serial_no int64 1 24.2k | cuda_source stringlengths 11 9.01M |
|---|---|
20,301 | #include <iostream>
#include <cassert>
__device__
void cube(double* xi) {
*xi = (*xi) * (*xi) * (*xi);
}
__global__
void cube_kernel(double* x, int size) {
int tid = blockIdx.x * blockDim.x + threadIdx.x;
if (tid < size) {
cube(&x[tid]);
}
}
int main(int argc, char* argv[]) {
double* x = NULL;
int size = 100;
cudaError_t cuda_status = cudaSuccess;
cuda_status = cudaMallocManaged(&x, sizeof(double) * size);
assert(cuda_status == cudaSuccess);
for (int i = 0; i < size; i++) {
x[i] = 2.0;
}
int numThreads = std::min(32, size);
int numBlocks = static_cast<int>(ceil(((double) size)/((double) numThreads)));
cube_kernel<<<numBlocks, numThreads>>>(x, size);
cuda_status = cudaDeviceSynchronize();
assert(cuda_status == cudaSuccess);
double xsum = 0.0;
for (int i = 0; i < size; i++) {
xsum += x[i];
}
std::cout << "sum of elementwise cubed x is: " << xsum << std::endl;
if (xsum == 800.0) std::cout << "SUCCESS!" << std::endl;
else std::cout << "ERROR!" << std::endl;
return 0;
}
|
20,302 | #include "includes.h"
__device__ __forceinline__ size_t gpu_fieldn_index(unsigned int x, unsigned int y, unsigned int z, unsigned int d)
{
return (NX*(NY*(NZ*(d-1)+z)+y)+x);
}
__device__ __forceinline__ size_t gpu_scalar_index(unsigned int x, unsigned int y, unsigned int z)
{
return NX*(NY*z + y)+x;
}
__device__ __forceinline__ size_t gpu_field0_index(unsigned int x, unsigned int y, unsigned int z)
{
return NX*(NY*z + y)+x;
}
__global__ void gpu_init_equilibrium(double *f0, double *f1, double *h0, double *h1, double *temp0, double *temp1, double *r, double *c, double *u, double *v, double *w, double *ex, double *ey, double *ez, double*temp)
{
unsigned int y = blockIdx.y;
unsigned int z = blockIdx.z;
unsigned int x = blockIdx.x*blockDim.x+threadIdx.x;
double rho = r[gpu_scalar_index(x,y,z)];
double ux = u[gpu_scalar_index(x,y,z)];
double uy = v[gpu_scalar_index(x,y,z)];
double uz = w[gpu_scalar_index(x,y,z)];
double charge = c[gpu_scalar_index(x,y,z)];
double Ex = ex[gpu_scalar_index(x,y,z)];
double Ey = ey[gpu_scalar_index(x,y,z)];
double Ez = ez[gpu_scalar_index(x,y,z)];
double Temp = temp[gpu_scalar_index(x,y,z)];
// load equilibrium
// feq_i = w_i rho [1 + 3(ci . u) + (9/2) (ci . u)^2 - (3/2) (u.u)]
// feq_i = w_i rho [1 - 3/2 (u.u) + (ci . 3u) + (1/2) (ci . 3u)^2]
// feq_i = w_i rho [1 - 3/2 (u.u) + (ci . 3u){ 1 + (1/2) (ci . 3u) }]
// temporary variables
double w0r = w0*rho;
double wsr = ws*rho;
double war = wa*rho;
double wdr = wd*rho;
double w0c = w0*charge;
double wsc = ws*charge;
double wac = wa*charge;
double wdc = wd*charge;
double w0t = w0*Temp;
double wst = ws*Temp;
double wat = wa*Temp;
double wdt = wd*Temp;
double omusq = 1.0 - 0.5*(ux*ux+uy*uy+uz*uz)/cs_square;
double omusq_c = 1.0 - 0.5*((ux + K*Ex)*(ux + K*Ex) + (uy + K*Ey)*(uy + K*Ey) + (uz + K*Ez)*(uz + K*Ez)) / cs_square;
double tux = ux / cs_square / CFL;
double tuy = uy / cs_square / CFL;
double tuz = uz / cs_square / CFL;
double tux_c = (ux + K*Ex) / cs_square / CFL;
double tuy_c = (uy + K*Ey) / cs_square / CFL;
double tuz_c = (uz + K*Ez) / cs_square / CFL;
// zero weight
f0[gpu_field0_index(x,y,z)] = w0r*(omusq);
h0[gpu_field0_index(x,y,z)] = w0c*(omusq_c);
temp0[gpu_field0_index(x, y, z)] = w0t*(omusq);
// adjacent weight
// flow
double cidot3u = tux;
f1[gpu_fieldn_index(x,y,z,1)] = wsr*(omusq + cidot3u*(1.0+0.5*cidot3u));
cidot3u = -tux;
f1[gpu_fieldn_index(x,y,z,2)] = wsr*(omusq + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = tuy;
f1[gpu_fieldn_index(x,y,z,3)] = wsr*(omusq + cidot3u*(1.0+0.5*cidot3u));
cidot3u = -tuy;
f1[gpu_fieldn_index(x,y,z,4)] = wsr*(omusq + cidot3u*(1.0+0.5*cidot3u));
cidot3u = tuz;
f1[gpu_fieldn_index(x,y,z,5)] = wsr*(omusq + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = -tuz;
f1[gpu_fieldn_index(x,y,z,6)] = wsr*(omusq + cidot3u*(1.0 + 0.5*cidot3u));
// charge
cidot3u = tux_c;
h1[gpu_fieldn_index(x,y,z,1)] = wsc*(omusq_c + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = -tux_c;
h1[gpu_fieldn_index(x,y,z,2)] = wsc*(omusq_c + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = tuy_c;
h1[gpu_fieldn_index(x,y,z,3)] = wsc*(omusq_c + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = -tuy_c;
h1[gpu_fieldn_index(x,y,z,4)] = wsc*(omusq_c + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = tuz_c;
h1[gpu_fieldn_index(x,y,z,5)] = wsc*(omusq_c + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = -tuz_c;
h1[gpu_fieldn_index(x,y,z,6)] = wsc*(omusq_c + cidot3u*(1.0 + 0.5*cidot3u));
// temperature
cidot3u = tux;
temp1[gpu_fieldn_index(x, y, z, 1)] = wst*(omusq + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = -tux;
temp1[gpu_fieldn_index(x, y, z, 2)] = wst*(omusq + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = tuy;
temp1[gpu_fieldn_index(x, y, z, 3)] = wst*(omusq + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = -tuy;
temp1[gpu_fieldn_index(x, y, z, 4)] = wst*(omusq + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = tuz;
temp1[gpu_fieldn_index(x, y, z, 5)] = wst*(omusq + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = -tuz;
temp1[gpu_fieldn_index(x, y, z, 6)] = wst*(omusq + cidot3u*(1.0 + 0.5*cidot3u));
// diagonal weight
// flow
cidot3u = tux+tuy;
f1[gpu_fieldn_index(x,y,z,7)] = war*(omusq + cidot3u*(1.0+0.5*cidot3u));
cidot3u = -tuy-tux;
f1[gpu_fieldn_index(x,y,z,8)] = war*(omusq + cidot3u*(1.0+0.5*cidot3u));
cidot3u = tux+tuz;
f1[gpu_fieldn_index(x,y,z,9)] = war*(omusq + cidot3u*(1.0+0.5*cidot3u));
cidot3u = -tux-tuz;
f1[gpu_fieldn_index(x,y,z,10)] = war*(omusq + cidot3u*(1.0+0.5*cidot3u));
cidot3u = tuz + tuy;
f1[gpu_fieldn_index(x,y,z,11)] = war*(omusq + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = -tuy - tuz;
f1[gpu_fieldn_index(x,y,z,12)] = war*(omusq + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = tux - tuy;
f1[gpu_fieldn_index(x,y,z,13)] = war*(omusq + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = tuy - tux;
f1[gpu_fieldn_index(x,y,z,14)] = war*(omusq + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = tux - tuz;
f1[gpu_fieldn_index(x,y,z,15)] = war*(omusq + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = tuz - tux;
f1[gpu_fieldn_index(x,y,z,16)] = war*(omusq + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = tuy - tuz;
f1[gpu_fieldn_index(x,y,z,17)] = war*(omusq + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = tuz - tuy;
f1[gpu_fieldn_index(x,y,z,18)] = war*(omusq + cidot3u*(1.0 + 0.5*cidot3u));
// charge
cidot3u = tux_c + tuy_c;
h1[gpu_fieldn_index(x, y, z, 7)] = wac*(omusq_c + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = -tuy_c - tux_c;
h1[gpu_fieldn_index(x, y, z, 8)] = wac*(omusq_c + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = tux_c + tuz_c;
h1[gpu_fieldn_index(x, y, z, 9)] = wac*(omusq_c + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = -tux_c - tuz_c;
h1[gpu_fieldn_index(x, y, z, 10)] = wac*(omusq_c + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = tuy_c + tuz_c;
h1[gpu_fieldn_index(x, y, z, 11)] = wac*(omusq_c + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = -tuy_c - tuz_c;
h1[gpu_fieldn_index(x, y, z, 12)] = wac*(omusq_c + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = tux_c - tuy_c;
h1[gpu_fieldn_index(x, y, z, 13)] = wac*(omusq_c + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = tuy_c - tux_c;
h1[gpu_fieldn_index(x, y, z, 14)] = wac*(omusq_c + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = tux_c - tuz_c;
h1[gpu_fieldn_index(x, y, z, 15)] = wac*(omusq_c + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = tuz_c - tux_c;
h1[gpu_fieldn_index(x, y, z, 16)] = wac*(omusq_c + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = tuy_c - tuz_c;
h1[gpu_fieldn_index(x, y, z, 17)] = wac*(omusq_c + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = tuz_c - tuy_c;
h1[gpu_fieldn_index(x, y, z, 18)] = wac*(omusq_c + cidot3u*(1.0 + 0.5*cidot3u));
// temperature
cidot3u = tux + tuy;
temp1[gpu_fieldn_index(x, y, z, 7)] = wat*(omusq + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = -tuy - tux;
temp1[gpu_fieldn_index(x, y, z, 8)] = wat*(omusq + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = tux + tuz;
temp1[gpu_fieldn_index(x, y, z, 9)] = wat*(omusq + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = -tux - tuz;
temp1[gpu_fieldn_index(x, y, z, 10)] = wat*(omusq + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = tuy + tuz;
temp1[gpu_fieldn_index(x, y, z, 11)] = wat*(omusq + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = -tuy - tuz;
temp1[gpu_fieldn_index(x, y, z, 12)] = wat*(omusq + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = tux - tuy;
temp1[gpu_fieldn_index(x, y, z, 13)] = wat*(omusq + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = tuy - tux;
temp1[gpu_fieldn_index(x, y, z, 14)] = wat*(omusq + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = tux - tuz;
temp1[gpu_fieldn_index(x, y, z, 15)] = wat*(omusq + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = tuz - tux;
temp1[gpu_fieldn_index(x, y, z, 16)] = wat*(omusq + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = tuy - tuz;
temp1[gpu_fieldn_index(x, y, z, 17)] = wat*(omusq + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = tuz - tuy;
temp1[gpu_fieldn_index(x, y, z, 18)] = wat*(omusq + cidot3u*(1.0 + 0.5*cidot3u));
// 3d diagonal
//flow
cidot3u = tux + tuy + tuz;
f1[gpu_fieldn_index(x, y, z, 19)] = wdr*(omusq + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = -tuy - tux - tuz;
f1[gpu_fieldn_index(x, y, z, 20)] = wdr*(omusq + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = tux + tuy - tuz;
f1[gpu_fieldn_index(x, y, z, 21)] = wdr*(omusq + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = tuz - tux - tuy;
f1[gpu_fieldn_index(x, y, z, 22)] = wdr*(omusq + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = tux + tuz - tuy;
f1[gpu_fieldn_index(x, y, z, 23)] = wdr*(omusq + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = tuy - tux - tuz;
f1[gpu_fieldn_index(x, y, z, 24)] = wdr*(omusq + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = tuy + tuz - tux;
f1[gpu_fieldn_index(x, y, z, 25)] = wdr*(omusq + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = tux - tuy - tuz;
f1[gpu_fieldn_index(x, y, z, 26)] = wdr*(omusq + cidot3u*(1.0 + 0.5*cidot3u));
//charge
cidot3u = tux_c + tuy_c + tuz_c;
h1[gpu_fieldn_index(x, y, z, 19)] = wdc*(omusq_c + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = -tux_c -tuy_c - tuz_c;
h1[gpu_fieldn_index(x, y, z, 20)] = wdc*(omusq_c + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = tux_c + tuy_c - tuz_c;
h1[gpu_fieldn_index(x, y, z, 21)] = wdc*(omusq_c + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = tuz_c - tux_c - tuy_c;
h1[gpu_fieldn_index(x, y, z, 22)] = wdc*(omusq_c + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = tux_c + tuz_c - tuy_c;
h1[gpu_fieldn_index(x, y, z, 23)] = wdc*(omusq_c + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = tuy_c - tux_c - tuz_c;
h1[gpu_fieldn_index(x, y, z, 24)] = wdc*(omusq_c + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = tuy_c + tuz_c - tux_c;
h1[gpu_fieldn_index(x, y, z, 25)] = wdc*(omusq_c + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = tux_c - tuy_c - tuz_c;
h1[gpu_fieldn_index(x, y, z, 26)] = wdc*(omusq_c + cidot3u*(1.0 + 0.5*cidot3u));
//temperature
cidot3u = tux + tuy + tuz;
temp1[gpu_fieldn_index(x, y, z, 19)] = wdt*(omusq + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = -tux - tuy - tuz;
temp1[gpu_fieldn_index(x, y, z, 20)] = wdt*(omusq + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = tux + tuy - tuz;
temp1[gpu_fieldn_index(x, y, z, 21)] = wdt*(omusq + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = tuz - tux - tuy;
temp1[gpu_fieldn_index(x, y, z, 22)] = wdt*(omusq + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = tux + tuz - tuy;
temp1[gpu_fieldn_index(x, y, z, 23)] = wdt*(omusq + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = tuy - tux - tuz;
temp1[gpu_fieldn_index(x, y, z, 24)] = wdt*(omusq + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = tuy + tuz - tux;
temp1[gpu_fieldn_index(x, y, z, 25)] = wdt*(omusq + cidot3u*(1.0 + 0.5*cidot3u));
cidot3u = tux - tuy - tuz;
temp1[gpu_fieldn_index(x, y, z, 26)] = wdt*(omusq + cidot3u*(1.0 + 0.5*cidot3u));
} |
20,303 | #include "Main.cuh"
#include "Load.cuh"
#include <iostream>
#include <fstream>
#include <sstream>
#include <string>
using namespace std;
//Load the image
void loadImage(int *image_array, int &width, int &height, int &grayscale, string &file)
{
string line;
string dimensions[2];
int i = 0;
cout << endl << "Loading image:" + file << endl;
//Load .pgm (P2/ASCII Format) image into a 2D array.
ifstream image_in(file);
if (image_in)
{
getline(image_in, line);//Get .pgm "Magic number"
if (line == "P2")
{
cout << "Valid P2/ASCII .pgm file found." << endl;
}
else{
errorExit("Not a valid File. Must be .pgm P2/ASCII.");
}
//Skip over any comments
getline(image_in, line);
while (line.at(0) == '#'){
getline(image_in, line);
}
//Get width and height dimensions of image
//Use string stream to break line up into a string array then convert to ints
stringstream ssin(line);
while (ssin && i < 2){
ssin >> dimensions[i];
i++;
}
width = stoi(dimensions[0]);//Now convert strings to integers
height = stoi(dimensions[1]);
if (width > 1024 || height > 1024){//Change this check to accommodate larger images. Make sure enough memory is allocated.
errorExit("Incorrect image dimensions. Max size is 1024x1024.");
}
else{
cout << "The image dimensions are : " << width << "x" << height << endl;
}
//Get grayscale value
getline(image_in, line);
grayscale = stoi(line);//String to int conversion
cout << "The grayscale range for this image is 0 to " << grayscale << "." << endl;
//Store numbers into the 2D int array
for (int i = 0; i < width * height; i++){
image_in >> ws;//Extracts as many whitespace characters as possible from the current position in the input sequence.
getline(image_in, line, ' ');
image_array[i] = stoi(line);
}
image_in.close();//close ifstream
}
else{
errorExit("File not found.");
}
cout << "Finished loading image." << endl << endl;
}
|
20,304 | #include "includes.h"
__global__ void kernel_push_atomic2( int *g_terminate, int *g_push_reser, int *s_push_reser, int *g_block_num, int width1)
{
int x = __umul24( blockIdx.x, blockDim.x ) + threadIdx.x ;
int y = __umul24( blockIdx.y , blockDim.y ) + threadIdx.y ;
int thid = __umul24( y , width1 ) + x ;
if( s_push_reser[thid] - g_push_reser[thid] != 0)
{
g_terminate[blockIdx.y * (*g_block_num) + blockIdx.x] = 1 ;
}
} |
20,305 | #include <png.h>
#include <zlib.h>
#include <cassert>
#include <cmath>
#include <cstdlib>
#include <iostream>
#define MASK_N 2
#define MASK_X 5
#define MASK_Y 5
#define SCALE 8
// clang-format off
__device__ int mask[MASK_N][MASK_X][MASK_Y] = {
{{ -1, -4, -6, -4, -1},
{ -2, -8,-12, -8, -2},
{ 0, 0, 0, 0, 0},
{ 2, 8, 12, 8, 2},
{ 1, 4, 6, 4, 1}},
{{ -1, -2, 0, 2, 1},
{ -4, -8, 0, 8, 4},
{ -6,-12, 0, 12, 6},
{ -4, -8, 0, 8, 4},
{ -1, -2, 0, 2, 1}}
};
// clang-format on
int read_png(const char* filename, unsigned char** image, unsigned* height, unsigned* width,
unsigned* channels) {
unsigned char sig[8];
FILE* infile;
infile = fopen(filename, "rb");
fread(sig, 1, 8, infile);
if (!png_check_sig(sig, 8)) return 1; /* bad signature */
png_structp png_ptr;
png_infop info_ptr;
png_ptr = png_create_read_struct(PNG_LIBPNG_VER_STRING, NULL, NULL, NULL);
if (!png_ptr) return 4; /* out of memory */
info_ptr = png_create_info_struct(png_ptr);
if (!info_ptr) {
png_destroy_read_struct(&png_ptr, NULL, NULL);
return 4; /* out of memory */
}
png_init_io(png_ptr, infile);
png_set_sig_bytes(png_ptr, 8);
png_read_info(png_ptr, info_ptr);
int bit_depth, color_type;
png_get_IHDR(png_ptr, info_ptr, width, height, &bit_depth, &color_type, NULL, NULL, NULL);
png_uint_32 i, rowbytes;
png_bytep row_pointers[*height];
png_read_update_info(png_ptr, info_ptr);
rowbytes = png_get_rowbytes(png_ptr, info_ptr);
*channels = (int)png_get_channels(png_ptr, info_ptr);
if ((*image = (unsigned char*)malloc(rowbytes * *height)) == NULL) {
png_destroy_read_struct(&png_ptr, &info_ptr, NULL);
return 3;
}
for (i = 0; i < *height; ++i) {
row_pointers[i] = *image + i * rowbytes;
}
png_read_image(png_ptr, row_pointers);
png_read_end(png_ptr, NULL);
png_destroy_read_struct(&png_ptr, &info_ptr, NULL);
return 0;
}
void write_png(const char* filename, png_bytep image, const unsigned height, const unsigned width,
const unsigned channels) {
FILE* fp = fopen(filename, "wb");
png_structp png_ptr = png_create_write_struct(PNG_LIBPNG_VER_STRING, NULL, NULL, NULL);
png_infop info_ptr = png_create_info_struct(png_ptr);
png_init_io(png_ptr, fp);
png_set_IHDR(png_ptr, info_ptr, width, height, 8, PNG_COLOR_TYPE_RGB, PNG_INTERLACE_NONE,
PNG_COMPRESSION_TYPE_DEFAULT, PNG_FILTER_TYPE_DEFAULT);
png_set_filter(png_ptr, 0, PNG_NO_FILTERS);
png_write_info(png_ptr, info_ptr);
png_set_compression_level(png_ptr, 0);
png_bytep row_ptr[height];
for (int i = 0; i < height; ++i) {
row_ptr[i] = image + i * width * channels * sizeof(unsigned char);
}
png_write_image(png_ptr, row_ptr);
png_write_end(png_ptr, NULL);
png_destroy_write_struct(&png_ptr, &info_ptr);
fclose(fp);
}
__global__ void sobel_gpu(unsigned char* s, unsigned char* t, unsigned height, unsigned width, unsigned channels){
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
int i, v, u;
int R, G, B;
double val[MASK_N * 3] = {0.0};
int adjustX, adjustY, xBound, yBound;
if((x>=0 && x < width) && (y>=0 && y<height))
{
for (i = 0; i < MASK_N; ++i) {
adjustX = (MASK_X % 2) ? 1 : 0;
adjustY = (MASK_Y % 2) ? 1 : 0;
xBound = MASK_X / 2;
yBound = MASK_Y / 2;
val[i * 3 + 2] = 0.0;
val[i * 3 + 1] = 0.0;
val[i * 3] = 0.0;
for (v = -yBound; v < yBound + adjustY; ++v) {
for (u = -xBound; u < xBound + adjustX; ++u) {
if ((x + u) >= 0 && (x + u) < width && y + v >= 0 && y + v < height) {
R = s[channels * (width * (y + v) + (x + u)) + 2];
G = s[channels * (width * (y + v) + (x + u)) + 1];
B = s[channels * (width * (y + v) + (x + u)) + 0];
val[i * 3 + 2] += R * mask[i][u + xBound][v + yBound];
val[i * 3 + 1] += G * mask[i][u + xBound][v + yBound];
val[i * 3 + 0] += B * mask[i][u + xBound][v + yBound];
}
}
}
}
double totalR = 0.0;
double totalG = 0.0;
double totalB = 0.0;
for (i = 0; i < MASK_N; ++i) {
totalR += val[i * 3 + 2] * val[i * 3 + 2];
totalG += val[i * 3 + 1] * val[i * 3 + 1];
totalB += val[i * 3 + 0] * val[i * 3 + 0];
}
totalR = sqrt(totalR) / SCALE;
totalG = sqrt(totalG) / SCALE;
totalB = sqrt(totalB) / SCALE;
const unsigned char cR = (totalR > 255.0) ? 255 : totalR;
const unsigned char cG = (totalG > 255.0) ? 255 : totalG;
const unsigned char cB = (totalB > 255.0) ? 255 : totalB;
t[channels * (width * y + x) + 2] = cR;
t[channels * (width * y + x) + 1] = cG;
t[channels * (width * y + x) + 0] = cB;
}
}
int main(int argc, char** argv) {
assert(argc == 3);
unsigned height, width, channels;
unsigned char* src_img = NULL;
read_png(argv[1], &src_img, &height, &width, &channels);
assert(channels == 3);
// src in GPU (device)
unsigned char* src_cuda;
cudaMalloc(&src_cuda, sizeof(unsigned char)* height * width * channels);
cudaMemcpy(src_cuda, src_img, sizeof(unsigned char)* height * width * channels, cudaMemcpyHostToDevice);
// dst in GPU (device)
unsigned char* dst_img = (unsigned char*)malloc(height * width * channels * sizeof(unsigned char));
unsigned char* dst_cuda;
cudaMalloc(&dst_cuda, sizeof(unsigned char)* height * width * channels);
// 1204 threads in a block
dim3 threadsPerBlock(32, 32);
dim3 blocksPerGrid((width+31)/32, (height+31)/32);
sobel_gpu<<<blocksPerGrid, threadsPerBlock>>>(src_cuda, dst_cuda, height, width, channels);
cudaThreadSynchronize();
// passing dst_cuda to dst_img from device to host
cudaMemcpy(dst_img, dst_cuda, sizeof(unsigned char)* height * width * channels, cudaMemcpyDeviceToHost);
write_png(argv[2], dst_img, height, width, channels);
cudaFree(src_cuda);
cudaFree(dst_cuda);
free(src_img);
free(dst_img);
return 0;
}
|
20,306 | #include <stdlib.h>
#include <stdio.h>
// Number of elements to put in the test array
#define TEST_SIZE 16
#define NUM_BINS 10
////////////////////////////////////////////////////////////////
////////////////// COPY EVERYTHING BELOW HERE //////////////////
////////////////////////////////////////////////////////////////
// Number of threads per block (1-d blocks)
#define BLOCK_WIDTH 4
// Functions to reduce with
#define ADD 0
#define MIN 1
#define MAX 2
// Device functions
__device__ float addOp(float a, float b) {
return a + b;
}
__device__ float minOp(float a, float b) {
return a < b ? a : b;
}
__device__ float maxOp(float a, float b) {
return a > b ? a : b;
}
// Perform a partial reduction
// Only reduces per block, so this kernel may need to be called
// multiple times to generate a complete reduction
__global__ void reduceKernel(float* array,
const size_t array_size,
const unsigned int op,
const size_t step)
{
__shared__ float temp[BLOCK_WIDTH];
int bx = blockIdx.x;
int tx = threadIdx.x;
int index = BLOCK_WIDTH * bx + tx;
if(index < array_size) {
temp[tx] = array[index * step];
}
__syncthreads();
// Reduce
for(int offset = BLOCK_WIDTH >> 1; offset > 0; offset >>= 1) {
if(tx < offset) {
switch(op) {
case ADD:
temp[tx] = addOp(temp[tx], temp[tx + offset]);
break;
case MIN:
temp[tx] = minOp(temp[tx], temp[tx + offset]);
break;
case MAX:
temp[tx] = maxOp(temp[tx], temp[tx + offset]);
break;
default:
break;
}
}
__syncthreads();
}
if(index < array_size) {
array[BLOCK_WIDTH * bx] = temp[0];
}
}
// Create a histogram with atomics
__global__ void histogramKernel(unsigned int* d_hist,
const float* const d_array,
const size_t array_size,
float max,
float min,
const size_t numBins)
{
int bx = blockIdx.x;
int tx = threadIdx.x;
int index = BLOCK_WIDTH * bx + tx;
float range = max - min;
// Initialize temp
if(index < numBins) {
d_hist[index] = 0;
}
__syncthreads();
if(index < array_size) {
size_t bin = (size_t)((d_array[index] - min) * numBins / range );
atomicAdd(&d_hist[bin], 1);
}
}
// This performs a partial exclusive scan (blockwise) using Blelloch's method
__global__ void scanKernel(unsigned int* d_cdf,
unsigned int* d_input,
const size_t array_size)
{
__shared__ unsigned int temp[BLOCK_WIDTH<<1];
int bx = blockIdx.x;
int tx = threadIdx.x;
int index = BLOCK_WIDTH * bx + tx;
int offset = 1;
if(2*index + 1 < array_size) {
temp[2*index] = d_input[2*index];
temp[2*index + 1] = d_input[2*index + 1];
}
// Up-sweep
for(int powOf2 = (2*BLOCK_WIDTH)>>1; powOf2 > 0; powOf2 >>= 1) {
__syncthreads();
if(tx < powOf2) {
int idx1 = offset*(2*tx + 1) - 1 + 2*BLOCK_WIDTH*bx;
int idx2 = offset*(2*tx + 2) - 1 + 2*BLOCK_WIDTH*bx;
temp[idx2] += temp[idx1];
}
offset <<= 1;
}
__syncthreads();
///// The below will need to be remembered for multiple blocks /////
if(tx == 0) {
temp[2*BLOCK_WIDTH*(bx + 1) - 1] = 0;
}
// Down-sweep
for(int powOf2 = 1; powOf2 < 2*BLOCK_WIDTH; powOf2 <<= 1) {
offset >>= 1;
__syncthreads();
if(tx < powOf2) {
int idx1 = offset*(2*tx + 1) - 1 + 2*BLOCK_WIDTH*bx;
int idx2 = offset*(2*tx + 2) - 1 + 2*BLOCK_WIDTH*bx;
unsigned int t = temp[idx1];
temp[idx1] = temp[idx2];
temp[idx2] += t;
}
}
__syncthreads();
if(2*index + 1 < array_size) {
d_cdf[2*index] = temp[2*index];
d_cdf[2*index + 1] = temp[2*index + 1];
}
}
void reduce(float* d_array,
const size_t array_size,
float* result,
unsigned int op)
{
float *d_array_copy;
size_t mem_size = sizeof(float) * array_size;
cudaMalloc((void**) &d_array_copy, mem_size);
cudaMemcpy(d_array_copy, d_array, mem_size, cudaMemcpyDeviceToDevice);
// First pass:
size_t numBlocks = 1 + ((array_size - 1) / BLOCK_WIDTH);
reduceKernel<<<numBlocks, BLOCK_WIDTH>>>(d_array_copy, array_size, op, 1);
// Second pass:
reduceKernel<<<1, BLOCK_WIDTH>>>(d_array_copy, array_size, op, BLOCK_WIDTH);
cudaMemcpy(result, d_array_copy, sizeof(float), cudaMemcpyDeviceToHost);
cudaFree(d_array_copy);
}
//d_hist, d_array, TEST_SIZE, reduce_result_max, reduce_result_min, numBins
void histogram(unsigned int** d_hist,
const float* const d_array,
const size_t array_size,
float max,
float min,
const size_t numBins)
{
cudaMalloc((void**) d_hist, sizeof(unsigned int) * numBins);
size_t numBlocks = 1 + ((array_size - 1) / BLOCK_WIDTH);
histogramKernel<<<numBlocks, BLOCK_WIDTH>>>(
*d_hist,
d_array,
array_size,
max,
min,
numBins
);
}
void scan(unsigned int** d_cdf,
unsigned int* d_input,
const size_t array_size)
{
cudaMalloc((void**) d_cdf, sizeof(unsigned int) * array_size);
// Note the divide by 2 (a block can handle array of size 2*BLOCK_WIDTH)
size_t numBlocks = (1 + ((array_size - 1) / BLOCK_WIDTH))/2;
scanKernel<<<numBlocks, BLOCK_WIDTH>>>(*d_cdf, d_input, array_size);
}
////////////////////////////////////////////////////////////////
//////////////// EXCLUDE EVERYTHING BELOW HERE /////////////////
////////////////////////////////////////////////////////////////
void generateAndCopyTestValues(float** h_A, float** d_A, size_t size) {
unsigned int mem_size = sizeof(float) * size;
*h_A = (float*)malloc(mem_size);
cudaMalloc((void**) d_A, mem_size);
for(int i = 0; i < size; i++) {
(*h_A)[i] = i+1;
}
cudaMemcpy(*d_A, *h_A, mem_size, cudaMemcpyHostToDevice);
}
void genTestValsCDF(unsigned int** h_A, unsigned int** d_A, size_t size) {
unsigned int mem_size = sizeof(unsigned int) * size;
*h_A = (unsigned int*)malloc(mem_size);
cudaMalloc((void**) d_A, mem_size);
for(int i = 0; i < size; i++) {
(*h_A)[i] = i+1;
}
cudaMemcpy(*d_A, *h_A, mem_size, cudaMemcpyHostToDevice);
}
void prettyprint(float *h_A, size_t size) {
// Lots of magic numbers
if(size <= 16) {
for(int i = 0; i < size; i++) {
printf("%0.1f ", h_A[i]);
}
} else {
for(int i = 0; i < 8; i++) {
printf("%0.1f ", h_A[i]);
}
printf("... ");
for(int i = 0; i < 8; i++) {
printf("%0.1f ", h_A[size +i -8]);
}
}
printf("\n");
}
void prettyprint(unsigned int *h_A, size_t size) {
// Lots of magic numbers
if(size <= 16) {
for(int i = 0; i < size; i++) {
printf("%d ", h_A[i]);
}
} else {
for(int i = 0; i < 8; i++) {
printf("%d ", h_A[i]);
}
printf("... ");
for(int i = 0; i < 8; i++) {
printf("%d ", h_A[size +i -8]);
}
}
printf("\n");
}
int main(int argc, char** argv) {
// Reduce
float *h_array;
float *d_array;
float reduce_result_add;
float reduce_result_max;
float reduce_result_min;
// Histogram
unsigned int *h_hist;
unsigned int *d_hist;
size_t numBins = NUM_BINS;
// CDF (exclusive scan - prefix sum)
unsigned int *h_toBeScanned;
unsigned int *d_toBeScanned;
unsigned int *h_cdf;
unsigned int *d_cdf;
generateAndCopyTestValues(&h_array, &d_array, TEST_SIZE);
printf("h_array = ");
prettyprint(h_array, TEST_SIZE);
// Perform reduce
reduce(d_array, TEST_SIZE, &reduce_result_add, ADD);
reduce(d_array, TEST_SIZE, &reduce_result_min, MIN);
reduce(d_array, TEST_SIZE, &reduce_result_max, MAX);
printf("reduce_result_add = %0.1f\n", reduce_result_add);
printf("reduce_result_min = %0.1f\n", reduce_result_min);
printf("reduce_result_max = %0.1f\n", reduce_result_max);
// Perform histogram
histogram(&d_hist, d_array, TEST_SIZE, reduce_result_max, reduce_result_min, numBins);
// Host histogram (not to be used in student_func)
h_hist = (unsigned int*)malloc(sizeof(unsigned int) * numBins);
cudaMemcpy(h_hist, d_hist, sizeof(unsigned int) * numBins, cudaMemcpyDeviceToHost);
printf("h_hist = ");
prettyprint(h_hist, numBins);
// Set up and perform exclusive scan
genTestValsCDF(&h_toBeScanned, &d_toBeScanned, TEST_SIZE);
printf("\nh_toBeScanned = ");
prettyprint(h_toBeScanned, TEST_SIZE);
scan(&d_cdf, d_toBeScanned, TEST_SIZE);
h_cdf = (unsigned int*)malloc(sizeof(unsigned int) * TEST_SIZE);
cudaMemcpy(h_cdf, d_cdf, sizeof(unsigned int) * TEST_SIZE, cudaMemcpyDeviceToHost);
printf("h_cdf = ");
prettyprint(h_cdf, TEST_SIZE);
// Clean up
free(h_array);
free(h_hist);
free(h_cdf);
free(h_toBeScanned);
cudaFree(d_array);
cudaFree(d_hist);
cudaFree(d_cdf);
cudaFree(d_toBeScanned);
return 0;
} |
20,307 | /*
* Copyright 1993-2015 NVIDIA Corporation. All rights reserved.
*
* Please refer to the NVIDIA end user license agreement (EULA) associated
* with this source code for terms and conditions that govern your use of
* this software. Any use, reproduction, disclosure, or distribution of
* this software and related documentation outside the terms of the EULA
* is strictly prohibited.
*
*/
// Device code
extern "C" __global__ void memMapIpc_kernel(char *ptr, int sz, char val)
{
// Dummy kernel
int idx = blockIdx.x * blockDim.x + threadIdx.x;
for (; idx < sz; idx += (gridDim.x * blockDim.x)) {
ptr[idx] = val;
}
}
|
20,308 | #include <stdio.h>
//__device__ float Determinant(float *a,int n,float *temp);
__device__ __shared__ float result[3];
__device__ void MatrixDeterminant(void *param)
{
float *input = (float *) param;
int warp_size=32;
int n = (int)input[0];
float* matrix = input+1;
int thread = threadIdx.x % warp_size;
float value =0;
float *det = matrix +n*n;
if(n < 1){
//Error return 0
value = 0;
}
else {
if(n==1)
value = matrix[0];
else if(n==2)
value = matrix[0] * matrix[3] - matrix[2] * matrix[1];
else if (n==3){
if(thread < 3){
result[thread] = pow(-1.0,thread) *(matrix[thread]*(matrix[1*n + (thread+1)%3]*matrix[2*n + (thread+2)%3] - matrix[1*n + (thread+2)%3]*matrix[2 *n + (thread+1)%3]));
}
}
else
value = 0;//This program works only for n=1 to 3
}
if(n==3 && thread ==0)
{
for(int i=0; i < n; i++)
{
value = value + result[i];
}
*det = value;
}
else if(n<3)
*det = value;
}
//Recursive function not working
/*
__device__ float Determinant(float *a,int n,float *m)
{
int i,j,j1,j2;
float det = 0;
printf("%dInput\n",n);
if (n < 1) { * Error
} else if (n == 1) { /* Shouldn't get used
det = a[0];
} else if (n == 2) {
det = a[0] * a[3] - a[2] * a[1];
} else {
det = 0;
for (j1=0;j1<n;j1++) {
// m = (float *)malloc((n-1)*(n-1) * sizeof(float));
// for (i=0;i<n-1;i++)
// m[i] = (float *)malloc((n-1)*sizeof(float));
for (i=1;i<n;i++) {
j2 = 0;
for (j=0;j<n;j++) {
if (j == j1)
continue;
m[(i-1)*n+j2] = a[i * n + j];
j2++;
}
}
det += pow(-1.0,1.0+j1+1.0) * a[j1] * Determinant(m,n-1,a);
printf("%f Intermidiate det\n", det);
// free(m);
}
}
return(det);
}*/
|
20,309 | // #CSCS CUDA Training
//
// #Example 3 - transpose matrix
//
// #Author Ugo Varetto
//
// #Goal: compute the transpose of a matrix
//
// #Rationale: shows how to perform operations on a 2D grid and how to
// use the GPU for data initializaion
//
// #Solution: straightworwad, simply compute the thread id associated with the element
// and copy transposed data into output matrix
//
// #Code: typical flow:
// 1) compute launch grid configuration
// 2) allocate data on host(cpu) and device(gpu)
// 3) initialize data directly on the GPU
// 4) launch kernel
// 5) read data back
// 6) consume data (in this case print result)
// 7) free memory
//
// #Compilation: nvcc -arch=sm_13 3_0_transpose.cu -o transpose
//
// #Execution: ./transpose
//
// #Note: kernel invocations ( foo<<<...>>>(...) ) are *always* asynchronous and a call to
// cudaThreadSynchronize() is required to wait for the end of kernel execution from
// a host thread; in case of synchronous copy operations like cudaMemcpy(...,cudaDeviceToHost)
// kernel execution is guaranteed to be terminated before data are copied
//
// #Note: the code is C++ also because the default compilation mode for CUDA is C++, all functions
// are named with C++ convention and the syntax is checked by default against C++ grammar rules
//
// #Note: -arch=sm_13 allows the code to run on every card with hw architecture GT200 (gtx 2xx) or better
//
// #Note: -arch=sm_13 is the lowest architecture version that supports double precision
//
// #Note: the example can be extended to read configuration data and matrix size from the command line
//#include <cuda_runtime.h> // automatically added by nvcc
#include <vector>
#include <iostream>
typedef float real_t;
__global__ void transpose( const real_t* in, real_t *out, int num_rows, int num_columns ) {
const int row = blockIdx.x * blockDim.x + threadIdx.x;
const int col = blockIdx.y * blockDim.y + threadIdx.y;
const int input_index = row * num_columns + col;
const int output_index = col * num_rows + row;
out[ output_index ] = in[ input_index ];
}
__global__ void init_matrix( real_t* in ) {
const int c = threadIdx.x + blockDim.x * blockIdx.x;
const int r = threadIdx.y + blockDim.y * blockIdx.y;
const int idx = c + gridDim.x * blockDim.x * r;
in[ idx ] = (real_t) idx;
}
void print_matrix( const real_t* m, int r, int c ) {
for( int i = 0; i != r; ++i ) {
for( int j = 0; j != c; ++j ) std::cout << m[ i * c + j ] << ' ';
std::cout << '\n';
}
std::cout << std::endl;
}
//------------------------------------------------------------------------------
int main(int argc, char** argv ) {
const int ROWS = 16;
const int COLUMNS = 16;
const dim3 BLOCKS( 4, 4 );
const dim3 THREADS_PER_BLOCK( 4, 4 );
const size_t SIZE = ROWS * COLUMNS * sizeof( real_t );
// device(gpu) storage
real_t* dev_in = 0;
real_t* dev_out = 0;
cudaMalloc( &dev_in, SIZE );
cudaMalloc( &dev_out, SIZE );
// host(cpu) storage
std::vector< real_t > outmatrix( ROWS * COLUMNS );
// initialize data with gpu kernel; faster than CPU for loops
init_matrix<<<dim3( COLUMNS, ROWS ), 1>>>( dev_in );
cudaMemcpy( &outmatrix[ 0 ], dev_in, SIZE, cudaMemcpyDeviceToHost );
std::cout << "INPUT MATRIX - " << ROWS << " rows, " << COLUMNS << " columns" << std::endl;
print_matrix( &outmatrix[ 0 ], ROWS, COLUMNS );
// invoke transpose kernel
transpose<<<BLOCKS, THREADS_PER_BLOCK>>>( dev_in, dev_out, ROWS, COLUMNS );
// copy output data from device(gpu) to host(cpu)
cudaMemcpy( &outmatrix[ 0 ], dev_out, SIZE, cudaMemcpyDeviceToHost );
// print result
std::cout << "\nOUTPUT MATRIX - " << COLUMNS << " rows, " << ROWS << " columns" << std::endl;
print_matrix( &outmatrix[ 0 ], COLUMNS, ROWS );
// free memory
cudaFree( dev_in );
cudaFree( dev_out );
return 0;
}
|
20,310 | #if GOOGLE_CUDA
#define EIGEN_USE_GPU
extern "C" __global__ void default_function_kernel0(const float* __restrict__ Data,
const float* __restrict__ K0,
const float* __restrict__ K1,
const float* __restrict__ K2,
float* __restrict__ Output) {
float Output_local[8];
__shared__ float pad_temp_shared[256];
__shared__ float K0_shared[6];
__shared__ float K1_shared[6];
__shared__ float K2_shared[24];
for (int nn_inner_outer = 0; nn_inner_outer < 4; ++nn_inner_outer) {
for (int hh_inner_outer = 0; hh_inner_outer < 2; ++hh_inner_outer) {
for (int ww_inner_outer = 0; ww_inner_outer < 2; ++ww_inner_outer) {
Output_local[0] = 0.000000e+00f;
Output_local[1] = 0.000000e+00f;
Output_local[2] = 0.000000e+00f;
Output_local[3] = 0.000000e+00f;
Output_local[4] = 0.000000e+00f;
Output_local[5] = 0.000000e+00f;
Output_local[6] = 0.000000e+00f;
Output_local[7] = 0.000000e+00f;
for (int rc_outer = 0; rc_outer < 16; ++rc_outer) {
__syncthreads();
pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + (((int)threadIdx.x) * 2))] = (((((1 - (((int)threadIdx.x) / 8)) - (((int)threadIdx.y) * 2)) <= ((((int)blockIdx.y) * 16) + (hh_inner_outer * 8))) && ((1 - ((((int)threadIdx.x) * 2) % 16)) <= (ww_inner_outer * 16))) ? Data[((((((((((nn_inner_outer * 32768) + ((((((int)threadIdx.y) * 16) + ((int)threadIdx.x)) / 64) * 16384)) + (((int)threadIdx.z) * 16384)) + (rc_outer * 1024)) + (((int)blockIdx.y) * 512)) + (hh_inner_outer * 256)) + (((((((int)threadIdx.y) * 32) + (((int)threadIdx.x) * 2)) % 128) / 16) * 32)) + (ww_inner_outer * 16)) + ((((int)threadIdx.x) * 2) % 16)) - 33)] : 0.000000e+00f);
pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + (((int)threadIdx.x) * 2)) + 1)] = (((((1 - (((((int)threadIdx.x) * 2) + 1) / 16)) - (((int)threadIdx.y) * 2)) <= ((((int)blockIdx.y) * 16) + (hh_inner_outer * 8))) && ((1 - (((((int)threadIdx.x) * 2) + 1) % 16)) <= (ww_inner_outer * 16))) ? Data[((((((((((nn_inner_outer * 32768) + (((((((int)threadIdx.y) * 32) + (((int)threadIdx.x) * 2)) + 1) / 128) * 16384)) + (((int)threadIdx.z) * 16384)) + (rc_outer * 1024)) + (((int)blockIdx.y) * 512)) + (hh_inner_outer * 256)) + ((((((((int)threadIdx.y) * 32) + (((int)threadIdx.x) * 2)) + 1) % 128) / 16) * 32)) + (ww_inner_outer * 16)) + (((((int)threadIdx.x) * 2) + 1) % 16)) - 33)] : 0.000000e+00f);
if ((((int)threadIdx.z) * 3) < ((6 - ((int)threadIdx.y)) - ((int)threadIdx.x))) {
if (((int)threadIdx.x) < (3 - ((int)threadIdx.y))) {
if (((int)threadIdx.x) < 1) {
if (((((((int)threadIdx.z) * 3) + ((int)threadIdx.x)) + ((int)threadIdx.y)) / 6) < (16 - rc_outer)) {
K0_shared[(((((int)threadIdx.z) * 3) + ((int)threadIdx.x)) + ((int)threadIdx.y))] = K0[((((rc_outer * 6) + (((int)threadIdx.z) * 3)) + ((int)threadIdx.x)) + ((int)threadIdx.y))];
}
}
}
}
if ((((int)threadIdx.z) * 3) < ((6 - ((int)threadIdx.y)) - ((int)threadIdx.x))) {
if (((int)threadIdx.x) < (3 - ((int)threadIdx.y))) {
if (((int)threadIdx.x) < 1) {
if ((((int)threadIdx.z) * 3) < ((18 - ((int)threadIdx.y)) - ((int)threadIdx.x))) {
K1_shared[(((((int)threadIdx.z) * 3) + ((int)threadIdx.x)) + ((int)threadIdx.y))] = K1[((((((((int)threadIdx.z) * 3) + ((int)threadIdx.x)) + ((int)threadIdx.y)) / 6) * 18) + ((((((int)threadIdx.z) * 3) + ((int)threadIdx.x)) + ((int)threadIdx.y)) % 6))];
}
}
}
}
if ((((int)threadIdx.z) * 3) < (6 - (((((int)threadIdx.y) * 3) + ((int)threadIdx.x)) / 4))) {
if (((((int)threadIdx.z) * 12) + (((int)threadIdx.y) * 3)) < (24 - ((int)threadIdx.x))) {
if ((((int)threadIdx.y) * 3) < (12 - ((int)threadIdx.x))) {
if (((int)threadIdx.x) < 3) {
K2_shared[(((((int)threadIdx.z) * 12) + (((int)threadIdx.y) * 3)) + ((int)threadIdx.x))] = K2[((((((int)threadIdx.z) * 48) + ((((((int)threadIdx.y) * 3) + ((int)threadIdx.x)) / 4) * 16)) + (((int)blockIdx.z) * 4)) + (((((int)threadIdx.y) * 3) + ((int)threadIdx.x)) % 4))];
}
}
}
}
__syncthreads();
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[0]) * K1_shared[0]) * K2_shared[0]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[0]) * K1_shared[0]) * K2_shared[0]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[0]) * K1_shared[0]) * K2_shared[1]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[0]) * K1_shared[0]) * K2_shared[1]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[0]) * K1_shared[0]) * K2_shared[2]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[0]) * K1_shared[0]) * K2_shared[2]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[0]) * K1_shared[0]) * K2_shared[3]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[0]) * K1_shared[0]) * K2_shared[3]));
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[1]) * K1_shared[1]) * K2_shared[4]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[1]) * K1_shared[1]) * K2_shared[4]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[1]) * K1_shared[1]) * K2_shared[5]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[1]) * K1_shared[1]) * K2_shared[5]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[1]) * K1_shared[1]) * K2_shared[6]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[1]) * K1_shared[1]) * K2_shared[6]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[1]) * K1_shared[1]) * K2_shared[7]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[1]) * K1_shared[1]) * K2_shared[7]));
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[2]) * K1_shared[2]) * K2_shared[8]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[2]) * K1_shared[2]) * K2_shared[8]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[2]) * K1_shared[2]) * K2_shared[9]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[2]) * K1_shared[2]) * K2_shared[9]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[2]) * K1_shared[2]) * K2_shared[10]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[2]) * K1_shared[2]) * K2_shared[10]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[2]) * K1_shared[2]) * K2_shared[11]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[2]) * K1_shared[2]) * K2_shared[11]));
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[3]) * K1_shared[3]) * K2_shared[12]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[3]) * K1_shared[3]) * K2_shared[12]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[3]) * K1_shared[3]) * K2_shared[13]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[3]) * K1_shared[3]) * K2_shared[13]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[3]) * K1_shared[3]) * K2_shared[14]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[3]) * K1_shared[3]) * K2_shared[14]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[3]) * K1_shared[3]) * K2_shared[15]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[3]) * K1_shared[3]) * K2_shared[15]));
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[4]) * K1_shared[4]) * K2_shared[16]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[4]) * K1_shared[4]) * K2_shared[16]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[4]) * K1_shared[4]) * K2_shared[17]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[4]) * K1_shared[4]) * K2_shared[17]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[4]) * K1_shared[4]) * K2_shared[18]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[4]) * K1_shared[4]) * K2_shared[18]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[4]) * K1_shared[4]) * K2_shared[19]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[4]) * K1_shared[4]) * K2_shared[19]));
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[5]) * K1_shared[5]) * K2_shared[20]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[5]) * K1_shared[5]) * K2_shared[20]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[5]) * K1_shared[5]) * K2_shared[21]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[5]) * K1_shared[5]) * K2_shared[21]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[5]) * K1_shared[5]) * K2_shared[22]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[5]) * K1_shared[5]) * K2_shared[22]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[5]) * K1_shared[5]) * K2_shared[23]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[5]) * K1_shared[5]) * K2_shared[23]));
__syncthreads();
pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + (((int)threadIdx.x) * 2))] = ((((1 - (((int)threadIdx.x) / 8)) - (((int)threadIdx.y) * 2)) <= ((((int)blockIdx.y) * 16) + (hh_inner_outer * 8))) ? Data[((((((((((nn_inner_outer * 32768) + ((((((int)threadIdx.y) * 16) + ((int)threadIdx.x)) / 64) * 16384)) + (((int)threadIdx.z) * 16384)) + (rc_outer * 1024)) + (((int)blockIdx.y) * 512)) + (hh_inner_outer * 256)) + (((((((int)threadIdx.y) * 32) + (((int)threadIdx.x) * 2)) % 128) / 16) * 32)) + (ww_inner_outer * 16)) + ((((int)threadIdx.x) * 2) % 16)) - 32)] : 0.000000e+00f);
pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + (((int)threadIdx.x) * 2)) + 1)] = ((((1 - (((((int)threadIdx.x) * 2) + 1) / 16)) - (((int)threadIdx.y) * 2)) <= ((((int)blockIdx.y) * 16) + (hh_inner_outer * 8))) ? Data[((((((((((nn_inner_outer * 32768) + (((((((int)threadIdx.y) * 32) + (((int)threadIdx.x) * 2)) + 1) / 128) * 16384)) + (((int)threadIdx.z) * 16384)) + (rc_outer * 1024)) + (((int)blockIdx.y) * 512)) + (hh_inner_outer * 256)) + ((((((((int)threadIdx.y) * 32) + (((int)threadIdx.x) * 2)) + 1) % 128) / 16) * 32)) + (ww_inner_outer * 16)) + (((((int)threadIdx.x) * 2) + 1) % 16)) - 32)] : 0.000000e+00f);
if ((((int)threadIdx.z) * 3) < ((6 - ((int)threadIdx.y)) - ((int)threadIdx.x))) {
if (((int)threadIdx.x) < (3 - ((int)threadIdx.y))) {
if (((int)threadIdx.x) < 1) {
if (((((((int)threadIdx.z) * 3) + ((int)threadIdx.x)) + ((int)threadIdx.y)) / 6) < (16 - rc_outer)) {
K0_shared[(((((int)threadIdx.z) * 3) + ((int)threadIdx.x)) + ((int)threadIdx.y))] = K0[((((rc_outer * 6) + (((int)threadIdx.z) * 3)) + ((int)threadIdx.x)) + ((int)threadIdx.y))];
}
}
}
}
if ((((int)threadIdx.z) * 3) < ((6 - ((int)threadIdx.y)) - ((int)threadIdx.x))) {
if (((int)threadIdx.x) < (3 - ((int)threadIdx.y))) {
if (((int)threadIdx.x) < 1) {
if ((((int)threadIdx.z) * 3) < ((18 - ((int)threadIdx.y)) - ((int)threadIdx.x))) {
K1_shared[(((((int)threadIdx.z) * 3) + ((int)threadIdx.x)) + ((int)threadIdx.y))] = K1[(((((((((int)threadIdx.z) * 3) + ((int)threadIdx.x)) + ((int)threadIdx.y)) / 6) * 18) + ((((((int)threadIdx.z) * 3) + ((int)threadIdx.x)) + ((int)threadIdx.y)) % 6)) + 6)];
}
}
}
}
if ((((int)threadIdx.z) * 3) < (6 - (((((int)threadIdx.y) * 3) + ((int)threadIdx.x)) / 4))) {
if (((((int)threadIdx.z) * 12) + (((int)threadIdx.y) * 3)) < (24 - ((int)threadIdx.x))) {
if ((((int)threadIdx.y) * 3) < (12 - ((int)threadIdx.x))) {
if (((int)threadIdx.x) < 3) {
K2_shared[(((((int)threadIdx.z) * 12) + (((int)threadIdx.y) * 3)) + ((int)threadIdx.x))] = K2[((((((int)threadIdx.z) * 48) + ((((((int)threadIdx.y) * 3) + ((int)threadIdx.x)) / 4) * 16)) + (((int)blockIdx.z) * 4)) + (((((int)threadIdx.y) * 3) + ((int)threadIdx.x)) % 4))];
}
}
}
}
__syncthreads();
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[0]) * K1_shared[0]) * K2_shared[0]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[0]) * K1_shared[0]) * K2_shared[0]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[0]) * K1_shared[0]) * K2_shared[1]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[0]) * K1_shared[0]) * K2_shared[1]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[0]) * K1_shared[0]) * K2_shared[2]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[0]) * K1_shared[0]) * K2_shared[2]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[0]) * K1_shared[0]) * K2_shared[3]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[0]) * K1_shared[0]) * K2_shared[3]));
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[1]) * K1_shared[1]) * K2_shared[4]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[1]) * K1_shared[1]) * K2_shared[4]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[1]) * K1_shared[1]) * K2_shared[5]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[1]) * K1_shared[1]) * K2_shared[5]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[1]) * K1_shared[1]) * K2_shared[6]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[1]) * K1_shared[1]) * K2_shared[6]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[1]) * K1_shared[1]) * K2_shared[7]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[1]) * K1_shared[1]) * K2_shared[7]));
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[2]) * K1_shared[2]) * K2_shared[8]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[2]) * K1_shared[2]) * K2_shared[8]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[2]) * K1_shared[2]) * K2_shared[9]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[2]) * K1_shared[2]) * K2_shared[9]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[2]) * K1_shared[2]) * K2_shared[10]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[2]) * K1_shared[2]) * K2_shared[10]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[2]) * K1_shared[2]) * K2_shared[11]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[2]) * K1_shared[2]) * K2_shared[11]));
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[3]) * K1_shared[3]) * K2_shared[12]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[3]) * K1_shared[3]) * K2_shared[12]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[3]) * K1_shared[3]) * K2_shared[13]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[3]) * K1_shared[3]) * K2_shared[13]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[3]) * K1_shared[3]) * K2_shared[14]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[3]) * K1_shared[3]) * K2_shared[14]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[3]) * K1_shared[3]) * K2_shared[15]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[3]) * K1_shared[3]) * K2_shared[15]));
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[4]) * K1_shared[4]) * K2_shared[16]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[4]) * K1_shared[4]) * K2_shared[16]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[4]) * K1_shared[4]) * K2_shared[17]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[4]) * K1_shared[4]) * K2_shared[17]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[4]) * K1_shared[4]) * K2_shared[18]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[4]) * K1_shared[4]) * K2_shared[18]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[4]) * K1_shared[4]) * K2_shared[19]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[4]) * K1_shared[4]) * K2_shared[19]));
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[5]) * K1_shared[5]) * K2_shared[20]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[5]) * K1_shared[5]) * K2_shared[20]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[5]) * K1_shared[5]) * K2_shared[21]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[5]) * K1_shared[5]) * K2_shared[21]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[5]) * K1_shared[5]) * K2_shared[22]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[5]) * K1_shared[5]) * K2_shared[22]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[5]) * K1_shared[5]) * K2_shared[23]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[5]) * K1_shared[5]) * K2_shared[23]));
__syncthreads();
pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + (((int)threadIdx.x) * 2))] = (((((1 - (((int)threadIdx.x) / 8)) - (((int)threadIdx.y) * 2)) <= ((((int)blockIdx.y) * 16) + (hh_inner_outer * 8))) && ((ww_inner_outer * 16) < (31 - ((((int)threadIdx.x) * 2) % 16)))) ? Data[((((((((((nn_inner_outer * 32768) + ((((((int)threadIdx.y) * 16) + ((int)threadIdx.x)) / 64) * 16384)) + (((int)threadIdx.z) * 16384)) + (rc_outer * 1024)) + (((int)blockIdx.y) * 512)) + (hh_inner_outer * 256)) + (((((((int)threadIdx.y) * 32) + (((int)threadIdx.x) * 2)) % 128) / 16) * 32)) + (ww_inner_outer * 16)) + ((((int)threadIdx.x) * 2) % 16)) - 31)] : 0.000000e+00f);
pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + (((int)threadIdx.x) * 2)) + 1)] = (((((1 - (((((int)threadIdx.x) * 2) + 1) / 16)) - (((int)threadIdx.y) * 2)) <= ((((int)blockIdx.y) * 16) + (hh_inner_outer * 8))) && ((ww_inner_outer * 16) < (31 - (((((int)threadIdx.x) * 2) + 1) % 16)))) ? Data[((((((((((nn_inner_outer * 32768) + (((((((int)threadIdx.y) * 32) + (((int)threadIdx.x) * 2)) + 1) / 128) * 16384)) + (((int)threadIdx.z) * 16384)) + (rc_outer * 1024)) + (((int)blockIdx.y) * 512)) + (hh_inner_outer * 256)) + ((((((((int)threadIdx.y) * 32) + (((int)threadIdx.x) * 2)) + 1) % 128) / 16) * 32)) + (ww_inner_outer * 16)) + (((((int)threadIdx.x) * 2) + 1) % 16)) - 31)] : 0.000000e+00f);
if ((((int)threadIdx.z) * 3) < ((6 - ((int)threadIdx.y)) - ((int)threadIdx.x))) {
if (((int)threadIdx.x) < (3 - ((int)threadIdx.y))) {
if (((int)threadIdx.x) < 1) {
if (((((((int)threadIdx.z) * 3) + ((int)threadIdx.x)) + ((int)threadIdx.y)) / 6) < (16 - rc_outer)) {
K0_shared[(((((int)threadIdx.z) * 3) + ((int)threadIdx.x)) + ((int)threadIdx.y))] = K0[((((rc_outer * 6) + (((int)threadIdx.z) * 3)) + ((int)threadIdx.x)) + ((int)threadIdx.y))];
}
}
}
}
if ((((int)threadIdx.z) * 3) < ((6 - ((int)threadIdx.y)) - ((int)threadIdx.x))) {
if (((int)threadIdx.x) < (3 - ((int)threadIdx.y))) {
if (((int)threadIdx.x) < 1) {
if ((((int)threadIdx.z) * 3) < ((18 - ((int)threadIdx.y)) - ((int)threadIdx.x))) {
K1_shared[(((((int)threadIdx.z) * 3) + ((int)threadIdx.x)) + ((int)threadIdx.y))] = K1[(((((((((int)threadIdx.z) * 3) + ((int)threadIdx.x)) + ((int)threadIdx.y)) / 6) * 18) + ((((((int)threadIdx.z) * 3) + ((int)threadIdx.x)) + ((int)threadIdx.y)) % 6)) + 12)];
}
}
}
}
if ((((int)threadIdx.z) * 3) < (6 - (((((int)threadIdx.y) * 3) + ((int)threadIdx.x)) / 4))) {
if (((((int)threadIdx.z) * 12) + (((int)threadIdx.y) * 3)) < (24 - ((int)threadIdx.x))) {
if ((((int)threadIdx.y) * 3) < (12 - ((int)threadIdx.x))) {
if (((int)threadIdx.x) < 3) {
K2_shared[(((((int)threadIdx.z) * 12) + (((int)threadIdx.y) * 3)) + ((int)threadIdx.x))] = K2[((((((int)threadIdx.z) * 48) + ((((((int)threadIdx.y) * 3) + ((int)threadIdx.x)) / 4) * 16)) + (((int)blockIdx.z) * 4)) + (((((int)threadIdx.y) * 3) + ((int)threadIdx.x)) % 4))];
}
}
}
}
__syncthreads();
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[0]) * K1_shared[0]) * K2_shared[0]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[0]) * K1_shared[0]) * K2_shared[0]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[0]) * K1_shared[0]) * K2_shared[1]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[0]) * K1_shared[0]) * K2_shared[1]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[0]) * K1_shared[0]) * K2_shared[2]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[0]) * K1_shared[0]) * K2_shared[2]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[0]) * K1_shared[0]) * K2_shared[3]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[0]) * K1_shared[0]) * K2_shared[3]));
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[1]) * K1_shared[1]) * K2_shared[4]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[1]) * K1_shared[1]) * K2_shared[4]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[1]) * K1_shared[1]) * K2_shared[5]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[1]) * K1_shared[1]) * K2_shared[5]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[1]) * K1_shared[1]) * K2_shared[6]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[1]) * K1_shared[1]) * K2_shared[6]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[1]) * K1_shared[1]) * K2_shared[7]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[1]) * K1_shared[1]) * K2_shared[7]));
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[2]) * K1_shared[2]) * K2_shared[8]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[2]) * K1_shared[2]) * K2_shared[8]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[2]) * K1_shared[2]) * K2_shared[9]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[2]) * K1_shared[2]) * K2_shared[9]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[2]) * K1_shared[2]) * K2_shared[10]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[2]) * K1_shared[2]) * K2_shared[10]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[2]) * K1_shared[2]) * K2_shared[11]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[2]) * K1_shared[2]) * K2_shared[11]));
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[3]) * K1_shared[3]) * K2_shared[12]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[3]) * K1_shared[3]) * K2_shared[12]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[3]) * K1_shared[3]) * K2_shared[13]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[3]) * K1_shared[3]) * K2_shared[13]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[3]) * K1_shared[3]) * K2_shared[14]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[3]) * K1_shared[3]) * K2_shared[14]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[3]) * K1_shared[3]) * K2_shared[15]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[3]) * K1_shared[3]) * K2_shared[15]));
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[4]) * K1_shared[4]) * K2_shared[16]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[4]) * K1_shared[4]) * K2_shared[16]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[4]) * K1_shared[4]) * K2_shared[17]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[4]) * K1_shared[4]) * K2_shared[17]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[4]) * K1_shared[4]) * K2_shared[18]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[4]) * K1_shared[4]) * K2_shared[18]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[4]) * K1_shared[4]) * K2_shared[19]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[4]) * K1_shared[4]) * K2_shared[19]));
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[5]) * K1_shared[5]) * K2_shared[20]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[5]) * K1_shared[5]) * K2_shared[20]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[5]) * K1_shared[5]) * K2_shared[21]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[5]) * K1_shared[5]) * K2_shared[21]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[5]) * K1_shared[5]) * K2_shared[22]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[5]) * K1_shared[5]) * K2_shared[22]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[5]) * K1_shared[5]) * K2_shared[23]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[5]) * K1_shared[5]) * K2_shared[23]));
__syncthreads();
pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + (((int)threadIdx.x) * 2))] = (((1 - ((((int)threadIdx.x) * 2) % 16)) <= (ww_inner_outer * 16)) ? Data[((((((((((nn_inner_outer * 32768) + ((((((int)threadIdx.y) * 16) + ((int)threadIdx.x)) / 64) * 16384)) + (((int)threadIdx.z) * 16384)) + (rc_outer * 1024)) + (((int)blockIdx.y) * 512)) + (hh_inner_outer * 256)) + (((((((int)threadIdx.y) * 32) + (((int)threadIdx.x) * 2)) % 128) / 16) * 32)) + (ww_inner_outer * 16)) + ((((int)threadIdx.x) * 2) % 16)) - 1)] : 0.000000e+00f);
pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + (((int)threadIdx.x) * 2)) + 1)] = (((1 - (((((int)threadIdx.x) * 2) + 1) % 16)) <= (ww_inner_outer * 16)) ? Data[((((((((((nn_inner_outer * 32768) + (((((((int)threadIdx.y) * 32) + (((int)threadIdx.x) * 2)) + 1) / 128) * 16384)) + (((int)threadIdx.z) * 16384)) + (rc_outer * 1024)) + (((int)blockIdx.y) * 512)) + (hh_inner_outer * 256)) + ((((((((int)threadIdx.y) * 32) + (((int)threadIdx.x) * 2)) + 1) % 128) / 16) * 32)) + (ww_inner_outer * 16)) + (((((int)threadIdx.x) * 2) + 1) % 16)) - 1)] : 0.000000e+00f);
if ((((int)threadIdx.z) * 3) < ((6 - ((int)threadIdx.y)) - ((int)threadIdx.x))) {
if (((int)threadIdx.x) < (3 - ((int)threadIdx.y))) {
if (((int)threadIdx.x) < 1) {
if (((((((int)threadIdx.z) * 3) + ((int)threadIdx.x)) + ((int)threadIdx.y)) / 6) < (16 - rc_outer)) {
K0_shared[(((((int)threadIdx.z) * 3) + ((int)threadIdx.x)) + ((int)threadIdx.y))] = K0[((((rc_outer * 6) + (((int)threadIdx.z) * 3)) + ((int)threadIdx.x)) + ((int)threadIdx.y))];
}
}
}
}
if ((((int)threadIdx.z) * 3) < ((6 - ((int)threadIdx.y)) - ((int)threadIdx.x))) {
if (((int)threadIdx.x) < (3 - ((int)threadIdx.y))) {
if (((int)threadIdx.x) < 1) {
if ((((int)threadIdx.z) * 3) < ((12 - ((int)threadIdx.y)) - ((int)threadIdx.x))) {
K1_shared[(((((int)threadIdx.z) * 3) + ((int)threadIdx.x)) + ((int)threadIdx.y))] = K1[(((((((((int)threadIdx.z) * 3) + ((int)threadIdx.x)) + ((int)threadIdx.y)) / 6) * 18) + ((((((int)threadIdx.z) * 3) + ((int)threadIdx.x)) + ((int)threadIdx.y)) % 6)) + 18)];
}
}
}
}
if ((((int)threadIdx.z) * 3) < (6 - (((((int)threadIdx.y) * 3) + ((int)threadIdx.x)) / 4))) {
if (((((int)threadIdx.z) * 12) + (((int)threadIdx.y) * 3)) < (24 - ((int)threadIdx.x))) {
if ((((int)threadIdx.y) * 3) < (12 - ((int)threadIdx.x))) {
if (((int)threadIdx.x) < 3) {
K2_shared[(((((int)threadIdx.z) * 12) + (((int)threadIdx.y) * 3)) + ((int)threadIdx.x))] = K2[((((((int)threadIdx.z) * 48) + ((((((int)threadIdx.y) * 3) + ((int)threadIdx.x)) / 4) * 16)) + (((int)blockIdx.z) * 4)) + (((((int)threadIdx.y) * 3) + ((int)threadIdx.x)) % 4))];
}
}
}
}
__syncthreads();
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[0]) * K1_shared[0]) * K2_shared[0]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[0]) * K1_shared[0]) * K2_shared[0]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[0]) * K1_shared[0]) * K2_shared[1]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[0]) * K1_shared[0]) * K2_shared[1]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[0]) * K1_shared[0]) * K2_shared[2]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[0]) * K1_shared[0]) * K2_shared[2]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[0]) * K1_shared[0]) * K2_shared[3]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[0]) * K1_shared[0]) * K2_shared[3]));
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[1]) * K1_shared[1]) * K2_shared[4]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[1]) * K1_shared[1]) * K2_shared[4]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[1]) * K1_shared[1]) * K2_shared[5]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[1]) * K1_shared[1]) * K2_shared[5]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[1]) * K1_shared[1]) * K2_shared[6]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[1]) * K1_shared[1]) * K2_shared[6]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[1]) * K1_shared[1]) * K2_shared[7]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[1]) * K1_shared[1]) * K2_shared[7]));
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[2]) * K1_shared[2]) * K2_shared[8]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[2]) * K1_shared[2]) * K2_shared[8]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[2]) * K1_shared[2]) * K2_shared[9]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[2]) * K1_shared[2]) * K2_shared[9]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[2]) * K1_shared[2]) * K2_shared[10]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[2]) * K1_shared[2]) * K2_shared[10]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[2]) * K1_shared[2]) * K2_shared[11]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[2]) * K1_shared[2]) * K2_shared[11]));
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[3]) * K1_shared[3]) * K2_shared[12]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[3]) * K1_shared[3]) * K2_shared[12]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[3]) * K1_shared[3]) * K2_shared[13]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[3]) * K1_shared[3]) * K2_shared[13]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[3]) * K1_shared[3]) * K2_shared[14]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[3]) * K1_shared[3]) * K2_shared[14]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[3]) * K1_shared[3]) * K2_shared[15]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[3]) * K1_shared[3]) * K2_shared[15]));
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[4]) * K1_shared[4]) * K2_shared[16]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[4]) * K1_shared[4]) * K2_shared[16]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[4]) * K1_shared[4]) * K2_shared[17]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[4]) * K1_shared[4]) * K2_shared[17]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[4]) * K1_shared[4]) * K2_shared[18]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[4]) * K1_shared[4]) * K2_shared[18]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[4]) * K1_shared[4]) * K2_shared[19]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[4]) * K1_shared[4]) * K2_shared[19]));
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[5]) * K1_shared[5]) * K2_shared[20]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[5]) * K1_shared[5]) * K2_shared[20]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[5]) * K1_shared[5]) * K2_shared[21]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[5]) * K1_shared[5]) * K2_shared[21]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[5]) * K1_shared[5]) * K2_shared[22]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[5]) * K1_shared[5]) * K2_shared[22]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[5]) * K1_shared[5]) * K2_shared[23]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[5]) * K1_shared[5]) * K2_shared[23]));
__syncthreads();
pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + (((int)threadIdx.x) * 2))] = ((bool)1 ? Data[(((((((((nn_inner_outer * 32768) + ((((((int)threadIdx.y) * 16) + ((int)threadIdx.x)) / 64) * 16384)) + (((int)threadIdx.z) * 16384)) + (rc_outer * 1024)) + (((int)blockIdx.y) * 512)) + (hh_inner_outer * 256)) + (((((((int)threadIdx.y) * 32) + (((int)threadIdx.x) * 2)) % 128) / 16) * 32)) + (ww_inner_outer * 16)) + ((((int)threadIdx.x) * 2) % 16))] : 0.000000e+00f);
pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + (((int)threadIdx.x) * 2)) + 1)] = ((bool)1 ? Data[(((((((((nn_inner_outer * 32768) + (((((((int)threadIdx.y) * 32) + (((int)threadIdx.x) * 2)) + 1) / 128) * 16384)) + (((int)threadIdx.z) * 16384)) + (rc_outer * 1024)) + (((int)blockIdx.y) * 512)) + (hh_inner_outer * 256)) + ((((((((int)threadIdx.y) * 32) + (((int)threadIdx.x) * 2)) + 1) % 128) / 16) * 32)) + (ww_inner_outer * 16)) + (((((int)threadIdx.x) * 2) + 1) % 16))] : 0.000000e+00f);
if ((((int)threadIdx.z) * 3) < ((6 - ((int)threadIdx.y)) - ((int)threadIdx.x))) {
if (((int)threadIdx.x) < (3 - ((int)threadIdx.y))) {
if (((int)threadIdx.x) < 1) {
if (((((((int)threadIdx.z) * 3) + ((int)threadIdx.x)) + ((int)threadIdx.y)) / 6) < (16 - rc_outer)) {
K0_shared[(((((int)threadIdx.z) * 3) + ((int)threadIdx.x)) + ((int)threadIdx.y))] = K0[((((rc_outer * 6) + (((int)threadIdx.z) * 3)) + ((int)threadIdx.x)) + ((int)threadIdx.y))];
}
}
}
}
if ((((int)threadIdx.z) * 3) < ((6 - ((int)threadIdx.y)) - ((int)threadIdx.x))) {
if (((int)threadIdx.x) < (3 - ((int)threadIdx.y))) {
if (((int)threadIdx.x) < 1) {
if ((((int)threadIdx.z) * 3) < ((12 - ((int)threadIdx.y)) - ((int)threadIdx.x))) {
K1_shared[(((((int)threadIdx.z) * 3) + ((int)threadIdx.x)) + ((int)threadIdx.y))] = K1[(((((((((int)threadIdx.z) * 3) + ((int)threadIdx.x)) + ((int)threadIdx.y)) / 6) * 18) + ((((((int)threadIdx.z) * 3) + ((int)threadIdx.x)) + ((int)threadIdx.y)) % 6)) + 24)];
}
}
}
}
if ((((int)threadIdx.z) * 3) < (6 - (((((int)threadIdx.y) * 3) + ((int)threadIdx.x)) / 4))) {
if (((((int)threadIdx.z) * 12) + (((int)threadIdx.y) * 3)) < (24 - ((int)threadIdx.x))) {
if ((((int)threadIdx.y) * 3) < (12 - ((int)threadIdx.x))) {
if (((int)threadIdx.x) < 3) {
K2_shared[(((((int)threadIdx.z) * 12) + (((int)threadIdx.y) * 3)) + ((int)threadIdx.x))] = K2[((((((int)threadIdx.z) * 48) + ((((((int)threadIdx.y) * 3) + ((int)threadIdx.x)) / 4) * 16)) + (((int)blockIdx.z) * 4)) + (((((int)threadIdx.y) * 3) + ((int)threadIdx.x)) % 4))];
}
}
}
}
__syncthreads();
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[0]) * K1_shared[0]) * K2_shared[0]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[0]) * K1_shared[0]) * K2_shared[0]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[0]) * K1_shared[0]) * K2_shared[1]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[0]) * K1_shared[0]) * K2_shared[1]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[0]) * K1_shared[0]) * K2_shared[2]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[0]) * K1_shared[0]) * K2_shared[2]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[0]) * K1_shared[0]) * K2_shared[3]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[0]) * K1_shared[0]) * K2_shared[3]));
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[1]) * K1_shared[1]) * K2_shared[4]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[1]) * K1_shared[1]) * K2_shared[4]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[1]) * K1_shared[1]) * K2_shared[5]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[1]) * K1_shared[1]) * K2_shared[5]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[1]) * K1_shared[1]) * K2_shared[6]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[1]) * K1_shared[1]) * K2_shared[6]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[1]) * K1_shared[1]) * K2_shared[7]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[1]) * K1_shared[1]) * K2_shared[7]));
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[2]) * K1_shared[2]) * K2_shared[8]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[2]) * K1_shared[2]) * K2_shared[8]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[2]) * K1_shared[2]) * K2_shared[9]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[2]) * K1_shared[2]) * K2_shared[9]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[2]) * K1_shared[2]) * K2_shared[10]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[2]) * K1_shared[2]) * K2_shared[10]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[2]) * K1_shared[2]) * K2_shared[11]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[2]) * K1_shared[2]) * K2_shared[11]));
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[3]) * K1_shared[3]) * K2_shared[12]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[3]) * K1_shared[3]) * K2_shared[12]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[3]) * K1_shared[3]) * K2_shared[13]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[3]) * K1_shared[3]) * K2_shared[13]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[3]) * K1_shared[3]) * K2_shared[14]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[3]) * K1_shared[3]) * K2_shared[14]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[3]) * K1_shared[3]) * K2_shared[15]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[3]) * K1_shared[3]) * K2_shared[15]));
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[4]) * K1_shared[4]) * K2_shared[16]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[4]) * K1_shared[4]) * K2_shared[16]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[4]) * K1_shared[4]) * K2_shared[17]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[4]) * K1_shared[4]) * K2_shared[17]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[4]) * K1_shared[4]) * K2_shared[18]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[4]) * K1_shared[4]) * K2_shared[18]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[4]) * K1_shared[4]) * K2_shared[19]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[4]) * K1_shared[4]) * K2_shared[19]));
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[5]) * K1_shared[5]) * K2_shared[20]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[5]) * K1_shared[5]) * K2_shared[20]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[5]) * K1_shared[5]) * K2_shared[21]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[5]) * K1_shared[5]) * K2_shared[21]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[5]) * K1_shared[5]) * K2_shared[22]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[5]) * K1_shared[5]) * K2_shared[22]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[5]) * K1_shared[5]) * K2_shared[23]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[5]) * K1_shared[5]) * K2_shared[23]));
__syncthreads();
pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + (((int)threadIdx.x) * 2))] = (((ww_inner_outer * 16) < (31 - ((((int)threadIdx.x) * 2) % 16))) ? Data[((((((((((nn_inner_outer * 32768) + ((((((int)threadIdx.y) * 16) + ((int)threadIdx.x)) / 64) * 16384)) + (((int)threadIdx.z) * 16384)) + (rc_outer * 1024)) + (((int)blockIdx.y) * 512)) + (hh_inner_outer * 256)) + (((((((int)threadIdx.y) * 32) + (((int)threadIdx.x) * 2)) % 128) / 16) * 32)) + (ww_inner_outer * 16)) + ((((int)threadIdx.x) * 2) % 16)) + 1)] : 0.000000e+00f);
pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + (((int)threadIdx.x) * 2)) + 1)] = (((ww_inner_outer * 16) < (31 - (((((int)threadIdx.x) * 2) + 1) % 16))) ? Data[((((((((((nn_inner_outer * 32768) + (((((((int)threadIdx.y) * 32) + (((int)threadIdx.x) * 2)) + 1) / 128) * 16384)) + (((int)threadIdx.z) * 16384)) + (rc_outer * 1024)) + (((int)blockIdx.y) * 512)) + (hh_inner_outer * 256)) + ((((((((int)threadIdx.y) * 32) + (((int)threadIdx.x) * 2)) + 1) % 128) / 16) * 32)) + (ww_inner_outer * 16)) + (((((int)threadIdx.x) * 2) + 1) % 16)) + 1)] : 0.000000e+00f);
if ((((int)threadIdx.z) * 3) < ((6 - ((int)threadIdx.y)) - ((int)threadIdx.x))) {
if (((int)threadIdx.x) < (3 - ((int)threadIdx.y))) {
if (((int)threadIdx.x) < 1) {
if (((((((int)threadIdx.z) * 3) + ((int)threadIdx.x)) + ((int)threadIdx.y)) / 6) < (16 - rc_outer)) {
K0_shared[(((((int)threadIdx.z) * 3) + ((int)threadIdx.x)) + ((int)threadIdx.y))] = K0[((((rc_outer * 6) + (((int)threadIdx.z) * 3)) + ((int)threadIdx.x)) + ((int)threadIdx.y))];
}
}
}
}
if ((((int)threadIdx.z) * 3) < ((6 - ((int)threadIdx.y)) - ((int)threadIdx.x))) {
if (((int)threadIdx.x) < (3 - ((int)threadIdx.y))) {
if (((int)threadIdx.x) < 1) {
if ((((int)threadIdx.z) * 3) < ((12 - ((int)threadIdx.y)) - ((int)threadIdx.x))) {
K1_shared[(((((int)threadIdx.z) * 3) + ((int)threadIdx.x)) + ((int)threadIdx.y))] = K1[(((((((((int)threadIdx.z) * 3) + ((int)threadIdx.x)) + ((int)threadIdx.y)) / 6) * 18) + ((((((int)threadIdx.z) * 3) + ((int)threadIdx.x)) + ((int)threadIdx.y)) % 6)) + 30)];
}
}
}
}
if ((((int)threadIdx.z) * 3) < (6 - (((((int)threadIdx.y) * 3) + ((int)threadIdx.x)) / 4))) {
if (((((int)threadIdx.z) * 12) + (((int)threadIdx.y) * 3)) < (24 - ((int)threadIdx.x))) {
if ((((int)threadIdx.y) * 3) < (12 - ((int)threadIdx.x))) {
if (((int)threadIdx.x) < 3) {
K2_shared[(((((int)threadIdx.z) * 12) + (((int)threadIdx.y) * 3)) + ((int)threadIdx.x))] = K2[((((((int)threadIdx.z) * 48) + ((((((int)threadIdx.y) * 3) + ((int)threadIdx.x)) / 4) * 16)) + (((int)blockIdx.z) * 4)) + (((((int)threadIdx.y) * 3) + ((int)threadIdx.x)) % 4))];
}
}
}
}
__syncthreads();
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[0]) * K1_shared[0]) * K2_shared[0]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[0]) * K1_shared[0]) * K2_shared[0]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[0]) * K1_shared[0]) * K2_shared[1]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[0]) * K1_shared[0]) * K2_shared[1]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[0]) * K1_shared[0]) * K2_shared[2]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[0]) * K1_shared[0]) * K2_shared[2]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[0]) * K1_shared[0]) * K2_shared[3]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[0]) * K1_shared[0]) * K2_shared[3]));
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[1]) * K1_shared[1]) * K2_shared[4]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[1]) * K1_shared[1]) * K2_shared[4]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[1]) * K1_shared[1]) * K2_shared[5]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[1]) * K1_shared[1]) * K2_shared[5]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[1]) * K1_shared[1]) * K2_shared[6]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[1]) * K1_shared[1]) * K2_shared[6]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[1]) * K1_shared[1]) * K2_shared[7]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[1]) * K1_shared[1]) * K2_shared[7]));
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[2]) * K1_shared[2]) * K2_shared[8]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[2]) * K1_shared[2]) * K2_shared[8]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[2]) * K1_shared[2]) * K2_shared[9]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[2]) * K1_shared[2]) * K2_shared[9]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[2]) * K1_shared[2]) * K2_shared[10]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[2]) * K1_shared[2]) * K2_shared[10]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[2]) * K1_shared[2]) * K2_shared[11]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[2]) * K1_shared[2]) * K2_shared[11]));
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[3]) * K1_shared[3]) * K2_shared[12]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[3]) * K1_shared[3]) * K2_shared[12]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[3]) * K1_shared[3]) * K2_shared[13]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[3]) * K1_shared[3]) * K2_shared[13]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[3]) * K1_shared[3]) * K2_shared[14]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[3]) * K1_shared[3]) * K2_shared[14]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[3]) * K1_shared[3]) * K2_shared[15]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[3]) * K1_shared[3]) * K2_shared[15]));
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[4]) * K1_shared[4]) * K2_shared[16]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[4]) * K1_shared[4]) * K2_shared[16]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[4]) * K1_shared[4]) * K2_shared[17]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[4]) * K1_shared[4]) * K2_shared[17]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[4]) * K1_shared[4]) * K2_shared[18]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[4]) * K1_shared[4]) * K2_shared[18]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[4]) * K1_shared[4]) * K2_shared[19]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[4]) * K1_shared[4]) * K2_shared[19]));
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[5]) * K1_shared[5]) * K2_shared[20]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[5]) * K1_shared[5]) * K2_shared[20]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[5]) * K1_shared[5]) * K2_shared[21]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[5]) * K1_shared[5]) * K2_shared[21]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[5]) * K1_shared[5]) * K2_shared[22]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[5]) * K1_shared[5]) * K2_shared[22]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[5]) * K1_shared[5]) * K2_shared[23]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[5]) * K1_shared[5]) * K2_shared[23]));
__syncthreads();
pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + (((int)threadIdx.x) * 2))] = (((((((int)blockIdx.y) * 16) + (hh_inner_outer * 8)) < ((31 - (((int)threadIdx.x) / 8)) - (((int)threadIdx.y) * 2))) && ((1 - ((((int)threadIdx.x) * 2) % 16)) <= (ww_inner_outer * 16))) ? Data[((((((((((nn_inner_outer * 32768) + ((((((int)threadIdx.y) * 16) + ((int)threadIdx.x)) / 64) * 16384)) + (((int)threadIdx.z) * 16384)) + (rc_outer * 1024)) + (((int)blockIdx.y) * 512)) + (hh_inner_outer * 256)) + (((((((int)threadIdx.y) * 32) + (((int)threadIdx.x) * 2)) % 128) / 16) * 32)) + (ww_inner_outer * 16)) + ((((int)threadIdx.x) * 2) % 16)) + 31)] : 0.000000e+00f);
pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + (((int)threadIdx.x) * 2)) + 1)] = (((((((int)blockIdx.y) * 16) + (hh_inner_outer * 8)) < ((31 - (((((int)threadIdx.x) * 2) + 1) / 16)) - (((int)threadIdx.y) * 2))) && ((1 - (((((int)threadIdx.x) * 2) + 1) % 16)) <= (ww_inner_outer * 16))) ? Data[((((((((((nn_inner_outer * 32768) + (((((((int)threadIdx.y) * 32) + (((int)threadIdx.x) * 2)) + 1) / 128) * 16384)) + (((int)threadIdx.z) * 16384)) + (rc_outer * 1024)) + (((int)blockIdx.y) * 512)) + (hh_inner_outer * 256)) + ((((((((int)threadIdx.y) * 32) + (((int)threadIdx.x) * 2)) + 1) % 128) / 16) * 32)) + (ww_inner_outer * 16)) + (((((int)threadIdx.x) * 2) + 1) % 16)) + 31)] : 0.000000e+00f);
if ((((int)threadIdx.z) * 3) < ((6 - ((int)threadIdx.y)) - ((int)threadIdx.x))) {
if (((int)threadIdx.x) < (3 - ((int)threadIdx.y))) {
if (((int)threadIdx.x) < 1) {
if (((((((int)threadIdx.z) * 3) + ((int)threadIdx.x)) + ((int)threadIdx.y)) / 6) < (16 - rc_outer)) {
K0_shared[(((((int)threadIdx.z) * 3) + ((int)threadIdx.x)) + ((int)threadIdx.y))] = K0[((((rc_outer * 6) + (((int)threadIdx.z) * 3)) + ((int)threadIdx.x)) + ((int)threadIdx.y))];
}
}
}
}
if ((((int)threadIdx.z) * 3) < ((6 - ((int)threadIdx.y)) - ((int)threadIdx.x))) {
if (((int)threadIdx.x) < (3 - ((int)threadIdx.y))) {
if (((int)threadIdx.x) < 1) {
K1_shared[(((((int)threadIdx.z) * 3) + ((int)threadIdx.x)) + ((int)threadIdx.y))] = K1[(((((((((int)threadIdx.z) * 3) + ((int)threadIdx.x)) + ((int)threadIdx.y)) / 6) * 18) + ((((((int)threadIdx.z) * 3) + ((int)threadIdx.x)) + ((int)threadIdx.y)) % 6)) + 36)];
}
}
}
if ((((int)threadIdx.z) * 3) < (6 - (((((int)threadIdx.y) * 3) + ((int)threadIdx.x)) / 4))) {
if (((((int)threadIdx.z) * 12) + (((int)threadIdx.y) * 3)) < (24 - ((int)threadIdx.x))) {
if ((((int)threadIdx.y) * 3) < (12 - ((int)threadIdx.x))) {
if (((int)threadIdx.x) < 3) {
K2_shared[(((((int)threadIdx.z) * 12) + (((int)threadIdx.y) * 3)) + ((int)threadIdx.x))] = K2[((((((int)threadIdx.z) * 48) + ((((((int)threadIdx.y) * 3) + ((int)threadIdx.x)) / 4) * 16)) + (((int)blockIdx.z) * 4)) + (((((int)threadIdx.y) * 3) + ((int)threadIdx.x)) % 4))];
}
}
}
}
__syncthreads();
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[0]) * K1_shared[0]) * K2_shared[0]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[0]) * K1_shared[0]) * K2_shared[0]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[0]) * K1_shared[0]) * K2_shared[1]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[0]) * K1_shared[0]) * K2_shared[1]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[0]) * K1_shared[0]) * K2_shared[2]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[0]) * K1_shared[0]) * K2_shared[2]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[0]) * K1_shared[0]) * K2_shared[3]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[0]) * K1_shared[0]) * K2_shared[3]));
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[1]) * K1_shared[1]) * K2_shared[4]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[1]) * K1_shared[1]) * K2_shared[4]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[1]) * K1_shared[1]) * K2_shared[5]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[1]) * K1_shared[1]) * K2_shared[5]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[1]) * K1_shared[1]) * K2_shared[6]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[1]) * K1_shared[1]) * K2_shared[6]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[1]) * K1_shared[1]) * K2_shared[7]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[1]) * K1_shared[1]) * K2_shared[7]));
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[2]) * K1_shared[2]) * K2_shared[8]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[2]) * K1_shared[2]) * K2_shared[8]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[2]) * K1_shared[2]) * K2_shared[9]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[2]) * K1_shared[2]) * K2_shared[9]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[2]) * K1_shared[2]) * K2_shared[10]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[2]) * K1_shared[2]) * K2_shared[10]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[2]) * K1_shared[2]) * K2_shared[11]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[2]) * K1_shared[2]) * K2_shared[11]));
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[3]) * K1_shared[3]) * K2_shared[12]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[3]) * K1_shared[3]) * K2_shared[12]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[3]) * K1_shared[3]) * K2_shared[13]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[3]) * K1_shared[3]) * K2_shared[13]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[3]) * K1_shared[3]) * K2_shared[14]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[3]) * K1_shared[3]) * K2_shared[14]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[3]) * K1_shared[3]) * K2_shared[15]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[3]) * K1_shared[3]) * K2_shared[15]));
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[4]) * K1_shared[4]) * K2_shared[16]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[4]) * K1_shared[4]) * K2_shared[16]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[4]) * K1_shared[4]) * K2_shared[17]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[4]) * K1_shared[4]) * K2_shared[17]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[4]) * K1_shared[4]) * K2_shared[18]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[4]) * K1_shared[4]) * K2_shared[18]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[4]) * K1_shared[4]) * K2_shared[19]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[4]) * K1_shared[4]) * K2_shared[19]));
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[5]) * K1_shared[5]) * K2_shared[20]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[5]) * K1_shared[5]) * K2_shared[20]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[5]) * K1_shared[5]) * K2_shared[21]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[5]) * K1_shared[5]) * K2_shared[21]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[5]) * K1_shared[5]) * K2_shared[22]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[5]) * K1_shared[5]) * K2_shared[22]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[5]) * K1_shared[5]) * K2_shared[23]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[5]) * K1_shared[5]) * K2_shared[23]));
__syncthreads();
pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + (((int)threadIdx.x) * 2))] = ((((((int)blockIdx.y) * 16) + (hh_inner_outer * 8)) < ((31 - (((int)threadIdx.x) / 8)) - (((int)threadIdx.y) * 2))) ? Data[((((((((((nn_inner_outer * 32768) + ((((((int)threadIdx.y) * 16) + ((int)threadIdx.x)) / 64) * 16384)) + (((int)threadIdx.z) * 16384)) + (rc_outer * 1024)) + (((int)blockIdx.y) * 512)) + (hh_inner_outer * 256)) + (((((((int)threadIdx.y) * 32) + (((int)threadIdx.x) * 2)) % 128) / 16) * 32)) + (ww_inner_outer * 16)) + ((((int)threadIdx.x) * 2) % 16)) + 32)] : 0.000000e+00f);
pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + (((int)threadIdx.x) * 2)) + 1)] = ((((((int)blockIdx.y) * 16) + (hh_inner_outer * 8)) < ((31 - (((((int)threadIdx.x) * 2) + 1) / 16)) - (((int)threadIdx.y) * 2))) ? Data[((((((((((nn_inner_outer * 32768) + (((((((int)threadIdx.y) * 32) + (((int)threadIdx.x) * 2)) + 1) / 128) * 16384)) + (((int)threadIdx.z) * 16384)) + (rc_outer * 1024)) + (((int)blockIdx.y) * 512)) + (hh_inner_outer * 256)) + ((((((((int)threadIdx.y) * 32) + (((int)threadIdx.x) * 2)) + 1) % 128) / 16) * 32)) + (ww_inner_outer * 16)) + (((((int)threadIdx.x) * 2) + 1) % 16)) + 32)] : 0.000000e+00f);
if ((((int)threadIdx.z) * 3) < ((6 - ((int)threadIdx.y)) - ((int)threadIdx.x))) {
if (((int)threadIdx.x) < (3 - ((int)threadIdx.y))) {
if (((int)threadIdx.x) < 1) {
if (((((((int)threadIdx.z) * 3) + ((int)threadIdx.x)) + ((int)threadIdx.y)) / 6) < (16 - rc_outer)) {
K0_shared[(((((int)threadIdx.z) * 3) + ((int)threadIdx.x)) + ((int)threadIdx.y))] = K0[((((rc_outer * 6) + (((int)threadIdx.z) * 3)) + ((int)threadIdx.x)) + ((int)threadIdx.y))];
}
}
}
}
if ((((int)threadIdx.z) * 3) < ((6 - ((int)threadIdx.y)) - ((int)threadIdx.x))) {
if (((int)threadIdx.x) < (3 - ((int)threadIdx.y))) {
if (((int)threadIdx.x) < 1) {
K1_shared[(((((int)threadIdx.z) * 3) + ((int)threadIdx.x)) + ((int)threadIdx.y))] = K1[(((((((((int)threadIdx.z) * 3) + ((int)threadIdx.x)) + ((int)threadIdx.y)) / 6) * 18) + ((((((int)threadIdx.z) * 3) + ((int)threadIdx.x)) + ((int)threadIdx.y)) % 6)) + 42)];
}
}
}
if ((((int)threadIdx.z) * 3) < (6 - (((((int)threadIdx.y) * 3) + ((int)threadIdx.x)) / 4))) {
if (((((int)threadIdx.z) * 12) + (((int)threadIdx.y) * 3)) < (24 - ((int)threadIdx.x))) {
if ((((int)threadIdx.y) * 3) < (12 - ((int)threadIdx.x))) {
if (((int)threadIdx.x) < 3) {
K2_shared[(((((int)threadIdx.z) * 12) + (((int)threadIdx.y) * 3)) + ((int)threadIdx.x))] = K2[((((((int)threadIdx.z) * 48) + ((((((int)threadIdx.y) * 3) + ((int)threadIdx.x)) / 4) * 16)) + (((int)blockIdx.z) * 4)) + (((((int)threadIdx.y) * 3) + ((int)threadIdx.x)) % 4))];
}
}
}
}
__syncthreads();
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[0]) * K1_shared[0]) * K2_shared[0]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[0]) * K1_shared[0]) * K2_shared[0]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[0]) * K1_shared[0]) * K2_shared[1]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[0]) * K1_shared[0]) * K2_shared[1]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[0]) * K1_shared[0]) * K2_shared[2]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[0]) * K1_shared[0]) * K2_shared[2]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[0]) * K1_shared[0]) * K2_shared[3]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[0]) * K1_shared[0]) * K2_shared[3]));
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[1]) * K1_shared[1]) * K2_shared[4]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[1]) * K1_shared[1]) * K2_shared[4]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[1]) * K1_shared[1]) * K2_shared[5]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[1]) * K1_shared[1]) * K2_shared[5]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[1]) * K1_shared[1]) * K2_shared[6]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[1]) * K1_shared[1]) * K2_shared[6]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[1]) * K1_shared[1]) * K2_shared[7]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[1]) * K1_shared[1]) * K2_shared[7]));
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[2]) * K1_shared[2]) * K2_shared[8]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[2]) * K1_shared[2]) * K2_shared[8]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[2]) * K1_shared[2]) * K2_shared[9]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[2]) * K1_shared[2]) * K2_shared[9]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[2]) * K1_shared[2]) * K2_shared[10]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[2]) * K1_shared[2]) * K2_shared[10]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[2]) * K1_shared[2]) * K2_shared[11]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[2]) * K1_shared[2]) * K2_shared[11]));
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[3]) * K1_shared[3]) * K2_shared[12]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[3]) * K1_shared[3]) * K2_shared[12]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[3]) * K1_shared[3]) * K2_shared[13]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[3]) * K1_shared[3]) * K2_shared[13]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[3]) * K1_shared[3]) * K2_shared[14]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[3]) * K1_shared[3]) * K2_shared[14]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[3]) * K1_shared[3]) * K2_shared[15]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[3]) * K1_shared[3]) * K2_shared[15]));
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[4]) * K1_shared[4]) * K2_shared[16]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[4]) * K1_shared[4]) * K2_shared[16]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[4]) * K1_shared[4]) * K2_shared[17]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[4]) * K1_shared[4]) * K2_shared[17]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[4]) * K1_shared[4]) * K2_shared[18]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[4]) * K1_shared[4]) * K2_shared[18]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[4]) * K1_shared[4]) * K2_shared[19]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[4]) * K1_shared[4]) * K2_shared[19]));
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[5]) * K1_shared[5]) * K2_shared[20]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[5]) * K1_shared[5]) * K2_shared[20]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[5]) * K1_shared[5]) * K2_shared[21]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[5]) * K1_shared[5]) * K2_shared[21]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[5]) * K1_shared[5]) * K2_shared[22]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[5]) * K1_shared[5]) * K2_shared[22]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[5]) * K1_shared[5]) * K2_shared[23]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[5]) * K1_shared[5]) * K2_shared[23]));
__syncthreads();
pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + (((int)threadIdx.x) * 2))] = (((((((int)blockIdx.y) * 16) + (hh_inner_outer * 8)) < ((31 - (((int)threadIdx.x) / 8)) - (((int)threadIdx.y) * 2))) && ((ww_inner_outer * 16) < (31 - ((((int)threadIdx.x) * 2) % 16)))) ? Data[((((((((((nn_inner_outer * 32768) + ((((((int)threadIdx.y) * 16) + ((int)threadIdx.x)) / 64) * 16384)) + (((int)threadIdx.z) * 16384)) + (rc_outer * 1024)) + (((int)blockIdx.y) * 512)) + (hh_inner_outer * 256)) + (((((((int)threadIdx.y) * 32) + (((int)threadIdx.x) * 2)) % 128) / 16) * 32)) + (ww_inner_outer * 16)) + ((((int)threadIdx.x) * 2) % 16)) + 33)] : 0.000000e+00f);
pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + (((int)threadIdx.x) * 2)) + 1)] = (((((((int)blockIdx.y) * 16) + (hh_inner_outer * 8)) < ((31 - (((((int)threadIdx.x) * 2) + 1) / 16)) - (((int)threadIdx.y) * 2))) && ((ww_inner_outer * 16) < (31 - (((((int)threadIdx.x) * 2) + 1) % 16)))) ? Data[((((((((((nn_inner_outer * 32768) + (((((((int)threadIdx.y) * 32) + (((int)threadIdx.x) * 2)) + 1) / 128) * 16384)) + (((int)threadIdx.z) * 16384)) + (rc_outer * 1024)) + (((int)blockIdx.y) * 512)) + (hh_inner_outer * 256)) + ((((((((int)threadIdx.y) * 32) + (((int)threadIdx.x) * 2)) + 1) % 128) / 16) * 32)) + (ww_inner_outer * 16)) + (((((int)threadIdx.x) * 2) + 1) % 16)) + 33)] : 0.000000e+00f);
if ((((int)threadIdx.z) * 3) < ((6 - ((int)threadIdx.y)) - ((int)threadIdx.x))) {
if (((int)threadIdx.x) < (3 - ((int)threadIdx.y))) {
if (((int)threadIdx.x) < 1) {
if (((((((int)threadIdx.z) * 3) + ((int)threadIdx.x)) + ((int)threadIdx.y)) / 6) < (16 - rc_outer)) {
K0_shared[(((((int)threadIdx.z) * 3) + ((int)threadIdx.x)) + ((int)threadIdx.y))] = K0[((((rc_outer * 6) + (((int)threadIdx.z) * 3)) + ((int)threadIdx.x)) + ((int)threadIdx.y))];
}
}
}
}
if ((((int)threadIdx.z) * 3) < ((6 - ((int)threadIdx.y)) - ((int)threadIdx.x))) {
if (((int)threadIdx.x) < (3 - ((int)threadIdx.y))) {
if (((int)threadIdx.x) < 1) {
K1_shared[(((((int)threadIdx.z) * 3) + ((int)threadIdx.x)) + ((int)threadIdx.y))] = K1[(((((((((int)threadIdx.z) * 3) + ((int)threadIdx.x)) + ((int)threadIdx.y)) / 6) * 18) + ((((((int)threadIdx.z) * 3) + ((int)threadIdx.x)) + ((int)threadIdx.y)) % 6)) + 48)];
}
}
}
if ((((int)threadIdx.z) * 3) < (6 - (((((int)threadIdx.y) * 3) + ((int)threadIdx.x)) / 4))) {
if (((((int)threadIdx.z) * 12) + (((int)threadIdx.y) * 3)) < (24 - ((int)threadIdx.x))) {
if ((((int)threadIdx.y) * 3) < (12 - ((int)threadIdx.x))) {
if (((int)threadIdx.x) < 3) {
K2_shared[(((((int)threadIdx.z) * 12) + (((int)threadIdx.y) * 3)) + ((int)threadIdx.x))] = K2[((((((int)threadIdx.z) * 48) + ((((((int)threadIdx.y) * 3) + ((int)threadIdx.x)) / 4) * 16)) + (((int)blockIdx.z) * 4)) + (((((int)threadIdx.y) * 3) + ((int)threadIdx.x)) % 4))];
}
}
}
}
__syncthreads();
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[0]) * K1_shared[0]) * K2_shared[0]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[0]) * K1_shared[0]) * K2_shared[0]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[0]) * K1_shared[0]) * K2_shared[1]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[0]) * K1_shared[0]) * K2_shared[1]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[0]) * K1_shared[0]) * K2_shared[2]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[0]) * K1_shared[0]) * K2_shared[2]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[0]) * K1_shared[0]) * K2_shared[3]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[0]) * K1_shared[0]) * K2_shared[3]));
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[1]) * K1_shared[1]) * K2_shared[4]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[1]) * K1_shared[1]) * K2_shared[4]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[1]) * K1_shared[1]) * K2_shared[5]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[1]) * K1_shared[1]) * K2_shared[5]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[1]) * K1_shared[1]) * K2_shared[6]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[1]) * K1_shared[1]) * K2_shared[6]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[1]) * K1_shared[1]) * K2_shared[7]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[1]) * K1_shared[1]) * K2_shared[7]));
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[2]) * K1_shared[2]) * K2_shared[8]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[2]) * K1_shared[2]) * K2_shared[8]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[2]) * K1_shared[2]) * K2_shared[9]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[2]) * K1_shared[2]) * K2_shared[9]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[2]) * K1_shared[2]) * K2_shared[10]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[2]) * K1_shared[2]) * K2_shared[10]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[2]) * K1_shared[2]) * K2_shared[11]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[2]) * K1_shared[2]) * K2_shared[11]));
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[3]) * K1_shared[3]) * K2_shared[12]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[3]) * K1_shared[3]) * K2_shared[12]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[3]) * K1_shared[3]) * K2_shared[13]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[3]) * K1_shared[3]) * K2_shared[13]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[3]) * K1_shared[3]) * K2_shared[14]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[3]) * K1_shared[3]) * K2_shared[14]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[3]) * K1_shared[3]) * K2_shared[15]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[3]) * K1_shared[3]) * K2_shared[15]));
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[4]) * K1_shared[4]) * K2_shared[16]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[4]) * K1_shared[4]) * K2_shared[16]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[4]) * K1_shared[4]) * K2_shared[17]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[4]) * K1_shared[4]) * K2_shared[17]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[4]) * K1_shared[4]) * K2_shared[18]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[4]) * K1_shared[4]) * K2_shared[18]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[4]) * K1_shared[4]) * K2_shared[19]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[4]) * K1_shared[4]) * K2_shared[19]));
Output_local[0] = (Output_local[0] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[5]) * K1_shared[5]) * K2_shared[20]));
Output_local[1] = (Output_local[1] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[5]) * K1_shared[5]) * K2_shared[20]));
Output_local[2] = (Output_local[2] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[5]) * K1_shared[5]) * K2_shared[21]));
Output_local[3] = (Output_local[3] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[5]) * K1_shared[5]) * K2_shared[21]));
Output_local[4] = (Output_local[4] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[5]) * K1_shared[5]) * K2_shared[22]));
Output_local[5] = (Output_local[5] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[5]) * K1_shared[5]) * K2_shared[22]));
Output_local[6] = (Output_local[6] + (((pad_temp_shared[(((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x))] * K0_shared[5]) * K1_shared[5]) * K2_shared[23]));
Output_local[7] = (Output_local[7] + (((pad_temp_shared[((((((int)threadIdx.z) * 128) + (((int)threadIdx.y) * 32)) + ((int)threadIdx.x)) + 16)] * K0_shared[5]) * K1_shared[5]) * K2_shared[23]));
}
Output[((((((((nn_inner_outer * 32768) + (((int)threadIdx.z) * 16384)) + (((int)blockIdx.z) * 4096)) + (((int)blockIdx.y) * 512)) + (hh_inner_outer * 256)) + (((int)threadIdx.y) * 64)) + (ww_inner_outer * 16)) + ((int)threadIdx.x))] = Output_local[0];
Output[(((((((((nn_inner_outer * 32768) + (((int)threadIdx.z) * 16384)) + (((int)blockIdx.z) * 4096)) + (((int)blockIdx.y) * 512)) + (hh_inner_outer * 256)) + (((int)threadIdx.y) * 64)) + (ww_inner_outer * 16)) + ((int)threadIdx.x)) + 32)] = Output_local[1];
Output[(((((((((nn_inner_outer * 32768) + (((int)threadIdx.z) * 16384)) + (((int)blockIdx.z) * 4096)) + (((int)blockIdx.y) * 512)) + (hh_inner_outer * 256)) + (((int)threadIdx.y) * 64)) + (ww_inner_outer * 16)) + ((int)threadIdx.x)) + 1024)] = Output_local[2];
Output[(((((((((nn_inner_outer * 32768) + (((int)threadIdx.z) * 16384)) + (((int)blockIdx.z) * 4096)) + (((int)blockIdx.y) * 512)) + (hh_inner_outer * 256)) + (((int)threadIdx.y) * 64)) + (ww_inner_outer * 16)) + ((int)threadIdx.x)) + 1056)] = Output_local[3];
Output[(((((((((nn_inner_outer * 32768) + (((int)threadIdx.z) * 16384)) + (((int)blockIdx.z) * 4096)) + (((int)blockIdx.y) * 512)) + (hh_inner_outer * 256)) + (((int)threadIdx.y) * 64)) + (ww_inner_outer * 16)) + ((int)threadIdx.x)) + 2048)] = Output_local[4];
Output[(((((((((nn_inner_outer * 32768) + (((int)threadIdx.z) * 16384)) + (((int)blockIdx.z) * 4096)) + (((int)blockIdx.y) * 512)) + (hh_inner_outer * 256)) + (((int)threadIdx.y) * 64)) + (ww_inner_outer * 16)) + ((int)threadIdx.x)) + 2080)] = Output_local[5];
Output[(((((((((nn_inner_outer * 32768) + (((int)threadIdx.z) * 16384)) + (((int)blockIdx.z) * 4096)) + (((int)blockIdx.y) * 512)) + (hh_inner_outer * 256)) + (((int)threadIdx.y) * 64)) + (ww_inner_outer * 16)) + ((int)threadIdx.x)) + 3072)] = Output_local[6];
Output[(((((((((nn_inner_outer * 32768) + (((int)threadIdx.z) * 16384)) + (((int)blockIdx.z) * 4096)) + (((int)blockIdx.y) * 512)) + (hh_inner_outer * 256)) + (((int)threadIdx.y) * 64)) + (ww_inner_outer * 16)) + ((int)threadIdx.x)) + 3104)] = Output_local[7];
}
}
}
}
void Conv2dCpFusedNchwKernelLauncher(const float* U, const float* K0,
const float* K1, const float* K2, float* V){
dim3 gridDim0(1, 2, 4);
dim3 blockDim0(16, 4, 2);
default_function_kernel0<<<gridDim0, blockDim0>>>(U, K0, K1, K2, V);
cudaDeviceSynchronize();
}
#endif
|
20,311 | #include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <iostream>
#include <time.h>
#include <stdio.h>
#include <stdlib.h>
#define N 5
#define BLOCK_DIM 10
using namespace std;
__global__
void sum_Matrices_Normal (int *a, int *b, int *c) {
int columna = blockIdx.x * blockDim.x + threadIdx.x;
int fila = blockIdx.y * blockDim.y + threadIdx.y;
int id = columna + fila * N;
if (columna < N && fila < N) {
c[id] = a[id] + b[id];
}
}
__global__
void sum_Matrices_fila (int *a, int *b, int *c) {
int columna = blockIdx.x * blockDim.x + threadIdx.x;
int fila = blockIdx.y * blockDim.y + threadIdx.y;
for(int i=columna; i<N; i++){
int id = i + fila * N;
c[id] = a[id] + b[id];
}
}
__global__
void sum_Matrices_columna (int *a, int *b, int *c) {
int columna = blockIdx.x * blockDim.x + threadIdx.x;
int fila = blockIdx.y * blockDim.y + threadIdx.y;
for(int i=fila; i<N; i++){
int id = columna + i * N;
c[id] = a[id] + b[id];
}
}
void imprimir_Matriz(int matrix[N][N]){
for(int i=0;i<N;i++){
for(int j=0; j<N; j++){
cout<<matrix[i][j]<<' ';
}
cout<<endl;
}
}
void imprimir_vector(int vector[N]){
for(int j=0; j<N; j++){
cout<<vector[j]<<' ';
}
}
int main() {
int a[N][N], b[N][N], c[N][N];
int *dev_a, *dev_b, *dev_c;
int size = N * N * sizeof(int);
srand(time(NULL));
for(int i=0; i<N; i++)
for (int j=0; j<N; j++){
a[i][j] = 1;
b[i][j] = 1;
}
imprimir_Matriz(a);
cout<<endl;
imprimir_Matriz(b);
cudaMalloc((void**)&dev_a, size);
cudaMalloc((void**)&dev_b, size);
cudaMalloc((void**)&dev_c, size);
cudaMemcpy(dev_a, a, size, cudaMemcpyHostToDevice);
cudaMemcpy(dev_b, b, size, cudaMemcpyHostToDevice);
dim3 dimBlock(N,N); // cuantos threads se ejecutaran juntos y que compartiran memoria en un sigle proccessor
dim3 dimGrid(1,1); // un grupo de thread block que se ejecutan en un sigle cuda program logically in parallel
sum_Matrices_Normal<<<dimGrid,dimBlock>>>(dev_a,dev_b,dev_c);
//sum_Matrices_fila<<<dimGrid,dimBlock>>>(dev_a,dev_b,dev_c);
//sum_Matrices_columna<<<dimGrid,dimBlock>>>(dev_a,dev_b,dev_c);
cudaDeviceSynchronize();
cudaMemcpy(c, dev_c, size, cudaMemcpyDeviceToHost);
cout<<endl;
for(int i=0; i<N; i++){
for (int j=0; j<N; j++){
printf("%d ", c[i][j] );
}
printf("\n");
}
cudaFree(dev_a);
cudaFree(dev_b);
cudaFree(dev_c);
return 0;
}
|
20,312 | #include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <iostream>
#include <numeric>
#include <math.h>
using namespace std;
__global__ void max(int* input, int n)
{
const int tid = threadIdx.x;
int step_size = 1;
int number_of_threads = blockDim.x;
while (number_of_threads > 0)
{
if (tid < number_of_threads)
{
const int fst = tid * step_size * 2;
const int snd = fst + step_size;
if(snd < n)
{
if(input[snd] > input[fst])
input[fst] = input[snd];
}
}
step_size *= 2;
if(number_of_threads == 1)
break;
number_of_threads = (int)ceil((float)number_of_threads/2.0); // divide number of threads by 2
__syncthreads();
}
}
int main()
{
int count;
int result;
int* d;
cout<<"\nEnter the number of elements : ";
cin>>count;
const int size = count * sizeof(int);
int *h;
h = new int[count];
cout<<"\nEnter the elements : \n";
for(int i=0;i<count;i++)
cin>>h[i];
//h[i] = rand()%1000
cudaMalloc(&d, size);
cudaMemcpy(d, h, size, cudaMemcpyHostToDevice);
max <<<1, ceil((float)count/2.0) >>>(d , count);
cudaMemcpy(&result, d, sizeof(int), cudaMemcpyDeviceToHost);
cout << "Max is " << result << endl;
getchar();
cudaFree(d);
delete[] h;
return 0;
}
/*
PS D:\MyFiles\Projects\LP1-LabAsg\1-HPC> nvcc ParRedMax.cu -o ParRedMax
ParRedMax.cu
Creating library ParRedMax.lib and object ParRedMax.exp
PS D:\MyFiles\Projects\LP1-LabAsg\1-HPC> nvprof ./ParRedMax
Enter the number of elements : 4
Enter the elements :
67
3
78
32
==10688== NVPROF is profiling process 10688, command: ./ParRedMax
Max is 78
==10688== Profiling application: ./ParRedMax
==10688== Profiling result:
Type Time(%) Time Calls Avg Min Max Name
GPU activities: 60.33% 2.4330us 1 2.4330us 2.4330us 2.4330us max(int*, int)
25.39% 1.0240us 1 1.0240us 1.0240us 1.0240us [CUDA memcpy HtoD]
14.28% 576ns 1 576ns 576ns 576ns [CUDA memcpy DtoH]
API calls: 74.44% 154.08ms 1 154.08ms 154.08ms 154.08ms cudaMalloc
25.25% 52.275ms 1 52.275ms 52.275ms 52.275ms cuDevicePrimaryCtxRelease
0.13% 277.30us 97 2.8580us 100ns 178.30us cuDeviceGetAttribute
0.06% 130.30us 1 130.30us 130.30us 130.30us cudaFree
0.04% 82.600us 2 41.300us 24.900us 57.700us cudaMemcpy
0.04% 80.300us 1 80.300us 80.300us 80.300us cuModuleUnload
0.01% 28.900us 1 28.900us 28.900us 28.900us cudaLaunchKernel
0.01% 27.100us 1 27.100us 27.100us 27.100us cuDeviceTotalMem
0.00% 9.5000us 1 9.5000us 9.5000us 9.5000us cuDeviceGetPCIBusId
0.00% 2.3000us 2 1.1500us 200ns 2.1000us cuDeviceGet
0.00% 2.0000us 3 666ns 300ns 900ns cuDeviceGetCount
0.00% 800ns 1 800ns 800ns 800ns cuDeviceGetName
0.00% 400ns 1 400ns 400ns 400ns cuDeviceGetLuid
0.00% 300ns 1 300ns 300ns 300ns cuDeviceGetUuid
*/ |
20,313 | #include "includes.h"
#define THREADS 256
#define BLOCKS 32
#define NUM THREADS*BLOCKS
int seed_var =1239;
__global__ void work_efficient_scan_kernel(int *X, int *Y, int InputSize)
{
extern __shared__ int XY[];
int i= blockIdx.x*blockDim.x+ threadIdx.x;
if (i < InputSize)
{
XY[threadIdx.x] = X[i];
}
for (unsigned int stride = 1; stride < blockDim.x; stride *= 2)
{
__syncthreads();
int index = (threadIdx.x+1) * 2* stride -1;
if (index < blockDim.x)
{
XY[index] += XY[index -stride];
}
}
for (int stride = THREADS/4; stride > 0; stride /= 2)
{
__syncthreads();
int index = (threadIdx.x+1)*stride*2 -1;
if(index + stride < THREADS)
{
XY[index + stride] += XY[index];
}
}
__syncthreads();
Y[i] = XY[threadIdx.x];
//OWN CODE
__syncthreads();
if(threadIdx.x < blockIdx.x)
{
XY[threadIdx.x] = Y[threadIdx.x*blockDim.x + (blockDim.x-1)];
}
__syncthreads();
for(unsigned int stride =0; stride < blockIdx.x; stride++)
{
Y[i] += XY[stride];
}
__syncthreads();
} |
20,314 | #include <curand_kernel.h>
#include <curand.h>
#include <stdio.h>
#include <stdlib.h>
#include <chrono>
#include <iostream>
// #define NUM_ITER 10000000000
//#define grid_size 1
// #define BLOCK_SIZE 1
#define MAX_THREADS 2048*12
__global__
void calc_pi(uint64_t *counts, int iterations, int block_size)
{
extern __shared__ int counter[];
counter[threadIdx.x] = 0;
int id = threadIdx.x + blockDim.x * blockIdx.x;
int seed = id;
curandState_t rng;
curand_init(clock64(), seed, 0, &rng);
float x, y, z;
for(int i = 0; i < iterations; i++){
x = curand_uniform(&rng);
y = curand_uniform(&rng);
z = sqrt((x*x) + (y*y));
if(z <= 1){
counter[threadIdx.x] += 1;
}
}
__syncthreads();
// Do we need to sync warps??
if (threadIdx.x == 0) {
counts[blockIdx.x] = 0;
for (int i = 0; i < block_size; i++) {
counts[blockIdx.x] += counter[i];
}
}
}
int main(int argc, char* argv[])
{
uint64_t num_iter = std::stol(argv[1]);
int block_size = std::stol(argv[2]);
int grid_size = MAX_THREADS/block_size;
int n_thread_iterations = num_iter/(block_size * grid_size);
printf("Total iterations %ld grid_size %d \n", num_iter, grid_size);
uint64_t *h_total_counter, *d_total_counter;
h_total_counter = new uint64_t[grid_size];
cudaMalloc(&d_total_counter, sizeof(uint64_t) * grid_size);
auto gpu_start = std::chrono::steady_clock::now();
calc_pi<<<grid_size, block_size, block_size*sizeof(int)>>>(d_total_counter, n_thread_iterations, block_size);
cudaMemcpy(h_total_counter, d_total_counter, sizeof(uint64_t) * grid_size, cudaMemcpyDeviceToHost);
auto gpu_end = std::chrono::steady_clock::now();
std::chrono::duration<double> elapsed_gpu_seconds = gpu_end-gpu_start;
uint64_t total = 0;
for(int i = 0; i < grid_size; i++){
total += h_total_counter[i];
}
float pi = 4 * total/(float)num_iter;
printf("PI: %f\n", pi);
std::cout << "GPU Execution Time" << " " << elapsed_gpu_seconds.count() << std::endl;
} |
20,315 | /////////////////////////
// matrixVecMult.cu //
// Andrew Krepps //
// Module 6 Assignment //
// 3/12/2018 //
/////////////////////////
#include <chrono>
#include <stdio.h>
#include <stdlib.h>
#define MAX_SIZE 8192
///////////////////////////////////////////////////////////////////////////////
/// \brief perform matrix vector multiplication for a single output element
///
/// \param [in] inMat the input matrix
/// \param [in] inVec the input vector
/// \param [out] outVec the output vector
/// \param [in] m the number of matrix rows and the output vector length
/// \param [in] n the number of matrix columns and the input vector length
///////////////////////////////////////////////////////////////////////////////
__device__ void performMatVecMult(
const float* inMat,
const float* inVec,
float* outVec,
const unsigned int m,
const unsigned int n)
{
const unsigned int outIdx = blockIdx.x*blockDim.x + threadIdx.x;
if (outIdx < m) {
// intermediate results are stored in registers
// before being written back to the output
float sum = 0.0f;
unsigned int matRowStart = outIdx*n;
for (unsigned int i = 0; i < n; i++) {
unsigned int matIdx = matRowStart + i;
sum += inMat[matIdx]*inVec[i];
}
outVec[outIdx] = sum;
}
}
///////////////////////////////////////////////////////////////////////////////
/// \brief perform matrix vector multiplication using global memory
///
/// \param [in] inMat the input matrix
/// \param [in] inVec the input vector
/// \param [out] outVec the output vector
/// \param [in] m the number of matrix rows and the output vector length
/// \param [in] n the number of matrix columns and the input vector length
///////////////////////////////////////////////////////////////////////////////
__global__ void matVecMultGlobalMem(
const float* inMat,
const float* inVec,
float* outVec,
const unsigned int m,
const unsigned int n)
{
// we're just using global memory, so directly perform multiplication
performMatVecMult(inMat, inVec, outVec, m, n);
}
///////////////////////////////////////////////////////////////////////////////
// \brief constant memory for storing input vector
///////////////////////////////////////////////////////////////////////////////
__constant__ float inVecConstantMem[MAX_SIZE];
///////////////////////////////////////////////////////////////////////////////
/// \brief perform matrix vector mutiplication using constant memory
///
/// This assumes that the input vector has already been copied to constant
/// memory using the symbol inVecConstantMem
///
/// \param [in] inMat the input matrix
/// \param [out] outVec the output vector
/// \param [in] m the number of matrix rows and the output vector length
/// \param [in] n the number of matrix columns and the input vector length
///////////////////////////////////////////////////////////////////////////////
__global__ void matVecMultConstantMem(
const float* inMat,
float* outVec,
const unsigned int m,
const unsigned int n)
{
// perform the multiplication using input vector bound to constant memory
performMatVecMult(inMat, inVecConstantMem, outVec, m, n);
}
///////////////////////////////////////////////////////////////////////////////
/// \brief perform matrix vector multiplication using shared memory
///
/// \param [in] inMat the input matrix
/// \param [in] inVec the input vector
/// \param [out] outVec the output vector
/// \param [in] m the number of matrix rows and the output vector length
/// \param [in] n the number of matrix columns and the input vector length
///////////////////////////////////////////////////////////////////////////////
__global__ void matVecMultSharedMem(
const float* inMat,
const float* inVec,
float* outVec,
const unsigned int m,
const unsigned int n)
{
// load input vector into shared memory for each block
extern __shared__ float inVecSharedMem[];
const unsigned int localIdx = threadIdx.x;
// input vector could be larger than the block size,
// so we need to figure out which elements each thread
// is responsible for copying over to shared memory
const unsigned int elementsToCopy = n/blockDim.x + 1;
const unsigned int startElement = localIdx*elementsToCopy;
for (unsigned int i = 0; i < elementsToCopy; ++i) {
unsigned int dataIdx = startElement + i;
if (dataIdx < n) {
inVecSharedMem[dataIdx] = inVec[dataIdx];
}
}
__syncthreads();
// after all data is loaded, perform multiplication using vector in shared memory
performMatVecMult(inMat, inVecSharedMem, outVec, m, n);
}
///////////////////////////////////////////////////////////////////////////////
/// \brief initialize input data on the host
///
/// \param [out] mat the input matrix
/// \param [out] vec the input vector
/// \param [in] m the number of matrix rows
/// \param [in] n the number of matrix columns and the input vector length
///////////////////////////////////////////////////////////////////////////////
void initializeInputData(
float* mat,
float* vec,
const unsigned int m,
const unsigned int n)
{
for (unsigned int i = 0; i < m; ++i) {
for (unsigned int j = 0; j < n; ++j) {
const unsigned int matIdx = i*n + j;
mat[matIdx] = matIdx*0.01f;
}
vec[i] = i*0.1f;
}
}
///////////////////////////////////////////////////////////////////////////////
/// \brief launch a kernel to perform matrix vector multiplication
///
/// \param [in] kernel the kernel index (i.e., memory type) to use
/// \param [in] blockSize the number of threads per block to use
/// \param [in] inMat the input matrix (on the device)
/// \param [in] inVec the input vector (on the device)
/// \param [out] outVec the output vector (on the device)
/// \param [in] m the number of matrix rows and the output vector length
/// \param [in] n the number of matrix columns and the input vector length
///
/// \returns the kernel execution time (in ms)
///////////////////////////////////////////////////////////////////////////////
float launchKernel(
const unsigned int kernel,
const unsigned int blockSize,
const float* inMat,
const float* inVec,
float* outVec,
const unsigned int m,
const unsigned int n)
{
const unsigned int numBlocks = m/blockSize;
// start clock and launch kernel
auto start = std::chrono::high_resolution_clock::now();
switch (kernel) {
case 0:
matVecMultGlobalMem<<<numBlocks, blockSize>>>(inMat, inVec, outVec, m, n);
break;
case 1:
matVecMultConstantMem<<<numBlocks, blockSize>>>(inMat, outVec, m, n);
break;
case 2:
matVecMultSharedMem<<<numBlocks, blockSize, n*sizeof(float)>>>(inMat, inVec, outVec, m, n);
break;
default:
printf("Invalid kernel index: %d\n", kernel);
}
// wait for GPU kernel to finish
cudaThreadSynchronize();
// calculate execution time in ms
auto stop = std::chrono::high_resolution_clock::now();
std::chrono::duration<float> duration(stop - start);
return duration.count()*1000.0f;
}
///////////////////////////////////////////////////////////////////////////////
/// \brief allocate device memory and run a kernel to perform matrix vector
/// multiplication
///
/// \param [in] kernel the kernel index (i.e., memory type) to use
/// \param [in] kernel the kernel index (i.e., memory type) to use
/// \param [in] blockSize the number of threads per block to use
/// \param [in] inMat the input matrix (on the host)
/// \param [in] inVec the input vector (on the host)
/// \param [out] outVec the output vector (on the host)
/// \param [in] m the number of matrix rows and the output vector length
/// \param [in] n the number of matrix columns and the input vector length
///
/// \returns the kernel execution time (in ms) not including data transfer
///////////////////////////////////////////////////////////////////////////////
float runTimingTest(
const unsigned int kernel,
const unsigned int blockSize,
const float* inMat,
const float* inVec,
float* outVec,
const unsigned int m,
const unsigned int n)
{
// allocate device memory
float* d_inMat;
float* d_inVec;
float* d_outVec;
const unsigned int matrixElements = m*n;
const unsigned int matrixBytes = matrixElements*sizeof(float);
const unsigned int vectorBytes = n*sizeof(float);
cudaMalloc((void**)&d_inMat, matrixBytes);
cudaMalloc((void**)&d_inVec, vectorBytes);
cudaMalloc((void**)&d_outVec, vectorBytes);
// copy input data to device
cudaMemcpy(d_inMat, inMat, matrixBytes, cudaMemcpyHostToDevice);
cudaMemcpy(d_inVec, inVec, vectorBytes, cudaMemcpyHostToDevice);
float ms = launchKernel(kernel, blockSize, d_inMat, d_inVec, d_outVec, m, n);
// copy output data to host
cudaMemcpy(outVec, d_outVec, vectorBytes, cudaMemcpyDeviceToHost);
// free device memory
cudaFree(d_inMat);
cudaFree(d_inVec);
cudaFree(d_outVec);
return ms;
}
int main(int argc, char** argv)
{
// configure run
unsigned int dataSize = 512;
unsigned int blockSize = 256;
if (argc > 1) {
dataSize = atoi(argv[1]);
}
if (argc > 2) {
blockSize = atoi(argv[2]);
}
if (dataSize > MAX_SIZE) {
dataSize = MAX_SIZE;
printf("Warning: data size exceeds maximum limit. Setting to %d.\n", dataSize);
}
// allocate and initialize host memory
const unsigned int matrixBytes = dataSize*dataSize*sizeof(float);
const unsigned int vectorBytes = dataSize*sizeof(float);
float* inMat = (float*)malloc(matrixBytes);
float* inVec = (float*)malloc(vectorBytes);
float* outVec = (float*)malloc(vectorBytes);
initializeInputData(inMat, inVec, dataSize, dataSize);
// initialize input vector in constant memory for later
cudaMemcpyToSymbol(inVecConstantMem, inVec, vectorBytes);
// dummy executions to avoid startup performance hit
for (unsigned int kernel = 0; kernel < 3; ++kernel) {
runTimingTest(kernel, blockSize, inMat, inVec, outVec, dataSize, dataSize);
}
// run timing comparisons
for (unsigned int kernel = 0; kernel < 3; ++kernel) {
switch (kernel) {
case 0:
printf("Running global memory kernel: ");
break;
case 1:
printf("Running constant memory kernel: ");
break;
case 2:
printf("Running shared memory kernel ");
break;
default:
printf("Invalid kernel index: %d\n", kernel);
}
float ms = runTimingTest(kernel, blockSize, inMat, inVec, outVec, dataSize, dataSize);
printf("Kernel took %.6f ms to run\n", ms);
// print output of kernel
for (unsigned int i = 0; i < dataSize; ++i) {
//printf("outVec[%d] = %f\n", i, outVec[i]);
}
}
// free host memory
free(inMat);
free(inVec);
free(outVec);
}
|
20,316 | #include <stdio.h>
__global__ void print() {
printf("block = %d, thread = %d\n", blockIdx.x, threadIdx.x);
}
int main() {
print<<<3,3>>>();
cudaDeviceSynchronize();
}
|
20,317 | #include "includes.h"
__global__ void multiplyBy2_self(int size, long *inout) {
const int ix = threadIdx.x + blockIdx.x * blockDim.x;
if (ix < size) {
inout[ix] = inout[ix] * 2;
}
} |
20,318 | #include <cuda.h>
#include <thrust/device_vector.h>
#include <thrust/fill.h>
#include <thrust/host_vector.h>
#include <thrust/sequence.h>
#include <thrust/transform.h>
#include <iostream>
using namespace std;
#define N 10
#define V 0.2
#define T 2
#define CUDA_CHECK_RETURN(value) ((cudaError_t)value != cudaSuccess) ? printf("Error %s at line %d in the file %s\n", cudaGetErrorString((cudaError_t)value), __LINE__, __FILE__) : printf("")
struct functor
{
const float koef;
functor(float _koef) : koef(_koef){}
__host__ __device__ float operator()(float x, float y)
{
return y + koef * (x - y);
}
};
void iteration(float _koef, thrust::device_vector<float> &x, thrust::device_vector<float> &y)
{
functor func(_koef);
thrust::transform(x.begin(), x.end(), y.begin(), y.begin(), func);
}
__global__ void kernel(float *f, float *res)
{
int cur = threadIdx.x + blockDim.x * blockIdx.x;
int prev = cur - 1;
if(prev == -1)
{
prev = N - 1;
}
res[cur] = f[cur] + (V * T) * (f[prev] - f[cur]);
}
int main()
{
float *Function = new float[N];
float *FunctionData = new float[N];
float *frez;
float *tempa;
cudaEvent_t start, stop;
float time;
CUDA_CHECK_RETURN(cudaEventCreate(&start));
CUDA_CHECK_RETURN(cudaEventCreate(&stop));
for (int i = 0; i < N; i++)
{
FunctionData[i] = rand() % 100;
Function[i] = FunctionData[i];
}
CUDA_CHECK_RETURN(cudaMalloc((void **)&frez, N * sizeof(float)));
CUDA_CHECK_RETURN(cudaMalloc((void **)&tempa, N * sizeof(float)));
CUDA_CHECK_RETURN(cudaMemcpy(tempa, Function, N * sizeof(float), cudaMemcpyHostToDevice));
CUDA_CHECK_RETURN(cudaEventSynchronize(start));
CUDA_CHECK_RETURN(cudaEventRecord(start, 0));
for(int i = 0; i < 1000; i++)
{
kernel <<< 1, N >>> (tempa, frez);
CUDA_CHECK_RETURN(cudaMemcpy(Function, frez, N * sizeof(float), cudaMemcpyDeviceToHost));
//CUDA_CHECK_RETURN(cudaMemcpy(tempa, frez, N * sizeof(float), cudaMemcpyHostToDevice));
/*for(int i = 0; i < N; i++)
{
cout << Function[i] << " ";
}
cout << endl;*/
}
CUDA_CHECK_RETURN(cudaDeviceSynchronize());
CUDA_CHECK_RETURN(cudaEventRecord(stop, 0));
CUDA_CHECK_RETURN(cudaEventSynchronize(stop));
CUDA_CHECK_RETURN(cudaEventElapsedTime(&time, start, stop));
printf("CUDA: %f ms\n", time);
thrust::host_vector<float> cpumem1(N);
thrust::host_vector<float> cpumem2(N);
for (int i = 0; i < N; i++)
{
cpumem1[i] = FunctionData[i];
if(i - 1 >= 0)
{
cpumem2[i] = FunctionData[i - 1];
}
else
{
cpumem2[i] = FunctionData[N - 1];
}
}
thrust::device_vector<float> gpumem1 = cpumem1;
thrust::device_vector<float> gpumem2 = cpumem2;
CUDA_CHECK_RETURN(cudaEventSynchronize(start));
CUDA_CHECK_RETURN(cudaEventRecord(start, 0));
for(int i = 0; i < 1000; i++)
{
iteration(V * T, gpumem2, gpumem1);
/*for(int i = 0; i < N; i++)
{
cout << gpumem1[i] << " ";
}
cout << endl;*/
thrust::copy(gpumem1.begin(), gpumem1.end(), gpumem2.begin());
/* thrust::copy(cpumem1.begin(), cpumem1.end(), gpumem1.begin());
thrust::copy(cpumem2.begin(), cpumem2.end(), gpumem2.begin());*/
/*for(int i = 0; i < N; i++)
{
if(i - 1 >= 0)
{
gpumem2[i] = gpumem1[i - 1];
}
else
{
gpumem2[i] = gpumem1[N - 1];
}
}*/
}
CUDA_CHECK_RETURN(cudaDeviceSynchronize());
CUDA_CHECK_RETURN(cudaEventRecord(stop, 0));
CUDA_CHECK_RETURN(cudaEventSynchronize(stop));
CUDA_CHECK_RETURN(cudaEventElapsedTime(&time, start, stop));
printf("Thrust: %f ms\n", time);
return 0;
}
|
20,319 | struct boundingVolume{
float3 u_f_r;
float3 u_f_l;
float3 u_b_r;
float3 u_b_l;
float3 lo_f_r;
float3 lo_f_l;
float3 lo_b_r;
float3 lo_b_l;
};
// class CellIDs{
// public:
// int *cellIDArray;
// int *objectIDArray;
// CellIDs(){
// cellIDArray = (int*)malloc(sizeof(int)*8*OBJECT_COUNT);
// objectIDArray = (int*)malloc(sizeof(int)*8*OBJECT_COUNT);
// }
// };
// CellIDs h_cellIDs, d_cellIDS;
|
20,320 | #include "includes.h"
// fill an image with a chekcer_board (BGR)
__global__ void replace_image_by_distance_kernel(const unsigned char *pImage, const float* pDepth, const unsigned char *pBackground, unsigned char *result, const float max_value, const unsigned int width, const unsigned int height, const unsigned int image_channels)
{
const int x = blockIdx.x * blockDim.x + threadIdx.x;
const int y = blockIdx.y * blockDim.y + threadIdx.y;
if (y >= height || x >= width)
return;
// get the depth of the current pixel
float z_distance = pDepth[y * width + x];
// replace part of the view
int index = (y * width + x) * 3;
if (isfinite(z_distance) && (z_distance > max_value))
{
result[index] = pBackground[index];
result[index + 1] = pBackground[index + 1];
result[index + 2] = pBackground[index + 2];
}
else
{
if (image_channels == 1)//gray image
{
int img_index = y * width + x;
result[index] = pImage[img_index];
result[index + 1] = pImage[img_index];
result[index + 2] = pImage[img_index];
}
else//color image
{
int img_index = (y * width + x) * image_channels;
result[index] = pImage[img_index];
result[index + 1] = pImage[img_index + 1];
result[index + 2] = pImage[img_index + 2];
}
}
} |
20,321 | #ifndef __UTEPOCH_CU__
#define __UTEPOCH_CU__
#include <stdio.h>
#include "sac.cuh"
long long int time2utepoch(int year,int jday,int hour,int min,int sec,int msec,int usec)
{
long long int kk=1000000;
long long int k=1000;
long long int utepoch;
int a4=year/4-!(year & 3);
int a100=a4/25;
int a400=a100/4;
int intervening_leap_days=a4-a100+a400-477;
int days=(365*(year-1970)+intervening_leap_days+jday-1);
utepoch=(60*(60*((long long int)24*days+hour)+min)+sec)*kk+msec*k+usec;
return utepoch;
}
long long int sactime2utepoch(SACHEAD hd,char mark)
{
long long int kk=1000000;
long long int utepoch;
float marktime=0;
int year,jday,hour,min,sec,msec;
year=hd.nzyear;
jday=hd.nzjday;
hour=hd.nzhour;
min=hd.nzmin;
sec=hd.nzsec;
msec=hd.nzmsec;
if( mark=='b')
marktime = hd.b;
else
if(mark=='e')
marktime=hd.e;
utepoch=time2utepoch(year,jday,hour,min,sec,msec,0)+(long long int)((double)marktime*kk+0.5);
return(utepoch);
}
#endif |
20,322 | //pass
//--blockDim=1024 --gridDim=1 --no-inline
#include <cuda.h>
#include <stdio.h>
#define N 2 //1024
__global__ void definitions (int* A, unsigned int* B, unsigned long long int* C)
{
atomicMin(A,10);
atomicMin(B,1);
atomicMin(C,5);
}
|
20,323 | #include <fstream>
#include <iostream>
#include <string>
#include <vector>
#include <sstream>
#include <algorithm>
#include <cuda_runtime.h>
#include <math.h>
#include <device_launch_parameters.h>
int* readfile(const char* filename, int* size);
/*****************************************************
while !stable
propose in parallel
block
accept/reject in parallel
block
*****************************************************/
__device__ bool stable;
__device__ bool no_match;
__device__ bool gpu_reduced_size_empty;
__global__ void p1_proposal(int* preference_lists, int* proposal_to, int* proposed_to, int N) {
int row = blockIdx.x * blockDim.x + threadIdx.x;
stable = true;
if (row < N)
{
__syncthreads();
if (proposed_to[row] >= (N-1))
{
no_match = true;
return;
}
// if proposal was rejected, or havent proposed to anyone yet
if (proposal_to[row] == N)
{
proposal_to[row] = preference_lists[row * N + proposed_to[row] + 1];
}
}
}
__global__ void p1_rejection(int* preference_lists, int* proposal_to, int* proposal_from, int* proposed_to, int N) {
int row = blockIdx.x * blockDim.x + threadIdx.x;
int proposee;
if (row < N)
{
__syncthreads();
proposee = proposal_to[row];
int op = N;
for (int i = 0; i < (N-1); i++)
{
if (preference_lists[proposee * N + i + 1] == row)
{
op = i;
break;
}
}
if (op == N)
{
no_match = true;
return;
}
int op_curr = N;
for (int i = 0; i < (N-1); i++)
{
if (preference_lists[proposee * N + i + 1] == proposal_from[proposee*N])
{
op_curr = i;
break;
}
}
if (op < op_curr)
{
if (proposal_from[proposee*N] != N)
{
proposal_to[proposal_from[proposee*N]] = N;
stable = false;
}
proposal_from[proposee*N + op + 1] = row;
}
else if (op == op_curr)
{
}
else
{
stable = false;
}
atomicAdd(&proposed_to[row], 1);
__syncthreads();
}
}
__global__ void p1_accept(int* proposal_from, int* proposal_to, int* C, int* rank, int N) {
int i = threadIdx.x;
int j = blockIdx.x;
if (i < N && j < N)
{
C[j * N + i] = N;
if (proposal_from[j * N + i] != N)
{
C[j * N + i] = rank[j * N + proposal_from[j * N + i]];
}
for (int d = 1; d < N; d *= 2)
{
if (i - d >= 0)
{
if (C[j * N + i] > C[j * N + i - d])
{
C[j * N + i] = C[j * N + i - d];
}
}
__syncthreads();
}
if (C[j * N + i] != N)
{
proposal_from[j * N] = proposal_from[j * N + i];
}
if (i != 0)
{
proposal_from[j * N + i] = N;
}
}
}
__global__ void p1_evaluate(int* proposal_from, int* proposal_to, int N)
{
int i = threadIdx.x;
if (i < N)
{
proposal_to[i] = N;
__syncthreads();
if (proposal_from[i * N] != N) {
proposal_to[proposal_from[i * N]] = i;
}
else {
stable = false;
}
__syncthreads();
}
}
__global__ void p1_remove(int* preference_lists, int* proposal_from, int* rank, int N)
{
int i = threadIdx.x;
int j = blockIdx.x;
if (i < (N - 1) && j < N)
{
if (rank[j * N + i] > rank[j * N + proposal_from[j * N]] && rank[j * N + i] != N)
{
preference_lists[j * N + rank[j * N + i]] = N;
preference_lists[i * N + rank[i * N + j]] = N;
}
}
}
__global__ void p1_shift(int* preference_lists, int* reduced_size, int N)
{
int i = blockIdx.x * blockDim.x + threadIdx.x;
if (i < N)
{
int count = 0;
for (int d = 0; d < N; d++)
{
if (preference_lists[i * N + d] != N)
{
preference_lists[i * N + count++] = preference_lists[i * N + d];
}
}
reduced_size[i] = count - 1;
if (reduced_size[i] <= 0)
{
no_match = true;
return;
}
while (count < N)
{
preference_lists[i * N + count++] = N;
}
}
}
__global__ void get_rank(int* preference_lists, int N, int* rank, int* reduced_size)
{
int i = threadIdx.x;
int j = blockIdx.x;
if (i < N && j < N)
{
rank[j * N + i] = N;
if (i < reduced_size[j] + 1) {
rank[j * N + preference_lists[j * N + i]] = i;
}
if (i == j)
{
rank[j * N + i] = N;
}
}
__syncthreads();
}
__global__ void p2_remove(int* preference_lists, int* reduced_size, int* rotations, int N, int* rank, int count)
{
int i = blockIdx.x * blockDim.x + threadIdx.x;
__syncthreads();
if (i < count)
{
if (rotations[i] != N)
{
if (i % 2 == 1)//odd
{
preference_lists[rotations[i] * N + rank[rotations[i] * N + rotations[i - 1]]] = N;
atomicSub(&reduced_size[rotations[i]], 1);
if (reduced_size[rotations[i]] == 0)
{
no_match = true;
return;
}
}
else //even
{
preference_lists[rotations[i] * N + rank[rotations[i] * N + rotations[i + 1]]] = N;
atomicSub(&reduced_size[rotations[i]], 1);
if (reduced_size[rotations[i]] == 0)
{
no_match = true;
return;
}
}
}
}
}
__global__ void p2_getSecondLastChoice(int* preference_lists, int* last_choice, int* second_choice, int* reduced_size, int N)
{
int i = threadIdx.x;
int j = blockIdx.x;
if (i < N && j < N)
{
if (preference_lists[j * N + i] < N && reduced_size[j] > 1)
{
// second choice
if (i == 2)
{
second_choice[j] = preference_lists[j * N + i];
}
// last choice needs to be size
if (i == reduced_size[j])
{
last_choice[j] = preference_lists[j * N + i];
}
}
else if (reduced_size[j] == 0)
{
no_match = true;
return;
}
}
}
__global__ void get_reduced_sizes(int* reduced_size, int N)
{
int i = blockIdx.x * blockDim.x + threadIdx.x;
if (i < N)
{
if (reduced_size[i] <= 0)
{
gpu_reduced_size_empty = true;
}
}
}
__global__ void fill_matching_zeros(int* matching, int N)
{
int i = blockIdx.x * blockDim.x + threadIdx.x;
if (i < N)
{
matching[i] = 0;
}
}
__global__ void fill_matching(int* preference_lists, int* matching, int N)
{
int i = blockIdx.x * blockDim.x + threadIdx.x;
if (i < N)
{
matching[i] = preference_lists[i * N + 1];
}
}
std::vector<int> stable_roommate(std::vector<std::vector<int>> preference_lists_vector, int N) {
int NUM_BLOCKS;
int NUM_THREADS;
if (N % 32)
{
NUM_BLOCKS = N + (32 - N % 32);
NUM_THREADS = N + (32 - N % 32);
}
else
{
NUM_BLOCKS = N;
NUM_THREADS = N;
}
int* gpu_preference_lists;
int* gpu_proposal_to;
int* gpu_proposal_from;
int* gpu_proposed_to;
int* gpu_matching;
int* gpu_reduced_size;
int* gpu_second_choice;
int* gpu_last_choice;
int* gpu_rotations;
int* gpu_C;
int* preference_lists = new int[N*N];
for (int i = 0; i < N; i++)
{
preference_lists[i*N] = i;
for (int j = 1; j < N; j++)
{
preference_lists[i*N+j] = preference_lists_vector[i][j-1];
}
}
int* proposal_from = new int[N*N];
proposal_from = (int *)calloc(N*N, sizeof(*proposal_from));
std::replace(proposal_from, proposal_from + N*N, 0, N);
int *proposed_to = new int[N];
proposed_to = (int *)calloc(N, sizeof(*proposed_to));
int *proposal_to = new int[N];
proposal_to = (int *)calloc(N, sizeof(*proposal_to));
std::replace(proposal_to, proposal_to + N, 0, N);
int* reduced_size = new int[N];
reduced_size = (int*)calloc(N, sizeof(*reduced_size));
std::replace(reduced_size, reduced_size + N, 0, N-1);
int* second_choice = new int[N];
second_choice = (int*)calloc(N, sizeof(*second_choice));
std::replace(second_choice, second_choice + N, 0, N);
int* last_choice = new int[N];
last_choice = (int*)calloc(N, sizeof(*last_choice));
std::replace(last_choice, last_choice + N, 0, N);
int* rotations = new int[N * N];
rotations = (int*)calloc(N * N, sizeof(*rotations));
std::replace(rotations, rotations + N * N, 0, N);
int* C = new int[N * N];
C = (int*)calloc(N * N, sizeof(*C));
std::replace(C, C + N * N, 0, N);
int *matching = new int[N];
matching = (int *)calloc(N, sizeof(*matching));
cudaMalloc(&gpu_preference_lists, N * N * sizeof(int));
cudaMalloc(&gpu_proposal_to, N * sizeof(int));
cudaMalloc(&gpu_proposal_from, N * N * sizeof(int));
cudaMalloc(&gpu_proposed_to, N * sizeof(int));
cudaMalloc(&gpu_matching, N * sizeof(int));
cudaMalloc(&gpu_reduced_size, N * sizeof(int));
cudaMalloc(&gpu_second_choice, N * sizeof(int));
cudaMalloc(&gpu_last_choice, N * sizeof(int));
cudaMalloc(&gpu_rotations, N * N * sizeof(int));
cudaMalloc(&gpu_C, N * N * sizeof(int));
bool stable_host;
bool no_match_host;
bool reduced_size_empty;
stable_host = false;
no_match_host = false;
cudaMemcpy(gpu_preference_lists, preference_lists, N * N * sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(gpu_proposal_to, proposal_to, N * sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(gpu_proposal_from, proposal_from, N * N * sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(gpu_proposed_to, proposed_to, N * sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(gpu_C, C, N * N * sizeof(int), cudaMemcpyHostToDevice);
int* rank;
cudaMalloc(&rank, N * N * sizeof(int));
cudaMemcpy(gpu_reduced_size, reduced_size, N * sizeof(int), cudaMemcpyHostToDevice);
get_rank <<<NUM_BLOCKS, NUM_THREADS >>> (gpu_preference_lists, N, rank, gpu_reduced_size);
while (!(stable_host) && !(no_match_host))
{
p1_proposal<<<1, NUM_THREADS>>> (gpu_preference_lists, gpu_proposal_to, gpu_proposed_to, N);
cudaDeviceSynchronize();
p1_rejection<<<1, NUM_THREADS>>> (gpu_preference_lists, gpu_proposal_to, gpu_proposal_from, gpu_proposed_to, N);
cudaDeviceSynchronize();
p1_accept<<<NUM_BLOCKS, NUM_THREADS>>> (gpu_proposal_from, gpu_proposal_to, gpu_C, rank, N);
cudaDeviceSynchronize();
p1_evaluate <<<1, NUM_THREADS>>> (gpu_proposal_from, gpu_proposal_to, N);
cudaMemcpyFromSymbol(&stable_host, stable, sizeof(stable_host), 0, cudaMemcpyDeviceToHost);
cudaMemcpyFromSymbol(&no_match_host, no_match, sizeof(no_match_host), 0, cudaMemcpyDeviceToHost);
}
if (!no_match_host)
{
int* rank;
cudaMalloc(&rank, N * N * sizeof(int));
cudaMemcpy(gpu_reduced_size, reduced_size, N * sizeof(int), cudaMemcpyHostToDevice);
get_rank <<<NUM_BLOCKS, NUM_THREADS>>> (gpu_preference_lists, N, rank, gpu_reduced_size);
p1_remove <<<NUM_BLOCKS, NUM_THREADS>>> (gpu_preference_lists, gpu_proposal_from, rank, N);
cudaDeviceSynchronize();
cudaMemcpy(gpu_reduced_size, reduced_size, N * sizeof(int), cudaMemcpyHostToDevice);
p1_shift <<<1, NUM_THREADS>>> (gpu_preference_lists, gpu_reduced_size, N);
cudaDeviceSynchronize();
cudaMemcpyFromSymbol(&no_match_host, no_match, sizeof(no_match_host), 0, cudaMemcpyDeviceToHost);
p2_getSecondLastChoice <<<NUM_BLOCKS, NUM_THREADS>>> (gpu_preference_lists, gpu_last_choice, gpu_second_choice, gpu_reduced_size, N);
cudaDeviceSynchronize();
}
stable_host = false;
// get rotations
while (!(stable_host) && !(no_match_host))
{
stable_host = true;
for (int i = 0; i < N; i++)
{
cudaMemcpy(reduced_size, gpu_reduced_size, N * sizeof(int), cudaMemcpyDeviceToHost);
cudaMemcpy(second_choice, gpu_second_choice, N * sizeof(int), cudaMemcpyDeviceToHost);
cudaMemcpy(last_choice, gpu_last_choice, N * sizeof(int), cudaMemcpyDeviceToHost);
rotations = (int*)calloc(N * N, sizeof(*rotations));
std::replace(rotations, rotations + N * N, 0, N);
if (reduced_size[i] > 1)
{
stable_host = false;
int counter = 0;
bool cycle_complete = false;
rotations[counter] = second_choice[i];
counter++;
while (!cycle_complete)
{
if (counter % 2 == 0)
{
rotations[counter] = second_choice[rotations[counter-1]];
}
else
{
rotations[counter] = last_choice[rotations[counter-1]];
}
if (rotations[counter] == i)
{
cycle_complete = true;
}
counter++;
}
int* rank;
int num_threads_counter;
if (counter % 32)
{
num_threads_counter = counter + (32 - counter % 32);
}
else
{
num_threads_counter = counter;
}
cudaMalloc(&rank, N * N * sizeof(int));
cudaMemcpy(gpu_rotations, rotations, N * N * sizeof(int), cudaMemcpyHostToDevice);
get_rank <<<NUM_BLOCKS, NUM_THREADS>>> (gpu_preference_lists, N, rank, gpu_reduced_size);
cudaDeviceSynchronize();
p2_remove <<<1, num_threads_counter>>> (gpu_preference_lists, gpu_reduced_size, gpu_rotations, N, rank, counter);
cudaDeviceSynchronize();
p1_shift <<<1, NUM_THREADS>>> (gpu_preference_lists, gpu_reduced_size, N);
cudaDeviceSynchronize();
p2_getSecondLastChoice <<<NUM_BLOCKS, NUM_THREADS>>> (gpu_preference_lists, gpu_last_choice, gpu_second_choice, gpu_reduced_size, N);
cudaDeviceSynchronize();
cudaMemcpyFromSymbol(&no_match_host, no_match, sizeof(no_match_host), 0, cudaMemcpyDeviceToHost);
}
}
}
reduced_size_empty = false;
get_reduced_sizes <<<1, NUM_THREADS>>> (gpu_reduced_size, N);
cudaMemcpyFromSymbol(&reduced_size_empty, gpu_reduced_size_empty, sizeof(reduced_size_empty), 0, cudaMemcpyDeviceToHost);
cudaMemcpy(gpu_matching, matching, N * sizeof(int), cudaMemcpyHostToDevice);
if (no_match_host || reduced_size_empty)
{
fill_matching_zeros <<<1, NUM_THREADS>>> (gpu_matching, N);
}
else
{
fill_matching <<<1, NUM_THREADS>>> (gpu_preference_lists, gpu_matching, N);
}
cudaDeviceSynchronize();
cudaMemcpy(matching, gpu_matching, N * sizeof(int), cudaMemcpyDeviceToHost);
std::vector<int> matching_vector(matching, matching + N);
cudaFree(gpu_preference_lists);
cudaFree(gpu_proposal_to);
cudaFree(gpu_proposal_from);
cudaFree(gpu_proposed_to);
cudaFree(gpu_matching);
cudaFree(gpu_reduced_size);
cudaFree(gpu_second_choice);
cudaFree(gpu_last_choice);
cudaFree(gpu_rotations);
return matching_vector;
}
int main()
{
// 2d vector for the preference lists
std::vector<std::vector<int>> preference_lists;
int N = 0;
std::vector<int> matching;
// input file
std::ifstream f("inp.txt");
// get line
std::string line;
// while another line to get
while (std::getline(f, line))
{
// inner vector
std::vector<int> row;
std::stringstream ss(line);
std::string data;
// numbers are separated by commas
while (std::getline(ss, data, ','))
{
// put numbers in vector
row.push_back(std::stoi(data));
}
// put vector in 2d vector
preference_lists.push_back(row);
N++;
}
matching = stable_roommate(preference_lists, N);
// output to file
std::fstream file;
file.open("outp_p.txt", std::ios::out);
// if all 0s, no matches. fill with zeros
if (std::adjacent_find(matching.begin(), matching.end(), std::not_equal_to<>()) == matching.end())
{
// print results to text file
file << "NULL" << "\n";
}
else
{
for (int i = 0; i < matching.size(); i++)
{
file << matching[i] << "\n";
}
}
file.close();
return 0;
} |
20,324 |
/*
Group Members : Jose Garcia Kameron Bush
Collatz code for CS 4380 / CS 5351
Copyright (c) 2019 Texas State University. All rights reserved.
Redistribution in source or binary form, with or without modification,
is *not* permitted. Use in source and binary forms, with or without
modification, is only permitted for academic use in CS 4380 or CS 5351
at Texas State University.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Author: Martin Burtscher
*/
#include <cstdio>
#include <algorithm>
#include <cuda.h>
#include <sys/time.h>
static const int ThreadsPerBlock = 512;
static __global__ void collatz(const long upper, int const maxlen[])
{
// compute sequence lengths
//*********has to be only odds*************
const int i = (threadIdx.x + blockIdx.x * (long)blockDim.x) * 2 + 1;
long val = i;
int len = 1;
while (val != 1) {
len++;
if ((val % 2) == 0) {
val = val / 2; // even
} else {
val = 3 * val + 1; // odd
}
}
int * temp = (int *)maxlen[0];
if (len > *temp) { //***********check if pointer correct********
atomicMax(temp, len);
int temp2 = *temp;
maxlen = &temp2;
}
}
static void CheckCuda()
{
cudaError_t e;
cudaDeviceSynchronize();
if (cudaSuccess != (e = cudaGetLastError())) {
fprintf(stderr, "CUDA error %d: %s\n", e, cudaGetErrorString(e));
exit(-1);
}
}
int main(int argc, char *argv[])
{
printf("Collatz v1.2\n");
// check command line
if (argc != 2) {fprintf(stderr, "USAGE: %s upper_bound\n", argv[0]); exit(-1);}
const long upper = atol(argv[1]);
if (upper < 5) {fprintf(stderr, "ERROR: upper_bound must be at least 5\n"); exit(-1);}
if ((upper % 2) != 1) {fprintf(stderr, "ERROR: upper_bound must be an odd number\n"); exit(-1);}
printf("upper bound: %ld\n", upper);
//Initalize
int* const maxlen = new int [1];
maxlen[0] = 0;
// allocate vectors on GPU
int* d_maxlen;
if (cudaSuccess != cudaMalloc((void **)&d_maxlen, 1)) {fprintf(stderr, "ERROR: could not allocate memory\n"); exit(-1);}
// initialize vectors on GPU
if (cudaSuccess != cudaMemcpy(d_maxlen, maxlen, 1, cudaMemcpyHostToDevice)) {fprintf(stderr, "ERROR: copying to device failed\n"); exit(-1);}
// start time
timeval start, end;
gettimeofday(&start, NULL);
// execute timed code
//**********************see if number of threads correct
collatz<<<(upper + ThreadsPerBlock - 1) / ThreadsPerBlock, ThreadsPerBlock>>>(upper, d_maxlen);
cudaDeviceSynchronize();
// end time
gettimeofday(&end, NULL);
const double runtime = end.tv_sec - start.tv_sec + (end.tv_usec - start.tv_usec) / 1000000.0;
printf("compute time: %.4f s\n", runtime);
// get result from GPU
CheckCuda();
if (cudaSuccess != cudaMemcpy(maxlen, d_maxlen, 1, cudaMemcpyDeviceToHost)) {fprintf(stderr, "ERROR: copying from device failed\n"); exit(-1);}
// print result
printf("longest sequence: %d elements\n", maxlen[0]);
cudaFree(d_maxlen);
delete [] maxlen;
return 0;
}
|
20,325 | #include <stdio.h>
#include <stdlib.h>
#include <math.h>
// CUDA kernel. Each thread takes care of one element of c
__global__ void matAdd(double *a, double *b, double *c, int n)
{
// Get our global thread ID
int id = blockIdx.x*blockDim.x+threadIdx.x;
// Make sure we do not go out of bounds - should be the same as before
if (id < n)
c[id] = a[id] + b[id];
}
// CUDA Kernel. Each thread adds a whole row.
__global__ void matAddRow(double *a, double *b, double *c, int n){
// Get our global thread ID
int id = blockIdx.x*blockDim.x+threadIdx.x;
// loop over your row
int add = id*n;
int i;
for(i = 0; i < n; i++){
c[add + i] = a[add + i] + b[add + i];
}
}
// CUDA Kernel. Each thread adds a whole column.
__global__ void matAddCol(double *a, double *b, double *c, int n){
// Get our global thread ID
int id = blockIdx.x*blockDim.x+threadIdx.x;
// loop over column
int i;
for( i = 0; i < n ; i++){
c[i*n + id] = a[i*n + id] + b[i*n + id];
}
}
int main( int argc, char* argv[] )
{
// Size of matrices col * row
int n = 1024;
//how many data points there are 1024*1024
int nsquared = 1048576;
// Host input matrix
double *h_a;
double *h_b;
//Host output matrix
double *h_c;
// Device input matrix
double *d_a;
double *d_b;
//Device output matrix
double *d_c;
// Size, in bytes, of each matrix
size_t bytes = nsquared*sizeof(double);
// Allocate memory for each matrix on host
h_a = (double*)malloc(bytes);
h_b = (double*)malloc(bytes);
h_c = (double*)malloc(bytes);
// Allocate memory for each matrix on GPU
cudaMalloc(&d_a, bytes);
cudaMalloc(&d_b, bytes);
cudaMalloc(&d_c, bytes);
int row_id;
// Initialize matrix on host
// Simple initialize for now
for( row_id = 0; row_id < nsquared; row_id++ ) {
h_a[row_id] = 4;
h_b[row_id] = 1;
}
//print input matrix
int col_id;
printf("INPUT A: \n");
for(row_id = 0; row_id < 5; row_id++){
for(col_id = 0; col_id < 5; col_id++){
printf("%d ", (int)h_a[n*row_id + col_id]);
}
printf("\n");
}
printf("INPUT A: \n");
for(row_id = 0; row_id < 5; row_id++){
for(col_id = 0; col_id < 5; col_id++){
printf("%d ", (int)h_b[n*row_id + col_id]);
}
printf("\n");
}
// Copy host matrices to device
cudaMemcpy( d_a, h_a, bytes, cudaMemcpyHostToDevice);
cudaMemcpy( d_b, h_b, bytes, cudaMemcpyHostToDevice);
int blockSize, gridSize;
// Number of threads in each thread block
blockSize = 1024;
// Number of thread blocks in grid 1024
gridSize = (int)ceil((float)nsquared/blockSize);
// Execute the kernel add each thread for one output
matAdd<<<gridSize, blockSize>>>(d_a, d_b, d_c, nsquared);
// Copy array back to host
cudaMemcpy( h_c, d_c, bytes, cudaMemcpyDeviceToHost );
// Sum up vector c and print result divided by n, this should equal 1 within error
//print out 5x5
printf("OUPUT C: \n");
for( row_id = 0; row_id < 5; row_id++){
for( col_id = 0; col_id < 5; col_id++){
printf("%d ", (int)h_c[n*row_id + col_id]);
}
printf("\n");
}
// redo gridSize
gridSize = (int)ceil((float)n/blockSize);
// Execute the kernel, each thread for one row -- don't need to reset memory since dA and dB should be 1
matAddRow<<<gridSize, blockSize>>>(d_a, d_b, d_c, n);
// Copy array back to host
cudaMemcpy( h_c, d_c, bytes, cudaMemcpyDeviceToHost );
// execute other kernel for column -- same as above
matAddCol<<<gridSize, blockSize>>>(d_a, d_b, d_c, n);
// Copy array back to host
cudaMemcpy( h_c, d_c, bytes, cudaMemcpyDeviceToHost );
// Release device memory
cudaFree(d_a);
cudaFree(d_b);
cudaFree(d_c);
// Release host memory
free(h_a);
free(h_b);
free(h_c);
printf("done\n");
return 0;
} |
20,326 | /*
============================================================================
Name : cuda.c
Author :
Version :
Copyright : Your copyright notice
Description : Hello World in C, Ansi-style
============================================================================
*/
#include<stdio.h>
#include <cuda.h>
#include<cuda_runtime.h>
__global__ static void quicksort(int* values,int N) {
#define MAX_LEVELS 300
int pivot, L, R;
int idx = threadIdx.x + blockIdx.x * blockDim.x;
int start[MAX_LEVELS];
int end[MAX_LEVELS];
start[idx] = idx;
end[idx] = N - 1;
while (idx >= 0) {
L = start[idx];
R = end[idx];
if (L < R) {
pivot = values[L];
while (L < R) {
while (values[R] >= pivot && L < R)
R--;
if(L < R)
values[L++] = values[R];
while (values[L] < pivot && L < R)
L++;
if (L < R)
values[R--] = values[L];
}
values[L] = pivot;
start[idx + 1] = L + 1;
end[idx + 1] = end[idx];
end[idx++] = L;
if (end[idx] - start[idx] > end[idx - 1] - start[idx - 1]) {
// swap start[idx] and start[idx-1]
int tmp = start[idx];
start[idx] = start[idx - 1];
start[idx - 1] = tmp;
// swap end[idx] and end[idx-1]
tmp = end[idx];
end[idx] = end[idx - 1];
end[idx - 1] = tmp;
}
}
else
idx--;
}
}
int main(){
int x[20],size,i;
int *d_x,*d_size,*d_i;
printf("Enter size of the array: ");
scanf("%d",&size);
printf("Enter %d elements: ",size);
for(i=0;i<size;i++)
scanf("%d",&x[i]);
cudaMalloc((void **)&d_x,sizeof(int)*size);
cudaMalloc((void **)&d_size,sizeof(int));
cudaMalloc((void **)&d_i,sizeof(int));
cudaMemcpy(d_x, &x, sizeof( int)*size, cudaMemcpyHostToDevice);
cudaMemcpy(d_size, &size, sizeof( int), cudaMemcpyHostToDevice);
cudaMemcpy(d_i, &i, sizeof( int), cudaMemcpyHostToDevice);
quicksort<<<1,1,size>>>(d_x,size);
cudaMemcpy(x, d_x, sizeof(int)*size, cudaMemcpyDeviceToHost);
printf("Sorted elements: ");
for(i=0;i<size;i++)
printf(" %d",x[i]);
cudaFree(d_x);
cudaFree(d_size);
cudaFree(d_i);
return 0;
}
|
20,327 | /// @file
/// @copyright 2016- The Science and Technology Facilities Council (STFC)
/// @author Florent Lopez
#include <stdio.h>
#include <limits>
#include <cuda_runtime.h>
#include <cuda_runtime_api.h>
// #define BLOCK_SIZE 128 // Number of threads
// #define BLOCK_SIZE 16 // Number of threads
#define BLOCK_SIZE 32 // Number of threads
namespace /* anon */ {
template<typename T>
__global__ void
cu_calc_ld(
int m, int n,
T *const l, int ldl,
T *const d,
double *ld, int ldld) {
int bx = blockIdx.x;
int by = blockIdx.y;
int tx = threadIdx.x;
int ty = threadIdx.y;
// if (tx == 0 && ty == 0)
// printf("[cu_calc_ld] m = %d, n = %d\n", m, n);
for (int col = ty + by * blockDim.y; col < n; col += blockDim.y * gridDim.y) {
// Check if we are halfway trhough a 2x2 pivot
// if (col+1 < n && !std::isfinite(d[2*col]))
// continue;
if((col+1==n || std::isfinite(d[2*col+2])) && std::isfinite(d[2*col])) {
// 1x1 pivot
T d11 = d[2*col];
if(d11 != 0.0) d11 = 1/d11;
for(int row = tx + bx * blockDim.x; row < m; row += blockDim.x * gridDim.x)
ld[col*ldld+row] = d11 * l[col*ldl+row];
}
// else {
else if (std::isfinite(d[2*col])) {
// else if (d[2*col]==std::numeric_limits<T>::infinity()) {
// 2x2 pivot
T d11 = d[2*col];
T d21 = d[2*col+1];
T d22 = d[2*col+3];
// printf("[cu_calc_ld] d11 = %f, d21 = %f, d22 = %f\n", d11, d21, d22);
T det = d11*d22 - d21*d21;
d11 = d11/det;
d21 = d21/det;
d22 = d22/det;
for(int row = tx + bx * blockDim.x; row < m; row += blockDim.x * gridDim.x) {
T a1, a2;
a1 = l[col*ldl+row];
a2 = l[(col+1)*ldl+row];
ld[col*ldld+row] = d22*a1 - d21*a2;
ld[(col+1)*ldld+row] = -d21*a1 + d11*a2;
}
}
}
}
// template<typename T>
// __global__ void
// cu_calc_ld(
// int m,
// int n,
// T *const l, int ldl,
// T *const d,
// double *ld, int ldld) {
// // printf("[cu_calc_ld] blockIdx.x = %d\n", blockIdx.x);
// for (int col = 0; col < n; ) {
// if(col+1==n || std::isfinite(d[2*col+2])) {
// // 1x1 pivot
// // printf("[cu_calc_ld] 1x1\n");
// T d11 = d[2*col];
// if(d11 != 0.0) d11 = 1/d11;
// for(int row = threadIdx.x + blockIdx.x * blockDim.x; row < m; row += blockDim.x * gridDim.x)
// ld[col*ldld+row] = d11 * l[col*ldl+row];
// col++;
// }
// else {
// // 2x2 pivot
// // printf("[cu_calc_ld] 2x2\n");
// T d11 = d[2*col];
// T d21 = d[2*col+1];
// T d22 = d[2*col+3];
// // printf("[cu_calc_ld] d11 = %f, d21 = %f, d22 = %f\n", d11, d21, d22);
// T det = d11*d22 - d21*d21;
// d11 = d11/det;
// d21 = d21/det;
// d22 = d22/det;
// for(int row = threadIdx.x + blockIdx.x * blockDim.x; row < m; row += blockDim.x * gridDim.x) {
// T a1, a2;
// a1 = l[col*ldl+row];
// a2 = l[(col+1)*ldl+row];
// ld[col*ldld+row] = d22*a1 - d21*a2;
// ld[(col+1)*ldld+row] = -d21*a1 + d11*a2;
// }
// col += 2;
// }
// }
// }
} // end of anon namespace
namespace sylver {
namespace spldlt {
namespace gpu {
// template<typename T>
void calc_ld(
const cudaStream_t stream,
int m, int n,
double *const l, int ldl,
double *const d,
double *ld, int ldld
) {
dim3 threads(BLOCK_SIZE, BLOCK_SIZE);
dim3 grid((m + threads.x - 1) / threads.x, (n + threads.y -1) / threads.y);
cu_calc_ld
<double>
<<<grid, threads, 0, stream>>>
(m, n, l, ldl, d, ld, ldld);
}
}}} // End of namespace sylver::spldlt::gpu
|
20,328 | float h_A[]= {
0.6268283168399733, 0.5820438295956624, 0.7155419277465724, 0.6529364371559196, 0.9515301876238671, 0.5313043871151091, 0.6366937516486788, 0.5752237649250644, 0.6688301026236152, 0.7924564403991801, 0.7127780398297493, 0.8825410410557307, 0.7795304387612063, 0.5831883604271031, 0.7635014054558178, 0.5845028026419947, 0.9769193131500157, 0.5986569239478308, 0.9771872651135183, 0.5706982738878061, 0.5655504243511911, 0.9787784925351168, 0.7255151576091641, 0.504539561798399, 0.9189874417248736, 0.5868629115070443, 0.8464846089722025, 0.6129727880245557, 0.673414788248178, 0.9565603941768499, 0.6061016736060079, 0.7473135252864294, 0.6780880447591489, 0.753648536559429, 0.7061132839441507, 0.7768473632688374, 0.6069769413118016, 0.9264556466239561, 0.6065909564219512, 0.5077259046887153, 0.6181178904761713, 0.7426904423540224, 0.6870746981083349, 0.727992515672684, 0.5755021478174948, 0.7704955029109959, 0.8290585808986233, 0.7172770202136067, 0.8670126493379693, 0.6416605666808382, 0.7361344572399395, 0.8857544951946521, 0.894856029489604, 0.8041028419486409, 0.6414337678105697, 0.6896067553920224, 0.9381048029553873, 0.9983054138582781, 0.8221118023513856, 0.6782056810317401, 0.9348831435705958, 0.7772925849973444, 0.7242789674163963, 0.7671497769240274, 0.5929843214942244, 0.6266841390064901, 0.8721625803242479, 0.7394670229557045, 0.6733289999373473, 0.9857059898251795, 0.7419575787943071, 0.7611675479694354, 0.863407859426127, 0.7968135849999325, 0.5424049959709002, 0.6565173493113797, 0.9527903067093388, 0.8100655442379351, 0.5307470087675424, 0.9357281596482208, 0.9339681888221909, 0.6212402131595047, 0.8165316552621937, 0.5945191102978719, 0.5676324757227449, 0.8325496746166331, 0.8531059046428082, 0.8421314645571942, 0.515275023873797, 0.6420499650035507, 0.7596131563702928, 0.904692652058516, 0.9811611836375086, 0.5846138112658954, 0.623058994918902, 0.6257138334934635, 0.678412062105248, 0.9923598133201186, 0.5459650340358795, 0.6188969365850279, 0.6143977508549578, 0.6618754978867776, 0.8495101668480587, 0.7451017724712115, 0.8813852677028532, 0.5257665846970615, 0.519972727073298, 0.74664671528845, 0.8365206256549966, 0.9088600598871681, 0.9725335805569907, 0.9786312434940365, 0.8845018563257189, 0.7117619122982692, 0.9179418769665793, 0.7089459582165598, 0.8394228395637928, 0.8656050148514266, 0.5795181176031429, 0.872941094542371, 0.813666099172177, 0.7187804943021978, 0.8428308952479763, 0.864977538111175, 0.8848047194537303, 0.8215300842996032, 0.7180183401544314, 0.934509713179313, 0.578376160183924, 0.636159568667387, 0.901692427918481, 0.7872913105127601, 0.6250933904199936, 0.8597790547752882, 0.5588034225265055, 0.5710796269832461, 0.6104722755204925, 0.6394758876102078, 0.8758452823985676, 0.882999384074572, 0.7866775601381741, 0.6444402511801853, 0.6818587936716349, 0.9688860514305309, 0.6882358922200469, 0.7387648701546805, 0.8453672387134, 0.9482234004191339, 0.646858408319565, 0.6106777876888348, 0.9713280022251882, 0.8881580442994076, 0.8550287863676509, 0.6927927884682396, 0.5074304183364535, 0.9508587391976466, 0.9107054137659165, 0.819894464739874, 0.6960616007626284, 0.555150372492861, 0.9525837199849332, 0.7577596623798046, 0.8553268422390026, 0.6194396236366896, 0.703398669771603, 0.6107614040563882, 0.6505937825843336, 0.7090831484697298, 0.9413149526045425, 0.5215286288684062, 0.9915167686290243, 0.7367772455963502, 0.749227013762219, 0.6340712557516281, 0.7121364776402821, 0.904873684331938, 0.8955862812361157, 0.5625442351003411, 0.5513608750534146, 0.9835646162774531, 0.757335055275124, 0.7436221510095263, 0.6219419554463248, 0.8096442036995821, 0.5120952733336708, 0.7640231861211313, 0.9197960081468775, 0.976513573434648, 0.6629419787766371, 0.6502391506806318, 0.6414122453775282, 0.636461807374565, 0.9372635801713765, 0.657210022185293, 0.7009281525164495, 0.6754206470048036, 0.9737772413801391, 0.7302082515916004, 0.6282996333895543, 0.8101221647429779, 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0.6723248425356423, 0.6253482815050495, 0.6344455727675048, 0.6912684873172246, 0.7546635387134235, 0.6338174985472502, 0.766703420846848, 0.7535874704423106, 0.5853547275900401, 0.6495856336327949, 0.9441988566264903, 0.6002588618384952, 0.6666259387683705, 0.5603128430773621, 0.7599364097845875, 0.8558323320954593, 0.9649385633525303, 0.7960148090204131, 0.8558682714982162, 0.8363495864888146, 0.9495649403410122, 0.5626473560188052, 0.9140805858861714, 0.5907210234217728, 0.8692931696020769, 0.7813940212950713, 0.6643935236813541, 0.8054469291655701, 0.8931549490380551, 0.9254584191694466, 0.8984026991384332, 0.7715861335782999, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0};
int h_B[]= {
0, 2, 4, 6, 8, 10, 12, 14, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 47, 49, 51, 53, 56, 58, 60, 62, 64, 66, 68, 70, 73, 75, 77, 79, 81, 83, 85, 87, 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, 170, 172, 174, 176, 178, 180, 182, 184, 186, 188, 190, 192, 194, 196, 198, 200, 203, 205, 207, 209, 211, 213, 215, 217, 219, 221, 223, 225, 227, 229, 231, 233, 235, 237, 239, 241, 243, 245, 247, 249, 251, 253, 255, 257, 259, 261, 263, 265, 267, 269, 271, 273, 275, 277, 279, 281, 283, 285, 287, 289, 291, 293, 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, 486, 488, 491, 493, 495, 497, 499, 501, 503, 505, 507, 509, 512, 514, 516, 518, 521, 523, 525, 527, 529, 531, 533, 535, 537, 539, 541, 543, 545, 547, 549, 551, 553, 555, 557, 559, 561, 563, 565, 567, 569, 571, 573, 575, 577, 579, 581, 583, 585, 587, 589, 591, 593, 595, 597, 599, 601, 603, 605, 607, 609, 611, 614, 616, 618, 620, 622, 624, 626, 628, 630, 632, 634, 636, 638, 640, 642, 644, 648, 650, 652, 654, 656, 658, 660, 662, 664, 666, 668, 670, 672, 674, 676, 678, 682, 684, 686, 688, 691, 693, 695, 697, 699, 701, 703, 705, 707, 709, 711, 713, 715, 717, 719, 721, 723, 725, 727, 729, 731, 733, 735, 737, 739, 741, 743, 745, 747, 749, 751, 753, 755, 757, 759, 761, 763, 765, 767, 769, 771, 773, 775, 777, 779, 781, 783, 785, 787, 789, 791, 793, 795, 797, 799, 801, 803, 805, 807, 809, 811, 813, 817, 819, 821, 823, 825, 827, 829, 831, 833, 835, 837, 839, 841, 843, 845, 847, 849, 851, 853, 855, 857, 859, 861, 863, 865, 867, 869, 871, 873, 875, 877, 879, 881, 883, 885, 887, 889, 891, 893, 895, 897, 899, 901, 903, 905, 907, 909, 911, 913, 915, 917, 919, 921, 923, 925, 927, 929, 931, 933, 935, 937, 939, 941, 943, 947, 949, 951, 953, 955, 957, 959, 961, 963, 965, 967, 969, 972, 974, 976, 978, 980, 982, 984, 986, 988, 990, 992, 994, 996, 998, 1000, 1002, 1004, 1006, 1008, 1010, 1018, 1020, 1022, 1024, 1026, 1028, 1030, 1032, 1034, 1036, 1038, 1040, 1045, 1047, 1050, 1052, 1054, 1056, 1059, 1061, 1064, 1066, 1068, 1070, 1072, 1074, 1076, 1078, 1080, 1082, 1084, 1086, 1088, 1090, 1092, 1094, 1096, 1098, 1100, 1102, 1104, 1106, 1108, 1110, 1112, 1114, 1116, 1118, 1120, 1122, 1124, 1126, 1128, 1130, 1132, 1134, 1137, 1139, 1141, 1143, 1145, 1147, 1149, 1151, 1153, 1155, 1158, 1160, 1163, 1165, 1167, 1169, 1172, 1174, 1176, 1178, 1180, 1182, 1184, 1186, 1188, 1190, 1192, 1194, 1196, 1198, 1200, 1202, 1205, 1207, 1210, 1212, 1214, 1216, 1219, 1221, 1223, 1225, 1227, 1229, 1231, 1233, 1235, 1237, 1239, 1241, 1244, 1246, 1248, 1250, 1253, 1255, 1258, 1260, 1263, 1265, 1268, 1270, 1273, 1275, 1277, 1279, 1281, 1283, 1286, 1288, 1291, 1293, 1296, 1298, 1301, 1303, 1306, 1308, 1311, 1313, 1316, 1318, 1321, 1323, 1326, 1328, 1331, 1333, 1335, 1337, 1339, 1341, 1343, 1345, 1348, 1350, 1353, 1355, 1361, 1363, 1365, 1367, 1369, 1371, 1374, 1376, 1378, 1380, 1382, 1384, 1387, 1389, 1391, 1393, 1396, 1398, 1401, 1403, 1409, 1411, 1413, 1415, 1417, 1419, 1421, 1423, 1425, 1427, 1430, 1432, 1435, 1437, 1439, 1441, 1443, 1445, 1448, 1450, 1453, 1455, 1457, 1459, 1461, 1463, 1466, 1468, 1471, 1473, 1476, 1478, 1481, 1483, 1486, 1488, 1491, 1493, 1496, 1498, 1501, 1503, 1506, 1508, 1511, 1513, 1516, 1518, 1520, 1522, 1525, 1527, 1530, 1532, 1538, 1540, 1542, 1544, 1546, 1548, 1551, 1553, 1556, 1558, 1561, 1563, 1566, 1568, 1570, 1572, 1574, 1576, 1578, 1580, 1582, 1584, 1586, 1588, 1590, 1592, 1594, 1596, 1598, 1600, 1602, 1604, 1606, 1608, 1610, 1612, 1614, 1616, 1618, 1620, 1622, 1624, 1626, 1628, 1630, 1632, 1634, 1636, 1639, 1641, 1643, 1645, 1648, 1650, 1652, 1654, 1656, 1658, 1660, 1662, 1664, 1666, 1668, 1670, 1672, 1674, 1676, 1678, 1680, 1682, 1684, 1686, 1688, 1690, 1692, 1694, 1696, 1698, 1700, 1702, 1704, 1706, 1708, 1710, 1712, 1714, 1716, 1718, 1720, 1722, 1724, 1726, 1728, 1730, 1732, 1734, 1736, 1738, 1740, 1742, 1744, 1746, 1748, 1750, 1752, 1754, 1756, 1758, 1760, 1762, 1764, 1766, 1768, 1770, 1772, 1774, 1776, 1778, 1780, 1782, 1784, 1786, 1788, 1790, 1792, 1794, 1796, 1798, 1800, 1802, 1804, 1806, 1809, 1811, 1813, 1815, 1817, 1819, 1821, 1823, 1825, 1827, 1829, 1831, 1833, 1835, 1839, 1841, 1843, 1845, 1847, 1849, 1851, 1853, 1855, 1857, 1859, 1861, 1863, 1865, 1867, 1869, 1871, 1873, 1875, 1877, 1880, 1882, 1884, 1886, 1888, 1890, 1893, 1895, 1898, 1900, 1903, 1905, 1908, 1910, 1912, 1914, 1917, 1919, 1921, 1923, 1926, 1928, 1932, 1934, 1936, 1938, 1940, 1942, 1944, 1946, 1948, 1950, 1952, 1954, 1956, 1958, 1960, 1962, 1964, 1966, 1968, 1970, 1973, 1975, 1978, 1980, 1983, 1985, 1988, 1990, 1993, 1995, 1997, 1999, 2002, 2004, 2007, 2009, 2014, 2016, 2019, 2021, 2024, 2026, 2029, 2031, 2034, 2036, 2039, 2041, 2044, 2046, 2048, 2050, 2052, 2054, 2057, 2059, 2062, 2064, 2066, 2068, 2071, 2073, 2075, 2077, 2079, 2081, 2083, 2085, 2087, 2089, 2092, 2094, 2099, 2101, 2104, 2106, 2109, 2111, 2113, 2115, 2117, 2119, 2121, 2123, 2125, 2127, 2129, 2131, 2133, 2135, 2137, 2139, 2141, 2143, 2145, 2147, 2149, 2151, 2153, 2155, 2157, 2159, 2161, 2163, 2165, 2167, 2170, 2172, 2174, 2176, 2179, 2181, 2184, 2186, 2192, 2194, 2196, 2198, 2200, 2202, 2205, 2207, 2210, 2212, 2215, 2217, 2220, 2222, 2224, 2226, 2228, 2230, 2233, 2235, 2238, 2240, 2243, 2245, 2248, 2250, 2253, 2255, 2262, 2264, 2266, 2268, 2270, 2272, 2274, 2276, 2279, 2281, 2284, 2286, 2292, 2294, 2296, 2298, 2301, 2303, 2306, 2308, 2317, 2319, 2322, 2324, 2327, 2329, 2332, 2334, 2337, 2339, 2343, 2345, 2347, 2349, 2352, 2354, 2356, 2358, 2360, 2362, 2364, 2366, 2368, 2370, 2372, 2374, 2376, 2378, 2380, 2382, 2384, 2386, 2388, 2390, 2392, 2394, 2396, 2398, 2400, 2402, 2404, 2406, 2408, 2410, 2412, 2414, 2416, 2418, 2420, 2422, 2424, 2426, 2428, 2430, 2432, 2434, 2436, 2438, 2441, 2443, 2445, 2447, 2450, 2452, 2454, 2456, 2458, 2460, 2462, 2464, 2466, 2468, 2470, 2472, 2474, 2476, 2479, 2481, 2483, 2485, 2487, 2489, 2491, 2493, 2495, 2497, 2499, 2501, 2503, 2505, 2507, 2509, 2512, 2514, 2516, 2518, 2520, 2522, 2524, 2526, 2528, 2530, 2533, 2535, 2537, 2539, 2541, 2543, 2545, 2547, 2549, 2551, 2553, 2555, 2557, 2559, 2561, 2563, 2566, 2568, 2570, 2572, 2574, 2576, 2578, 2580, 2582, 2584, 2586, 2588, 2590, 2592, 2594, 2596, 2598, 2600, 2602, 2604, 2606, 2608, 2610, 2612, 2614, 2616, 2618, 2620, 2622, 2624, 2626, 2628, 2630, 2632, 2634, 2636, 2638, 2640, 2642, 2644, 2646, 2648, 2650, 2652, 2654, 2656, 2658, 2660, 2662, 2664, 2666, 2668, 2670, 2672, 2674, 2676, 2678, 2680, 2682, 2684, 2686, 2688, 2690, 2692, 2694, 2696, 2699, 2701, 2703, 2705, 2708, 2710, 2712, 2714, 2716, 2718, 2720, 2722, 2724, 2726, 2729, 2731, 2734, 2736, 2738, 2740, 2742, 2744, 2746, 2748, 2750, 2752, 2754, 2756, 2759, 2761, 2763, 2765, 2767, 2769, 2771, 2773, 2776, 2778, 2780, 2782, 2785, 2787, 2789, 2791, 2793, 2795, 2797, 2799, 2801, 2803, 2805, 2807, 2810, 2812, 2814, 2816, 2819, 2821, 2823, 2825, 2827, 2829, 2831, 2833, 2835, 2837, 2840, 2842, 2844, 2846, 2848, 2850, 2852, 2854, 2856, 2858, 2861, 2863, 2865, 2867, 2869, 2871, 2873, 2875, 2877, 2879, 2881, 2883, 2885, 2887, 2889, 2891, 2893, 2895, 2898, 2900, 2903, 2905, 2908, 2910, 2913, 2915, 2917, 2919, 2921, 2923, 2926, 2928, 2931, 2933, 2935, 2937, 2940, 2942, 2945, 2947, 2950, 2952, 2954, 2956, 2958, 2960, 2963, 2965, 2967, 2969, 2971, 2973, 2976, 2978, 2980, 2982, 2984, 2986, 2989, 2991, 2994, 2996, 2999, 3001, 3005, 3007, 3009, 3011, 3015, 3017, 3019, 3021, 3023, 3025, 3028, 3030, 3033, 3035, 3038, 3040, 3043, 3045, 3048, 3050, 3053, 3055, 3061, 3063, 3065, 3067, 3069, 3071, 3073, 3075, 3077, 3079, 3081, 3083, 3085, 3087, 3089, 3091, 3093, 3095, 3097, 3099, 3101, 3103, 3105, 3107, 3109, 3111, 3114, 3116, 3118, 3120, 3122, 3124, 3126, 3128, 3130, 3132, 3134, 3136, 3139, 3141, 3145, 3147, 3150, 3152, 3155, 3157, 3160, 3162, 3165, 3167, 3170, 3172, 3175, 3177, 3180, 3182, 3184, 3186, 3188, 3190, 3193, 3195, 3198, 3200, 3203, 3205, 3208, 3210, 3212, 3214, 3216, 3218, 3221, 3223, 3226, 3228, 3231, 3233, 3236, 3238, 3241, 3243, 3246, 3248, 3251, 3253, 3256, 3258, 3261, 3263, 3269, 3271, 3274, 3276, 3279, 3281, 3284, 3286, 3291, 3293, 3295, 3297, 3299, 3301, 3303, 3305, 3307, 3309, 3311, 3313, 3316, 3318, 3320, 3322, 3325, 3327, 3329, 3331, 3333, 3335, 3337, 3339, 3341, 3343, 3345, 3347, 3350, 3352, 3355, 3357, 3359, 3361, 3364, 3366, 3369, 3371, 3374, 3376, 3379, 3381, 3384, 3386, 3389, 3391, 3394, 3396, 3399, 3401, 3404, 3406, 3409, 3411, 3414, 3416, 3419, 3421, 3424, 3426, 3428, 3430, 3433, 3435, 3437, 3439, 3442, 3444, 3448, 3450, 3452, 3454, 3456, 3458, 3460, 3462, 3464, 3466, 3469, 3471, 3473, 3475, 3480, 3482, 3484, 3486, 3488, 3490, 3493, 3495, 3498, 3500, 3503, 3505, 3508, 3510, 3512, 3514, 3516, 3518, 3521, 3523, 3525, 3527, 3530, 3532, 3535, 3537, 3542, 3544, 3546, 3548, 3554, 3556, 3558, 3560, 3562, 3564, 3566, 3568, 3570, 3572, 3574, 3576, 3580, 3582, 3584, 3586, 3588, 3590, 3592, 3594, 3596, 3598, 3601, 3603, 3605, 3607, 3609, 3611, 3613, 3615, 3617, 3619, 3623, 3625, 3628, 3630, 3632, 3634, 3637, 3639, 3641, 3643, 3647, 3649, 3651, 3653, 3656, 3658, 3660, 3662, 3665, 3667, 3670, 3672, 3674, 3676, 3678, 3680, 3682, 3684, 3686, 3688, 3690, 3692, 3694, 3696, 3698, 3700, 3702, 3704, 3706, 3708, 3710, 3712, 3714, 3716, 3719, 3721, 3723, 3725, 3727, 3729, 3731, 3733, 3735, 3737, 3739, 3741, 3743, 3745, 3747, 3749, 3752, 3754, 3757, 3759, 3762, 3764, 3767, 3769, 3772, 3774, 3777, 3779, 3782, 3784, 3787, 3789, 3792, 3794, 3797, 3799, 3802, 3804, 3808, 3810, 3812, 3814, 3817, 3819, 3822, 3824, 3827, 3829, 3832, 3834, 3837, 3839, 3841, 3843, 3845, 3847, 3849, 3851, 3854, 3856, 3858, 3860, 3863, 3865, 3869, 3871, 3873, 3875, 3878, 3880, 3883, 3885, 3888, 3890, 3893, 3895, 3897, 3899, 3901, 3903, 3905, 3907, 3909, 3911, 3913, 3915, 3917, 3919, 3921, 3923, 3925, 3927, 3929, 3931, 3933, 3935, 3937, 3939, 3941, 3943, 3945, 3947, 3949, 3951, 3953, 3955, 3958, 3960, 3963, 3965, 3967, 3969, 3971, 3973, 3975, 3977, 3979, 3981, 3983, 3985, 3987, 3989, 3991, 3993, 3995, 3997, 3999, 4001, 4003, 4005, 4007, 4009, 4011, 4013, 4016, 4018, 4020, 4022, 4024, 4026, 4028, 4030, 4032, 4034, 4036, 4038, 4040, 4042, 4044, 4046, 4048, 4050, 4052, 4054, 4056, 4058, 4060, 4062, 4064, 4066, 4068, 4070, 4072, 4074, 4076, 4078, 4080, 4082, 4084, 4086, 4088, 4090, 4092, 4094, 4096, 4098, 4100, 4102, 4104, 4106, 4108, 4110, 4112, 4114, 4116, 4118, 4120, 4122, 4124, 4126, 4128, 4130, 4132, 4134, 4136, 4138, 4140, 4142, 4144, 4146, 4148, 4150, 4152, 4154, 4156, 4158, 4160, 4162, 4164, 4166, 4168, 4170, 4172, 4174, 4176, 4178, 4181, 4183, 4185, 4187, 4189, 4191, 4193, 4195, 4197, 4199, 4201, 4203, 4205, 4207, 4209, 4211, 4213, 4215, 4217, 4219, 4221, 4223, 4225, 4227, 4229, 4231, 4233, 4235, 4237, 4239, 4241, 4243, 4246, 4248, 4250, 4252, 4254, 4256, 4258, 4260, 4262, 4264, 4266, 4268, 4270, 4272, 4274, 4276, 4278, 4280, 4282, 4284, 4286, 4288, 4290, 4292, 4294, 4296, 4298, 4300, 4302, 4304, 4306, 4308, 4310, 4312, 4314, 4316, 4318, 4320, 4322, 4324, 4326, 4328, 4330, 4332, 4335, 4337, 4339, 4341, 4344, 4346, 4348, 4350, 4353, 4355, 4357, 4359, 4361, 4363, 4366, 4368, 4371, 4373, 4376, 4378, 4380, 4382, 4384, 4386, 4389, 4391, 4393, 4395, 4397, 4399, 4402, 4404, 4407, 4409, 4412, 4414, 4417, 4419, 4421, 4423, 4433, 4435, 4438, 4440, 4446, 4448, 4450, 4452, 4454, 4456, 4459, 4461, 4464, 4466, 4469, 4471, 4474, 4476, 4479, 4481, 4484, 4486, 4489, 4491, 4494, 4496, 4499, 4501, 4504, 4506, 4509, 4511, 4514, 4516, 4518, 4520, 4522, 4524, 4527, 4529, 4532, 4534, 4537, 4539, 4542, 4544, 4546, 4548, 4550, 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23390, 23467, 23386, 23390, 23575, 23572, 23467, 23386, 23390, 23575, 23572, 23401, 23516, 23426, 23520, 23518, 23418, 23528, 19549, 19548, 23418, 23499, 23497, 23503, 23501, 20992, 19530, 19542, 23516, 23426, 23520, 23518, 23428, 23432, 19549, 19548, 23442, 23446, 19549, 19548, 19540, 19539, 23544, 23586, 23480, 19543, 19542, 23516, 23514, 23520, 23481, 23485, 19545, 19544, 23528, 19549, 19548, 23499, 23497, 23503, 23501, 23516, 23514, 23520, 23518, 23528, 19549, 21155, 21153, 21155, 21153, 23586, 21198, 19566, 19565, 19564, 21198, 19566, 19565, 21213, 21214, 8430, 23731, 21222, 8485, 8485, 23718, 21233, 8430, 23652, 23662, 23674, 24673, 24674, 23675, 23685, 8430, 23700, 23706, 23718, 23737, 8485, 24105, 24103, 24060, 24122, 19722, 19721, 19720, 19719, 19722, 19721, 19720, 19719, 19722, 19721, 21490, 21488, 19736, 19735, 23775, 19814, 23788, 23794, 19814, 24437, 24435, 24443, 21490, 21488, 19736, 19735, 19736, 19735, 23856, 23854, 24075, 24079, 24095, 24097, 24096, 23864, 23862, 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228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 35330, 35331, 34634, 35334, 35336, 35340, 35341, 35342, 35328, 35344, 34896, 34899, 35351, 35352, 35356, 34634, 35359, 35361, 35364, 35365, 35366, 35368, 35369, 35375, 35376, 35328, 35381, 34634, 35155, 35387, 35390, 35162, 35393, 35396, 35398, 35168, 35407, 35354, 35411, 35412, 35348, 35347, 35413, 35354, 35355, 35414, 35415, 35416, 35379, 35374, 35373, 35372, 35371, 35379, 35417, 35418, 35419, 35379, 35380, 35420, 35421, 35422, 35174, 35173, 35400, 35175, 35403, 35176, 35404, 35423, 35424, 35425, 35188, 35186, 35410, 35426, 35427, 35428, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 35586, 35588, 35339, 35095, 35584, 35592, 35346, 35596, 35599, 35363, 35129, 35584, 35605, 35607, 35609, 35611, 35612, 35389, 35395, 35619, 35584, 35335, 35621, 35594, 35624, 35625, 35595, 35627, 35628, 35630, 35360, 35632, 35633, 35634, 35635, 35636, 35637, 35638, 35641, 35642, 35644, 35392, 35646, 35647, 35648, 35649, 35650, 35651, 35652, 35654, 35409, 35656, 35657, 35658, 35660, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 35333, 35843, 35844, 35358, 35850, 35851, 35383, 35860, 35861, 35842, 35846, 35863, 35866, 35623, 35353, 35868, 35870, 35849, 35370, 35872, 35874, 35377, 35877, 35378, 35879, 35386, 35857, 35881, 35858, 35882, 35884, 35886, 35888, 35890, 35893, 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, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 36096, 36105, 36098, 36106, 36107, 35865, 36110, 36111, 36099, 36113, 36101, 36114, 36115, 36117, 36119, 36120, 36102, 36121, 36122, 36124, 36103, 36125, 36128, 36129, 36130, 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, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 36352, 36354, 35862, 36356, 36357, 35867, 36360, 36362, 35871, 35876, 35878, 36368, 36372, 36373, 36375, 36123, 36123, 36123, 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, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 36611, 36609, 36623, 36104, 36359, 36615, 36624, 36112, 36367, 36118, 36364, 36620, 36625, 36369, 36376, 36374, 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, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 36865, 36867, 36868, 36864, 36869, 36871, 36872, 36873, 36874, 36875, 36877, 36878, 36879, 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, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 37120, 37123, 37124, 37126, 37129, 37131, 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, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 37376, 37122, 37378, 37379, 37380, 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, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 37381, 37635, 37633, 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, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 37888, 37889, 37890, 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, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 38144, 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, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 38400, 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, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255};
int h_C[]= {
1, 3, 5, 7, 9, 11, 13, 15, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 48, 50, 52, 54, 57, 59, 61, 63, 65, 67, 69, 71, 74, 76, 78, 80, 82, 84, 86, 88, 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, 171, 173, 175, 177, 179, 181, 183, 185, 187, 189, 191, 193, 195, 197, 199, 201, 204, 206, 208, 210, 212, 214, 216, 218, 220, 222, 224, 226, 228, 230, 232, 234, 236, 238, 240, 242, 244, 246, 248, 250, 252, 254, 256, 258, 260, 262, 264, 266, 268, 270, 272, 274, 276, 278, 280, 282, 284, 286, 288, 290, 292, 294, 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, 395, 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, 487, 489, 492, 494, 496, 498, 500, 502, 504, 506, 508, 510, 513, 515, 517, 519, 522, 524, 526, 528, 530, 532, 534, 536, 538, 540, 542, 544, 546, 548, 550, 552, 554, 556, 558, 560, 562, 564, 566, 568, 570, 572, 574, 576, 578, 580, 582, 584, 586, 588, 590, 592, 594, 596, 598, 600, 602, 604, 606, 608, 610, 612, 615, 617, 619, 621, 623, 625, 627, 629, 631, 633, 635, 637, 639, 641, 643, 645, 649, 651, 653, 655, 657, 659, 661, 663, 665, 667, 669, 671, 673, 675, 677, 679, 683, 685, 687, 689, 692, 694, 696, 698, 700, 702, 704, 706, 708, 710, 712, 714, 716, 718, 720, 722, 724, 726, 728, 730, 732, 734, 736, 738, 740, 742, 744, 746, 748, 750, 752, 754, 756, 758, 760, 762, 764, 766, 768, 770, 772, 774, 776, 778, 780, 782, 784, 786, 788, 790, 792, 794, 796, 798, 800, 802, 804, 806, 808, 810, 812, 814, 818, 820, 822, 824, 826, 828, 830, 832, 834, 836, 838, 840, 842, 844, 846, 848, 850, 852, 854, 856, 858, 860, 862, 864, 866, 868, 870, 872, 874, 876, 878, 880, 882, 884, 886, 888, 890, 892, 894, 896, 898, 900, 902, 904, 906, 908, 910, 912, 914, 916, 918, 920, 922, 924, 926, 928, 930, 932, 934, 936, 938, 940, 942, 944, 948, 950, 952, 954, 956, 958, 960, 962, 964, 966, 968, 970, 973, 975, 977, 979, 981, 983, 985, 987, 989, 991, 993, 995, 997, 999, 1001, 1003, 1005, 1007, 1009, 1011, 1019, 1021, 1023, 1025, 1027, 1029, 1031, 1033, 1035, 1037, 1039, 1041, 1046, 1048, 1051, 1053, 1055, 1057, 1060, 1062, 1065, 1067, 1069, 1071, 1073, 1075, 1077, 1079, 1081, 1083, 1085, 1087, 1089, 1091, 1093, 1095, 1097, 1099, 1101, 1103, 1105, 1107, 1109, 1111, 1113, 1115, 1117, 1119, 1121, 1123, 1125, 1127, 1129, 1131, 1133, 1135, 1138, 1140, 1142, 1144, 1146, 1148, 1150, 1152, 1154, 1156, 1159, 1161, 1164, 1166, 1168, 1170, 1173, 1175, 1177, 1179, 1181, 1183, 1185, 1187, 1189, 1191, 1193, 1195, 1197, 1199, 1201, 1203, 1206, 1208, 1211, 1213, 1215, 1217, 1220, 1222, 1224, 1226, 1228, 1230, 1232, 1234, 1236, 1238, 1240, 1242, 1245, 1247, 1249, 1251, 1254, 1256, 1259, 1261, 1264, 1266, 1269, 1271, 1274, 1276, 1278, 1280, 1282, 1284, 1287, 1289, 1292, 1294, 1297, 1299, 1302, 1304, 1307, 1309, 1312, 1314, 1317, 1319, 1322, 1324, 1327, 1329, 1332, 1334, 1336, 1338, 1340, 1342, 1344, 1346, 1349, 1351, 1354, 1356, 1362, 1364, 1366, 1368, 1370, 1372, 1375, 1377, 1379, 1381, 1383, 1385, 1388, 1390, 1392, 1394, 1397, 1399, 1402, 1404, 1410, 1412, 1414, 1416, 1418, 1420, 1422, 1424, 1426, 1428, 1431, 1433, 1436, 1438, 1440, 1442, 1444, 1446, 1449, 1451, 1454, 1456, 1458, 1460, 1462, 1464, 1467, 1469, 1472, 1474, 1477, 1479, 1482, 1484, 1487, 1489, 1492, 1494, 1497, 1499, 1502, 1504, 1507, 1509, 1512, 1514, 1517, 1519, 1521, 1523, 1526, 1528, 1531, 1533, 1539, 1541, 1543, 1545, 1547, 1549, 1552, 1554, 1557, 1559, 1562, 1564, 1567, 1569, 1571, 1573, 1575, 1577, 1579, 1581, 1583, 1585, 1587, 1589, 1591, 1593, 1595, 1597, 1599, 1601, 1603, 1605, 1607, 1609, 1611, 1613, 1615, 1617, 1619, 1621, 1623, 1625, 1627, 1629, 1631, 1633, 1635, 1637, 1640, 1642, 1644, 1646, 1649, 1651, 1653, 1655, 1657, 1659, 1661, 1663, 1665, 1667, 1669, 1671, 1673, 1675, 1677, 1679, 1681, 1683, 1685, 1687, 1689, 1691, 1693, 1695, 1697, 1699, 1701, 1703, 1705, 1707, 1709, 1711, 1713, 1715, 1717, 1719, 1721, 1723, 1725, 1727, 1729, 1731, 1733, 1735, 1737, 1739, 1741, 1743, 1745, 1747, 1749, 1751, 1753, 1755, 1757, 1759, 1761, 1763, 1765, 1767, 1769, 1771, 1773, 1775, 1777, 1779, 1781, 1783, 1785, 1787, 1789, 1791, 1793, 1795, 1797, 1799, 1801, 1803, 1805, 1807, 1810, 1812, 1814, 1816, 1818, 1820, 1822, 1824, 1826, 1828, 1830, 1832, 1834, 1836, 1840, 1842, 1844, 1846, 1848, 1850, 1852, 1854, 1856, 1858, 1860, 1862, 1864, 1866, 1868, 1870, 1872, 1874, 1876, 1878, 1881, 1883, 1885, 1887, 1889, 1891, 1894, 1896, 1899, 1901, 1904, 1906, 1909, 1911, 1913, 1915, 1918, 1920, 1922, 1924, 1927, 1929, 1933, 1935, 1937, 1939, 1941, 1943, 1945, 1947, 1949, 1951, 1953, 1955, 1957, 1959, 1961, 1963, 1965, 1967, 1969, 1971, 1974, 1976, 1979, 1981, 1984, 1986, 1989, 1991, 1994, 1996, 1998, 2000, 2003, 2005, 2008, 2010, 2015, 2017, 2020, 2022, 2025, 2027, 2030, 2032, 2035, 2037, 2040, 2042, 2045, 2047, 2049, 2051, 2053, 2055, 2058, 2060, 2063, 2065, 2067, 2069, 2072, 2074, 2076, 2078, 2080, 2082, 2084, 2086, 2088, 2090, 2093, 2095, 2100, 2102, 2105, 2107, 2110, 2112, 2114, 2116, 2118, 2120, 2122, 2124, 2126, 2128, 2130, 2132, 2134, 2136, 2138, 2140, 2142, 2144, 2146, 2148, 2150, 2152, 2154, 2156, 2158, 2160, 2162, 2164, 2166, 2168, 2171, 2173, 2175, 2177, 2180, 2182, 2185, 2187, 2193, 2195, 2197, 2199, 2201, 2203, 2206, 2208, 2211, 2213, 2216, 2218, 2221, 2223, 2225, 2227, 2229, 2231, 2234, 2236, 2239, 2241, 2244, 2246, 2249, 2251, 2254, 2256, 2263, 2265, 2267, 2269, 2271, 2273, 2275, 2277, 2280, 2282, 2285, 2287, 2293, 2295, 2297, 2299, 2302, 2304, 2307, 2309, 2318, 2320, 2323, 2325, 2328, 2330, 2333, 2335, 2338, 2340, 2344, 2346, 2348, 2350, 2353, 2355, 2357, 2359, 2361, 2363, 2365, 2367, 2369, 2371, 2373, 2375, 2377, 2379, 2381, 2383, 2385, 2387, 2389, 2391, 2393, 2395, 2397, 2399, 2401, 2403, 2405, 2407, 2409, 2411, 2413, 2415, 2417, 2419, 2421, 2423, 2425, 2427, 2429, 2431, 2433, 2435, 2437, 2439, 2442, 2444, 2446, 2448, 2451, 2453, 2455, 2457, 2459, 2461, 2463, 2465, 2467, 2469, 2471, 2473, 2475, 2477, 2480, 2482, 2484, 2486, 2488, 2490, 2492, 2494, 2496, 2498, 2500, 2502, 2504, 2506, 2508, 2510, 2513, 2515, 2517, 2519, 2521, 2523, 2525, 2527, 2529, 2531, 2534, 2536, 2538, 2540, 2542, 2544, 2546, 2548, 2550, 2552, 2554, 2556, 2558, 2560, 2562, 2564, 2567, 2569, 2571, 2573, 2575, 2577, 2579, 2581, 2583, 2585, 2587, 2589, 2591, 2593, 2595, 2597, 2599, 2601, 2603, 2605, 2607, 2609, 2611, 2613, 2615, 2617, 2619, 2621, 2623, 2625, 2627, 2629, 2631, 2633, 2635, 2637, 2639, 2641, 2643, 2645, 2647, 2649, 2651, 2653, 2655, 2657, 2659, 2661, 2663, 2665, 2667, 2669, 2671, 2673, 2675, 2677, 2679, 2681, 2683, 2685, 2687, 2689, 2691, 2693, 2695, 2697, 2700, 2702, 2704, 2706, 2709, 2711, 2713, 2715, 2717, 2719, 2721, 2723, 2725, 2727, 2730, 2732, 2735, 2737, 2739, 2741, 2743, 2745, 2747, 2749, 2751, 2753, 2755, 2757, 2760, 2762, 2764, 2766, 2768, 2770, 2772, 2774, 2777, 2779, 2781, 2783, 2786, 2788, 2790, 2792, 2794, 2796, 2798, 2800, 2802, 2804, 2806, 2808, 2811, 2813, 2815, 2817, 2820, 2822, 2824, 2826, 2828, 2830, 2832, 2834, 2836, 2838, 2841, 2843, 2845, 2847, 2849, 2851, 2853, 2855, 2857, 2859, 2862, 2864, 2866, 2868, 2870, 2872, 2874, 2876, 2878, 2880, 2882, 2884, 2886, 2888, 2890, 2892, 2894, 2896, 2899, 2901, 2904, 2906, 2909, 2911, 2914, 2916, 2918, 2920, 2922, 2924, 2927, 2929, 2932, 2934, 2936, 2938, 2941, 2943, 2946, 2948, 2951, 2953, 2955, 2957, 2959, 2961, 2964, 2966, 2968, 2970, 2972, 2974, 2977, 2979, 2981, 2983, 2985, 2987, 2990, 2992, 2995, 2997, 3000, 3002, 3006, 3008, 3010, 3012, 3016, 3018, 3020, 3022, 3024, 3026, 3029, 3031, 3034, 3036, 3039, 3041, 3044, 3046, 3049, 3051, 3054, 3056, 3062, 3064, 3066, 3068, 3070, 3072, 3074, 3076, 3078, 3080, 3082, 3084, 3086, 3088, 3090, 3092, 3094, 3096, 3098, 3100, 3102, 3104, 3106, 3108, 3110, 3112, 3115, 3117, 3119, 3121, 3123, 3125, 3127, 3129, 3131, 3133, 3135, 3137, 3140, 3142, 3146, 3148, 3151, 3153, 3156, 3158, 3161, 3163, 3166, 3168, 3171, 3173, 3176, 3178, 3181, 3183, 3185, 3187, 3189, 3191, 3194, 3196, 3199, 3201, 3204, 3206, 3209, 3211, 3213, 3215, 3217, 3219, 3222, 3224, 3227, 3229, 3232, 3234, 3237, 3239, 3242, 3244, 3247, 3249, 3252, 3254, 3257, 3259, 3262, 3264, 3270, 3272, 3275, 3277, 3280, 3282, 3285, 3287, 3292, 3294, 3296, 3298, 3300, 3302, 3304, 3306, 3308, 3310, 3312, 3314, 3317, 3319, 3321, 3323, 3326, 3328, 3330, 3332, 3334, 3336, 3338, 3340, 3342, 3344, 3346, 3348, 3351, 3353, 3356, 3358, 3360, 3362, 3365, 3367, 3370, 3372, 3375, 3377, 3380, 3382, 3385, 3387, 3390, 3392, 3395, 3397, 3400, 3402, 3405, 3407, 3410, 3412, 3415, 3417, 3420, 3422, 3425, 3427, 3429, 3431, 3434, 3436, 3438, 3440, 3443, 3445, 3449, 3451, 3453, 3455, 3457, 3459, 3461, 3463, 3465, 3467, 3470, 3472, 3474, 3476, 3481, 3483, 3485, 3487, 3489, 3491, 3494, 3496, 3499, 3501, 3504, 3506, 3509, 3511, 3513, 3515, 3517, 3519, 3522, 3524, 3526, 3528, 3531, 3533, 3536, 3538, 3543, 3545, 3547, 3549, 3555, 3557, 3559, 3561, 3563, 3565, 3567, 3569, 3571, 3573, 3575, 3577, 3581, 3583, 3585, 3587, 3589, 3591, 3593, 3595, 3597, 3599, 3602, 3604, 3606, 3608, 3610, 3612, 3614, 3616, 3618, 3620, 3624, 3626, 3629, 3631, 3633, 3635, 3638, 3640, 3642, 3644, 3648, 3650, 3652, 3654, 3657, 3659, 3661, 3663, 3666, 3668, 3671, 3673, 3675, 3677, 3679, 3681, 3683, 3685, 3687, 3689, 3691, 3693, 3695, 3697, 3699, 3701, 3703, 3705, 3707, 3709, 3711, 3713, 3715, 3717, 3720, 3722, 3724, 3726, 3728, 3730, 3732, 3734, 3736, 3738, 3740, 3742, 3744, 3746, 3748, 3750, 3753, 3755, 3758, 3760, 3763, 3765, 3768, 3770, 3773, 3775, 3778, 3780, 3783, 3785, 3788, 3790, 3793, 3795, 3798, 3800, 3803, 3805, 3809, 3811, 3813, 3815, 3818, 3820, 3823, 3825, 3828, 3830, 3833, 3835, 3838, 3840, 3842, 3844, 3846, 3848, 3850, 3852, 3855, 3857, 3859, 3861, 3864, 3866, 3870, 3872, 3874, 3876, 3879, 3881, 3884, 3886, 3889, 3891, 3894, 3896, 3898, 3900, 3902, 3904, 3906, 3908, 3910, 3912, 3914, 3916, 3918, 3920, 3922, 3924, 3926, 3928, 3930, 3932, 3934, 3936, 3938, 3940, 3942, 3944, 3946, 3948, 3950, 3952, 3954, 3956, 3959, 3961, 3964, 3966, 3968, 3970, 3972, 3974, 3976, 3978, 3980, 3982, 3984, 3986, 3988, 3990, 3992, 3994, 3996, 3998, 4000, 4002, 4004, 4006, 4008, 4010, 4012, 4014, 4017, 4019, 4021, 4023, 4025, 4027, 4029, 4031, 4033, 4035, 4037, 4039, 4041, 4043, 4045, 4047, 4049, 4051, 4053, 4055, 4057, 4059, 4061, 4063, 4065, 4067, 4069, 4071, 4073, 4075, 4077, 4079, 4081, 4083, 4085, 4087, 4089, 4091, 4093, 4095, 4097, 4099, 4101, 4103, 4105, 4107, 4109, 4111, 4113, 4115, 4117, 4119, 4121, 4123, 4125, 4127, 4129, 4131, 4133, 4135, 4137, 4139, 4141, 4143, 4145, 4147, 4149, 4151, 4153, 4155, 4157, 4159, 4161, 4163, 4165, 4167, 4169, 4171, 4173, 4175, 4177, 4179, 4182, 4184, 4186, 4188, 4190, 4192, 4194, 4196, 4198, 4200, 4202, 4204, 4206, 4208, 4210, 4212, 4214, 4216, 4218, 4220, 4222, 4224, 4226, 4228, 4230, 4232, 4234, 4236, 4238, 4240, 4242, 4244, 4247, 4249, 4251, 4253, 4255, 4257, 4259, 4261, 4263, 4265, 4267, 4269, 4271, 4273, 4275, 4277, 4279, 4281, 4283, 4285, 4287, 4289, 4291, 4293, 4295, 4297, 4299, 4301, 4303, 4305, 4307, 4309, 4311, 4313, 4315, 4317, 4319, 4321, 4323, 4325, 4327, 4329, 4331, 4333, 4336, 4338, 4340, 4342, 4345, 4347, 4349, 4351, 4354, 4356, 4358, 4360, 4362, 4364, 4367, 4369, 4372, 4374, 4377, 4379, 4381, 4383, 4385, 4387, 4390, 4392, 4394, 4396, 4398, 4400, 4403, 4405, 4408, 4410, 4413, 4415, 4418, 4420, 4422, 4424, 4434, 4436, 4439, 4441, 4447, 4449, 4451, 4453, 4455, 4457, 4460, 4462, 4465, 4467, 4470, 4472, 4475, 4477, 4480, 4482, 4485, 4487, 4490, 4492, 4495, 4497, 4500, 4502, 4505, 4507, 4510, 4512, 4515, 4517, 4519, 4521, 4523, 4525, 4528, 4530, 4533, 4535, 4538, 4540, 4543, 4545, 4547, 4549, 4551, 4553, 4555, 4557, 4559, 4561, 4563, 4565, 4567, 4569, 4571, 4573, 4575, 4577, 4579, 4581, 4583, 4585, 4587, 4589, 4591, 4593, 4595, 4597, 4599, 4601, 4603, 4605, 4608, 4610, 4612, 4614, 4616, 4618, 4620, 4622, 4625, 4627, 4629, 4631, 4633, 4635, 4637, 4639, 4643, 4645, 4647, 4649, 4652, 4654, 4657, 4659, 4665, 4667, 4670, 4672, 4678, 4680, 4683, 4685, 4691, 4693, 4695, 4697, 4700, 4702, 4704, 4706, 4710, 4712, 4714, 4716, 4718, 4720, 4722, 4724, 4726, 4728, 4730, 4732, 4735, 4737, 4739, 4741, 4743, 4745, 4747, 4749, 4753, 4755, 4757, 4759, 4761, 4763, 4765, 4767, 4769, 4771, 4773, 4775, 4779, 4781, 4783, 4785, 4788, 4790, 4793, 4795, 4801, 4803, 4806, 4808, 4811, 4813, 4816, 4818, 4820, 4822, 4824, 4826, 4829, 4831, 4833, 4835, 4837, 4839, 4842, 4844, 4847, 4849, 4852, 4854, 4857, 4859, 4861, 4863, 4865, 4867, 4870, 4872, 4874, 4876, 4878, 4880, 4882, 4884, 4886, 4888, 4891, 4893, 4896, 4898, 4901, 4903, 4905, 4907, 4909, 4911, 4913, 4915, 4917, 4919, 4921, 4923, 4925, 4927, 4929, 4931, 4933, 4935, 4937, 4939, 4941, 4943, 4945, 4947, 4949, 4951, 4953, 4955, 4957, 4959, 4962, 4964, 4966, 4968, 4970, 4972, 4974, 4976, 4978, 4980, 4982, 4984, 4987, 4989, 4991, 4993, 4995, 4997, 5000, 5002, 5005, 5007, 5010, 5012, 5018, 5020, 5023, 5025, 5028, 5030, 5032, 5034, 5036, 5038, 5041, 5043, 5049, 5051, 5054, 5056, 5058, 5060, 5062, 5064, 5066, 5068, 5070, 5072, 5074, 5076, 5078, 5080, 5082, 5084, 5086, 5088, 5090, 5092, 5094, 5096, 5098, 5100, 5102, 5104, 5106, 5108, 5110, 5112, 5115, 5117, 5121, 5123, 5125, 5127, 5129, 5131, 5134, 5136, 5139, 5141, 5144, 5146, 5149, 5151, 5153, 5155, 5157, 5159, 5162, 5164, 5166, 5168, 5170, 5172, 5175, 5177, 5180, 5182, 5185, 5187, 5190, 5192, 5194, 5196, 5199, 5201, 5203, 5205, 5209, 5211, 5213, 5215, 5218, 5220, 5222, 5224, 5227, 5229, 5231, 5233, 5236, 5238, 5241, 5243, 5245, 5247, 5249, 5251, 5253, 5255, 5257, 5259, 5261, 5263, 5265, 5267, 5269, 5271, 5273, 5275, 5277, 5279, 5281, 5283, 5286, 5288, 5290, 5292, 5295, 5297, 5299, 5301, 5303, 5305, 5307, 5309, 5312, 5314, 5316, 5318, 5321, 5323, 5325, 5327, 5329, 5331, 5333, 5335, 5337, 5339, 5341, 5343, 5346, 5348, 5350, 5352, 5354, 5356, 5358, 5360, 5362, 5364, 5366, 5368, 5370, 5372, 5375, 5377, 5379, 5381, 5383, 5385, 5388, 5390, 5393, 5395, 5398, 5400, 5403, 5405, 5408, 5410, 5413, 5415, 5418, 5420, 5423, 5425, 5428, 5430, 5433, 5435, 5438, 5440, 5443, 5445, 5448, 5450, 5453, 5455, 5458, 5460, 5462, 5464, 5466, 5468, 5470, 5472, 5474, 5476, 5478, 5480, 5483, 5485, 5487, 5489, 5491, 5493, 5496, 5498, 5501, 5503, 5507, 5509, 5512, 5514, 5517, 5519, 5521, 5523, 5525, 5527, 5530, 5532, 5535, 5537, 5540, 5542, 5545, 5547, 5550, 5552, 5554, 5556, 5558, 5560, 5563, 5565, 5568, 5570, 5572, 5574, 5576, 5578, 5580, 5582, 5585, 5587, 5589, 5591, 5594, 5596, 5598, 5600, 5602, 5604, 5606, 5608, 5610, 5612, 5614, 5616, 5618, 5620, 5622, 5624, 5626, 5628, 5630, 5632, 5634, 5636, 5638, 5640, 5642, 5644, 5646, 5648, 5650, 5652, 5654, 5656, 5658, 5660, 5662, 5664, 5666, 5668, 5670, 5672, 5674, 5676, 5678, 5680, 5682, 5684, 5686, 5688, 5690, 5692, 5694, 5696, 5698, 5700, 5702, 5704, 5706, 5708, 5710, 5712, 5714, 5716, 5718, 5720, 5722, 5724, 5726, 5728, 5730, 5732, 5734, 5736, 5738, 5740, 5742, 5744, 5746, 5748, 5750, 5752, 5754, 5756, 5758, 5760, 5762, 5764, 5766, 5768, 5770, 5772, 5774, 5776, 5778, 5780, 5782, 5784, 5786, 5788, 5790, 5792, 5794, 5796, 5798, 5800, 5802, 5804, 5806, 5808, 5810, 5812, 5814, 5816, 5818, 5820, 5822, 5824, 5827, 5829, 5831, 5833, 5836, 5838, 5840, 5842, 5844, 5846, 5848, 5850, 5852, 5854, 5856, 5858, 5860, 5862, 5864, 5866, 5868, 5870, 5872, 5874, 5876, 5878, 5880, 5882, 5885, 5887, 5890, 5892, 5894, 5896, 5899, 5901, 5903, 5905, 5908, 5910, 5912, 5914, 5916, 5918, 5920, 5922, 5924, 5926, 5928, 5930, 5933, 5935, 5938, 5940, 5942, 5944, 5946, 5948, 5950, 5952, 5954, 5956, 5958, 5960, 5962, 5964, 5966, 5968, 5970, 5972, 5974, 5976, 5978, 5980, 5982, 5984, 5987, 5989, 5991, 5993, 5995, 5997, 5999, 6001, 6003, 6005, 6008, 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101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 35840, 35591, 13415, 35848, 35604, 13463, 35855, 13517, 13552, 35841, 35845, 13557, 13562, 35864, 35847, 35629, 13571, 35601, 35852, 35873, 35875, 35853, 35640, 35854, 35643, 35856, 35613, 13594, 35616, 35883, 35885, 35887, 35653, 13607, 35659, 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, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 35585, 13553, 36097, 13556, 35622, 36108, 13565, 35869, 35598, 13572, 36100, 13575, 36116, 13581, 13586, 35880, 35610, 13592, 13593, 13595, 35859, 36126, 35889, 35891, 35894, 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, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 13551, 13554, 36355, 36109, 35626, 36358, 13570, 13573, 36363, 36365, 36366, 13591, 13596, 36127, 35892, 36370, 36370, 36370, 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, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 36612, 36353, 13615, 36608, 36613, 36361, 13620, 36614, 36618, 36617, 36616, 36371, 13626, 36619, 36622, 36621, 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, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 13614, 13616, 13617, 36610, 13619, 13621, 13622, 13623, 13624, 13625, 13627, 13628, 13629, 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, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 36866, 13618, 36870, 37127, 36876, 37132, 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, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 37121, 37377, 37125, 37128, 37130, 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, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 37636, 37634, 37632, 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, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 13630, 13631, 13632, 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, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 38145, 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, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 38146, 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, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255};
bool h_Op[]= {
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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#define THREADS_PER_BLOCK 256
#define BLOCKS_PER_GRID 1
#define SIZE_OF_IN 13824
#define SIZE_OF_AC 25088
__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[152*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];
__syncthreads();
for (int iter=0; iter< n_iter; iter++) {
R[i + 54*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 + 55*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 + 56*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 + 57*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 + 58*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 + 59*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 + 60*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 + 61*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 + 62*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 + 63*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 + 64*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 + 65*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 + 66*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 + 67*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 + 68*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]];
__syncthreads();
R[i + 69*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 + 70*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 + 71*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 + 72*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 + 73*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 + 74*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 + 75*t] = Op[i + 21*t] ? R[B[i + 21*t]] * R[C[i + 21*t]] : R[B[i + 21*t]] + R[C[i + 21*t]];
R[i + 76*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 + 77*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]];
__syncthreads();
R[i + 78*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 + 79*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 + 80*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 + 81*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 + 82*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 + 83*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 + 84*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 + 85*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 + 86*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 + 87*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]];
__syncthreads();
R[i + 88*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 + 89*t] = Op[i + 35*t] ? R[B[i + 35*t]] * R[C[i + 35*t]] : R[B[i + 35*t]] + R[C[i + 35*t]];
R[i + 90*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 + 91*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 + 92*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 + 93*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 + 94*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 + 95*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 + 96*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]];
__syncthreads();
R[i + 97*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 + 98*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 + 99*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 + 100*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 + 101*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]];
__syncthreads();
R[i + 102*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 + 103*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 + 104*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 + 105*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 + 106*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 + 107*t] = Op[i + 53*t] ? R[B[i + 53*t]] * R[C[i + 53*t]] : R[B[i + 53*t]] + R[C[i + 53*t]];
R[i + 108*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]];
__syncthreads();
R[i + 109*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 + 110*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 + 111*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 + 112*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 + 113*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 + 114*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]];
__syncthreads();
R[i + 115*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 + 116*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 + 117*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 + 118*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]];
__syncthreads();
R[i + 119*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 + 120*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 + 121*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 + 122*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]];
__syncthreads();
R[i + 123*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 + 124*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 + 125*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 + 126*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 + 127*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 + 128*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]];
__syncthreads();
R[i + 129*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 + 130*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]];
__syncthreads();
R[i + 131*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 + 132*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]];
__syncthreads();
R[i + 133*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]];
__syncthreads();
R[i + 134*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]];
__syncthreads();
R[i + 135*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]];
__syncthreads();
R[i + 136*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]];
__syncthreads();
R[i + 137*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]];
__syncthreads();
R[i + 138*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]];
__syncthreads();
R[i + 139*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 + 140*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]];
__syncthreads();
R[i + 141*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]];
__syncthreads();
R[i + 142*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]];
__syncthreads();
R[i + 143*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]];
__syncthreads();
R[i + 144*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]];
__syncthreads();
R[i + 145*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]];
__syncthreads();
R[i + 146*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]];
__syncthreads();
R[i + 147*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]];
__syncthreads();
R[i + 148*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]];
__syncthreads();
R[i + 149*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]];
__syncthreads();
R[i + 150*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]];
__syncthreads();
R[i + 151*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]];
if (i==0) { final += R[151*t]; }
__syncthreads();
}
if (i==0) { A[0]= final;}
}
|
20,329 | #include "includes.h"
__global__ void fillZero(int *c_red, int size)
{
int id = threadIdx.x + blockIdx.x * blockDim.x;
int stride = blockDim.x * gridDim.x;
for (int i = id; i < size; i+=stride)
{
c_red[i] = 0;
}
} |
20,330 | #include <stdio.h>
#include <stdlib.h>
#include <iostream>
#include <math.h>
using namespace std;
// Blocksize
#define BLOCKSIZE 1024
//*************************************************
// GLOBAL MEMORY VERSION OF THE ALGORITHM
// ************************************************
__global__ void vectorNS(float *in, float *out, int n, int ex) {
int i = threadIdx.x + blockDim.x * blockIdx.x + 2;
int iB = i - 2;
if (iB < n) {
float Aim2 = in[i - 2];
float Aim1 = in[i - 1];
float Ai = in[i];
float Aip1 = in[i + 1];
float Aip2 = in[i + 2];
out[iB] = (pow(Aim2, ex) + 2.0 * pow(Aim1, ex) + pow(Ai, ex) - 3.0 * pow(Aip1, ex) + 5.0 * pow(Aip2, ex)) / 24.0;
}
}
//*************************************************
// TILING VERSION (USES SHARED MEMORY) OF THE ALGORITHM
// ************************************************
__global__ void vectorS(float *in, float *out, int n, int ex) {
int li = threadIdx.x + 2; //local index in shared memory vector
int gi = blockDim.x * blockIdx.x + threadIdx.x + 2; // global memory index
int lstart = 0;
__shared__ float s_in[BLOCKSIZE + 4]; //shared mem. vector
// Load Tile in shared memory
if (gi < n + 3) {
s_in[li] = in[gi];
}
if (threadIdx.x == 0) { // First Thread (in the current block)
s_in[lstart] = in[gi - 2];
s_in[lstart + 1] = in[gi - 1];
}
if ((gi >= n + 1) || (threadIdx.x == BLOCKSIZE - 1)) { // Last Block || Last Thread
s_in[li + 1] = in[gi + 1];
s_in[li + 2] = in[gi + 2];
}
__syncthreads();
int iB = gi - 2;
if (iB < n) {
float Aim2 = s_in[li - 2];
float Aim1 = s_in[li - 1];
float Ai = s_in[li];
float Aip1 = s_in[li + 1];
float Aip2 = s_in[li + 2];
out[iB] = (pow(Aim2, ex) + 2.0 * pow(Aim1, ex) + pow(Ai, ex) - 3.0 * pow(Aip1, ex) + 5.0 * pow(Aip2, ex)) / 24.0;
}
}
//**************************************************************************
// FIND MAX IN VECTOR
__global__ void reduceMax(float * V_in, float * V_out, const int N) {
extern __shared__ float sdata[];
int tid = threadIdx.x;
int mid = blockDim.x/2;
int i = blockIdx.x * (blockDim.x * 2) + threadIdx.x;
sdata[tid] = ((i < N) ? V_in[i] : -1);
sdata[tid + blockDim.x] = ((i + blockDim.x) < N ? V_in[i + blockDim.x] : -1);
__syncthreads();
for(int s = mid; s > 0; s >>= 1) {
if (tid < s) {
if(sdata[tid] < sdata[tid + s]) {
sdata[tid] = sdata[tid + s];
}
} else if((i + blockDim.x + s) < N) {
if(sdata[tid + mid] < sdata[tid + mid + s]) {
sdata[tid + mid] = sdata[tid + mid + s];
}
}
__syncthreads();
}
if (tid == 0) {
V_out[blockIdx.x * 2] = sdata[0];
} else if (tid == mid) {
V_out[(blockIdx.x * 2) + 1] = sdata[tid + mid];
}
}
//**************************************************************************
int main(int argc, char *argv[]) {
//******************************
//Get GPU information
int devID;
cudaDeviceProp props;
cudaError_t err;
err = cudaGetDevice(&devID);
if (err != cudaSuccess) {
cout << "ERRORRR" << endl;
}
cudaGetDeviceProperties(&props, devID);
printf("Device %d: \"%s\" with Compute %d.%d capability\n", devID, props.name, props.major, props.minor);
int N;
int ex;
if (argc != 3) {
cout << "Uso: transformacion Num_elementos Potencia " << endl;
return (0);
}
else {
N = atoi(argv[1]);
ex = atoi(argv[2]);
}
//* pointers to host memory */
float *A, *B;
//* Allocate arrays a, b and c on host*/
A = new float[N + 4];
B = new float[N];
float mx; // maximum of B
//* Initialize array A */
for (int i = 2; i < N + 2; i++)
A[i] = (float)(1 - (i % 100) * 0.001);
// Impose Boundary Conditions
A[0] = 0.0;
A[1] = 0.0;
A[N + 2] = 0.0;
A[N + 3] = 0.0;
//**************************
// GPU phase
//**************************
float *B_GPU_global = new float[N];
float *B_GPU_shared = new float[N];
int Nsize = N * sizeof(float);
int NsizeWithBound = (N + 4) * sizeof(float);
// Allocation in device mem
float *A_GPU = NULL;
err = cudaMalloc((void **)&A_GPU, NsizeWithBound);
if (err != cudaSuccess) {
cout << "ALLOCATION ERROR" << endl;
}
float *out = NULL;
err = cudaMalloc((void **)&out, Nsize);
if (err != cudaSuccess) {
cout << "ALLOCATION ERROR" << endl;
}
// Copy A values to device memory
err = cudaMemcpy(A_GPU, A, NsizeWithBound, cudaMemcpyHostToDevice);
if (err != cudaSuccess) {
cout << "GPU COPY ERROR" << endl;
}
int blocksPerGrid = (int)ceil((float)(N) / BLOCKSIZE);
// Shared memory
// Take initial time
cout << "Start GPU Shared" << endl;
cudaDeviceSynchronize();
double gst1 = clock();
int smemSizeVec = (BLOCKSIZE + 4)*sizeof(float);
cout << endl;
// ********* Kernel Launch ************************************
vectorS<<<blocksPerGrid, BLOCKSIZE, smemSizeVec>>>(A_GPU, out, N, ex);
// ************************************************************
cudaDeviceSynchronize();
double TgpuS = clock();
TgpuS = (TgpuS - gst1) / CLOCKS_PER_SEC;
cout << "End GPU shared" << endl;
err = cudaGetLastError();
if (err != cudaSuccess) {
fprintf(stderr, "Failed to launch kernel! %d \n", err);
exit(EXIT_FAILURE);
}
cudaMemcpy(B_GPU_shared, out, Nsize, cudaMemcpyDeviceToHost);
// Global memory
// Take initial time
cout << "Start GPU Global" << endl;
cudaDeviceSynchronize();
double ggt1 = clock();
cout << endl;
// ********* Kernel Launch ************************************
vectorNS<<<blocksPerGrid, BLOCKSIZE>>>(A_GPU, out, N, ex);
// ************************************************************
cudaDeviceSynchronize();
double TgpuG = clock();
TgpuG = (TgpuG - ggt1) / CLOCKS_PER_SEC;
cout << "End GPU global" << endl;
err = cudaGetLastError();
if (err != cudaSuccess) {
fprintf(stderr, "Failed to launch kernel! %d \n", err);
exit(EXIT_FAILURE);
}
cudaMemcpy(B_GPU_global, out, Nsize, cudaMemcpyDeviceToHost);
//**************************
// CPU phase
//**************************
cout << "Start CPU" << endl;
// Time measurement
double ct1 = clock();
float Ai, Aim1, Aim2, Aip1, Aip2;
// Compute B[i] and mx
for (int i = 2; i < N + 2; i++) {
const int iB = i - 2;
Aim2 = A[i - 2];
Aim1 = A[i - 1];
Ai = A[i];
Aip1 = A[i + 1];
Aip2 = A[i + 2];
B[iB] = (pow(Aim2, ex) + 2.0 * pow(Aim1, ex) + pow(Ai, ex) - 3.0 * pow(Aip1, ex) + 5.0 * pow(Aip2, ex)) / 24.0;
mx = (iB == 0) ? B[0] : max(B[iB], mx);
}
double Tcpu = clock();
Tcpu = (Tcpu - ct1) / CLOCKS_PER_SEC;
cout << "End CPU" << endl;
//**************************
// CPU-GPU comparison and error checking
//**************************
int passed = 1;
int i = 0;
while (passed && i < N) {
float diff = fabs(B[i] - B_GPU_global[i]);
if (diff > 0) {
passed = 0;
cout << endl << i << endl;
cout << "DIFF= " << diff << endl;
}
i++;
}
if (passed) {
cout << "PASSED TEST GLOBAL MEMORY!!!" << endl;
}
else {
cout << "ERROR IN TEST GLOBAL MEMORY!!!" << endl;
}
passed = 1;
i = 0;
while (passed && i < N) {
float diff = fabs(B[i] - B_GPU_shared[i]);
if (diff > 0) {
passed = 0;
cout << endl << i << endl;
cout << "DIFF= " << diff << endl;
}
i++;
}
if (passed) {
cout << "PASSED TEST SHARED MEMORY!!!" << endl;
}
else {
cout << "ERROR IN TEST SHARED MEMORY!!!" << endl;
}
// c_d Maximum computation on GPU
dim3 threadsPerBlock(BLOCKSIZE);
dim3 numBlocks( ceil ((float)(N / 2)/threadsPerBlock.x));
// Maximum vector on CPU
float * vmax;
vmax = (float*) malloc(2*numBlocks.x*sizeof(float));
// Maximum vector to be computed on GPU
float *vmax_d;
cudaMalloc ((void **) &vmax_d, sizeof(float)*2*numBlocks.x);
float smemSize = 2*threadsPerBlock.x*sizeof(float);
// Kernel launch to compute Minimum Vector
reduceMax<<<numBlocks, threadsPerBlock, smemSize>>>(out,vmax_d, N);
/* Copy data from device memory to host memory */
cudaMemcpy(vmax, vmax_d, 2*numBlocks.x*sizeof(float),cudaMemcpyDeviceToHost);
// Perform final reduction in CPU
float max_gpu = -1;
for (int i=0; i<numBlocks.x; i++) {
max_gpu =max(max_gpu,vmax[i]);
}
if (N < 16) {
for (int i = 0; i < N; i++) {
cout << "CPU[" << i << "] = " << B[i] << ", GPU[" << i << "] = " << B_GPU_global[i] << endl;
}
}
cout << "................................." << endl;
cout << "................................." << endl
<< "El valor máximo en B es (CPU): " << mx << endl;
cout << "................................." << endl
<< "El valor máximo en B es (GPU): " << max_gpu << endl;
cout << endl
<< "Tiempo gastado CPU= " << Tcpu << endl
<< endl;
cout << endl
<< "Tiempo gastado GPU (SHARED MEMORY)= " << TgpuS << endl
<< endl;
cout << endl
<< "Speedup GPU (SHARED MEMORY)= " << Tcpu / TgpuS << endl
<< endl;
cout << endl
<< "Tiempo gastado GPU (GLOBAL MEMORY)= " << TgpuG << endl
<< endl;
cout << endl
<< "Speedup GPU (GLOBAL MEMORY)= " << Tcpu / TgpuG << endl
<< endl;
//* Free the memory */
delete (A);
delete (B);
cudaFree(A_GPU);
cudaFree(B_GPU_global);
cudaFree(out);
}
|
20,331 |
// System includes
#include <stdio.h>
#include <assert.h>
// CUDA runtime
#include <cuda_runtime.h>
// Matrices are stored in row-major order:
// M(row, col) = *(M.elements + row * M.width + col)
typedef struct {
int width;
int height;
int stride;
double* elements;
} Matrix;
// Thread block size
#define BLOCK_SIZE 32
// Matrix size
#define MATRIX_SIZE 5760
void constantInit(double *data, int size, double val)
{
for (int i = 0; i < size; ++i)
{
data[i] = val;
}
}
__device__ double GetElement(const Matrix A, int row, int col)
{
return A.elements[row * A.stride + col];
}
__device__ void SetElement(const Matrix A, int row, int col, double value)
{
A.elements[row * A.stride + col] = value;
}
__device__ Matrix GetSubMatrix(Matrix A, int row, int col)
{
Matrix Asub;
Asub.width = BLOCK_SIZE;
Asub.height = BLOCK_SIZE;
Asub.stride = A.stride;
Asub.elements = &A.elements[A.stride * BLOCK_SIZE * row + BLOCK_SIZE * col];
return Asub;
}
__global__ void MatMulKernel(const Matrix A, const Matrix B, Matrix C)
{
int blockRow = blockIdx.y;
int blockCol = blockIdx.x;
Matrix Csub = GetSubMatrix(C, blockRow, blockCol);
double Cvalue = 0;
int row = threadIdx.y;
int col = threadIdx.x;
for(int m=0; m < (A.width / BLOCK_SIZE); ++m)
{
Matrix Asub = GetSubMatrix(A, blockRow, m);
Matrix Bsub = GetSubMatrix(B, m, blockCol);
__shared__ double As[BLOCK_SIZE][BLOCK_SIZE];
__shared__ double Bs[BLOCK_SIZE][BLOCK_SIZE];
As[row][col] = GetElement(Asub, row, col);
Bs[row][col] = GetElement(Bsub, row, col);
__syncthreads();
for(int e=0; e<BLOCK_SIZE; ++e)
{
Cvalue += As[row][e] * Bs[e][col];
}
__syncthreads();
}
SetElement(Csub, row, col, Cvalue);
}
int main(int argc, char **argv)
{
printf("[Share memory Matrix Multiply Using CUDA] - Starting...\n");
// By default, we use device 0, otherwise change here
int devID = 0;
cudaSetDevice(devID);
cudaError_t error;
cudaDeviceProp deviceProp;
error = cudaGetDevice(&devID);
error = cudaGetDeviceProperties(&deviceProp, devID);
if (deviceProp.computeMode == cudaComputeModeProhibited)
{
fprintf(stderr, "Error: device is running in <Compute Mode Prohibited>, no threads can use ::cudaSetDevice().\n");
exit(EXIT_SUCCESS);
}
printf("GPU Device %d: \"%s\" with compute capability %d.%d\n\n", devID, deviceProp.name, deviceProp.major, deviceProp.minor);
//Alloc and init host matrix
Matrix A,B,C;
A.width = A.height = A.stride = MATRIX_SIZE;
B.width = B.height = B.stride = MATRIX_SIZE;
C.width = C.height = C.stride = MATRIX_SIZE;
printf("MatrixA(%d,%d), MatrixB(%d,%d)\n", A.width, A.height, B.width, B.height);
unsigned int size_A = A.width * A.height;
unsigned int mem_size_A = sizeof(double) * size_A;
A.elements = (double *)malloc(mem_size_A);
unsigned int size_B = B.width * B.height;
unsigned int mem_size_B = sizeof(double) * size_B;
B.elements = (double *)malloc(mem_size_B);
unsigned int size_C = C.width * C.height;
unsigned int mem_size_C = sizeof(double) * size_C;
C.elements = (double *)malloc(mem_size_C);
const double valA = 1.0f;
const double valB = 0.01f;
constantInit(A.elements, size_A, valA);
constantInit(B.elements, size_B, valB);
/*
for(int i=0; i<size_A; i++)
{
printf("%f\n", A.elements[i]);
printf("%f\n", B.elements[i]);
}
*/
//Alloc and init device matrix
Matrix d_A;
d_A.width = A.width;
d_A.height = A.height;
d_A.stride = A.stride;
cudaMalloc(&d_A.elements, mem_size_A);
cudaMemcpy(d_A.elements, A.elements, mem_size_A, cudaMemcpyHostToDevice);
Matrix d_B;
d_B.width = B.width;
d_B.height = B.height;
d_B.stride = B.stride;
cudaMalloc(&d_B.elements, mem_size_B);
cudaMemcpy(d_B.elements, B.elements, mem_size_B, cudaMemcpyHostToDevice);
Matrix d_C;
d_C.width = C.width;
d_C.height = C.height;
d_C.stride = C.stride;
cudaMalloc(&d_C.elements, mem_size_C);
//launch kernel
dim3 dimBlock(BLOCK_SIZE, BLOCK_SIZE);
dim3 dimGrid(C.width / dimBlock.x, C.height / dimBlock.y);
MatMulKernel<<<dimGrid, dimBlock>>>(d_A, d_B, d_C);
//copy back
cudaMemcpy(C.elements, d_C.elements, mem_size_C, cudaMemcpyDeviceToHost);
//result check
printf("Checking computed result for correctness: ");
bool correct = true;
// test relative error by the formula
// |<x, y>_cpu - <x,y>_gpu|/<|x|, |y|> < eps
double eps = 1.e-6 ; // machine zero
for (int i = 0; i < (int)(C.width * C.height); i++)
{
double abs_err = fabs(C.elements[i] - (A.width * valB));
double dot_length = A.width;
double abs_val = fabs(C.elements[i]);
double rel_err = abs_err/abs_val/dot_length ;
if (rel_err > eps)
{
printf("Error! Matrix[%05d]=%.8f, ref=%.8f error term is > %E\n", i, C.elements[i], A.width*valB, eps);
correct = false;
}
}
printf("%s\n", correct ? "Result = PASS" : "Result = FAIL");
/*
for(int i=0; i<size_C; i++)
{
printf("%f\n", C.elements[i]);
}
*/
cudaDeviceSynchronize();
//Get Gflops
cudaEvent_t start;
error = cudaEventCreate(&start);
cudaEvent_t stop;
error = cudaEventCreate(&stop);
int nIter = 10;
// Record the start event
error = cudaEventRecord(start, NULL);
for (int j = 0; j < nIter; j++)
{
MatMulKernel<<<dimGrid, dimBlock>>>(d_A, d_B, d_C);
}
error = cudaEventRecord(stop, NULL);
error = cudaEventSynchronize(stop);
float msecTotal = 0.0f;
error = cudaEventElapsedTime(&msecTotal, start, stop);
if (error != cudaSuccess)
{
fprintf(stderr, "Failed to get time elapsed between events (error code %s)!\n", cudaGetErrorString(error));
exit(EXIT_FAILURE);
}
// Compute and print the performance
float msecPerMatrixMul = msecTotal / nIter;
double flopsPerMatrixMul = 2.0 * (double)A.width * (double)A.height * (double)B.width;
double gigaFlops = (flopsPerMatrixMul * 1.0e-9f) / (msecPerMatrixMul / 1000.0f);
printf(
"Performance= %.2f GFlop/s, Time= %.3f msec, Size= %.0f Ops, WorkgroupSize= %u threads/block\n",
gigaFlops,
msecPerMatrixMul,
flopsPerMatrixMul,
dimBlock.x * dimBlock.y);
cudaDeviceSynchronize();
free(A.elements);
free(B.elements);
free(C.elements);
cudaFree(d_A.elements);
cudaFree(d_B.elements);
cudaFree(d_C.elements);
return 0;
}
|
20,332 | #include <cuda.h>
#include <cuComplex.h>
#include <math_constants.h>
// Use symmetry
// Use max_half_support threads only
// Perhaps it's not very cache friendly, but it is
// very simple to perform work-distribution for this variant
#define __SET_MAP \
const int \
x = blockIdx.x * blockDim.x + threadIdx.x \
, y = blockIdx.y * blockDim.y + threadIdx.y \
, xl = max_half_support - x \
, xr = max_half_support + x \
, yl = max_half_support - y \
, yr = max_half_support + y \
;
template <int max_half_support>
__device__ __inline__
void ucs_common(
cuDoubleComplex mesh[max_half_support * 2 + 1][max_half_support * 2 + 1]
, double t2
){
__SET_MAP
double
t2_div_sc = t2 / double(max_half_support)
, xs = double(x) * t2_div_sc
, ys = double(y) * t2_div_sc
;
cuDoubleComplex r2 = make_cuDoubleComplex(xs * xs + ys * ys, 0.0);
mesh[xl][yl] = r2;
mesh[xl][yr] = r2;
mesh[xr][yl] = r2;
mesh[xr][yr] = r2;
}
extern "C" __global__ void r2(cuDoubleComplex mesh[257][257], double t2) {
ucs_common<128>(mesh, t2);
}
#if 0
template <int max_half_support>
__device__
void calc_inplace(
cuDoubleComplex mesh[max_half_support * 2 + 1][max_half_support * 2 + 1]
, double w
){
__SET_MAP
// mesh is symmetric, thus we need no recalc ph
double ph = w * (1.0 - sqrt(1.0 - mesh[xl][yl].x));
double s, c;
sincos(2.0 * CUDART_PI * ph, &s, &c);
cuDoubleComplex res = make_cuDoubleComplex(c, -s); // to get rid of conj later
mesh[xl][yl] = res;
mesh[xl][yr] = res;
mesh[xr][yl] = res;
mesh[xr][yr] = res;
}
// test instantiation
template __device__
void calc_inplace<128>(
cuDoubleComplex mesh[257][257]
, double w
);
#endif
template <int max_half_support>
__device__ __inline__
void calc(
cuDoubleComplex dst[max_half_support * 2 + 1][max_half_support * 2 + 1]
, const cuDoubleComplex src[max_half_support * 2 + 1][max_half_support * 2 + 1]
, double w
){
__SET_MAP
double ph = w * (1.0 - sqrt(1.0 - src[xl][yl].x));
double s, c;
sincos(2.0 * CUDART_PI * ph, &s, &c);
cuDoubleComplex res = make_cuDoubleComplex(c, -s); // to get rid of conj later
dst[xl][yl] = res;
dst[xl][yr] = res;
dst[xr][yl] = res;
dst[xr][yr] = res;
}
extern "C" __global__ void wkernff(
cuDoubleComplex dst[257][257]
, const cuDoubleComplex src[257][257]
, double w
){
calc<128>(dst, src, w);
}
template <
int max_half_support
, int oversample
>
__device__ __inline__
void copy_ucs_2_over(
cuDoubleComplex dst[(max_half_support * 2 + 1) * oversample][(max_half_support * 2 + 1) * oversample]
, const cuDoubleComplex src[max_half_support * 2 + 1][max_half_support * 2 + 1]
){
const int dst_center = (max_half_support * 2 + 1) * oversample / 2;
__SET_MAP
dst[dst_center - x][dst_center - y] = src[xl][yl];
dst[dst_center - x][dst_center + y] = src[xl][yr];
dst[dst_center + x][dst_center - y] = src[xr][yl];
dst[dst_center + x][dst_center + y] = src[xr][yr];
}
extern "C" __global__ void copy_2_over(
cuDoubleComplex dst[2056][2056]
, const cuDoubleComplex src[257][257]
){
copy_ucs_2_over<128,8>(dst, src);
}
#ifdef __SMALL_EXTRACT
template <
int max_half_support
, int oversample
>
__device__ __inline__
void extract_over(
int overx
, int overy
, cuDoubleComplex dst[max_half_support * 2 + 1][max_half_support * 2 + 1]
, const cuDoubleComplex src[(max_half_support * 2 + 1) * oversample][(max_half_support * 2 + 1) * oversample]
) {
__SET_MAP
const int
sxl = xl * oversample + overx
, sxr = xr * oversample + overx
, syl = yl * oversample + overy
, syr = yr * oversample + overy
;
dst[xl][yl] = src[sxl][syl];
dst[xl][yr] = src[sxl][syr];
dst[xr][yl] = src[sxr][syl];
dst[xr][yr] = src[sxr][syr];
}
extern "C" __global__ void wextract0(
int overx
, int overy
, cuDoubleComplex dst[257][257]
, const cuDoubleComplex src[2056][2056]
){
extract_over<128,8>(overx, overy, dst, src);
}
#else
// We use 3rd grid dimension to cover oversample range
template <
int max_half_support
, int oversample
>
__device__ __inline__
void transpose_over(
cuDoubleComplex dst[oversample][oversample][max_half_support * 2 + 1][max_half_support * 2 + 1]
, const cuDoubleComplex src[(max_half_support * 2 + 1) * oversample][(max_half_support * 2 + 1) * oversample]
) {
__SET_MAP
const int
overx = blockIdx.z / oversample
, overy = blockIdx.z % oversample
, sxl = xl * oversample + overx
, sxr = xr * oversample + overx
, syl = yl * oversample + overy
, syr = yr * oversample + overy
;
dst[overx][overy][xl][yl] = src[sxl][syl];
dst[overx][overy][xl][yr] = src[sxl][syr];
dst[overx][overy][xr][yl] = src[sxr][syl];
dst[overx][overy][xr][yr] = src[sxr][syr];
}
extern "C" __global__ void transpose_over0(
cuDoubleComplex dst[8][8][257][257]
, const cuDoubleComplex src[2056][2056]
){
transpose_over<128,8>(dst, src);
}
#endif
template <
int max_half_support
, int oversample
>
__device__ __inline__
void cut_out(
int half_supp
, cuDoubleComplex * dst
, const cuDoubleComplex src[max_half_support * 2 + 1][max_half_support * 2 + 1]
) {
__SET_MAP
const int supp = half_supp * 2 + 1;
if (x > half_supp || y > half_supp) return;
const int
dxl = (half_supp - x) * supp
, dxr = (half_supp + x) * supp
, dyl = half_supp - y
, dyr = half_supp + y
;
dst[dxl + dyl] = src[xl][yl];
dst[dxl + dyr] = src[xl][yr];
dst[dxr + dyl] = src[xr][yl];
dst[dxr + dyr] = src[xr][yr];
}
extern "C" __global__ void wextract1(
int half_supp
, cuDoubleComplex * dst
, const cuDoubleComplex src[257][257]
){
cut_out<128,8>(half_supp, dst, src);
}
|
20,333 | #include "includes.h"
__global__ void calcSoftmaxDivForwardGPU(float *out, float *sum, int batch_size, int in_size_x, unsigned int n)
{
// int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
unsigned int index = threadIdx.x + blockIdx.x * blockDim.x;
if(index<n && *(sum + blockIdx.x)>0.0){
// out[id] = out[id] / *sum;
out[index] = out[index] / *(sum + blockIdx.x);
}
/* original
for ( int i = 0; i < in.size.x; ++i ){
out( b, i, 0, 0 ) = out( b, i, 0, 0 ) / sum;
}
*/
} |
20,334 | //CUDA code for matrix multiplicationn . The values of a,b,c,q have to changed according to N
#include<stdlib.h>
#include<stdio.h>
#include<iostream>
#include<cuda_runtime.h>
__global__ void Product (float *a, float *b, float *c)
{
// Out of all the threads created each one computes 1 value of C and stores into cval
float cval = 0.00;
int R = blockIdx.y * blockDim.y + threadIdx.y; //Row of the matrix
int C = blockIdx.x * blockDim.x + threadIdx.x; //Column of the matrix
//Defining the size of the matrix//
int N=1000;
if(R> N || C > N ){
return;
}
for (int j = 0; j < N; j++)
{
cval += a[R * N+ j] *b[j * N + C];
}
c[R * N + C]+= cval;
}
using namespace std;
int main(){
//The timing function
cudaEvent_t start,stop;
float time;
int N=5000;
cudaEventCreate(&start);
cudaEventCreate(&stop);
cudaEventRecord(start,0);
static float a[25000000],b[25000000];
static float c[25000000];
//Inputting values in the matrix
long int q = 25000000; // Standard int runs out of memory so long int used
int i=0;
//For checking the matrix multiplication all entries are 1
while(i != q)
{
a[i] = 1;
b[i] = 1;
i++;
}
int o=0;
//for(int m=0;m<N;m++){
//for(int n=0;n<N;n++){
//a[o]=m+n;
//b[o]=m*n;
//o=o+1;
//}}
//This section is the GPU part
float *device_a, *device_b, *device_c;
dim3 griddimension(500,500); // The dimension of the total grid (Blocks)
dim3 blockdimension(10,10); // The dimension of one block ( threads in one block)
//Allocating memory in the device for the matrices: device_a,b,c are device variables
cudaMalloc( (void**)&device_c, q * sizeof(float) );
cudaMalloc( (void**)&device_b, q * sizeof(float) );
cudaMalloc( (void**)&device_a, q * sizeof(float) );
//Copying the variables from CPU to GPU
cudaMemcpy( device_a,a,q * sizeof(float),cudaMemcpyHostToDevice );
cudaMemcpy( device_b,b,q * sizeof(float),cudaMemcpyHostToDevice );
cudaMemcpy( device_c,c,q * sizeof(float),cudaMemcpyHostToDevice );
Product<<<griddimension, blockdimension>>>( device_a, device_b, device_c ); //The device function Product is called
cudaMemcpy( c,device_c,q * sizeof(float),cudaMemcpyDeviceToHost );
cudaFree( device_a );
cudaFree( device_b );
cudaFree( device_c );
cudaEventRecord(stop,0);
cudaEventSynchronize(stop);
cudaEventElapsedTime(&time,start,stop);
cout<<"\n\nTime = "<<time<<" ms";
//For printing the matrix
long int g=N*N,d=0;
while(d!=g){
printf("%f\n",c[d]);
d=d+1;
}
//}
}
|
20,335 | #include <stdio.h>
#include <stdlib.h>
#include <thrust/sort.h>
#define num_thread 64
#define thread 16
__global__ void count(int *data,int input, int *result)
{
int i = blockIdx.x * blockDim.x + threadIdx.x;
if(data[i] == input)
{
int a = 1;
atomicAdd(result,a);
}
}
int main(int argc, char *argv[]){
int Data[num_thread], *arr,input,*result;
int i;
int resultarr[1];
int size = sizeof(int)*num_thread;
srand(123456846);
printf(" Generate Ok\n");
cudaSetDevice(0);
for(i = 0; i < num_thread; i++)
{
Data[i] = rand() % 50;
printf("%d ",Data[i]);
}
printf(" \n Working Ok\n");
printf("Input value to find: ");
scanf("%d",&input);
cudaMalloc( (void**) &arr, size);
cudaMalloc( (void**) &result, sizeof(int));
printf(" Malloc Ok\n");
cudaMemcpy(arr,Data,size, cudaMemcpyHostToDevice);
printf(" Copy Ok\n");
count<<<num_thread/thread,thread>>>(arr,input,result);
printf(" Function Ok\n");
cudaMemcpy(resultarr,result,sizeof(int),cudaMemcpyDeviceToHost);
printf(" Copy Back Ok\n");
cudaFree(result);
cudaFree(arr);
printf(" Value %d to search occurrences Data found: %d",input,resultarr[0]);
printf("\n");
return 0;
}
|
20,336 | #include "includes.h"
__global__ void LowPassColMulti(float *d_Result, float *d_Data, int width, int pitch, int height)
{
__shared__ float data[CONVCOL_W*(CONVCOL_H + 2*RADIUS)];
const int tx = threadIdx.x;
const int ty = threadIdx.y;
const int block = blockIdx.x/(NUM_SCALES+3);
const int scale = blockIdx.x - (NUM_SCALES+3)*block;
const int miny = blockIdx.y*CONVCOL_H;
const int maxy = min(miny + CONVCOL_H, height) - 1;
const int totStart = miny - RADIUS;
const int totEnd = maxy + RADIUS;
const int colStart = block*CONVCOL_W + tx;
const int colEnd = colStart + (height-1)*pitch;
const int sStep = CONVCOL_W*CONVCOL_S;
const int gStep = pitch*CONVCOL_S;
float *kernel = d_Kernel + scale*16;
const int size = pitch*height*scale;
d_Result += size;
d_Data += size;
if (colStart<width) {
float *sdata = data + ty*CONVCOL_W + tx;
int gPos = colStart + (totStart + ty)*pitch;
for (int y = totStart+ty;y<=totEnd;y+=blockDim.y){
if (y<0)
sdata[0] = d_Data[colStart];
else if (y>=height)
sdata[0] = d_Data[colEnd];
else
sdata[0] = d_Data[gPos];
sdata += sStep;
gPos += gStep;
}
}
__syncthreads();
if (colStart<width) {
float *sdata = data + ty*CONVCOL_W + tx;
int gPos = colStart + (miny + ty)*pitch;
for (int y=miny+ty;y<=maxy;y+=blockDim.y) {
d_Result[gPos] =
(sdata[0*CONVCOL_W] + sdata[8*CONVCOL_W])*kernel[0] +
(sdata[1*CONVCOL_W] + sdata[7*CONVCOL_W])*kernel[1] +
(sdata[2*CONVCOL_W] + sdata[6*CONVCOL_W])*kernel[2] +
(sdata[3*CONVCOL_W] + sdata[5*CONVCOL_W])*kernel[3] +
sdata[4*CONVCOL_W]*kernel[4];
sdata += sStep;
gPos += gStep;
}
}
} |
20,337 | #include <stdio.h>
#include <stdlib.h>
#include <time.h>
void printArr( int arr[], int n )
{
int i;
for ( i = 0; i < n; ++i )
printf( "%d ", arr[i] );
}
__device__ int d_size;
__global__ void partition (int *arr, int *arr_l, int *arr_h, int n)
{
int z = blockIdx.x*blockDim.x+threadIdx.x;
d_size = 0;
__syncthreads();
if (z<n)
{
int h = arr_h[z];
int l = arr_l[z];
int x = arr[h];
int i = (l - 1);
int temp;
for (int j = l; j <= h- 1; j++)
{
if (arr[j] <= x)
{
i++;
temp = arr[i];
arr[i] = arr[j];
arr[j] = temp;
}
}
temp = arr[i+1];
arr[i+1] = arr[h];
arr[h] = temp;
int p = (i + 1);
if (p-1 > l)
{
int ind = atomicAdd(&d_size, 1);
arr_l[ind] = l;
arr_h[ind] = p-1;
}
if ( p+1 < h )
{
int ind = atomicAdd(&d_size, 1);
arr_l[ind] = p+1;
arr_h[ind] = h;
}
}
}
void quickSortIterative (int arr[], int l, int h)
{
int lstack[ h - l + 1 ], hstack[ h - l + 1];
int top = -1, *d_d, *d_l, *d_h;
lstack[ ++top ] = l;
hstack[ top ] = h;
cudaMalloc(&d_d, (h-l+1)*sizeof(int));
cudaMemcpy(d_d, arr,(h-l+1)*sizeof(int),cudaMemcpyHostToDevice);
cudaMalloc(&d_l, (h-l+1)*sizeof(int));
cudaMemcpy(d_l, lstack,(h-l+1)*sizeof(int),cudaMemcpyHostToDevice);
cudaMalloc(&d_h, (h-l+1)*sizeof(int));
cudaMemcpy(d_h, hstack,(h-l+1)*sizeof(int),cudaMemcpyHostToDevice);
int n_t = 1;
int n_b = 1;
int n_i = 1;
while ( n_i > 0 )
{
partition<<<n_b,n_t>>>( d_d, d_l, d_h, n_i);
int answer;
cudaMemcpyFromSymbol(&answer, d_size, sizeof(int), 0, cudaMemcpyDeviceToHost);
if (answer < 1024)
{
n_t = answer;
}
else
{
n_t = 1024;
n_b = answer/n_t + (answer%n_t==0?0:1);
}
n_i = answer;
cudaMemcpy(arr, d_d,(h-l+1)*sizeof(int),cudaMemcpyDeviceToHost);
}
}
int main()
{
int arr[5000];
srand(time(NULL));
for (int i = 0; i<5000; i++)
{
arr[i] = rand ()%10000;
}
int n = sizeof( arr ) / sizeof( *arr );
quickSortIterative( arr, 0, n - 1 );
printArr( arr, n );
return 0;
}
|
20,338 | #include <stdio.h>
#include <string.h>
#include <stdbool.h>
#include <cuda.h>
#include <cuda_runtime_api.h>
#define V 9
#define E 14
long int* get_graph_dim(char* filename)
{
FILE* file;
file = fopen(filename, "r");
if (file == NULL)
{
printf("Unable to read the CSR file: %s.", filename);
exit(1);
}
long int* dim = (long int*)malloc(2*sizeof(long int));
fscanf(file, "%ld %ld", &dim[0], &dim[1]);
return dim;
}
long int* read_csr(char* filename, long int v, long int e)
{
FILE* file;
file = fopen(filename, "r");
if (file == NULL)
{
printf("Unable to read the CSR file: %s.", filename);
exit(1);
}
long int* csr = (long int*)malloc((v+1+(2*e))*sizeof(long int));
fscanf(file, "%ld %ld", &v, &e);
long int i;
for(i=0; i<v+1; i++)
fscanf(file, "%ld", &csr[i]);
for(; i<v+1+2*e; i++)
fscanf(file, "%ld", &csr[i]);
return csr;
}
__global__ void between_centre(double* bc, long int* R, long int* C, long int *vert, long int *edge)
{
//long int V = *vert;
//long int E = *edge;
long int idx = threadIdx.x;
long int s = blockIdx.x;
__shared__ long int d[V], sigma[V];
double dep[V];
__shared__ long int P[V][V];
__shared__ long int p_top[V];
long int k, r;
//Initialize d and sigma
// for(k=idx; k<V; k+=blockDim.x)
// {
// if(k == s)
// {
// d[k] = 0;
// sigma[k] = 1;
// }
// else
// {
// d[k] = INT_MAX;
// sigma[k] = 0;
// }
//
// p_top[k] = 0;
//
// }
//
// __shared__ long int Q[V];
// __shared__ long int Q2[V];
// __shared__ long int Q_len;
// __shared__ long int Q2_len;
//
// __shared__ long int S[V];
// __shared__ long int s_top;
//
// if(idx == 0)
// {
// Q[0] = s;
// Q_len = 1;
// Q2_len = 0;
// s_top = 0;
// }
// __syncthreads();
//
// while(1)
// {
//
// for(k=idx; k<Q_len; k+=blockDim.x)
// {
// int v = Q[k];
//
// atomicAdd((int*)&s_top, 1);
// S[s_top] = v;
//
// for(r=R[v]; r<R[v+1]; r++)
// {
// long int w = C[r];
//
// if(atomicCAS((int*)&d[w],INT_MAX,(int)d[v]+1) == INT_MAX)
// {
// int t = atomicAdd((int*)&Q2_len,1);
// Q2[t] = w;
// }
//
// if(d[w] == (d[v]+1))
// {
// atomicAdd((int*)&sigma[w],sigma[v]);
// atomicAdd((int*)&p_top[w], 1);
// atomicAdd((int*)&P[w][p_top[w]-1], v);
// }
// }
// }
// __syncthreads();
//
// if(Q2_len == 0)
// break;
//
// else
// {
// for(k=idx; k<Q2_len; k+=blockDim.x)
// Q[k] = Q2[k];
//
// __syncthreads();
//
// if(idx == 0)
// {
// Q_len = Q2_len;
// Q2_len = 0;
// }
// __syncthreads();
// }
// }
//
// while(s_top!=0)
// {
// atomicAdd((int*)&s_top, -1);
// long int w = S[s_top];
//
// for(k = 0; k < P[w][p_top[w]-1]; k++)
// dep[k] += (double)(sigma[k] * (1 + dep[w]) / sigma[w]);
//
// if(w!=s)
// atomicAdd((float*)&bc[w], (float)dep[w]);
//
// __syncthreads();
// }
}
int main()
{
long int* dim;
dim = get_graph_dim("01.txt");
printf("Hello!\n");
long int v = dim[0], e = dim[1];
long int* csr;
csr = read_csr("01.txt", v, e);
printf("Holla!!\n");
long int *r, *c;
double *bc;
r = (long int*)malloc((v+1)*sizeof(long int));
c = (long int*)malloc(2*e*sizeof(long int));
bc = (double*)malloc(v*sizeof(double));
memcpy(r, csr, (v+1)*sizeof(long int));
memcpy(c, csr+v+1, 2*e*sizeof(long int));
free(csr);
long int *d_v, *d_e;
long int *d_r, *d_c;
double *d_bc;
printf("Sui!\n");
cudaMalloc((void**)&d_v, sizeof(long int));
cudaMalloc((void**)&d_e, sizeof(long int));
cudaMalloc((void**)&d_bc, v * sizeof(double));
cudaMalloc((void**)&d_r, (v+1) * sizeof(long int));
cudaMalloc((void**)&d_c, 2*e * sizeof(long int));
cudaMemcpy(d_v, &v, sizeof(long int), cudaMemcpyHostToDevice);
cudaMemcpy(d_e, &e, sizeof(long int), cudaMemcpyHostToDevice);
cudaMemcpy(d_r, r, (v+1)*sizeof(long int), cudaMemcpyHostToDevice);
cudaMemcpy(d_c, c, 2*e*sizeof(long int), cudaMemcpyHostToDevice);
printf("Namaskara!\n");
dim3 dimGrid(v);
dim3 dimBlock(1024);
cudaEvent_t start, stop;
float timer;
cudaEventCreate(&start);
cudaEventCreate(&stop);
cudaEventRecord(start, 0);
between_centre<<<dimGrid, dimBlock>>>(d_bc, d_r, d_c, d_v, d_e);
cudaEventRecord(stop, 0);
cudaEventSynchronize(stop);
cudaEventElapsedTime(&timer, start, stop);
cudaEventDestroy(start);
cudaEventDestroy(stop);
cudaMemcpy(bc, d_bc, v*sizeof(double), cudaMemcpyDeviceToHost);
long int k;
for(k = 0; k < v; k++)
printf("%.2f ", bc[k]);
printf("\nElapsed Time: %lf\n", timer);
cudaFree(d_v);
cudaFree(d_e);
cudaFree(d_bc);
cudaFree(d_r);
cudaFree(d_c);
free(r);
free(c);
}
|
20,339 | /* 159.735 Semester 2, 2016. Ian Bond, 3/10/2016
Sequential version of the N-sphere counting problem for Assignment
5. Two alternative algorithms are presented.
Note: a rethink will be needed when implementing a GPU version of
this. You can't just cut and paste code.
To compile: g++ -O3 -o nsphere nsphere.cpp
(you will get slightly better performance with the O3 optimization flag)
*/
#include <cstdlib>
#include <cmath>
#include <iostream>
#include <string>
#include <cuda.h>
#include <vector>
#include <fstream>
using namespace std;
const long MAXDIM = 8;
const double RMIN = 2.0;
const double RMAX = 8.0;
const int MAX_POINTS_PER_THREAD = 500;
double diffclock(clock_t clock1, clock_t clock2)
{
double diffticks = clock1 - clock2;
double diffms = (diffticks * 1000) / CLOCKS_PER_SEC;
return diffms; // Time difference in milliseconds
}
/*
* Evaluate n**k where both are long integers
*/
long powlong(long n, long k)
{
long p = 1;
for (long i = 0; i < k; ++i) p *= n;
return p;
}
/*
* Convert a decimal number into another base system - the individual
* digits in the new base are stored in the index array.
*/
void convert(long num, long base, std::vector<long>& index)
{
const long ndim = index.size();
for (long i = 0; i < ndim; ++i) index[i] = 0;
long idx = 0;
while (num != 0) {
long rem = num % base;
num = num / base;
index[idx] = rem;
++idx;
}
}
long count_in_v1(long ndim, double radius)
{
const long halfb = static_cast<long>(floor(radius));
const long base = 2 * halfb + 1;
const double rsquare = radius * radius;
const long ntotal = powlong(base, ndim);
cout << "ntotal:"<<ntotal<<endl;
long count = 0;
std::vector<long> index(ndim, 0);
for (long n = 0; n < ntotal; ++n) {
convert(n, base, index);
double rtestsq = 0;
for (long k = 0; k < ndim; ++k) {
double xk = index[k] - halfb;
rtestsq += xk * xk;
}
if (rtestsq < rsquare) ++count;
}
return count;
}
// kernel
__global__ void cuda_func_count(int ndim, double radius, long nfrom, long nto, long nthreads, int* counter)
{
long id = blockIdx.x * blockDim.x + threadIdx.x;
counter[id] = 0;
if (id >= nto)
return;
const long halfb = static_cast<long>(floor(radius));
const long base = 2 * halfb + 1;
const double rsquare = radius*radius;
long index = 0;
long num = nfrom + id;
while (num < nto)
{
double rtestsq = 0;
for (int i=0; i<ndim; i++)
{
long rem = num % base;
num = num / base;
double xk = rem - halfb;
rtestsq += xk * xk;
}
if (rtestsq < rsquare )
{
atomicAdd(&counter[id], 1);
}
index++;
num = nfrom + id + nthreads*index;
}
}
long count_in_gpu(long ndim, double radius)
{
const long halfb = static_cast<long>(floor(radius));
const long base = 2 * halfb + 1;
const long ntotal = powlong(base, ndim);
cout << "ntotal:"<<ntotal<<endl;
const int threadsPerBlock_x = (ntotal<1024)?ntotal:1024;
int blocksPerGrid = ntotal / 1024 + 1;
if (blocksPerGrid > 1024)
{
blocksPerGrid = 1024;
}
const long nthreads = threadsPerBlock_x*blocksPerGrid; //maximum 1024*1024 threads
int* counters = new int[nthreads];
memset(counters, 0, sizeof(int)*nthreads);
int* d_counters;
cudaMalloc(&d_counters, sizeof(int)*nthreads);
long total_count = 0;
//invoke the kernel
//std::cout << "Launching a grid of " << nthreads << " threads" << std::endl;
const long points_for_each = MAX_POINTS_PER_THREAD * nthreads;
long nfrom = 0;
long nto = points_for_each;
do
{
if (nto > ntotal){
nto = ntotal;
}
cuda_func_count <<<blocksPerGrid, threadsPerBlock_x>>>(ndim, radius, nfrom, nto, nthreads, d_counters);
//copyback the counters to host
cudaMemcpy(counters, d_counters, sizeof(int)*nthreads, cudaMemcpyDeviceToHost);
//coculate all counters amount
for (long i = 0; i < nthreads; i++)
{
total_count += counters[i];
}
nfrom = nto;
nto += points_for_each;
}while (nfrom < ntotal);
cudaFree(d_counters);
delete[] counters;
return total_count;
}
int main(int argc, char* argv[])
{
const double r = atof(argv[1]);
const long nd = atol(argv[2]);
clock_t tstart = clock();
// const long seq_count = count_in_v1(nd, r);
// double seq_t_cost = diffclock(clock(), tstart);
// tstart = clock();
/**********************************************/
const long cuda_count = count_in_gpu(nd, r);
double cuda_t_cost = diffclock(clock(), tstart);
cout <<"r:"<< r << "; nd:" << nd
// << "; seq_count:" << seq_count << "; seq_t_cost:" << seq_t_cost
<<"; cuda_count:"<< cuda_count << "; cuda_t_cost"<< cuda_t_cost <<endl;
return 0;
}
|
20,340 | #include <iostream>
#include <stdlib.h>
#include <math.h>
#include <stdio.h>
#define PI 3.141592654
using namespace std;
const int nOrder = 3;
const int nTimePreSnap = 100;
typedef struct {
int nx, nz;
int Nx, Nz;
int sx, sz;
int npx, npz;
float dx, dz;
} dim;
typedef struct {
float *vp, *vs, *rho;
} media;
typedef struct {
float *vxx, *vxz, *vzx, *vzz,
*txxx, *txxz, *tzzx, *tzzz,
*txzx, *txzz;
float *vxt, *vzt, *txxt, *tzzt, *txzt;
} wave;
typedef struct {
float dt;
float d0x, d0z;
float C[nOrder];
} coeff;
typedef struct {
float *f0, *f1, *f2, *f3, *f4, *f5, *f6, *f7, *f8, *f9;
} factor;
__global__ void pre_eval(wave W, media M, dim D, coeff C, factor F) {
int ix = threadIdx.x + blockIdx.x*blockDim.x;
int iz = threadIdx.y + blockIdx.y*blockDim.y;
int idx = iz*D.Nx + ix;
float dpmlx = 0.0, dpmlz = 0.0;
float lambda, mu;
if(ix < D.Nx && iz < D.Nz) {
mu = M.rho[idx]*M.vs[idx]*M.vs[idx];
lambda = M.rho[idx]*M.vp[idx]*M.vp[idx] - 2*mu;
if(ix < D.npx + nOrder && ix >= nOrder)
dpmlx = C.d0x*pow(1.0*(D.npx + nOrder - ix)/D.npx, 2);
if(ix >= D.Nx - D.npx - nOrder && ix < D.Nx - nOrder)
dpmlx = C.d0x*pow(1.0*(ix + D.npx + nOrder + 1 - D.Nx)/D.npx, 2);
if(iz < D.npz + nOrder && iz >= nOrder)
dpmlz = C.d0z*pow(1.0*(D.npz + nOrder - iz)/D.npz, 2);
if(iz >= D.Nz - D.npz - nOrder && iz < D.Nz - nOrder)
dpmlz = C.d0z*pow(1.0*(iz + D.npz + nOrder + 1 - D.Nz)/D.npz, 2);
F.f0[idx] = (2 - C.dt*dpmlx)/(2 + C.dt*dpmlx);
F.f1[idx] = (2 - C.dt*dpmlz)/(2 + C.dt*dpmlz);
F.f2[idx] = 2*C.dt/(2 + C.dt*dpmlx)/M.rho[idx]/D.dx;
F.f3[idx] = 2*C.dt/(2 + C.dt*dpmlz)/M.rho[idx]/D.dz;
F.f4[idx] = 2*C.dt/(2 + C.dt*dpmlx)*(lambda + 2*mu)/D.dx;
F.f5[idx] = 2*C.dt/(2 + C.dt*dpmlz)*lambda/D.dz;
F.f6[idx] = 2*C.dt/(2 + C.dt*dpmlx)*lambda/D.dx;
F.f7[idx] = 2*C.dt/(2 + C.dt*dpmlz)*(lambda + 2*mu)/D.dz;
F.f8[idx] = 2*C.dt/(2 + C.dt*dpmlx)*mu/D.dx;
F.f9[idx] = 2*C.dt/(2 + C.dt*dpmlz)*mu/D.dz;
W.vxx [idx] = 0.0; W.vxz [idx] = 0.0; W.vxt [idx] = 0.0;
W.vzx [idx] = 0.0; W.vzz [idx] = 0.0; W.vzt [idx] = 0.0;
W.txxx[idx] = 0.0; W.txxz[idx] = 0.0; W.txxt[idx] = 0.0;
W.tzzx[idx] = 0.0; W.tzzz[idx] = 0.0; W.tzzt[idx] = 0.0;
W.txzx[idx] = 0.0; W.txzz[idx] = 0.0; W.txzt[idx] = 0.0;
}
}
__global__ void vel_eval(wave W, dim D, coeff C, factor F, int sidx) {
int ix = threadIdx.x + blockIdx.x*blockDim.x;
int iz = threadIdx.y + blockIdx.y*blockDim.y;
int idx = iz*D.Nx + ix;
int i;
float Psum;
if(ix >= nOrder && ix < D.Nx - nOrder && iz >= nOrder && iz < D.Nz - nOrder) {
Psum = 0.0;
for(i = 0; i < nOrder; i++)
Psum += C.C[i]*(W.txxt[idx + i] - W.txxt[idx - i - 1]);
W.vxx[idx] = F.f0[idx]*W.vxx[idx] + F.f2[idx]*Psum;
Psum = 0.0;
for(i = 0; i < nOrder; i++)
Psum += C.C[i]*(W.txzt[idx + i*D.Nx] - W.txzt[idx - (i + 1)*D.Nx]);
W.vxz[idx] = F.f1[idx]*W.vxz[idx] + F.f3[idx]*Psum;
W.vxt[idx] = W.vxx[idx] + W.vxz[idx];
Psum = 0.0;
for(i = 0; i < nOrder; i++)
Psum += C.C[i]*(W.txzt[idx + i + 1] - W.txzt[idx - i]);
W.vzx[idx] = F.f0[idx]*W.vzx[idx] + F.f2[idx]*Psum;
Psum = 0.0;
for(i = 0; i < nOrder; i++)
Psum += C.C[i]*(W.tzzt[idx + (i + 1)*D.Nx] - W.tzzt[idx - i*D.Nx]);
W.vzz[idx] = F.f1[idx]*W.vzz[idx] + F.f3[idx]*Psum;
W.vzt[idx] = W.vzx[idx] + W.vzz[idx];
}
}
__global__ void str_eval(wave W, dim D, coeff C, int sidx, float srclet, factor F) {
int ix = threadIdx.x + blockIdx.x*blockDim.x;
int iz = threadIdx.y + blockIdx.y*blockDim.y;
int idx = iz*D.Nx + ix;
int i;
float Psum;
if(ix >= nOrder && ix < D.Nx - nOrder && iz >= nOrder && iz < D.Nz - nOrder) {
Psum = 0.0;
for(i = 0; i < nOrder; i++)
Psum += C.C[i]*(W.vxt[idx + i + 1] - W.vxt[idx - i]);
W.txxx[idx] = F.f0[idx]*W.txxx[idx] + F.f4[idx]*Psum;
W.tzzx[idx] = F.f0[idx]*W.tzzx[idx] + F.f6[idx]*Psum;
Psum = 0.0;
for(i = 0; i < nOrder; i++)
Psum += C.C[i]*(W.vzt[idx + i*D.Nx] - W.vzt[idx - (i + 1)*D.Nx]);
W.txxz[idx] = F.f1[idx]*W.txxz[idx] + F.f5[idx]*Psum;
W.tzzz[idx] = F.f1[idx]*W.tzzz[idx] + F.f7[idx]*Psum;
Psum = 0.0;
for(i = 0; i < nOrder; i++)
Psum += C.C[i]*(W.vzt[idx + i] - W.vzt[idx - i - 1]);
W.txzx[idx] = F.f0[idx]*W.txzx[idx] + F.f8[idx]*Psum;
Psum = 0.0;
for(i = 0; i < nOrder; i++)
Psum += C.C[i]*(W.vxt[idx + (i + 1)*D.Nx] - W.vxt[idx - i*D.Nx]);
W.txzz[idx] = F.f1[idx]*W.txzz[idx] + F.f9[idx]*Psum;
W.txzt[idx] = W.txzx[idx] + W.txzz[idx];
if(idx == sidx) {
W.txxx[idx] += srclet/4;
W.txxz[idx] += srclet/4;
W.tzzx[idx] += srclet/4;
W.tzzz[idx] += srclet/4;
}
W.txxt[idx] = W.txxx[idx] + W.txxz[idx];
W.tzzt[idx] = W.tzzx[idx] + W.tzzz[idx];
}
}
void exp_wave(dim D, char *filename, float *P) {
//
FILE *fp = fopen(filename, "wb");
fwrite(&D.nx, sizeof(float), 1, fp);
fwrite(&D.nz, sizeof(float), 1, fp);
for(int i = 0; i < D.nz; i++) {
fwrite(&P[(i + D.npz + nOrder)*D.Nx + D.npx + nOrder], sizeof(float), D.nx, fp);
}
fclose(fp);
/*
FILE *fp = fopen(filename, "wt");
for(int i = 0; i < D.nz; i++) {
for(int j = 0; j < D.nx; j++)
fprintf(fp, "%lf, ", (double)P[(i + D.npz + nOrder)*D.Nx + D.npx + nOrder + j]);
fprintf(fp, "\n");
}
fclose(fp);
*/
}
int main(int argc, char *argv[]) {
int nx = 500, nz = 600;
int npmlx = 20, npmlz = 20;
int sx = 80, sz = 80;
float dx = 5.0, dz = 5.0;
int nt = 1000;
float dt = 1.0e-3;
int nppw = 12;
float ampl = 1.0e0;
wave W; media M; dim D; coeff C; factor F;
int Nx, Nz;
size_t memSize;
int i, j;
cout << "Input nt = ";
cin >> nt;
int prod1, prod2;
for(int m = 1; m < nOrder + 1; m++) {
prod1 = 1;
for(i = 1; i <= nOrder; i++)
if(i != m) prod1 *= (2*i - 1)*(2*i - 1);
prod2 = 1;
for(i = 1; i <= nOrder; i++)
if(i != m) prod2 *= abs((2*m - 1)*(2*m - 1) - (2*i - 1)*(2*i - 1));
C.C[m - 1] = pow(-1.0, m + 1)*prod1/(2*m - 1)/prod2;
}
Nx = nx + 2*npmlx + 2*nOrder;
Nz = nz + 2*npmlz + 2*nOrder;
memSize = Nx*Nz*sizeof(float);
D.nx = nx; D.nz = nz;
D.Nx = Nx; D.Nz = Nz;
D.sx = sx; D.sz = sz;
D.npx = npmlx; D.npz = npmlz;
D.dx = dx; D.dz= dz;
float *Vp = (float*) malloc(memSize);
float *Vs = (float*) malloc(memSize);
float *Rho = (float*) malloc(memSize);
for(i = 0; i < Nx*Nz; i++) {
Vp [i] = 2000.0;
Vs [i] = 1000.0;
Rho[i] = 1000.0;
}
cudaMalloc((float**) &M.vp , memSize);
cudaMalloc((float**) &M.vs , memSize);
cudaMalloc((float**) &M.rho, memSize);
cudaMemcpy(M.vp , Vp , memSize, cudaMemcpyHostToDevice);
cudaMemcpy(M.vs , Vs , memSize, cudaMemcpyHostToDevice);
cudaMemcpy(M.rho, Rho, memSize, cudaMemcpyHostToDevice);
float *vsmin = Vs, *vsmax = Vs;
for(i = 1; i < Nx*Nz; i++) {
if(*vsmin > Vs[i]) vsmin = &Vs[i];
if(*vsmax < Vs[i]) vsmax = &Vs[i];
}
float f0, t0;
f0 = (*vsmin)/(max(dx, dz)*nppw);
t0 = 1.0/f0;
C.dt = dt;
C.d0x = 3*(*vsmax)/dx*(8.0/15 - 3.0/100*npmlx + 1.0/1500*npmlx*npmlx);
C.d0z = 3*(*vsmax)/dz*(8.0/15 - 3.0/100*npmlz + 1.0/1500*npmlz*npmlz);
cudaMalloc((float**) &F.f0, memSize);
cudaMalloc((float**) &F.f1, memSize);
cudaMalloc((float**) &F.f2, memSize);
cudaMalloc((float**) &F.f3, memSize);
cudaMalloc((float**) &F.f4, memSize);
cudaMalloc((float**) &F.f5, memSize);
cudaMalloc((float**) &F.f6, memSize);
cudaMalloc((float**) &F.f7, memSize);
cudaMalloc((float**) &F.f8, memSize);
cudaMalloc((float**) &F.f9, memSize);
cudaMalloc((float**) &W.vxx , memSize);
cudaMalloc((float**) &W.vxz , memSize);
cudaMalloc((float**) &W.vzx , memSize);
cudaMalloc((float**) &W.vzz , memSize);
cudaMalloc((float**) &W.txxx, memSize);
cudaMalloc((float**) &W.txxz, memSize);
cudaMalloc((float**) &W.tzzx, memSize);
cudaMalloc((float**) &W.tzzz, memSize);
cudaMalloc((float**) &W.txzx, memSize);
cudaMalloc((float**) &W.txzz, memSize);
cudaMalloc((float**) &W.vxt , memSize);
cudaMalloc((float**) &W.vzt , memSize);
cudaMalloc((float**) &W.txxt, memSize);
cudaMalloc((float**) &W.tzzt, memSize);
cudaMalloc((float**) &W.txzt, memSize);
dim3 Block(32, 16);
dim3 Grid(ceil(1.0*Nx/Block.x), ceil(1.0*Nz/Block.y));
cout << "Block = " << Block.x << " " << Block.y << endl;
cout << "Grid = " << Grid.x << " " << Grid.y << endl;
float *P;
P = (float*) malloc(memSize);
float srclet;
int sidx = npmlx + sx + nOrder - 1 + (npmlz + sz + nOrder - 1)*D.Nx;
pre_eval <<< Grid, Block >>> (W, M, D, C, F);
int it = 0;
char file[200];
for(i = 0; i*nTimePreSnap < nt; i++) {
printf("calculating and exporting for step %5d ...\n", it);
for(j = 0; j < nTimePreSnap; j++, it++) {
if(it > nt) break;
srclet = ampl*(1 - 2*pow(PI*f0*(dt*it - t0), 2))*exp( - pow(PI*f0*(dt*it - t0), 2));
str_eval <<< Grid, Block >>> (W, D, C, sidx, srclet, F);
vel_eval <<< Grid, Block >>> (W, D, C, F, sidx);
if((it - 1)%nTimePreSnap == 0) {
cudaDeviceSynchronize();
sprintf(file, "P%05d.bin", it - 1);
cudaMemcpy(P, W.txxt, memSize, cudaMemcpyDeviceToHost);
exp_wave(D, file, P);
}
}
}
free(Vp); free(Vs); free(Rho);
cudaFree(M.vp); cudaFree(M.vs); cudaFree(M.rho);
free(P);
cudaFree(W.vxx ); cudaFree(W.vxz ); cudaFree(W.vxt );
cudaFree(W.vzx ); cudaFree(W.vzz ); cudaFree(W.vzt );
cudaFree(W.txxx); cudaFree(W.txxz); cudaFree(W.txxt);
cudaFree(W.tzzx); cudaFree(W.tzzz); cudaFree(W.tzzt);
cudaFree(W.txzx); cudaFree(W.txzz); cudaFree(W.txzt);
return 0;
}
|
20,341 | #include <thrust/device_vector.h>
#include <thrust/host_vector.h>
#include <thrust/random/linear_congruential_engine.h>
#include <thrust/random/uniform_real_distribution.h>
#include <iostream>
// nvcc -std=c++14 -O3 tarefa6_multSeed.cu -o t6 && ./t6
struct raw_access
{
int SEED;
__device__ __host__ double
operator()(const int &i)
{
thrust::minstd_rand rng(SEED * i);
thrust::uniform_real_distribution<double> dist(0, 1);
double x = dist(rng);
double y = dist(rng);
if (x * x + y * y <= 1)
return 1.0;
return 0.0;
}
};
int main()
{
int N = 100000;
thrust::device_vector<double> vetor(N);
thrust::counting_iterator<int> iter(0);
raw_access ra = {.SEED = 1230};
thrust::transform(iter, iter + vetor.size(), vetor.begin(), ra);
double sum = thrust::reduce(vetor.begin(), vetor.end(), 0.0, thrust::plus<double>());
std::cout << sum << "\n";
double pi = (double)4 * sum / N;
std::cout << "PI monte carlo: " << pi << "\n";
}
|
20,342 | #include <iostream>
#include <math.h>
#include <time.h>
#include <stdlib.h>
#include <random>
#include <vector>
#include <chrono>
#define TILE_DIM 32
__global__ void multiplyNaive(const int *mat_1, const int *mat_2, int *mat_prod, const int n, const int m, const int p) {
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
if (y < n && x < p) {
int sum = 0;
for (int i = 0; i < m; i++) {
sum += mat_1[y*m+i] * mat_2[i*p+x];
}
mat_prod[y*p+x] = sum;
}
}
__global__ void multiplySharedMem(const int *mat_1, const int *mat_2, int *mat_prod, const int n, const int m, const int p, const int start, const int end) {
__shared__ int aTile[TILE_DIM][TILE_DIM], bTile[TILE_DIM][TILE_DIM];
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
aTile[threadIdx.y][threadIdx.x] = mat_1[y*m+threadIdx.x+start];
bTile[threadIdx.y][threadIdx.x] = mat_2[(threadIdx.y+start)*p+x];
__syncthreads();
int dims_tile = end-start;
if (y < n && x < p) {
int sum = 0;
for (int i = 0; i < dims_tile; i++) {
sum += aTile[threadIdx.y][i] * bTile[i][threadIdx.x];
}
mat_prod[y*p+x] += sum;
}
}
std::vector<std::vector<int>> random_matrix(const int num_rows, const int num_cols, const int min_val=0.0, const int max_val=1000.0) {
std::vector<std::vector<int>> my_arr;
static std::random_device rd;
static std::mt19937 mte(rd());
std::uniform_int_distribution<int> dist(min_val, max_val);
for (int i = 0; i < num_rows; i++) {
std::vector<int> my_arr_col;
for (int j = 0; j < num_cols; j++) {
my_arr_col.push_back(dist(mte));
}
my_arr.push_back(my_arr_col);
}
return my_arr;
}
bool check_correctness(const int *mat_1, const int *mat_2, int *mat_prod, const int n, const int m, const int p) {
for (int i = 0; i < n; i++) {
for (int j = 0; j < p; j++) {
int sum = 0;
for (int k = 0; k < m; k++) {
sum += mat_1[i*m+k] * mat_2[k*p+j];
}
if (sum != mat_prod[i*p+j]) {
return false;
}
}
}
return true;
}
int main(void) {
int n = 2000;
int m = 1000;
int p = 5000;
dim3 dimGrid((p + TILE_DIM - 1)/TILE_DIM, (n + TILE_DIM - 1)/TILE_DIM, 1);
dim3 dimBlock(TILE_DIM, TILE_DIM, 1);
int *mat_1, *mat_2, *mat_prod;
cudaMallocManaged(&mat_1, n*m*sizeof(int));
cudaMallocManaged(&mat_2, m*p*sizeof(int));
cudaMallocManaged(&mat_prod, n*p*sizeof(int));
std::vector<std::vector<int>> my_arr_1 = random_matrix(n, m, 0, 10);
std::vector<std::vector<int>> my_arr_2 = random_matrix(m, p, 0, 10);
for (int i = 0; i < n; i++) {
for (int j = 0; j < m; j++) {
mat_1[m*i + j] = my_arr_1[i][j];
}
}
for (int i = 0; i < m; i++) {
for (int j = 0; j < p; j++) {
mat_2[p*i + j] = my_arr_2[i][j];
}
}
int num_parts = (m+TILE_DIM-1)/TILE_DIM;
cudaStream_t stream[num_parts];
auto t1 = std::chrono::high_resolution_clock::now();
for (int i = 0; i < num_parts; i++) {
int start = i*TILE_DIM;
int end = m < ((i+1)*TILE_DIM)?m:((i+1)*TILE_DIM);
cudaStreamCreate(&stream[i]);
multiplySharedMem<<<dimGrid, dimBlock, 0, stream[i]>>>(mat_1, mat_2, mat_prod, n, m, p, start, end);
}
cudaDeviceSynchronize();
auto t2 = std::chrono::high_resolution_clock::now();
auto duration = std::chrono::duration_cast<std::chrono::milliseconds>( t2 - t1 ).count();
std::cout << duration << std::endl;
std::cout << check_correctness(mat_1, mat_2, mat_prod, n, m, p) << std::endl;
for (int i = 0; i < num_parts; i++) {
cudaStreamDestroy(stream[i]);
}
cudaFree(mat_1);
cudaFree(mat_2);
cudaFree(mat_prod);
return 0;
}
|
20,343 | #include <stdio.h>
#include <stdlib.h>
#include <cuda_runtime.h>
#include <time.h>
#define N 10000
__global__ void add(int *d_a, int *d_b, int *d_c){
d_c[blockIdx.x] = d_a[blockIdx.x] + d_b[blockIdx.x];
}
int main(){
int *a, *b, *c, *gold_c;
int *d_a, *d_b, *d_c;
int i;
int pass = 1;
a = (int*)malloc(N*sizeof(int));
b = (int*)malloc(N*sizeof(int));
c = (int*)malloc(N*sizeof(int));
gold_c = (int*)malloc(N*sizeof(int));
for(i=0; i<N; i++){
a[i] = rand()%100;
b[i] = rand()%100;
}
struct timespec t_start, t_end;
double elapsedTimeCPU;
// start time
clock_gettime( CLOCK_REALTIME, &t_start);
for(i=0; i<N; i++){
gold_c[i] = a[i] + b[i];
}
// stop time
clock_gettime( CLOCK_REALTIME, &t_end);
// compute and print the elapsed time in millisec
elapsedTimeCPU = (t_end.tv_sec - t_start.tv_sec) * 1000.0;
elapsedTimeCPU += (t_end.tv_nsec - t_start.tv_nsec) / 1000000.0;
printf("CPU elapsedTime: %lf ms\n", elapsedTimeCPU);
cudaMalloc((void**)&d_a, N * sizeof(int));
cudaMalloc((void**)&d_b, N * sizeof(int));
cudaMalloc((void**)&d_c, N * sizeof(int));
cudaMemcpy(d_a, a, N*sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(d_b, b, N*sizeof(int), cudaMemcpyHostToDevice);
// Get start time event
cudaEvent_t start, stop;
cudaEventCreate(&start);
cudaEventCreate(&stop);
cudaEventRecord(start, 0);
add<<<N, 1>>>(d_a, d_b, d_c);
// Get stop time event
cudaEventRecord(stop, 0);
cudaEventSynchronize(stop);
// Compute execution time
float elapsedTime;
cudaEventElapsedTime(&elapsedTime, start, stop);
printf("GPU time: %13f msec\n", elapsedTime);
cudaEventDestroy(start);
cudaEventDestroy(stop);
cudaMemcpy(c, d_c, N*sizeof(int), cudaMemcpyDeviceToHost);
for(i=0; i<N; i++){
if(gold_c[i]!=c[i]){
pass = 0;
break;
}
}
if(pass==1)
printf("test pass!\n");
else
printf("error...\n");
return 0;
}
|
20,344 | #include <cuda_runtime_api.h>
#include <iostream>
////////////////////////////////////////////////////////////////////////////////
// Program main
////////////////////////////////////////////////////////////////////////////////
using namespace std;
int main( int argc, char** argv)
{
int deviceCount = 0;
cudaError_t error_id = cudaGetDeviceCount(&deviceCount);
if ( error_id != cudaSuccess )
{
cerr << "cudaGetDeviceCount failed" << endl;
return -1;
}
int driverVersion = 0;
int runtimeVersion = 0;
cudaDriverGetVersion( &driverVersion );
cudaRuntimeGetVersion( &runtimeVersion );
cout << "CUDA Driver Version = " << driverVersion << endl;
cout << "CUDA Runtime Version = " << runtimeVersion << endl;
for (int dev = 0; dev < deviceCount; ++dev)
{
cudaDeviceProp deviceProp;
cudaGetDeviceProperties( &deviceProp, dev );
cout << "Device " << dev << ": " << deviceProp.name << endl;
cout << " total amount of global memory = " << deviceProp.totalGlobalMem << " Byte"
<< " = " << deviceProp.totalGlobalMem / ( 1024.0 * 1024.0 ) << " MByte" << endl;
cout << " compute capability = " << deviceProp.major << "." << deviceProp.minor << endl;
}
}
|
20,345 | #include "includes.h"
__global__ void vecAddKernel(float* A, float* B, float* C, int n) {
// Calculate global thread index based on the block and thread indices ----
//INSERT KERNEL CODE HERE
int i = blockDim.x*blockIdx.x+threadIdx.x;
// Use global index to determine which elements to read, add, and write ---
//INSERT KERNEL CODE HERE
if (i<n) C[i] = A[i] + B[i];
} |
20,346 | #include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <algorithm>
#include <map>
#include <iostream> |
20,347 | #include <iostream>
using namespace std;
#include <thrust/reduce.h>
#include <thrust/sequence.h>
#include <thrust/host_vector.h>
#include <thrust/device_vector.h>
int main()
{
const int N = 50000;
thrust::device_vector<int> a(N);
thrust::sequence(a.begin(), a.end(), 0);
int sumA = thrust::reduce(a.begin(), a.end(), 0);
int sumCheck = 0;
for (int i = 0; i < N; i++) sumCheck += i;
if (sumA == sumCheck) cout << "Test Succeeded!" << endl;
else {
cout << "Test Failed" << endl;
return 1;
}
return 0;
} |
20,348 | #include "includes.h"
__global__ void sReduceSingle(int *idata,int *single,unsigned int ncols) {
int i;
unsigned int tid = threadIdx.x;
extern __shared__ int sdata[];
unsigned int startPos = blockDim.x + threadIdx.x;
int colsPerThread = ncols/blockDim.x;
int myPart = 0;
for(i=0;i<colsPerThread;i++) {
myPart+=idata[startPos+i];
}
sdata[tid]=myPart;
__syncthreads();
unsigned int s;
for(s=blockDim.x/2;s>0;s>>=1) {
if(tid<s) {
sdata[tid] += sdata[tid+s];
}
__syncthreads();
}
if(tid==0)*single=sdata[0];
} |
20,349 |
/* This is a automatically generated test. Do not modify */
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
__global__
void compute(float comp, int var_1,float var_2,int var_3,int var_4,float var_5,float var_6,float var_7,float var_8,float var_9,float var_10,float var_11,float var_12,float var_13,float var_14,float var_15,float var_16,float var_17,float var_18) {
if (comp < (-1.2964E-35f / var_2)) {
for (int i=0; i < var_1; ++i) {
comp = (var_5 - (-1.0299E-43f * (-0.0f + acosf((-1.7670E-37f - +1.1236E-8f / -1.0999E-36f - floorf((var_6 + var_7)))))));
for (int i=0; i < var_3; ++i) {
comp += (var_8 - -1.4604E-44f);
}
for (int i=0; i < var_4; ++i) {
comp += var_9 / +1.0312E-29f;
comp = var_10 + var_11;
}
if (comp <= tanhf(var_12 / sqrtf((var_13 + +1.1098E-27f * (-1.8034E-3f - var_14))))) {
comp += +0.0f - atan2f(var_15 + (var_16 + (+1.6843E36f - +1.5738E-21f - var_17 - +1.5894E-37f)), -1.2096E15f);
comp = (var_18 / -1.2181E-12f);
}
}
}
printf("%.17g\n", comp);
}
float* initPointer(float v) {
float *ret = (float*) malloc(sizeof(float)*10);
for(int i=0; i < 10; ++i)
ret[i] = v;
return ret;
}
int main(int argc, char** argv) {
/* Program variables */
float tmp_1 = atof(argv[1]);
int tmp_2 = atoi(argv[2]);
float tmp_3 = atof(argv[3]);
int tmp_4 = atoi(argv[4]);
int tmp_5 = atoi(argv[5]);
float tmp_6 = atof(argv[6]);
float tmp_7 = atof(argv[7]);
float tmp_8 = atof(argv[8]);
float tmp_9 = atof(argv[9]);
float tmp_10 = atof(argv[10]);
float tmp_11 = atof(argv[11]);
float tmp_12 = atof(argv[12]);
float tmp_13 = atof(argv[13]);
float tmp_14 = atof(argv[14]);
float tmp_15 = atof(argv[15]);
float tmp_16 = atof(argv[16]);
float tmp_17 = atof(argv[17]);
float tmp_18 = atof(argv[18]);
float tmp_19 = atof(argv[19]);
compute<<<1,1>>>(tmp_1,tmp_2,tmp_3,tmp_4,tmp_5,tmp_6,tmp_7,tmp_8,tmp_9,tmp_10,tmp_11,tmp_12,tmp_13,tmp_14,tmp_15,tmp_16,tmp_17,tmp_18,tmp_19);
cudaDeviceSynchronize();
return 0;
}
|
20,350 | __global__ void dijkstra(int* V, int* E, int* W, int* n, int* vis, int* dist, int* predist){
const int u0 = threadIdx.z * blockDim.x * blockDim.y + threadIdx.y * blockDim.x + threadIdx.x;
const int offset = blockDim.x * blockDim.y * blockDim.z;
__shared__ int quickBreak[1];
int u = -1;
for(int i = 0; i < (*n); i++){
quickBreak[0] = 0;
u = u0;
while(u < *n){
if(vis[u] == 0){
vis[u] = 1;
for(int j = V[u]; j < V[u + 1]; j++){
atomicMin(&predist[E[j]], dist[u] + W[j]);
}
}
u += offset;
}
__syncthreads();
u = u0;
while(u < *n){
if(predist[u] < dist[u]){
dist[u] = predist[u];
vis[u] = 0;
quickBreak[0] = 1;
}
u += offset;
}
__syncthreads();
if(quickBreak[0] == 0)
break;
__syncthreads();
}
}
__global__ void divide(int* V, int* E, int* W, int* n, int* flag, int* base, int* part, int* vis, int* dist, int* predist){
const int u0 = threadIdx.z * blockDim.x * blockDim.y + threadIdx.y * blockDim.x + threadIdx.x;
const int offset = blockDim.x * blockDim.y * blockDim.z;
int u = -1;
int l = -1;
int r = -1;
int localBase = base[0];
int localPart = part[0];
u = u0; //u + l is the vertex index
while(u < (*n)){
if(V[u + 1] <= localBase){ // self right
u += offset;
continue; // illegal
}
else if(V[u] >= localBase + localPart){ // self left
u += offset;
continue; // illegal
}
if(vis[u]){
atomicSub(&vis[u], 1);
l = localBase>V[u]?localBase:V[u];
r = (localBase + localPart)<V[u + 1]?(localBase + localPart):V[u + 1];
for(int j = l; j < r; j++){
atomicMin(&predist[E[j - localBase]], dist[u] + W[j - localBase]);
}
}
u += offset;
}
__syncthreads();
u = u0;
while(u < (*n)){
if(predist[u] < dist[u]){
dist[u] = predist[u];
vis[u] = (V[u + 1] + localPart - 1) / localPart - V[u] / localPart; // re calc the ability of updating
flag[0] = 1;
}
u += offset;
}
}
|
20,351 | #include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <limits.h>
#include <time.h>
#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);
}
}
typedef struct Edge {
int fromIndex;
int toIndex;
int departure;
int arrival;
} Edge;
/*
********************************************************************************************************
Cuda functions
********************************************************************************************************
*/
__global__
void edgeScan(int n, unsigned int* labels, Edge* edges, int offset) {
int i = threadIdx.x + blockDim.x * blockIdx.x;
if (i < n) {
Edge e = edges[offset + i];
int fromIndex = e.fromIndex;
int toIndex = e.toIndex;
int departure = e.departure;
int arrival = e.arrival;
if (departure >= labels[fromIndex]) {
atomicMin(&(labels[toIndex]), arrival);
}
}
}
__global__
void initLabels(int n, unsigned int* labels) {
int i = threadIdx.x + blockDim.x * blockIdx.x;
if (i < n) {
labels[i] = INT_MAX;
}
}
__global__
void setSource(unsigned int* labels, int source, int time) {
labels[source] = time;
}
/*
********************************************************************************************************
Function Definitions
********************************************************************************************************
*/
int stringToInt(char* str, int len);
int compare(const void* a, const void* b);
// berlin
int numOfBatches = 1495;
int numOfEdges = 1209980;
int numOfVertices = 12746;
char file1[] = "e:\\data\\berlin.txt.meta";
char file2[] = "e:\\data\\berlin.txt.edges";
// wikipedia-growth
// int numOfBatches = 700;
// int numOfEdges = 39953145;
// int numOfVertices = 1870709;
// char file1[] = "e:\\data\\out.wikipedia-growth.meta";
// char file2[] = "e:\\data\\out.wikipedia-growth.edges";
// munmun_digg_reply
// int numOfBatches = 30;
// int numOfEdges = 86203;
// int numOfVertices = 30360;
// char file1[] = "e:\\data\\out.munmun_digg_reply.meta";
// char file2[] = "e:\\data\\out.munmun_digg_reply.edges";
// loans
// int numOfBatches = 59;
// int numOfEdges = 3343284;
// int numOfVertices = 89269;
// char file1[] = "e:\\data\\out.prosper-loans.meta";
// char file2[] = "e:\\data\\out.prosper-loans.edges";
// digg-friends
// int numOfBatches = 4623;
// int numOfEdges = 1731653;
// int numOfVertices = 279630;
// char file1[] = "e:\\data\\out.digg-friends.meta";
// char file2[] = "e:\\data\\out.digg-friends.edges";
int numOfRuns = 100;
const int max_thread = 512;
bool printResult = false;
/*
********************************************************************************************************
Entry point
********************************************************************************************************
*/
int main() {
/*
********************************************************************************************************
Read meta file
********************************************************************************************************
*/
printf("\nReading file %s...\n", file1);
FILE *fp;
fp = fopen(file1,"r"); // read mode
if(fp == NULL) {
perror("Error while opening the file.\n");
exit(EXIT_FAILURE);
}
int* batchSizes = (int*)malloc(numOfBatches* sizeof(int));
char line[256];
int count = 0;
while (fgets(line, sizeof(line), fp) != NULL) {
/* note that fgets don't strip the terminating \n, checking its
presence would allow to handle lines longer that sizeof(line) */
int size = stringToInt(line, strlen(line) - 1);
batchSizes[count] = size;
count++;
// printf("New batch size added: %d...\n", size);
}
fclose(fp);
/*
********************************************************************************************************
Read data file
********************************************************************************************************
*/
printf("\nReading file %s...\n", file2);
fp = fopen(file2,"r"); // read mode
if(fp == NULL) {
perror("Error while opening the file.\n");
exit(EXIT_FAILURE);
}
Edge* edges = (Edge *)malloc(numOfEdges * sizeof(Edge));
// Initialize all values of the array to 0
// for(int i = 0; i < numOfEdges; i++) {
// memset(&(edges[i]),0,sizeof(Edge));
// }
count = 0;
while (fgets(line, sizeof(line), fp) != NULL) {
/* note that fgets don't strip the terminating \n, checking its
presence would allow to handle lines longer that sizeof(line) */
char copy[256] = "";
strncpy(copy, line, strlen(line));
char* parts;
parts = strtok(copy," ");
if (parts != NULL) {
edges[count].fromIndex = stringToInt(parts, strlen(parts));
parts = strtok(NULL, " ");
edges[count].toIndex = stringToInt(parts, strlen(parts));
parts = strtok(NULL, " ");
edges[count].departure = stringToInt(parts, strlen(parts));
parts = strtok(NULL, " ");
edges[count].arrival = stringToInt(parts, strlen(parts) - 1);
}
count++;
}
fclose(fp);
/*
********************************************************************************************************
Get shortest paths
********************************************************************************************************
*/
// GPU implementation
unsigned int* labels = (unsigned int*)malloc(numOfVertices * sizeof(unsigned int));
unsigned int* gpuLabels;
Edge* gpuEdges;
const int labelSize = numOfVertices * sizeof(unsigned int);
const int edgeSize = numOfEdges * sizeof(Edge);
gpuErrchk(cudaMalloc((void**)&gpuLabels, labelSize));
gpuErrchk(cudaMalloc((void**)&gpuEdges, edgeSize));
gpuErrchk(cudaMemcpy(gpuEdges, edges, edgeSize, cudaMemcpyHostToDevice));
gpuErrchk(cudaDeviceSynchronize());
clock_t begin, end;
double time_spent;
begin = clock();
for (int i = 0; i < numOfRuns; i++) {
// Initialize labels
initLabels<<<numOfVertices / max_thread + 1, max_thread>>>(numOfVertices, gpuLabels);
gpuErrchk(cudaPeekAtLastError());
gpuErrchk(cudaDeviceSynchronize());
// end = clock();
// time_spent = (double)(end - begin) / CLOCKS_PER_SEC;
// printf("Up to init label running time: %f\n", time_spent);
setSource<<<1, 1>>>(gpuLabels, i, 0);
gpuErrchk(cudaPeekAtLastError());
gpuErrchk(cudaDeviceSynchronize());
// end = clock();
// time_spent = (double)(end - begin) / CLOCKS_PER_SEC;
// printf("Up to set source running time: %f\n", time_spent);
// Start edge scan
int offset = 0;
for (int j = 0; j < numOfBatches; j++) {
int size = batchSizes[j];
edgeScan<<<size / max_thread + 1, max_thread>>>(size, gpuLabels, gpuEdges, offset);
gpuErrchk(cudaPeekAtLastError());
gpuErrchk(cudaDeviceSynchronize());
offset = offset + size;
}
// end = clock();
// time_spent = (double)(end - begin) / CLOCKS_PER_SEC;
// printf("Up to GPU computing running time: %f\n", time_spent);
gpuErrchk(cudaMemcpy(labels, gpuLabels, labelSize, cudaMemcpyDeviceToHost));
gpuErrchk(cudaDeviceSynchronize());
if (printResult) {
int numOfReachable = 0;
for (int j = 0; j < numOfVertices; j++) {
//printf("Label[%d]=%d\n", j, labels[j]);
if (labels[j] != INT_MAX) {
numOfReachable++;
}
}
printf("Number of reachable vertices: %d...\n", numOfReachable);
}
}
end = clock();
time_spent = (double)(end - begin) / CLOCKS_PER_SEC;
printf("Total running time: %f\n", time_spent);
// Clean up GPU memory
cudaFree(gpuLabels);
cudaFree(gpuEdges);
//cudaFree(gpuBatchSizes);
cudaDeviceSynchronize();
/*
********************************************************************************************************
Cleanup
********************************************************************************************************
*/
free(labels);
free(batchSizes);
free(edges);
return 0;
}
int stringToInt(char* str, int len) {
int size = 0;
for(int i = 0; i < len; i++) {
size = size * 10 + (str[i] - '0');
}
return size;
}
int compare(const void* a, const void* b) {
int int_a = * ( (int*) a );
int int_b = * ( (int*) b );
if ( int_a == int_b ) return 0;
else if ( int_a < int_b ) return -1;
else return 1;
}
|
20,352 |
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <device_functions.h>
#include <device_launch_parameters.h>
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
#include <cuda.h>
CUcontext hContext = 0;
#define CUDA_CHECK( fn ) do { \
CUresult status = (fn); \
if ( CUDA_SUCCESS != status ) { \
const char* errstr; \
cuGetErrorString(status, &errstr); \
printf("CUDA Driver Failure (line %d of file %s):\n\t%s returned 0x%x (%s)\n", __LINE__, __FILE__, #fn, status, errstr); \
exit(EXIT_FAILURE); \
} \
} while (0)
void gflops(const char* ident, int N, float ms, int repeat)
{
double msecPerMatrixMul = ms / repeat;
long int Gflops = (128*16*1024)*1024*2.0*1e-9f;//*2 for MUL and ADD
double gigaFlops = (Gflops) / (msecPerMatrixMul) * 1000.0f ;
printf("ms = %f \n", msecPerMatrixMul);
printf("%s GFLOPS: %.2f (size: %d, iterations: %d)\n", ident, gigaFlops, N, repeat);
}
int main()
{
//-----------------sample_data_config---------------------
int NBLOCK = 1024;
int N = 2048*NBLOCK + 1024*30;//1024032;//1023985; 160768.0*4/1024/1024=0.61328125MB
int M = 1024;//16;
int P = 2048*NBLOCK;
size_t sizeSampleFloat = N * sizeof(float);
size_t sizeFilterFloat = M * sizeof(float);//16 * 4;
size_t sizeResultFloat = P * sizeof(float);
int repeat = 4;
dim3 threads(128, 1, 1);
dim3 grid(NBLOCK, 1, 1);
cudaError_t error;
char deviceName[32];
int count, ordinal, major, minor;
CUdevice hDevice;
CUevent hStart, hStop;
CUdeviceptr devH, devX, devY;
// ------Initialize the Driver API and find a device-----
CUDA_CHECK(cuInit(0));
CUDA_CHECK(cuDeviceGetCount(&count));
for (ordinal = 0; ordinal < count; ordinal++)
{
CUDA_CHECK(cuDeviceGet(&hDevice, ordinal));
CUDA_CHECK(cuDeviceGetAttribute(&major, CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR, hDevice));
CUDA_CHECK(cuDeviceGetAttribute(&minor, CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR, hDevice));
CUDA_CHECK(cuDeviceGetName(deviceName, sizeof(deviceName), hDevice));
if (major >= 5 && minor >= 2)
{
//printf("Using: Id:%d %s (%d.%d)\n\n", ordinal, deviceName, major, minor);
break;
}
}
if (ordinal == count)
{
printf("No compute 5.0 device found, exiting.\n");
exit(EXIT_FAILURE);
}
//-----------------device_test------------------------
int device = 0;
error = cudaSetDevice(0);
if (error != cudaSuccess)
{
printf("device error");
exit(EXIT_FAILURE);
}
else printf("device: %d \n", device);
cudaDeviceProp deviceProp;
error = cudaGetDeviceProperties(&deviceProp, 0);
if (error != cudaSuccess)
{
printf("DeviceProperties error");
exit(EXIT_FAILURE);
}
//-----------------------host----------------------------
float* H = (float*)malloc(sizeFilterFloat);
float* X = (float*)malloc(sizeSampleFloat);
float* Y = (float*)malloc(sizeResultFloat);
float* T = (float*)malloc(sizeResultFloat);
for (int i = 0; i < N ; i++) //
{
X[i] = (float)rand()/1000;//(float)1.0;//
//if(X[i] == (float)16) X[i]=0;
}
for (int i = 0; i < M; i++) //
{
H[i] = (float)rand()/1000;//(float)i;// (i % 2);//(float)rand();//(float)1.0;//
}
for (int i = 0; i < P; i++) //
{
Y[i] = (float)0.0;
T[i] = (float)0.0;
}
//conv calculate
for (int i = 0; i < P; i++)
{
int k = i;
for (int j = 1024; j > 0; j--)
{
T[i] += H[j - 1] * X[k];
k++;
}
}
//-----------------------Dev----------------------------
CUDA_CHECK(cuCtxCreate(&hContext, 0, hDevice));
CUDA_CHECK(cuEventCreate(&hStart, CU_EVENT_BLOCKING_SYNC)); // CU_EVENT_DEFAULT
CUDA_CHECK(cuEventCreate(&hStop, CU_EVENT_BLOCKING_SYNC));
CUDA_CHECK(cuMemAlloc(&devH, sizeFilterFloat));
CUDA_CHECK(cuMemAlloc(&devX, sizeSampleFloat));
CUDA_CHECK(cuMemAlloc(&devY, sizeResultFloat));
CUDA_CHECK(cuMemcpyHtoD(devH, H, sizeFilterFloat));
CUDA_CHECK(cuMemcpyHtoD(devX, X, sizeSampleFloat));
//---------------------Kernel----------------------------
printf("Computing result using CUDA Kernel...\n");
// Load the cubin
CUmodule hModule;
CUDA_CHECK(cuModuleLoad(&hModule, "conv.cubin"));
// Load the kernel function
CUfunction hKernel;
CUDA_CHECK(cuModuleGetFunction(&hKernel, hModule, "conv_kernel_128"));
void * params[] = {&devH, &devX, &devY};
float totalTime = 0;
// Launch the kernel repeat times.. but break it up into pieces so as not to lock things up.
CUDA_CHECK(cuEventCreate(&hStart, CU_EVENT_BLOCKING_SYNC)); // CU_EVENT_DEFAULT
CUDA_CHECK(cuEventCreate(&hStop, CU_EVENT_BLOCKING_SYNC));
while (repeat > 0)
{
float ms;
int r = repeat;
CUDA_CHECK(cuEventRecord(hStart, NULL));
for (int i = 0; i < repeat; i++)
CUDA_CHECK(cuLaunchKernel(hKernel, grid.x, 1, 1, threads.x, 1, 1, 0, 0, params, 0));
CUDA_CHECK(cuEventRecord(hStop, NULL));
CUDA_CHECK(cuEventSynchronize(hStop));
CUDA_CHECK(cuEventElapsedTime(&ms, hStart, hStop));
totalTime += ms;
//gflops("conv_kernel_128", P, totalTime, repeat);
repeat -= r;
}
//CUDA_CHECK(cuLaunchKernel(hKernel, grid.x, grid.y, 1, threads.x, 1, 1, 0, 0, params, 0));
//CUDA_CHECK(cuLaunchKernel(hKernel, grid.x, grid.y, 1, threads.x, 1, 1, 0, 0, params, 0));
CUDA_CHECK(cuModuleUnload(hModule));
// Copy result from device to host
CUDA_CHECK(cuMemcpyDtoH(Y, devY, sizeResultFloat));
//CUDA_CHECK(cuMemcpyDtoH(H, devH, sizeFilterFloat));
//CUDA_CHECK(cuMemcpyDtoH(X, devX, sizeSampleFloat));
//for (int i = 0; i<20; i++) {
//if (Y[i] != 0.0f)
// printf("Y[%d] = %f \n", i, Y[i]);
//}
//for (int i = 2048*0; i<2048*780; i++) {
// if (Y[i] != T[i])//1024.0f)
// printf("Y[%d] = %f \n", i, Y[i]);
//}
//-----------------------free----------------------------
// Cleanup and shutdown of cuda
CUDA_CHECK(cuMemFree(devH));
CUDA_CHECK(cuMemFree(devX));
CUDA_CHECK(cuMemFree(devY));
for (int i = 0; i<1024*1; i++)
printf("Y[%d] = %f --- and --- T[%d] = %f error = %f\n", i, Y[i], i, T[i], T[i] - Y[i]);
for (int i = 0; i<P; i++)
{
if ( (Y[i]-T[i] > 1) || (Y[i]-T[i] < -1) )
printf("Y[%d] = %f --- but --- T[%d] = %f error = %f\n", i, Y[i], i, T[i], T[i] - Y[i]);
}
//for (int i = 2048*0; i<2048*1; i++) {
//if (Y[i] != 1024.0f)
// printf("T[%d] = %f \n", i, T[i]);
//}
free(H);
free(X);
free(Y);
free(T);
CUDA_CHECK(cuEventDestroy(hStart));
CUDA_CHECK(cuEventDestroy(hStop));
CUDA_CHECK(cuCtxDestroy(hContext));
hContext = 0;
cudaDeviceReset();
printf("done\n");
return EXIT_SUCCESS;
}
|
20,353 | #include <cuda.h>
#include <stdio.h>
#include <iostream>
#include <string>
#include <cstdio>
#include <cstdlib>
__global__ void calc(int *dA){
int index = blockIdx.x * blockDim.x + threadIdx.x;
dA[index] = blockIdx.x + threadIdx.x;
}
int main(void) {
using namespace std;
int *dA;
int size = sizeof(int);
cudaMalloc(&dA, 16 * size);
calc <<< 2 , 8 >>>(dA);
cudaDeviceSynchronize();
int *hA = (int *)malloc(16 * size);
cudaMemcpy(hA, dA, 16*sizeof(int), cudaMemcpyDeviceToHost);
for (int i = 0; i < 16; i++) {
if (i < 15)
cout << hA[i] << " ";
else {
cout << hA[i] << endl;
}
}
free(hA);
cudaFree(&dA);
return 0;
} |
20,354 | #define _POSIX_C_SOURCE 200809L
#include <stdio.h>
#include <cuda.h>
#include <string>
#include <iostream>
#include <fstream>
#include "f_eval.cuh"
using namespace std;
__inline__ __host__ __device__ double f_eval(double* p_x, int m);
double* readFile(string input, int *m, int *n);
void writeFile(string output, double* A, int m, int n);
int main(int argc, char* argv[])
{
if(argc != 4){
printf("USAGE: %s <inputArray.inp> <outfile> <epsilon>\n", argv[0]);
return 1;
}
string inputFN = argv[1];
string outputFN = argv[2];
double epsilon = atof(argv[3]);
int m, n;
// Read file
double* A = readFile(inputFN, &m, &n);
double *B = (double*)malloc(sizeof(double)*m*n);
struct timespec start, stop;
double time = 0;
clock_gettime(CLOCK_MONOTONIC, &start);
for(int i = 0; i < n; i++){
for(int j = 0; j < m; j++){
A[i*m+j] += epsilon;
B[i*m+j] = f_eval(A+i*m,m);
A[i*m+j] -= 2*epsilon;
B[i*m+j] -= f_eval(A+i*m,m);
A[i*m+j] += epsilon;
B[i*m+j] /= 2*epsilon;
}
}
clock_gettime(CLOCK_MONOTONIC, &stop);
time += (stop.tv_sec - start.tv_sec) * 1000;
time += (stop.tv_nsec - start.tv_nsec) / 1000.0 / 1000.0;
writeFile(outputFN, B, m, n);
cout << n << "," << m << "," << time << endl;
free(A);
free(B);
A = NULL;
B = NULL;
}
double* readFile(string input, int *m, int *n){
string line;
ifstream in (input);
if (in.is_open())
{
//read n
getline(in,line);
*n = atoi(line.c_str());
//read m
getline(in,line);
*m = atoi(line.c_str());
// Allocate pinned memory
double *A = (double*)malloc(sizeof(double)*(*m)*(*n));
for(int i = 0; i < *n; i++){
for(int j = 0; j < *m-1; j++){
getline(in,line,',');
A[i*(*m)+j] = atof(line.c_str());
}
getline(in,line);
A[(i+1)*(*m)-1] = atof(line.c_str());
}
in.close();
return A;
}
else{
*m = -1;
*n = -1;
cout << "Unable to open input file!";
return NULL;
}
}
void writeFile(string output, double* A, int m, int n){
ofstream out (output);
for(int i = 0; i < n; i++){
for(int j = 0; j < m-1; j++){
out << A[i*m+j] << ",";
}
out << A[(i+1)*m-1] << endl;
}
out.close();
} |
20,355 | #include <stdlib.h>
#include <stdio.h>
#include <cuda.h>
// allocate memory on gpu
extern "C++" void cu_safe_falloc(float **g_f, size_t n_elem) {
void *gptr;
cudaError_t crc = cudaMalloc(&gptr, n_elem*sizeof(float));
if(crc) {
printf("cudaMalloc Error=%d:%s\n", crc, cudaGetErrorString(crc));
exit(1);
}
*g_f = (float*) gptr;
}
// free memory on gpu
extern "C++" void cu_free(void *g_d) {
cudaError_t crc = cudaFree(g_d);
if (crc) {
printf("cudaFree Error=%d:%s\n", crc, cudaGetErrorString(crc));
exit(1);
}
}
// copy from cpu space f to gpu space g_f
extern "C++" void memcpy_htod(float *g_f, float *f, size_t n_elem) {
cudaError_t crc = cudaMemcpy((void*)g_f, f, sizeof(float)*n_elem,
cudaMemcpyHostToDevice);
if (crc) {
printf("cudaMemcpyHostToDevice float Error=%d:%s\n",crc,
cudaGetErrorString(crc));
exit(1);
}
}
// copy from gpu space g_f to cpu space f
extern "C++" void memcpy_dtoh(float *f, float *g_f, size_t n_elem) {
cudaError_t crc = cudaMemcpy(f, (void*)g_f, sizeof(float)*n_elem,
cudaMemcpyDeviceToHost);
if (crc) {
printf("cudaMemcpyDeviceToHost float Error=%d:%s\n",crc,
cudaGetErrorString(crc));
exit(1);
}
return;
}
|
20,356 | #include <stdio.h>
#include <ctime>
#define CUDA_KERNEL_LOOP(i, n) \
for (int i = blockIdx.x * blockDim.x + threadIdx.x; \
i < (n); \
i += blockDim.x * gridDim.x)
__global__ void distance(float *xSquare, float *ySquare, int *result, int testNum) {
// int tid = threadIdx.x;
// int bid = blockIdx.x;
// int idx = tid + blockDim.x * bid;
// printf("idx is: %d\n", idx);
CUDA_KERNEL_LOOP(index, testNum) {
// if (index < num)
{
float temp = (xSquare[index] - 1) * (xSquare[index] - 1) + (ySquare[index] - 1) * (ySquare[index] - 1);
if (temp < 1) {
result[index] = 1;
}
else {
result[index] = 0;
// printf("**********************************************************");
}
}
}
}
__global__ void sum_array(int *a, int *b, int testNum) {
int tid = threadIdx.x;
b[0] = 0;
__shared__ float sData[512];
for (int count = 0; count < ceilf(testNum / 512); count++) {
if (tid + count * 512 < testNum) {
sData[tid] = a[tid + count * 512];
__syncthreads();
}
for (int i = 512 / 2; i > 0; i /= 2) {
if (tid < i) {
sData[tid] = sData[tid] + sData[tid + i];
}
__syncthreads();
}
if (tid == 0) {
b[0] += sData[0];
}
}
}
int main(int argc, char* argv[]) {
int testNum = 100000000;
srand((int)time(0));
float *xSquare = new float[testNum];
float *ySquare = new float[testNum];
for (int i = 0; i < testNum; i++) {
xSquare[i] = rand() % 10000 * 1.0 / 10000;
ySquare[i] = rand() % 10000 * 1.0 / 10000;
}
float *xSquareGpu;
cudaMalloc((void**)&xSquareGpu, testNum * sizeof(float));
cudaMemcpy(xSquareGpu, xSquare, testNum * sizeof(float), cudaMemcpyHostToDevice);
float *ySquareGpu;
cudaMalloc((void**)&ySquareGpu, testNum * sizeof(float));
cudaMemcpy(ySquareGpu, ySquare, testNum * sizeof(float), cudaMemcpyHostToDevice);
int threadNum = 1024;
int blockNum = 512;
int *resultGpu;
cudaMalloc((void**)&resultGpu, testNum * sizeof(int));
distance<<<blockNum, threadNum>>>(xSquareGpu, ySquareGpu, resultGpu, testNum);
int *result = new int[testNum];
cudaMemcpy(result, resultGpu, testNum * sizeof(int), cudaMemcpyDeviceToHost);
for (int i = 0; i < 10; i++) {
printf("result[%d] is %d\n", i, result[i]);
}
int *bGpu;
cudaMalloc((void**)&bGpu, 1 * sizeof(int));
sum_array<<<1, 512>>>(resultGpu, bGpu, testNum);
int b[1];
cudaMemcpy(b, bGpu, 1 * sizeof(int), cudaMemcpyDeviceToHost);
printf("the b[0] is: %d\n", b[0]);
printf("the appropriate pi is: %2.6f\n", float(b[0])/testNum*4);
return 0;
}
|
20,357 | #include "includes.h"
#ifndef __CUDACC__
#define __CUDACC__
#endif
// generate a random square matrix
__global__ void matMulKernel4(float* P, float* M, float* N, int width) {
__shared__ float Mds4[4][4];
__shared__ float Nds4[4][4];
int bx = blockIdx.x; int by = blockIdx.y;
int tx = threadIdx.x; int ty = threadIdx.y;
int row = by*4 + ty; int col = bx*4 + tx;
float pVal = 0;
for(int ph = 0; ph < width/4; ++ph) {
Mds4[ty][tx] = M[row*width + ph*4 + tx];
Nds4[ty][tx] = N[(ph*4 + ty)*width + col];
__syncthreads();
for(int k = 0; k < 4; ++k)
pVal += Mds4[ty][k]*Nds4[k][tx];
__syncthreads();
}
P[row*width + col] = pVal;
} |
20,358 | #include <thrust/tuple.h>
#include <thrust/device_vector.h>
#include <thrust/host_vector.h>
#include <thrust/transform.h>
#include <thrust/fill.h>
#include <thrust/iterator/zip_iterator.h>
#include <cstdio>
#define N 32
struct rotate_tuple
{
__host__ __device__ thrust::tuple<float, float, float> operator()(thrust::tuple<float&, float&, float&> t)
{
float x = thrust::get<0>(t);
float y = thrust::get<1>(t);
float z = thrust::get<2>(t);
float rx = 0.36 * x + 0.48 * y - 0.80 * z;
float ry = -0.80f * x + 0.60 * y + 0.00f * z;
float rz = 0.48f * x + 0.64f * y + 0.60f * z;
return thrust::make_tuple(rx, ry, rz);
}
};
struct range_functor
{
float h;
range_functor(float _h) : h(_h) {}
__host__ __device__ float operator() (float x)
{
return x * h;
}
};
int main()
{
thrust::device_vector<float> x(N), y(N), z(N);
thrust::fill(x.begin(), x.end(), 2.0);
thrust::fill(y.begin(), y.end(), 3.0);
thrust::fill(z.begin(), z.end(), 5.0);
thrust::transform(
thrust::make_zip_iterator(
thrust::make_tuple(x.begin(), y.begin(), z.begin() )),
thrust::make_zip_iterator(
thrust::make_tuple(x.end(), y.end(), z.end() )),
thrust::make_zip_iterator(
thrust::make_tuple(x.begin(), y.begin(), z.begin() )),
rotate_tuple() );
thrust::host_vector<float> hx(N), hy(N), hz(N);
hx = x;
hy = y;
hz = z;
for (int i = 0; i < N; i++)
printf("%g\t%g\t%g\n", hx[i], hy[i], hz[i]);
return 0;
}
|
20,359 | // CUDA programming
// Exercise n. 02
#include <errno.h>
#include <cuda.h>
#include <stdio.h>
#define BLOCKS 1
#define THREADS 1
// Prototype
__global__ void add(int *a, int *b, int *c);
int main(void)
{
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 = sizeof(int);
// Setup input values
a = 5;
b = 9;
// Allocate space for device copies of a, b, c
cudaMalloc((void **)&d_a, size);
cudaMalloc((void **)&d_b, size);
cudaMalloc((void **)&d_c, size);
// Copy inputs to device
cudaMemcpy(d_a, &a, size, cudaMemcpyHostToDevice);
cudaMemcpy(d_b, &b, size, cudaMemcpyHostToDevice);
// Call the kernel on GPU
add<<< BLOCKS, THREADS >>>(d_a, d_b, d_c);
// Copy result back to host
cudaMemcpy(&c, d_c, size, cudaMemcpyDeviceToHost);
// Cleanup
cudaFree(d_a);
cudaFree(d_b);
cudaFree(d_c);
return(EXIT_SUCCESS);
}
// Addition (on device)
__global__ void add(int *a, int *b, int *c)
{
*c = *a + *b;
}
|
20,360 | #include <string.h>
#include <stdlib.h>
#include <math.h>
#include <stdint.h>
#include <stdio.h>
#include <unistd.h>
// **********************************************
// For floats vector on device
// **********************************************
typedef struct {
float x;
float y;
float z;
} Vec;
__device__ Vec vec_add(Vec v1, Vec v2) {
Vec res;
res.x = v1.x + v2.x;
res.y = v1.y + v2.y;
res.z = v1.z + v2.z;
return res;
}
__device__ Vec vec_sub(Vec v1, Vec v2) {
Vec res;
res.x = v1.x - v2.x;
res.y = v1.y - v2.y;
res.z = v1.z - v2.z;
return res;
}
__device__ Vec vec_scale(Vec v, float i) {
Vec res;
res.x = i * v.x;
res.y = i * v.y;
res.z = i * v.z;
return res;
}
__device__ float random_float(uint64_t * seed){
const uint64_t m = 9223372036854775808ULL;
const uint64_t a = 2806196910506780709ULL;
const uint64_t c = 1ULL;
*seed = (a * (*seed) + c)%m;
float res = (float) (*seed)/(float)m;
return res;
}
__device__ uint64_t forward(uint64_t seed, uint64_t n){
const uint64_t m = 9223372036854775808ULL;
uint64_t a = 2806196910506780709ULL;
uint64_t c = 1ULL;
n = n % m;
uint64_t a_new = 1;
uint64_t c_new = 0;
while(n>0){
if(n & 1){
a_new *= a;
c_new = c_new *a + c;
}
c *= (a + 1);
a *= a;
n >>= 1;
}
return (a_new * seed + c_new) % m;
}
__device__ Vec vec_sample_unit(uint64_t i) {
Vec res;
uint64_t seed = forward(i, i*200);
float pho = random_float(&seed) * 2 * 3.141592653;
float cos_theta = random_float(&seed) * 2 - 1;
float sin_theta = sqrt( 1 - cos_theta * cos_theta);
res.x = sin_theta * cos(pho);
res.y = sin_theta * sin(pho);
res.z = cos_theta;
return res;
}
__device__ float vec_dot_product(Vec v1, Vec v2) {
float res;
res = v1.x * v2.x + v1.y * v2.y + v1.z * v2.z;
return res;
}
__device__ float vec_norm(Vec v) {
float res;
res = sqrt(v.x * v.x + v.y * v.y + v.z * v.z);
return res;
}
// **********************************************
// ray_trace_kernel
// **********************************************
inline void cuchk(cudaError_t err){
if (err != cudaSuccess) {
printf("%s in %s at line %d\n",
cudaGetErrorString(err), __FILE__, __LINE__);
exit(EXIT_FAILURE);
}
}
__global__ void ray_trace_kernel(float* d_window, int width, int ray_num) {
//tried to use the shared mem, but it's too small to store the whole window 1000*1000*4bytes = 4 mb
//issue the threads to do the work
__shared__ int ray_chunck;
__shared__ Vec C;
__shared__ Vec L;
__shared__ float w_y;
__shared__ float w_max;
__shared__ float R;
int tx = blockIdx.x * gridDim.x + threadIdx.x;
ray_chunck = ray_num/gridDim.x;
C.x = 0.0;
C.y = 12.0;
C.z = 0.0;
L.x = 4.0;
L.y = 4.0;
L.z = -1;
w_y = 10;
w_max = 10;
R = 6.0;
for (int i = 0; i < ray_chunck; i+=blockDim.x*gridDim.x){
Vec V, W;
uint64_t seed = tx;
while(1){
seed += ray_num;
V = vec_sample_unit(seed);
if (V.y == 0) continue;
W = vec_scale(V, (w_y/V.y));
float temp = vec_dot_product(V, C);
if (fabs(W.x) < w_max && fabs(W.z) < w_max && temp * temp + R * R - vec_dot_product(C,C) > 0) break;
}
float temp2 = vec_dot_product(V,C);
float t = temp2 - sqrt(temp2 * temp2 + R * R - vec_dot_product(C,C));
Vec II = vec_scale(V, t);
Vec N = vec_scale(vec_sub(II,C), 1.0/vec_norm(vec_sub(II,C)));
Vec S = vec_scale(vec_sub(L,II), 1.0/vec_norm(vec_sub(L,II)));
float b = vec_dot_product(S,N);
b = 0 >= b ? 0 : b;
int x = floor((W.x + w_max) /(2 * w_max) * (width - 1));
int z = floor((W.z + w_max) /(2 * w_max) * (width - 1));
atomicAdd(&d_window[x*width + z], b);
}
}
// **********************************************
// get the args
// **********************************************
void get_input(int argc, char *argv[], int* num_rays, int* len, int *grid_dim, int *block_dim){
*num_rays = 1000000;
*len = 1000;
*grid_dim = -1;
*block_dim = 256;
int opt;
while((opt = getopt(argc, argv, "r:l:g:b:")) != -1) {
switch (opt) {
case 'r':
*num_rays = atoi(optarg);
break;
case 'l':
*len = atoi(optarg);
break;
case 'g':
*grid_dim = atoi(optarg);
break;
case 'b':
*block_dim = atoi(optarg);
break;
default:break;
}
}
if (*grid_dim == -1) {
*grid_dim = (*num_rays + *block_dim-1) / *block_dim;
}
}
int main(int argc, char* argv[]) {
int num_rays;
int len;
int grid_dim;
int block_dim;
get_input(argc, argv, &num_rays, &len, &grid_dim, &block_dim);
size_t size = len * len * sizeof(float);
float* d_window;
cudaMalloc((void **) &d_window, size);
cudaMemset((void *) d_window, 0.0, size);
cudaEvent_t start ,end;
cudaEventCreate(&start);
cudaEventCreate(&end);
cudaEventRecord(start, 0);
ray_trace_kernel<<<grid_dim,block_dim>>>(d_window, len, num_rays);
cudaEventRecord(end, 0);
float time;
cudaEventSynchronize(end);
cudaEventElapsedTime(&time, start, end);
time = time / 1000;
printf("ray_num\tgrid_dim\tblock_dim\ttime\n");
printf("%d\t%d\t%d\t%f\n", num_rays, grid_dim, block_dim, time);
float* window = (float*) malloc(size);
cudaMemcpy(window, d_window, size, cudaMemcpyDeviceToHost);
char filename[] = "ball.dat";
FILE *f = fopen(filename, "wb");
if (f != NULL) {
fwrite(window, sizeof(float), len * len, f);
fclose(f);
} else {
fprintf(stderr, "Error opening %s: ", filename);
perror("");
free(window);
cuchk(cudaFree(d_window));
exit(EXIT_FAILURE);
}
free(window);
cuchk(cudaFree(d_window));
return 0;
}
|
20,361 | #include <iostream>
#include <cstdio>
#include <cstring>
#include <thrust/device_vector.h>
// ---------------------------------------------------------------------------------------------------------------------------
// -------------------------------------------------- Kernels ----------------------------------------------------------------
// Compute an histogram of how many 0's and 1's are set using the current mask
__global__ void Gpu_Histogram (unsigned int *in, unsigned int *bins, const unsigned int mask, const int i)
{
int tid = threadIdx.x;
unsigned int bin = (in[tid] & mask) >> i;
atomicAdd(&bins[bin],1);
}
// Compute an exclusive scan on the input array and store the result on the output array
__global__ void Gpu_ExScan (unsigned int *in, unsigned int *out, const size_t n)
{
extern __shared__ unsigned int cache2[];
int tid = threadIdx.y * blockDim.x + threadIdx.x;
int offset = 1;
// Load the input into shared memory
cache2[2*tid] = in[2*tid];
cache2[2*tid+1] = in[2*tid+1];
__syncthreads();
// Build sum in place up the tree
for (int d = n >> 1; d > 0; d >>= 1)
{
__syncthreads();
if (tid < d)
{
int ai = offset*(2*tid+1)-1;
int bi = offset*(2*tid+2)-1;
cache2[bi] += cache2[ai];
}
offset *= 2;
}
// Clear the last element
if (tid == 0)
{
cache2[n-1] = 0;
}
// Transverse down and build scan
for (int d = 1; d < n; d *= 2)
{
offset >>= 1;
__syncthreads();
if (tid < d)
{
int ai = offset*(2*tid+1)-1;
int bi = offset*(2*tid+2)-1;
// Avoiding read-write bugs
int aux = cache2[ai];
cache2[ai] = cache2[bi];
cache2[bi] += aux;
}
}
__syncthreads();
// Write results to output
out[2*tid] = cache2[2*tid];
out[2*tid+1] = cache2[2*tid+1];
offset = out[1];
//__syncthreads();
//if (tid == 0)
// printf("Binscan 0 = %u || Binscan 1 = %u\n",out[0],out[1]);
}
// Calculate the predicate of each element on the input
__global__ void Gpu_Predicate (unsigned int *in, unsigned int *out, unsigned int *nout, const int mask, const int i)
{
int tid = threadIdx.x;
// Predicate format = Check if the ith bit of the element is set
unsigned int bit = (in[tid] & mask) >> i;
out[tid] = bit;
nout[tid] = !bit;
}
// Map the correct position of each element on input based on the scan array of the predicate
__global__ void Gpu_Mapping (unsigned int *in, unsigned int *out, unsigned int *bscan, unsigned int *scan, unsigned int *nscan, const int mask, const int i)
{
int tid = threadIdx.x;
unsigned int bit = (in[tid] & mask) >> i;
// If bit is 1, than pred[tid] is active
if (bit)
{
int offset = bscan[1];
out[scan[tid] + offset] = in[tid];
}
// Bit 0 is active in npred[tid]
else
{
out[nscan[tid]] = in[tid];
}
}
// ---------------------------------------------------------------------------------------------------------------------------
// -------------------------------------------------- CPU Functions ----------------------------------------------------------
void Print (unsigned int *v, unsigned int N)
{
for (int i = 0; i < N; i++)
printf("%4u\n",v[i]);
printf("\n");
}
void GetInput (unsigned int *v, unsigned int N)
{
for (int i = 0; i < N; i++)
scanf("%u",&v[i]);
}
void Swap (unsigned int* &a, unsigned int* &b)
{
unsigned int *temp = a;
a = b;
b = temp;
}
void RadixSort (unsigned int *in, unsigned int *out, const unsigned int N)
{
// Define kernel dimension
dim3 gridSize(1,1);
dim3 blockSize(N,1);
const int numBits = 1;
const int numBins = 2;
// Shared memory sizes
size_t sizeBins = sizeof(unsigned int)*numBins;
size_t sizeN = sizeof(unsigned int)*N;
// Allocate GPU memory
unsigned int *binHistogram, *binScan, *pred, *npred, *scan, *nscan;
cudaMalloc(&binHistogram,sizeof(unsigned int)*numBins);
cudaMalloc(&binScan,sizeof(unsigned int)*numBins);
cudaMalloc(&pred,sizeof(unsigned int)*N);
cudaMalloc(&npred,sizeof(unsigned int)*N);
cudaMalloc(&scan,sizeof(unsigned int)*N);
cudaMalloc(&nscan,sizeof(unsigned int)*N);
for (int i = 0; i < 8 * sizeof(unsigned int); i += numBits)
{
unsigned int mask = numBits << i;
// Reset the bins
cudaMemset(binHistogram,0,sizeof(unsigned int)*numBins);
cudaMemset(binScan,0,sizeof(unsigned int)*numBins);
// Call kernel to compute histogram
Gpu_Histogram<<<gridSize,blockSize>>>(in,binHistogram,mask,i);
// Call kernel to compute the exclusive scan on the histogram
Gpu_ExScan<<<1,numBins,2*sizeBins>>>(binHistogram,binScan,numBins);
// Call kernel that calculate the predicate
Gpu_Predicate<<<gridSize,blockSize>>>(in,pred,npred,mask,i);
// Call kernel to compute the exclusive scan on the predicates
Gpu_ExScan<<<gridSize,blockSize,sizeN>>>(pred,scan,N);
Gpu_ExScan<<<gridSize,blockSize,sizeN>>>(npred,nscan,N);
// Call kernel to do the final mapping using the scan array
Gpu_Mapping<<<gridSize,blockSize>>>(in,out,binScan,scan,nscan,mask,i);
Swap(out,in);
}
cudaMemcpy(out,in,sizeof(unsigned int)*N,cudaMemcpyDeviceToDevice);
// Free memory
cudaFree(binHistogram);
cudaFree(binScan);
cudaFree(pred);
cudaFree(npred);
cudaFree(scan);
cudaFree(nscan);
}
void Usage (char pName[])
{
printf("============================================\n");
printf("Usage:> %s < 'input_filename'\n",pName);
printf("Example:> %s < input\n",pName);
printf("============================================\n");
}
// ---------------------------------------------------------------------------------------------------------------------------
// ------------------------------------------------ MAIN FUNCTION ------------------------------------------------------------
int main (int argc, char *argv[])
{
if (argc-1 != 0)
{
Usage(argv[0]);
exit(1);
}
// Read size of the input
unsigned int N;
scanf("%u",&N);
// Declare and allocate memory for the host
unsigned int *in, *out;
unsigned int *d_in, *d_out;
in = new unsigned int[N]();
out = new unsigned int[N]();
cudaMalloc(&d_in,sizeof(unsigned int)*N);
cudaMalloc(&d_out,sizeof(unsigned int)*N);
// Get or Generate the array to sort
GetInput(in,N);
Print(in,N);
// Copy the array to the GPU
cudaMemcpy(d_in,in,sizeof(unsigned int)*N,cudaMemcpyHostToDevice);
// Initialize output array
cudaMemset(d_out,0,sizeof(unsigned int)*N);
// Sort the array using RadixSort
RadixSort(d_in,d_out,N);
// Print the result
cudaMemcpy(out,d_out,sizeof(unsigned int)*N,cudaMemcpyDeviceToHost);
Print(out,N);
delete [] in;
delete [] out;
cudaFree(d_in);
cudaFree(d_out);
return 0;
} |
20,362 | #include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include <math.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <cuda.h>
#include <device_launch_parameters.h>
#define LIST_SIZE 100000
extern "C" __device__ unsigned long long zeroList[LIST_SIZE];
extern "C" __device__ unsigned long long oneList[LIST_SIZE];
extern "C" __device__ unsigned long long record_flag;
void bambooLogRecordOff(){
long long local_record = 0;
cudaMemcpyToSymbol(record_flag, &local_record, sizeof(long long), 0, cudaMemcpyHostToDevice);
}
void bambooLogKernelBegin(long long i) {
i = 1;
cudaMemcpyToSymbol(record_flag, &i, sizeof(long long), 0, cudaMemcpyHostToDevice);
}
void bambooLogKernelEnd()
{
#ifdef KERNELTRACE
cudaDeviceSynchronize();
#endif
unsigned long long zero_result[LIST_SIZE] = {0};
unsigned long long one_result[LIST_SIZE] = {0};
cudaMemcpyFromSymbol(zero_result, zeroList, LIST_SIZE * sizeof(long long), 0, cudaMemcpyDeviceToHost);
cudaMemcpyFromSymbol(one_result, oneList, LIST_SIZE * sizeof(long long), 0, cudaMemcpyDeviceToHost);
FILE *profileFile = fopen("profile_cmp_prob_result.txt", "w");
for(long long i=0; i < LIST_SIZE; i++){
if(zero_result[i] != 0 || one_result[i] != 0){
fprintf(profileFile, "%lld: %lld %lld\n", i, zero_result[i], one_result[i]);
}
}
fclose(profileFile);
}
|
20,363 | #include <iostream>
#include <string>
#include <cassert>
#include <ctime>
using namespace std;
struct cuda_exception {
explicit cuda_exception(const char *err) : error_info(err) {}
explicit cuda_exception(const string &err) : error_info(err) {}
string what() const throw() { return error_info; }
private:
string error_info;
};
void checkCudaError(const char *msg) {
cudaError_t err = cudaGetLastError();
if (cudaSuccess != err) {
string error_info(msg);
error_info += " : ";
error_info += cudaGetErrorString(err);
throw cuda_exception(error_info);
}
}
template<typename T>
void random_matrix(T *M, size_t height, size_t width, int p = 2) {
for(size_t i = 0; i < height; ++i) {
for (size_t j = 0; j < width; ++j) {
M[i * width + j] = rand() % p;
}
}
}
template<typename T>
void print_matrix(const T *M, size_t height, size_t width) {
if (height >= 32 || width >= 32) {
cout << "a matrix of height " << height << ", of width " << width << endl;
return;
}
for(size_t i = 0; i < height; ++i) {
for (size_t j = 0; j < width; ++j) {
cout << M[i * width + j] << " ";
}
cout << endl;
}
cout << endl;
}
#define BLOCK_SIZE 16
/**
* CUDA kernel for matrix multiplication, blockwise multiplcation
*
* @C, the output matrix C = A * B
* @A, the first input matrix
* @B, the second input matrix
* @wa, is the width of A
* @wb, is the width of B
*
* returns void.
*
* The heights of A and B are not passed since wa, wb and
* the block indices are sufficient to determine the leading
* position of the matrix tile that a given thread is working on
*
* One thread block is computing a tile in C, thus, it
* computes a dot-product of tile vectors.
* Each element in C is computed by one thread of one
* thread block.
* Each thread computes its C-element as a sum of
* "small" dot-products, where each of these dot-products
* is a dot-product of two vectors in a tile.
*
*/
template <typename T>
__global__ void matrix_mul_ker(T* C, const T *A, const T *B,
size_t wa, size_t wb)
{
// Block index; WARNING: should be at most 2^16 - 1
int bx = blockIdx.x;
int by = blockIdx.y;
// Thread index
int tx = threadIdx.x;
int ty = threadIdx.y;
// Index of the first submatrix of A processed by the block
// This is the y-coordinate of the NW corner of the working tile
int aBegin = wa * BLOCK_SIZE * by;
// Index of the last submatrix of A processed by the block
// This is the y-coordinate of the SE corner of the working tile
int aEnd = aBegin + wa - 1;
// Step size used to iterate through the submatrices of A
//
int aStep = BLOCK_SIZE;
// Index of the first submatrix of B processed by the block
// This is the x-coordinate of the NW corner of the working tile
int bBegin = BLOCK_SIZE * bx;
// Step size used to iterate through the submatrices of B
int bStep = BLOCK_SIZE * wb;
// The element of the block submatrix that is computed by the thread
// WARNING: This is a local variable for the working thread
int Csub = 0;
// Loop over all the submatrices of A and B required to
// compute the block submatrix
// This loop iterates through the tiles in A ndn B that
// contribute to the working tile of C
for(int a = aBegin, b = bBegin; a <= aEnd; a += aStep, b += bStep) {
// shared memory for the submatrix of A
__shared__ int As[BLOCK_SIZE][BLOCK_SIZE];
// shared memory for the submatrix of B
__shared__ int Bs[BLOCK_SIZE][BLOCK_SIZE];
// Load the matrices from global memory to shared memory
// each thread loads one element of each matrix
As[ty][tx] = A[a + wa * ty + tx];
Bs[ty][tx] = B[b + wb * ty + tx];
// synchronize to make sure the matrices are loaded
__syncthreads();
// Multiply the two matrices together
// each thread computes one element of the block submatrix
for(int k = 0; k < BLOCK_SIZE; ++k) {
Csub += As[ty][k] * Bs[k][tx];
}
// synchronize to make sure that the preceding computation is
// done before loading two new submatrices of A dnd B in the next iteration
__syncthreads();
}
// Write the block submatrix to global memory;
// each thread writes one element
int c = wb * BLOCK_SIZE * by + BLOCK_SIZE * bx;
C[c + wb * ty + tx] = Csub;
}
template <typename T>
void matrix_mul_dev(T* C, const T* A, const T* B, int ha, int wa, int wb) {
assert(wa % BLOCK_SIZE == 0);
assert(wb % BLOCK_SIZE == 0);
// load A and B to the device
size_t mem_size = ha * wa * sizeof(T);
T* Ad;
cudaMalloc((void **)&Ad, mem_size);
checkCudaError("allocate GPU memory for the first matrix");
cudaMemcpy(Ad, A, mem_size, cudaMemcpyHostToDevice);
T* Bd;
mem_size = wa * wb * sizeof(T);
cudaMalloc((void **)&Bd, mem_size);
checkCudaError("allocate GPU memory for the second matrix");
cudaMemcpy(Bd, B, mem_size, cudaMemcpyHostToDevice);
// allocate C on the device
T* Cd;
mem_size = ha * wb * sizeof(int);
cudaMalloc((void**)&Cd, mem_size);
checkCudaError("allocate GPU memory for the output matrix");
// compute the execution configure
// assume that the matrix dimensions are multiples of BLOCK_SIZE
dim3 dimBlock(BLOCK_SIZE, BLOCK_SIZE);
size_t dgx = wb / dimBlock.x;
size_t dgy = ha / dimBlock.y;
dim3 dimGrid(dgx, dgy);
// launch the device computation
matrix_mul_ker<<<dimGrid, dimBlock>>>(Cd, Ad, Bd, wa, wb);
cudaThreadSynchronize();
checkCudaError("call the matrix multiplication kernel");
// read C from the device
cudaMemcpy(C, Cd, mem_size, cudaMemcpyDeviceToHost);
// Free device memory
cudaFree(Ad);
cudaFree(Bd);
cudaFree(Cd);
}
/**
* Returns the time spent in seconds
*/
template <typename T>
double matrix_mul_gpu(T* C, const T* A, const T* B, int ha, int wa, int wb) {
clock_t t1 = clock();
// do the multiplication
matrix_mul_dev(C, A, B, ha, wa, wb);
clock_t t2 = clock();
return (t2 - t1) / double(CLOCKS_PER_SEC);
}
/**
* ha = 2^eha,
* wa = 2^ewa,
* wb = 2^ewb;
*
* If no parameter entered, then default values are used.
* If one parameter rentered, it is eha = ewa = ewb = argv[1].
* If three parameters rentered, there are eha, ewa, and ewb, respectively.
*
*/
int matrix_mul_test(int argc, char **argv) {
int *A, *B, *C;
size_t eha = 4;
size_t ewa = 4;
size_t ewb = 4;
if (argc == 2) {
eha = ewa = ewb = atoi(argv[1]);
} else if (argc >= 3) {
eha = atoi(argv[1]);
ewa = atoi(argv[2]);
ewb = atoi(argv[3]);
}
size_t ha = (1L << eha);
size_t wa = (1L << ewa);
size_t wb = (1L << ewb);
try {
A = new int[ha * wa];
B = new int[wa * wb];
C = new int[ha * wb];
random_matrix(A, ha, wa);
random_matrix(B, wa, wb);
// timing gpu based method
cout << matrix_mul_gpu(C, A, B, ha, wa, wb) << "(seconds)" << endl;
} catch (cuda_exception& err) {
cout << err.what() << endl;
delete [] A;
delete [] B;
delete [] C;
return EXIT_FAILURE;
} catch (...) {
delete [] A;
delete [] B;
delete [] C;
cout << "unknown exeception" << endl;
return EXIT_FAILURE;
}
print_matrix(A, ha, wa);
print_matrix(B, wa, wb);
print_matrix(C, ha, wb);
delete [] A;
delete [] B;
delete [] C;
return 0;
}
int main(int argc, char** argv) {
matrix_mul_test(argc, argv);
return 0;
}
|
20,364 | #include <stdio.h>
#include <cuda_runtime_api.h>
#include <time.h>
/****************************************************************************
* An experiment with cuda kernel invocation parameters. 2x3x4 threads on
* one block should yield 24 kernel invocations.
*
* Compile with:
* nvcc -o cupass cupass.cu
*
* Dr Kevan Buckley, University of Wolverhampton, January 2018
*****************************************************************************/
__device__ int is_a_match(char *attempt){
char plain_password1[] ="AA1111";
char plain_password2[] ="AA1112";
char plain_password3[] ="AA1113";
char plain_password4[] ="AA1114";
char *q = attempt;
char *w = attempt;
char *e = attempt;
char *r = attempt;
char *pp1 = plain_password1;
char *pp2 = plain_password2;
char *pp3 = plain_password3;
char *pp4 = plain_password4;
while(*q ==*pp1){
if(*q == '\0')
{
printf("password:%s\n", plain_password1);
break;
}
q++;
pp1++;
}
while(*w ==*pp2){
if(*w == '\0')
{
printf("password:%s\n", plain_password2);
break;
}
w++;
pp2++;
}
while(*e ==*pp3){
if(*e == '\0')
{
printf("password:%s\n", plain_password3);
break;
}
e++;
pp3++;
}
while(*r ==*pp4){
if(*r == '\0')
{
printf("password: %s\n", plain_password4);
return 1;
}
r++;
pp4++;
}
return 0;
}
__global__ void kernel(){
char i1, i2, i3, i4;
char password[7];
password[6] ='\0';
int i = blockIdx.x +65;
int j = threadIdx.x+65;
char firstMatch =i;
char secondMatch =j;
password[0] =firstMatch;
password[1] =secondMatch;
for(i1='0'; i1<='9'; i1++){
for(i2='0'; i2<='9'; i2++){
for(i3='0'; i3<='9'; i3++){
for(i4='0'; i4<='9'; i4++){
password[2] =i1;
password[3] =i2;
password[4] =i3;
password[5] =i4;
if(is_a_match(password)){
}
else{
//printf("tried: %s\n",password);
}
}
}
}
}
}
int time_difference(struct timespec *start, struct timespec *finish,long long int *difference) {
long long int ds = finish->tv_sec - start->tv_sec;
long long int dn = finish->tv_nsec - start->tv_nsec;
if(dn < 0 ) {
ds--;
dn += 1000000000;
}
*difference = ds * 1000000000 + dn;
return !(*difference > 0);
}
int main() {
struct timespec start, finish;
long long int time_elapsed;
clock_gettime(CLOCK_MONOTONIC, &start);
kernel<<<26,26>>>();
cudaThreadSynchronize();
clock_gettime(CLOCK_MONOTONIC, &finish);
time_difference(&start, &finish, &time_elapsed);
printf("Time elapsed was %lldns or %0.9lfs\n", time_elapsed,
(time_elapsed/1.0e9));
return 0;
}
|
20,365 | #include <thrust/host_vector.h>
#include <thrust/device_vector.h>
#include <thrust/iterator/counting_iterator.h>
#include <iostream>
#if 0
typedef thrust::tuple<float,float,float> Float3;
struct DotProduct : public thrust::binary_function<Float3,Float3,float>
{
const float params[4];
__host__ __device__
float operator()(const Float3& a, const Float3& b) const
{
return thrust::get<0>(a) * thrust::get<0>(b) + // x components
thrust::get<1>(a) * thrust::get<1>(b) + // y components
thrust::get<2>(a) * thrust::get<2>(b); // z components
}
};
#endif
void preparemake3D(const float params[4], thrust::host_vector<float> & tW, thrust::host_vector<float> & tH,int w, int h)
{
for(int i = 0; i < w; i++)
{
tW[i] = (i-params[1]+0.5)/params[0];
}
for (int i = 0; i < h; i++)
{
tH[i] = -(i-params[3]+0.5)/params[2];
}
}
// x output = pw[c]*z
// y output = +-ph[c]*z
// z output = z
//
// xyz are coalesced and computed independently
// 1) replicate pw and ph across x and y
// 2) multiply by z
/*
auto poc = (uint8_t*)(po+3);
const float rx = pw[c]*z;
const float ry = w*z;
po[0] = rx;
po[1] = -ry;
po[2] = z;
poc[0] = pc[2];
poc[1] = pc[1];
poc[2] = pc[0];
good++;
poi += 3*4+3;
*/
struct unprojectx
{
__host__ __device__
unprojectx(const thrust::device_vector<float> & p, int w) :p_(thrust::raw_pointer_cast(&p[0])), w_(w) {}
__host__ __device__
float operator()(const float & d, const int & c)
{
return d*p_[c % w_];
}
const float * p_;
const int w_;
};
struct unprojecty
{
__host__ __device__
unprojecty(const thrust::device_vector<float> & p, int w) :p_(thrust::raw_pointer_cast(&p[0])), w_(w) {}
__host__ __device__
float operator()(const float & d, const int & c)
{
return d*p_[c / w_];
}
const float* p_;
const int w_;
};
int main(void)
{
float params[4] = {1,2,3,4}; // fx cx fy cy
int w = 512;
int h = 424;
thrust::host_vector<float> tW(w);
thrust::host_vector<float> tH(h);
preparemake3D(params,tW,tH,w,h);
// Copy host_vector H to device_vector D
thrust::device_vector<float> dtW = tW,dtH = tH;
thrust::host_vector<float> tD(h*w); // depths
thrust::device_vector<float> dtD = tD;
thrust::device_vector<float> dtPx(h*w); // points (all points)
thrust::device_vector<float> dtPy(h*w); // points (all points)
// works on: 1..w by 1..h
// input: tD depths
// output expanded dtP
// uses dtW and dtH as argument
thrust::counting_iterator<int> co(0);
cudaEvent_t start, stop;
cudaEventCreate(&start);
cudaEventCreate(&stop);
cudaEventRecord(start, 0);
thrust::transform(dtD.begin(),dtD.end(), co, dtPx.begin(),unprojectx(tW,w));
cudaEventRecord(stop, 0);
cudaEventSynchronize(stop);
float elapsedTime;
cudaEventElapsedTime(&elapsedTime , start, stop);
printf("Avg. time is %f ms", elapsedTime/100);
thrust::transform(dtD.begin(),dtD.end(),co,dtPy.begin(),unprojecty(tH,w));
// filter by z
return 0;
} |
20,366 | /*
* Face Factor Distance
* (MP3, Fall 2019, GPU Programming/Yifan Liu)
*/
#include <assert.h>
#include <cuda.h>
#include <stdio.h>
#include <math.h>
#include <iostream>
#include <fstream>
#include <ctime>
#include <string>
#include <sstream>
/* Usage message displayed when invalid command line arguments are supplied */
#define USAGE \
"MP3 takes a (m x k) matrix M \n" \
"and compute the distance betwen rows and save teh result if two rows' distance is smaller than 0.3\n" \
"The values of m, k must be >= 1.\n" \
"\n" \
"Usage: mp3 m k\n"
/* Tile size*/
#ifndef TILE_WIDTH
# define TILE_WIDTH 16
#endif
/* If a CUDA call fails, display an error message and exit */
#define CUDA_CHECK(e) { \
cudaError_t err = (e); \
if (err != cudaSuccess) \
{ \
fprintf(stderr, "CUDA error: %s, line %d, %s: %s\n", \
__FILE__, __LINE__, #e, cudaGetErrorString(err)); \
exit(EXIT_FAILURE); \
} \
}
/* assert() is only supported on devices of compute capability >= 2.0 */
#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ < 200)
# undef assert
# define assert(arg)
#endif
/*getDistance calculate the distance among rows*/
__global__ static void getDistance_gpu(float *d_M, float *d_P, int m, int k) {
assert(blockDim.x == TILE_WIDTH && blockDim.y == TILE_WIDTH);
int row = blockIdx.y * TILE_WIDTH + threadIdx.y;
int col= blockIdx.x * TILE_WIDTH + threadIdx.x;
if(row >= m || col >= m)
return;
if(row == col){
d_P[row*m+col] = 100;
return;
}
float expected = 0.0;
for (int i = 0; i < k; i++)
{
expected += pow(d_M[row*k+i] - d_M[col*k+i], 2);
}
expected = sqrt(expected);
d_P[row*m+col] = expected;
}
/* Displays one row of the given matrix */
static void printRow(int row, float *matrix, int cols)
{
printf("[");
if (cols >= 1) printf(" %3.3f", matrix[row*cols+0]);
if (cols >= 2) printf(" %3.3f", matrix[row*cols+1]);
if (cols >= 3) printf(" %3.3f", matrix[row*cols+2]);
if (cols >= 6) printf(" ...");
if (cols >= 5) printf(" %3.3f", matrix[row*cols+(cols-2)]);
if (cols >= 4) printf(" %3.3f", matrix[row*cols+(cols-1)]);
printf(" ]\n");
}
/* Displays the given matrix */
static void printMatrix(float *matrix, int rows, int cols)
{
if (rows >= 1) printRow(0, matrix, cols);
if (rows >= 2) printRow(1, matrix, cols);
if (rows >= 3) printRow(2, matrix, cols);
if (rows >= 6) printf(" ...\n");
if (rows >= 5) printRow(rows-2, matrix, cols);
if (rows >= 4) printRow(rows-1, matrix, cols);
}
/* Program entrypoint. */
int main()
{
/* read in m and k here */
std::cout << "Loading matrices...\n";
clock_t begin = clock();
int m, k;
std::ifstream in1;
in1.open("descriptor.txt");
if(in1.is_open())
printf("File opened successfully\n");
else
printf("File opened unsuccessfully\n");
std::string line, temp;
// read in m and k
while ((std::getline(in1, line))){
if (line == "end header")
break;
std::istringstream ss(line);
std::cout << line << std::endl;
if(line.find("line_number")!=-1)
ss >> temp >> m;
else if(line.find("vector_dimension")!=-1)
ss >> temp >> k;
}
printf("The matrix is %d x %d\n", m, k);
if (m < 1 || k < 1)
{
fprintf(stderr, USAGE);
fprintf(stderr, "Invalid value for m or k (%d, %d)\n", m, k);
system("Pause");
return EXIT_FAILURE;
}
size_t bytesForM = m * k * sizeof(float);
float *h_M = (float *)malloc(bytesForM);
/* Fill M (on host) */
for (int i = 0; i < m*k; ++i)
in1 >> h_M[i];
in1.close();
clock_t end = clock();
double elapsed_secs = double(end - begin) / CLOCKS_PER_SEC;
printf("Reading input file took %f seconds\n", elapsed_secs);
printf("M =\n"); printMatrix(h_M, m, k);
printf("using (%d x %d) tiles.\n", TILE_WIDTH, TILE_WIDTH);
/********************************************/
/* M is (m x k), P is (m x m) */
/********************************************/
size_t bytesForP = m * m * sizeof(float);
float *h_P = (float *)malloc(bytesForP);
if (h_M == NULL || h_P == NULL)
{
fprintf(stderr, "Unable to allocate host memory\n");
system("Pause");
return EXIT_FAILURE;
}
/* Allocate device memory for matrices */
float *d_M, *d_P;
CUDA_CHECK(cudaMalloc((void **)&d_M, bytesForM));
CUDA_CHECK(cudaMalloc((void **)&d_P, bytesForP));
/* Copy M to device global memory */
CUDA_CHECK(cudaMemcpy(d_M, h_M, bytesForM, cudaMemcpyHostToDevice));
/* Launch the CUDA kernel */
dim3 dimGrid((m+TILE_WIDTH-1)/TILE_WIDTH, (m+TILE_WIDTH-1)/TILE_WIDTH);
dim3 dimBlock(TILE_WIDTH, TILE_WIDTH);
printf("matMul called from host\n");
getDistance_gpu<<<dimGrid, dimBlock>>>(d_M, d_P, m, k);
CUDA_CHECK(cudaDeviceSynchronize());
/* Copy result matrix from device global memory back to host memory */
CUDA_CHECK(cudaMemcpy(h_P, d_P, bytesForP, cudaMemcpyDeviceToHost));
printf(" product received from host\n");
printf("P =\n"); printMatrix(h_P, m, m);
printf("Saving result\n");
std::ofstream out;
out.open("matrix.txt");
for (int i = 0; i < m; i++){
for (int j = 0; j < m; j++){
if (h_P[i*m+j] < 0.3)
out << j+1 << " ";
}
out << std::endl;
}
out.close();
/* Free device global memory */
CUDA_CHECK(cudaFree(d_M));
CUDA_CHECK(cudaFree(d_P));
/* Free host memory */
free(h_M);
free(h_P);
/* Reset the device (unnecessary if not profiling, but good practice) */
CUDA_CHECK(cudaDeviceReset());
printf("Done\n");
system("Pause");
return EXIT_SUCCESS;
}
|
20,367 | #include "includes.h"
#define B 2
/*
*/
__global__ void cudaAcc_GetPowerSpectrum_kernel2( int NumDataPoints, float2* FreqData, float* PowerSpectrum)
{
const int i = blockIdx.x * blockDim.x*B + threadIdx.x;
float ax[B];
float ay[B];
#pragma unroll
for (int k=0;k<B;k++)
{
ax[k] = FreqData[i+k*blockDim.x].x;
ay[k] = FreqData[i+k*blockDim.x].y;
}
// PowerSpectrum[i] = freqData.x * freqData.x + freqData.y * freqData.y;
#pragma unroll
for (int k=0;k<B;k++)
{
PowerSpectrum[i+k*blockDim.x] = __fadd_rn( __fmul_rn(ax[k],ax[k]),__fmul_rn(ay[k],ay[k]));
}
} |
20,368 | #include "assignmentHPC1.cuh"
#include <iostream>
#include <cstdlib>
#include <chrono>
#include <limits>
using namespace std;
using namespace std::chrono;
double find_max_cpu(double *arr_host, unsigned int N) {
double result = numeric_limits<double>::min();
for(unsigned int i = 0; i < N; i++) {
if(arr_host[i] > result) {
result = arr_host[i];
}
}
return result;
}
void find_max(double *arr_host, unsigned int N) {
// ----------------------------------------- CPU Code -------------------------------------------------
auto startCPU = high_resolution_clock::now();
double result = find_max_cpu(arr_host, N);
auto stopCPU = high_resolution_clock::now();
cout<<"\n\n--------------- CPU ---------------\n"<<endl;
cout<<"Answer CPU : "<<result<<endl;
cout<<"\nTime on CPU : "<<duration_cast<microseconds>(stopCPU - startCPU).count()/1000<<" milli seconds\n\n"<<endl;
// ----------------------------------------- GPU Code -------------------------------------------------
} |
20,369 | #include <math.h>
#include <stdio.h>
#include <stdlib.h>
/*
* Wei Wu
* CAAM 520
*/
// TO compile:
// nvcc -o hw04 hw04.c -lm
// TO run with tolerance 1e-4 and 4x4 loop currents
// ./hw04 4 1e-4
#define PI 3.14159265359
#define MAX(a,b) (((a)>(b))?(a):(b))
#define p_Nthreads 32
#define SERIAL false
// kernel for one iteration of Jacobi
__global__ void Jacobi(float* unew_c, float* u_c, float* f_c, int N){
float invD = 1./4.0f; // factor of h cancels out
const int i = blockIdx.x*blockDim.x + threadIdx.x;
const int j = blockIdx.y*blockDim.y + threadIdx.y;
const int id = i + j*(N+2); // x-index first
if (i >= 1 && j >= 1 && i <= N && j <= N){
const float Ru = -u_c[id-(N+2)]-u_c[id+(N+2)]
-u_c[id-1]-u_c[id+1];
const float rhs = invD*(f_c[id]-Ru);
unew_c[id] = rhs;
}
}
// sequential addressing reduction kernel. Modified from class notes
__global__ void reduce2(int N, float *x1, float *x2, float *xout){
__shared__ float s_x[p_Nthreads];
const int tid = threadIdx.x;
const int i = blockIdx.x*blockDim.x + tid;
// load smem
s_x[tid] = 0;
if (i < N){
s_x[tid] = (x1[i] - x2[i])*(x1[i] - x2[i]);
}
__syncthreads();
for (unsigned int s = blockDim.x/2; s > 0; s /= 2){
if (tid < s){
s_x[tid] += s_x[tid+s]; // fewer bank conflicts
}
__syncthreads();
}
if (tid==0){
xout[blockIdx.x] = s_x[0];
}
}
// compute square of residual
float residual_square(float* x1, float* x2, int N){
float res2 = 0.0f;
float *xout_c;
int Nthreads = p_Nthreads;
int Nblocks = ((N+2)*(N+2)+Nthreads-1)/Nthreads;
dim3 threadsPerBlock(Nthreads,1,1);
dim3 blocks(Nblocks,1,1);
float *xout = (float*)malloc(Nblocks*sizeof(float));
cudaMalloc(&xout_c, Nblocks*sizeof(float));
reduce2 <<< blocks, threadsPerBlock >>> ((N+2)*(N+2), x1, x2, xout_c);
cudaMemcpy(xout, xout_c, Nblocks*sizeof(float), cudaMemcpyDeviceToHost);
for (int i = 0; i < Nblocks; i++){
res2 += xout[i];
}
cudaFree(xout_c);
return res2;
}
// parallely solve for solution vector u
int parallel_solve(const int N, const float tol, float * u, float * f){
float *unew = (float*)calloc((N+2)*(N+2),sizeof(float));
float res2 = 1.0f;
unsigned int iter = 0;
// allocate CUDA global memory and copy from host to device
float *unew_c, *u_c, *f_c;
cudaMalloc(&unew_c, (N+2)*(N+2)*sizeof(float));
cudaMalloc(&u_c, (N+2)*(N+2)*sizeof(float));
cudaMalloc(&f_c, (N+2)*(N+2)*sizeof(float));
cudaMemcpy(unew_c, unew, (N+2)*(N+2)*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(u_c, u, (N+2)*(N+2)*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(f_c, f, (N+2)*(N+2)*sizeof(float), cudaMemcpyHostToDevice);
int Nthreads = p_Nthreads;
int Nblocks = ((N+2)+Nthreads-1)/Nthreads;
dim3 threadsPerBlock(Nthreads,Nthreads,1);
dim3 blocks(Nblocks,Nblocks,1);
while(res2>tol*tol){
// update interior nodes using Jacobi
Jacobi <<<blocks, threadsPerBlock>>> (unew_c, u_c, f_c, N);
cudaError_t err = cudaGetLastError();
if (err != cudaSuccess)
printf("Error: %s\n", cudaGetErrorString(err));
// compute residual using reduction
res2 = residual_square(unew_c,u_c,N);
// update u on device
float* pingPong = u_c;
u_c = unew_c;
unew_c = pingPong;
++iter;
if(!(iter%500)){
printf("at iter %d: residual = %g\n", iter, sqrt(res2));
}
}
// copy from device back to host
cudaError_t cudaStatus = cudaMemcpy(u, u_c, (N+2)*(N+2)*sizeof(float),
cudaMemcpyDeviceToHost);
cudaFree(unew_c);
cudaFree(u_c);
cudaFree(f_c);
return iter;
}
// serialy solve for solution vector u
int serial_solve(const int N, const float tol, float * u, float * f){
float *unew = (float*)calloc((N+2)*(N+2),sizeof(float));
//float w = 1.0f; // not used for Pure Jacobi
float invD = 1./4.f; // factor of h cancels out
float res2 = 1.0f;
unsigned int iter = 0;
while(res2>tol*tol){
res2 = 0.0f;
// update interior nodes using (Pure) Jacobi
for(int i=1; i<=N; ++i){
for(int j=1; j<=N; ++j){
const int id = i + j*(N+2); // x-index first
const float Ru = -u[id-(N+2)]-u[id+(N+2)]-u[id-1]-u[id+1];
const float rhs = invD*(f[id]-Ru);
const float oldu = u[id];
// const float newu = w*rhs + (1.0-w)*oldu;
const float newu = rhs;
// compute residual
res2 += (newu-oldu)*(newu-oldu);
unew[id] = newu;
}
}
for (int i = 0; i < (N+2)*(N+2); ++i){
u[i] = unew[i];
}
++iter;
if(!(iter%500)){
printf("at iter %d: residual = %g\n", iter, sqrt(res2));
}
}
return iter;
}
int main(int argc, char **argv){
if(argc!=3){
printf("Usage: ./main N tol\n");
exit(-1);
}
int N = atoi(argv[1]);
float tol = atof(argv[2]);
float *u = (float*) calloc((N+2)*(N+2), sizeof(float));
float *f = (float*) calloc((N+2)*(N+2), 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 = -1.0 + i*h;
const float y = -1.0 + j*h;
f[i + j*(N+2)] = sin(PI*x)*sin(PI*y) * h*h;
}
}
int iter;
if(!SERIAL){
iter = parallel_solve(N, tol, u, f);
}else{
iter = serial_solve(N, tol, u, f);
}
float err = 0.0f;
for (int i = 0; i < (N+2)*(N+2); ++i){
err = MAX(err,fabs(u[i] - f[i]/(h*h*2.0*PI*PI)));
}
printf("Iters: %d\n", iter);
printf("Max error: %lg\n", err);
free(u);
free(f);
}
|
20,370 | #include <cuda.h>
#include <cuda_runtime.h>
#include <iostream>
#include <vector>
#define N 10
__global__ void add(float *a, float *b, float *c) {
c[blockIdx.x] = a[blockIdx.x] + b[blockIdx.x];
}
void cuda_check(cudaError_t status) {
if (status != cudaSuccess) {
std::cout << "Error could not allocate memory result " << status << std::endl;
exit(1);
}
}
int main(void) {
std::vector<float> vec_a;
std::vector<float> vec_b;
std::vector<float> vec_c;
float *a, *b, *c;
float *d_a, *d_b, *d_c;
int size = N * sizeof(int);
cudaMalloc((void**)&d_a, size);
cudaMalloc((void**)&d_b, size);
cudaMalloc((void**)&d_c, size);
cuda_check(cudaHostAlloc((void **)&a, size, cudaHostAllocPortable));
cuda_check(cudaHostAlloc((void **)&b, size, cudaHostAllocPortable));
cuda_check(cudaHostAlloc((void **)&c, size, cudaHostAllocPortable));
// a = new int[N];
// b = new int[N];
// c = new int[N];
for(int i = 0; i < N; ++i) {
vec_a.push_back(i);
vec_b.push_back(i);
//a[i] = i;
//b[i] = i;
}
memcpy(a, &vec_a[0], size);
memcpy(b, &vec_b[0], size);
cudaMemcpy(d_a, a, size, cudaMemcpyHostToDevice);
cudaMemcpy(d_b, b, size, cudaMemcpyHostToDevice);
add<<<N, 1>>>(d_a, d_b, d_c);
cudaMemcpy(c, d_c, size, cudaMemcpyDeviceToHost);
for(int i = 0; i < N; ++i) {
std::cout << c[i] << std::endl;
}
//free(a); free(b); free(c);
cudaFreeHost(a); cudaFreeHost(b); cudaFreeHost(c);
cudaFree(d_a); cudaFree(d_b); cudaFree(d_c);
return 0;
}
|
20,371 | #include "includes.h"
__global__ void convertKinectDisparityToRegularDisparity_kernel( float *d_regularDisparity, int d_regularDisparityPitch, const float *d_KinectDisparity, int d_KinectDisparityPitch, int width, int height) {
const int x = blockIdx.x * blockDim.x + threadIdx.x;
const int y = blockIdx.y * blockDim.y + threadIdx.y;
if ((x < width) & (y < height)) { // are we in the image?
float d_in =
*((float *)((char *)d_KinectDisparity + y * d_KinectDisparityPitch) +
x);
float d_out = (d_in == 0.0f) ? nanf("") : -d_in;
*((float *)((char *)d_regularDisparity + y *d_regularDisparityPitch) + x) =
d_out;
}
} |
20,372 | #include <cuda_runtime.h>
#include <stdio.h>
#include <stdlib.h>
#include <ctime>
#include <iostream>
#define DATA_SIZE 1048576
#define THREAD_SIZE 256
#define BLOCK_SIZE 32
int data[DATA_SIZE];
void Generate(int* number, int size) {
for (int i = 0; i < size; ++i)
number[i] = rand() % 10;
}
__global__ static void sumOf(int* data, int* result, clock_t* time) {
const int tid = threadIdx.x;
const int bid = blockIdx.x;
int sum = 0;
clock_t start;
if (tid == 0) time[bid] = clock();
for (int i = bid * THREAD_SIZE + tid; i < DATA_SIZE; i += BLOCK_SIZE * THREAD_SIZE) {
sum += data[i];
}
result[bid * THREAD_SIZE + tid] = sum;
if (tid == 0) time[bid + BLOCK_SIZE] = clock();
}
int main(int argc, char** argv) {
Generate(data, DATA_SIZE);
int* gpudata, *result;
clock_t* time;
cudaMalloc((void**)&gpudata, sizeof(int)* DATA_SIZE);
cudaMalloc((void**)&result, sizeof(int)* THREAD_SIZE * BLOCK_SIZE);
cudaMalloc((void**)&time, sizeof(int) * 2 * BLOCK_SIZE);
cudaMemcpy(gpudata, data, sizeof(int)* DATA_SIZE, cudaMemcpyHostToDevice);
sumOf<<<BLOCK_SIZE, THREAD_SIZE, 0 >>>(gpudata, result, time);
clock_t usetime[BLOCK_SIZE * 2];
int sum[THREAD_SIZE * BLOCK_SIZE];
cudaMemcpy(sum, result, sizeof(int)* THREAD_SIZE * BLOCK_SIZE, cudaMemcpyDeviceToHost);
cudaMemcpy(usetime, time, sizeof(int) * 2 * BLOCK_SIZE, cudaMemcpyDeviceToHost);
cudaFree(gpudata);
cudaFree(result);
cudaFree(time);
int finalsum = 0;
for (int i = 0; i < THREAD_SIZE * BLOCK_SIZE; ++i)
finalsum += sum[i];
clock_t minp, maxp;
minp = usetime[0], maxp = usetime[BLOCK_SIZE];
for (int i = 0; i < BLOCK_SIZE; ++i){
if (usetime[i] < minp) minp = usetime[i];
if (usetime[i + BLOCK_SIZE] > maxp) maxp = usetime[i + BLOCK_SIZE];
}
printf("%d %d\n", finalsum, maxp - minp);
system("pause");
return 0;
} |
20,373 | #include "includes.h"
/*
WRITE CUDA KERNEL FOR TRANSPOSE HERE
*/
const int CHUNK_SIZE = 32;
const int CHUNK_ROWS = 8;
__global__ void matrix_t(int* data, int* out, int* rows, int* cols){
__shared__ int chunk[CHUNK_SIZE][CHUNK_SIZE];
int x = blockIdx.x * CHUNK_SIZE + threadIdx.x;
int y = blockIdx.y * CHUNK_SIZE + threadIdx.y;
for (int i=0; i<CHUNK_SIZE; i+= CHUNK_ROWS) {
chunk[threadIdx.x][threadIdx.y+i] = data[(y + i) * *cols + x];
}
__syncthreads();
x = blockIdx.y * CHUNK_SIZE + threadIdx.x;
y = blockIdx.x * CHUNK_SIZE + threadIdx.y;
for (int i=0; i<CHUNK_SIZE; i+= CHUNK_ROWS) {
if (x < *rows && y+i < *cols) {
out[(y + i) * *rows + x] = chunk[threadIdx.y + i][threadIdx.x];
// out[(y + i) * *rows + x] = 1;
}
}
} |
20,374 | #include <cuda.h>
#include <cuda_runtime.h>
#include <iostream>
#include <vector>
extern "C" __global__ void switch_test(long *src_ptr, long *dst_ptr, int num_rows, int* output_rows)
{
int idx;
long val;
int pos_start = blockIdx.x * blockDim.x + threadIdx.x;
if(pos_start >= num_rows) return;
val = src_ptr[pos_start];
switch(val){
case 7016889694419943424L:
case 3688448094816436224L:
case 3761631588761206784L:
case 7089228763434582016L:
case 7161567832449220608L:{
idx = atomicAdd(output_rows,1);
dst_ptr[idx] = val;
break;
}
default: break;
}
return;
}
int main()
{
int64_t *src_ptr,*dst_ptr;
int32_t num_rows = 10;
auto cuda_err = cudaMallocManaged(&src_ptr,sizeof(int64_t)*num_rows);
if(cuda_err) return -6;
std::cout << "alloc src_ptr success" << std::endl;
cuda_err = cudaMallocManaged(&dst_ptr,sizeof(int64_t)*num_rows);
if(cuda_err) return -7;
std::cout << "alloc dst_ptr success" << std::endl;
int32_t* output_rows;
cuda_err = cudaMallocManaged(&output_rows,sizeof(int32_t));
// for(int i=0;i<num_rows;++i) src_ptr[i] = i;
src_ptr[0] = 3688729569793146880L;
src_ptr[1] = 3617516400685350912L;
src_ptr[2] = 3688729569793146880L;
src_ptr[3] = 3761631588761206784L;
src_ptr[4] = 3688448094816436224L;
src_ptr[5] = 3689292519746568192L;
src_ptr[6] = 3618642300592193536L;
src_ptr[7] = 3618360825615482880L;
src_ptr[8] = 3688729569793146880L;
src_ptr[9] = 3617516400685350912L;
*output_rows = 0;
switch_test<<<2,16>>>(src_ptr,dst_ptr,num_rows,output_rows);
cudaDeviceSynchronize();
cuda_err = cudaGetLastError();
if(cuda_err){
std::cout << "cudaDeviceSynchronize failed, error code = " << cuda_err << std::endl;
}
std::cout << "cudaDeviceSynchronize success" << std::endl;
for(int i=0; i<*output_rows; ++i){
std::cout << i << ":" << dst_ptr[i] << std::endl;
}
return 0;
}
|
20,375 | #include <iostream>
using namespace std;
int main() {
cudaEvent_t start, stop, done_offload;
cudaEventCreate(&start);
cudaEventCreate(&stop);
cudaEventCreate(&done_offload);
void *p, *q;
long size = 1024l * 1024 * 200;
cudaMalloc(&p, size);
cudaMallocHost(&q, size);
cout << "without split by event\n";
int N = 100;
cudaEventRecord(start);
for (int i = 0; i < N; i++) {
cudaMemcpyAsync(q, p, size, cudaMemcpyDeviceToHost, NULL);
}
cudaEventRecord(stop);
cudaEventSynchronize(stop);
float milli;
cudaEventElapsedTime(&milli, start, stop);
cout << "Time(ms): " << milli << endl;
cout << "with split by event\n";
cudaEventRecord(start);
for (int i = 0; i < N; i++) {
cudaMemcpyAsync(q, p, size, cudaMemcpyDeviceToHost, NULL);
cudaEventRecord(done_offload, NULL);
cudaEventSynchronize(done_offload);
}
cudaEventRecord(stop);
cudaEventSynchronize(stop);
cudaEventElapsedTime(&milli, start, stop);
cout << "Time(ms): " << milli << endl;
} |
20,376 | #include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <stdio.h>
__device__ const char *STR = "HELLO WORLD!";
const char STR_LENGTH = 12;
__global__ void hello()
{
printf("%c\n", STR[threadIdx.x]);
}
int main(void)
{
int num_threads = STR_LENGTH;
int num_blocks = 1;
hello <<< num_blocks, num_threads >>>();
cudaDeviceSynchronize();
return 0;
} |
20,377 | #include <stdio.h>
#include <stdlib.h>
#include <curand.h>
#include <curand_kernel.h>
#define MAX 100
/* this GPU kernel function calculates a random number and stores it in the parameter */
__global__ void random(float* result1, float* result2) {
/* CUDA's random number library uses curandState_t to keep track of the seed value
we will store a random state for every thread */
curandState_t state;
/* we have to initialize the state */
curand_init(0, /* the seed controls the sequence of random values that are produced */
0, /* the sequence number is only important with multiple cores */
0, /* the offset is how much extra we advance in the sequence for each call, can be 0 */
&state);
/* curand works like rand - except that it takes a state as a parameter */
*result1 = curand_normal(&state);
*result2 = curand_normal(&state);
}
int main( ) {
/* allocate an int on the GPU */
float* gpu_x1, * gpu_x2;
cudaMalloc((void**) &gpu_x1, sizeof(float));
cudaMalloc((void**) &gpu_x2, sizeof(float));
/* invoke the GPU to initialize all of the random states */
random<<<1, 1>>>(gpu_x1, gpu_x2);
/* copy the random number back */
float x1, x2;
cudaMemcpy(&x1, gpu_x1, sizeof(float), cudaMemcpyDeviceToHost);
cudaMemcpy(&x2, gpu_x2, sizeof(float), cudaMemcpyDeviceToHost);
printf("Random number = %f.\n", x1);
printf("Random number = %f.\n", x2);
/* free the memory we allocated */
cudaFree(gpu_x1);
cudaFree(gpu_x2);
return 0;
}
|
20,378 | #include <stdio.h>
#include <time.h>
#include <sys/time.h>
// GPU: Impressão dos índices
__global__ void fIndice() {
printf ("%d\t%d\t%d\n", threadIdx.x, blockIdx.x, blockDim.x);
}
// CPU: Função principal
int main (int argc, char ** argv) {
int nblocos = 0;
int nthreads = 0;
// Tratamento dos paramêtros
if (argc == 3) {
nblocos = atoi(argv[1]);
nthreads = atoi(argv[2]);
} else {
printf ("\n ############# \n");
printf ("./07_kernel <nblocos> <nthreads>\n");
printf ("./07_kernel 32 128 | sort -n | tail -32\n");
exit(1);
}
// Limite bloco 1024, limite de thread 1024
// Limite MAX (bloco * thread) = 4096
fIndice<<<nblocos,nthreads>>>();
cudaDeviceSynchronize();
printf ("threadIdx.x blockIdx.x blockDim.x\n");
return 0;
}
|
20,379 | #include "includes.h"
__global__ void refine_dilateFPPlaneDepthMapXpYp_kernel(float* fpPlaneDepthMap, int fpPlaneDepthMap_p, float* maskMap, int maskMap_p, int width, int height, int xp, int yp, float fpPlaneDepth)
{
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
if((x + xp >= 0) && (y + yp >= 0) && (x + xp < width) && (y + yp < height) && (x < width) && (y < height))
{
float depth = maskMap[y * maskMap_p + x];
if(depth > 0.0f)
{
fpPlaneDepthMap[(y + yp) * fpPlaneDepthMap_p + (x + xp)] = fpPlaneDepth;
};
};
} |
20,380 | #include <cuda_runtime.h>
#include <device_launch_parameters.h>
__global__
void square(float* d_out, float* d_in)
{
int idx = threadIdx.x;
float f = d_in[idx];
d_out[idx] = f * f;
}
void your_square(int array_size, float* d_out, float* d_in)
{
square<<<1, array_size >>> (d_out, d_in);
} |
20,381 | #include "includes.h"
__global__ void kernel_End( int *g_stochastic, int *g_count_blocks, int *g_counter)
{
int thid = blockIdx.x * blockDim.x + threadIdx.x ;
if( thid < ( *g_counter ) )
{
if( g_stochastic[thid] == 1 )
atomicAdd(g_count_blocks,1);
//(*g_count_blocks) = (*g_count_blocks) + 1 ;
}
} |
20,382 | #include "includes.h"
__global__ void conductance_move_spikes_towards_synapses_kernel( int* d_spikes_travelling_to_synapse, float current_time_in_seconds, int* circular_spikenum_buffer, int* spikeid_buffer, int bufferloc, int buffersize, int total_number_of_synapses, float* d_time_of_last_spike_to_reach_synapse, int* postsynaptic_neuron_indices, float * neuron_wise_conductance_trace, int * synaptic_decay_id, int total_number_of_neurons, float * d_synaptic_efficacies_or_weights, float * d_biological_conductance_scaling_constants_lambda, float timestep){
int indx = threadIdx.x + blockIdx.x * blockDim.x;
while (indx < circular_spikenum_buffer[bufferloc]) {
int idx = spikeid_buffer[bufferloc*total_number_of_synapses + indx];
// Update Synapses
d_time_of_last_spike_to_reach_synapse[idx] = current_time_in_seconds;
int postsynaptic_neuron_id = postsynaptic_neuron_indices[idx];
int trace_id = synaptic_decay_id[idx];
float synaptic_efficacy = d_biological_conductance_scaling_constants_lambda[idx] * d_synaptic_efficacies_or_weights[idx];
atomicAdd(&neuron_wise_conductance_trace[total_number_of_neurons*trace_id + postsynaptic_neuron_id], synaptic_efficacy);
indx += blockDim.x * gridDim.x;
}
__syncthreads();
} |
20,383 | #include <stdio.h>
#include <unistd.h>
#include <sys/stat.h>
#include <fcntl.h>
#include <string.h>
#include <stdlib.h>
#include <stdint.h>
#include <math.h>
#include <cuda.h>
#define PI (3.14159265358979323)
#define BLOCK_DIM (32)
#define vidx(rr, r, R) (((rr) + (r) < 0) ? 0 : (((rr) + (r) >= (R)) ? (R) - 1 : (rr) + (r)))
//void blur_kernel(float *H, uint8_t *vals, uint8_t *blur, int N, int k, int nrows, int ncols, int ii, int jj)
//{
// int xv, yv, i, j;
// float val = 0;
// for (i = -k; i <= k; ++i) {
// xv = vidx(ii, i, nrows);
// for (j = -k; j <= k; ++j) {
// yv = vidx(jj, j, ncols);
// val += H[(i+k)*N + (j+k)]*vals[xv*ncols + yv];
// }
// }
//
// blur[ii*ncols + jj] = val;
//}
__global__ void blur_kernel_cuda(float *H, uint8_t *vals, uint8_t *blur, int N, int k, int nrows, int ncols)
{
int ii = blockIdx.x * blockDim.x + threadIdx.x;
int jj = blockIdx.y * blockDim.y + threadIdx.y;
if (ii >= nrows || jj >= ncols) return;
int xv, yv;
float val = 0;
for (int i = -k; i <= k; ++i) {
xv = vidx(ii, i, nrows);
for (int j = -k; j <= k; ++j) {
yv = vidx(jj, j, ncols);
val += H[(i+k)*N + (j+k)]*vals[xv*ncols + yv];
}
}
blur[ii*ncols + jj] = val;
}
int main(int argc, char *argv[])
{
float sigma, ss;
int N, k, nrows, ncols, max_pixel;
uint8_t *vals_d, *vals_h, *blur_d, *blur_h;
if (argc != 4) {
fprintf(stderr, "Usage: ./gaussian_blur_serial <input_pgm> output_pgm> <sigma>\n");
return 1;
}
sigma = atof(argv[3]);
ss = sigma * sigma;
N = ceil(6*sigma);
if (N % 2 == 0) N++;
k = N / 2;
char header[128], n1[32], n2[32];
FILE *fp1 = fopen(argv[1], "rb");
fgets(header, 128, fp1);
char *eol = strchr(header, '\n');
*eol = '\0';
if (strcmp(header, "P5")) {
fprintf(stderr, "'%s' must be in P5 format\n", argv[1]);
fclose(fp1);
return 1;
}
fgets(header, 128, fp1);
eol = strchr(header, '\n');
*eol = '\0';
char *hp = header;
for (; *hp != ' '; ++hp);
*hp = '\0';
strcpy(n1, header);
strcpy(n2, hp + 1);
ncols = atoi(n1);
nrows = atoi(n2);
fgets(header, 128, fp1);
eol = strchr(header, '\n');
*eol = '\0';
max_pixel = atoi(header);
vals_h = (uint8_t *) malloc(nrows * ncols);
fread(vals_h, 1, nrows * ncols, fp1);
fclose(fp1);
float H[N*N];
float *H_d;
for (int i = 0, x = -k; i < N; ++i, ++x)
for (int j = 0, y = -k; j < N; ++j, ++y)
H[i*N + j] = exp(-(x*x + y*y)/(2.*ss))/(2.*PI*ss);
blur_h = (uint8_t *) malloc(nrows * ncols);
cudaMalloc((void **)&vals_d, nrows * ncols);
cudaMalloc((void **)&blur_d, nrows * ncols);
cudaMalloc((void **)&H_d, N * N * sizeof(float));
cudaMemcpy(vals_d, vals_h, nrows * ncols, cudaMemcpyHostToDevice);
cudaMemcpy(H_d, H, N * N * sizeof(float), cudaMemcpyHostToDevice);
dim3 block(BLOCK_DIM, BLOCK_DIM);
dim3 grid(ceil(nrows/(BLOCK_DIM + 0.0)), ceil(ncols/(BLOCK_DIM + 0.0)));
blur_kernel_cuda<<<grid, block>>>(H_d, vals_d, blur_d, N, k, nrows, ncols);
//for (int ii = 0; ii < nrows; ++ii)
// for (int jj = 0; jj < ncols; ++jj)
// blur_kernel(H, vals_h, blur_h, N, k, nrows, ncols, ii, jj);
cudaMemcpy(blur_h, blur_d, nrows * ncols, cudaMemcpyDeviceToHost);
cudaFree(blur_d);
cudaFree(vals_d);
cudaFree(H_d);
FILE *fp2 = fopen(argv[2], "wb");
fprintf(fp2, "P5\n%d %d\n%d\n", ncols, nrows, max_pixel);
fwrite(blur_h, 1, nrows * ncols, fp2);
free(blur_h);
free(vals_h);
fclose(fp2);
return 0;
}
|
20,384 | /******************************************************************
File : lcsExclusiveScanForInt.cu
Author : Mingcheng Chen
Last Update : January 29th, 2013
*******************************************************************/
#include <stdio.h>
#define BLOCK_SIZE 512
#define NUM_BANKS 32
#define LOG_NUM_BANKS 5
#define CONFLICT_FREE_OFFSET(n) ((n) >> (LOG_NUM_BANKS))
#define POSI(n) ((n) + CONFLICT_FREE_OFFSET(n))
__global__ void ScanKernel(int *globalArray, int length, int step) {
__shared__ int localArray[POSI(BLOCK_SIZE << 1) + 1];
int localID = threadIdx.x;
int groupID = blockIdx.x;
int groupSize = blockDim.x;
int startOffset = (groupSize << 1) * groupID * step;
int posi1 = startOffset + localID * step;
int posi2 = posi1 + groupSize * step;
localArray[POSI(localID)] = posi1 < length ? globalArray[posi1] : 0;
localArray[POSI(localID + groupSize)] = posi2 < length ? globalArray[posi2] : 0;
// Up-sweep
for (int stride = 1, d = groupSize; stride <= groupSize; stride <<= 1, d >>= 1) {
__syncthreads();
if (localID < d) {
posi1 = stride * ((localID << 1) + 1) - 1;
posi2 = posi1 + stride;
localArray[POSI(posi2)] += localArray[POSI(posi1)];
}
}
// Down-sweep
for (int stride = groupSize, d = 1; stride >= 1; stride >>= 1, d <<= 1) {
__syncthreads();
if (localID < d) {
posi1 = stride * ((localID << 1) + 1) - 1;
posi2 = POSI(posi1 + stride);
posi1 = POSI(posi1);
int t = localArray[posi1];
localArray[posi1] = localArray[posi2];
localArray[posi2] = localArray[posi2] * !!localID + t;
}
}
__syncthreads();
// Write to global memory
posi1 = startOffset + localID * step;
posi2 = posi1 + groupSize * step;
if (posi1 < length) globalArray[posi1] = localArray[POSI(localID)];
if (posi2 < length) globalArray[posi2] = localArray[POSI(localID + groupSize)];
}
__global__ void ReverseUpdateKernel(int *globalArray, int length, int step) {
int localID = threadIdx.x;
int groupID = blockIdx.x;
int groupSize = blockDim.x;
int startOffset = groupID * (groupSize << 1) * step;
if (groupID) {
int value = globalArray[startOffset];
int posi1 = startOffset + localID * step;
int posi2 = posi1 + groupSize * step;
if (posi1 < length && localID) globalArray[posi1] += value;
if (posi2 < length) globalArray[posi2] += value;
}
}
extern "C"
int ExclusiveScanForInt(int *d_arr, int length) {
cudaError_t err;
// Get the work group size
int localWorkSize = BLOCK_SIZE;
// Up-sweep and down-sweep
static int records[10];
int problemSize = length;
int numOfRecords = 0;
int d_step = 1;
/// DEBUG ///
//printf("length = %d\n", length);
dim3 dimBlock(BLOCK_SIZE, 1, 1);
dim3 dimGrid(1, 1, 1);
for (; problemSize > 1; problemSize = (problemSize - 1) / (localWorkSize * 2) + 1) {
if (numOfRecords) d_step *= localWorkSize * 2;
records[numOfRecords++] = problemSize;
dimGrid.x = (problemSize - 1) / (localWorkSize * 2) + 1;
ScanKernel<<<dimGrid, dimBlock>>>(d_arr, length, d_step);
err = cudaDeviceSynchronize();
if (err) {
printf("Fail to finish scan kernel");
cudaGetErrorString(err);
exit(0);
}
}
int sum;
err = cudaMemcpy(&sum, d_arr, sizeof(int), cudaMemcpyDeviceToHost);
if (err) {
cudaGetErrorString(err);
exit(0);
}
err = cudaMemset(d_arr, 0, sizeof(int));
if (err) {
cudaGetErrorString(err);
exit(0);
}
// Reverse updates
for (int i = numOfRecords - 1; i >= 0; i--, d_step /= localWorkSize * 2) {
dimGrid.x = (records[i] - 1) / (localWorkSize * 2) + 1;
ReverseUpdateKernel<<<dimGrid, dimBlock>>>(d_arr, length, d_step);
err = cudaDeviceSynchronize();
if (err) {
printf("Fail to finish reverse update kernel");
cudaGetErrorString(err);
exit(0);
}
}
return sum;
}
|
20,385 | #include "includes.h"
__global__ void mergeGPU1d( unsigned char *image1, unsigned char *image2, unsigned char *res, int pixels ) {
int i = threadIdx.x + blockIdx.x*blockDim.x;
if( i < pixels ) {
int idx = 3*i;
int r1 = image1[ idx+2 ];
int g1 = image1[ idx+1 ];
int b1 = image1[ idx ];
int r2 = image2[ idx+2 ];
int g2 = image2[ idx+1 ];
int b2 = image2[ idx ];
int r = (int)( ( (float)r1 + (float)r2 )*0.5f );
int g = (int)( ( (float)g1 + (float)g2 )*0.5f );
int b = (int)( ( (float)b1 + (float)b2 )*0.5f );
res[ idx+2 ] = (unsigned char)r;
res[ idx+1 ] = (unsigned char)g;
res[ idx ] = (unsigned char)b;
}
} |
20,386 | #include "includes.h"
/*-----This is a vector addition--*/
/*---- @ Cuda/c ------*/
/*---- __NS__Bologna__2020__*/
__global__ void vectorAdd(int* a, int* b, int* c, int n){
// calculate index thread
int tid = blockIdx.x * blockDim.x + threadIdx.x;
// Make sure we stay in-bounds
if(tid < n)
// Vector add
c[tid] = a[tid] + b[tid];
} |
20,387 | #include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <stdio.h>
#include <stdlib.h>
#include <cstring>
#include <time.h>
__global__ void memtransf(int * arr)
{
int gid = blockIdx.x * blockDim.x + threadIdx.x;
printf("tid : %d, gid : %d, value : %d\n", threadIdx.x, gid, arr[gid]);
}
int main()
{
int shape = 128;
int size = shape * sizeof(int);
int * h_arr;
h_arr = (int *)malloc(size);
for(int i=0; i<shape;i++)
{
h_arr[i]=(int)(rand() & 0xff);
}
int * d_arr;
cudaMalloc((void**)&d_arr, size);
cudaMemcpy(d_arr, h_arr, size, cudaMemcpyHostToDevice);
dim3 block(64);
dim3 grid(2);
memtransf<<<grid, block>>>(d_arr);
cudaDeviceSynchronize();
cudaFree(d_arr);
free(h_arr);
cudaDeviceReset();
return 0;
} |
20,388 | template<typename T, typename U>
__global__ void addCUDA(T *a, U *b, T *c){
*c = *a + *b;
}
template<typename T, typename U>
__global__ void subCUDA(T *a, U *b, T *c){
*c = *a - *b;
}
template<typename T, typename U>
__global__ void mulCUDA(T *a, U *b, T *c){
*c = *a * *b;
}
template<typename T, typename U>
__global__ void divCUDA(T *a, U *b, T *c){
*c = *a / *b;
}
//array op array
template<typename T, typename U>
__global__ void addVecCUDA(T *a, U *b, T *c){
int index = blockIdx.x * blockDim.x + threadIdx.x;
c[index] = a[index] + b[index];
}
template<typename T, typename U>
__global__ void subVecCUDA(T *a, U *b, T *c){
/* insert code to calculate the index properly using blockIdx.x, blockDim.x, threadIdx.x */
int index = blockIdx.x * blockDim.x + threadIdx.x;
c[index] = a[index] - b[index];
}
template<typename T, typename U>
__global__ void mulVecCUDA(T *a, U *b, T *c){
/* insert code to calculate the index properly using blockIdx.x, blockDim.x, threadIdx.x */
int index = blockIdx.x * blockDim.x + threadIdx.x;
c[index] = a[index] * b[index];
}
template<typename T, typename U>
__global__ void divVecCUDA(T *a, U *b, T *c){
/* insert code to calculate the index properly using blockIdx.x, blockDim.x, threadIdx.x */
int index = blockIdx.x * blockDim.x + threadIdx.x;
c[index] = a[index] / b[index];
}
template<typename T, typename U>
__global__ void accVecCUDA(T *a, U *b, T *c, int count){
/* insert code to calculate the index properly using blockIdx.x, blockDim.x, threadIdx.x */
int index = blockIdx.x * blockDim.x + threadIdx.x;
for (int i = 0; i < count; ++i){
c[index] = c[index] + a[index] + b[index];
}
}
//pi
template<typename T>
__global__ void pi(T *result, int n) {
T sum = T(0);
for (int i = 0; i < n; i+=2){
sum = sum + (T(1) / (T(2) * T(i) + T(1)));
sum = sum - (T(1) / (T(2) * T(i+1) + T(1)));
}
*result = T(4)*sum;
} |
20,389 |
__global__
void otp(int *v, int *k) {
v[threadIdx.x] = v[threadIdx.x] ^ (*k);
}
|
20,390 | #include <math.h>
#include <stdlib.h>
#include <stdio.h>
#include "unistd.h"
#include "time.h"
#include "string.h"
// Stores output value computed in inner loop for each thread
__shared__ float localvalue[4008];
// Stores temporary shift values
__constant__ float dm_shifts[1024];
// -------------------------- The Dedispersion Loop -----------------------------------
__global__ void dedisperse_loop(float *outuff, float *buff, int nsamp, int nchans, float tsamp,
int chanfactor, float startdm, float dmstep, int inshift, int outshift)
{
int samp, s, c, indx, soffset;
float shift_temp;
/* dedispersing loop over all samples in this buffer */
s = threadIdx.x + blockIdx.x * blockDim.x;
shift_temp = (startdm + blockIdx.y * dmstep) / tsamp;
for (samp = 0; s + samp < nsamp; samp += blockDim.x * gridDim.x) {
soffset = (s + samp);
/* clear array element for storing dedispersed subband */
localvalue[threadIdx.x] = 0.0;
/* loop over the channels */
for (c = 0; c < nchans; c ++) {
indx = (soffset + (int)(dm_shifts[c * chanfactor] * shift_temp)) * nchans + c;
localvalue[threadIdx.x] += buff[inshift + indx];
}
outuff[outshift + blockIdx.y * nsamp + soffset] = localvalue[threadIdx.x];
}
}
// -------------------------- Main Program -----------------------------------
float fch1 = 126, foff = -6, tsamp = 5e-6, dmstep = 0.065, startdm = 0;
int nchans = 128, nsamp = 1024, tdms = 128;
int gridsize = 128, blocksize = 128;
// Process command-line parameters
void process_arguments(int argc, char *argv[])
{
int i = 1;
while((fopen(argv[i], "r")) != NULL)
i++;
while(i < argc) {
if (!strcmp(argv[i], "-nchans"))
nchans = atoi(argv[++i]);
else if (!strcmp(argv[i], "-nsamp"))
nsamp = atoi(argv[++i]);
else if (!strcmp(argv[i], "-dmstep"))
dmstep = atof(argv[++i]);
else if (!strcmp(argv[i], "-startdm"))
startdm = atof(argv[++i]);
else if (!strcmp(argv[i], "-tdms"))
tdms = atoi(argv[++i]);
else if (!strcmp(argv[i], "-gridsize"))
gridsize = atoi(argv[++i]);
else if (!strcmp(argv[i], "-blocksize"))
blocksize = atoi(argv[++i]);
i++;
}
foff = foff / (float) nchans;
tsamp = tsamp * nchans;
}
// Fill buffer with data (blocking call)
void generate_data(float* buffer, int nsamp, int nchans)
{
for(int i = 0; i < nsamp * nchans; i++)
buffer[i] = 01;
}
// DM delay calculation
float dmdelay(float f1, float f2)
{
return(4148.741601 * ((1.0 / f1 / f1) - (1.0 / f2 / f2)));
}
int main(int argc, char *argv[])
{
float *input, *output, *d_input, *d_output;
int maxshift;
process_arguments(argc, argv);
printf("nsamp: %d, nchans: %d, tsamp: %f, startdm: %f, dmstep: %f, tdms: %d, fch1: %f, foff: %f\n",
nsamp, nchans, tsamp, startdm, dmstep, tdms, fch1, foff);
// Calculate temporary DM-shifts
float *dmshifts = (float *) malloc(nchans * sizeof(float));
for (unsigned i = 0; i < nchans; i++)
dmshifts[i] = dmdelay(fch1 + (foff * i), fch1);
// Calculate maxshift
maxshift = dmshifts[nchans - 1] * (startdm + dmstep * tdms) / tsamp;
// Initialise input buffer
input = (float *) malloc( (nsamp + maxshift) * nchans * sizeof(float));
output = (float *) malloc( nsamp * tdms * sizeof(float));
// Allocate arrays
input = (float *) malloc( (nsamp + maxshift) * nchans * sizeof(float));
output = (float *) malloc( nsamp * tdms * sizeof(float));
// Initialise CUDA stuff
( cudaSetDevice(0));
cudaEvent_t event_start, event_stop;
float timestamp, kernelTime;
dim3 gridDim(gridsize, tdms);
cudaEventCreate(&event_start);
cudaEventCreate(&event_stop);
// Allocate CUDA memory and copy dmshifts
( cudaMalloc((void **) &d_input, (nsamp + maxshift) * nchans * sizeof(float)));
( cudaMalloc((void **) &d_output, nsamp * tdms * sizeof(float)));
( cudaMemset(d_output, 0, nsamp * tdms * sizeof(float)));
( cudaMemcpyToSymbol(dm_shifts, dmshifts, nchans * sizeof(int)) );
time_t start = time(NULL);
// Copy input to GPU
cudaEventRecord(event_start, 0);
( cudaMemcpy(d_input, input, (nsamp + maxshift) * nchans * sizeof(float), cudaMemcpyHostToDevice) );
cudaEventRecord(event_stop, 0);
cudaEventSynchronize(event_stop);
cudaEventElapsedTime(×tamp, event_start, event_stop);
printf("Copied to GPU in: %lf\n", timestamp);
// Dedisperse
cudaEventRecord(event_start, 0);
dedisperse_loop<<<gridDim, blocksize >>>(d_output, d_input, nsamp, nchans, tsamp, 1, startdm, dmstep, 0, 0);
cudaEventRecord(event_stop, 0);
cudaEventSynchronize(event_stop);
cudaEventElapsedTime(×tamp, event_start, event_stop);
printf("Processed in: %lf\n", timestamp);
kernelTime = timestamp;
// Copy output from GPU
cudaEventRecord(event_start, 0);
( cudaMemcpy(output, d_output, nsamp * tdms * sizeof(float), cudaMemcpyDeviceToHost) );
cudaEventRecord(event_stop, 0);
cudaEventSynchronize(event_stop);
cudaEventElapsedTime(×tamp, event_start, event_stop);
printf("Copied from GPU in: %lf\n", timestamp);
printf("Total time: %d\n", (int) (time(NULL) - start));
printf("Performance: %lf Gflops\n", (nchans * tdms) * (nsamp * 1.0 / kernelTime / 1.0e6));
}
|
20,391 | #include "includes.h"
__global__ void matrixAdd_A_Kernel(float* A, float* B, float* C, size_t pitch, int width){
//compute indexes
int row = blockIdx.x * blockDim.x + threadIdx.x;
int col = blockIdx.y * blockDim.y + threadIdx.y;
int rowWidthWithPad = pitch/sizeof(float);
if(row < width && col < width)
C[row * rowWidthWithPad + col] = A[row * rowWidthWithPad + col] + B[row * rowWidthWithPad + col];
} |
20,392 | #include <stdio.h>
#include <stdlib.h>
#include "cs_whm_encode.h"
__global__ void d_do_a_pair_32_2_32( int *a, int size, int offset )
{
int tid = blockIdx.x*blockDim.x + threadIdx.x;
int f, ff ;
if ( tid < ( size >> 1 ))
{
f = ( tid / offset ) * ( offset << 1 ) ;
tid = f + tid % offset ;
f = a[ tid ] ;
ff = a[ tid + offset ] ;
a[ tid ] = f + ff ;
a[ tid + offset ] = f - ff ;
}
}
__global__ void d_do_a_pair_8_2_32( unsigned char *from, int size, int offset, int *to )
{
int tid = blockIdx.x*blockDim.x + threadIdx.x;
int f, ff ;
if ( tid < ( size >> 1 ))
{
f = ( tid / offset ) * ( offset << 1 ) ;
tid = f + tid % offset ;
f = from[ tid ] ;
ff = from[ tid + offset ] ;
to[ tid ] = f + ff ;
to[ tid + offset ] = f - ff ;
}
}
void
cs_whm_measurement( char *d_from, int *d_to, int n )
{
int first, loop, offset ;
int nThreadsPerBlock = 512;
int nBlocks = ( n + ( nThreadsPerBlock - 1 )) / nThreadsPerBlock ;
first = 1 ;
loop = n ;
loop >>= 1 ;
offset = 1 ;
while ( loop > 0 )
{
#ifdef CUDA_OBS
fprintf( stderr, "cs_whm_measurement: f %p t %p cnt %d first %d "
"loop %d offset %d nblk %d \n", d_from, d_to, n, first, loop,
offset, nBlocks ) ;
#endif
if ( first )
{
d_do_a_pair_8_2_32 <<< nBlocks, nThreadsPerBlock >>>
(( unsigned char * )d_from, n, offset, d_to ) ;
first = 0 ;
} else
d_do_a_pair_32_2_32 <<< nBlocks, nThreadsPerBlock >>>
( d_to, n, offset ) ;
cudaThreadSynchronize() ;
offset <<= 1 ;
loop >>= 1 ;
}
}
|
20,393 | /*
* Please write your name and net ID below
*
* Last name: Boran
* First name: Tudor
* Net ID: N13059231
*
* I have attached a readme, you can also compile with make (which I used to get this in nsight, because I love IDEs)
*/
/*
* This file contains the code for doing the heat distribution problem.
* You do not need to modify anything except starting gpu_heat_dist() at the bottom
* of this file.
* In gpu_heat_dist() you can organize your data structure and the call to your
* kernel(s) that you need to write too.
*
* You compile with:
* nvcc -o heatdist -arch=sm_60 heatdist.cu
*/
#include <cuda.h>
#include <stdlib.h>
#include <stdio.h>
#include <time.h>
/* To index element (i,j) of a 2D array stored as 1D */
#define index(i, j, N) ((i)*(N)) + (j)
/* Block size = BLOCK_WIDTH*BLOCK_WIDTH */
#define BLOCK_WIDTH 8
/*****************************************************************/
// Function declarations: Feel free to add any functions you want.
void seq_heat_dist(float *, unsigned int, unsigned int);
void gpu_heat_dist(float *, unsigned int, unsigned int);
void check_err(cudaError_t, const char *);
__global__ void gpu_heat_dist_kernel(float *, float *, unsigned int);
/*****************************************************************/
/**** Do NOT CHANGE ANYTHING in main() function ******/
int main(int argc, char * argv[])
{
unsigned int N; /* Dimention of NxN matrix */
int type_of_device = 0; // CPU or GPU
int iterations = 0;
int i;
/* The 2D array of points will be treated as 1D array of NxN elements */
float * playground;
// to measure time taken by a specific part of the code
double time_taken;
clock_t start, end;
if(argc != 4)
{
fprintf(stderr, "usage: heatdist num iterations who\n");
fprintf(stderr, "num = dimension of the square matrix (50 and up)\n");
fprintf(stderr, "iterations = number of iterations till stopping (1 and up)\n");
fprintf(stderr, "who = 0: sequential code on CPU, 1: GPU execution\n");
exit(1);
}
type_of_device = atoi(argv[3]);
N = (unsigned int) atoi(argv[1]);
iterations = (unsigned int) atoi(argv[2]);
/* Dynamically allocate NxN array of floats */
playground = (float *)calloc(N*N, sizeof(float));
if( !playground )
{
fprintf(stderr, " Cannot allocate the %u x %u array\n", N, N);
exit(1);
}
/* Initialize it: calloc already initalized everything to 0 */
// Edge elements to 70F
for(i = 0; i < N; i++)
playground[index(0,i,N)] = 70;
for(i = 0; i < N; i++)
playground[index(i,0,N)] = 70;
for(i = 0; i < N; i++)
playground[index(i,N-1, N)] = 70;
for(i = 0; i < N; i++)
playground[index(N-1,i,N)] = 70;
// from (0,10) to (0,30) inclusive are 100F
for(i = 10; i <= 30; i++)
playground[index(0,i,N)] = 100;
// from (n-1,10) to (n-1,30) inclusive are 150F
for(i = 10; i <= 30; i++)
playground[index(N-1,i,N)] = 150;
if( !type_of_device ) // The CPU sequential version
{
start = clock();
seq_heat_dist(playground, N, iterations);
end = clock();
}
else // The GPU version
{
start = clock();
gpu_heat_dist(playground, N, iterations);
end = clock();
}
time_taken = ((double)(end - start))/ CLOCKS_PER_SEC;
printf("Time taken for %s is %lf\n", type_of_device == 0? "CPU" : "GPU", time_taken);
free(playground);
return 0;
}
/***************** The CPU sequential version (DO NOT CHANGE THAT) **************/
void seq_heat_dist(float * playground, unsigned int N, unsigned int iterations)
{
// Loop indices
int i, j, k;
int upper = N-1;
// number of bytes to be copied between array temp and array playground
unsigned int num_bytes = 0;
float * temp;
/* Dynamically allocate another array for temp values */
/* Dynamically allocate NxN array of floats */
temp = (float *)calloc(N*N, sizeof(float));
if( !temp )
{
fprintf(stderr, " Cannot allocate temp %u x %u array\n", N, N);
exit(1);
}
num_bytes = N*N*sizeof(float);
/* Copy initial array in temp */
memcpy((void *)temp, (void *) playground, num_bytes);
for( k = 0; k < iterations; k++)
{
/* Calculate new values and store them in temp */
for(i = 1; i < upper; i++)
for(j = 1; j < upper; j++)
temp[index(i,j,N)] = (playground[index(i-1,j,N)] +
playground[index(i+1,j,N)] +
playground[index(i,j-1,N)] +
playground[index(i,j+1,N)])/4.0;
/* Move new values into old values */
memcpy((void *)playground, (void *) temp, num_bytes);
}
}
/***************** The GPU version: Write your code here *********************/
/* This function can call one or more kernels if you want ********************/
void gpu_heat_dist(float * playground, unsigned int N, unsigned int iterations)
{
// number of bytes to be copied between array temp and array playground
size_t count = N*N;
unsigned int num_bytes = count*sizeof(float);
unsigned int k;
float *d_temp = NULL, *d_playground = NULL;
/* Dynamically allocate NxN array of floats */
cudaError_t err;
err = cudaMalloc((void**)&d_playground, num_bytes);
/* Dynamically allocate another array for temp values */
err = cudaMalloc((void**)&d_temp, num_bytes);
check_err(err, "allocating memory on device.");
err = cudaMemcpy(d_temp, playground, count*sizeof(float), cudaMemcpyHostToDevice);
err = cudaMemcpy(d_playground, playground, count*sizeof(float), cudaMemcpyHostToDevice);
check_err(err, "copying array to device memory.");
dim3 block(BLOCK_WIDTH, BLOCK_WIDTH, 1);
dim3 grid(N/BLOCK_WIDTH + 1, N/BLOCK_WIDTH + 1, 1);
for (k = 0; k < iterations; k++)
{
gpu_heat_dist_kernel<<<grid, block>>>(d_playground, d_temp, N);
cudaThreadSynchronize();
err = cudaMemcpy(d_playground, d_temp, count*sizeof(float),cudaMemcpyDeviceToDevice);
}
err = cudaMemcpy(playground, d_playground, count*sizeof(float), cudaMemcpyDeviceToHost);
check_err(err, "copying array back to host.");
cudaFree(d_playground);
cudaFree(d_temp);
}
__global__
void gpu_heat_dist_kernel(float *d_playground, float *d_temp, unsigned int N)
{
unsigned int upper = N-1;
unsigned int i, j;
i = blockIdx.x*blockDim.x + threadIdx.x;
j = blockIdx.y*blockDim.y + threadIdx.y;
if (i > 0 && i < upper && j > 0 && j < upper)
{
d_temp[index(i,j,N)] = (d_playground[index(i-1,j,N)] +
d_playground[index(i+1,j,N)] +
d_playground[index(i,j-1,N)] +
d_playground[index(i,j+1,N)])/4.0;
}
}
void check_err(cudaError_t err, const char *msg)
{
if (err != cudaSuccess)
{
fprintf(stderr, "CUDA Error: %s\n", msg);
exit(1);
}
}
|
20,394 | #include<iostream>
#include <cuda.h>
__global__ void matmul_kernel(const float* A, const float* B, float* C, unsigned int n)
{
extern __shared__ float sm[];
float *sA = &sm[0];
float *sB = &sA[blockDim.x*blockDim.x];
int r = blockIdx.y*blockDim.x+ threadIdx.y;
int c = blockIdx.x*blockDim.x + threadIdx.x;
float opval = 0.0;
int tx = threadIdx.x; int ty = threadIdx.y;
int bx = blockIdx.x; int by = blockIdx.y;
for (int q = 0; q < (blockDim.x + n - 1)/blockDim.x; q++)
{
if (q*blockDim.x + threadIdx.x < n && r < n)
sA[blockDim.x*ty+ tx] = A[r*n + q*blockDim.x + threadIdx.x];
else
sA[blockDim.x*ty+ tx] = 0.0;
if (q*blockDim.x+ threadIdx.y < n && c< n)
sB[blockDim.x*ty+ tx] = B[(q*blockDim.x + threadIdx.y)*n + c];
else
sB[blockDim.x*ty+ tx] = 0.0;
__syncthreads();
for (int j = 0; j < blockDim.x; ++j)//Multiplying Elements present in tile
{
opval += sA[blockDim.x*ty+ j] * sB[blockDim.x*j+ tx];
}
__syncthreads();
}
int cindex = (by * blockDim.x + threadIdx.y)*n+(bx*blockDim.x)+threadIdx.x;
if (r < n && c < n)
C[cindex]=opval;
}
__host__ void matmul(const float* A, const float* B, float* C, unsigned int n, unsigned int block_dim)
{
dim3 dimBlock(block_dim,block_dim);
int gridsize= (n + block_dim -1)/ block_dim;
dim3 dimGrid(gridsize,gridsize);
matmul_kernel<<<dimGrid,dimBlock, 2*block_dim*block_dim* sizeof(float)>>>(A, B, C, n);
cudaDeviceSynchronize();
}
|
20,395 | #include <stdio.h>
#include <cstdlib>
#include <iostream>
#include <vector>
#define BS 32
#define NUM_BLOCKS 1500
#define NUM_THREADS_PER_BLOCK 1500
#define SIZE NUM_BLOCKS*NUM_THREADS_PER_BLOCK
using namespace std;
cudaEvent_t start, stop;
// These are specific to measure the execution of only the kernel execution - might be useful
void startKernelTime (void) {
cudaEventCreate(&start);
cudaEventCreate(&stop);
cudaEventRecord(start);
}
void stopKernelTime (void) {
cudaEventRecord(stop);
cudaEventSynchronize(stop);
float milliseconds = 0;
cudaEventElapsedTime(&milliseconds, start, stop);
cout << milliseconds << " ms have elapsed for the CUDA execution" << endl;
}
void checkCUDAError (const char *msg) {
cudaError_t err = cudaGetLastError();
if( cudaSuccess != err) {
cerr << "Cuda error: " << msg << ", " << cudaGetErrorString( err) << endl;
exit(-1);
}
}
// Fill the input parameters and kernel qualifier
__global__
void dotKernel (float *dev_m1, float *dev_m2, float *dev_output) {
__shared__ float shareA[BS][BS];
__shared__ float shareB[BS][BS];
int bx = blockIdx.x; int by = blockIdx.y;
int tx = threadIdx.x; int ty = threadIdx.y;
int row = by * BS + ty;
int col = bx * BS + tx;
float tmp_sum = 0.0f;
for(int i = 0; i < NUM_BLOCKS/BS; ++i){
shareA[ty][tx] = dev_m1[row* NUM_BLOCKS + (i * BS + tx)];
shareB[ty][tx] = dev_m2[(i * BS + ty) * NUM_BLOCKS + col];
__syncthreads();
for(int k = 0; k < BS; ++k){
tmp_sum += shareA[ty][k] * shareB[k][tx];
__syncthreads();
}
dev_output[row * NUM_BLOCKS + col] = tmp_sum;
}
}
// Fill with the code required for the GPU dot (mem allocation, transfers, kernel launch of dotKernel)
float* dotGPU (float *m1, float *m2) {
float *dev_m1, *dev_m2, *dev_output;
float *array_output = new float [SIZE];
// allocate the memory on the device
cudaMalloc((void**) &dev_m1, sizeof(float) * SIZE);
cudaMalloc((void**) &dev_m2, sizeof(float) * SIZE);
cudaMalloc((void**) &dev_output, sizeof(float) * SIZE);
startKernelTime();
// copy inputs to the device
cudaMemcpy(dev_m1, m1, sizeof(float) * SIZE, cudaMemcpyHostToDevice);
cudaMemcpy(dev_m2, m2, sizeof(float) * SIZE, cudaMemcpyHostToDevice);
cudaMemcpy(dev_output, array_output, sizeof(float) * SIZE, cudaMemcpyHostToDevice);
dim3 blocksPerGrid(BS, BS, 1);
dim3 threadsPerBlock(NUM_THREADS_PER_BLOCK/BS, NUM_THREADS_PER_BLOCK/BS, 1);
// launch the kernel
dotKernel <<< blocksPerGrid, threadsPerBlock >>> (dev_m1, dev_m2, dev_output);
// copy the output to the host
cudaMemcpy(array_output, dev_output, sizeof(float) * SIZE, cudaMemcpyDeviceToHost);
stopKernelTime();
for(size_t i = 0; i < 512; i++) {
cout << array_output[i] << '\n';
}
// free the device memory
cudaFree(dev_m1);
cudaFree(dev_m2);
cudaFree(dev_output);
return array_output;
}
int main (int argc, char** argv) {
float array1 [SIZE];
float array2 [SIZE];
// initialize array with random values
for (unsigned i = 0; i < SIZE; i++) {
array1[i] = ((float) rand()) / ((float) RAND_MAX) * 10;
array2[i] = 1;
}
for(size_t i = 0; i < 4; i++) {
cout << array1[i * NUM_BLOCKS] << '\n';
}
dotGPU(array1, array2);
return 0;
}
|
20,396 | #include <stdio.h>
#include <stdlib.h>
#include <cstdlib>
#include <math.h>
#define MATRIX_WIDTH 2025
#define MATRIX_HEIGHT 2025
#define MATRIX_SIZE MATRIX_WIDTH*MATRIX_HEIGHT
#define TILE_WIDTH 45
#define TILE_HEIGHT 45
#define INIT_THREADS_PER_BLOCK 256
#define INIT_ELEMENTS_PER_THREAD 90
#define INIT_ELEMENTS_PER_BLOCK INIT_ELEMENTS_PER_THREAD*INIT_THREADS_PER_BLOCK
/*
* Matrix transpose - device function
* ----------------------------------------
* Description:
* this function transpose matrix element block by block
* and line by line
* Parms:
* - idata (int*) - input matrix
* - odata (int*) - output matix (allocated alrea)
*/
__global__ void transpose_matrix(int* idata, int* odata, int block_per_row) {
// allocate shared memory
__shared__ int shared_input[TILE_HEIGHT][TILE_WIDTH];
__shared__ int shared_output[TILE_WIDTH][TILE_HEIGHT];
// estimate current position
int line = threadIdx.x;
int block = blockIdx.x;
// local data from global memory (idata) to shared memory (shared_input)
int block_row = (block/block_per_row);
int block_column = block % block_per_row;
// cpy
int block_start_pos =(block_row * (TILE_HEIGHT*MATRIX_WIDTH) + (block_column*TILE_WIDTH + (line*MATRIX_WIDTH))); //(block_row*TILE_HEIGHT+(line*MATRIX_WIDTH));
//int block_shift = TILE_WIDTH*block;
for(int i=0; i < TILE_WIDTH; i++) {
shared_input[line][i] = idata[block_start_pos+i];
}
// single transposition blok
for(int i=0;i<TILE_WIDTH;i++)
{
shared_output[i][line] = shared_input[line][i];
}
__syncthreads();
if(block == 0) {
block_start_pos = (block_column * (TILE_WIDTH*MATRIX_HEIGHT) + (block_row*TILE_HEIGHT + (line*MATRIX_HEIGHT)));
for(int i=0; i<TILE_HEIGHT; i++) {
odata[block_start_pos+i] = shared_output[line][i];
}
//v 2 memcpy
//memcpy( odata, shared_output, TILE_HEIGHT * sizeof(int) ); // TODO: napisac poprawnie zapisywanie wynikow w macierzy wynikowej
}
}
/*
* Initialize matrix - device function
* ----------------------------------------
* Description:
* this function initialze matrix INIT_ELEMENTS_PER_THREAD elements by elements
* Parms:
* - idata (int*) - matrix
* - size (int) - matrix size (number of elements)
*/
__global__ void init_matrix( int *idata, int size ) {
int elements_count;
int start_id = (threadIdx.x * INIT_ELEMENTS_PER_THREAD );
// set elements count
if ( start_id+INIT_ELEMENTS_PER_THREAD > size ) {
elements_count = size-start_id;
}else{
elements_count = INIT_ELEMENTS_PER_THREAD;
}
// set elementsPerThread elements
int value = start_id + ( blockIdx.x * INIT_ELEMENTS_PER_BLOCK );
for( int i=0; i < elements_count; i++ )
idata[value+i] = value+i;
}
/*
* Print matrix window - host function
* ----------------------------------------
* Description:
* this function copy data form device
* and print output to screen
* host matrix size has to be the same as device matrix size
* Pamrs:
* - h_matrix (int*) - host matrix
* - d_matrix (int*) - device matrix
* - start_height, start_width, end_height, end_width (int) - displaying area
*/
void print_matrix_window(int* h_matrix, int* d_matrix, int start_height, int start_width, int end_height, int end_width ) {
cudaMemcpy( h_matrix, d_matrix, sizeof(int)*MATRIX_SIZE, cudaMemcpyDeviceToHost);
printf("--------------------\n");
for(int y=start_height; y < end_height; y++) {
for(int x=start_width; x < end_width; x++) {
printf("%d\t", h_matrix[x + ( y * MATRIX_WIDTH ) ]);
}
printf("\n");
}
}
/*
* return GB per second for given data
*/
double GBperSec(float runtime, double bytes) {
return 100*(bytes/1073741824)/runtime;
}
int main(void) {
int number_of_device,device_number;
cudaGetDeviceCount(&number_of_device);
if(number_of_device >0){
for(device_number=0;device_number<number_of_device; device_number++){
cudaDeviceProp device_prop;
cudaGetDeviceProperties(&device_prop, device_number);
printf("number of device: %d prop: %s\n",device_number, device_prop.name);
}
}
int *d_idata, *d_odata, *h_matrix, blocks_count, block_per_row;
cudaEvent_t init_start, init_end;
float init_time;
// create init events
cudaEventCreate(&init_start);
cudaEventCreate(&init_end);
cudaEvent_t transpose_start, transpose_end;
float transpose_time;
// create transpose events
cudaEventCreate(&transpose_start);
cudaEventCreate(&transpose_end);
// size validation
if ( MATRIX_WIDTH % TILE_WIDTH != 0
|| MATRIX_HEIGHT % TILE_HEIGHT != 0) {
printf("Invalid matrix size\n");
return 1;
}
cudaMalloc( (void**)&d_idata, sizeof(int) * MATRIX_SIZE );
cudaMalloc( (void**)&d_odata, sizeof(int) * MATRIX_SIZE );
h_matrix = new int[ MATRIX_SIZE ];
// matrix initialization
// set data to global memory
blocks_count = ceil(((MATRIX_SIZE+INIT_THREADS_PER_BLOCK-1)/INIT_THREADS_PER_BLOCK)/INIT_ELEMENTS_PER_THREAD);
cudaEventRecord(init_start, 0);
init_matrix<<< blocks_count, INIT_THREADS_PER_BLOCK >>>(d_idata, MATRIX_SIZE);
cudaEventRecord(init_end, 0);
cudaEventSynchronize(init_end);
cudaEventElapsedTime(&init_time, init_start, init_end);
printf("init matrix:\n\tblocks count: %d\n\tthreads per block: %d\n\telements per thread: %d\n\ttime: %f ms\n\tspeed: %lf GB/s\n",
blocks_count, INIT_THREADS_PER_BLOCK, INIT_ELEMENTS_PER_THREAD, init_time, GBperSec(init_time, MATRIX_SIZE*sizeof(int)));
print_matrix_window(h_matrix, d_idata, 0, 0, 10, 10);
blocks_count = MATRIX_SIZE / (TILE_WIDTH*TILE_HEIGHT);
block_per_row = MATRIX_WIDTH/TILE_WIDTH;
cudaEventRecord(transpose_start, 0);
transpose_matrix<<< blocks_count, TILE_HEIGHT >>>(d_idata, d_odata, block_per_row);
cudaEventRecord(transpose_end, 0);
cudaEventSynchronize(transpose_end);
cudaEventElapsedTime(&transpose_time, transpose_start, transpose_end);
printf("\ntranspose matrix:\n\tblocks count: %d\n\tthreads per block: %d\n\tblock per row: %d\n\ttime: %f ms\n\tspeed: %lf GB/s\n",
blocks_count, TILE_HEIGHT, block_per_row, transpose_time, GBperSec(transpose_time, MATRIX_SIZE*sizeof(int)));
print_matrix_window(h_matrix, d_odata, 0, 0, 10, 10);
// clear memory allocation
cudaFree( d_idata );
cudaFree( d_odata );
delete [] h_matrix;
return 0;
}
|
20,397 | /*
Troca os valores de posição em um vetor (inverte os valores no vetor).
Exemplo da necessidade da sincronização de threads de um bloco.
Exemplo para alocação dinâmica e estática de shared mem
Quando a função __syncthreads() no kernel está comentada, o resultado fica errado.
Os if's nos for's das saídas dos resultados mostram os casos errados.
*/
#include <stdio.h>
#include <stdlib.h>
#include <cuda.h>
#define N 512
__global__ void staticReverse(int *vetD_glb)
{
__shared__ int vetD_shd[N];
int t = threadIdx.x;
int tr = N-t-1;
vetD_shd[t] = vetD_glb[t];
// __syncthreads();
vetD_glb[t] = vetD_shd[tr];
}
__global__ void dynamicReverse(int *vetD_glb)
{
extern __shared__ int vetD_shd[];
int t = threadIdx.x;
int tr = N-t-1;
vetD_shd[t] = vetD_glb[t];
//__syncthreads();
vetD_glb[t] = vetD_shd[tr];
}
int main(void)
{
int vetA_h[N], vetCtrl_h[N], vetD_h[N];
int *vetD_d;
int i;
for (i = 0; i < N; i++) {
vetA_h[i] = i; // source
vetCtrl_h[i] = N-i-1; // just to check our results
vetD_h[i] = 0; // target
}
cudaMalloc(&vetD_d, N * sizeof(int));
// copy vet a into device global memory
cudaMemcpy(vetD_d, vetA_h, N*sizeof(int), cudaMemcpyHostToDevice);
// run version with static shared memory
staticReverse<<<1,N>>>(vetD_d);
// copy results from device to host memory
cudaMemcpy(vetD_h, vetD_d, N*sizeof(int), cudaMemcpyDeviceToHost);
printf("Static Results(%d): ", N);
for (i = 0; i < N; i++)
if (vetD_h[i] != vetCtrl_h[i])
printf("vetD_h[%d]=%d, vetCtrl_h[%d]=%d ", i, vetD_h[i], i, vetCtrl_h[i]);
printf("\nN=%d \n", N);
// *************************************************
// copy again vet a into device global memory
cudaMemcpy(vetD_d, vetA_h, N*sizeof(int), cudaMemcpyHostToDevice);
// run dynamic shared memory version
dynamicReverse<<<1,N,N*sizeof(int)>>>(vetD_d);
// copy results from device to host memory
cudaMemcpy(vetD_h, vetD_d, N * sizeof(int), cudaMemcpyDeviceToHost);
printf("Dynamic Results(%d): ", N);
for (i = 0; i < N; i++)
if (vetD_h[i] != vetCtrl_h[i])
printf("vetD_h[%d]=%d, vetCtrl_h[%d]=%d ", i, vetD_h[i], i, vetCtrl_h[i]);
printf("\nN=%d \n", N);
// device memory free!!!!
cudaFree(vetD_d);
exit(0);
}
|
20,398 | #include <stdio.h>
#include <stdlib.h>
#include <assert.h>
#include <iostream>
#include <bitset>
__global__ void
block_reduction (int *a, int len)
{
__shared__ int smem[256];
assert(blockDim.x <= 256);
smem[threadIdx.x] = threadIdx.x;
__syncthreads();
for (int i = blockDim.x/2; i > 0; i = i/2)
{
if(threadIdx.x < i)
{
int temp = smem[threadIdx.x] + smem[threadIdx.x + i];
smem[threadIdx.x] = temp;
}
__syncthreads();
}
a[threadIdx.x] = smem[0];
}
int
main (int args, char **argv)
{
int len = 256;
int *a = new int[len];
int *a_d;
cudaMalloc ((void **) &a_d, sizeof (int) * len);
block_reduction <<< 1, len >>> (a_d, len);
cudaMemcpy (a, a_d, sizeof (int) * len, cudaMemcpyDeviceToHost);
std::cout<<"Block id sum: "<< a[0]<<"\n";
int sum = 0;
for (int i = 0; i < 256; i ++)
sum += i;
std::cout<<"Correct result: "<<sum<<"\n";
return 0;
}
|
20,399 | #include "includes.h"
__global__ void setDiffVolumeKernel(float *d_fv, unsigned char *d_picture1, unsigned char *d_picture2, unsigned picWidth, unsigned picHeight) {
__shared__ float p1_section[10 * 10 * 4];
__shared__ float p2_section[10 * 10 * 4];
unsigned i;
// This thread's position in its block's subsection of the float volume
unsigned sx, sy, sz;
// Dimensions of the grid
unsigned gx, gy, gz;
// Position of this thread's block
unsigned bx, by, bz;
// This thread's position in the entire float volume
unsigned vx, vy, vz;
// The location of the colors that this thread will be comparing
unsigned c1, c2;
// Get the position of this thread in its subsection
sz = threadIdx.x % 10;
sy = threadIdx.x / 100;
sx = (threadIdx.x % 100) / 10;
// Get the dimensions of the grid
gz = picWidth / 10;
if(picWidth % 10) gz++;
gy = picHeight / 10;
if(picHeight % 10) gy++;
gx = picWidth / 10;
if(picWidth % 10) gx++;
// Get the position of this thread's block
bz = blockIdx.x % gz;
by = blockIdx.x / (gx * gz);
bx = (blockIdx.x % (gx * gz)) / gz;
// Get the position of this thread in entire float volume
vx = sx + 10 * bx;
vy = sy + 10 * by;
vz = sz + 10 * bz;
// Copy subpicture to shared memory
// See if this thread needs to copy from picture 1
// picture 1 covers width * height
// If the float volume z of this thread is zero,
// then it needs to copy from picture 1
if(sz == 0) {
// Check if this thread will get a pixel not in the picture
if(vx < picWidth && vy < picHeight) {
for(i = 0; i < 4; i++) {
p1_section[(sx + sy * 10) * 4 + i] =
(float) d_picture1[(vx + vy * picWidth) * 4 + i];
}
}
}
// See if this thread needs to copy from picture 2
// picture 2 covers depth * height
// If the float volume x of this thread is zero,
// then it needs to copy from picture 2
if(sx == 0) {
// Check if this thread will get a pixel not in the picture
if(vz < picWidth && vy < picHeight) {
for(i = 0; i < 4; i++) {
p2_section[(sz + sy * 10) * 4 + i] =
(float) d_picture2[(vz + vy * picWidth) * 4 + i];
}
}
}
__syncthreads();
// Now each of d_picture1 and d_picture2 are properly filled out
// Write difference into float volume
if(vx < picWidth && vy < picHeight && vz < picWidth) {
c1 = (sx + sy * 10) * 4;
c2 = (sz + sy * 10) * 4;
d_fv[vz + vx * picWidth + vy * picWidth * picWidth] =
sqrtf(
powf(p1_section[c1 + 0] - p2_section[c2 + 0], 2.f) +
powf(p1_section[c1 + 1] - p2_section[c2 + 1], 2.f) +
powf(p1_section[c1 + 2] - p2_section[c2 + 2], 2.f) +
powf(p1_section[c1 + 3] - p2_section[c2 + 3], 2.f)
);
}
} |
20,400 | #include <iostream>
#include <cuda_runtime_api.h>
#include <stdlib.h>
using namespace std;
#define LEN 100000000
__global__ void add_vec(int *v1, int *v2, int *res, size_t l) {
// cudaError_t status;
int i = blockIdx.x * blockDim.x + threadIdx.x;
int step = gridDim.x * blockDim.x;
for (; i < l; i+= step) {
res[i] = v1[i] + v2[i];
}
}
int main() {
int *v1_cpu, *v2_cpu, *res_cpu;
v1_cpu = (int*)calloc(LEN, sizeof(int));
v2_cpu = (int*)calloc(LEN, sizeof(int));
for (int i = 0; i < LEN; i++) {
v1_cpu[i] = i;
v2_cpu[i] = i * 40 + 2;
}
cudaError_t status;
int *v1_gpu, *v2_gpu, *res_gpu;
status = cudaMalloc((void**)&v1_gpu, sizeof(int) * LEN);
if (status != cudaSuccess) {
cout << cudaGetErrorString(status) << endl;
}
status = cudaMalloc((void**)&v2_gpu, sizeof(int) * LEN);
if (status != cudaSuccess) {
cout << cudaGetErrorString(status) << endl;
}
status = cudaMalloc((void**)&res_gpu, sizeof(int) * LEN);
if (status != cudaSuccess) {
cout << cudaGetErrorString(status) << endl;
}
status = cudaMemcpy(v1_gpu, v1_cpu, sizeof(int) * LEN, cudaMemcpyHostToDevice);
if (status != cudaSuccess) {
cout << cudaGetErrorString(status) << endl;
}
status = cudaMemcpy(v2_gpu, v2_cpu, sizeof(int) * LEN, cudaMemcpyHostToDevice);
if (status != cudaSuccess) {
cout << cudaGetErrorString(status) << endl;
}
free(v1_cpu);
free(v2_cpu);
add_vec<<<2, 32, 0>>>(v1_gpu, v2_gpu, res_gpu, LEN);
res_cpu = (int*)calloc(sizeof(int), LEN);
status = cudaMemcpy(res_cpu, res_gpu, sizeof(int) * LEN, cudaMemcpyDeviceToHost);
if (status != cudaSuccess) {
cout << cudaGetErrorString(status) << endl;
}
status = cudaFree(v1_gpu);
if (status != cudaSuccess) {
cout << cudaGetErrorString(status) << endl;
}
status = cudaFree(v2_gpu);
if (status != cudaSuccess) {
cout << cudaGetErrorString(status) << endl;
}
status = cudaFree(res_gpu);
if (status != cudaSuccess) {
cout << cudaGetErrorString(status) << endl;
}
free(res_cpu);
return 0;
}
|
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