dirvec[2])*\
rnorm3d(dirvec[0], dirvec[1], dirvec[2]);
if (status[id] == 0) {
v[3*id] += 0;
v[3*id+1] += 0;
v[3*id+2] += 0;
varr[id] = 0;
varr[numParticles+id] = 0;
varr[2*numParticles+id] = 0;
}
else {
// Update velocities of particles 1 through N-1
v[3*id] += m[0] * invdist * dirvec[0];
v[3*id+1] += m[0] * invdist * dirvec[1];
v[3*id+2] += m[0] * invdist * dirvec[2];
varr[id] = -m[id] * invdist * dirvec[0];
varr[numParticles+id] = -m[id] * invdist * dirvec[1];
varr[2*numParticles+id] = -m[id] * invdist * dirvec[2];
}
varr[0] = v[0];
varr[numParticles] = v[1];
varr[2*numParticles] = v[2];
}
}
// Execute the B operator when only embryo and other particles interact
__global__ void B_kernel(double *r, double *v, double *m, double *varr, double dt, int numParticles, double *status, double eps) {
size_t id = blockIdx.x * blockDim.x + threadIdx.x + 2;
double dirvec[3];
double invdist;
if (id < numParticles) {
dirvec[0] = r[3] - r[3*id];
dirvec[1] = r[3+1] - r[3*id+1];
dirvec[2] = r[3+2] - r[3*id+2];
invdist = status[id] * dt * rsqrt((dirvec[0]*dirvec[0] + dirvec[1]*dirvec[1] + dirvec[2]*dirvec[2] + eps*eps)*\
(dirvec[0]*dirvec[0] + dirvec[1]*dirvec[1] + dirvec[2]*dirvec[2] + eps*eps)*\
(dirvec[0]*dirvec[0] + dirvec[1]*dirvec[1] + dirvec[2]*dirvec[2] + eps*eps));
// update id'th satelitesimal
v[3*id] += m[1] * invdist * dirvec[0];
v[3*id+1] += m[1] * invdist * dirvec[1];
v[3*id+2] += m[1] * invdist * dirvec[2];
// update embryo
// Store forces on embryo for reduction
varr[0] = v[3];
varr[numParticles-1] = 0;
varr[numParticles] = v[4];
varr[2*numParticles-1] = 0;
varr[2*numParticles] = v[5];
varr[3*numParticles-1] = 0;
varr[id-1] = -m[id] * invdist * dirvec[0];
varr[numParticles+id-1] = -m[id] * invdist * dirvec[1];
varr[2*numParticles+id-1] = -m[id] * invdist * dirvec[2];
}
}
__global__ void mergeEject(double *r, double *status, int numParticles, double rH) {
size_t id = blockIdx.x * blockDim.x + threadIdx.x + 2;
double dist;
if (id < numParticles) {
dist = norm3d(r[0]-r[3*id], r[1]-r[3*id+1], r[2]-r[3*id+2]);
if (dist < 0.03*rH && status[id] != 0)
status[id] = 2;
else if (dist > rH && status[id] != 0)
status[id] = 3; // so that momentum conservation doesn't include ejected particles
// will be set to 0 in the consMomentum function
}
}
__global__ void consMomentum(double *v, double *m, double *status, int numParticles, double *rSatellites) {
for (int id = 2; id < numParticles; id++) {
if (status[id] == 2) {
status[id] = 0;
// use conservation of momentum to update central velocity
v[0] = 1./(m[0] + m[id]) * (m[0]*v[0] + m[id]*v[3*id]);
v[1] = 1./(m[0] + m[id]) * (m[0]*v[1] + m[id]*v[3*id+1]);
v[2] = 1./(m[0] + m[id]) * (m[0]*v[2] + m[id]*v[3*id+2]);
// conservation of mass
m[0] += m[id];
}
else if (status[id] == 4) {
status[id] = 0;
rSatellites[0] = cbrt((m[1]+m[2])/m[2])*rSatellites[1];
// use conservation of momentum to update velocity
v[3] = 1./(m[1] + m[id]) * (m[1]*v[3] + m[id]*v[3*id]);
v[4] = 1./(m[1] + m[id]) * (m[1]*v[4] + m[id]*v[3*id+1]);
v[5] = 1./(m[1] + m[id]) * (m[1]*v[5] + m[id]*v[3*id+2]);
// conservation of mass
m[1] += m[id];
}
else if (status[id] == 3)
status[id] = 0;
else
continue;
}
}
__global__ void statusUpdate(double *r, double *v, double *m, double *status, int numParticles) {
size_t id = blockIdx.x * blockDim.x + threadIdx.x;
m[id/3] *= status[id/3];
r[id] *= status[id/3];
v[id] *= status[id/3];
}
// Function to find
// cross product of two vector array.
__device__ void crossProduct(double *vect_A, double *vect_B, double *cross_P) {
cross_P[0] = vect_A[1] * vect_B[2] - vect_A[2] * vect_B[1];
cross_P[1] = vect_A[2] * vect_B[0] - vect_A[0] * vect_B[2];
cross_P[2] = vect_A[0] * vect_B[1] - vect_A[1] * vect_B[0];
}
__global__ void collision(double* r, double* v, double* status, double* rSatellites, int numParticles, double dt) {
size_t id = blockIdx.x * blockDim.x + threadIdx.x + 2;
double rTemp[3];
double vTemp[3];
double crossP[3];
double vecA[3];
double vecB[3];
double t;
double dist;
double d1;
double d2;
if (id < numParticles) {
// go to rest frame of embryo
vTemp[0] = v[3*id] - v[3];
vTemp[1] = v[3*id+1] - v[4];
vTemp[2] = v[3*id+2] - v[5];
// evolve satelitesimal
rTemp[0] = r[3*id] + vTemp[0] * dt/4.0;
rTemp[1] = r[3*id+1] + vTemp[1] * dt/4.0;
rTemp[2] = r[3*id+2] + vTemp[2] * dt/4.0;
// the equation ((r-r[1]) * (rTemp-r)) / |rTemp-r|^2 where r[1] is the embryo's
// position in its rest frame, r is the satelitesimal's original position and rTemp is the
// satelitesimal's updated position in the rest frame. * indicates a dot product in this case
// this is the time that minimizes the distance function from a line segment to a point
t = -1*((r[3*id]-r[3]) *(rTemp[0]-r[3*id]) +\
(r[3*id+1]-r[4]) *(rTemp[1]-r[3*id+1]) +\
(r[3*id+2]-r[5]) *(rTemp[2]-r[3*id+2])) /\
((rTemp[0]-r[3*id]) *(rTemp[0]-r[3*id]) +\
(rTemp[1]-r[3*id+1])*(rTemp[1]-r[3*id+1]) +\
(rTemp[2]-r[3*id+2])*(rTemp[2]-r[3*id+2]));
if (0 < t < 1) {
// the equation |(r[1]-r) x (r[1]-rTemp)|/|rTemp-r| where r[1] is the embryo's position
// in its rest frame, r is the satelitesimal's original position and rTemp is the
// satelitesimal's updated position in the rest frame
// if t is in this range, then the point in within line segment
vecA[0] = r[3]-r[3*id], vecA[1] = r[4]-r[3*id+1], vecA[2] = r[5]-r[3*id+2];
vecB[0] = r[3]-rTemp[0], vecB[1] = r[4]-rTemp[1], vecB[2] = r[5]-rTemp[2];
crossProduct(vecA, vecB, crossP);
dist = norm3d(crossP[0],crossP[1],crossP[2])*rnorm3d(rTemp[0]-r[3*id], rTemp[1]-r[3*id+1], rTemp[2]-r[3*id+2]);
}
/*else if (t > 1 || t < 0) {
// if t is not in the range, it does not lie within the line segment
// the equation |r-r[1]|
d1 = norm3d(r[3*id]-r[3], r[3*id+1]-r[4], r[3*id+2]-r[5]);
// the equation |rTemp-r[1]|
d2 = norm3d(rTemp[0]-r[3], rTemp[1]-r[4], rTemp[2]-r[5]);
dist = fmin(d1, d2);
}*/
if (dist < rSatellites[0] + rSatellites[1])
status[id] = 4;
}
}
// Find distance
__global__ void calcDist(double *r, double *dist) {
size_t id = blockIdx.x * blockDim.x + threadIdx.x;
dist[id] = norm3d(r[3*id], r[3*id+1], r[3*id+2]);
}
// Find eccentricity of all particles
__global__ void calcEccentricity(double *r, double *v, double *m, double *ecc, int numParticles) {
size_t id = blockIdx.x * blockDim.x + threadIdx.x + 1;
double L[3]; // angular momentum
double eccTemp[3]; // hold components of eccentricity vector
double mu; // standard gravitational parameter
double invdist; // inverse distance between particle and central planet
if (id < numParticles) {
mu = m[0] + m[id];
invdist = rnorm3d(r[3*id]-r[0], r[3*id+1]-r[1], r[3*id+2]-r[2]);
L[0] = (r[3*id+1]-r[1])*v[3*id+2] - (r[3*id+2]-r[2])*v[3*id+1];
L[1] = (r[3*id+2]-r[2])*v[3*id] - (r[3*id]-r[0])*v[3*id+2];
L[2] = (r[3*id]-r[0])*v[3*id+1] - (r[3*id+1]-r[1])*v[3*id];
eccTemp[0] = (1./mu) * (v[3*id+1]*L[2] - v[3*id+2]*L[1]) - (r[3*id]-r[0]) * invdist;
eccTemp[1] = (1./mu) * (v[3*id+2]*L[0] - v[3*id]*L[2]) - (r[3*id+1]-r[1]) * invdist;
eccTemp[2] = (1./mu) * (v[3*id]*L[1] - v[3*id+1]*L[0]) - (r[3*id+2]-r[2]) * invdist;
ecc[id] = norm3d(eccTemp[0], eccTemp[1], eccTemp[2]); // real eccentricity
}
}
// Reduce last warp (unrolled) in reduction for A2 operator
template <unsigned int blockSize>
__device__ void warpReduce(volatile double* sdata, int tid) {
// All statements evaluated at compile time
if (blockSize >= 64) sdata[tid] += sdata[tid + 32];
if (blockSize >= 32) sdata[tid] += sdata[tid + 16];
if (blockSize >= 16) sdata[tid] += sdata[tid + 8];
if (blockSize >= 8) sdata[tid] += sdata[tid + 4];
if (blockSize >= 4) sdata[tid] += sdata[tid + 2];
if (blockSize >= 2) sdata[tid] += sdata[tid + 1];
}
// Reduction kernel for A2 operator for particle 0
template <unsigned int blockSize>
__global__ void reduce(double *g_idata, double *g_odata, unsigned int n) {
extern __shared__ double sdata[];
unsigned int tid = threadIdx.x;
unsigned int i = blockIdx.x*(blockSize*2) + threadIdx.x;
unsigned int gridSize = blockSize*2*gridDim.x;
sdata[tid] = 0;
while (i < n) {
sdata[tid] += g_idata[i] + g_idata[i+blockSize];
i += gridSize;
}
__syncthreads();
if (blockSize >= 512) { if (tid < 256) { sdata[tid] += sdata[tid + 256]; } __syncthreads(); }
if (blockSize >= 256) { if (tid < 128) { sdata[tid] += sdata[tid + 128]; } __syncthreads(); }
if (blockSize >= 128) { if (tid < 64) { sdata[tid] += sdata[tid + 64]; } __syncthreads(); }
if (tid < 32) warpReduce<blockSize>(sdata, tid);
if (tid == 0) g_odata[blockIdx.x] = sdata[0];
}
/*__global__ void reduce(double *v, double *varr, int numParticles, int s) {
v[s] = thrust::reduce(thrust::device, &varr[0], &varr[numParticles]);
v[1+s] = thrust::reduce(thrust::device, &varr[numParticles], &varr[2*numParticles]);
v[2+s] = thrust::reduce(thrust::device, &varr[2*numParticles], &varr[3*numParticles]);
}*/
// Function to find
// cross product of two vector array.
void crossProduct2(double *vect_A, double *vect_B, double *cross_P) {
cross_P[0] = vect_A[1] * vect_B[2] - vect_A[2] * vect_B[1];
cross_P[1] = vect_A[2] * vect_B[0] - vect_A[0] * vect_B[2];
cross_P[2] = vect_A[0] * vect_B[1] - vect_A[1] * vect_B[0];
}
// used to calculate the total angular momentum of the system
void linMomentum(double* v, double* m, int numParticles, double *P) {
*P = 0; // angular momentum
double plin[3]; // linear momentum
for (int i = 0; i < numParticles; i++) {
plin[0] += m[i]*v[3*i], plin[1] += m[i]*v[3*i+1], plin[2] += m[i]*v[3*i+2];
*P = sqrt(pow(plin[0], 2) + pow(plin[1], 2) + pow(plin[2], 2));
}
}
void totalMass(double *m, int numParticles, double* M) {
*M = 0;
for (int i = 0; i < numParticles; i++)
*M += m[i];
}
// used to calculate the total angular momentum of the system
void angMomentum(double* r, double* v, double* m, int numParticles, double *L) {
*L = 0;
double Ltemp[3];
double crossP[3]; // store cross product result
double dirvec[3]; // distance from planet
double p[3]; // linear momentum
for (int i = 1; i < numParticles; i++) {
dirvec[0] = -r[0]+r[3*i], dirvec[1] = -r[1]+r[3*i+1], dirvec[2] = -r[2]+r[3*i+2];
p[0] = m[i]*v[3*i], p[1] = m[i]*v[3*i+1], p[2] = m[i]*v[3*i+2];
crossProduct2(dirvec, p, crossP);
Ltemp[0] += crossP[0], Ltemp[1] += crossP[1], Ltemp[2] += crossP[2];
}
*L = sqrt(pow(Ltemp[0], 2) + pow(Ltemp[1], 2) + pow(Ltemp[2], 2));
}
double energynew(double* r, double* v, double* m, int numParticles, double eps) {
double T = 0; // kinetic energy
double U = 0; // potential energy
// to hold the vector that points between particle i and particle j
double* dirvec = (double*)malloc(3 * sizeof(double));
for (int i = 0; i < numParticles; i++) {
T += 0.5 * m[i] * (pow(v[3*i], 2) + pow(v[3*i+1], 2) + pow(v[3*i+2], 2));
if (i > 0) {
for (int k = 0; k < 3; k++)
dirvec[k] = r[k] - r[3*i+k];
U -= m[0] * m[i] / sqrt(pow(dirvec[0], 2) + pow(dirvec[1], 2) + pow(dirvec[2], 2));
}
if (i > 1) {
for (int k = 0; k < 3; k++)
dirvec[k] = r[3+k] - r[3*i+k];
U -= m[1] * m[i] / sqrt(pow(dirvec[0], 2) + pow(dirvec[1], 2) + pow(dirvec[2], 2) + eps*eps);
}
}
free(dirvec);
return T + U;
}
// Convenience function for checking CUDA runtime API results
// can be wrapped around any runtime API call. No-op in release builds.
inline
cudaError_t checkCuda(cudaError_t result)
{
#if defined(DEBUG) || defined(_DEBUG)
if (result != cudaSuccess) {
fprintf(stderr, "CUDA Runtime Error: %s\n",
cudaGetErrorString(result));
assert(result == cudaSuccess);
}
#endif
return result;
}
// Perform the simulation
extern "C" {
void runSim(double *r_h, double *v_h, double *m_h, double dt, int numParticles, int n, double eps, int numSteps, double *ecc_h, double *status_h, double *rSatellites_h, double *dist_h) {
// Declare useful variables
size_t i, j;
const unsigned int warpSize = 32;
size_t N = 3 * numParticles;
size_t N_bytes = N * sizeof(double);
double rH = 5.37e10/8.8605e9; // scaled
double L; double P; double M; double K;
double L0; double P0; double M0; double K0;
double semMjrAxis;
// Make sure the number of particles is multiple of twice the warp size (2*32)
// for efficiency and reduction
if (numParticles % (warpSize) != 0) {
printf("Error: The number of particles must be a multiple of the warp size (32).\n");
return;
}
// Allocate arrays on device
double *r_d, *v_d, *m_d, *ecc_d, *varr_d, *rSatellites_d, *status_d, *vTemp_d, *dist_d;
cudaMalloc((void**) &r_d, N_bytes);
cudaMalloc((void**) &v_d, N_bytes);
cudaMalloc((void**) &m_d, N_bytes/3);
cudaMalloc((void**) &varr_d, N_bytes);
cudaMalloc((void**) &status_d, N_bytes/3);
cudaMalloc((void**) &ecc_d, N_bytes/3);
cudaMalloc((void**) &rSatellites_d, 2*sizeof(double));
cudaMalloc((void**) &vTemp_d, numParticles/512*sizeof(double));
cudaMalloc((void**) &dist_d, N_bytes/3);
// Copy arrays from host to device
cudaMemcpy(r_d, r_h, N_bytes, cudaMemcpyHostToDevice);
cudaMemcpy(v_d, v_h, N_bytes, cudaMemcpyHostToDevice);
cudaMemcpy(m_d, m_h, N_bytes/3, cudaMemcpyHostToDevice);
cudaMemcpy(status_d, status_h, N_bytes/3, cudaMemcpyHostToDevice);
cudaMemcpy(rSatellites_d, rSatellites_h, 2*sizeof(double), cudaMemcpyHostToDevice);
//for (i = 0; i < numSteps; i++) {
// One time step
/*for (j = 0; j < n; j++) {
collision<<<numParticles/64, 64>>>(r_d, v_d, status_d, rSatellites_d, numParticles, dt);
consMomentum<<<1, 1>>>(v_d, m_d, status_d, numParticles, rSatellites_d);
statusUpdate<<<N/64, 64>>>(r_d, v_d, m_d, status_d, numParticles);
A1_kernel<<<N/512, 512>>>(r_d, v_d, dt/(4*n));
mergeEject<<<numParticles/64, 64>>>(r_d, status_d, numParticles, rH);
consMomentum<<<1, 1>>>(v_d, m_d, status_d, numParticles, rSatellites_d);
statusUpdate<<<N/64, 64>>>(r_d, v_d, m_d, status_d, numParticles);
A2_kernel<<<numParticles/64, 64>>>(r_d, v_d, m_d, dt/(2*n), varr_d, status_d, numParticles);
reduce<512><<<1, numParticles/2, numParticles*sizeof(double)>>>(varr_d, &v_d[0], numParticles);
reduce<512><<<1, numParticles/2, numParticles*sizeof(double)>>>(varr_d+numParticles, &v_d[1], numParticles);
reduce<512><<<1, numParticles/2, numParticles*sizeof(double)>>>(varr_d+2*numParticles, &v_d[2], numParticles);
collision<<<numParticles/64, 64>>>(r_d, v_d, status_d, rSatellites_d, numParticles, dt);
consMomentum<<<1, 1>>>(v_d, m_d, status_d, numParticles, rSatellites_d);
statusUpdate<<<N/64, 64>>>(r_d, v_d, m_d, status_d, numParticles);
A1_kernel<<<N/512, 512>>>(r_d, v_d, dt/(4*n));
mergeEject<<<numParticles/64, 64>>>(r_d, status_d, numParticles, rH);
consMomentum<<<1, 1>>>(v_d, m_d, status_d, numParticles, rSatellites_d);
statusUpdate<<<N/64, 64>>>(r_d, v_d, m_d, status_d, numParticles);
}
B_kernel<<<numParticles/64, 64>>>(r_d, v_d, m_d, varr_d, dt, numParticles, status_d, eps);
reduce<512><<<1, numParticles/2, numParticles*sizeof(double)>>>(varr_d, &v_d[3], numParticles);
reduce<512><<<1, numParticles/2, numParticles*sizeof(double)>>>(varr_d+numParticles, &v_d[4], numParticles);
reduce<512><<<1, numParticles/2, numParticles*sizeof(double)>>>(varr_d+2*numParticles, &v_d[5], numParticles);
for (j = 0; j < n; j++) {
collision<<<numParticles/64, 64>>>(r_d, v_d, status_d, rSatellites_d, numParticles, dt);
consMomentum<<<1, 1>>>(v_d, m_d, status_d, numParticles, rSatellites_d);
statusUpdate<<<N/64, 64>>>(r_d, v_d, m_d, status_d, numParticles);
A1_kernel<<<N/512, 512>>>(r_d, v_d, dt/(4*n));
mergeEject<<<numParticles/64, 64>>>(r_d, status_d, numParticles, rH);
consMomentum<<<1, 1>>>(v_d, m_d, status_d, numParticles, rSatellites_d);
statusUpdate<<<N/64, 64>>>(r_d, v_d, m_d, status_d, numParticles);
A2_kernel<<<numParticles/64, 64>>>(r_d, v_d, m_d, dt/(2*n), varr_d, status_d, numParticles);
reduce<512><<<1, numParticles/2, numParticles*sizeof(double)>>>(varr_d, &v_d[0], numParticles);
reduce<512><<<1, numParticles/2, numParticles*sizeof(double)>>>(varr_d+numParticles, &v_d[1], numParticles);
reduce<512><<<1, numParticles/2, numParticles*sizeof(double)>>>(varr_d+2*numParticles, &v_d[2], numParticles);
collision<<<numParticles/64, 64>>>(r_d, v_d, status_d, rSatellites_d, numParticles, dt);
consMomentum<<<1, 1>>>(v_d, m_d, status_d, numParticles, rSatellites_d);
statusUpdate<<<N/64, 64>>>(r_d, v_d, m_d, status_d, numParticles);
A1_kernel<<<N/512, 512>>>(r_d, v_d, dt/(4*n));
mergeEject<<<numParticles/64, 64>>>(r_d, status_d, numParticles, rH);
consMomentum<<<1, 1>>>(v_d, m_d, status_d, numParticles, rSatellites_d);
statusUpdate<<<N/64, 64>>>(r_d, v_d, m_d, status_d, numParticles);
}*/
//}
/*for (i = 0; i < numParticles; i++)
printf("%f\n", status_h[i]);
angMomentum(r_h, v_h, m_h, numParticles, &L0);
linMomentum(v_h, m_h, numParticles, &P0);
totalMass(m_h, numParticles, &M0);
K0 = energynew(r_h, v_h, m_h, numParticles, eps);*/
/*calcEccentricity<<<numParticles/64, 64>>>(r_d, v_d, m_d, ecc_d, numParticles);
cudaMemcpy(ecc_h, ecc_d, N_bytes/3, cudaMemcpyDeviceToHost);
calcDist<<<numParticles/64, 64>>>(r_d, dist_d);
cudaMemcpy(dist_h, dist_d, N_bytes/3, cudaMemcpyDeviceToHost);*/
/*for (i = 0; i < numSteps; i++) {
// One time step
for (j = 0; j < n; j++) {
collision<<<numParticles/64, 64>>>(r_d, v_d, status_d, rSatellites_d, numParticles, dt);
consMomentum<<<1, 1>>>(v_d, m_d, status_d, numParticles, rSatellites_d);
statusUpdate<<<N/64, 64>>>(r_d, v_d, m_d, status_d, numParticles);
A1_kernel<<<N/512, 512>>>(r_d, v_d, dt/(4*n));
mergeEject<<<numParticles/64, 64>>>(r_d, status_d, numParticles, rH);
consMomentum<<<1, 1>>>(v_d, m_d, status_d, numParticles, rSatellites_d);
statusUpdate<<<N/64, 64>>>(r_d, v_d, m_d, status_d, numParticles);
A2_kernel<<<numParticles/64, 64>>>(r_d, v_d, m_d, dt/(2*n), varr_d, status_d, numParticles);
reduce<512><<<1, numParticles/2, numParticles*sizeof(double)>>>(varr_d, &v_d[0], numParticles);
reduce<512><<<1, numParticles/2, numParticles*sizeof(double)>>>(varr_d+numParticles, &v_d[1], numParticles);
reduce<512><<<1, numParticles/2, numParticles*sizeof(double)>>>(varr_d+2*numParticles, &v_d[2], numParticles);
collision<<<numParticles/64, 64>>>(r_d, v_d, status_d, rSatellites_d, numParticles, dt);
consMomentum<<<1, 1>>>(v_d, m_d, status_d, numParticles, rSatellites_d);
statusUpdate<<<N/64, 64>>>(r_d, v_d, m_d, status_d, numParticles);
A1_kernel<<<N/512, 512>>>(r_d, v_d, dt/(4*n));
mergeEject<<<numParticles/64, 64>>>(r_d, status_d, numParticles, rH);
consMomentum<<<1, 1>>>(v_d, m_d, status_d, numParticles, rSatellites_d);
statusUpdate<<<N/64, 64>>>(r_d, v_d, m_d, status_d, numParticles);
}
B_kernel<<<numParticles/64, 64>>>(r_d, v_d, m_d, varr_d, dt, numParticles, status_d, eps);
reduce<512><<<1, numParticles/2, numParticles*sizeof(double)>>>(varr_d, &v_d[3], numParticles);
reduce<512><<<1, numParticles/2, numParticles*sizeof(double)>>>(varr_d+numParticles, &v_d[4], numParticles);
reduce<512><<<1, numParticles/2, numParticles*sizeof(double)>>>(varr_d+2*numParticles, &v_d[5], numParticles);
for (j = 0; j < n; j++) {
collision<<<numParticles/64, 64>>>(r_d, v_d, status_d, rSatellites_d, numParticles, dt);
consMomentum<<<1, 1>>>(v_d, m_d, status_d, numParticles, rSatellites_d);
statusUpdate<<<N/64, 64>>>(r_d, v_d, m_d, status_d, numParticles);
A1_kernel<<<N/512, 512>>>(r_d, v_d, dt/(4*n));
mergeEject<<<numParticles/64, 64>>>(r_d, status_d, numParticles, rH);
consMomentum<<<1, 1>>>(v_d, m_d, status_d, numParticles, rSatellites_d);
statusUpdate<<<N/64, 64>>>(r_d, v_d, m_d, status_d, numParticles);
A2_kernel<<<numParticles/64, 64>>>(r_d, v_d, m_d, dt/(2*n), varr_d, status_d, numParticles);
reduce<512><<<1, numParticles/2, numParticles*sizeof(double)>>>(varr_d, &v_d[0], numParticles);
reduce<512><<<1, numParticles/2, numParticles*sizeof(double)>>>(varr_d+numParticles, &v_d[1], numParticles);
reduce<512><<<1, numParticles/2, numParticles*sizeof(double)>>>(varr_d+2*numParticles, &v_d[2], numParticles);
collision<<<numParticles/64, 64>>>(r_d, v_d, status_d, rSatellites_d, numParticles, dt);
consMomentum<<<1, 1>>>(v_d, m_d, status_d, numParticles, rSatellites_d);
statusUpdate<<<N/64, 64>>>(r_d, v_d, m_d, status_d, numParticles);
A1_kernel<<<N/512, 512>>>(r_d, v_d, dt/(4*n));
mergeEject<<<numParticles/64, 64>>>(r_d, status_d, numParticles, rH);
consMomentum<<<1, 1>>>(v_d, m_d, status_d, numParticles, rSatellites_d);
statusUpdate<<<N/64, 64>>>(r_d, v_d, m_d, status_d, numParticles);
}
//cudaMemcpy(r_h, r_d, N_bytes, cudaMemcpyDeviceToHost);
//cudaMemcpy(v_h, v_d, N_bytes, cudaMemcpyDeviceToHost);
//cudaMemcpy(m_h, m_d, N_bytes/3, cudaMemcpyDeviceToHost);
//cudaMemcpy(status_h, status_d, N_bytes/3, cudaMemcpyDeviceToHost);
//cudaMemcpy(rSatellites_h, rSatellites_d, 2*sizeof(double), cudaMemcpyDeviceToHost);
//cudaMemcpy(dist_h, dist_d, N_bytes/3, cudaMemcpyDeviceToHost);
//angMomentum(r_h, v_h, m_h, numParticles, &L);
//linMomentum(v_h, m_h, numParticles, &P);
//totalMass(m_h, numParticles, &M);
//K = energynew(r_h, v_h, m_h, numParticles, eps);
//semMjrAxis = (m_h[0]+m_h[1])*sqrt(r_h[0]*r_h[0]+r_h[1]*r_h[1]+r_h[2]*r_h[2])/(2*(m_h[0]+m_h[1])-sqrt((r_h[0]-r_h[3])*(r_h[0]-r_h[3])+(r_h[1]-r_h[4])*(r_h[1]-r_h[4])+\
// (r_h[2]-r_h[5])*(r_h[2]-r_h[5]))*sqrt(v_h[3]*v_h[3]+v_h[4]*v_h[4]+v_h[5]*v_h[5])*sqrt(v_h[3]*v_h[3]+v_h[4]*v_h[4]+v_h[5]*v_h[5]));
//printf("%.15lf %.15lf %.15lf %.15lf %.15lf %.15lf\n", abs((L-L0)/L0), abs((P-P0)/P0), abs((M-M0)/M0), abs((K-K0)/K0), ecc_h[1], semMjrAxis);
}*/
calcEccentricity<<<numParticles/64, 64>>>(r_d, v_d, m_d, ecc_d, numParticles);
calcDist<<<numParticles/64, 64>>>(r_d, dist_d);
cudaMemcpy(dist_h, dist_d, N_bytes/3, cudaMemcpyDeviceToHost);
cudaMemcpy(ecc_h, ecc_d, N_bytes/3, cudaMemcpyDeviceToHost);
/*for (i = 0; i < numSteps; i++) {
// One time step
for (j = 0; j < n; j++) {
A1_kernel<<<N/512, 512>>>(r_d, v_d, dt/(4*n));
A2_kernel<<<numParticles/512, 512>>>(r_d, v_d, m_d, dt/(2*n), varr_d, status_d, numParticles);
reduce<512><<<numParticles/512, 512, 1024*sizeof(double)>>>(varr_d, vTemp_d, numParticles);
//reduce<SIZE><<<1, numParticles/1024, numParticles/512*sizeof(double)>>>(vTemp_d, &v_d[0], numParticles/512);
reduce<512><<<numParticles/512, 512, 1024*sizeof(double)>>>(varr_d+numParticles, vTemp_d, numParticles);
//reduce<SIZE><<<1, numParticles/1024, numParticles/512*sizeof(double)>>>(vTemp_d, &v_d[1], numParticles/512);
reduce<512><<<numParticles/512, 512, 1024*sizeof(double)>>>(varr_d+2*numParticles, vTemp_d, numParticles);
//reduce<SIZE><<<1, numParticles/1024, numParticles/512*sizeof(double)>>>(vTemp_d, &v_d[2], numParticles/512);
A1_kernel<<<N/512, 512>>>(r_d, v_d, dt/(4*n));
}
B_kernel<<<numParticles/512, 512>>>(r_d, v_d, m_d, varr_d, dt, numParticles, status_d, eps);
reduce<512><<<numParticles/512, 512, 1024*sizeof(double)>>>(varr_d, vTemp_d, numParticles);
//reduce<SIZE><<<1, numParticles/1024, numParticles/512*sizeof(double)>>>(vTemp_d, &v_d[3], numParticles/512);
reduce<512><<<numParticles/512, 512, 1024*sizeof(double)>>>(varr_d+numParticles, vTemp_d, numParticles);
//reduce<SIZE><<<1, numParticles/1024, numParticles/512*sizeof(double)>>>(vTemp_d, &v_d[4], numParticles/512);
reduce<512><<<numParticles/512, 512, 1024*sizeof(double)>>>(varr_d+2*numParticles, vTemp_d, numParticles);
//reduce<SIZE><<<1, numParticles/1024, numParticles/512*sizeof(double)>>>(vTemp_d, &v_d[5], numParticles/512);
for (j = 0; j < n; j++) {
A1_kernel<<<N/512, 512>>>(r_d, v_d, dt/(4*n));
A2_kernel<<<numParticles/512, 512>>>(r_d, v_d, m_d, dt/(2*n), varr_d, status_d, numParticles);
reduce<512><<<numParticles/512, 512, 1024*sizeof(double)>>>(varr_d, vTemp_d, numParticles);
//reduce<SIZE><<<1, numParticles/1024, numParticles/512*sizeof(double)>>>(vTemp_d, &v_d[0], numParticles/512);
reduce<512><<<numParticles/512, 512, 1024*sizeof(double)>>>(varr_d+numParticles, vTemp_d, numParticles);
//reduce<SIZE><<<1, numParticles/1024, numParticles/512*sizeof(double)>>>(vTemp_d, &v_d[1], numParticles/512);
reduce<512><<<numParticles/512, 512, 1024*sizeof(double)>>>(varr_d+2*numParticles, vTemp_d, numParticles);
//reduce<SIZE><<<1, numParticles/1024, numParticles/512*sizeof(double)>>>(vTemp_d, &v_d[2], numParticles/512);
A1_kernel<<<N/512, 512>>>(r_d, v_d, dt/(4*n));
}
}*/
/*for (i = 0; i < numSteps; i++) {
// One time step
for (j = 0; j < n; j++) {
collision<<<1, numParticles>>>(r_d, v_d, status_d, rSatellites_d, numParticles, dt);
consMomentum<<<1, 1>>>(v_d, m_d, status_d, numParticles, 1, rSatellites_d);
statusUpdate<<<1, N>>>(r_d, v_d, m_d, status_d, numParticles);
A1_kernel<<<1, N>>>(r_d, v_d, dt/(4*n));
mergeEject<<<1, numParticles>>>(r_d, status_d, numParticles, rH);
consMomentum<<<1, 1>>>(v_d, m_d, status_d, numParticles, 0, rSatellites_d);
statusUpdate<<<1, N>>>(r_d, v_d, m_d, status_d, numParticles);
A2_kernel<<<1, numParticles>>>(r_d, v_d, m_d, dt/(2*n), varr_d, status_d, numParticles);
reduce<2><<<1, numParticles/2, numParticles*sizeof(double)>>>(varr_d, &v_d[0], numParticles);
reduce<2><<<1, numParticles/2, numParticles*sizeof(double)>>>(varr_d+numParticles, &v_d[1], numParticles);
reduce<2><<<1, numParticles/2, numParticles*sizeof(double)>>>(varr_d+2*numParticles, &v_d[2], numParticles);
collision<<<1, numParticles>>>(r_d, v_d, status_d, rSatellites_d, numParticles, dt);
consMomentum<<<1, 1>>>(v_d, m_d, status_d, numParticles, 1, rSatellites_d);
statusUpdate<<<1, N>>>(r_d, v_d, m_d, status_d, numParticles);
A1_kernel<<<1, N>>>(r_d, v_d, dt/(4*n));
mergeEject<<<1, numParticles>>>(r_d, status_d, numParticles, rH);
consMomentum<<<1, 1>>>(v_d, m_d, status_d, numParticles, 0, rSatellites_d);
statusUpdate<<<1, N>>>(r_d, v_d, m_d, status_d, numParticles);
}
B_kernel<<<1, numParticles>>>(r_d, v_d, m_d, varr_d, dt, numParticles, status_d, eps);
reduce<2><<<1, numParticles/2, numParticles*sizeof(double)>>>(varr_d, &v_d[3], numParticles);
reduce<2><<<1, numParticles/2, numParticles*sizeof(double)>>>(varr_d+numParticles, &v_d[4], numParticles);
reduce<2><<<1, numParticles/2, numParticles*sizeof(double)>>>(varr_d+2*numParticles, &v_d[5], numParticles);
for (j = 0; j < n; j++) {
collision<<<1, numParticles>>>(r_d, v_d, status_d, rSatellites_d, numParticles, dt);
consMomentum<<<1, 1>>>(v_d, m_d, status_d, numParticles, 1, rSatellites_d);
statusUpdate<<<1, N>>>(r_d, v_d, m_d, status_d, numParticles);
A1_kernel<<<1, N>>>(r_d, v_d, dt/(4*n));
mergeEject<<<1, numParticles>>>(r_d, status_d, numParticles, rH);
consMomentum<<<1, 1>>>(v_d, m_d, status_d, numParticles, 0, rSatellites_d);
statusUpdate<<<1, N>>>(r_d, v_d, m_d, status_d, numParticles);
A2_kernel<<<1, numParticles>>>(r_d, v_d, m_d, dt/(2*n), varr_d, status_d, numParticles);
reduce<2><<<1, numParticles/2, numParticles*sizeof(double)>>>(varr_d, &v_d[0], numParticles);
reduce<2><<<1, numParticles/2, numParticles*sizeof(double)>>>(varr_d+numParticles, &v_d[1], numParticles);
reduce<2><<<1, numParticles/2, numParticles*sizeof(double)>>>(varr_d+2*numParticles, &v_d[2], numParticles);
collision<<<1, numParticles>>>(r_d, v_d, status_d, rSatellites_d, numParticles, dt);
consMomentum<<<1, 1>>>(v_d, m_d, status_d, numParticles, 1, rSatellites_d);
statusUpdate<<<1, N>>>(r_d, v_d, m_d, status_d, numParticles);
A1_kernel<<<1, N>>>(r_d, v_d, dt/(4*n));
mergeEject<<<1, numParticles>>>(r_d, status_d, numParticles, rH);
consMomentum<<<1, 1>>>(v_d, m_d, status_d, numParticles, 0, rSatellites_d);
statusUpdate<<<1, N>>>(r_d, v_d, m_d, status_d, numParticles);
}
}*/
// Copy arrays from device to host
/*cudaMemcpy(r_h, r_d, N_bytes, cudaMemcpyDeviceToHost);
cudaMemcpy(v_h, v_d, N_bytes, cudaMemcpyDeviceToHost);
cudaMemcpy(m_h, m_d, N_bytes/3, cudaMemcpyDeviceToHost);
cudaMemcpy(status_h, status_d, N_bytes/3, cudaMemcpyDeviceToHost);
cudaMemcpy(rSatellites_h, rSatellites_d, 2*sizeof(double), cudaMemcpyDeviceToHost);
int h = 0;
printf("Embryo radius = %.16lf\n", rSatellites_h[0]);
for (int kk = 0; kk < numParticles; kk++) {
if (status_h[kk] == 0) {
printf("Index: %d\n", kk);
printf("New Position\n");
printf("%.16lf %.16lf %.16lf\n", r_h[3*kk], r_h[3*kk+1], r_h[3*kk+2]);
printf("New Velocity\n");
printf("%.16lf %.16lf %.16lf\n", v_h[3*kk], v_h[3*kk+1], v_h[3*kk+2]);
h += 1;
}
}
printf("%d\n", h);
printf("New Mass Planet\n");
printf("%.16lf\n", m_h[0]);
printf("New Velocity Planet\n");
printf("%.16lf %.16lf %.16lf\n", v_h[0], v_h[1], v_h[2]);
printf("New Mass Embryo\n");
printf("%.16lf\n", m_h[1]);
printf("New Velocity Embryo\n");
printf("%.16lf %.16lf %.16lf\n", v_h[3], v_h[4], v_h[5]);
printf("After %d time step(s):\n", numSteps);
printf("r\n");
for (i = 0; i < 9; i += 3)
printf("%.16lf %.16lf %.16lf\n", r_h[i], r_h[i+1], r_h[i+2]);
printf("...\n");
for (i = 3*numParticles - 9; i < 3*numParticles; i += 3)
printf("%.16lf %.16lf %.16lf\n", r_h[i], r_h[i+1], r_h[i+2]);
printf("\n");
printf("v\n");
for (i = 0; i < 9; i += 3)
printf("%.16lf %.16lf %.16lf\n", v_h[i], v_h[i+1], v_h[i+2]);
printf("\n");
printf("...\n");
for (i = 3*numParticles - 9; i < 3*numParticles; i += 3)
printf("%.16lf %.16lf %.16lf\n", v_h[i], v_h[i+1], v_h[i+2]);*/
// Free allocated memory on host and device
cudaFree(r_d);
cudaFree(v_d);
cudaFree(m_d);
cudaFree(varr_d);
cudaFree(status_d);
cudaFree(ecc_d);
cudaFree(dist_d);
cudaFree(rSatellites_d);
}
}
|
1,056
|
#include "includes.h"
__device__ inline float stableSigmoid(float x) {
if(x >= 0) {
float z = expf(-x);
return 1.0 / (1.0 + z);
} else {
float z = expf(x);
return z / (1.0 + z);
}
}
__global__ void gGRUFastBackward(float* outState, float* outXW, float* outSU, float* outB, const float* state, const float* xW, const float* sU, const float* b, const float* mask, const float* adj, size_t rows, size_t cols, bool final) {
for(int bid = 0; bid < rows; bid += gridDim.x) {
int j = bid + blockIdx.x;
if(j < rows) {
float m = !mask || mask[j];
float* rowOutState = outState + j * cols;
float* rowOutXW = outXW + j * cols * 3;
float* rowOutSU = outSU + j * cols * 3;
const float* rowState = state + j * cols;
const float* rowXW = xW + j * cols * 3;
const float* rowSU = sU + j * cols * 3;
const float* rowAdj = adj + j * cols;
for(int tid = 0; tid < cols; tid += blockDim.x) {
int i = tid + threadIdx.x;
if(i < cols) {
int k = i + cols;
int l = i + 2 * cols;
float r = stableSigmoid(rowXW[i] + rowSU[i] + b[i]);
float z = stableSigmoid(rowXW[k] + rowSU[k] + b[k]);
float h;
if(final)
h = tanhf(rowXW[l] + (rowSU[l] + b[l]) * r);
else
h = tanhf(rowXW[l] + rowSU[l] * r + b[l]);
float adj = rowAdj[i];
float t = (1 - z) * (1 - h * h);
// df/ds
if(outState)
rowOutState[i] += (m * z - m + 1) * adj;
// df/d(xW_r) ...
float dfdxW_r = m * r * (1 - r) * t * adj;
if(final)
dfdxW_r *= rowSU[l] + b[l];
else
dfdxW_r *= rowSU[l];
if(outXW)
rowOutXW[i] += dfdxW_r;
if(outSU)
rowOutSU[i] += dfdxW_r;
if(outB)
atomicAdd(outB + i, dfdxW_r);
// df/d(xW_z) ...
float dfdxW_z = m * (1 - z) * z * (rowState[i] - h) * adj;
if(outXW)
rowOutXW[k] += dfdxW_z;
if(outSU)
rowOutSU[k] += dfdxW_z;
if(outB)
atomicAdd(outB + k, dfdxW_z);
// df/d(xW_x) ...
float dfdxW_x = m * t * adj;
if(outXW)
rowOutXW[l] += dfdxW_x;
if(outSU)
rowOutSU[l] += dfdxW_x * r;
if(outB)
if(final)
atomicAdd(outB + l, dfdxW_x * r);
else
atomicAdd(outB + l, dfdxW_x);
}
}
}
}
}
|
1,057
|
/*
Program perform matrix multiplication
*/
#include<stdio.h>
#include<cuda.h>
#include<assert.h>
#include<stdlib.h>
#include<sys/time.h>
#define VAL_LIMIT 10
#define DEBUG 0
#define TILE_WIDTH 32
cudaError_t err;
/*
* @PRAM : Number of rows and columns
* @RETURN : Pointer to created Matrix
* @DESC :
* @SEE :
* @TODO :
*
*/
float* createMatrix(int r,int c)
{
float *temp;
temp = (float*) malloc(sizeof(float)*r*c);
return temp;
}
/*
* @DESC : Frees the memory allocated to the matrix
* @PRAM : pointer to the matrix
* @RETURN : Nothing
* @SEE :
* @TODO :
*
*/
void destroyMAtrix(float *mat)
{
free(mat);
}
/*
* @PRAM : Device pointer, number of rows and columns
* @RETURN : Nothing
* @DESC : Creates a matrix of float * rows * columns on device
* @SEE :
* @TODO :
*
*/
void createMatrixDevice(float **m, int r, int c)
{
int size = sizeof(float)*r*c;
err = cudaSuccess;
err = cudaMalloc(m, size);
if (err != cudaSuccess)
{
fprintf(stderr,"%s, %d.\n %s.",__FILE__,__LINE__,cudaGetErrorString(err));
exit(EXIT_FAILURE);
}
}
/*
* @PRAM : Host pointer, Device pointer, Number of rows and columns
* @RETURN : Nothing
* @DESC : Copies data from host pointer to device pointer
* @SEE :
* @TODO :
*
*/
void transferToDevice(float *hostptr, float *deviceptr, int r, int c)
{
int size = sizeof(float) * r*c;
err = cudaSuccess;
err = cudaMemcpy(deviceptr,hostptr,size,cudaMemcpyHostToDevice);
if (err != cudaSuccess)
{
//fprintf(stderr,"%s, %d.\n %s.",__FILE__,__LINE__,cudaGetErrorString(err));
fprintf(stderr,"%s",cudaGetErrorString(err));
exit(EXIT_FAILURE);
}
}
void transferFromDevice(float *hostptr, float *deviceptr, int r, int c)
{
int size = sizeof(float) * r*c;
err = cudaSuccess;
err = cudaMemcpy(hostptr,deviceptr,size,cudaMemcpyDeviceToHost);
if (err != cudaSuccess)
{
fprintf(stderr,"%s, %d.\n %s.",__FILE__,__LINE__,cudaGetErrorString(err));
exit(EXIT_FAILURE);
}
}
void initMatrix(float *m,int r,int c)
{
for(int i=0;i<r;i++)
{
for(int j=0;j<c;j++)
{
m[ i*c +j ] = (float) (rand()%VAL_LIMIT);
}
}
}
void matMul(float *A, float *B, float *C, int Aw, int Ah, int Bw)
{
for( int i=0 ; i<Ah; i++)
{
for( int j=0; j<Bw; j++)
{
float sum=0;
for( int k=0; k<Aw; k++)
{
float a = A[i*Aw+k];
float b = B[k*Bw +j];
sum += a*b;
if(DEBUG)
printf(" %d * %d +",i*Aw+k,k*Bw+j);
}
C[i*Bw+j] = sum;
if(DEBUG)
printf("%d\n",i*Bw+j);
}
}
}
__global__
void matMulKernel(float *A, float *B, float *C, int Ac, int Ar, int Bc)
{
int row = blockIdx.x * TILE_WIDTH + threadIdx.x;
int col = blockIdx.y * TILE_WIDTH + threadIdx.y;
int sum = 0;
for ( int i=0 ; i<Ar; i++)
{
sum += A[row *Ac + i] * B[ i *Bc + col];
}
C[row*Bc+col] = sum;
}
void pMatMul(float *A,float *B,float *C, int Ac, int Ar, int Bw)
{
dim3 gridProp(ceil(Bw/TILE_WIDTH), ceil(Ar/TILE_WIDTH), 1);
dim3 blockProp(TILE_WIDTH,TILE_WIDTH,1);
matMulKernel<<<gridProp,blockProp>>>(A, B, C, Ac, Ar, Bw);
}
void printMat(float *mat, int r, int c)
{
for(int i=0;i<r;i++)
{
for(int j=0;j<c;j++)
{
printf("%4.1f \t",mat[i*c+j]);
}
printf("\n");
}
}
bool check(float *mat, float *mat2, int r, int c)
{
for(int i=0;i<r;i++)
{
for(int j=0;j<c;j++)
{
if( mat2[i*c+j] != mat[i*c+j])
return false;
}
}
return true;
}
int main()
{
float *h_A, *h_B, *h_C,*h_D;
float *d_A, *d_B, *d_C;
float milisecs;
unsigned int Ar=1024, Ac=1024;
unsigned int Br=1024, Bc=1024;
unsigned int Cr=1024, Cc=1024;
assert(Ac == Br);
cudaEvent_t start,stop;
cudaEventCreate(&start);
cudaEventCreate(&stop);
h_A = createMatrix(Ar, Ac);
assert(h_A != NULL);
h_B = createMatrix(Br, Bc);
assert(h_B != NULL);
h_C = createMatrix(Cr, Cc);
assert(h_C != NULL);
h_D = createMatrix(Cr, Cc);
assert(h_D != NULL);
initMatrix(h_A, Ar, Ac);
initMatrix(h_B, Br, Bc);
if(DEBUG){
printf("Matrix A:\n");
printMat(h_A, Ar, Ac);
printf("Matrix B:\n");
printMat(h_B, Br, Bc);
}
matMul(h_A, h_B, h_C, Ac, Ar, Bc);
if(DEBUG){
printf("Matrix C:\n");
printMat(h_C, Cr, Cc);
}
createMatrixDevice(&d_A, Ar, Ac);
createMatrixDevice(&d_B, Br, Bc);
createMatrixDevice(&d_C, Cr, Cc);
transferToDevice(h_A, d_A, Ar, Ac);
transferToDevice(h_B, d_B, Br, Bc);
cudaEventRecord(start);
pMatMul(d_A, d_B, d_C, Ac, Ar, Bc);
cudaEventRecord(stop);
transferFromDevice(h_D, d_C, Cr, Cc);
cudaEventSynchronize(stop);
cudaEventElapsedTime(&milisecs,start,stop);
printf("Time required for parallel execution %f\n",milisecs);
if(DEBUG){
printf("Matrix D:\n");
printMat(h_D, Cr, Cc);
}
if(check(h_D, h_C, Cr, Cc))
printf("Success !! :) \n");
else
printf("Failed !! :( \n");
cudaFree(d_A);
cudaFree(d_B);
cudaFree(d_C);
destroyMAtrix(h_A);
destroyMAtrix(h_B);
destroyMAtrix(h_D);
destroyMAtrix(h_C);
return 0;
}
|
1,058
|
//A practice code of using constant memory in dot product
//Author: Zhaoyuan "Maxwell" Cui
//Jun 13, 2017
#include<stdio.h>
#include<iostream>
#define VEC_DIM 120
//Define constant memory
__constant__ float d_vec[VEC_DIM];
//Prototype the kernal
__global__ void kernal(float* input,float* d_result);
int main()
{
//Declare variables on host and device
float *c_vec=new float [VEC_DIM];
float *c_result=new float;
float *d_result,*d_input;
//Allocate memory for device variable
cudaMalloc(&d_result,sizeof(float));
cudaMalloc(&d_input,sizeof(float)*VEC_DIM);
//Initialize host vector
for(int i=0;i<VEC_DIM;i++)
{
c_vec[i]=1;
}
//Copy memory to constant memory
cudaMemcpyToSymbol(d_vec,c_vec,sizeof(float)*VEC_DIM);
//Copy memory to device input vector
cudaMemcpy(d_input,c_vec,sizeof(float)*VEC_DIM,cudaMemcpyHostToDevice);
//RUN!!
kernal<<<1,VEC_DIM>>>(d_input,d_result);
//Copy result from device to host
cudaMemcpy(c_result,d_result,sizeof(float),cudaMemcpyDeviceToHost);
//SYNC...
cudaDeviceSynchronize();
//Show the result
std::cout<<"The result is: "<<*c_result<<std::endl;
//Free allocated memory
delete [] c_vec;
delete c_result;
cudaFree(d_result);
cudaFree(d_input);
return 0;
}
__global__ void kernal(float* d_input,float* d_result)
{
//Declare and acquire thread id
int tid;
tid=blockIdx.x*blockDim.x+threadIdx.x;
//Declare shared memory
__shared__ float cache[VEC_DIM];
//do the calculation
if(tid<VEC_DIM)
{
cache[tid]=d_input[tid]*d_vec[tid];
}
//Wait for all threads
__syncthreads();
//Initialize the return variable
*d_result=0;
//Caluclate the result
for(int i=0;i<VEC_DIM;i++)
{
*d_result+=cache[i];
}
}
|
1,059
|
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <iostream>
int sorting(int argc, char** argv)
{
std::cout << "not implemented" << std::endl;
return 0;
}
|
1,060
|
__global__ void gaussKernel(float *ptr, int width, int height, int sigma2)
{
int x = threadIdx.x + blockIdx.x * blockDim.x;
int y = threadIdx.y + blockIdx.y * blockDim.y;
if(x < width && y < height)
{
int xc = width / 2;
int yc = height / 2;
int idx = (width * y + x);
ptr += idx;
float pi = 3.1415926f;
*ptr = expf(-((x - xc)*(x - xc)+(y - yc)*(y - yc)) / (2*sigma2)) / (2 * pi * sigma2);
}
}
extern "C"
float *makeGaussianGpu(int width, int height, float sigma)
{
float *ptr = NULL;
cudaMalloc((void**) &ptr, width * height * sizeof(float));
float sigma2 = sigma * sigma;
dim3 threads(16, 16);
dim3 blocks((width + threads.x - 1) / threads.x, (height + threads.y - 1) / threads.y);
gaussKernel<<<blocks, threads>>>(ptr, width, height, sigma2);
return ptr;
}
|
1,061
|
#include "includes.h"
__constant__ float *c_Kernel;
__global__ void convolutionColumnsKernel_down_smp( float *d_Dst, float *d_Src, int imageW, int imageH, int n_imageH, int pitch, int filter_Rad, int Halo_steps )
{
extern __shared__ float s_Data[];
//Offset to the upper halo edge
const int baseX = blockIdx.x * COLUMNS_BLOCKDIM_X + threadIdx.x;
const int baseY = (blockIdx.y * 2 * COLUMNS_RESULT_STEPS - Halo_steps) * COLUMNS_BLOCKDIM_Y + threadIdx.y;
const int baseY1 = (blockIdx.y * COLUMNS_RESULT_STEPS) * COLUMNS_BLOCKDIM_Y + threadIdx.y;
if (baseX < imageW)
{
d_Src += baseY * pitch + baseX;
d_Dst += baseY1 * pitch + baseX;
//Upper halo
#pragma unroll
for (int i = 0; i < Halo_steps; i++)
{
s_Data[(threadIdx.x*(2 * COLUMNS_RESULT_STEPS + 2 * Halo_steps) *COLUMNS_BLOCKDIM_Y) + threadIdx.y + i * COLUMNS_BLOCKDIM_Y] = (baseY + i * COLUMNS_BLOCKDIM_Y >= 0) ? d_Src[i * COLUMNS_BLOCKDIM_Y * pitch] : 0;
}
//Lower halo + Main data
#pragma unroll
for (int i = Halo_steps; i < Halo_steps + 2 * COLUMNS_RESULT_STEPS + Halo_steps; i++)
{
s_Data[(threadIdx.x*(2 * COLUMNS_RESULT_STEPS + 2 * Halo_steps) *COLUMNS_BLOCKDIM_Y) + threadIdx.y + i * COLUMNS_BLOCKDIM_Y] = (baseY + i * COLUMNS_BLOCKDIM_Y < imageH) ? d_Src[i * COLUMNS_BLOCKDIM_Y * pitch] : 0;
}
//Compute and store results
__syncthreads();
#pragma unroll
for (int i = 0; i < COLUMNS_RESULT_STEPS; ++i)
{
float sum = 0;
if (baseY1 + i * COLUMNS_BLOCKDIM_Y < n_imageH)
{
#pragma unroll
for (int j = -filter_Rad; j <= filter_Rad; ++j)
{
sum += c_Kernel[filter_Rad - j] * s_Data[(threadIdx.x*(2 * COLUMNS_RESULT_STEPS + 2 * Halo_steps) *COLUMNS_BLOCKDIM_Y) + 2 * threadIdx.y + 2 * i * COLUMNS_BLOCKDIM_Y + Halo_steps * COLUMNS_BLOCKDIM_Y + j];
}
d_Dst[i * COLUMNS_BLOCKDIM_Y * pitch] = sum;
}
}
}
}
|
1,062
|
#include <stdlib.h>
#include <stdio.h>
#include <math.h>
#define BLOCK_SIZE 3
#define WA 3
#define HA 3
#define WB 3
#define HB 3
#define WC 3
#define HC 3
void Init(float * data ,int size)
{
for(int i = 0; i < size; ++i)
data[i] = i;
}
__global__ void matrixMul(float* A,float* B,float* C,int wA,int wB)
{
int tx = threadIdx.x;
int ty = threadIdx.y;
float value = 0;
for(int i = 0; i < wA; ++i)
{
float elementA = A[ty * wA + i];
float elementB = B[i * wB + tx];
value += elementA * elementB;
}
// write to device mem
C[ty * wA + tx] = value;
}
int main(int argc ,char** argv)
{
srand(2006);
unsigned int size_A = WA * HA;
unsigned int mem_size_A =sizeof(float) * size_A;
float* h_A = (float*) malloc(mem_size_A);
unsigned int size_B = WB * HB;
unsigned int mem_size_B =sizeof(float) * size_B;
float * h_B = (float*) malloc(mem_size_B);
unsigned int size_C = WC * HC;
unsigned int mem_size_C =sizeof(float) * size_C;
float * h_C = (float *) malloc(mem_size_C);
Init(h_A, size_A);
Init(h_B, size_B);
printf("\n\nMatrix A\n");
for(int i = 0; i < size_A; i++)
{
printf("%f ", h_A[i]);
if(((i + 1) % WA) == 0)
printf("\n");
}
printf("\n\nMatrix B\n");
for(int i = 0; i < size_B; i++)
{
printf
("%f ", h_B[i]);
if(((i + 1) % WB) == 0)
printf("\n");
}
float* d_A;
float* d_B;
float* d_C;
cudaMalloc((void**) &d_A, mem_size_A);
cudaMalloc((void**) &d_B, mem_size_B);
cudaMalloc((void**) &d_C, mem_size_C);
cudaMemcpy(d_A, h_A,mem_size_A ,cudaMemcpyHostToDevice);
cudaMemcpy(d_B, h_B,mem_size_B ,cudaMemcpyHostToDevice);
dim3 threads(BLOCK_SIZE , BLOCK_SIZE);
dim3 grid(WC / threads.x, HC / threads.y);
matrixMul<<< grid , threads >>>(d_A,d_B, d_C, WA, WB);
cudaMemcpy(h_C, d_C, mem_size_C ,cudaMemcpyDeviceToHost);
printf("\n\nMatrix C (Results) \n");
for(int i = 0;i<size_C; i ++){
printf("%f ",h_C[i]);
if(((i+ 1) % WC) == 0)
printf("\n");
}
printf("\n");
cudaFree(d_A);
cudaFree(d_B);
cudaFree(d_C);
free(h_A);
free(h_B);
free(h_C);
}
|
1,063
|
#include <stdio.h>
#include <stdlib.h>
#include <stdint.h>
#include <math.h>
#include <complex.h>
#include <cuda_runtime.h>
#include <utility>
#include <sys/time.h>
#define K 3
#define BLCH 8
#define BLCW 32
int compute(float *img, float *f, float * out, int bh, int bw, int imgH, int imgW, int imgN, int nF, int convH, int convW) {
int inm1, inm2, inm3, inm4, inf, ind1, ind2, ind3;
inm1 = 0;
inf = 0;
ind1 = 0;
for (int mi = 0; mi < imgN; mi++){
ind1 += convW * convH;
inm1 += imgW * imgH;
for (int mj = 0; mj < convH; mj++){
ind2 = ind1 + convW * mj;
inm2 = inm1 + imgW * mj;
for (int mk = 0; mk < convW; mk++){
ind3 = ind2 + mk;
inm3 = inm2 + mk;
for (int fi = 0; fi < nF; fi++){
inm4 = inm3 + imgW * fi;
inf = ind3*nF*nF + fi*nF;
for (int fj = 0; fj < nF; fj++){
out[ind3] += img[inm4+fj] * f[inf+fj];
}
}
}
}
}
return 0;
}
int compute_csr(float *img, float *f_csr, int *pos, int *coor, float * out, int bh, int bw, int imgH, int imgW, int imgN, int nF, int convH, int convW) {
int inm1, inm2, inm3, inm4, inf, inf1, inf2, ind1, ind2, ind3;
for (int mi = 0; mi < imgN; mi++){
ind1 = mi * convW * convH;
inm1 = mi * imgW * imgH;
for (int mj = 0; mj < convH; mj++){
ind2 = ind1 + convW * mj;
inm2 = inm1 + imgW * mj;
for (int mk = 0; mk < convW; mk++){
ind3 = ind2 + mk;
inm3 = inm2 + mk;
inf = nF*nF*ind3;
for (int fi = 0; fi < nF; fi++){
inf1 = pos[fi+inf];
inf2 = pos[fi+1+inf];
inm4 = inm3 + fi*imgW;
for (int fj = inf1; fj < inf2; fj++){
out[ind3] += img[inm4+coor[fj]] * f_csr[fj+inf];
}
}
}
}
}
return 0;
}
__global__ void compute_gpu(float *img, float *f, float * out, int bh, int bw, int imgH, int imgW, int imgN, int nF, int convH, int convW){
int idY = blockDim.y * blockIdx.y + threadIdx.y;
int idX = blockDim.x * blockIdx.x + threadIdx.x;
int inm1, inm2, inm3, inm4, inf, ind1, ind2, ind3;
inm1 = 0;
inf = 0;
ind1 = 0;
for (int mi = 0; mi < imgN; mi++){
ind1 += convW * convH;
inm1 += imgW * imgH;
if (idX < convH && idY < convW){
ind2 = ind1 + convW * idX;
inm2 = inm1 + imgW * idX;
ind3 = ind2 + idY;
inm3 = inm2 + idY;
for (int fi = 0; fi < nF; fi++){
inm4 = inm3 + imgW * fi;
inf = ind3*nF*nF + fi*nF;
for (int fj = 0; fj < nF; fj++){
out[ind3] += img[inm4+fj] * f[inf+fj];
}
}
}
}
}
__global__ void compute_gpu_csr(float *img, float *f_csr, int *pos, int *coor, float *out, int bh, int bw, int imgH, int imgW, int imgN, int nF, int convH, int convW){
int idX = blockDim.x * blockIdx.x + threadIdx.x;
int idY = blockDim.y * blockIdx.y + threadIdx.y;
int con, imgg, inm1, inm2, inm3, inm4, inf, inf1, inf2, ind1, ind2, ind3;
for (int mi = 0; mi < imgN; mi++){
ind1 = mi * convW * convH;
inm1 = mi * imgW * imgH;
if (idX < convH && idY < convW){
ind2 = ind1 + convW * idX;
inm2 = inm1 + imgW * idX;
ind3 = ind2 + idY;
inm3 = inm2 + idY;
inf = nF*nF*ind3;
for (int fi = 0; fi < nF; fi++){
inf1 = pos[fi+inf];
inf2 = pos[fi+1+inf];
inm4 = inm3 + fi*imgW;
for (int fj = inf1; fj < inf2; fj++){
out[ind3] += img[inm4+coor[fj]] * f_csr[fj+inf];
}
}
}
}
}
int main(int argc, char **argv){
//create parameters
int imgH = 2048;
int imgW = 2048;
int imgN = 10;
int blcH = BLCH;
int blcW = BLCW;
int k = K;
int s = 1;
int nB = (imgH * imgW) / (blcH * blcW);
//int nT = (blcW+k) * (blcH+k);
int nT = blcW * blcH;
int imgDims = imgH * imgW * imgN;
int imgSize = imgDims * sizeof(float);
int num=0;
srand (time(NULL));
// create host array that can hold pixel intensity values
float *h_img = new float[imgDims];
for(int i=0; i<imgDims; i++){
h_img[i] = (float)(rand()%10485)/10485;
}
// create host and device array that holds the convoluted matrix
int convH = ( (imgH - k) / s ) + 1;
int convW = ( (imgW - k) / s ) + 1;
int convDims = convH * convW;
int convSize = convDims * sizeof(float);
float *h_convolved = new float[convDims];
for(int i=0; i<convDims; i++){
h_convolved[i] = 0.0;
}
// create filter and copy to constant memory
int filterDims = k * k * convDims;
int filterSize = filterDims * sizeof(float);
float *filter = new float[filterDims];
num = 0;
for(int i=0; i<filterDims; i++){
if(rand()/40 == 0){
num++;
filter[i] = (float)(rand()%10485)/10485;
}
else{
filter[i] = 0;
}
}
//create index arrays of CSR
int *pos = new int[(k+1)*convDims];
int *coor = new int[num];
float *h_fcsr = new float[num];
int csrposSize = (k+1)*convDims*sizeof(int);
int csrcooSize = num*sizeof(int);
int fSize = num*sizeof(float);
for(int ki=0; ki<convDims; ki++){
pos[0+ki*(k+1)] = 0;
int index_p=0;
int z = ki*k*k;
for(int i=0; i<k; i++){
for (int j=0; j<k; j++){
if (filter[i*k+j+z] != 0){
coor[index_p] = j;
h_fcsr[index_p] = filter[i*k+j+z];
index_p++;
}
}
pos[i+1+ki*(k+1)] = index_p;
}
}
// create device array that can hold pixel intensity values in GPU GM
float *d_img;
float *d_convolved;
float *d_filter;
int *d_pos;
int *d_coor;
cudaMalloc((void **) &d_filter, fSize);
cudaMemcpyToSymbol(d_filter, h_fcsr, fSize, cudaMemcpyHostToDevice);
cudaMalloc((void **) &d_convolved, convSize);
cudaMemcpy(d_convolved, h_convolved, convSize, cudaMemcpyHostToDevice);
cudaMalloc((void **) &d_img, imgSize);
cudaMemcpy(d_img, h_img, imgSize, cudaMemcpyHostToDevice);
cudaMalloc((void **) &d_pos, csrposSize);
cudaMemcpy(d_pos, pos, csrposSize, cudaMemcpyHostToDevice);
cudaMalloc((void **) &d_coor, csrcooSize);
cudaMemcpy(d_coor, coor, csrcooSize, cudaMemcpyHostToDevice);
struct timeval starttime, endtime;
double elapsed = 0.0;
for (int i = 0; i<10000; i++){
gettimeofday(&starttime,NULL);
// call the kernel
//compute_gpu<<<nB, nT>>>(d_img, d_convolved, blcH, blcW, imgH, imgW, imgN, k, convH, convW);
compute_gpu_csr<<<nB, nT>>>(d_img, d_filter, d_pos, d_coor, d_convolved, blcH, blcW, imgH, imgW, imgN, k, convH, convW);
gettimeofday(&endtime,NULL);
elapsed += ((endtime.tv_sec-starttime.tv_sec)*1000000 + endtime.tv_usec-starttime.tv_usec)/1000000.0;
}
cudaMemcpy(h_convolved, d_convolved, convSize, cudaMemcpyDeviceToHost);
cudaDeviceReset();
printf("Input imgH: %d imgW: %d imgN: %d\n", imgH, imgW, imgN);
printf("Tile width: %d height: %d\n", blcW, blcH);
printf("Block number: %d, block size: %d \n", nB, nT);
printf("time: %f \n", elapsed);
delete h_img;
delete h_convolved;
delete pos;
delete h_fcsr;
delete coor;
return 0;
}
|
1,064
|
#include <stdio.h>
#include <cuda_runtime.h>
#include <device_launch_parameters.h>
#include <chrono>
using namespace std;
__global__
void addKernel( int* a, int* b, int* c )
{
int idx = threadIdx.x;
c[idx] = a[idx] + b[idx];
}
void testCudaAdd() {
const int COUNT = 10;
int* a = new int[COUNT];
int* b = new int[COUNT];
int* c = new int[COUNT];
for( int i=0;i<COUNT;++i) {
a[i] = i;
b[i] = i*100;
}
int *devArrayA = 0, *devArrayB = 0, *devArrayC = 0;
cudaMalloc( &devArrayA, sizeof(int) * COUNT );
cudaMalloc( &devArrayB, sizeof(int) * COUNT );
cudaMalloc( &devArrayC, sizeof(int) * COUNT );
// ̓f[^̓].
cudaMemcpy( devArrayA, a, sizeof(int)*COUNT, cudaMemcpyHostToDevice );
cudaMemcpy( devArrayB, b, sizeof(int)*COUNT, cudaMemcpyHostToDevice );
// s.
addKernel<<<1, COUNT>>>(devArrayA, devArrayB, devArrayC );
cudaDeviceSynchronize();
// ʂ̓ǂݖ߂.
cudaMemcpy( c, devArrayC, sizeof(int)*COUNT, cudaMemcpyDeviceToHost );
for(int i=0;i<COUNT;++i) {
printf( "%d ", c[i] );
}
printf( "\n" );
cudaFree( devArrayC );
cudaFree( devArrayB );
cudaFree( devArrayA );
delete[] a;
delete[] b;
delete[] c;
}
__global__
void multMatrix( float* a, float* b, float* c, int COUNT )
{
int idx = blockDim.x * threadIdx.y + threadIdx.x;
int idxCol = blockDim.x * blockIdx.x + threadIdx.x;
int idxRow = blockDim.y * blockIdx.y + threadIdx.y;
float scanSum = 0;
for( int i=0;i<COUNT;++i) {
if( idxCol >= COUNT || idxRow >= COUNT ) {
continue;
}
#if 01
scanSum += a[ idxRow*COUNT + i ] * b[ idxCol + i*COUNT ];
#else
// x̖肪o.
scanSum = __fadd_rn( scanSum, __fmul_rn( a[idxRow*COUNT+i], b[idxCol+i*COUNT] ) );
#endif
}
if( idxCol < COUNT && idxRow < COUNT ) {
c[idxCol+idxRow*COUNT] = scanSum;
}
}
void testCudaMult() {
const int COUNT = 1024;
const int SIZE = COUNT*COUNT; // sTCY
float* a = new float[SIZE];
float* b = new float[SIZE];
float* c = new float[SIZE];
for( int i=0;i<SIZE;++i) {
a[i] = float(0.001f * i );
b[i] = float(0.005f * i );
}
chrono::high_resolution_clock::time_point start, stop;
start = chrono::high_resolution_clock::now();
for( int i=0;i<COUNT;++i ) {
for( int j=0;j<COUNT;++j ) {
float tmp = float(0);
for(int t=0;t<COUNT;++t) {
tmp += a[t+i*COUNT] * b[j+COUNT*t];
}
c[i*COUNT+j] = tmp;
}
}
stop = chrono::high_resolution_clock::now();
#if 0
for(int i=0;i<COUNT;++i) {
for(int j=0;j<COUNT;++j) {
printf( "%f ", c[i*COUNT+j] );
}
printf( "\n" );
}
#endif
chrono::microseconds cpuTime = chrono::duration_cast<chrono::microseconds>(stop-start);
printf( "CPU: %d (us)\n", cpuTime.count() );
printf( "\n" );
float* gpuC = new float[SIZE];
for( int i=0;i<SIZE;++i) {
a[i] = float(0.001f * i );
b[i] = float(0.005f * i );
gpuC[i] = 0.0f;
}
float *devA, *devB, *devC;
cudaMalloc( &devA, sizeof(float) * SIZE );
cudaMalloc( &devB, sizeof(float) * SIZE );
cudaMalloc( &devC, sizeof(float) * SIZE );
cudaMemcpy( devA, a, sizeof(float)*SIZE, cudaMemcpyHostToDevice );
cudaMemcpy( devB, b, sizeof(float)*SIZE, cudaMemcpyHostToDevice );
start = chrono::high_resolution_clock::now();
const int thrCount=32;
int blockXY = ( COUNT + (thrCount-1) ) / thrCount;
dim3 blk( blockXY,blockXY);
dim3 thr( thrCount,thrCount);
multMatrix<<<blk,thr>>>( devA, devB, devC, COUNT );
cudaDeviceSynchronize();
stop = chrono::high_resolution_clock::now();
cudaMemcpy( gpuC, devC, sizeof(float)*SIZE, cudaMemcpyDeviceToHost );
#if 0
for(int i=0;i<COUNT;++i) {
for(int j=0;j<COUNT;++j) {
printf( "%.2f(%.2f) ", gpuC[i*COUNT+j], c[i*COUNT+j] );
}
printf( "\n" );
}
#endif
int mismatchCount = 0;
for(int i=0;i<SIZE;++i) {
if( c[i] != gpuC[i] ) {
mismatchCount++;
}
}
if( mismatchCount > 0 ) {
printf( "mismatchCount= %d (%d)\n", mismatchCount, SIZE );
}
chrono::microseconds gpuTime = chrono::duration_cast<chrono::microseconds>(stop-start);
printf( "GPU: %d (us)\n", gpuTime.count() );
printf( "\n" );
printf( "rate = %.4f (%dx%d matrix)\n", cpuTime.count() / (double)gpuTime.count(), COUNT, COUNT );
cudaFree( devA );
cudaFree( devB );
cudaFree( devC );
}
int main() {
printf( "Hello,CUDA\n" );
testCudaAdd();
testCudaMult();
cudaDeviceReset();
return 0;
}
|
1,065
|
#include <stdio.h>
#include <sys/time.h>
#include <vector>
#include <iostream>
using namespace std;
int problem_size = 20000000;
float random_float(float a, float b)
{
float random = ((float) rand()) / (float) RAND_MAX;
float diff = b - a;
float r = random * diff;
return a + r;
}
int random_int(int min, int max)
{
return rand() % (max - min) + min + 1;
}
__global__
void infect_sweep(int *InfStats, int *Travelling, float *HouseInf, int *Absent, float *WAIFW_Matrix, float *AgeSusceptibility,
int *Age, float *Susceptibility, int *Treated, int *Vaccinated, float *Results)
{
int i = (blockIdx.x * blockDim.x) + threadIdx.x;
float FOI = HouseInf[i];
for (int j = i * 10; j < (i + 1) * 10; j++)
{
FOI *= (1 + 2 * Absent[j]);
}
for (int j = i * 10; j < (i + 1) * 10; j++)
{
int host_age_group = Age[j] / 17;
int infector_age_group = Age[i] / 17;
float infectee_susceptibility = AgeSusceptibility[host_age_group] * Susceptibility[j];
FOI *= WAIFW_Matrix[host_age_group * 17 + infector_age_group] * infectee_susceptibility;
FOI *= (1.0 - Treated[j] * 2) * (1.0 - Vaccinated[j] * 2) * Travelling[i] * InfStats[i];
Results[j] = FOI;
}
}
int main(void)
{
int *InfStats_h = (int *)malloc(problem_size*sizeof(int));
int *Travelling_h = (int *)malloc(problem_size*sizeof(int));
int *HouseInf_h = (int *)malloc(problem_size*sizeof(float));
float *WAIFW_Matrix_h = (float *)malloc(100*sizeof(float));
float *AgeSusceptibility_h = (float *)malloc(100*sizeof(float));
int *Absent_h = (int *)malloc(15*problem_size*sizeof(int));
int *Age_h = (int *)malloc(15*problem_size*sizeof(int));
int *Treated_h = (int *)malloc(15*problem_size*sizeof(int));
int *Vaccinated_h = (int *)malloc(15*problem_size*sizeof(int));
int *Susceptibility_h = (int *)malloc(15*problem_size*sizeof(float));
int *Results_h = (int *)malloc(15*problem_size*sizeof(float));
{
for (int i = 0; i < problem_size; i++)
{
InfStats_h[i] = 1;
Travelling_h[i] = 0;
HouseInf_h[i] = random_float(0.1, 1);
}
for (int i = 0; i < 100; i++)
{
AgeSusceptibility_h[i] = random_float(0.1, 1);
WAIFW_Matrix_h[i] = random_float(0.1, 1);
}
for (int i = 0; i < problem_size * 10; i++)
{
Age_h[i] = random_int(0, 100);
Susceptibility_h[i] = random_float(0.1, 1);
Treated_h[i] = 0;
Vaccinated_h[i] = 1;
Results_h[i] = 0;
}
}
int *InfStats_d, *Travelling_d, *Absent_d, *Age_d, *Treated_d, *Vaccinated_d;
float *HouseInf_d, *WAIFW_Matrix_d, *AgeSusceptibility_d, *Susceptibility_d, *Results_d;
cudaMalloc(&InfStats_d, problem_size*sizeof(int));
cudaMalloc(&Travelling_d, problem_size*sizeof(int));
cudaMalloc(&Absent_d, 15*problem_size*sizeof(int));
cudaMalloc(&Age_d, 15*problem_size*sizeof(int));
cudaMalloc(&Treated_d, 15*problem_size*sizeof(int));
cudaMalloc(&Vaccinated_d, 15*problem_size*sizeof(int));
cudaMalloc(&HouseInf_d, problem_size*sizeof(float));
cudaMalloc(&WAIFW_Matrix_d, 100*sizeof(float));
cudaMalloc(&AgeSusceptibility_d, 100*sizeof(float));
cudaMalloc(&Susceptibility_d, 15*problem_size*sizeof(float));
cudaMalloc(&Results_d, 15*problem_size*sizeof(float));
struct timeval t1, t2;
gettimeofday(&t1, 0);
cudaMemcpy(InfStats_d, InfStats_h, problem_size*sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(Travelling_d, Travelling_h, problem_size*sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(Absent_d, Absent_h, problem_size*sizeof(int)*15, cudaMemcpyHostToDevice);
cudaMemcpy(Age_d, Age_h, problem_size*sizeof(int)*15, cudaMemcpyHostToDevice);
cudaMemcpy(Treated_d, Treated_h, problem_size*sizeof(int)*15, cudaMemcpyHostToDevice);
cudaMemcpy(Vaccinated_d, Vaccinated_h, problem_size*sizeof(int)*15, cudaMemcpyHostToDevice);
cudaMemcpy(HouseInf_d, HouseInf_h, problem_size*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(WAIFW_Matrix_d, WAIFW_Matrix_h, 100*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(AgeSusceptibility_d, AgeSusceptibility_h, 100*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(Susceptibility_d, Susceptibility_h, problem_size*sizeof(float)*15, cudaMemcpyHostToDevice);
cudaMemcpy(Results_d, Results_h, problem_size*sizeof(float)*15, cudaMemcpyHostToDevice);
infect_sweep<<<(problem_size+255)/256, 256>>>(InfStats_d, Travelling_d, HouseInf_d, Absent_d, WAIFW_Matrix_d, AgeSusceptibility_d,
Age_d, Susceptibility_d, Treated_d, Vaccinated_d, Results_d);
cudaMemcpy(Results_h, Results_d, problem_size*15*sizeof(float), cudaMemcpyDeviceToHost);
gettimeofday(&t2, 0);
double time = (1000000.0*(t2.tv_sec-t1.tv_sec) + t2.tv_usec-t1.tv_usec)/1000.0;
printf("Population size: %d. Time to generate: %3.1f ms.\n", (problem_size*10), time);
free(InfStats_h);
free(Travelling_h);
free(HouseInf_h);
free(WAIFW_Matrix_h);
free(AgeSusceptibility_h);
free(Absent_h);
free(Age_h);
free(Treated_h);
free(Vaccinated_h);
free(Susceptibility_h);
free(Results_h);
cudaFree(InfStats_d);
cudaFree(Travelling_d);
cudaFree(Absent_d);
cudaFree(Age_d);
cudaFree(Treated_d);
cudaFree(Vaccinated_d);
cudaFree(InfStats_d);
cudaFree(HouseInf_d);
cudaFree(WAIFW_Matrix_d);
cudaFree(AgeSusceptibility_d);
cudaFree(Susceptibility_d);
cudaFree(Results_d);
}
|
1,066
|
// Dot product
#include <iostream>
#define N 1024
__global__ void dot( int*a, int*b, int*c ) {
__shared__ int temp[N];
temp[threadIdx.x] = a[threadIdx.x] * b[threadIdx.x];
__syncthreads();
// Thread 0 sums the pairwiseproducts
if( 0 == threadIdx.x ) {
int sum = 0;
for( int i = N-1; i >= 0 ; i-- )
sum += temp[i];
c[0] = sum;
}
}
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <string.h>
int main( void ) {
int *a, *b, *c ; // host copies of a,b,c
int *dev_a, *dev_b, *dev_c; // device copies of a, b, c
int size = N *sizeof( int); // we need space for N integers
// allocate device copies of a, b, c
cudaMalloc( (void**)&dev_a, size );
cudaMalloc( (void**)&dev_b, size );
cudaMalloc( (void**)&dev_c, size );
a = (int*)malloc( size );
b = (int*)malloc( size );
c = (int*)malloc( size );
for (int i=0; i<N; i++)
{ a[i] = 1;
};
for (int i=0; i<N; i++)
{ b[i] = 1;
};
// copy inputs to device
cudaMemcpy( dev_a, a, size, cudaMemcpyHostToDevice);
cudaMemcpy( dev_b, b, size, cudaMemcpyHostToDevice);
// launch dot() kernel with N parallel blocks
dot<<< 1,N >>>( dev_a, dev_b, dev_c);
// copy device result back to host copy of c
cudaMemcpy( c, dev_c, size , cudaMemcpyDeviceToHost);
printf("a %i b %i ; c %i; \n ",a[0],b[0],c[0]);
free( a ); free( b ); free( c );
cudaFree( dev_a);
cudaFree( dev_b);
cudaFree( dev_c);
return 0;
}
|
1,067
|
#include "includes.h"
__global__ void add(float *A, float *C)
{
int columna = threadIdx.x;
//indice de las filas
int fila = threadIdx.y;
//indice lineal
int Id = columna + fila * blockDim.x;
int id1 = (columna - 1) + fila * blockDim.x;
int id2 = (columna + 1) + fila * blockDim.x;
int id3 = columna + (fila - 1) * blockDim.x;
int id4 = columna + (fila + 1) * blockDim.x;
if ((fila > 0 && fila < N - 1) && (columna > 0 && columna < N - 1)) {
C[Id] = A[id1] + A[id2] + A[id3] + A[id4];
}
else
{
C[Id] = A[Id];
}
}
|
1,068
|
#include <iostream>
#include <ctime>
#include <cstdlib>
#include <cmath>
#include "cuda_runtime.h"
#include <thrust/scan.h>
using namespace std;
#define TPB 1024
#define RANGE 10
#define min(a,b) ((a < b) ? a : b)
__global__
void incl_pfsum (float * array, int size) {
int bsize = blockIdx.x * blockDim.x;
int tid = bsize + threadIdx.x;
int tmp;
size = min(size, TPB);
__syncthreads();
for (int step = 2; step <= size ; step *= 2) {
if (tid % step == (step - 1) && (tid - (step / 2) >= bsize)) {
array[tid] += array[tid - (step / 2)];
}
__syncthreads();
}
__syncthreads();
if (tid % TPB == 0) { array[bsize - 1] = 0; }
__syncthreads();
for (int step = size; step > 0; step /= 2) {
if (tid % step == (step - 1) && (tid - (step / 2) >= bsize)) {
tmp = array[tid];
//__syncthreads();
array[tid] += array[tid - (step / 2)];
//__syncthreads();
array[tid - (step / 2)] = tmp;
//__syncthreads();
}
__syncthreads();
}
__syncthreads();
}
__global__
void scat_part_sum(float * array, float * array_psums) {
int tid = (blockIdx.x * blockDim.x) + threadIdx.x;
__syncthreads();
array[tid] += array_psums[blockIdx.x];
__syncthreads();
}
__global__
void upsweep (float * array, float * array_aggr1, int size, int size_aggr1) {
int bid = blockIdx.x * blockDim.x;
int tid = (blockIdx.x * blockDim.x) + threadIdx.x;
__syncthreads();
for (int step = 2; step <= size ; step *= 2) {
if (tid % step == (step - 1) && (tid - (step / 2) >= bid)) {
array[tid] += array[tid - (step / 2)];
}
__syncthreads();
}
__syncthreads();
if (threadIdx.x == (TPB - 1)) {
if (tid < size) {
array_aggr1[blockIdx.x] = array[tid];
}
else {
array_aggr1[blockIdx.x] = array[size - 1];
}
}
__syncthreads();
}
__global__
void downsweep (float * array) {
int next_bid = (blockIdx.x + 1) * blockDim.x;
int tid = (blockIdx.x * blockDim.x) + threadIdx.x;
__syncthreads();
for (int step = TPB / 2; step > 1; step /= 2) {
if (tid % step == (step - 1) && (tid + (step / 2) < next_bid)) {
array[tid + (step / 2)] += array[tid];
}
__syncthreads();
}
__syncthreads();
/*
if (tid >= (1<<step) && (tid < size)) {
tmp = array[tid - (1<<step)];
__syncthreads();
array[tid] = tmp + array[tid];
__syncthreads();
}
*/
}
int main(int argc, char** argv)
{
if (argc != 2) {
cout << "Takes one argument - the number of elements in an array" << endl;
return 0;
}
int size = atoi(argv[1]);
int size_div1 = int(ceil(float(size) / float(TPB)));
int size_div2 = int(ceil(float(size_div1) / float(TPB)));
int nblocks = int(ceil(float(size) / float(TPB)));
int nblocks_div1 = int(ceil(float(nblocks) / float(TPB)));
int nblocks_div2 = int(ceil(float(nblocks_div1) / float(TPB)));
cout << "First stage blocks: " << nblocks << endl;
cout << "Second stage blocks: " << nblocks_div1 << endl;
cout << "Third stage blocks: " << nblocks_div2 << endl;
cout << "First stage size: " << size << endl;
cout << "Second stage size: " << size_div1 << endl;
cout << "Third stage size: " << size_div2 << endl;
cout << "Malloc'ing\n";
float *x = (float*)malloc(size * sizeof(float));
float *x1 = (float*)malloc(size_div1 * sizeof(float));
float *x2 = (float*)malloc(size_div2 * sizeof(float));
float *y = (float*)malloc(size * sizeof(float));
float *d_x, *d_x1, *d_x2;
cudaMalloc(&d_x, size * sizeof(float));
cudaMalloc(&d_x1, size_div1 * sizeof(float));
cudaMalloc(&d_x2, size_div2 * sizeof(float));
cout << "Generating Array\n";
srand(time(NULL));
for (int i = 0; i < size; i++) {
x[i] = rand() % RANGE;
y[i] = x[i];
}
for (int i = 1; i < size; i++) {
y[i] = y[i] + y[i - 1];
}
/*
for (int i = 1; i < size; i++) {
y[i] = y[i] + y[i - 1];
}
*/
cudaMemcpy(d_x, x, size * sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(d_x1, x1, size_div1 * sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(d_x2, x2, size_div2 * sizeof(float), cudaMemcpyHostToDevice);
cout << "Up-Sweep\n" << endl;
upsweep <<<nblocks, TPB>>> (d_x, d_x1, size, nblocks_div1);
cudaDeviceSynchronize();
cout << "Down-Sweep\n" << endl;
downsweep <<<nblocks, TPB>>> (d_x);
cudaDeviceSynchronize();
/*
cout << "Up-Sweep 2\n" << endl;
upsweep <<<nblocks_div1, TPB>>> (d_x1, d_x2, size_div1, nblocks_div2);
cudaDeviceSynchronize();
cout << "Down-Sweep 2\n" << endl;
downsweep <<<nblocks_div1, TPB>>> (d_x1);
cudaDeviceSynchronize();
*/
cout << "Inclusive Sum 1\n" << endl;
incl_pfsum <<<nblocks_div1, TPB>>> (d_x1, size_div1);
cudaDeviceSynchronize();
cudaMemcpy(x1, d_x1, size_div1 * sizeof(float), cudaMemcpyDeviceToHost);
cudaDeviceSynchronize();
for (int i = 0; i < size_div1; i++) {
cout << i << " " << x1[i] << endl;
}
cout << "Inclusive Sum 2\n" << endl;
incl_pfsum <<<nblocks_div2, TPB>>> (d_x2, size_div2);
cudaDeviceSynchronize();
cudaMemcpy(x2, d_x2, size_div2 * sizeof(float), cudaMemcpyDeviceToHost);
cudaDeviceSynchronize();
for (int i = 0; i < size_div2; i++) {
cout << i << " " << x2[i] << endl;
}
cout << "Scatter Partial Sums 2\n" << endl;
scat_part_sum <<<nblocks_div1, TPB>>> (d_x1, d_x2);
cudaDeviceSynchronize();
/*
cudaMemcpy(x1, d_x1, size_div1 * sizeof(float), cudaMemcpyDeviceToHost);
cudaDeviceSynchronize();
for (int i = 0; i < size_div1; i++) {
cout << i << " " << x1[i] << endl;
}
*/
cout << "Scatter Partial Sums 1\n" << endl;
scat_part_sum <<<nblocks, TPB>>> (d_x, d_x1);
cudaDeviceSynchronize();
/*
for (int i = 0; i < size; i++) {
cout << i << " " << x[i] << endl;
}
*/
/*
thrust::inclusive_scan(x, x + size, x);
cudaDeviceSynchronize();
thrust::inclusive_scan(x, x + size, x);
cudaDeviceSynchronize();
*/
cudaMemcpy(x, d_x, size * sizeof(float), cudaMemcpyDeviceToHost);
for (int i = 0; i < size; i++) {
cout << i << " " << x[i] << " " << y[i] << endl;
if (x[i] != y[i]) {
//cout << i << " " << x[i] << " " << y[i] << endl;
//cout << "Not the same" << endl;
//return 0;
}
}
cout << "arrays are the same" << endl;
return 0;
}
|
1,069
|
#include "includes.h"
__global__ void arrayReduce(int *m, int *ms){
int id = threadIdx.x + blockIdx.x * blockDim.x;
if (m[id] > -1)
m[id] = m[id] - ms[blockIdx.x];
}
|
1,070
|
#include "includes.h"
__global__ void worker(double * a, long n) {
int i = blockDim.x * blockIdx.x + threadIdx.x;
if (i < n) {
a[i] += i;
}
}
|
1,071
|
__global__ void create_escape_carry_index(char *file, long n, char *escape_carry_index) {
int index = blockIdx.x * blockDim.x + threadIdx.x;
int stride = blockDim.x * gridDim.x;
long normal_chars_per_thread = max((n+stride-1) / stride, 64L);
long chars_per_thread = ((normal_chars_per_thread + 64 - 1) / 64) * 64;
long start = index * chars_per_thread;
long end = start + chars_per_thread;
// There are essentially two cases:
// - The last character in the previous block is an escape character.
// - The last character in the previous block is not an escape character.
// However, we don't know in advance which one it is, because
// we are not sequential. So, here we'll basically
// calculate the carry of each thread assuming the initial
// carry is 0.
char carry = 0;
for (long i = start; i < end && i < n; i += 1) {
if (file[i] == '\\') {
carry = 1 ^ carry;
} else {
carry = 0;
}
}
escape_carry_index[index] = carry;
}
|
1,072
|
/* CUDA exercise to convert a simple serial code for a brute force
largest prime number search into CUDA. This initial code is serial,
but it is written as CUDA code for your convenience, so should be
compiled with nvcc (see below). Your task is to convert the serial
computation to a kernel computation. In the simplest case, use
atomicMax to find the globally largest prime number.
All prime numbers can be expressed as 6*k-1 or 6*k+1, k being an
integer. We provide the range of k to probe as macro parameters
KMIN and KMAX (see below).
You should get a speedup ~22 (with KMIN=100000000, KMAX=100100000,
BLOCK_SIZE=256, and default number of blocks per kernel NBLOCKS=560).
This is a 64-bit (long long int, instead of int) version - so in principle
you can find primes up to 2^64-1, or 1.8e19.
Hints:
* You can still use atomicMax, even in this 64-bit version, if you use
it not with prime numbers themselves (xmax), but with differences
between the prime number and the starting prime number candidate
value for the current kernel (__device__ int d_xmax), which should
fit in a 32-bit integer for any realistic size kernel.
* On the host, computation should be organized in a while loop, which
sets the initial prime candidate value for the loop, x0, computes number
of blocks for the main kernel, initializes d_xmax in a single-thread
kernel, and then submit the main kernel to device. Then you should copy
the current value of d_xmax back to the host, and compute the largest
found prime (this time using 64-bit integers) for the loop, as x0+d_xmax.
* It's very convenient to use a two-dimensional grid of blocks,
defined as "dim3 Nblocks (NBLOCKS, 2, 1);". The second grid
dimension is used to derive the two values of j=(-1; 1) inside the
kernel: "int j = 2*blockIdx.y - 1;". This way, there will be only
one loop inside the kernel - for y.
* When you get a failure (not a prime) inside the y loop, you can exit
the thread with "return" (no need to use "break").
To compile:
nvcc -arch=sm_20 -O2 primes64.cu -o primes64
*/
#include <sys/time.h>
#include <ctype.h>
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <time.h>
#include <unistd.h>
// Range of k-numbers for primes search:
#define KMIN 100000000
#define KMAX 100100000
// Number of threads in one block (possible range is 32...1024):
#define BLOCK_SIZE 256
/* Subtract the `struct timeval' values X and Y,
storing the result in RESULT.
Return 1 if the difference is negative, otherwise 0. */
// It messes up with y!
int
timeval_subtract (double *result, struct timeval *x, struct timeval *y)
{
struct timeval result0;
/* Perform the carry for the later subtraction by updating y. */
if (x->tv_usec < y->tv_usec) {
int nsec = (y->tv_usec - x->tv_usec) / 1000000 + 1;
y->tv_usec -= 1000000 * nsec;
y->tv_sec += nsec;
}
if (x->tv_usec - y->tv_usec > 1000000) {
int nsec = (y->tv_usec - x->tv_usec) / 1000000;
y->tv_usec += 1000000 * nsec;
y->tv_sec -= nsec;
}
/* Compute the time remaining to wait.
tv_usec is certainly positive. */
result0.tv_sec = x->tv_sec - y->tv_sec;
result0.tv_usec = x->tv_usec - y->tv_usec;
*result = ((double)result0.tv_usec)/1e6 + (double)result0.tv_sec;
/* Return 1 if result is negative. */
return x->tv_sec < y->tv_sec;
}
//++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
// Kernel(s) should go here:
int main (int argc,char **argv)
{
struct timeval tdr0, tdr1, tdr;
double restime;
int devid, devcount, error, success;
long long int k, j, x, xmax, y, ymax;
if (BLOCK_SIZE>1024)
{
printf ("Bad BLOCK_SIZE: %d\n", BLOCK_SIZE);
exit (1);
}
/* find number of device in current "context" */
cudaGetDevice(&devid);
/* find how many devices are available */
if (cudaGetDeviceCount(&devcount) || devcount==0)
{
printf ("No CUDA devices!\n");
exit (1);
}
else
{
cudaDeviceProp deviceProp;
cudaGetDeviceProperties (&deviceProp, devid);
printf ("Device count, devid: %d %d\n", devcount, devid);
printf ("Device: %s\n", deviceProp.name);
printf("[deviceProp.major.deviceProp.minor] = [%d.%d]\n\n", deviceProp.major, deviceProp.minor);
}
//--------------------------------------------------------------------------------
if (error = cudaDeviceSynchronize())
{
printf ("Error %d\n", error);
exit (error);
}
gettimeofday (&tdr0, NULL);
// This serial computation will have to be replaced by calls to kernel(s):
xmax = 0;
for (k=KMIN; k<=KMAX; k++)
{
// testing "-1" and "+1" cases:
for (j=-1; j<2; j=j+2)
{
// Prime candidate:
x = 6*k + j;
// We should be dividing by numbers up to sqrt(x):
ymax = (long long int)ceil(sqrt((double)x));
// Primality test:
for (y=3; y<=ymax; y=y+2)
{
// To be a success, the modulus should not be equal to zero:
success = x % y;
if (!success)
break;
}
if (success && x > xmax)
{
xmax = x;
}
}
}
if (error = cudaDeviceSynchronize())
{
printf ("Error %d\n", error);
exit (error);
}
gettimeofday (&tdr1, NULL);
tdr = tdr0;
timeval_subtract (&restime, &tdr1, &tdr);
printf ("%ld\n", xmax);
printf ("Time: %e\n", restime);
//--------------------------------------------------------------------------------
return 0;
}
|
1,073
|
#include<iostream>
#include <stdlib.h>
#include <time.h>
#include <math.h>
#include "../inc/WeightedGraph.cuh"
WeightedGraph::WeightedGraph(int s,int p){
size=s;
cudaMallocManaged(&adjmat,size*sizeof(float*));
srand(time(NULL));
for(int i=0;i<size;i++){
cudaMallocManaged(&adjmat[i],size*sizeof(float));
for(int j=0;j<size;j++){
//Graph is supposed to be undirected
if(j<i){ adjmat[i][j]=adjmat[j][i]; }
else{
if(rand()%100<p){ adjmat[i][j]=(float)rand()/(float)RAND_MAX; }
else{ adjmat[i][j]=0; }
}
}
}
}
float **WeightedGraph::getAdjmat(){ return adjmat;}
int WeightedGraph::getSize(){ return size;}
void WeightedGraph::print(){
for(int i=0;i<size;i++){
for(int j=0;j<size;j++)
std::cout<<adjmat[i][j]<<" ";
std::cout<<"\n";
}
}
|
1,074
|
#include "includes.h"
// Device input vectors
int *d_a;
//Device output vector
int *d_b;
__global__ void setLastToCero(int *A, int size) {
const int index = blockIdx.x * blockDim.x + threadIdx.x;
if (index == size - 1) {
A[index] = 0;
}
}
|
1,075
|
//ulimit -s unlimited
//gcc -lm -std=c99 NRCDML1RegLog.c && ./a.out
//nvcc CE.cu -arch sm_20 && ./a.out
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <math.h>
#include <time.h>
#include <stdint.h>
#include <cuda.h>
#include "device_functions.h"
#include <curand.h>
#include <curand_kernel.h>
#include <assert.h>
#include <cuda_runtime.h>
void checkCUDAError(const char *msg);
// Part 3 of 5: implement the kernel
__global__ void myFirstKernel(float *d_a) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
int i;
float tmp=0;
for (i = 0; i < 1000; i++) {
if (i > 500)
tmp = tmp+ idx + i;
else
tmp = idx + i;
}
d_a[idx]=tmp;
}
////////////////////////////////////////////////////////////////////////////////
// Program main
////////////////////////////////////////////////////////////////////////////////
void cudaTest1() {
// pointer for host memory
float *h_a;
// pointer for device memory
float *d_a;
// define grid and block size
int numBlocks = 10000 ;
int numThreadsPerBlock = 1024 ;
// Part 1 of 5: allocate host and device memory
size_t memSize = numBlocks * numThreadsPerBlock * sizeof(float);
h_a = (float *) malloc(memSize);
cudaMalloc((float **) &d_a, memSize);
// Part 2 of 5: launch kernel
dim3 dimGrid( numBlocks);
dim3 dimBlock( numThreadsPerBlock);
clock_t t1, t2;
t1 = clock();
myFirstKernel<<< dimGrid, dimBlock >>>( d_a );
cudaThreadSynchronize();
t2 = clock();
float diff = ((float) t2 - (float) t1) / 1000000.0F;
checkCUDAError("kernel execution");
printf("sorting:%f\n", diff);
float pole[numBlocks * numThreadsPerBlock];
t1 = clock();
for (int i = 0; i < numBlocks * numThreadsPerBlock; i++) {
float tmp=0;
for (int j = 0; j < 1000; j++) {
if (i > 500)
tmp = tmp+ i + j;
else
tmp = i + j;
}
pole[i]=tmp;
}
t2 = clock();
diff = ((float) t2 - (float) t1) / 1000000.0F;
printf("sorting:%f\n", diff);
// block until the device has completed
// check if kernel execution generated an error
// Part 4 of 5: device to host copy
cudaMemcpy(h_a, d_a, memSize, cudaMemcpyDeviceToHost);
// Check for any CUDA errors
checkCUDAError("cudaMemcpy");
// Part 5 of 5: verify the data returned to the host is correct
for (int i = 0; i < numBlocks; i++) {
for (int j = 0; j < numThreadsPerBlock; j++) {
assert(h_a[i * numThreadsPerBlock + j] == pole[i
* numThreadsPerBlock + j]);
}
}
// free device memory
cudaFree(d_a);
// free host memory
free(h_a);
// If the program makes it this far, then the results are correct and
// there are no run-time errors. Good work!
printf("Correct!\n");
}
int main(void) {
srand(1);
printf("start solving\n");
cudaTest1();
printf("end solving\n");
}
void checkCUDAError(const char *msg) {
cudaError_t err = cudaGetLastError();
if (cudaSuccess != err) {
fprintf(stderr, "Cuda error: %s: %s.\n", msg, cudaGetErrorString(err));
exit(-1);
}
}
|
1,076
|
#include<stdio.h>
#define N 833
void add(int *X, int* Y, int* Z) {
for(int i = 0; i < N; i++) {
for(int j = 0; j < N; j++) {
Z[i*N+j] = X[i*N+j] + Y[i*N+j];
}
}
}
__global__ void add_kernel(int *X, int *Y, int *Z) {
int i = threadIdx.x;
int j = threadIdx.y;
if(i < N && j < N) {
Z[i*N+j] = X[i*N+j] + Y[i*N+j];
}
}
int main () {
//Input matrix
int X[N*N];
int Y[N*N];
cudaEvent_t start, stop;
cudaEventCreate(&start);
cudaEventCreate(&stop);
for(int i = 0; i < N; i++) {
for(int j = 0; j < N; j++) {
X[i*N+j] = -1;
Y[i*N+j] = 1;
}
}
//Output matrix
int Z[N*N];
int *d_X, *d_Y, *d_Z;
cudaMalloc((void**) &d_X, (N*N)*sizeof(int));
cudaMalloc((void**) &d_Y, (N*N)*sizeof(int));
cudaMalloc((void**) &d_Z, (N*N)*sizeof(int));
cudaMemcpy(d_X, &X, (N*N)*sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(d_Y, &Y, (N*N)*sizeof(int), cudaMemcpyHostToDevice);
dim3 dimGrid(32, 32, 1);
dim3 dimBlock(32, 32, 1);
//Timed add_kernel function
cudaEventRecord(start);
add_kernel<<<dimGrid, dimBlock>>>(d_X, d_Y, d_Z);
cudaEventRecord(stop);
//add(X, Y, Z);
cudaMemcpy(&Z, d_Z, (N*N)*sizeof(int), cudaMemcpyDeviceToHost);
cudaEventSynchronize(stop);
float milliseconds = 0;
cudaEventElapsedTime(&milliseconds, start, stop);
cudaFree(d_X);
cudaFree(d_Y);
cudaFree(d_Z);
int sum = 0;
for(int i = 0; i < N; i++) {
for(int j = 0; j < N; j++) {
//printf("%d ", Z[i*N+j]);
sum += Z[i*N+j];
}
//printf("\n");
}
if(sum == 0)
printf("All 0s! With N = %d\n", N);
else {
printf("Something is wrong!!!\n");
}
printf("Time used: %f milliseconds\n", milliseconds);
return -1;
}
|
1,077
|
////////////////////////////////////////////////////////////////////////////
// Calculate scalar products of VectorN vectors of ElementN elements on CPU.
// Straight accumulation in double precision.
////////////////////////////////////////////////////////////////////////////
#include <iostream>
//extern "C"
void Kernel_2_Max_CPU_MPI(int *Max_All_CPU, int *A_Location_All_CPU, int *B_Location_All_CPU,
int *Max_K1 , int *A_Location_K1 , int *B_Location_K1,
int K1_Max_Report, int Number, int Kerene2Max)
{
for (int k=0; k<K1_Max_Report; k++)
{
int check=0;
do
{
check=0;
int Max_Val_MPI = 0;
int Max_Loc_MPI = 0;
for (int i=0; i<Number; i++)
{
if (Max_Val_MPI<Max_All_CPU[i])
{
Max_Val_MPI = Max_All_CPU[i];
Max_Loc_MPI = i;
}
}
// printf(" --- %i %i %i \n", k, Max_Val_MPI, Max_Loc_MPI);
if (Max_Val_MPI==0)
{
check=1;
k=K1_Max_Report+10;
}
// printf("----- Ata ------ %i %i %i %i %i \n", k, Max_Val_MPI,Max_Loc_MPI, K1_Max_Report, Number);
if (check!=0){
for (int i=0; i<k; i++)
{
if ((A_Location_K1[i*Kerene2Max]==A_Location_All_CPU[Max_Loc_MPI*Kerene2Max]) && (B_Location_K1 [i*Kerene2Max]==B_Location_All_CPU[Max_Loc_MPI*Kerene2Max]))
{
check=1;
Max_All_CPU[Max_Loc_MPI]=0;
// printf("----- %i %i %i %i %i %i \n", k, i, A_Location_K1[i],A_Location_All_CPU[Max_Loc_MPI], Max_K1[i],Max_Val_MPI);
i=k+2*Number;
}
}
}
if (check==0)
{
Max_K1[k] = Max_Val_MPI;
// printf("------ %i\n", Max_Val_MPI);
for (int j=0; j<Kerene2Max; j++)
{
A_Location_K1 [k*Kerene2Max+j]= A_Location_All_CPU[Max_Loc_MPI*Kerene2Max+j];
B_Location_K1 [k*Kerene2Max+j]= B_Location_All_CPU[Max_Loc_MPI*Kerene2Max+j];
}
Max_All_CPU[Max_Loc_MPI]=0;
Max_Val_MPI = 0;
}
}while (check!=0);
}
}
|
1,078
|
#include <stdio.h>
#define N 32
void add(int *X, int *Y, int *Z){
for(int i=0; i<N; i++){
for(int j=0; j<N; j++){
Z[i*N+j] = X[i*N+j] + Y[i*N+j] ;
}
}
}
__global__ void add_kernel(int *X, int *Y, int *Z){
int i = threadIdx.x ;
int j = threadIdx.y ;
if(i < N && j < N){
Z[i*N+j] = X[i*N+j] + Y[i*N+j] ;
}
}
int main(){
int X[N*N] ;
int Y[N*N] ;
for(int i=0; i<N; i++){
for(int j=0; j<N; j++){
X[i*N+j] = 0 ;
Y[i*N+j] = 1 ;
}
}
int Z[N*N] ;
int *d_X, *d_Y, *d_Z ;
cudaMalloc((void **) &d_X, (N*N)*sizeof(int)) ;
cudaMalloc((void **) &d_Y, (N*N)*sizeof(int)) ;
cudaMalloc((void **) &d_Z, (N*N)*sizeof(int)) ;
cudaMemcpy(d_X, &X, (N*N)*sizeof(int), cudaMemcpyHostToDevice) ;
cudaMemcpy(d_Y, &Y, (N*N)*sizeof(int), cudaMemcpyHostToDevice) ;
dim3 dimGrid(32,32,1) ;
dim3 dimBlock(32,32,1) ;
cudaEvent_t start, stop ;
cudaEventCreate(&start) ;
cudaEventCreate(&stop) ;
cudaEventRecord(start) ;
add_kernel<<<dimGrid, dimBlock>>>(d_X, d_Y, d_Z) ;
//add(X, Y, Z) ;
cudaEventRecord(stop) ;
cudaMemcpy(&Z, d_Z, (N*N)*sizeof(int), cudaMemcpyDeviceToHost) ;
cudaEventSynchronize(stop) ;
float millisec = 0 ;
cudaEventElapsedTime(&millisec, start, stop) ;
cudaFree(d_X) ;
cudaFree(d_Y) ;
cudaFree(d_Z) ;
for(int i=0; i<N; i++){
for(int j=0; j<N; j++){
printf("%d ", Z[i*N+j]) ;
}
printf("\n") ;
}
printf("Time : %lf\n", millisec) ;
}
|
1,079
|
#include "learn_kernels.cuh"
__global__ void kMultiplyBySigmoidGrad(float* act, float* target, const unsigned int len) {
const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
const unsigned int numThreads = blockDim.x * gridDim.x;
for(unsigned int i = idx; i < len; i+= numThreads) {
target[i] = target[i] * act[i] * (1.0f - act[i]);
}
}
|
1,080
|
#include "includes.h"
__global__ void reduce_to_first_index_h(float *X, int height, int width) {
int t = blockIdx.x * blockDim.x + threadIdx.x;
float tmp = 0;
if (t < width) {
for (int i = 0; i < height; i++) {
tmp += X[i * width + t];
}
X[t] = tmp;
}
}
|
1,081
|
#include <stdio.h>
#include <stdlib.h>
#include <fstream>
#include <iostream>
#include <float.h>
using namespace std;
#define TILE_WIDTH 32
//#define THREADS_PER_BLOCK 32;
void MatrixMulOnHost(float* M, float* N, float* P, int Width)
{
for (int i = 0; i < Width; ++i)
for (int j = 0; j < Width; ++j) {
float sum = 0;
for (int k = 0; k < Width; ++k) {
float a = M[i * Width + k];
float b = N[k * Width + j];
sum += a * b;
}
P[i * Width + j] = sum;
}
}
void llenar(int* a, int n)
{
int i;
for (i = 0; i < n*n; ++i)
a[i] = rand()%5+1;
}
__global__
void matrixMulti(int *c, int *a, int *b,int n)
{
int row = blockIdx.y * blockDim.y + threadIdx.y ;
int col = blockIdx.x * blockDim.x + threadIdx.x ;
if ((row <n) && (col<n))
{
int suma=0;
for(int i=0;i<n;++i)
{
suma+=a[row*n+i]*b[i*n+col];
}
c[row*n+col] = suma;
}
}
__global__ void MatrixMulTiled(int * d_P, int * d_M, int* d_N,int Width)
{
__shared__ int Mds[TILE_WIDTH][TILE_WIDTH];
__shared__ int 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;
int Pvalue = 0;
// Loop over the d_M and d_N tiles required to compute d_P element
for (int ph = 0; ph < Width/TILE_WIDTH; ++ph)
{
// Collaborative loading of d_M and d_N tiles into shared memory
if ((Row< Width) && (ph*TILE_WIDTH+tx)< Width)
Mds[ty][tx] = d_M[Row*Width + ph*TILE_WIDTH + tx];
if ((ph*TILE_WIDTH+ty)<Width && Col<Width)
Nds[ty][tx] = d_N[(ph*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;
}
__global__ void MatrixMulTiledMod(int * d_P, int * d_M, int* d_N,int Width)
{
__shared__ int Mds[TILE_WIDTH][TILE_WIDTH];
__shared__ int Nds[TILE_WIDTH][TILE_WIDTH];
__shared__ int Nds2[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*2 + tx;
int Pvalue =0 , Pvalue2=0;
Mds[ty][tx]=0;
Nds[ty][tx]=0;
Nds2[ty][tx]=0;
__syncthreads();
// Loop over the d_M and d_N tiles required to compute d_P element
if((Row < Width) && (Col < Width)){
for (int ph = 0; ph <Width/TILE_WIDTH; ph++)
{
// Collaborative loading of d_M and d_N tiles into shared memory
//printf("%i - %i -%i \n",ph, Row, Col );
if ((Row< Width) && (ph*TILE_WIDTH+tx)< Width)
Mds[ty][tx] = d_M[Row*Width + ph*TILE_WIDTH + tx];
if ((ph*TILE_WIDTH+ty)<Width && Col<Width)
Nds[ty][tx] = d_N[(ph*TILE_WIDTH + ty)*Width + Col];
//printf("%i %i\n",(ph*TILE_WIDTH+ty),Col+TILE_WIDTH);
if (((ph*TILE_WIDTH + ty)*Width + Col+TILE_WIDTH)<(Width*Width))
{
Nds2[ty][tx] = d_N[(ph*TILE_WIDTH + ty)*Width + Col+TILE_WIDTH];
}
__syncthreads();
for (int k = 0; k < TILE_WIDTH; k++)
{
Pvalue += Mds[ty][k] * Nds[k][tx];
Pvalue2 += Mds[ty][k] * Nds2[k][tx];
}
__syncthreads();
}
d_P[Row*Width + Col] = Pvalue;
d_P[Row*Width + Col +TILE_WIDTH] = Pvalue2;
}
}
void printMatrix( int *a , int tam){
for(int i=0;i<tam;i++)
{
for(int j=0;j<tam;j++)
{
cout<<a[i*tam+j]<<" ";
}
cout<<endl;
}
}
int main(int argc, char *argv[])
{
srand (time(NULL));
int N= strtol(argv[1], NULL, 10);
int THREADS_PER_BLOCK=TILE_WIDTH;
//cout<<N<<endl; return 1;
//printf("Storage size for float : %d \n", sizeof(float));
//printf("Storage size for int : %d \n", sizeof(int));
int *a, *b, *c; // host copies of a, b, c
int *d_a, *d_b, *d_c; //device copies of a,b,c
//int size = N*N*sizeof(int);
int size=N*N*sizeof(int);
cudaMalloc((void **)&d_a, size);
cudaMalloc((void **)&d_b, size);
cudaMalloc((void **)&d_c, size);
a = (int *)malloc(size);
llenar(a, N);
b = (int *)malloc(size);
llenar(b, N);
c = (int *)malloc(size);
cudaMemcpy(d_a, a, size, cudaMemcpyHostToDevice);
cudaMemcpy(d_b, b, size, cudaMemcpyHostToDevice);
int blocks= (N + THREADS_PER_BLOCK -1)/THREADS_PER_BLOCK;
dim3 dimGrid((blocks+THREADS_PER_BLOCK -1)/2, blocks, 1);
dim3 dimBlock(THREADS_PER_BLOCK,THREADS_PER_BLOCK, 1);
cout<<"N: "<<N<<"\tBloques : "<<blocks<<"\t Hebras/Bloque: "<<THREADS_PER_BLOCK<<endl;
cudaEvent_t start, stop;
float elapsedTime;
cudaEventCreate(&start);
cudaEventRecord(start,0);
//matrixMulti<<<dimGrid,dimBlock>>>(d_c, d_a, d_b, N);
//MatrixMulTiled<<<dimGrid,dimBlock>>>(d_c, d_a, d_b, N);
MatrixMulTiledMod<<<dimGrid,dimBlock>>>(d_c, d_a, d_b, N);
//matrixMulti<<<dimGrid,dimBlock>>>(d_c, d_a, d_b, N);
//MatrixMulTiled<<<dimGrid,dimBlock>>>(d_c, d_a, d_b, N); cudaEventElapsedTime()
cudaEventCreate(&stop);
cudaEventRecord(stop,0);
cudaEventSynchronize(stop);
cudaEventElapsedTime(&elapsedTime, start,stop);
cudaEventDestroy(start);
cudaEventDestroy(stop);
printf("Tiempo : %f ms\n" ,elapsedTime);
cudaMemcpy(c, d_c, size, cudaMemcpyDeviceToHost);
//cout<<"------A------------"<<endl;
//printMatrix(a,N);
//cout<<"------B------------"<<endl;
//printMatrix(b,N);
//cout<<"------C------------"<<endl;
//printMatrix(c,N);
free(a); free(b); free(c);
cudaFree(d_a); cudaFree(d_b); cudaFree(d_c);
return 0;
}
|
1,082
|
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
#include <sys/time.h>
#include <math.h>
__global__ void cuda_hello(){
printf("Hello World from GPU!\n");
}
int main(){
cuda_hello<<<1,1>>>();
printf("Hello World from CPU!\n");
cudaGetLastError();
return 0;
}
|
1,083
|
// matrix multiplication between square matrices using bidimensional indexes.
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
#include <ctype.h>
#include <sys/types.h>
#include <sys/time.h>
// #define MATRIXSIZE 10
// #define MAX_THREADS 5
// #define NUM_BLOCKS MATRIXSIZE / MAX_THREADS
double cclock()
/* Returns elepsed seconds past from the last call to timer rest */
{
struct timeval tmp;
double sec;
gettimeofday( &tmp, (struct timezone *)0 );
sec = tmp.tv_sec + ((double)tmp.tv_usec)/1000000.0;
return sec;
}
// print the matrix M
void print_matrix(int rows, int cols, double *M) {
int i, j;
for (i = 0; i < rows; i++) {
for (j = 0; j < cols; j++) {
printf("%d ", (int)M[i * cols + j]);
}
printf("\n");
}
printf("\n");
}
// kernel function
__global__ void matrix_init(double * M, int SIZE){
int col = ( blockIdx.x * blockDim.x ) + threadIdx.x;
int row = ( blockIdx.y * blockDim.y ) + threadIdx.y;
int idx = row * SIZE + col;
if( idx < SIZE*SIZE) {
M[idx] = 1;
}
}
__global__ void matrix_mult(double * A, double * B, double * C, int SIZE){
int k;
int col = ( blockIdx.x * blockDim.x ) + threadIdx.x;
int row = ( blockIdx.y * blockDim.y ) + threadIdx.y;
// every block of threads computes the i-th column of matrix C
if( row < SIZE && col < SIZE) {
C[row * SIZE + col] = 0;
for( k = 0; k < SIZE; k++ ) {
C[row * SIZE + col] += A[row * SIZE + k] * B[k * SIZE + col];
}
}
}
int main(int argc, char *argv[]) {
// double * h_A, * h_B,
double *h_C; // host pointers
double * d_A, * d_B, *d_C; // device pointers
// int i;
int size_in_bytes, MATRIXSIZE;
double t_start, t_end;
MATRIXSIZE = atoi(argv[1]);
size_in_bytes = MATRIXSIZE * MATRIXSIZE * sizeof( double );
if( MATRIXSIZE < 1 ){
fprintf( stderr, "Error. Inconsistent parameters.\nProgram exit ...\n");
exit(1);
}
// allocate the pointers
// h_A = ( double * ) malloc( size_in_bytes );
// h_B = ( double * ) malloc( size_in_bytes );
h_C = ( double * ) malloc( size_in_bytes );
cudaMalloc( (void**) &d_A, size_in_bytes );
cudaMalloc( (void**) &d_B, size_in_bytes );
cudaMalloc( (void**) &d_C, size_in_bytes );
dim3 dimBlock(4,4); //4 threads per block
dim3 dimGrid(MATRIXSIZE/dimBlock.x, MATRIXSIZE/dimBlock.y); // MATRIXSIZE/dimBlock blocks per grid
// // initialize the matrices
// srand(time(NULL));
// for( i = 0; i < MATRIXSIZE * MATRIXSIZE; i++ ){
// h_A[i] = (rand() % 1000 + 1);
// h_B[i] = (rand() % 1000 + 1);
// h_C[i] = 0;
// }
// copy from CPU to GPU
//cudaMemcpy( dest, source, sizeinbytes, cudaMemcpyHostToDevice | cudaMemcpyDeviceToHost );
// cudaMemcpy( d_A, h_A, size_in_bytes, cudaMemcpyHostToDevice );
// cudaMemcpy( d_B, h_B, size_in_bytes, cudaMemcpyHostToDevice );
// cudaMemcpy( d_C, h_C, size_in_bytes, cudaMemcpyHostToDevice );
matrix_init<<< dimGrid, dimBlock >>>(d_A, MATRIXSIZE);
matrix_init<<< dimGrid, dimBlock >>>(d_B, MATRIXSIZE);
t_start=cclock();
matrix_mult<<< dimGrid, dimBlock >>>(d_A, d_B, d_C, MATRIXSIZE);
t_end=cclock();
// copy from GPU to CPU
cudaMemcpy( h_C, d_C, size_in_bytes, cudaMemcpyDeviceToHost );
print_matrix(MATRIXSIZE, MATRIXSIZE, h_C);
fprintf( stdout, "multiplication executed. Time Elapsed %9.4f secs\n", t_end-t_start );
// free the memory
// free( h_A );
// free( h_B );
free( h_C );
cudaFree( d_A );
cudaFree( d_B );
cudaFree( d_C );
return 0;
}
|
1,084
|
// for this simple illustration, it is assumed that the code runs in
// just one block, and that the number of threads evenly divides n
// improvements that could be made:
// 1. change to multiple blocks, to try to use all SMs
// 2. possibly use shared memory
// 3. have each thread work on staggered elements of dx, rather than
// on contiguous ones, to get more efficient bank access
#include <cuda.h>
#include <stdio.h>
__global__ void cumulker(int *dx, int n)
{
int me = threadIdx.x;
int csize = n / blockDim.x;
int start = me * csize;
int i,j,base;
for (i = 1; i < csize; i++) {
j = start + i;
dx[j] = dx[j-1] + dx[j];
}
__syncthreads();
if (me > 0) {
base = 0;
for (j = 0; j < me; j++)
base += dx[(j+1)*csize-1];
}
if (me > 0) {
for (i = start; i < start + csize; i++)
dx[i] += base;
}
}
int main(int argc, char **argv)
{
int n = atoi(argv[1]), // length of array
nth = atoi(argv[2]); // number of threads
int *ha, // host array
*da, // device array
nint = n * sizeof(int);
ha = (int *) malloc(nint);
// test example
for (int i = 0; i < n; i++) ha[i] = i*i % 5;
if (n < 100) for(int i=0; i<n; i++) printf("%d ",ha[i]);
printf("\n");
cudaMalloc((void **)&da,nint);
cudaMemcpy(da,ha,nint,cudaMemcpyHostToDevice);
dim3 dimGrid(1,1);
dim3 dimBlock(n/nth,1,1);
cumulker<<<dimGrid,dimBlock>>>(da,n);
cudaDeviceSynchronize();
cudaMemcpy(ha,da,nint,cudaMemcpyDeviceToHost);
if (n < 100) for(int i=0; i<n; i++) printf("%d ",ha[i]);
printf("\n");
free(ha);
cudaFree(da);
}
|
1,085
|
#include <stdio.h>
__constant__ int const_symbol;
/********************/
/* CUDA ERROR CHECK */
/********************/
#define gpuErrchk(ans) { gpuAssert((ans), __FILE__, __LINE__); }
inline void gpuAssert(cudaError_t code, const char *file, int line, bool abort=true)
{
if (code != cudaSuccess)
{
fprintf(stderr,"GPUassert: %s %s %d\n", cudaGetErrorString(code), file, line);
if (abort) exit(code);
}
}
/***************/
/* TEST KERNEL */
/***************/
__global__ void kernel() {
printf("Address of symbol from device = %p\n", &const_symbol);
}
/********/
/* MAIN */
/********/
int main()
{
const int N = 16;
int *pointer = NULL;
gpuErrchk(cudaGetSymbolAddress((void**)&pointer, const_symbol));
kernel<<<1,1>>>();
printf("Address of symbol from host = %p\n", pointer);
return 0;
}
|
1,086
|
// standard lib stuff
#include <stdio.h>
#include <iostream>
#include <math.h>
#include <algorithm>
#include <random>
// thrust library
#include <thrust/host_vector.h>
#include <thrust/device_vector.h>
#include <thrust/tuple.h>
#include <thrust/generate.h>
#include <thrust/random.h>
#include <thrust/sort.h>
#include <thrust/copy.h>
#include <cstdlib>
// For random number generation
#include <curand.h>
#include <curand_kernel.h>
// Program global variables. Many will be overridden during execution.
const double MAX = 5.12;
const double MIN = -5.12;
const int SIZE_PARENT_POOL = 7;
// Default settings if not overriden at runtime.
int POPULATION_SIZE = 200;
int N_PARAMETERS = 10;
int BLOCKSIZE = 256;
int TOTALTHREADS = 1024;
/* wrapper function to check cuda calls
* ref: https://stackoverflow.com/questions/14038589/what-is-the-canonical-way-to-check-for-errors-using-the-cuda-runtime-api
*/
#define gpuErrchk(ans) { gpuAssert((ans), __FILE__, __LINE__); }
inline void gpuAssert(cudaError_t code, const char *file, int line, bool abort=true)
{
if (code != cudaSuccess)
{
fprintf(stderr,"GPUassert: %s %s %d\n", cudaGetErrorString(code), file, line);
if (abort) exit(code);
}
}
/* Generate uniform distribution
*
* Used by thrust transform functions to create large numbers of
* random numbers in a uniform distribution.
*/
struct prg
{
double a, b;
__host__ __device__
prg(double _a=0.f, double _b=1.f) : a(_a), b(_b) {
};
__host__ __device__
double operator()(const unsigned int n) const
{
thrust::default_random_engine rng;
//thrust::default_random_engine rng( 5555555 );
thrust::uniform_real_distribution<double> dist(a, b);
rng.discard(n);
return dist(rng);
}
};
/* Generate normal distribution
*
* Used by thrust transform functions to create large numbers of
* random numbers from a gaussian (normal) distribution.
*/
struct normal
{
double a, b;
__host__ __device__
normal(double _a=0.f, double _b=0.1f) : a(_a), b(_b) {
};
__host__ __device__
double operator()(const unsigned int n) const
{
thrust::default_random_engine rng;
thrust::normal_distribution<double> dist(a, b);
rng.discard(n);
return dist(rng);
}
};
/* Return the larger of the two elements in the tuple
*
* Used by thrust reduce functions to find the maximum element
* in an array.
*/
template <class T>
struct larger_tuple {
__device__ __host__
thrust::tuple<T,int> operator()(const thrust::tuple<T,int> &a, const thrust::tuple<T,int> &b)
{
if (a > b) return a;
else return b;
}
};
/* min_index returns the index of the smallest element in the array
*
* Similar to NumPy argmin() functionality, this returns the index of the
* smallest element in the input array.
*/
template <class T>
int min_index(thrust::device_vector<T>& vec) {
thrust::counting_iterator<int> begin(0); thrust::counting_iterator<int> end(vec.size());
thrust::tuple<T,int> init(vec[0],0);
thrust::tuple<T,int> largest;
largest = thrust::reduce(thrust::make_zip_iterator(thrust::make_tuple(vec.begin(), begin)), thrust::make_zip_iterator(thrust::make_tuple(vec.end(), end)),
init, larger_tuple<T>());
return thrust::get<1>(largest);
}
/* score evaluates the fitness of each member in the population
*
* Each member (represented by a N_PARAMETERS sized section of the population
* array) is evaulated against the desired input function. For this example,
* this is hard-coded to be the "offset sphere problem".
*
* Higher numbers represent better fitness.
*/
__global__ void score(unsigned int n, unsigned int np, double *source, double *score) {
int index = blockIdx.x * blockDim.x + threadIdx.x;
int stride = blockDim.x * gridDim.x;
double value;
if (index < n) {
for (int i=index; i < n; i += stride) {
// score = 1/ (sqrt(sum((xi - 0.5)**2)) + 1)
value = 0;
for (int p=0; p<np; p++) {
value += std::pow(source[i*np+p]-0.5, 2);
}
value = (double) std::sqrt( (double) value);
score[i] = (double) 1.0 / (double) (value+1.0);
}
}
}
/* pickParents generates the set of parents to breed into the next generation
*
* The method here is a sort of limited tournament style. Each parent
* in the output array is the member with the best fitness drawn from a random
* pool of SIZE_PARENT_POOL that has been generated ahead of time.
*
* The brings in a controlled amount of natural selction, while still tending
* to bring the best members forward to the next generation. Note many parents
* can and will breed multiple times.
*/
__global__ void pickParents(unsigned int n, unsigned int np, int *randParents, double *score, int *pool) {
int index = blockIdx.x * blockDim.x + threadIdx.x;
int stride = blockDim.x * gridDim.x;
// Pick a parent n times
for (int i=index; i<n; i+=stride) {
double best = -1.0;
int best_index = -1;
int idx;
// Grab the SIZE_PARENT_POOL randomely chosen individuals,
// and pick the best one to output.
for (int j=0; j<SIZE_PARENT_POOL; j++) {
idx = randParents[i*SIZE_PARENT_POOL+j];
if (score[idx] > best) {
best = score[idx];
best_index = idx;
}
}
pool[i] = best_index;
}
}
/* breedGeneration uses the chosen parents to create a new derived generation
*
* 10% of the time, the parent will produce no children, but will move straight
* into the new generation.
*
* 90% of the time, a child will be produced from the two parents. In this
* case, half the values from one parent and half from the other are used
* to create the child. In this event, 5% of the time a random mutation
* will also happen to the child's values.
*/
__global__ void breedGeneration(unsigned int n, unsigned int np, int *randomParameters, double *population, double *newPopulation, int *parentsPool, double *mutations) {
int index = blockIdx.x * blockDim.x + threadIdx.x;
int stride = blockDim.x * gridDim.x;
int parentA;
int parentB;
int probCopyParents;
int crossover;
int probChildAMutate;
int mutationPoint;
// Once for each individual
for (int i=index; i<n; i+=stride) {
probCopyParents = randomParameters[i*4];
crossover = randomParameters[(i+1)*4] % np;
probChildAMutate = randomParameters[(i+2)*4];
mutationPoint = randomParameters[(i+3)*4] % np;
parentA = parentsPool[i];
parentB = parentsPool[i+1];
// Place parent directly into the subsequent generation and
if (probCopyParents < 10) {
for (int j=0; j<np; j++) {
newPopulation[(i*np) + j] = population[(parentA*np) + j];
continue;
}
}
for (int j=0; j<np; j++) {
if (j < crossover) {
newPopulation[(i*np) + j] = population[(parentA*np) + j];
} else {
newPopulation[(i*np) + j] = population[(parentB*np) + j];
}
}
if (probChildAMutate < 5) {
double newval = newPopulation[(i*np) + mutationPoint] + mutations[i];
newPopulation[(i*np) + mutationPoint] = fminf(newval, MAX);
newPopulation[(i*np) + mutationPoint] = fmaxf(newval, MIN);
}
}
}
/* init sets up the random states
*/
__global__ void init(unsigned int seed, curandState_t* states) {
int idx = threadIdx.x+blockDim.x*blockIdx.x;
curand_init(seed, idx, 0, &states[idx]);
}
/* setRandom generates a random array modulo the max parameter.
*/
__global__ void setRandom(curandState_t* states, int* numbers, int max) {
int idx = threadIdx.x+blockDim.x*blockIdx.x;
for (int i=0; i<SIZE_PARENT_POOL; i++) {
numbers[idx+i] = curand(&states[idx]) % max;
}
}
int main(int argc, char** argv) {
int generation = 0;
// Create thrust arrays to hold the population, scores, and the new
// population that is created each generation.
thrust::device_vector<double> population(POPULATION_SIZE * N_PARAMETERS);
thrust::device_vector<double> popScores(POPULATION_SIZE);
thrust::device_vector<double> newPopulation(POPULATION_SIZE * N_PARAMETERS);
// Create raw pointers to the arrays createde by thrust. When using
// device calls outside the thrust library, this is the appropriate input
double* popPtr = thrust::raw_pointer_cast(&population[0]);
double* newPopPtr = thrust::raw_pointer_cast(&newPopulation[0]);
double* scoresPtr = thrust::raw_pointer_cast(&popScores[0]);
// Allocated random state for the initial population
curandState_t* states;
gpuErrchk(cudaMalloc((void**) &states, POPULATION_SIZE*SIZE_PARENT_POOL*sizeof(curandState_t)));
// Create random state for the random potential parents for each generation
int *randParents;
gpuErrchk(cudaMalloc((void**) &randParents, POPULATION_SIZE*SIZE_PARENT_POOL*sizeof(int)));
// Allocate array for the actual parents used to breed in each generation
int *parentsPool_d;
gpuErrchk(cudaMalloc((void**) &parentsPool_d, POPULATION_SIZE*sizeof(int)));
// Create random states and array for the parameters needed to breed children;
// - the probability of placing the parent directly into the next gen
// - the crossover point for the child
// - probability of mutating the child
// - index to mutate the child
curandState_t* childStates;
gpuErrchk(cudaMalloc((void**) &childStates, POPULATION_SIZE*4*sizeof(curandState_t)));
int *randParams_d;
gpuErrchk(cudaMalloc((void**)&randParams_d, POPULATION_SIZE*4*sizeof(int)));
// Parse Argument variables
if (argc >= 2) {
POPULATION_SIZE = atoi(argv[1]);
}
if (argc >= 3) {
N_PARAMETERS = atoi(argv[2]);
}
if (argc >= 4) {
TOTALTHREADS = atoi(argv[3]);
}
if (argc >= 5) {
BLOCKSIZE = atoi(argv[4]);
}
// Cude Device Properties to see if the BLOCKSIZE can be handled
cudaDeviceProp prop;
cudaGetDeviceProperties(&prop, 0);
if (BLOCKSIZE > prop.maxThreadsPerBlock) {
std::cout << "Device only supports a BLOCKSIZE of " << prop.maxThreadsPerBlock <<" threads/block" << std::endl;
std::cout << "Try again with a smaller BLOCKSIZE" << std::endl;
return 1;
}
std::cout << "Running with " << TOTALTHREADS << " threads and a BLOCKSIZE of ";
std::cout << BLOCKSIZE << std::endl;
int numBlocks = TOTALTHREADS/BLOCKSIZE;
// validate command line arguments and re-size if needed
if (TOTALTHREADS % BLOCKSIZE != 0) {
++numBlocks;
TOTALTHREADS = numBlocks*BLOCKSIZE;
printf("Warning: Total thread count is not evenly divisible by the block size\n");
printf("The total number of threads will be rounded up to %d\n", TOTALTHREADS);
}
std::cout << "Running " << numBlocks << " total blocks." << std::endl;
// initialize random generator for the initial population
std::default_random_engine generator;
std::normal_distribution<double> distribution(0, .1);
// Generate initial random population representing generation 0
thrust::counting_iterator<unsigned int> index_sequence_begin(0);
thrust::transform(index_sequence_begin,
index_sequence_begin + POPULATION_SIZE * N_PARAMETERS,
population.begin(),
prg(MIN, MAX));
// Evaluate every member of the population and find the most fit individual
score<<<TOTALTHREADS, BLOCKSIZE>>>(POPULATION_SIZE, N_PARAMETERS, popPtr, scoresPtr);
double best = *(thrust::max_element(popScores.begin(), popScores.end()));
int best_index = min_index(popScores);
std::cout << "Initial generation best score: " << best << " at index: " << best_index << " ";
for (int i=0; i<N_PARAMETERS; i++) {
std::cout << population[best_index * N_PARAMETERS + i] << " ";
}
std::cout << std::endl;
// Create successive generations until convergence is achieved.
while (best < .999) {
// Create a new set of random parents to breed into the next generation
init<<<POPULATION_SIZE*SIZE_PARENT_POOL, 1>>>(time(0), states);
gpuErrchk(cudaPeekAtLastError());
setRandom<<<POPULATION_SIZE*SIZE_PARENT_POOL, 1>>>(states, randParents, POPULATION_SIZE);
gpuErrchk(cudaPeekAtLastError());
pickParents<<<TOTALTHREADS, BLOCKSIZE>>>(POPULATION_SIZE, N_PARAMETERS, randParents, scoresPtr, parentsPool_d);
gpuErrchk(cudaPeekAtLastError());
// Generate new random states for the breeding parameters
init<<<POPULATION_SIZE*4, 1>>>(time(0), childStates);
gpuErrchk(cudaPeekAtLastError());
setRandom<<<POPULATION_SIZE*4, 1>>>(childStates, randParams_d, 100);
gpuErrchk(cudaPeekAtLastError());
// Generate new random parameters for breeding and child mutation
thrust::device_vector<double> mutations(POPULATION_SIZE);
thrust::counting_iterator<unsigned int> index_sequence_begin(0);
thrust::transform(index_sequence_begin,
index_sequence_begin + POPULATION_SIZE,
mutations.begin(),
normal(0.0, 0.001));
double* mutPtr = thrust::raw_pointer_cast(&mutations[0]);
// Breed members and copy over to the new generation
breedGeneration<<<TOTALTHREADS, BLOCKSIZE>>>(POPULATION_SIZE, N_PARAMETERS, randParams_d, popPtr, newPopPtr, parentsPool_d, mutPtr);
gpuErrchk(cudaPeekAtLastError());
thrust::copy(thrust::device, newPopulation.begin(), newPopulation.end(), population.begin());
// Evaluate all members and identify the most fit individual
score<<<TOTALTHREADS, BLOCKSIZE>>>(POPULATION_SIZE, N_PARAMETERS, popPtr, scoresPtr);
gpuErrchk(cudaPeekAtLastError());
best = *(thrust::min_element(popScores.begin(), popScores.end()));
best_index = min_index(popScores);
std::cout << "Bred generation " << generation << " Best score: " << best << " at index: " << best_index << " ";
for (int i=0; i<N_PARAMETERS; i++) {
std::cout << population[best_index * N_PARAMETERS + i] << " ";
}
std::cout << std::endl;
// Keep track of how many generations have occured.
generation++;
}
cudaFree(states);
cudaFree(randParents);
cudaFree(parentsPool_d);
cudaFree(childStates);
cudaFree(randParams_d);
return 0;
}
|
1,087
|
#include "includes.h"
__global__ void __word2vecEvalPos(int nrows, int ncols, int *W, int *LB, int *UB, float *A, float *B, float *Retval) {}
|
1,088
|
#include <stdio.h>
__global__ void helloFromGPU()
{
printf("Hello, World from GPU!\n");
}
void helloFromCPU()
{
printf("Hello, World from CPU!\n");
}
int main()
{
helloFromCPU();
helloFromGPU<<<1,1>>>();
cudaDeviceSynchronize();
helloFromCPU();
}
|
1,089
|
template <typename T>
struct A {
T* p;
int *b;
A(int a) {
p = new T[a];
b = new int[a];
}
};
int main() {
A<int> x(10);
return 0;
}
|
1,090
|
__device__ int ave(int a, int b)
{
return (a+b)/2;
}
__global__ void simple(int *data)
{
int tid = blockIdx.x * blockDim.x + threadIdx.x;
data[tid] = ave(tid, tid);
}
|
1,091
|
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include <math.h>
#include <time.h>
#define WA 10000 // Matrix A width
#define HA 10000 // Matrix A height
#define WB 10000 // Matrix B width
#define HB WA // Matrix B height
#define WC WB // Matrix C width
#define HC HA // Matrix C height
#define N 100
#define M 100
#define BLOCK_SIZE 16
// Allocates a matrix with random float entries
void randomInit(float* data, int size)
{
for (int i = 0; i < size; ++i)
data[i] = rand() / (float)RAND_MAX;
}
// Code running on GPU //
__global__ void
matrixMul_naive( float* C, float* A, float* B, int wA, int wB)
{
int bx = blockIdx.x;
int by = blockIdx.y;
int tx = threadIdx.x;
int ty = threadIdx.y;
int i = by * blockDim.y + ty;
int j = bx * blockDim.x + tx;
float accu = 0.0;
for(int k=0; k<wA; k++){
accu = accu + A[ i * wA + k ] * B[ k * wB + j ];
}
C[ i * wB + j ] = accu;
}
int main(){
cudaEvent_t start, stop;
float msecTotal;
cudaEventCreate(&start);
unsigned int size_A = WA * HA;
unsigned int mem_size_A = sizeof(float) * size_A;
float* h_A = (float*) malloc(mem_size_A);
unsigned int size_B = WB * HB;
unsigned int mem_size_B = sizeof(float) * size_B;
float* h_B = (float*) malloc(mem_size_B);
randomInit(h_A, size_A);
randomInit(h_B, size_B);
float* d_A;
cudaMalloc((void**) &d_A, mem_size_A);
float* d_B;
cudaMalloc((void**) &d_B, mem_size_B);
unsigned int size_C = WC * HC;
unsigned int mem_size_C = sizeof(float) * size_C;
float* d_C;
cudaMalloc((void**) &d_C, mem_size_C);
float* h_C = (float*) malloc(mem_size_C);
cudaMemcpy(d_A, h_A, mem_size_A, cudaMemcpyHostToDevice);
cudaMemcpy(d_B, d_B, mem_size_B, cudaMemcpyHostToDevice);
dim3 dimBlock(16, 16);
dim3 dimGrid((N)/dimBlock.x, (M)/dimBlock.y);
cudaEventRecord(start, NULL);
// execute the kernel
matrixMul_naive<<< dimGrid, dimBlock >>>(d_C, d_A, d_B,WA,WB);
cudaEventCreate(&stop);
cudaEventRecord(stop, NULL);
cudaEventSynchronize(stop);
cudaEventElapsedTime(&msecTotal, start, stop);
cudaMemcpy(h_C, d_C, mem_size_C, cudaMemcpyDeviceToHost);
printf("GPU Execution time: %f (ms) \n", msecTotal);
return 0;
}
|
1,092
|
#include<iostream>
using namespace std;
__global__ void average(int *a, float *b, int n)
{
int tid=threadIdx.x;
int sum=0;
for(int i=0;i<n;i++)
{
sum+=a[i];
}
float mean=sum/(n*1.0);
b[tid]=mean;
}
__global__ void standardDev(int *a, float *b, float mean, int n)
{
int tid=blockIdx.x;
b[0]=0.0;
for(int i=0;i<n;i++)
{
b[0] += (a[i] - mean) * (a[i] - mean);
}
b[0]=b[0]/n;
}
int main()
{
int n=10;
int *a=(int*)malloc(n*sizeof(int));
cudaEvent_t start, end;
for(int i=0;i<n;i++)
{
a[i]=i+1;
}
cudaEventCreate(&start);
cudaEventCreate(&end);
int *dev_a;
float *dev_b;
int size=n*sizeof(int);
cudaMalloc(&dev_a,size);
cudaMalloc(&dev_b,sizeof(float));
cudaMemcpy(dev_a,a,size,cudaMemcpyHostToDevice);
cudaEventRecord(start);
average<<<1, n>>>(dev_a, dev_b, n);
float *mean=(float *)malloc(sizeof(float));
cudaEventRecord(end);
cudaEventSynchronize(end);
float time=0;
cudaEventElapsedTime(&time, start, end);
cudaMemcpy(mean, dev_b, sizeof(float),cudaMemcpyDeviceToHost);
cout<<"\nMean is : "<<mean[0];
float *std=(float*)malloc(sizeof(float));
standardDev<<<n,1>>>(dev_a, dev_b, mean[0], n);
cudaMemcpy(std, dev_b, sizeof(float), cudaMemcpyDeviceToHost);
cout<<"\nStandard Deviation is : "<<sqrt(std[0])<<endl;
cout<<"\nTime taken : "<<time;
return 0;
}
|
1,093
|
#include <iostream>
#include <cstdlib>
#include <stdio.h>
#include <math.h>
#include <assert.h>
#include <sys/time.h>
__global__ void fPixelGenerator(int width, unsigned char* pic) {
int frame = threadIdx.x; // ID da thread
for (int row = 0; row < width; row++) {
for (int col = 0; col < width; col++) {
float fx = col - 1024/2;
float fy = row - 1024/2;
float d = sqrtf( fx * fx + fy * fy );
unsigned char color = (unsigned char) (160.0f + 127.0f * cos(d/10.0f - frame/7.0f) / (d/50.0f + 1.0f));
pic[frame * width * width + row * width + col] = (unsigned char) color;
}
}
}
static void writeBMP(const int x, const int y, const unsigned char* const bmp, const char* const name) {
const unsigned char bmphdr[54] = {66, 77, 255, 255, 255, 255, 0, 0, 0, 0, 54, 4, 0, 0, 40, 0, 0, 0, 255, 255, 255, 255, 255, 255, 255, 255, 1, 0, 8, 0, 0, 0, 0, 0, 255, 255, 255, 255, 196, 14, 0, 0, 196, 14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
unsigned char hdr[1078];
int i, j, c, xcorr, diff;
FILE* f;
xcorr = (x + 3) >> 2 << 2; // BMPs have to be a multiple of 4 pixels wide
diff = xcorr - x;
for (i = 0; i < 54; i++) hdr[i] = bmphdr[i];
*((int*)(&hdr[18])) = xcorr;
*((int*)(&hdr[22])) = y;
*((int*)(&hdr[34])) = xcorr * y;
*((int*)(&hdr[2])) = xcorr * y + 1078;
for (i = 0; i < 256; i++) {
j = i * 4 + 54;
hdr[j+0] = i; // blue ColorTable
hdr[j+1] = 0; // green
hdr[j+2] = 0; // red
hdr[j+3] = 0; // dummy
}
f = fopen(name, "wb"); assert(f != NULL);
c = fwrite(hdr, 1, 1078, f); assert(c == 1078);
if (diff == 0) {
c = fwrite(bmp, 1, x * y, f); assert(c == x * y);
}
else {
*((int*)(&hdr[0])) = 0; // need up to three zero bytes
for (j = 0; j < y; j++) {
c = fwrite(&bmp[j * x], 1, x, f); assert(c == x);
c = fwrite(hdr, 1, diff, f); assert(c == diff);
}
}
fclose(f);
}
int main(int argc, char *argv[]) {
// check command line
if (argc != 3) {
fprintf(stderr, "usage: %s frame_width num_frames\n", argv[0]);
exit(-1);
}
int width = atoi(argv[1]);
if (width < 100) {
fprintf(stderr, "error: frame_width must be at least 100\n");
exit(-1);
}
int frames = atoi(argv[2]);
if (frames < 1) {
fprintf(stderr, "error: num_frames must be at least 1\n");
exit(-1);
}
printf("computing %d frames of %d by %d picture\n", frames, width, width);
// allocate picture array
unsigned char* pic = NULL;
cudaMallocManaged(&pic, frames * width * width * sizeof(unsigned char));
// start time
timeval start, end;
gettimeofday(&start, NULL);
// Pixel Generator
fPixelGenerator<<<1, frames>>>(width, pic);
// Wait for GPU to finish before accessing on host
cudaDeviceSynchronize();
// end time
gettimeofday(&end, NULL);
double runtime = end.tv_sec + end.tv_usec / 1000000.0 - start.tv_sec - start.tv_usec / 1000000.0;
printf("compute time: %.4f s\n", runtime);
// verify result by writing frames to BMP files
if ((width <= 256) && (frames <= 100)) {
for (int frame = 0; frame < frames; frame++) {
char name[32];
sprintf(name, "wave%d.bmp", frame + 1000);
writeBMP(width, width, &pic[frame * width * width], name);
}
}
// Free memory
cudaFree(pic);
return 0;
}
|
1,094
|
#include "includes.h"
__global__ void vectorAddKernel(float *a, float *b, float *c, int n) {
// Escribir en c la suma de a y b
};
|
1,095
|
#include "includes.h"
__global__ void l2_regularize_kernel(int factors, float regularization, float * YtY) {
YtY[threadIdx.x * factors + threadIdx.x] += regularization;
}
|
1,096
|
#include<stdio.h>
#include<stdlib.h>
#include<math.h>
double randd() {
return (double)rand() / (RAND_MAX + 1.0);
}
//Kernel -- To multiply matrix with it's transpose
__global__ void multiply_serial(double **d_a,double **d_c,int mSize)
{
int i,j,k;
//start with matrix multplication calcultaion
for(i=0;i<mSize;i++)
{
for(j=0;j<mSize;j++)
{
for(k=0;k<mSize;k++)
{
*(*(d_c+i)+j) = (*(*(d_a+k)+i)) * (*(*(d_a+k)+j));
}
}
}
}
// End of function
int main()
{
double **h_a; //Pointer for host memory
double **d_a; //Pointer for device memory
double **d_c;// anotehr pointer to device memory
int dimA = 1024*1024;
int i,j;
//define thread hierarchy
int nblocks = 1;
int tpb = 1024;
//allocate host and device memory.
size_t memSize;
memSize = dimA * sizeof(double*);
h_a = (double**)malloc(sizeof(double*)*1024);
for(i=0;i<1024;i++)
*(h_a+i) = (double*)malloc(sizeof(double*)*1024);
cudaMalloc((void**)&d_a,memSize);
cudaMalloc((void**)&d_c,memSize);
//initialize host array
for(i=0;i<dimA;i++)
{
for(j=0;j<dimA;j++)
{
*(*(h_a+i)+j)= randd();
}
}
//Copy contents to device
cudaMemcpy(d_a, h_a,memSize, cudaMemcpyHostToDevice);
//Launch kernel
dim3 dimGrid(nblocks);
dim3 dimBlock(tpb);
multiply_serial<<<dimGrid,dimBlock>>>(d_a,d_c,dimA);
//get the output
cudaMemcpy(h_a, d_c,memSize, cudaMemcpyDeviceToHost);
// Print the ouput(i.e final result after multiplication)
for(i=0;i<dimA;i++)
{
for(j=0;j<dimA;j++)
{
printf(" ",h_a[i][j]);
}
printf("\n");
}
}
|
1,097
|
#include "includes.h"
/* This file is copied from https://github.com/jzbonter/mc-cnn */
extern "C" {
}
#define TB 128
#define DISP_MAX 256
__global__ void rho(float *x, int size, float lambda)
{
int id = blockIdx.x * blockDim.x + threadIdx.x;
if (id < size) {
x[id] = 1 - exp(-x[id] / lambda);
}
}
|
1,098
|
// Approximation of Pi using a simple, and not optimized, CUDA program
// Copyleft Alessandro Re
#include <stdio.h>
#include <cuda.h>
#include <curand_kernel.h>
typedef unsigned long long Count;
const Count WARP_SIZE = 32; // Warp size
const Count NBLOCKS = 64; // Number of total cuda cores on my GPU
__global__ void picount(Count *totals, Count iterPerThread) {
__shared__ Count counter[WARP_SIZE];
int tid = threadIdx.x + blockIdx.x * blockDim.x;
curandState_t rng;
curand_init(clock64(), tid, 0, &rng);
Count cnt = 0;
// Computation loop
for (int i = 0; i < iterPerThread; i++) {
float x = curand_uniform(&rng);
float y = curand_uniform(&rng);
if(x*x + y*y <= 1.0) cnt++;
}
counter[threadIdx.x] = cnt;
if (threadIdx.x == 0) {
totals[blockIdx.x] = 0;
for (int i = 0; i < WARP_SIZE; i++) {
totals[blockIdx.x] += counter[i];
}
}
}
int main(int argc, char **argv) {
int nDevices;
cudaGetDeviceCount(&nDevices);
if (nDevices < 1) {
printf("CUDA device missing! Do you need to use optirun?\n");
return 1;
}
printf("------CUDA Devices------\n");
for (int i = 0; i < nDevices; i++) {
cudaDeviceProp prop;
cudaGetDeviceProperties(&prop, i);
printf("Device Number: %d\n", i);
printf(" Device name: %s\n", prop.name);
printf(" Memory Clock Rate (KHz): %d\n",
prop.memoryClockRate);
}
Count totalIterations;
scanf("%lld", &totalIterations);
Count totalThreads = (WARP_SIZE * NBLOCKS);
Count iterPerThread = (totalIterations + totalThreads - 1LL)/(totalThreads);
totalIterations = iterPerThread * totalThreads;
printf("\nStarting simulation with %lld blocks, %lld threads per block (warps), and a total of %lld iterations\n", NBLOCKS, WARP_SIZE, totalIterations);
Count *hOut, *dOut;
hOut = new Count[NBLOCKS];
cudaMalloc(&dOut, sizeof(Count) * NBLOCKS);
picount<<<NBLOCKS, WARP_SIZE>>>(dOut, iterPerThread);
cudaMemcpy(hOut, dOut, sizeof(Count) * NBLOCKS, cudaMemcpyDeviceToHost);
cudaFree(dOut);
Count total = 0;
for (int i = 0; i < NBLOCKS; i++) {
total += hOut[i];
}
printf("Approximated PI using %lld random tests\n", totalIterations);
double pi = 4.0 * (double)total/(double)totalIterations;
printf("PI ~= %.9lf\n", pi);
return 0;
}
|
1,099
|
#include<iostream>
using namespace std;
#define N 512
__global__ void ArithmeticMean (int *a,int *o)
{
int of = N/2;
int tid = threadIdx.x;
for(of;of>0;of = of/2)
{
if(tid < of)
{
a[tid]+=a[tid+of];
}
}
o[0] = a[0];
}
int main()
{
int *h_a,*d_a,*o_a,*oh_a;
int size = N*sizeof(int);
h_a = (int *)malloc(size);
oh_a = (int *)malloc(size);
cudaMalloc((void**)&d_a,size);
cudaMalloc((void**)&o_a,size);
for(int i = 1; i <= N;i++)
{
h_a[i-1] = i;
}
cudaMemcpy(d_a,h_a,size,cudaMemcpyHostToDevice);
ArithmeticMean<<<1,N/2>>>(d_a,o_a);
cudaMemcpy(h_a,d_a,size,cudaMemcpyDeviceToHost);
cudaMemcpy(oh_a,o_a,size,cudaMemcpyDeviceToHost);
float AM =(float) oh_a[0]/N;
cout<<"AM is "<<AM;
cudaFree(d_a);
free(h_a);
}
|
1,100
|
#if GOOGLE_CUDA
#define EIGEN_USE_GPU
__global__ void default_function_kernel0(const float* __restrict__ U, const float* __restrict__ K0, float* __restrict__ U0) {
float U0_local[1];
__shared__ float U_shared[128];
__shared__ float K0_shared[4];
for (int n_inner_outer = 0; n_inner_outer < 2; ++n_inner_outer) {
for (int h_inner_outer = 0; h_inner_outer < 2; ++h_inner_outer) {
for (int r_inner_outer = 0; r_inner_outer < 6; ++r_inner_outer) {
U0_local[0] = 0.000000e+00f;
for (int k0_c_outer = 0; k0_c_outer < 4; ++k0_c_outer) {
__syncthreads();
for (int ax0_ax1_ax2_ax3_fused_fused_fused_inner_inner_inner = 0; ax0_ax1_ax2_ax3_fused_fused_fused_inner_inner_inner < 4; ++ax0_ax1_ax2_ax3_fused_fused_fused_inner_inner_inner) {
U_shared[(((((int)threadIdx.z) * 64) + (((int)threadIdx.y) * 4)) + ax0_ax1_ax2_ax3_fused_fused_fused_inner_inner_inner)] = U[(((((((((((((int)blockIdx.z) / 8) * 32768) + (((((((int)threadIdx.z) * 64) + (((int)threadIdx.y) * 4)) + ax0_ax1_ax2_ax3_fused_fused_fused_inner_inner_inner) / 128) * 16384)) + (n_inner_outer * 16384)) + ((((int)blockIdx.z) % 8) * 2048)) + (h_inner_outer * 1024)) + ((((((((int)threadIdx.z) * 64) + (((int)threadIdx.y) * 4)) + ax0_ax1_ax2_ax3_fused_fused_fused_inner_inner_inner) % 128) / 64) * 512)) + (((int)blockIdx.y) * 256)) + ((((((int)threadIdx.y) * 4) + ax0_ax1_ax2_ax3_fused_fused_fused_inner_inner_inner) / 4) * 16)) + (k0_c_outer * 4)) + ax0_ax1_ax2_ax3_fused_fused_fused_inner_inner_inner)];
}
if ((((int)threadIdx.z) * 2) < (4 - ((int)threadIdx.y))) {
if (((int)threadIdx.y) < 2) {
if (((k0_c_outer * 4) + (((int)threadIdx.z) * 2)) < (16 - ((int)threadIdx.y))) {
K0_shared[((((int)threadIdx.z) * 2) + ((int)threadIdx.y))] = K0[((((k0_c_outer * 24) + (((int)threadIdx.z) * 12)) + (((int)threadIdx.y) * 6)) + r_inner_outer)];
}
}
}
__syncthreads();
for (int k0_c_inner = 0; k0_c_inner < 4; ++k0_c_inner) {
U0_local[0] = (U0_local[0] + (U_shared[(((((int)threadIdx.z) * 64) + (((int)threadIdx.y) * 4)) + k0_c_inner)] * K0_shared[k0_c_inner]));
}
}
U0[(((((((((((int)blockIdx.z) / 8) * 12288) + (n_inner_outer * 6144)) + ((((int)blockIdx.z) % 8) * 768)) + (h_inner_outer * 384)) + (((int)threadIdx.z) * 192)) + (((int)blockIdx.y) * 96)) + (((int)threadIdx.y) * 6)) + r_inner_outer)] = U0_local[0];
}
}
}
}
void Cp0NhwcKernelLauncher(const float* U, const float* K0, float* U0){
dim3 gridDim0(1, 2, 32);
dim3 blockDim0(1, 16, 2);
default_function_kernel0<<<gridDim0, blockDim0>>>(U, K0, U0);
cudaDeviceSynchronize();
}
#endif
|
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