serial_no int64 1 24.2k | cuda_source stringlengths 11 9.01M |
|---|---|
11,501 | /*
Program to add two integers using the GPU instead of the CPU.
This program does not use shared memory.
Terrible example to start with as the CPU can execute the opertaion 100x faster than the GPU.
Benchmarking timings to compare speeds of execution.
*/
/*
Note that there is a considerable dependency of the ratio of execution times of the CPU and GPU on the
hardware which is being used to execute the run the program.
*/
// Importing the required headers
#include<stdio.h>
#include<cuda.h>
#include<time.h>
// Returns the duration from start to end times in sec
double time_elapsed(struct timespec *start, struct timespec *end)
{
double t;
t = (end->tv_sec - start->tv_sec); // diff in seconds
t += (end->tv_nsec - start->tv_nsec) * 0.000000001; //diff in nanoseconds
return t;
}
// GPU Kernel to add two numbers
__global__ void add(int *a, int *b)
{
a[0] += b[0]; //add the numbers and store the result in 'a'.
}
int GPU_ADD(int a, int b)
{
int *d_a; //pointer to device memory
int *d_b; //pointer to device memory
cudaMalloc(&d_a, sizeof(int)); //malloc space in device
cudaMalloc(&d_b, sizeof(int)); //malloc space in device
cudaMemcpy(d_a, &a, sizeof(int), cudaMemcpyHostToDevice); //copy value of variable to device memory/ram
cudaMemcpy(d_b, &b, sizeof(int), cudaMemcpyHostToDevice); //copy value of variable to device memory/ram
add<<<1,1>>>(d_a, d_b); //call to the gpu function with the launch configuration
cudaMemcpy(&a, d_a, sizeof(int), cudaMemcpyDeviceToHost); //copy the result from device ram back to main memory
cudaFree(d_a); //free device space
cudaFree(d_b); //free device space
return a; //return result
}
// CPU function to add two numbers
int CPU_ADD(int a, int b)
{
return a+b; //return result
}
// Code execution begins here
int main()
{
struct timespec start1, end1; //variables to store time for GPU
struct timespec start2, end2; //variables to store time for CPU
int a = 10000;
int b = 10000;
//printf("Enter the value of a: "); //get value of a
//scanf("%d", &a);
//printf("Enter the value of b: "); //get value of b
//scanf("%d", &b);
clock_gettime(CLOCK_REALTIME, &start1); //start timestamp
int sum1 = GPU_ADD(a, b);
clock_gettime(CLOCK_REALTIME, &end1); //end timestamp
clock_gettime(CLOCK_REALTIME, &start2); //start timestamp
int sum2 = CPU_ADD(a, b);
clock_gettime(CLOCK_REALTIME, &end2); //end timestamp
printf("\nThe sum of the two numbers using GPU is: %d\n", sum1);
printf("Time taken by GPU is: %E\n\n", time_elapsed(&start1, &end1)); //print result for GPU
printf("The sum of the two numbers using CPU is: %d\n", sum2);
printf("Time taken by CPU is: %E\n", time_elapsed(&start2, &end2)); //print result for CPU
return 0;
}
|
11,502 | #include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <stdio.h>
__global__ void hello()
{
printf("Hello from block %d\n", blockIdx.x);
}
int main()
{
hello <<<16, 1>>> ();
cudaDeviceSynchronize();
printf("Bye!\n");
return 0;
} |
11,503 | #include "includes.h"
__global__ void rinit(float * init, const unsigned int * fsum, const float * ncrs) {
int idx = threadIdx.x + blockIdx.x*blockDim.x;
init[idx] = sqrtf((float)fsum[idx] / ncrs[idx]);
} |
11,504 | #include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <stdio.h>
#include <time.h>
#define N (1024*1024)
#define M (1000)
#define THREADS_PER_BLOCK 1024
void serial_add(double *a, double *b, double *c, int n, int m)
{
for(int index=0;index<n;index++)
{
for(int j=0;j<m;j++)
{
c[index] = a[index]*a[index] + b[index]*b[index];
}
}
}
__global__ void vector_add(double *a, double *b, double *c)
{
int index = blockIdx.x * blockDim.x + threadIdx.x;
for(int j=0;j<M;j++)
{
c[index]=a[index]*a[index]+b[index]*b[index];
}
}
int main()
{
clock_t start,end;
double *a, *b, *c;
int size=N*sizeof( double );
a= (double *)malloc( size );
b= (double *)malloc( size );
c= (double *)malloc( size );
for(int i=0;i<N;i++)
{
a[i]=b[i]=i;
c[i]=0;
}
start=clock();
serial_add(a,b,c,N,M);
printf("c[%d]=%f\n",0,c[0]);
printf("c[%d]=%f\n",N-1,c[N-1]);
end=clock();
float time1=((float)(end-start))/CLOCKS_PER_SEC;
printf("CPU: %f seconds\n",time1);
double *d_a,*d_b,*d_c;
cudaMalloc((void **) &d_a,size);
cudaMalloc((void **) &d_b,size);
cudaMalloc((void **) &d_c,size);
cudaMemcpy(d_a,a,size,cudaMemcpyHostToDevice);
cudaMemcpy(d_b,b,size,cudaMemcpyHostToDevice);
start=clock();
vector_add<<<(N+(THREADS_PER_BLOCK-1))/THREADS_PER_BLOCK,THREADS_PER_BLOCK>>>(d_a,d_b,d_c);
cudaMemcpy(c,d_c,size,cudaMemcpyDeviceToHost);
printf("c[%d]=%f\n",0,c[0]);
printf("c[%d]=%f\n",N-1,c[N-1]);
end=clock();
free(a);
free(b);
free(c);
cudaFree(d_a);
cudaFree(d_b);
cudaFree(d_c);
float time2 = ((float)(end-start))/CLOCKS_PER_SEC;
printf("CUDA: %f seconds, Speedup: %f\n",time2,time1/time2);
return 0;
} |
11,505 | #include "includes.h"
__global__ void fixPVandGain(float* input, float* output, float gain, int lowestIndx, int length) {
int i = threadIdx.x + blockDim.x*blockIdx.x;
int j = i<<1;
if (j >= lowestIndx && j < length) {
if (isnan(output[j])) // LIKELY, THERE IS A PERFORMANCE LOSS HERE
output[j] = 0.0f; // set to zero any invalid amplitude
if (output[j+1] == -1.0f) { // LIKELY, THERE IS A PERFORMANCE LOSS HERE
output[j] = 0.0f; // set to zero the amp related to any undefined frequency
}
else
output[j] *= gain; // scale all amplitudes by the gain factor
}
if (j == 0) output[0] = input[0]; // keep original DC amplitude
if (j == length-2) output[length-2] = input[length-2]; // keep original Nyquist amplitude
} |
11,506 | #include <assert.h>
#include <stdio.h>
__global__ void addKernel(int a, int b, int* c) {
*c = a + b;
}
int main(void) {
int h_c;
int* d_c;
const int C_BYTES = 1 * sizeof(int);
// Save memory
cudaMalloc((void**)&d_c, C_BYTES);
// Call the kernel
addKernel<<<1, 1>>>(2, 7, d_c);
// Copy memory from Device to Host
cudaMemcpy(&h_c, d_c, sizeof(int), cudaMemcpyDeviceToHost);
assert(2 + 7 == h_c);
printf("-: successful execution :-\n");
// Free device memory
cudaFree(d_c);
return 0;
}
|
11,507 | #include<stdio.h>
#include<cuda.h>
#define height 50
#define width 50
// Device code
__global__ void kernel(float* devPtr, int pitch)
{
for (int r = 0; r < height; ++r) {
float* row = (float*)((char*)devPtr + r * pitch);
for (int c = 0; c < width; ++c) {
float element = row[c];
}
}
}
//Host Code
int main()
{
float* devPtr;
size_t pitch;
cudaMallocPitch((void**)&devPtr, &pitch, width * sizeof(float), height);
printf("%d\n", (int)pitch);
kernel<<<100, 512>>>(devPtr, pitch);
return 0;
}
|
11,508 | /**
* Calculates the average and standard deviation filters for the provided input.
* Note that it does not tamper with the channels BUT creates an output with 3 channels.
* Assumes that the input of the textures has the ALPHA component at the beginning.
*
*/
#define O_TILE_WIDTH 16
extern "C"
__global__ void newFilter(uchar4 *input, const int iPitchInElements,
float4 *average, float4 *stdDev, const int oPitchInElements,
const int width, const int height,
const int maskWidth) {
// get indices
int tx = threadIdx.x;
int ty = threadIdx.y;
int col_o = blockIdx.x * O_TILE_WIDTH + tx;
int row_o = blockIdx.y * O_TILE_WIDTH + ty;
int row_i = row_o - maskWidth / 2;
int col_i = col_o - maskWidth / 2;
__shared__ uchar4 tile[30][30];
if (row_i >= 0 && row_i < height && col_i >= 0 && col_i < iPitchInElements)
tile[ty][tx] = input[row_i * iPitchInElements + col_i];
// else
// tile[ty][tx] = make_uchar4(255, 0, 0, 0);
__syncthreads();
int count = maskWidth * maskWidth;
// first calculate the average
float4 sum = {0, 0, 0, 0};
float4 sumSd = {0, 0, 0, 0};
uchar4 fetched;
if (ty < O_TILE_WIDTH && tx < O_TILE_WIDTH) {
for (int i = 0 ; i < maskWidth ; i++)
for (int j = 0 ; j < maskWidth ; j++) {
fetched = tile[i + ty][j + tx];
sum.x += fetched.x;
sum.y += fetched.y;
sum.z += fetched.z;
sum.w += fetched.w;
}
sum.x /= count;
sum.y /= count;
sum.z /= count;
sum.w /= count;
for (int i = 0 ; i < maskWidth ; i++)
for (int j = 0 ; j < maskWidth ; j++) {
fetched = tile[i + ty][j + tx];
sumSd.x += pow(fetched.x - sum.x, 2);
sumSd.y += pow(fetched.y - sum.y, 2);
sumSd.z += pow(fetched.z - sum.z, 2);
sumSd.w += pow(fetched.w - sum.w, 2);
}
sumSd.x /= count;
sumSd.y /= count;
sumSd.z /= count;
sumSd.w /= count;
sumSd.x = sqrt(sumSd.x);
sumSd.y = sqrt(sumSd.y);
sumSd.z = sqrt(sumSd.z);
sumSd.w = sqrt(sumSd.w);
if (row_o < height && col_o < oPitchInElements) {
average[row_o * oPitchInElements + col_o] = sum;
stdDev[row_o * oPitchInElements + col_o] = sumSd;
}
}
}
|
11,509 | # include <stdio.h>
// # define n 4096
__global__ void Add( int n, float *A, float *B, float *C)
{
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if(idx < n)
C[idx]=A[idx]+B[idx];
}
int main ( int argc, char * argv [] ) {
if (argc != 2) return 1;
int n = atoi(argv[1]);
printf("n = %d\n", n);
// int i, steps;
float *hA, *hB, *hC;
float * devA, *devB, *devC;
hA = (float *) malloc(n*sizeof(float));
hB = (float *) malloc(n*sizeof(float));
hC = (float *) malloc(n*sizeof(float));
cudaMalloc ( &devA, n*sizeof(float));
cudaMalloc ( &devB, n*sizeof(float));
cudaMalloc ( &devC, n*sizeof(float));
for (int i = 0; i < n; i++) {
hA[i] = i;
hB[i] = n-i;
}
// blocks=4; blocksize=64;
// steps=(int)n/(blocks*blocksize);
dim3 block(512);
dim3 grid((n-1)/512 + 1);
printf("block = %d, grid = %d, threads = %d\n", block.x, grid.x, block.x*grid.x);
cudaMemcpy ( devA, hA, n*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy ( devB, hB, n*sizeof(float), cudaMemcpyHostToDevice);
Add<<<grid, block>>> ( n, devA, devB, devC);
cudaMemcpy ( hC, devC, n*sizeof(float), cudaMemcpyDeviceToHost );
for (int i = n-10; i < n; i++) printf("devC[%d] = %.5f\n", i, hC[i]);
cudaFree (devC);
cudaFree (devA);
cudaFree (devB);
free(hA);
free(hB);
free(hC);
return 0;
} |
11,510 | #define THREADS_X 16
#define THREADS_Y 16
__global__
void convolve2(int *out_ptr, int *signal_ptr, int nBBS0, int *out_strides, int *out_dims, int *signal_strides, int *signal_dims,
int nBBS1, int o2, int o3, int s2, int s3, int expand, int fLen0, int fLen1)
{
const unsigned C_SIZE = 512;
__shared__ int shrdMem[512];
const int radius0 = fLen0-1;
const int radius1 = fLen1-1;
const int padding0 = 2*radius0;
const int padding1 = 2*radius1;
const int shrdLen0 = THREADS_X + padding0;
const int shrdLen1 = THREADS_Y + padding1;
unsigned b0 = blockIdx.x / nBBS0;
unsigned b1 = (blockIdx.y + blockIdx.z * gridDim.y) / nBBS1;
int *dst = (int *)out_ptr + (b0 * out_strides[2] + /* activated with batched input signal */
o2 * out_strides[2] + /* activated with batched input filter */
b1 * out_strides[3] + /* activated with batched input signal */
o3 * out_strides[3]); /* activated with batched input filter */
const int *src = (const int *)signal_ptr + (b0 * signal_strides[2] + /* activated with batched input signal */
s2 * signal_strides[2] + /* activated with batched input filter */
b1 * signal_strides[3] + /* activated with batched input signal */
s3 * signal_strides[3]); /* activated with batched input filter */
int lx = threadIdx.x;
int ly = threadIdx.y;
int gx = THREADS_X * (blockIdx.x-b0*nBBS0) + lx;
int gy = THREADS_Y * ((blockIdx.y + blockIdx.z * gridDim.y) -b1*nBBS1) + ly;
if(b1 >= out_dims[3])
return;
int s0 = signal_strides[0];
int s1 = signal_strides[1];
int d0 = signal_dims[0];
int d1 = signal_dims[1];
// below loops are traditional loops, they only run multiple
// times filter length is more than launch size
for (int b=ly, gy2=gy; b<shrdLen1; b+=THREADS_Y, gy2+=THREADS_Y) {
int j = gy2-radius1;
bool is_j = j>=0 && j<d1;
// move row_set THREADS_Y along coloumns
for (int a=lx, gx2=gx; a<shrdLen0; a+=THREADS_X, gx2+=THREADS_X) {
int i = gx2-radius0;
bool is_i = i>=0 && i<d0;
shrdMem[b*shrdLen0+a] = (is_i && is_j ? src[i*s0+j*s1] : 0);
}
}
__syncthreads();
if (gx<out_dims[0] && gy<out_dims[1]) {
int ci = lx + radius0 + (expand ? 0 : fLen0>>1);
int cj = ly + radius1 + (expand ? 0 : fLen1>>1);
int accum = 0;
for(int fj=0; fj<fLen1; ++fj) {
for(int fi=0; fi<fLen0; ++fi) {
int s_val = shrdMem[(cj-fj)*shrdLen0 + (ci-fi)];
accum = accum + s_val;
}
}
dst[gy*out_strides[1]+gx] = (int)accum;
}
} |
11,511 | #include <cuda.h>
#include <bits/stdc++.h>
using namespace std;
#define BLOCK_SIZE 1024
int sz(int a, int b) {
return (a + b -1) / b;
}
//vector initialization
void init(int *A,int n, int d){
for(int i = 0; i < n; i++)
A[i] = d;
}
//vector summatory
void vecSum(int *h_A, int *h_B, int n){
for(int i = 0; i < n; i++)
*h_B += h_A[i];
}
//Parallel kernel
__global__ void vecSumP (int *A, int *out, int n){
__shared__ int tmp[BLOCK_SIZE];
size_t t = threadIdx.x;
size_t i = blockDim.x * blockIdx.x + t;
if(i < n)
tmp[t] = A[i];
else
tmp[t] = 0;
__syncthreads();
for(size_t st = blockDim.x/2; st > 0; st >>= 1){
if(t < st)
tmp[t] += tmp[t + st];
__syncthreads();
}
__syncthreads();
if (t == 0) out[blockIdx.x] = tmp[0];
}
int main(){
int n; cin>>n;
cout<<n<<endl;
cudaError_t error = cudaSuccess;
int size = n * sizeof(int);
int *h_A = (int *)malloc(size);
int *h_tmp = (int *)malloc(size);
int *h_B = (int *)malloc(sizeof(int));
//int *h_C = (int *)malloc(sz(size,BLOCK_SIZE));
int *h_C = (int *)malloc(size);
int *d_A, *d_B;
init(h_A, n, 1);
*h_B = 0;
double a, b;
clock_t t = clock();
//Secuencial ***************
vecSum(h_A, h_B, n);
t = clock() - t;
a = ((float)t)/CLOCKS_PER_SEC;
cout<<a<<endl;
//paralelo*****************
t = clock();
error = cudaMalloc(&d_A, size);
if(error != cudaSuccess){
printf("Error reservando memoria para d_A");
exit(0);
}
error = cudaMalloc(&d_B, sz(n, BLOCK_SIZE) * sizeof(int));
if(error != cudaSuccess){
printf("Error reservando memoria para d_B");
exit(0);
}
error = cudaMemcpy(d_A, h_A, size, cudaMemcpyHostToDevice);
if(error != cudaSuccess){
printf("Error copiando h_A en d_A");
exit(0);
}
while(n > 1) {
vecSumP<<< sz(n, BLOCK_SIZE), BLOCK_SIZE>>> (d_A, d_B, n);
error = cudaGetLastError();
if ( cudaSuccess != error ){
cout<<"Error en el kernel!"<<endl;
cout<<cudaGetErrorString( error )<<endl;
exit(0);
}
n = sz(n, BLOCK_SIZE);
error = cudaMemcpy(d_A, d_B, n * sizeof(int), cudaMemcpyDeviceToDevice);
if(error != cudaSuccess){
printf("Error copiando d_A en d_B");
cout<<cudaGetErrorString( error )<<endl;
exit(0);
}
cudaDeviceSynchronize();
}
//Allocate memory for device
// cudaMalloc(&d_A, size);
// cudaMalloc(&d_B, sizeof(int));
//Copy Data from host to device
//cudaMemcpy(d_A, h_A, size, cudaMemcpyHostToDevice);
//Blocks and Grids
//Launch Kernel
//vecSumP<<< (n + BLOCK_SIZE - 1)/BLOCK_SIZE, BLOCK_SIZE>>> (d_A, d_B,n);
cudaDeviceSynchronize();
//Copy from device, free device memory
cudaMemcpy (h_C, d_B, sizeof(int), cudaMemcpyDeviceToHost);
//matMultP(A,B,D,size);
t = clock() - t;
b = ((float)t)/CLOCKS_PER_SEC;
cout<<b<<endl;
cout<<(a / b)<<endl;
//printmat(C,n);
//printmat(D,n);
//if(compare(C,D,n)) cout<<"Work :)"<<endl;
//else cout<<"No work :("<<endl;
free(h_A);
free(h_B);
free(h_C);
cudaFree(d_A);
cudaFree(d_B);
return 0;
}
|
11,512 | #include "includes.h"
__global__ void MatrixMulKernel(float* M, float* N, float* P, int Width) {
int Row = blockIdx.y*blockDim.y+threadIdx.y;// Calculate the row index of the P element and M
int Col = blockIdx.x*blockDim.x+threadIdx.x;// Calculate the column index of P and N
if ((Row < Width) && (Col < Width)) {
float Pvalue = 0;
for (int k = 0; k < Width; ++k) {
Pvalue += M[Row*Width+k]*N[k*Width+Col];// each thread computes one element of the block sub-matrix
}
P[Row*Width+Col] = Pvalue;
}
} |
11,513 | // --------------------------------------------------------------
// Author: Tao Chen
// Citation:
// Homework pdf;
// Course slices;
// Previous homework.
// --------------------------------------------------------------
#include<stdio.h>
#include<stdlib.h>
#include<unistd.h>
#include<stdbool.h>
#include <sys/types.h>
#include <sys/stat.h>
#include <fcntl.h>
#include <cuda.h>
#include <cuda_runtime.h>
// Result from last compute of world.
unsigned char* g_resultData = NULL;
// Current state of world.
unsigned char* g_data = NULL;
// Current width of world.
size_t g_worldWidth = 0;
/// Current height of world.
size_t g_worldHeight = 0;
/// Current data length (product of width and height)
size_t g_dataLength = 0; // g_worldWidth * g_worldHeight
// I added two ghost rows to the original world directly, so the worldHeight would become 2 more larger
void gol_initAllZeros(size_t worldWidth, size_t worldHeight)
{
g_worldWidth = worldWidth;
g_worldHeight = worldHeight+2;
g_dataLength = g_worldWidth * g_worldHeight;
// calloc init's to all zeros
cudaMallocManaged(&g_data,(g_dataLength*sizeof(unsigned char)));
cudaMallocManaged(&g_resultData,(g_dataLength*sizeof(unsigned char)));
}
void gol_initAllOnes(size_t worldWidth, size_t worldHeight)
{
int i;
g_worldWidth = worldWidth;
g_worldHeight = worldHeight+2;
g_dataLength = g_worldWidth * g_worldHeight;
cudaMallocManaged(&g_data,(g_dataLength*sizeof(unsigned char)));
// set all rows of world to true
for (i = g_worldWidth; i < g_dataLength - g_worldWidth; i++)
{
g_data[i] = 1;
}
cudaMallocManaged(&g_resultData,(g_dataLength*sizeof(unsigned char)));
}
void gol_initOnesInMiddle(size_t worldWidth, size_t worldHeight)
{
int i;
g_worldWidth = worldWidth;
g_worldHeight = worldHeight+2;
g_dataLength = g_worldWidth * g_worldHeight;
cudaMallocManaged(&g_data,(g_dataLength*sizeof(unsigned char)));
// set the real last row's 127 to 137 to true
for (i = (g_worldHeight-2) * g_worldWidth; i < (g_worldHeight - 1) * g_worldWidth; i++)
{
if ((i >= ((g_worldHeight - 2) * g_worldWidth + 127)) && (i < ((g_worldHeight - 2) * g_worldWidth + 137)))
{
g_data[i] = 1;
}
}
cudaMallocManaged(&g_resultData,(g_dataLength*sizeof(unsigned char)));
}
void gol_initOnesAtCorners(size_t worldWidth, size_t worldHeight)
{
g_worldWidth = worldWidth;
g_worldHeight = worldHeight+2;
g_dataLength = g_worldWidth * g_worldHeight;
cudaMallocManaged(&g_data,(g_dataLength*sizeof(unsigned char)));
g_data[worldWidth] = 1; // upper left
g_data[2*worldWidth - 1] = 1; // upper right
g_data[(g_worldHeight - 2) * worldWidth] = 1; // lower left
g_data[(g_worldHeight - 2) * worldWidth + worldWidth - 1] = 1; // lower right
cudaMallocManaged(&g_resultData,(g_dataLength*sizeof(unsigned char)));
}
void gol_initSpinnerAtCorner(size_t worldWidth, size_t worldHeight)
{
g_worldWidth = worldWidth;
g_worldHeight = worldHeight+2;
g_dataLength = g_worldWidth * g_worldHeight;
cudaMallocManaged(&g_data,(g_dataLength*sizeof(unsigned char)));
g_data[worldWidth] = 1; // upper left
g_data[worldWidth + 1] = 1; // upper left +1
g_data[2 * worldWidth - 1] = 1; // upper right
cudaMallocManaged(&g_resultData,(g_dataLength*sizeof(unsigned char)));
}
// called from main file to generate world
extern "C" void gol_initMaster(int rankid, int cudaDeviceCount, unsigned int pattern, size_t worldWidth, size_t worldHeight)
{
// check if MPI can connect with CUDA correctly
int cE;
if( (cE = cudaGetDeviceCount( &cudaDeviceCount)) != cudaSuccess )
{
printf(" Unable to determine cuda device count, error is %d, count is %d\n",
cE, cudaDeviceCount );
exit(-1);
}
if( (cE = cudaSetDevice( rankid % cudaDeviceCount )) != cudaSuccess )
{
printf(" Unable to have rank %d set to cuda device %d, error is %d \n",
rankid, (rankid % cudaDeviceCount), cE);
exit(-1);
}
switch (pattern)
{
case 0:
gol_initAllZeros(worldWidth, worldHeight);
break;
case 1:
gol_initAllOnes(worldWidth, worldHeight);
break;
case 2:
gol_initOnesInMiddle(worldWidth, worldHeight);
break;
case 3:
gol_initOnesAtCorners(worldWidth, worldHeight);
break;
case 4:
gol_initSpinnerAtCorner(worldWidth, worldHeight);
break;
default:
printf("Pattern %u has not been implemented \n", pattern);
exit(-1);
}
}
void gol_swap(unsigned char** pA, unsigned char** pB)
{
// You write this function - it should swap the pointers of pA and pB.
unsigned char *temp = *pA; // set a temporary pointer to save pA
*pA = *pB; // set pB to pA
*pB = temp; // set temp to pB
}
__global__
void gol_kernel(const unsigned char* d_data, unsigned int worldWidth,
unsigned int worldHeight,unsigned char* d_resultData){
// The CUDA parallel thread hierarchy can be seen as the 1-D array just like hw1
size_t index = blockIdx.x *blockDim.x + threadIdx.x;
// we just would like to update the inner part of the world
if(index < worldWidth || index >= worldWidth*(worldHeight-1)) return;
for(;index < worldWidth*worldHeight;index +=blockDim.x * gridDim.x){
// Index is like the 1-D array index, it should be 0 when index < worldWidth and 1 when worldWidth <= index < 2*worldWidth
size_t x = index % worldWidth;
// y is exactly index % worldWidth. But to reduce computing time, I replace % in the following way
size_t y = index / worldWidth;
// the calculation of x0,x1,x2,y0,y1,y2 is the same with hw1, the only difference is that, we changeed "%" for reducing computing time
size_t y0 = ((y+worldHeight-1)% worldHeight) * worldWidth;
size_t y1 = y * worldWidth;
size_t y2 = ((y + 1) % worldHeight) * worldWidth;
size_t x1 = x;
size_t x0 = (x1 + worldWidth - 1) % worldWidth;
size_t x2 = (x1 + 1) % worldWidth;
// I can add the surround cells together directly since 1 is live, 0 is dead. So, by adding them together, it's the number of living cells aournd x1,y1
int count = d_data[x0+y0] + d_data[x0+y1] + d_data[x0+y2] + d_data[x1+y0] +
d_data[x1+y2] + d_data[x2+y0] + d_data[x2+y1] + d_data[x2+y2];
// if previous world's cell is alive
if(d_data[x1+y1] == 1){
// if the number of alive cells around it is less than 2 or greater than 3, it will die. set new world as 0
if(count < 2) d_resultData[x1+y1] = 0;
else if(count > 3) d_resultData[x1+y1] = 0;
// else, it will still be alive. set new world as 1
else d_resultData[x1+y1] = 1;
}
// if previous world's cell is dead
else{
// only if the number of alive cells around it is 3, it will be alive. set new world as 1
if(count == 3) d_resultData[x1+y1] = 1;
// else, it will still be dead.
else d_resultData[x1+y1] = 0;
}
}
}
// called from main file to lauch CUDA kernel
extern "C" void gol_kernelLaunch(unsigned char** d_data, unsigned char** d_resultData,
size_t worldWidth, size_t worldHeight,unsigned short threadsCount) {
// calculate the number of blocks by divide the overall threads that are needed by the number of threads in one block
size_t blocksCount = worldWidth * worldHeight / threadsCount;
// call the CUDA Kernel
gol_kernel <<<blocksCount, threadsCount>>> (*d_data, worldWidth, worldHeight, *d_resultData);
// Wait for GPU to finish before accessing on host
cudaDeviceSynchronize();
// swap the old word with the new world
gol_swap(d_data, d_resultData);
}
|
11,514 | #include <iostream>
#include <fstream>
#include <string>
#include <math.h>
#include <vector>
#include <time.h>
#include <algorithm>
#include <sstream>
using namespace std;
const double PI = 3.141592653589793238463;
#define HANDLE_ERROR( err ) (HandleError( err, __FILE__, __LINE__ ))
#define BLOCK_WIDTH 32 //predifined block width for uniform use across the code
#define BLOCK_HEIGHT 32 //predifined block height for uniform use across the code
// 32 x 32 as the max threads suppoted are 1024
struct RGB
{
char r, g, b; //char values to store the three channel RGB PPM image
};
struct ImageRGB
{
int w, h; //width and height of the Image
vector<RGB> data; //vector of RGB structure as it is easier to resize the vector for
}; //different image sizes
struct LoGKernal
{
vector<float> kernal; //Laplacian of Gaussian Kernal structure that stores one karnel
}; //with the float values
struct LoGKernalsList
{
vector<LoGKernal> KernalIndex; //Laplacian of Gaussian Kernal list structure stores multiple kernals
}; //of different sizes as they are required for scale space computation
void eat_comment(ifstream &f) // to remove comment from line and jump to EOF
{
char linebuf[1024];
char ppp;
while (ppp = f.peek(), ppp == '\n' || ppp == '\r') //parsing through the lines
f.get();
if (ppp == '#') //checking if the character is a #
f.getline(linebuf, 1023); //jumping to the end of the line
}
void load_ppm(ImageRGB &img, string &name) //function to load the PPM image
{
ifstream file(name.c_str(), ios::binary); //using streams to read the file in binary format
if (file.fail()) //throw error on screen if file opening fails
{
cout << "Could not open file: " << name << endl;
return;
}
string imageFormat; //stores the Image format
int bits = 0;
file >> imageFormat; //Reads the image format from the PPM input image file
eat_comment(file); //removes comments if any in the input file
file >> img.w; // get width
file >> img.h; // get height
file >> bits; // get the binary values
if (imageFormat != "P6" || img.w < 1 || img.h < 1 || bits != 255) //error check, only reads P6 types of images and throws error
{
cout << "Please select correct Image" << endl;
file.close();
return;
}
img.data.resize(img.w * img.h); //resizing image to the size of the imput image
file.get();
file.read((char*)&img.data[0], img.data.size() * 3); //reading the file into the image structure
file.close(); //always close the file
}
void store_ppm(ImageRGB &img, string &name)
{
ofstream file(name.c_str(), ios::binary); //create stream to write the file
if (file.fail()) //throw error if creating the file stream fails
{
cout << "Could not store file: " << name << endl;
return;
}
string imageFormat = "P6"; //stores only P6 format..first line of the PPM is format
file << imageFormat << "\n"; //store the image format
file << img.w << " "; // set width
file << img.h << endl; // set height
file << "255" << endl; // Set bits.. always 225
file.write((char*)&img.data[0], img.data.size() * 3); //writing the file from the image passed to the function
file.close(); //always close the file
}
static void HandleError(cudaError_t err, const char *file, int line) { //error handling for the cuda errors
if (err != cudaSuccess) { //anything other than successs status is printed on the screen
std::cout << cudaGetErrorString(err) << " in " << file << " at line " << line << endl;
}
}
vector<float> createLoGKernal(int sigma, int sizeOfKernal) //CPU function that creates the Laplacian of Gaussian kernal
{
vector<float> lKernal; //creating object of the kernal struct
lKernal.resize(sizeOfKernal); //resing to the passed size of the kernal
float sum = 0.0f;
float c1 = (-1 / (PI * pow(sigma,4)) ); //calcuating the value of the first component of the equation
//refer the report to see the entire equation
int index = 0;
for (int i = -sizeOfKernal / 2; i < sizeOfKernal / 2 + 1; i++) //loop to calculatet the values of the kernal
{
float c2 = ( 1 - ( (i*i) / (2 * sigma * sigma))); //calcuating the value of the second component of the equation
float c3 = exp( (-1 *i*i) /(2 * sigma * sigma) ); //calcuating the value of the third component of the equation
lKernal[index] = c1 * c2 * c3; //evaluate the Laplacian of Gaussian kernal
sum+=lKernal[index]; //calculating the sum to normalize the kernal later
index++; //index of the kernal array
}
for (int i = 0; i < lKernal.size(); i++) //Normalizing the kernal distribution
lKernal[i] /= sum;
return lKernal;
}
void medianFilterCPU(char *Input_Image, char *Output_Image, int Image_Width, int Image_Height) //CPU implementation of the median filter
{
char RNeighbor[9]; //R values of the neighbouring pixels
char GNeighbor[9]; //G values of the neighbouring pixels
char BNeighbor[9]; //B values of the neighbouring pixels
int iterator;
for(int x = 1; x < Image_Width - 1; x++) //looping through the width of the image.. -1 for the edge case
for(int y = 1; y < Image_Height - 1; y++) //looping through the height of the image.. -1 for the edge case
{
iterator = 0;
for (int r = x - 1; r <= x + 1; r++) { //finding the next neighbour pixels along the x axis
for (int c = y - 1; c <= y + 1; c++) { //finding the next neighbour pixels along the y axis
size_t innerIndex = (c * Image_Width + r) * 3; //getting the 1 D index of the pixel location
RNeighbor[iterator] = Input_Image[innerIndex + 0]; //storing the neighboring red pixels
GNeighbor[iterator] = Input_Image[innerIndex + 1]; //storing the neighboring green pixels
BNeighbor[iterator] = Input_Image[innerIndex + 2]; //storing the neighboring blue pixels
iterator++;
}
}
for (int i=0; i<5; ++i) //using bubble sort to sort the 9 pixels
{
int minvalR=i; //store red value in the temporary pixel..easier access..clearer code
int minvalG=i; //store green value in the temporary pixel..easier access..clearer code
int minvalB=i; //store blue value in the temporary pixel..easier access..clearer code
for (int l=i+1; l<9; ++l)
{
if (RNeighbor[l] < RNeighbor[minvalR]) //condition to check the red minimum from the array
minvalR=l;
if (GNeighbor[l] < GNeighbor[minvalG]) //condition to check the green minimum from the array
minvalG=l;
if (BNeighbor[l] < BNeighbor[minvalB]) //condition to check the blue minimum from the array
minvalB=l;
}
// --- Put found minimum element in its place
char tempR = RNeighbor[i]; //swap using the temporary variable
RNeighbor[i]=RNeighbor[minvalR]; //if the min red value is found
RNeighbor[minvalR]=tempR;
char tempG = GNeighbor[i]; //swap using the temporary variable
GNeighbor[i]=GNeighbor[minvalG];
GNeighbor[minvalG]=tempG; //if the min green value is found
char tempB = BNeighbor[i]; //swap using the temporary variable
BNeighbor[i]=BNeighbor[minvalB];
BNeighbor[minvalB]=tempB; //if the min blue value is found
}
int out_index = (y*Image_Width+x )* 3; //calculating the result 1 D index
Output_Image[out_index + 0]=RNeighbor[4]; //storing the middle value..the median value of the red channel
Output_Image[out_index + 1]=GNeighbor[4]; //storing the middle value..the median value of the green channel
Output_Image[out_index + 2]=BNeighbor[4]; //storing the middle value..the median value of the blue channel
}
}
__global__ void LoGConvolutionFunction(char* gpu_result_img_ptr, char* gpu_orig_img_ptr, size_t w, size_t h, float* KernalValues, size_t K){
extern __shared__ unsigned char S[]; //defined a shared memory pointer
//calculate the index into the output image
size_t bxi = blockIdx.x * blockDim.x; //upper left corner of the block
size_t xi = bxi + threadIdx.x; //x-index for the current thread
size_t yi = blockIdx.y * blockDim.y + threadIdx.y; //y-index for the current thread
if(xi >= w || yi >= h ) //exit if the output pixel is outside of the output image
return;
if(xi >= w - K + 1 || yi >= h - K + 1) //for indexes greater than the kernal width..the edge cases
{
size_t inner = (yi * (w ) + xi) * 3; //calculating the 1 D index the image
gpu_result_img_ptr[inner + 0] = 0; //change value to 0 for the edge pixel values
gpu_result_img_ptr[inner + 1] = 0;
gpu_result_img_ptr[inner + 2] = 0;
return;
}
size_t i = (yi * (w ) + xi) * 3; //calculating the 1 D index for the output image
//create registers for shared memory access
size_t Sw = blockDim.x + K - 1; //width of shared memory
size_t syi = threadIdx.y ; //shared memory y-index
for(size_t sxi = threadIdx.x; sxi < Sw; sxi += blockDim.x){ //copy block and curtain data from global memory to shared memory
S[(syi * Sw + sxi) * 3 + 0] = gpu_orig_img_ptr[(yi * w + bxi + sxi) * 3 + 0]; //copying to the shared memory
S[(syi * Sw + sxi) * 3 + 1] = gpu_orig_img_ptr[(yi * w + bxi + sxi) * 3 + 1];
S[(syi * Sw + sxi) * 3 + 2] = gpu_orig_img_ptr[(yi * w + bxi + sxi) * 3 + 2];
}
__syncthreads(); //synchronize threads after the global->shared copy
float3 sum; //allocate a register to store the pixel sum
sum.x = sum.y = sum.z = 0.0f; //intialize the register values to zero
size_t ypart = (syi * Sw + threadIdx.x) * 3;
for(size_t kxi = 0; kxi < K; kxi++){ //for each element in the kernel
if( sum.x > 255.0f) //if the values overflow
sum.x = sum.x - 255; //basically taking modulus
else
sum.x += ( S[ypart + kxi * 3 + 0] * KernalValues[kxi] ); //summming and adding..convoluting the image with the kernal
if( sum.y > 255.0f) //if the values overflow
sum.y = sum.y - 255; //basically taking modulus
else
sum.y += ( S[ypart + kxi * 3 + 1] * KernalValues[kxi] );
if( sum.z > 255.0f) //if the values overflow
sum.z = sum.z - 255; //basically taking modulus
else
sum.z += ( S[ypart + kxi * 3 + 2] * KernalValues[kxi] );
}
//output the result for each channel
gpu_result_img_ptr[i + 0] = sum.x ; //for the red channel
gpu_result_img_ptr[i + 1] = sum.y ; //for the green channel
gpu_result_img_ptr[i + 2] = sum.z ; //for the blue channel
}
__global__ void medianFilterKernel(char *Input_Image, char *Output_Image, int Image_Width, int Image_Height) {
char RNeighbor[9]; //R values of the neighbouring pixels
char GNeighbor[9]; //G values of the neighbouring pixels
char BNeighbor[9]; //B values of the neighbouring pixels
int iterator;
const int x = blockDim.x * blockIdx.x + threadIdx.x; //x dimension index of the threads
const int y = blockDim.y * blockIdx.y + threadIdx.y; //y dimension index of the threads
if( (x >= (Image_Width - 1)) || (y >= Image_Height - 1) ) //if the thread indexes are greater than the image
return;
iterator = 0;
for (int r = x - 1; r <= x + 1; r++) { //finding the next neighbour pixels along the x axis
for (int c = y - 1; c <= y + 1; c++) { //finding the next neighbour pixels along the y axis
size_t innerIndex = (c * Image_Width + r) * 3; //getting the 1 D index of the pixel location
RNeighbor[iterator] = Input_Image[innerIndex + 0]; //storing the neighboring red pixels
GNeighbor[iterator] = Input_Image[innerIndex + 1]; //storing the neighboring green pixels
BNeighbor[iterator] = Input_Image[innerIndex + 2]; //storing the neighboring blue pixels
iterator++;
}
}
for (int i=0; i<5; ++i) {
// --- Find the position of the minimum element
int minvalR=i;
int minvalG=i;
int minvalB=i;
for (int l=i+1; l<9; ++l)
{
if (RNeighbor[l] < RNeighbor[minvalR]) //condition to check the red minimum from the array
minvalR=l;
if (GNeighbor[l] < GNeighbor[minvalG]) //condition to check the green minimum from the array
minvalG=l;
if (BNeighbor[l] < BNeighbor[minvalB]) //condition to check the blue minimum from the array
minvalB=l;
}
// --- Put found minimum element in its place
char tempR = RNeighbor[i]; //swap using the temporary variable
RNeighbor[i]=RNeighbor[minvalR]; //if the min red value is found
RNeighbor[minvalR]=tempR;
char tempG = GNeighbor[i]; //swap using the temporary variable
GNeighbor[i]=GNeighbor[minvalG];
GNeighbor[minvalG]=tempG; //if the min green value is found
char tempB = BNeighbor[i]; //swap using the temporary variable
BNeighbor[i]=BNeighbor[minvalB];
BNeighbor[minvalB]=tempB; //if the min blue value is found
}
int out_index = (y*Image_Width+x )* 3; //calculating the result 1 D index
Output_Image[out_index + 0]=RNeighbor[4]; //storing the middle value..the median value of the red channel
Output_Image[out_index + 1]=GNeighbor[4]; //storing the middle value..the median value of the green channel
Output_Image[out_index + 2]=BNeighbor[4]; //storing the middle value..the median value of the blue channel
}
__global__ void medianFilterKernelSharedMem(char *Input_Image, char *Output_Image, int Image_Width, int Image_Height) {
__shared__ char RNeighbor[BLOCK_WIDTH*BLOCK_HEIGHT][9]; //shared memory to store the neighbor red values
__shared__ char GNeighbor[BLOCK_WIDTH*BLOCK_HEIGHT][9]; //shared memory to store the neighbor greeen values
__shared__ char BNeighbor[BLOCK_WIDTH*BLOCK_HEIGHT][9]; //shared memory to store the neighbor blue values
int iterator;
const int x = blockDim.x * blockIdx.x + threadIdx.x; //x dimension index of the input threads
const int y = blockDim.y * blockIdx.y + threadIdx.y; //y dimension index of the input threads
const int tid = threadIdx.y * blockDim.x + threadIdx.x; //thread index
if( (x >= (Image_Width - 1)) || (y >= Image_Height - 1) ) return; //if threads are out of the image size range
//Fill shared memory
iterator = 0;
for (int r = x - 1; r <= x + 1; r++) { //finding the next neighbour pixels along the x axis
for (int c = y - 1; c <= y + 1; c++) { //finding the next neighbour pixels along the y axis
size_t innerIndex = (c * Image_Width + r) * 3; //calculating the 1 D index of the neighboring values
RNeighbor[tid][iterator] = Input_Image[innerIndex + 0]; //storing the neighboring red pixels
GNeighbor[tid][iterator] = Input_Image[innerIndex + 1]; //storing the neighboring green pixels
BNeighbor[tid][iterator] = Input_Image[innerIndex + 2]; //storing the neighboring blue pixels
iterator++;
}
}
__syncthreads(); //synchronize the threads to complete the copy to shared memory
for (int i=0; i<5; ++i) {
int minvalR=i;
int minvalG=i;
int minvalB=i;
for (int l=i+1; l<9; ++l)
{
if (RNeighbor[tid][l] < RNeighbor[tid][minvalR]) //condition to check the red minimum from the array
minvalR=l;
if (GNeighbor[tid][l] < GNeighbor[tid][minvalG]) //condition to check the green minimum from the array
minvalG=l;
if (BNeighbor[tid][l] < BNeighbor[tid][minvalB]) //condition to check the blue minimum from the array
minvalB=l;
}
// --- Put found minimum element in its place
char tempR = RNeighbor[tid][i];
RNeighbor[tid][i]=RNeighbor[tid][minvalR]; //swap the values using a temporary variable
RNeighbor[tid][minvalR]=tempR;
char tempG = GNeighbor[tid][i];
GNeighbor[tid][i]=GNeighbor[tid][minvalG]; //swap the values using a temporary variable
GNeighbor[tid][minvalG]=tempG;
char tempB = BNeighbor[tid][i];
BNeighbor[tid][i]=BNeighbor[tid][minvalB]; //swap the values using a temporary variable
BNeighbor[tid][minvalB]=tempB;
}
__syncthreads();
int out_index = (y * Image_Width + x ) * 3; //calculating the result 1 D index
Output_Image[out_index + 0]=RNeighbor[tid][4]; //store the result in the result array from the shared memory
Output_Image[out_index + 1]=GNeighbor[tid][4];
Output_Image[out_index + 2]=BNeighbor[tid][4];
__syncthreads(); //synchronize the threads upon compeltion
}
__global__ void blobMaximaKernal(size_t* BlobIx, int sigma, int orig_h, int orig_w, char* img1, char* img2, char* img3){
size_t bxi = blockIdx.x * blockDim.x; //upper left corner of the block
size_t xi = bxi + threadIdx.x; //x-index for the current thread
size_t yi = blockIdx.y * blockDim.y + threadIdx.y; //y-index for the current thread
if(xi >= orig_w || yi >= orig_h ) //exit if the output pixel is outside of the output image
return;
size_t i = (yi * orig_w + xi) * 3; //1D index for the output image
int widthJump = 0; //width offset index of the image in scale space(image above and below)
if (i/orig_w % 2 == 0 )
widthJump = (i/orig_w) * 3;
else
widthJump = (i/orig_w + 1 ) * 3;
//calcualting the maximum of the 26 neighbours of the pixel
if( img2[i + 0] > img2[i + 3] && img2[i + 0] > img2[i - 3] // both the side edges R value
&& img2[i + 0] > img2[widthJump - 1] && img2[i + 0] > img2[widthJump - 4] && img2[i + 0] > img2[widthJump - 7]
&& img2[i + 0] > img2[widthJump + 1] && img2[i + 0] > img2[widthJump + 4] && img2[i + 0] > img2[widthJump + 7]
//checking the image below in the scale
&& img2[i + 0] > img1[i + 0] && img2[i + 0] > img1[i + 3] && img2[i + 0] > img1[i - 3]
&& img2[i + 0] > img1[widthJump - 1] && img2[i + 0] > img1[widthJump - 4] && img2[i + 0] > img1[widthJump - 7]
&& img2[i + 0] > img1[widthJump + 1] && img2[i + 0] > img1[widthJump + 4] && img2[i + 0] > img1[widthJump + 7]
//checking the image above in the scale
&& img2[i + 0] > img3[i + 0] && img2[i + 0] > img3[i + 3] && img2[i + 0] > img3[i - 3]
&& img2[i + 0] > img3[widthJump - 1] && img2[i + 0] > img3[widthJump - 4] && img2[i + 0] > img3[widthJump - 7]
&& img2[i + 0] > img3[widthJump + 1] && img2[i + 0] > img3[widthJump + 4] && img2[i + 0] > img3[widthJump + 7]
//for the G channel checking all the 26 neighbours
&& img2[i + 1] > img2[i + 4 ] && img2[i + 1] > img2[i - 4] // both the side edges
&& img2[i + 1] > img2[widthJump - 2] && img2[i + 1] > img2[widthJump - 5] && img2[i + 1] > img2[widthJump - 8]
&& img2[i + 1] > img2[widthJump + 2] && img2[i + 1] > img2[widthJump + 5] && img2[i + 1] > img2[widthJump + 8]
&& img2[i + 1] > img1[i + 1] && img2[i + 1] > img1[i + 4] && img2[i + 1] > img1[i - 4]
&& img2[i + 1] > img1[widthJump - 2] && img2[i + 1] > img1[widthJump - 5] && img2[i + 1] > img1[widthJump - 8]
&& img2[i + 1] > img1[widthJump + 2] && img2[i + 1] > img1[widthJump + 5] && img2[i + 1] > img1[widthJump + 8]
&& img2[i + 1] > img3[i + 1] && img2[i + 1] > img3[i + 4] && img2[i + 1] > img3[i - 4]
&& img2[i + 1] > img3[widthJump - 2] && img2[i + 1] > img3[widthJump - 5] && img2[i + 1] > img3[widthJump - 8]
&& img2[i + 1] > img3[widthJump + 2] && img2[i + 1] > img3[widthJump + 5] && img2[i + 1] > img3[widthJump + 8]
//for the Blue channel checking all the 26 neighbours
&& img2[i + 2] > img2[i + 5 ] && img2[i + 2] > img2[i - 5] // both the side edges
&& img2[i + 2] > img2[widthJump - 3] && img2[i + 2] > img2[widthJump - 6] && img2[i + 2] > img2[widthJump - 9]
&& img2[i + 2] > img2[widthJump + 3] && img2[i + 2] > img2[widthJump + 6] && img2[i + 2] > img2[widthJump + 9]
&& img2[i + 2] > img1[i + 2] && img2[i + 2] > img1[i + 5] && img2[i + 2] > img1[i - 5]
&& img2[i + 2] > img1[widthJump - 3] && img2[i + 2] > img1[widthJump - 6] && img2[i + 2] > img1[widthJump - 9]
&& img2[i + 2] > img1[widthJump + 3] && img2[i + 2] > img1[widthJump + 6] && img2[i + 2] > img1[widthJump + 9]
&& img2[i + 2] > img3[i + 2] && img2[i + 2] > img3[i + 5] && img2[i + 2] > img3[i - 5]
&& img2[i + 2] > img3[widthJump - 3] && img2[i + 2] > img3[widthJump - 6] && img2[i + 2] > img3[widthJump - 9]
&& img2[i + 2] > img3[widthJump + 3] && img2[i + 2] > img3[widthJump + 6] && img2[i + 2] > img3[widthJump + 9]
)
{
BlobIx[i] = i; //if the pixel is the maxima, then store it
}
return;
}
int circle(int x, int y, int cx, int cy, int radius){ //function to calculate the circumference indexes of the circle
if ((int)sqrt((x-cx)*(x-cx) + (y-cy)*(y-cy)) == radius) //equation of the circle
return 1; //returns true if the index lies on the circumference
else
return 0; //returns false if the index lies on the circumference
}
int isNearCircle(int x, int y, int cx, int cy, int radius){ //function to find if the index lies near to a given circle
if ((int)sqrt((x-cx)*(x-cx) + (y-cy)*(y-cy)) <= (int)(radius*1.5) ) //equation of the circle with an increased raduis to reduce overlapping
return 1; //returns true if the index lies near the circle
else
return 0; //returns false if the index is not near the circle
}
int main(int argc, char** argv) {
if (argc != 6) //There should be six command line arguments
{
cout << "Enter corrrect arguments as ./a.out image.ppm A B C D"<< endl;
cout << " Float A = t used as sigma = exp(t) " << endl;
cout << " Float B = increasing_factor used as t+=B" << endl;
cout << " Int C = numberOfIterations" << endl;
cout << " String D = blob or median" << endl;
cout << " Ex1 : ./a.out applegreen.ppm 0 0.5 5 median" << endl << "Ex1 : ./a.out butterfly.ppm 0 0.5 5 blob" << endl ;
return 1; //exit and return an error
}
string InputImagefilename = argv[1]; //getting the input filename from the command line arguments
float t = atof(argv[2]); //getting the first sigma value in the scale space
float increasingFactor = atof(argv[3]); //the value with which the sigma is to be incremented in scale space
int numberOfIterations = atoi(argv[4]); //the number of the scales of the scale space
string typeOfFilter = argv[5]; //type of filter to be applied to the image - Blob Filter/Median filter
//Caluclating the sigma values for the number of times the LoG has to be applied
int *sigmaList; //Declare pointer to type of array
sigmaList = new int[numberOfIterations]; //dynamic allocation of the number of scales
for(int i = 0; i < numberOfIterations; i++){ //calculating sigma in exponential factors
sigmaList[i] = exp(t);
t += increasingFactor;
}
LoGKernalsList kList; //declaring list of kernals to store the different kernal for each sigma
kList.KernalIndex.resize(numberOfIterations); //resizing the kernal vector to the scales
for(int i = 0; i < numberOfIterations; i++) //creating Laplacian of Gaussian (LoG) Kernals
{
int sizeOfKernal = 6 * sigmaList[i]; //calculate size of the kernal, keeping 99% of the variance
if (sizeOfKernal % 2 == 0) //kernal size should be odd
sizeOfKernal++;
kList.KernalIndex[i].kernal.resize(sizeOfKernal);
kList.KernalIndex[i].kernal = createLoGKernal(sigmaList[i], sizeOfKernal); //call to create the laplacian of gaussian karnel
}
ImageRGB InputImg; //original image
load_ppm(InputImg, InputImagefilename); //Loading the PPM input image
int count; //stores the number of CUDA compatible devices
HANDLE_ERROR(cudaGetDeviceCount(&count)); //get the number of devices with compute capability < 1.0
if (count < 1) //throw error if no CUDA device is found
{
cout << "No cuda device " << endl;
return 1; //exit and return an error
}
char* gpu_orig_img_ptr;
HANDLE_ERROR(cudaMalloc(&gpu_orig_img_ptr, InputImg.h * InputImg.w * 3 * sizeof(char))); //memory allocation on the device for the original image
//copying image to device
HANDLE_ERROR(cudaMemcpy(gpu_orig_img_ptr, &InputImg.data[0] , InputImg.h * InputImg.w * 3 * sizeof(char), cudaMemcpyHostToDevice));
////-----------------------------------------------------------------------/////
////------------------------BEGENNING OF MEDIAN FILTERING------------------/////
if(typeOfFilter == "median")
{
cout << "Executing in the CPU " << endl;
//CPU Implementation
ImageRGB CPUmedianFImage; //result image generated from the CPU execution
CPUmedianFImage.h = InputImg.h ;
CPUmedianFImage.w = InputImg.w ;
CPUmedianFImage.data.resize(CPUmedianFImage.h * CPUmedianFImage.w); //resing the image vector
const clock_t begin_time = clock(); //begin timer to calcualte the CPU execution time
//calling the function to execute on the CPU
medianFilterCPU(&InputImg.data[0].r , &CPUmedianFImage.data[0].r , CPUmedianFImage.w, CPUmedianFImage.h);
float cpuTime = ( clock () - begin_time ) * 1000 / CLOCKS_PER_SEC ; //calcuating the execution time in milliseconds
std::cout << "CPU execution time = " << cpuTime << " milliseconds";
string CPUmedianfile = "Median_Filter_Image_CPU.ppm";
store_ppm(CPUmedianFImage , CPUmedianfile); //storing the CPU image
//GPU Implementation
ImageRGB medianFImage; //resultant image from median filtering
medianFImage.h = InputImg.h ;
medianFImage.w = InputImg.w ;
medianFImage.data.resize(medianFImage.h * medianFImage.w); //resizing the resultant image
char* gpu_medianF_ptr; //pointer to the gpu result image
//memory allocation fro the result image
HANDLE_ERROR(cudaMalloc(&gpu_medianF_ptr, medianFImage.h * medianFImage.w * 3 * sizeof(char)));
dim3 threadsMedian(32, 32); //create a square block of 1024 threads
dim3 blocksMedian(medianFImage.w /threadsMedian.x + 1, medianFImage.h / threadsMedian.y + 1); //calculate # of blocks
cudaEvent_t start1, stop1, start2, stop2 ; //declare a start and stop event
cudaEventCreate(&start1); //create both start events
cudaEventCreate(&stop1);
cudaEventCreate(&start2); //create both stop events
cudaEventCreate(&stop2);
cudaEventRecord(start1); //insert the start event into the stream
//call the kernal function with the above configuration
medianFilterKernel<<<blocksMedian,threadsMedian>>>(gpu_orig_img_ptr, gpu_medianF_ptr, medianFImage.w, medianFImage.h);
cudaEventRecord(stop1); //insert the stop event into the stream
cudaEventSynchronize(stop1); //wait for the stop event, if it isn’t done
float milliseconds1 = 0; //declare a variable to store runtime
cudaEventElapsedTime(&milliseconds1, start1, stop1); //get the elapsed time
cout << "\nTime taken for simple Implementation: " << milliseconds1 << " milliseconds "<< endl;
cudaEventRecord(start2); //insert the start event into the stream
//Call the kernal using the shared memory implementation
medianFilterKernelSharedMem<<<blocksMedian,threadsMedian>>>(gpu_orig_img_ptr, gpu_medianF_ptr, medianFImage.w, medianFImage.h);
cudaEventRecord(stop2); //insert the stop event into the stream
cudaEventSynchronize(stop2); //wait for the stop event, if it isn’t done
float milliseconds2 = 0; //declare a variable to store runtime
cudaEventElapsedTime(&milliseconds2, start2, stop2); //get the elapsed time
cout << "\nTime taken for shared memory Implementation: " << milliseconds2 << " milliseconds "<< endl;
//copying image from device to host
HANDLE_ERROR(cudaMemcpy(&medianFImage.data[0], gpu_medianF_ptr, medianFImage.h * medianFImage.w * 3 * sizeof(char), cudaMemcpyDeviceToHost));
string medianFilterFileName = "Median_Filter_Image.ppm";
store_ppm(medianFImage , medianFilterFileName); //storing the GPU image
cudaFree(gpu_orig_img_ptr); //freeing threads
cudaFree(gpu_medianF_ptr); //very essential to free the threads
}
////-----------------------------------------------------------------------/////
////--------------------------END OF MEDIAN FILTERING----------------------/////
////---------------------------------------------------------------------/////
////------------------------BEGENNING OF BLOB DETECTION------------------/////
else if(typeOfFilter == "blob")
{
char** LoG_result_img_ptr; //Declare pointer to type of array
LoG_result_img_ptr = new char*[numberOfIterations]; //Array of pointers of the kernal
for(int j = 0; j < numberOfIterations; j++) //Looping for each kernal for different sigma values
{
float* LoGKernal_ptr; //declare pointer to each kernal evrytime in the loop
int kernalSize = kList.KernalIndex[j].kernal.size(); //resize this pointer to the kernal size for each sizma value
HANDLE_ERROR(cudaMalloc(&LoGKernal_ptr, kernalSize * sizeof(float))); //alloate the memory on the device for the kernal
//copying kernal to device
HANDLE_ERROR(cudaMemcpy(LoGKernal_ptr, &kList.KernalIndex[j].kernal[0] , kernalSize * sizeof(float), cudaMemcpyHostToDevice));
ImageRGB LoGImage; //object of the convolved image
LoGImage.h = InputImg.h ;
LoGImage.w = InputImg.w ;
LoGImage.data.resize(LoGImage.h * LoGImage.w); //keeping same size of the input image..makes life easy for
//finding the maxima, else its hell matching the indexes in scale space
//allocating the memory for each convolved image
HANDLE_ERROR(cudaMalloc(&LoG_result_img_ptr[j], LoGImage.h * LoGImage.w * 3 * sizeof(char)));
//create a CUDA grid configuration
dim3 threads(32, 32); //create a square block of 1024 threads
dim3 blocks(LoGImage.w /threads.x + 1, LoGImage.h / threads.y + 1); //calculate # of blocks
//calculate the required size of shared memory
size_t Sbytes = ((threads.x + kernalSize - 1) * threads.y * sizeof(char)) * 3;
cudaDeviceProp props;
cudaGetDeviceProperties(&props, 0); //get properties to check if there is enough shared memory
if(props.sharedMemPerBlock < Sbytes){
cout<<"ERROR: insufficient shared memory"<<std::endl;
exit(1);
}
//Calling the kernal function to convolve the image
LoGConvolutionFunction <<<blocks, threads, Sbytes >>>(LoG_result_img_ptr[j], gpu_orig_img_ptr, InputImg.w, InputImg.h, LoGKernal_ptr, kernalSize);
//copying image from device to host
HANDLE_ERROR(cudaMemcpy(&LoGImage.data[0], LoG_result_img_ptr[j], LoGImage.h * LoGImage.w * 3 * sizeof(char), cudaMemcpyDeviceToHost));
std::stringstream sstm;
sstm << "Convolved_Image_" << j << ".ppm";
string LoGFileName = sstm.str();
store_ppm(LoGImage, LoGFileName); //storing gpu image
cudaFree(LoG_result_img_ptr); //freeing the threads
cudaFree(LoGKernal_ptr);
}
//Getting the maximum value of the pixel from the genreated images through the Laplacian of Gaussian kernal
size_t* blob;
int numberOfBLobs = numberOfIterations - 2; //number of the scales in which the blob can be calculated
//the first sigma and the last sigma images will be included
//as they donot have the required 26 neighbors in scale space
int sizeofBLobImages = InputImg.w * InputImg.h * 3 ; //resizing to all the indexes of the images size
size_t *blobIdx;
blobIdx = new size_t[sizeofBLobImages * numberOfBLobs]; //dynmically allocating the number of blob images array
memset(blobIdx, 0, sizeofBLobImages * numberOfBLobs * sizeof(size_t) ) ; //setting all the indexes to zero
int indexOfBLobCenters = 0 ;
for(int scales = 1; scales < numberOfIterations-1; scales++) //looping through the scale space
{
HANDLE_ERROR(cudaMalloc(&blob, InputImg.w * InputImg.h * 3 * sizeof(size_t) )); // mem allication to get the max size for all possible blobs
HANDLE_ERROR(cudaMemset(blob, 0, InputImg.w * InputImg.h * 3 * sizeof(size_t) ) ); //setting the values to zero
dim3 threadsBlob(32, 32); //create a square block of 1024 threads
dim3 blocksBlob(InputImg.w /threadsBlob.x + 1, InputImg.h / threadsBlob.y + 1); //calculate # of blocks
//Call the function to calculate the maxima in scale space
blobMaximaKernal <<<blocksBlob, threadsBlob >>>(blob, sigmaList[scales], InputImg.h, InputImg.w,
LoG_result_img_ptr[scales - 1] , LoG_result_img_ptr[scales], LoG_result_img_ptr[scales + 1]);
//copying image from device to host
HANDLE_ERROR(cudaMemcpy(&blobIdx[indexOfBLobCenters], blob, InputImg.h * InputImg.w * 3 * sizeof(size_t), cudaMemcpyDeviceToHost));
int raduis = 1.5 * sigmaList[scales]; //radius of the blob is given by this formula
cout << "Removing Duplicates of the same size " << raduis << endl;
int county = 0; //counter index
for(int i=0; i< InputImg.w * InputImg.h * 3; i++)
{
if(blobIdx[indexOfBLobCenters + i] != 0) //for each blob center
{
int cy = (blobIdx[indexOfBLobCenters + i] / 3) % InputImg.w; //calculate the y index in 2D space of the blob
int cx = (blobIdx[indexOfBLobCenters + i] / 3) / InputImg.w; //calculate the x index in 2D space of the blob
for(int k = i+raduis ; k < InputImg.w * InputImg.h * 3 ; k++)
{ //for all pixels in the image after this index
int kcy = (blobIdx[indexOfBLobCenters + k] / 3) % InputImg.w; //calculate the y index in 2D space of the image
int kcx = (blobIdx[indexOfBLobCenters + k] / 3) / InputImg.w; //calculate the x index in 2D space of the image
if(isNearCircle(cx, cy, kcx, kcy, raduis)) //call the function to check if the index is near the blob
{
blobIdx[indexOfBLobCenters + i] = 0; //remove that blob..prevent overlapping blobs
//if(scales == (numberOfIterations-2))
//for(int pk = 0; pk < numberOfIterations - 2; pk++){
//blobIdx[indexOfBLobCenters + k - ( pk* sizeofBLobImages)] = 0;
}
}
}
county++;
}
indexOfBLobCenters += sizeofBLobImages; //incrementing the index of the blob pixels by the image size
}
cout << "Removing concentric circles of different sizes" << endl;
//Removing overlapping circles of different sizes..concentric circles
for(int i=(sizeofBLobImages * numberOfBLobs) - sizeofBLobImages ; i< sizeofBLobImages * numberOfBLobs; i++)
{
int raduis = 1.5 * sigmaList[numberOfBLobs + 1]; //formula to get the radius of the blob
if(blobIdx[i] != 0) //for all blob indexes not zero
{
int cy = (blobIdx[i] / 3) % InputImg.w; //calculate the y index in 2D space of the blob
int cx = (blobIdx[i] / 3) / InputImg.w; //calculate the x index in 2D space of the blob
for(int k = i+1 ; k < InputImg.w * InputImg.h * 3 - i ; k++){
int kcy = (blobIdx[k] / 3) % InputImg.w; //calculate the y index in 2D space of the blob in other scale space
int kcx = (blobIdx[k] / 3) / InputImg.w; //calculate the x index in 2D space of the blob in other scale space
if(isNearCircle(cx, cy, kcx, kcy, raduis)){ //if the blobs are near over lapping each other
for(int pk = 0; pk < numberOfBLobs-1; pk++){ //set those indexes to xero for smaller scales
blobIdx[i - pk*sizeofBLobImages] = 0;
}
}
}
}
}
//drawing the circles on the blobs
cout << "Drawing circles ... this will take some time if you have taken a very large image" << endl;
for(int index = 0 ; index < sizeofBLobImages * numberOfBLobs; index++)
{
int raduis = 1.5 * sigmaList[(index / sizeofBLobImages) + 1]; //formula to get the raduis of the circle
if(blobIdx[index] != 0) //for all indexes with blobs
{
int cy = (blobIdx[index] / 3) % InputImg.w; //calculate the y index in 2D space of the blob
int cx = (blobIdx[index] / 3) / InputImg.w; //calculate the x index in 2D space of the blob
for(int x=0; x<InputImg.h; x++) //for each x index of the image
for(int y=0; y<InputImg.w; y++) //for each y index of the image
if (circle(x, y, cx, cy, raduis)) //if the x and y cordinates are in the circumference of the blob
{
InputImg.data[x * InputImg.w + y].r = 255; //keep only the red values
InputImg.data[x * InputImg.w + y].g = 0; //to draw a red circle aroud the blob
InputImg.data[x * InputImg.w + y].b = 0;
}
}
}
string blobResultName = "Result_Blob_Image.ppm";
store_ppm(InputImg, blobResultName); //storing the blob image
cudaFree(blob); //freeing the threads
cudaFree(gpu_orig_img_ptr);
cudaFree(LoG_result_img_ptr);
}
////----------------------------------------------------------------
////------------------------END OF BLOB DETECTION------------------
cudaFree(gpu_orig_img_ptr); //freeing the original image
return 0;
}
|
11,515 | #ifdef _WIN32
# define NOMINMAX
#endif
#define NUM_ELEMENTS 512
// includes, system
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include <math.h>
#include <float.h>
// includes, kernels
#include "vector_reduction_kernel.cu"
////////////////////////////////////////////////////////////////////////////////
// declaration, forward
void runTest( int argc, char** argv);
int ReadFile(float*, char* file_name);
float computeOnDevice(float* h_data, int array_mem_size);
extern "C"
void computeGold( float* reference, float* idata, const unsigned int len);
////////////////////////////////////////////////////////////////////////////////
// Program main
////////////////////////////////////////////////////////////////////////////////
int
main( int argc, char** argv)
{
runTest( argc, argv);
return EXIT_SUCCESS;
}
////////////////////////////////////////////////////////////////////////////////
//! Run naive scan test
////////////////////////////////////////////////////////////////////////////////
void
runTest( int argc, char** argv)
{
int num_elements = NUM_ELEMENTS;
int errorM = 0;
const unsigned int array_mem_size = sizeof( float) * num_elements;
// allocate host memory to store the input data
float* h_data = (float*) malloc( array_mem_size);
// * No arguments: Randomly generate input data and compare against the
// host's result.
// * One argument: Read the input data array from the given file.
switch(argc-1)
{
case 1: // One Argument
errorM = ReadFile(h_data, argv[1]);
if(errorM != num_elements)
{
printf("Error reading input file!\n");
exit(1);
}
break;
default: // No Arguments or one argument
// initialize the input data on the host to be integer values
// between 0 and 1000
for( unsigned int i = 0; i < num_elements; ++i)
{
h_data[i] = floorf(1000*(rand()/(float)RAND_MAX));
}
break;
}
// compute reference solution
float reference = 0.0f;
computeGold(&reference , h_data, num_elements);
// **===-------- Modify the body of this function -----------===**
float result = computeOnDevice(h_data, num_elements);
// **===-----------------------------------------------------------===**
// We can use an epsilon of 0 since values are integral and in a range
// that can be exactly represented
float epsilon = 0.0f;
unsigned int result_regtest = (abs(result - reference) <= epsilon);
printf( "Test %s\n", (1 == result_regtest) ? "PASSED" : "FAILED");
printf( "device: %f host: %f\n", result, reference);
// cleanup memory
free( h_data);
}
// Read a floating point vector into M (already allocated) from file
int ReadFile(float* V, char* file_name)
{
unsigned int data_read = NUM_ELEMENTS;
FILE* input = fopen(file_name, "r");
unsigned i = 0;
for (i = 0; i < data_read; i++)
fscanf(input, "%f", &(V[i]));
return data_read;
}
// **===----------------- Modify this function ---------------------===**
// Take h_data from host, copies it to device, setup grid and thread
// dimentions, excutes kernel function, and copy result of reduction back
// to h_data.
// Note: float* h_data is both the input and the output of this function.
float computeOnDevice(float* h_data, int num_elements)
{
int size=num_elements*sizeof(float);
printf("%d",size);
// placeholder
float* g_data;
int n;
cudaMalloc((void**)&g_data, size);
// cudaMalloc(&n, sizeof(int));
dim3 dimBlock(16,1,1);
dim3 dimGrid(ceil(num_elements/32.0),1,1);
cudaMemcpy(g_data, h_data, size,cudaMemcpyHostToDevice);
reduction<<<dimGrid,dimBlock>>>(g_data,num_elements);
//cudaMemcpy(n,num_elements, size,cudaMemcpyHostToDevice);
cudaMemcpy(h_data,g_data, size, cudaMemcpyDeviceToHost);
//reduction<<<dimGrid,dimBlock>>>(g_data,num_elements);
printf("%f",h_data[0]);
cudaFree(g_data);
for(int i=1;i<ceil(num_elements/32.0);i++)
h_data[0]+=h_data[i];
//cudaFree(n);
return h_data[0];
}
|
11,516 | //pass
//--blockDim=10 --gridDim=64 --no-inline
#include "cuda.h"
__global__ void foo() {
__shared__ int A[10];
A[threadIdx.x] = 0;
}
|
11,517 | /* Author: Ross Bearden
Instructor: Dr. Pettey
Class: CSCI 4330
Date: 05/03/17
Purpose: This program will calculate the sum of two matrices using GPUs and the cuda
language to do the calculations
*/
#include <stdio.h>
#include <stdlib.h>
__global__
void vectorAdd(
int* vector1,
int* vector2,
int* vectorResult)
{
vectorResult[threadIdx.x] =
vector1[threadIdx.x] +
vector2[threadIdx.x];
}
int main(int argc, char* argv[])
{
int rows = 16;
int columns = 32;
int i,j;
int firstArray[rows][columns];
int secondArray[rows][columns];
//Read the matrices
for(i = 0; i < rows; i++){
for(j = 0; j < columns; j++){
scanf("%d", &firstArray[i][j]);
}
}
for(i = 0; i < rows; i++){
for(j = 0; j < columns; j++){
scanf("%d", &secondArray[i][j]);
}
}
/*
for(i = 0; i < rows; i++){
for(j = 0; j < columns; j++){
printf("%d", firstArray[i][j]);
}
}
for(i = 0; i < rows; i++){
for(j = 0; j < columns; j++){
printf("%d", secondArray[i][j]);
}
}
*/
int* vector1;
int* vector2;
int sizeOfVector = 512;
int* deviceResults;
int hostResults[512];
cudaMalloc((void**) &vector1, sizeof(int) * sizeOfVector);
cudaMalloc((void**) &vector2, sizeof(int) * sizeOfVector);
cudaMalloc((void**) &deviceResults, sizeof(int) * sizeOfVector);
cudaMemcpy(vector1, firstArray, sizeof(int) * sizeOfVector,
cudaMemcpyHostToDevice);
cudaMemcpy(vector2, secondArray, sizeof(int) * sizeOfVector,
cudaMemcpyHostToDevice);
vectorAdd<<<1, sizeOfVector>>> (vector1, vector2, deviceResults);
cudaMemcpy(hostResults, deviceResults, sizeof(int) * sizeOfVector,
cudaMemcpyDeviceToHost);
for(i = 0; i < rows; i++){
for(j = 0; j < columns; j++){
printf("%i ", hostResults[j]);
}
printf("\n");
}
cudaFree(vector1);
cudaFree(vector2);
cudaFree(deviceResults);
return 0;
}
|
11,518 | #include <stdio.h>
#include <cuda.h>
__global__ void initialize(int * arr) {
arr[blockIdx.x] = 0;
}
int main(int argc, char ** argv) {
int * arr;
int * d_arr;
int n = 1024;
int size = n * sizeof(int);
arr = (int *)malloc(size);
cudaMalloc((void **) &d_arr, size);
cudaMemcpy(d_arr, arr, size, cudaMemcpyHostToDevice);
initialize<<<n, 1>>>(d_arr);
cudaMemcpy(arr, d_arr, size, cudaMemcpyDeviceToHost);
for (int i = 0; i < n; i++) {
printf("%d ", arr[i]);
}
printf("\n");
}
|
11,519 | #include "includes.h"
__global__ void concat(float* output, float* input1, float* input2, float* input3, float* input4, const int size, const int in_channel1, const int in_channel2, const int in_channel3, const int in_channel4)
{
const int pos = blockIdx.x * blockDim.x + threadIdx.x;
const int out_channel = in_channel1 + in_channel2 + in_channel3 + in_channel4; // # of channel for output
const int N = size * size; // total elements per channel
if(pos < N){
for(int n = 0; n < out_channel; n++){
const int row = pos / size;
const int col = pos % size;
if(n < in_channel1){ // first input
output[(n * size + col) * size + row] = input1[(n * size + col) * size + row];
}
else if(n < in_channel1 + in_channel2){ // second input
output[(n * size + col) * size + row] = input2[((n - in_channel1) * size + col) * size + row];
}
else if(n < in_channel1 + in_channel2 + in_channel3){ // third input
output[(n * size + col) * size + row] = input3[((n - in_channel1 - in_channel2) * size + col) * size + row];
}
else{ // last input
output[(n * size + col) * size + row] = input4[((n - in_channel1 - in_channel2 - in_channel3) * size + col) * size + row];
}
}
}
} |
11,520 | #include <thrust/host_vector.h>
#include <thrust/device_vector.h>
#include <iostream>
// TEST to see if i can resize vector in a function
int new_size = 10;
int dev_init = 33;
void resize_vec(thrust::host_vector<int> &vec){
vec.resize(new_size);
// for( int i = 0; i< new_size; i++)
// vec[i] = i;
thrust::device_vector<int> d_vec(new_size, dev_init);
int* d_vec_ptr = thrust::raw_pointer_cast(&d_vec[0]);
int* vec_ptr = thrust::raw_pointer_cast(&vec[0]);
cudaMemcpy(vec_ptr, d_vec_ptr, (size_t)(new_size*sizeof(int)), cudaMemcpyDeviceToHost);
}
int main(){
thrust::host_vector<int> test_vec(0);
std::cout << "Old size = " << test_vec.size() << std::endl;
resize_vec(test_vec);
std::cout << "new size = " << test_vec.size() << std::endl;
for( int i = 0; i< new_size; i++)
std::cout << test_vec[i] << std::endl;
return 0;
} |
11,521 | // 2nd order FD scheme for gradient with uniform mesh spacing
#include <stdio.h>
__constant__ int mx, my, mz;
__constant__ double dxInv, dyInv, dzInv;
__global__ void gradient_x(double* f, double* df) {
//f: vector of function values
//df: vector of derivatives (output)
int i = blockIdx.x*blockDim.x + threadIdx.x;
int j = blockIdx.y*blockDim.y + threadIdx.y;
int k = blockIdx.z;
int globalIdx = k * mx * my + j * mx + i;
if (i > 0 && i < mx-1) {
df[globalIdx] = (-0.5*f[globalIdx-1] + 0.5*f[globalIdx+1])*dxInv;
} else if (i == 0 && mx > 1) {
df[globalIdx] = (-f[globalIdx] + f[globalIdx+1])*dxInv;
} else if (i == mx-1 && mx > 1) {
df[globalIdx] = (-f[globalIdx-1] + f[globalIdx])*dxInv;
}
}
__global__ void gradient_y(double* f, double* df) {
//f: vector of function values
//df: vector of derivatives (output)
int i = blockIdx.x*blockDim.x + threadIdx.x;
int j = blockIdx.y*blockDim.y + threadIdx.y;
int k = blockIdx.z;
int globalIdx = k * mx * my + j * mx + i;
if (j > 0 && j < my-1) {
df[globalIdx] = (-0.5*f[globalIdx-mx] + 0.5*f[globalIdx+mx])*dyInv;
} else if (j == 0 && my > 1) {
df[globalIdx] = (-f[globalIdx] + f[globalIdx+mx])*dyInv;
} else if (j == my-1 && my > 1) {
df[globalIdx] = (-f[globalIdx-mx] + f[globalIdx])*dyInv;
}
}
__global__ void gradient_z(double* f, double* df) {
//f: vector of function values
//df: vector of derivatives (output)
int i = blockIdx.x*blockDim.x + threadIdx.x;
int j = blockIdx.y*blockDim.y + threadIdx.y;
int k = blockIdx.z;
int globalIdx = k * mx * my + j * mx + i;
if (k > 0 && k < mz-1) {
df[globalIdx] = (-0.5*f[globalIdx-mx*my] + 0.5*f[globalIdx+mx*my])*dzInv;
} else if (k == 0 && mz > 1) {
df[globalIdx] = (-f[globalIdx] + f[globalIdx+mx*my])*dzInv;
} else if (k == mz-1 && mz > 1) {
df[globalIdx] = (-f[globalIdx-mx*my] + f[globalIdx])*dzInv;
}
}
|
11,522 |
__global__ void mykernel( double *aplusb,
const double *a,
const double *b,
const int N
) {
/* "const" because the value is not changing in kernel, N = size of vectors */
// compute index of this kernel
int n = blockIdx.x*blockDim.x + threadIdx.x;
// if index is smaller than size, then compute something
if(n<N){
aplusb[n] = a[n] + b[n];
} else {
/* put your feet on the table */
}
}
|
11,523 | #include <thrust/device_vector.h>
#include <thrust/host_vector.h>
#include <iostream>
int main() {
thrust::host_vector<double> host;
while (std::cin.good()) {
double t;
std::cin >> t;
host.push_back(t);
}
thrust::device_vector<double> dev(host);
std::cout << dev.size() << "\n";
}
|
11,524 | #include <iostream>
#include <limits>
#include "kernels/kernels_linear.cu"
const float F_EPSILON = 0.00001f;
extern "C"
bool eq_mats(float *lhs, float *rhs, size_t len) {
size_t matrix_size = len * sizeof(float);
float *d_lhs, *d_rhs;
cudaMalloc(&d_lhs, matrix_size);
cudaMalloc(&d_rhs, matrix_size);
cudaMemcpy(d_lhs, lhs, matrix_size, cudaMemcpyHostToDevice);
cudaMemcpy(d_rhs, rhs, matrix_size, cudaMemcpyHostToDevice);
bool *equal_flag;
cudaMallocManaged(&equal_flag, sizeof(bool));
*equal_flag = true;
int threads_per_block = 256;
int blocks_per_grid = 1 + ((len - 1) / threads_per_block);
kernel_eq_mats<<<blocks_per_grid, threads_per_block>>>
(d_lhs, d_rhs, len, F_EPSILON, equal_flag);
cudaDeviceSynchronize();
bool equal = *equal_flag;
cudaFree(equal_flag);
cudaFree(d_lhs);
cudaFree(d_rhs);
return equal;
}
extern "C"
void add_mats(float *lhs1, float *lhs2, float *rhs, size_t len) {
size_t matrix_size = len * sizeof(float);
float *d_lhs1, *d_lhs2, *d_rhs;
cudaMalloc(&d_lhs1, matrix_size);
cudaMalloc(&d_lhs2, matrix_size);
cudaMalloc(& d_rhs, matrix_size);
cudaMemcpy(d_lhs1, lhs1, matrix_size, cudaMemcpyHostToDevice);
cudaMemcpy(d_lhs2, lhs2, matrix_size, cudaMemcpyHostToDevice);
int threads_per_block = 256;
int blocks_per_grid = 1 + ((len - 1) / threads_per_block);
kernel_add_mats<<<blocks_per_grid, threads_per_block>>>
(d_lhs1, d_lhs2, d_rhs, len);
cudaDeviceSynchronize();
cudaMemcpy(rhs, d_rhs, matrix_size, cudaMemcpyDeviceToHost);
cudaFree(d_lhs1);
cudaFree(d_lhs2);
cudaFree(d_rhs);
}
extern "C"
void mul_scalar_mat(float scalar, float *lhs, float *rhs, size_t len) {
size_t matrix_size = len * sizeof(float);
float *d_lhs, *d_rhs;
cudaMalloc(&d_lhs, matrix_size);
cudaMalloc(&d_rhs, matrix_size);
cudaMemcpy(d_lhs, lhs, matrix_size, cudaMemcpyHostToDevice);
int threads_per_block = 256;
int blocks_per_grid = 1 + ((len - 1) / threads_per_block);
kernel_mul_scalar_mat<<<blocks_per_grid, threads_per_block>>>
(scalar, d_lhs, d_rhs, len);
cudaDeviceSynchronize();
cudaMemcpy(rhs, d_rhs, matrix_size, cudaMemcpyDeviceToHost);
cudaFree(d_lhs);
cudaFree(d_rhs);
}
extern "C"
void mul_mats
(float *lhs1, size_t lhs1_rows, size_t lhs1_cols,
float *lhs2, size_t lhs2_rows, size_t lhs2_cols,
float *rhs)
{
size_t lhs1_size = lhs1_cols * lhs1_rows * sizeof(float);
size_t lhs2_size = lhs2_cols * lhs2_rows * sizeof(float);
size_t rhs_size = lhs1_rows * lhs2_cols * sizeof(float);
float *d_lhs1, *d_lhs2, *d_rhs;
cudaMalloc(&d_lhs1, lhs1_size);
cudaMalloc(&d_lhs2, lhs2_size);
cudaMalloc(&d_rhs, rhs_size);
cudaMemcpy(d_lhs1, lhs1, lhs1_size, cudaMemcpyHostToDevice);
cudaMemcpy(d_lhs2, lhs2, lhs2_size, cudaMemcpyHostToDevice);
Matrix lhs1_mat = { lhs1_rows, lhs1_cols, d_lhs1 };
Matrix lhs2_mat = { lhs2_rows, lhs2_cols, d_lhs2 };
Matrix rhs_mat = { lhs1_rows, lhs2_cols, d_rhs };
dim3 threads_per_block(SUB_MATRIX_DIM, SUB_MATRIX_DIM);
dim3 blocks_per_grid
((lhs2_cols / SUB_MATRIX_DIM) + (lhs2_cols % SUB_MATRIX_DIM != 0),
(lhs1_rows / SUB_MATRIX_DIM) + (lhs1_rows % SUB_MATRIX_DIM != 0));
kernel_mul_mats<<<blocks_per_grid, threads_per_block>>>
(lhs1_mat, lhs2_mat, rhs_mat);
cudaDeviceSynchronize();
cudaMemcpy(rhs, d_rhs, rhs_size, cudaMemcpyDeviceToHost);
cudaFree(d_lhs1);
cudaFree(d_lhs2);
cudaFree(d_rhs);
}
extern "C"
void transpose_mat(float *lhs, size_t lhs_rows, size_t lhs_cols, float *rhs) {
size_t lhs_size = lhs_rows * lhs_cols * sizeof(float);
size_t rhs_size = lhs_size;
float *d_lhs, *d_rhs;
cudaMalloc(&d_lhs, lhs_size);
cudaMalloc(&d_rhs, rhs_size);
cudaMemcpy(d_lhs, lhs, lhs_size, cudaMemcpyHostToDevice);
Matrix lhs_mat = { lhs_rows, lhs_cols, d_lhs };
Matrix rhs_mat = { lhs_cols, lhs_rows, d_rhs };
dim3 threads_per_block(SUB_MATRIX_DIM, SUB_MATRIX_DIM);
dim3 blocks_per_grid
((lhs_cols / SUB_MATRIX_DIM) + (lhs_cols % SUB_MATRIX_DIM != 0),
(lhs_rows / SUB_MATRIX_DIM) + (lhs_rows % SUB_MATRIX_DIM != 0));
kernel_transpose_mat<<<blocks_per_grid, threads_per_block>>>
(lhs_mat, rhs_mat);
cudaDeviceSynchronize();
cudaMemcpy(rhs, d_rhs, rhs_size, cudaMemcpyDeviceToHost);
cudaFree(d_lhs);
cudaFree(d_rhs);
}
extern "C"
void dot_vecs(float *lhs1, float *lhs2, float *rhs, size_t len) {
size_t vec_size = len * sizeof(float);
float *d_lhs1, *d_lhs2, *d_rhs;
cudaMalloc(&d_lhs1, vec_size);
cudaMalloc(&d_lhs2, vec_size);
cudaMalloc(& d_rhs, sizeof(float));
cudaMemcpy(d_lhs1, lhs1, vec_size, cudaMemcpyHostToDevice);
cudaMemcpy(d_lhs2, lhs2, vec_size, cudaMemcpyHostToDevice);
int threads_per_block = SUB_VECTOR_LEN;
int blocks_per_grid = (len / SUB_VECTOR_LEN) + (len % SUB_VECTOR_LEN != 0);
kernel_dot_vecs<<<blocks_per_grid, threads_per_block>>>
(d_lhs1, d_lhs2, d_rhs);
cudaDeviceSynchronize();
cudaMemcpy(rhs, d_rhs, sizeof(float), cudaMemcpyDeviceToHost);
cudaFree(d_lhs1);
cudaFree(d_lhs2);
cudaFree(d_rhs);
}
extern "C"
void p_norm_vec(float *lhs, float p, float *rhs, size_t len) {
size_t vec_size = len * sizeof(float);
float *d_lhs, *d_rhs;
cudaMalloc(&d_lhs, vec_size);
cudaMalloc(&d_rhs, sizeof(float));
cudaMemcpy(d_lhs, lhs, vec_size, cudaMemcpyHostToDevice);
int threads_per_block = SUB_VECTOR_LEN;
int blocks_per_grid = (len / SUB_VECTOR_LEN) + (len % SUB_VECTOR_LEN != 0);
if (isinf(p))
kernel_inf_norm_vec<<<blocks_per_grid, threads_per_block>>>
(d_lhs, d_rhs);
else
kernel_p_norm_vec<<<blocks_per_grid, threads_per_block>>>
(d_lhs, p, d_rhs);
cudaDeviceSynchronize();
cudaMemcpy(rhs, d_rhs, sizeof(float), cudaMemcpyDeviceToHost);
cudaFree(d_lhs);
cudaFree(d_rhs);
}
|
11,525 | #include <stdio.h>
#include <sys/time.h>
#define N (65535)
#define HANDLE_ERROR( err ) (HandleError( err, __FILE__, __LINE__))
static void HandleError( cudaError_t err, const char *file, int line)
{
if (err != cudaSuccess)
{
printf("%s in %s at line %d\n", cudaGetErrorString (err ), file, line);
exit( EXIT_FAILURE );
}
}
double get_time()
{
struct timeval tv;
gettimeofday(&tv, NULL);
return (double)tv.tv_sec + (double)1e-6 * tv.tv_usec;
}
__global__ void add (int *a, int *b, int *c)
{
/* PLEASE COMPLETE THE CODE */
}
void add_cpu (int *a, int *b, int *c)
{
/* PLEASE COMPLETE THE CODE */
}
int main(void)
{
int a[N], b[N], c[N];
int *dev_a, *dev_b, *dev_c;
double start_time, end_time;
//INITIALIZE THE ARRAY
for (int i=0; i<N; i++)
{
a[i] = i;
b[i] = i*i;
}
//GPU MEMORY ALLOCATION for dev_a, dev_b, and dev_c.
/* PLEASE COMPLETE THE CODE */
//COPY a and b to ALLOCATED GPU MEMORY
/* PLEASE COMPLETE THE CODE */
//GPU CALCULATION TAKES PLACE
start_time = get_time();
/* PLEASE COMPLETE THE CODE */
end_time = get_time();
printf("[GPU]: %f sec Elpased\n", end_time - start_time);
//COPY c to HOST
/* PLEASE COMPLETE THE CODE */
//CHECK FOR THE INTEGRITY
bool success = true;
for (int i=0; i<N; i++)
{
if ((a[i] + b[i])!=c[i])
{
printf("%d + %d != %d\n", a[i], b[i], c[i]);
success = false;
}
}
if (success)
{
printf("GPU TEST SUCCESSFUL!\n");
}
//FREE the CUDA MEMORY
/* PLEASE COMPLETE THE CODE */
//CPU CALCULATION TAKES PLACE
start_time = get_time();
add_cpu(a, b, c);
end_time = get_time();
printf("[CPU]: %f sec Elpased\n", end_time - start_time);
//CHECK FOR THE INTEGRITY
for (int i=0; i<N; i++)
{
if ((a[i] + b[i])!=c[i])
{
printf("%d + %d != %d\n", a[i], b[i], c[i]);
success = false;
}
}
if (success)
{
printf("CPU TEST SUCCESSFUL!\n");
}
}
|
11,526 | #include "includes.h"
__global__ void vis2ints(double scale, double2 *vis_in, int2* vis_out, int npts) {
for (int q=threadIdx.x+blockIdx.x*blockDim.x;
q<npts;
q+=gridDim.x*blockDim.x) {
double2 inn = vis_in[q];
inn.x *= scale;
inn.y *= scale;
int main_y = floor(inn.y);
int sub_y = floor(GCF_GRID*(inn.y-main_y));
int main_x = floor(inn.x);
int sub_x = floor(GCF_GRID*(inn.x-main_x));
vis_out[q].x = main_x*GCF_GRID+sub_x;
vis_out[q].y = main_y*GCF_GRID+sub_y;
}
} |
11,527 | extern "C"
__global__ void ipc(double *CMass, double *SMass, double *HMass, double *NMass, double *OMass, double *CP, double *SP, double *HP, double *NP, double *OP, int *arrayCombination, int *indexCombination, double *MassOutput, double *POutput)
{
int cMax = arrayCombination[0];
int sMax = arrayCombination[1];
int hMax = arrayCombination[2];
int nMax = arrayCombination[3];
int oMax = arrayCombination[4];
double *LocalCMass = new double[cMax];
double *LocalSMass = new double[sMax];
double *LocalHMass = new double[hMax];
double *LocalNMass = new double[nMax];
double *LocalOMass = new double[oMax];
double *LocalCP = new double[cMax];
double *LocalSP = new double[sMax];
double *LocalHP = new double[hMax];
double *LocalNP = new double[nMax];
double *LocalOP = new double[oMax];
int index = blockIdx.x;
int indexC = index * 5;
int indexS = index * 5 + 1;
int indexH = index * 5 + 2;
int indexN = index * 5 + 3;
int indexO = index * 5 + 4;
int CNumber = indexCombination[indexC];
int SNumber = indexCombination[indexS];
int HNumber = indexCombination[indexH];
int NNumber = indexCombination[indexN];
int ONumber = indexCombination[indexO];
for(int i = 0; i != cMax; i++){
LocalCMass[i] = CMass[cMax * (CNumber - 1) + i];
LocalCP[i] = CP[cMax * (CNumber - 1) + i];
}
for(int i = 0; i != sMax; i++){
if(SNumber > 0){
LocalSMass[i] = SMass[sMax * (SNumber - 1) + i];
LocalSP[i] = SP[sMax * (SNumber - 1) + i];
}else{
LocalSMass[i] = 0;
LocalSP[i] = 1;
}
}
for(int i = 0; i != hMax; i++){
LocalHMass[i] = HMass[hMax * (HNumber - 1) + i];
LocalHP[i] = HP[hMax * (HNumber - 1) + i];
}
for(int i = 0; i != nMax; i++){
LocalNMass[i] = NMass[nMax * (NNumber - 1) + i];
LocalNP[i] = NP[nMax * (NNumber - 1) + i];
}
for(int i = 0; i != oMax; i++){
LocalOMass[i] = OMass[oMax * (ONumber - 1) + i];
LocalOP[i] = OP[oMax * (ONumber - 1) + i];
}
int count = 0;
int total = cMax * sMax * hMax * nMax * oMax;
for(int i = 0; i != cMax; i++){
for(int j = 0; j != sMax; j++){
for(int k = 0; k != hMax; k++){
for(int l = 0; l != nMax; l++){
for(int m = 0; m != oMax; m++){
if(SNumber > 0){
MassOutput[total * index + count] = LocalCMass[i] + LocalSMass[j] + LocalHMass[k] + LocalNMass[l] + LocalOMass[m] + 5 * 0.00054858;
}else{
MassOutput[total * index + count] = LocalCMass[i] + LocalSMass[j] + LocalHMass[k] + LocalNMass[l] + LocalOMass[m] + 4 * 0.00054858;
}
POutput[total * index + count] = LocalCP[i] * LocalSP[j] * LocalHP[k] * LocalNP[l] * LocalOP[m];
count++;
}
}
}
}
}
}
|
11,528 | #include "includes.h"
__global__ void bcnn_op_cuda_relu_kernel(int n, float *x, float *y) {
int i = (blockIdx.x + blockIdx.y * gridDim.x) * blockDim.x + threadIdx.x;
if (i < n) {
y[i] = x[i] * (x[i] > 0);
}
return;
} |
11,529 | #include "includes.h"
__global__ void calcSumTable(const float *rowCumSum, float *SumTable, int rowNumberN, int colNumberM) {
int xIndex = threadIdx.x + blockIdx.x * blockDim.x;
if (xIndex >= colNumberM) return;
for (int i = 1; i < rowNumberN; i++) {
SumTable[i * colNumberM + xIndex] +=
rowCumSum[(i - 1) * colNumberM + xIndex];
}
} |
11,530 | #include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <math.h>
#include "number_types.cuh"
__device__
Cplx::Cplx() {
re = 0.;
im = 0.;
}
__device__
Cplx::Cplx(double real) {
re = real;
im = 0.;
}
__device__
Cplx::Cplx(double real, double imag) {
re = real;
im = imag;
}
__device__
Cplx Cplx::operator + (Cplx const& obj) {
Cplx res;
res.re = re + obj.re;
res.im = im + obj.im;
return res;
}
__device__
Cplx Cplx::operator * (Cplx const& obj) {
Cplx res;
res.re = re * obj.re - im * obj.im;
res.im = re * res.im + im * res.re;
return res;
}
__device__
Cplx operator * (double lhs, const Cplx& rhs) {
Cplx res;
res.re = rhs.re * lhs;
res.im = rhs.im * lhs;
return res;
}
__device__
Cplx square(Cplx Z) {
return Cplx(Z.re * Z.re - Z.im * Z.im, 2. * Z.re * Z.im);
}
// returns square of magnitude of argument
__device__
double norm(Cplx Z) {
return Z.re * Z.re + Z.im * Z.im;
}
__device__
double abs(Cplx rect) {
return sqrt(rect.re * rect.re + rect.im * rect.im);
}
// returns phase of argument, counter-clockwise from +'ve x axis
__device__
double phase(Cplx rect) {
return atan2(rect.im, rect.re);
}
__device__
Cplx polar(Cplx rect) { // Converts argument to polar form (magnitude, phase)
return Cplx(abs(rect), phase(rect));
}
__device__
Cplx rect(Cplx polar) { // Converts argument to rectangular form (real, imaginary)
return Cplx(polar.re * cos(polar.im), polar.re * sin(polar.im));
}
/*
class Cplx
{
public:
double re;
double im;
__device__
Cplx() {
re = 0.;
im = 0.;
}
__device__
Cplx(double real) {
re = real;
im = 0.;
}
__device__
Cplx(double real, double imag) {
re = real;
im = imag;
}
__device__
Cplx operator + (Cplx const& obj) {
Cplx res;
res.re = re + obj.re;
res.im = im + obj.im;
return res;
}
__device__
Cplx operator * (Cplx const& obj) {
Cplx res;
res.re = re * obj.re - im * obj.im;
res.im = re * res.im + im * res.re;
return res;
}
};
__device__
Cplx operator * (double lhs, const Cplx& rhs) {
Cplx res;
res.re = rhs.re * lhs;
res.im = rhs.im * lhs;
return res;
}
__device__
Cplx square(Cplx Z) {
return Cplx(Z.re * Z.re - Z.im * Z.im, 2. * Z.re * Z.im);
}
// returns square of magnitude of argument
__device__
double norm(Cplx Z) {
return Z.re * Z.re + Z.im * Z.im;
}
__device__
double abs(Cplx rect) {
return sqrt(rect.re * rect.re + rect.im * rect.im);
}
// returns phase of argument, counter-clockwise from +'ve x axis
__device__
double phase(Cplx rect) {
return atan2(rect.im, rect.re);
}
__device__
Cplx polar(Cplx rect) { // Converts argument to polar form (magnitude, phase)
return Cplx(abs(rect), phase(rect));
}
__device__
Cplx rect(Cplx polar) { // Converts argument to rectangular form (real, imaginary)
return Cplx(polar.re * cos(polar.im), polar.re * sin(polar.im));
}
*/ |
11,531 | //pass
//--blockDim=[16,16] --gridDim=[16,16]
#include <cuda.h>
//////////////////////////////////////////////////////////////////////////////
//// THIS CODE AND INFORMATION IS PROVIDED "AS IS" WITHOUT WARRANTY OF
//// ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING BUT NOT LIMITED TO
//// THE IMPLIED WARRANTIES OF MERCHANTABILITY AND/OR FITNESS FOR A
//// PARTICULAR PURPOSE.
////
//// Copyright (c) Microsoft Corporation. All rights reserved
//////////////////////////////////////////////////////////////////////////////
//----------------------------------------------------------------------------
// File: Matrixmult.cpp
//
// Implement GPU based matrix multiplication
//----------------------------------------------------------------------------
#define _type float
#define M 256
#define N 256
#define W 256
#define tile_size 16
#define X_DIMENSION 0
#define Y_DIMENSION 1
//----------------------------------------------------------------------------
// Implement tiled version of matrix multiplication
// M, N and W are sizes of matrix
// input matrix - va is of size (M * N), vb is (N * W)
// output matrix - vresult (M * W)
//----------------------------------------------------------------------------
__global__ void mxm_amp_tiled(const _type * va, const _type * vb, _type * vresult)
{
{
__shared__ _type localB[tile_size][tile_size];
__shared__ _type localA[tile_size][tile_size];
_type temp_c = 0;
int localIdxX = threadIdx.x;
int localIdxY = threadIdx.y;
int globalIdxX = blockIdx.x*blockDim.x + threadIdx.x;
int globalIdxY = blockIdx.y*blockDim.y + threadIdx.y;
for (int i = 0;
i < N; i += tile_size)
{
localA[localIdxY][localIdxX] = va[globalIdxY*M + i + localIdxX];
localB[localIdxY][localIdxX] = vb[(i + localIdxY)*N + globalIdxX];
#ifndef MUTATION
/* BUGINJECT: REMOVE_BARRIER, DOWN */
__syncthreads();
#endif
for (unsigned int k = 0; k < tile_size; k++)
{
temp_c += localA[localIdxY][k] * localB[k][localIdxX];
}
__syncthreads();
}
vresult[globalIdxY*M + globalIdxX] = temp_c;
}
}
|
11,532 | #include <stdio.h>
#include <stdlib.h>
__global__ void GPU_Matrix_Multiply_Kernel(double *A, double *B, double *C, int N) {
//2D Thread ID
int tx = threadIdx.x;
int ty = threadIdx.y;
//Cvalue stores the C element that is computed by the thread
float Cvalue = 0;
//Looping through to compute entries of C
for(int k = 0; k < N ; ++k) {
float Mdelement = A[ty*N + k];
float Ndelement = B[k*N + tx];
Cvalue += (Mdelement*Ndelement);
}
//Storing the value into C
C[ty*N + tx] = Cvalue;
}
|
11,533 | __global__ void simple_loop(int *a, int size) {
int tid = threadIdx.x;
for (int i = 0; i < size; i++) {
a[tid * 3] += a[tid * 3 + i];
}
}
|
11,534 | #include "includes.h"
__global__ void addVectorsInto(float *result, float *a, float *b, int N)
{
int idx = threadIdx.x + blockIdx.x * blockDim.x;
int stride = gridDim.x * blockDim.x;
for(int i = idx; i < N; i += stride)
{
result[i] = a[i] + b[i];
}
} |
11,535 | #include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <stdio.h>
#include <iostream>
#include <time.h>
#include <cstdlib>
using namespace std;
#define N 2048
#define Iteration 100
__global__ void matrixTransposeShared(const int *a, int *b)
{
__shared__ int mat[32][33];
int bx = blockIdx.x * 32;
int by = blockIdx.y * 32;
int i = by + threadIdx.y; int j = bx + threadIdx.x; //input
int ti = bx + threadIdx.y; int tj = by + threadIdx.x;
//output
if(i < N && j < N)
mat[threadIdx.x][threadIdx.y] = a[i * N + j];
__syncthreads(); //Wait for all data to be copied
if(tj < N && ti < N)
b[ti * N + tj] = mat[threadIdx.y][threadIdx.x];
}
int main(){
int *a, *b;
int *d_a, *d_b;
int size = N*N*sizeof(int);
clock_t start, end;
// Alloc space for device copies of a, b
cudaMalloc((void **)&d_a, size);
cudaMalloc((void **)&d_b, size);
// Alloc space for host copies of a, b, and setup input values
a = (int*)malloc(size);
b = (int*)malloc(size);
for(int i = 0; i < N; i++)
for(int j = 0; j < N; j++)
a[i*N+j] = j;
dim3 grid(64, 64);
dim3 block(32, 32);
start = clock();
// Copy inputs to device
cudaMemcpy(d_a, a, size, cudaMemcpyHostToDevice);
//cudaMemcpy(d_b, b, size, cudaMemcpyHostToDevice);
//Launch kernel
for(int i = 0; i < Iteration; i++)
matrixTransposeShared<<<grid, block>>>(d_a, d_b);
// Copy result back to host
cudaMemcpy(b, d_b, size, cudaMemcpyDeviceToHost);
end = clock();
for(int i = 0; i < 10; i++){
for(int j = 0; j < 10; j++)
cout<<b[i*N+j]<<" ";
cout<<endl;
}
//Cleanup
free(a); free(b);
cudaFree(d_a); cudaFree(d_b);
cout << "Totle Time : " <<(double)(end - start)<< "ms" << endl;
return 0;
} |
11,536 | #include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <stdio.h>
#include <time.h>
#include <stdlib.h>
#define INITIAL_CAPACITY 1024
/******************** Find the min value **************************/
__global__ void minCompare(int *a, int *set, bool *check, int *capacity) {
int cap = capacity[0];
int offset = set[0];
int idx = threadIdx.x + blockIdx.x * blockDim.x;
int idy = threadIdx.y + blockIdx.y * blockDim.y;
int tabx = idx + cap + offset;
int taby = idy + cap + offset;
if (idx == idy) { return; }
int xval = a[tabx];
int yval = a[taby];
if(yval == 0) {}
else if (xval == 0) {
check[idx] = false;
} else if (xval > yval) {
check[idx] = false;
}
}
__global__ void cudaMin(int *a, int *set, bool *check, int* min, int *capacity) {
int idx = blockIdx.x;
if (check[idx]) {
min[0] = a[idx + capacity[0] + set[0]];
}
}
/************************* Find the max value **********************/
__global__ void maxCompare(int *a, bool *check) {
int idx = threadIdx.x + blockIdx.x * blockDim.x;
int idy = threadIdx.y + blockIdx.y * blockDim.y;
if (idx == idy) { return; }
int xval = a[idx];
int yval = a[idy];
if (xval < yval) {
check[idx] = false;
}
}
__global__ void cudaMax(int *a, bool *check, int* max) {
int idx = blockIdx.x;
if (check[idx]) {
max[0] = a[idx];
}
}
/*********************** Helper Methods ********************************************/
__global__ void cudaBoolFill(bool *arr, int length) {
int i = threadIdx.x + blockIdx.x * blockDim.x;
if (i < length) {
arr[i] = true;
}
}
/********************** Min and Max Functions ******************************************/
void findMin(int *arr, const int length, const int offset, int *minimum, int *capacity) {
//length - 1 = row, offset = location of first element
bool *check;
int *set;
int *row = (int*) malloc(sizeof(int));
const int intSize = sizeof(int);
const int bsize = length * sizeof(bool);
cudaMalloc((void**)&check, bsize);
cudaBoolFill<<< dim3(length, 1), 1 >>>(check, length);
cudaMalloc((void**)&set, intSize);
cudaMemcpy(set, (int*)&offset, intSize, cudaMemcpyHostToDevice);
cudaMemcpy(row, capacity, intSize, cudaMemcpyDeviceToHost);
row[0] = row[0] * (length - 1);
printf("offset = %d length = %d row = %d\n", offset, length, row[0]);
int *row2;
cudaMalloc((void**) &row2, intSize);
cudaMemcpy(row2, row, intSize, cudaMemcpyHostToDevice);
minCompare<<< dim3(length, length), 1 >>>(arr, set, check, row2);
cudaMin<<< dim3(length, 1), 1 >>>(arr, set, check, minimum, row2);
cudaFree(check);
}
int findMax(int *arr, const int length) {
bool *check;
int *max;
const int intSize = sizeof(int);
const int bsize = length * sizeof(bool);
cudaMalloc((void**)&check, bsize);
cudaBoolFill<<< dim3(length, 1), 1 >>>(check, length);
cudaMalloc((void**)&max, intSize);
maxCompare<<< dim3(length, length), 1 >>>(arr, check);
cudaMax<<< dim3(length, 1), 1 >>>(arr, check, max);
int maxhost[1];
cudaMemcpy(maxhost, max, intSize, cudaMemcpyDeviceToHost);
cudaFree(max);
cudaFree(check);
return maxhost[0];
}
/********************* Find the Curl *****************************************/
int findCurl(int *sequence, int *table, int length, int capacity){
int *tempResults;
cudaMalloc((void **) &tempResults, (length >> 1) * sizeof(int));
int *cap;
cudaMalloc((void **) &cap, sizeof(int));
cudaMemcpy(cap, (int*)&capacity, sizeof(int), cudaMemcpyHostToDevice);
for(int i(0); i < (length >> 1); ++i) {
//int *p = &(table[i][(length - 1) - i]);
//findMin(p, length, &(tempResults[i]));
findMin(table, i+1, (length - 1) - i, &(tempResults[i]), cap);
}
int *results = (int *) malloc((length >> 1) * sizeof(int));
cudaMemcpy(results, tempResults, (length >> 1) * sizeof(int), cudaMemcpyDeviceToHost);
for(int i(0); i < (length >> 1); ++i) {
printf("%d ", results[i]);
}
printf("\n");
int curl = findMax(tempResults, length);
cudaFree(tempResults);
return curl;
}
void printTable(int *table, int length, int capacity) {
int *CPUTable;
CPUTable = (int *) malloc(capacity * capacity * sizeof(int));
cudaMemcpy(CPUTable, table, capacity * capacity * sizeof(int), cudaMemcpyDeviceToHost);
for(int i(0); i < length; ++i) {
for(int j(0); j < length; ++j) {
printf("%d ", CPUTable[(i * capacity) + j]);
}
printf("\n");
}
free(CPUTable);
}
__global__ void fillColumn(int *sequence, int *table, int *seqPosition, int *cap) {
int row = threadIdx.x + blockIdx.x * blockDim.x;
int index = *seqPosition;
int capacity = *cap;
int value = 1;
if(row == index){}
else if(sequence[index - (row + 1)] == sequence[index]) {
int t = table[(row * capacity) + (index - (row + 1))];
if(t == 0) {
value = 2;
} else {
value = table[(row * capacity) + (index - (row + 1))] + 1;
}
}
table[(row * capacity) + index] = value;
}
void initializeTable(int *sequence, int *table, int length, int capacity) {
int *index;
cudaMalloc((void **)&index, sizeof(int));
int *cap;
cudaMalloc((void **)&cap, sizeof(int));
cudaMemcpy(cap, (void *)&capacity, sizeof(int), cudaMemcpyHostToDevice);
for(int i(0); i < length; ++i) {
cudaMemcpy(index, (void *)&i, sizeof(int), cudaMemcpyHostToDevice);
fillColumn<<< dim3(i + 1, 1), 1 >>>(sequence, table, index, cap);
}
cudaFree(index);
}
int main() {
int *table;
int capacity = INITIAL_CAPACITY;
cudaMalloc((void**)&table, (INITIAL_CAPACITY * INITIAL_CAPACITY) * sizeof(int));
while (1) {
cudaMemset(table, 0, (capacity * capacity) * sizeof(int));
char buffer[100];
printf("Input a sequence to curl:\n");
scanf("%s", buffer);
int i(0);
int sequence[INITIAL_CAPACITY];
for (; buffer[i] != '\0'; ++i) {
sequence[i] = buffer[i] - '0';
}
int seqLength = i;
int sequenceByteSize = seqLength * sizeof(int);
int *cudaSequence;
cudaMalloc((void**)&cudaSequence, sequenceByteSize);
cudaMemcpy(cudaSequence, sequence, sequenceByteSize, cudaMemcpyHostToDevice);
initializeTable(cudaSequence, table, seqLength, capacity);
clock_t start = clock();
int *size;
cudaMalloc((void**)&size, sizeof(int));
int *cap;
cudaMalloc((void **)&cap, sizeof(int));
cudaMemcpy(cap, (void *)&capacity, sizeof(int), cudaMemcpyHostToDevice);
int curl = (seqLength == 1) ? 1: 0;
while(curl != 1) {
curl = findCurl(cudaSequence, table, seqLength, capacity);
printf("curl = %d\n", curl);
printTable(table, seqLength, capacity);
sequence[seqLength] = curl;
cudaMemcpy(size, (int*)&seqLength, sizeof(int), cudaMemcpyHostToDevice);
sequenceByteSize = ++seqLength * sizeof(int);
cudaMalloc((void**)&cudaSequence, sequenceByteSize);
cudaMemcpy(cudaSequence, sequence, sequenceByteSize, cudaMemcpyHostToDevice);
fillColumn<<< dim3(seqLength, 1), 1 >>>(cudaSequence, table, size, cap);
}
clock_t stop = clock();
double elapsed = ((double)(stop - start)) / CLOCKS_PER_SEC;
printf("Elapsed time: %.3fs\n", elapsed);
printf("curl is %d\n\nsequence = ", curl);
for(i = 0; i < seqLength; ++i){
printf("%d ", sequence[i]);
}
printf("\n\n");
cudaFree(cudaSequence);
}
return 0;
}
|
11,537 | #include <stdio.h>
__global__ void hello_cuda() {
printf("Hello Cuda!\n");
}
int main() {
hello_cuda<<<2,1>>>();;
cudaDeviceSynchronize();
cudaDeviceReset();
return 0;
}
|
11,538 | #include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <ctime>
#include <stdio.h>
#include <iostream>
#include <string>
__global__ void squareKernel(int* data);
int main(int argc, char** argv)
{
int* h_data;
int* d_data;
//количество квадратов + 1
int n = 10;
std::string name;
// выделяем page-locked память на хосте
// эту функцию лучше всего использовать экономно для выделения промежуточных областей для обмена данными между хостом и устройством.
cudaHostAlloc(&h_data, n * sizeof(int), cudaHostAllocPortable);
//cudaMemcpy(h_data, arr, n * sizeof(int), cudaMemcpyHostToDevice);
// выделяем память на устройстве
cudaMalloc(&d_data, n * sizeof(int));
dim3 block(512);
dim3 grid((n + block.x - 1) / block.x);
//grid - количество блоков
//block - размер блока
squareKernel<<<grid, block>>>(d_data);
//копируем данные с устройства (d_data) на хост (h_data)
cudaMemcpy(h_data, d_data, n * sizeof(int), cudaMemcpyDeviceToHost);
for (int j = 0; j < n; j++)
{
name += std::to_string(h_data[j]);
}
for (int j = 1; j < name.size()-1; j+=2)
{
std::cout << name[j] << name[j+1] << std::endl;
}
return 0;
}
__global__ void squareKernel(int* data)
{
//threadIdx – номер нити в блоке
//blockIdx – номер блока, в котором находится нить
//blockDim – размер блока
//глобальный индекс нити внутри сети
int i = blockIdx.x * blockDim.x + threadIdx.x;
data[i] = powf(2, threadIdx.x);
}
|
11,539 | #include <stdio.h>
#include <stdlib.h>
#include <iostream>
using namespace std;
__global__ void insert(int *a, int t){
int i = blockIdx.x*blockDim.x+threadIdx.x;
int cont = 0;
while(cont <= i*10000){
cont++;
}
if(i < t){
for(int k = 0; k < t-1; k++){
if(a[k] > a[k+1]){
int aux = a[k];
a[k] = a[k+1];
a[k+1] = aux;
}
}
}
}
void imp(int *a, int n){
for(int i = 0; i < n; i++){
cout << a[i]<<endl;
}
cout<<endl<<endl;
}
void cuda(int *a, int n){
int *array;
cudaMalloc((void**)&array, n * sizeof(int));
cudaMemcpy(array,a,n * sizeof(int),cudaMemcpyHostToDevice);
insert<<<1024,1>>>(array,n);
cudaMemcpy(a,array,n * sizeof(int),cudaMemcpyDeviceToHost);
cudaFree(array);
}
int main(){
int n;
cin >> n;
int a[n];
int *vec;
vec = (int*)malloc(n*sizeof(int));
for(int i = 0; i < n; i++){
cin >> a[i];
vec[i] = a[i];
}
cuda(vec,n);
imp(vec,n);
free(vec);
return 0;
}
|
11,540 | //fail
//--blockDim=256 --gridDim=1 --no-inline
#include <curand_kernel.h>
//#include <curand_mtgp32_host.h>
#include <stdio.h>
#define N 32 //16
__global__ void curand_test(curandStateMtgp32_t *state, float *A) {
if (threadIdx.x == 0) {
A[blockIdx.x] = curand(state);
}
}
|
11,541 | #include "includes.h"
__global__ void add_bias_kernel(float *output, float *biases, int batch, int filters, int spatial, int current_size)
{
const int index = blockIdx.x*blockDim.x + threadIdx.x;
if (index >= current_size) return;
int f = (index / spatial) % filters;
output[index] += biases[f];
} |
11,542 | float h_A[]= {
0.8703008385205668, 0.5830223508542586, 0.6083998850548107, 0.8613826609056426, 0.8941829119961082, 0.7440206703449481, 0.5236462909698196, 0.9306115221510955, 0.5561366958876625, 0.8324129440018487, 0.514491201400072, 0.6766741199881685, 0.6450739164628181, 0.6401033321099301, 0.5341903140138176, 0.5577902209866439, 0.7131968013795262, 0.6151141552692605, 0.5099160161273872, 0.5165272921725694, 0.9923824281911264, 0.866683304864855, 0.9499118813452812, 0.6260139601390774, 0.8117376439701834, 0.8957260953757002, 0.7792252752876919, 0.6849803937158705, 0.5599050198127209, 0.5720343013247882, 0.8746925802448947, 0.5140611196813153, 0.6660258279863636, 0.8874134916581521, 0.8528045716193791, 0.8510990789229385, 0.7603289536570277, 0.6534786924510203, 0.5842108204895216, 0.795017430404067, 0.7095826804606984, 0.6909501076685165, 0.6511638282553871, 0.6214561661384591, 0.781236229137249, 0.7320811943828842, 0.7540106051042632, 0.8704823216789586, 0.8662282025491246, 0.9803073980523442, 0.7472876531923529, 0.6140098729372864, 0.9179083540278892, 0.6130540109071678, 0.9675837183565494, 0.9608370583535455, 0.9989781937940019, 0.7203371426337121, 0.6351607563398054, 0.5899822461118807, 0.5610135378423229, 0.5620030542223522, 0.7458163475459259, 0.7391137404907409, 0.563372265823519, 0.5764965010246359, 0.5758511336406027, 0.5135419566952751, 0.8741115061451306, 0.6024297389544451, 0.8046107818322059, 0.7831092981519152, 0.9058258195486992, 0.8862822761425588, 0.5463550747673702, 0.690205661620356, 0.7440900559251067, 0.9303431788120698, 0.7066685869579924, 0.7849575984813206, 0.7636766871127134, 0.9666905070462338, 0.8748384027410168, 0.5425164755807429, 0.902066867847278, 0.8936977943143283, 0.9855649556870001, 0.9350372373598941, 0.8887206841200354, 0.9301770742633377, 0.6651625497923268, 0.5314231692003042, 0.5569755391156991, 0.8825524951505463, 0.506936524127362, 0.5717877797924347, 0.6626546281494299, 0.9962766856772274, 0.8241848628735429, 0.7722295247279554, 0.6645837504269616, 0.9300619249162243, 0.739673385606789, 0.6493903832926655, 0.6321316316110075, 0.562466511754744, 0.7373063865997558, 0.6462325201681133, 0.585334135339967, 0.8475435802653185, 0.5113468231925795, 0.9905797544259746, 0.65494520399653, 0.9391030281566615, 0.9753333051285997, 0.8438044478323983, 0.7571607284356825, 0.9414061227166093, 0.8260949048976964, 0.6275469720045876, 0.5116831351491651, 0.7478273530786146, 0.717397128106839, 0.5329487673387874, 0.5789701807507699, 0.8321621484350745, 0.5123750215881795, 0.5260552869058885, 0.5055504095418578, 0.6575127415125458, 0.8485979435669206, 0.6682944697332082, 0.9232634659431406, 0.9311580417183094, 0.8674713689473991, 0.9468696950849238, 0.723304294117563, 0.6295698468887327, 0.6075468779872333, 0.7170793415790715, 0.5006801654107043, 0.5466490373210209, 0.7350343057272216, 0.587720426381332, 0.8105431058908923, 0.8609374233573024, 0.5603379727088089, 0.5974046312409036, 0.8753699215183814, 0.9039474846994564, 0.8381857280744284, 0.7886881849394644, 0.6422837883768344, 0.8481540340132079, 0.7392102908773486, 0.9190171381993062, 0.5233986127733232, 0.8031756527584595, 0.7341318801457257, 0.9943557490354075, 0.7044125403493122, 0.9610739929024581, 0.8134049238834868, 0.8264221904648806, 0.7663114951943777, 0.5968705700827727, 0.6916991732450724, 0.8073466547950534, 0.5656790505912273, 0.8662639676562499, 0.804789212672504, 0.5295054095908969, 0.8302264926797329, 0.9670051069954286, 0.6352352934278249, 0.8750078906856709, 0.5115207144237994, 0.5080883045734175, 0.808641272550682, 0.9501114897854248, 0.9487084356437616, 0.5544772721200596, 0.7554642604972555, 0.5368559538739465, 0.7682318140277173, 0.7709292774015644, 0.5117632083501833, 0.9058911085150807, 0.9367171683467808, 0.5058214221619158, 0.7662701297440252, 0.5585489310802079, 0.6790429769753821, 0.8048476918467969, 0.9694933621526414, 0.9510489437573554, 0.9069867215290965, 0.8917630857688341, 0.7050014215821283, 0.9983026324967869, 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0.8701631045968914, 0.9221562992255901, 0.5406144532478712, 0.7405735479600584, 0.604882994483317, 0.7526975896554873, 0.7210608556917464, 0.8442936410728015, 0.7933964782246106, 0.5175984442863056, 0.8397753935443906, 0.8037712814582727, 0.5775395216828477, 0.5347654277108655, 0.9182569990332552, 0.7682746359493394, 0.5527753468452743, 0.934968113546614, 0.8966836900036901, 0.5531799855505113, 0.7601655495402616, 0.6393086895743154, 0.9630072710557924, 0.8258115296522927, 0.5289018423304995, 0.5740693938732013, 0.7886407785826957, 0.5396899452440803, 0.7316719826848441, 0.6973772539373739, 0.8995471674295412, 0.6917511536886838, 0.8381128091372896, 0.9103735236820578, 0.8849916309332329, 0.7779722835326615, 0.7561934429082124, 0.5370561442815114, 0.9948201260697653, 0.8551367067013731, 0.6708186205917919, 0.7609804180398888, 0.5475055268782227, 0.6946087012445713, 0.8204337786493683, 0.5214584970578318, 0.790733780794133, 0.5763839246779139, 0.6915332219846404, 0.7349431251974285, 0.778541451214618, 0.560489098906591, 0.9352956295886861, 0.917698570788454, 0.8119606517798484, 0.9618445972022055, 0.5641718571734805, 0.9087100227534834, 0.8234211886127889, 0.5337593140784956, 0.954522371597001, 0.765260940368492, 0.6444084047920458, 0.7227918814895826, 0.5756853539613791, 0.5219623370377682, 0.5634636439897545, 0.9088505381639406, 0.9634653776702033, 0.5313106592396346, 0.5988676121769283, 0.6036461550601697, 0.5942489981752779, 0.8982474977008328, 0.7004117802872798, 0.550066153019497, 0.9426320976253669, 0.9768943986449696, 0.9189987961550281, 0.7875347670239541, 0.6825884087669986, 0.5623362622886057, 0.6731668226191697, 0.8298990531148925, 0.5221371178221904, 0.886939831978423, 0.7241573307783613, 0.732291797538378, 0.5835273238437941, 0.5935535215459786, 0.7147039292048408, 0.9988140714074594, 0.8513286732444103, 0.6795537303634238, 0.7433098071889848, 0.7190276735853841, 0.6401156464264536, 0.7998585663361686, 0.7016350384240304, 0.7776422144933259, 0.8659516010385554, 0.9125176800408258, 0.7211453904716273, 0.6631422831848448, 0.9688923024436447, 0.8777787779497979, 0.6064045800505209, 0.8420966620126753, 0.9074017818524673, 0.993136751749861, 0.8759385669604616, 0.8428178272509916, 0.9752663899716211, 0.5583865501839871, 0.5767416621593746, 0.6970907681197195, 0.9424792441201464, 0.6541191410311507, 0.8758324921232213, 0.7885141178703442, 0.6608849361284597, 0.7943053284937712, 0.5808000079279569, 0.5615292771878642, 0.5360004952667607, 0.5868493187682227, 0.5709221514473821, 0.8333881636999886, 0.9596738566704955, 0.8661742599148584, 0.5918257467438204, 0.8566644773153498, 0.6058780192717242, 0.7712567068234952, 0.582602400947668, 0.7894572207000401, 0.8362173315968828, 0.6945771397093927, 0.8350231677436559, 0.8089280534720436, 0.8320356701458125, 0.8359434820266067, 0.7316724408293962, 0.6030141521544925, 0.8454360913302711, 0.886786933900616, 0.613687533023538, 0.7071495803740655, 0.7985280728770503, 0.7884232872887298, 0.7382626075614361, 0.8071447089046591, 0.7672650498678366, 0.6891188796363038, 0.8994186783587081, 0.5522348491377266, 0.651517299582953, 0.5163235884358885, 0.5829648164006656, 0.9317619151925713, 0.5558129065722536, 0.5758740866391974, 0.5157015021053635, 0.6791346460208185, 0.7051557155044865, 0.6224841239054186, 0.7112198839662512, 0.8311137443702443, 0.5215857871631843, 0.984775918750703, 0.6584311480033453, 0.6136104660077502, 0.5197102460968769, 0.8562149221278081, 0.6257513013737506, 0.6489124874374824, 0.9007116955705913, 0.6299387487125869, 0.5424002023919852, 0.9373085373476697, 0.6904737194566772, 0.859798673718188, 0.996021012810842, 0.712836782901458, 0.9295362923036348, 0.649736769092248, 0.7561332071491399, 0.856411508596638, 0.701603004068744, 0.5431650132381681, 0.6484297504510814, 0.915420199624522, 0.7097888841122453, 0.92888997139864, 0.6744863435984374, 0.9963922400301479, 0.6106298179907392, 0.9995756483145419, 0.8288166164247996, 0.5715254403057246, 0.5756370943881204, 0.5354235272892021, 0.8499320297821363, 0.9284039987274558, 0.8669127536450824, 0.85119368027696, 0.7159796894441488, 0.7256654734101653, 0.9726054114648711, 0.8920613133237958, 0.5129946139489945, 0.5043170744585945, 0.6540154557044473, 0.5640202429286866, 0.8592109963828602, 0.5425870430970059, 0.8090798283963888, 0.7275210151223548, 0.9081513127587374, 0.5079950900200697, 0.678217087530693, 0.6042751954701742, 0.5339257739003822, 0.8370955713421515, 0.6007089756109133, 0.527331814781791, 0.5393718337493731, 0.6882417892304605, 0.6061217291598857, 0.841047316582992, 0.6653755361951291, 0.5369340870207107, 0.8055140695589245, 0.5577177839796112, 0.661663953144831, 0.7578657044228352, 0.7720631140326626, 0.916388296087548, 0.6819388491303084, 0.5341458688678118, 0.9180827411848338, 0.6180633817102084, 0.5287752852076226, 0.7659165132869787, 0.6635512390298374, 0.9090490228492586, 0.6247634038709399, 0.5955403022328187, 0.7554969808616316, 0.7277481125930325, 0.635581681039719, 0.5577155587732148, 0.642552250521873, 0.5034973883387863, 0.6689595403980242, 0.9102031570926934, 0.9821332393931645, 0.5665732205296529, 0.6544491816524168, 0.550149151897732, 0.7468778807499408, 0.506621565040486, 0.5644617212123939, 0.7135067378656577, 0.581867786005896, 0.6355115814875529, 0.8964698216815656, 0.6056005575138628, 0.8003770573725082, 0.8623155202790562, 0.6360242935541907, 0.5127024880539524, 0.9223453833208367, 0.7730579765914024, 0.9276059223403585, 0.6198980592295298, 0.7677315021258284, 0.6369371524441649, 0.5151144132032299, 0.67877992146738, 0.5236610938509809, 0.6845228616777211, 0.7482134861411369, 0.6220092375556646, 0.9320581625613988, 0.5582223436261176, 0.5291599398426079, 0.8794293318475384, 0.9623154718959321, 0.5805512763742693, 0.570114512790617, 0.6002874148819002, 0.6529338877792464, 0.8797606824087343, 0.7001052767518501, 0.9492669106774123, 0.9651818659533443, 0.6219186635568451, 0.7082139230951436, 0.735597895637552, 0.7588713638018113, 0.7199591947137649, 0.7383663989882191, 0.879959327205587, 0.8398242671122316, 0.6660936726020321, 0.5697076899768438, 0.9562345136064265, 0.8244032552686779, 0.6962019949580223, 0.9020372873947551, 0.6445420030491404, 0.5909227941739021, 0.8261094051834874, 0.8867580479083659, 0.67808317991132, 0.5787048292018399, 0.7314583035939191, 0.8684634960130971, 0.6960943299986524, 0.7469723092121212, 0.920947874435188, 0.5260603135731832, 0.9075851857153867, 0.7309782524825255, 0.7791553799511519, 0.5732282978129067, 0.7208459942541006, 0.648551152809431, 0.8102736795799039, 0.5938720599699392, 0.7110863050384777, 0.7602550204565839, 0.5167961543708668, 0.9042783259278973, 0.8947946397740892, 0.7153464282297596, 0.7058447670382244, 0.7083097135616594, 0.5735061118662259, 0.9270523310803889, 0.7936783076628142, 0.5764272784792284, 0.5759728785799058, 0.6497693566495168, 0.9640704315928392, 0.717485087262488, 0.5321850228256703, 0.55077004451464, 0.515703903739225, 0.7832489090995033, 0.5879419561423006, 0.9055172380110981, 0.5475741136788416, 0.9357918381920307, 0.5238391022332063, 0.7677271832902652, 0.8448383388383156, 0.971823256327859, 0.7984362920715528, 0.943354570821429, 0.7543368391721801, 0.7100476126632593, 0.9264446908795596, 0.9155148088732479, 0.8233057683067806, 0.6196669459649813, 0.7950937799858881, 0.6661132578782377, 0.7961532517558784, 0.8724722600414099, 0.7319283950717758, 0.8588610247526003, 0.763951026366823, 0.8437990709336709, 0.6113195585978706, 0.8815614256913455, 0.6703239020714293, 0.8907386666011253, 0.6476374825400418, 0.5408610717915963, 0.858188357454277, 0.576190044345319, 0.5647142943230925, 0.5130217756988955, 0.8281097518307314, 0.6800365617389641, 0.7316907134716484, 0.7830249834462892, 0.7362145251947175, 0.9235767146309295, 0.925781098798704, 0.93054730158938, 0.7782650069212893, 0.7734247603684035, 0.5867839668746999, 0.8966760516478389, 0.7815672199672393, 0.5058733725765672, 0.56154494880356, 0.7540679635499328, 0.5547110796720629, 0.5350185750717871, 0.6131662059154023, 0.7681100335249451, 0.8679864102817695, 0.9058515327523387, 0.9719033300568207, 0.9307487036412736, 0.9754011740791838, 0.7271629604535517, 0.7232288911704399, 0.7797392521568081, 0.5621229267537466, 0.6785914275499663, 0.7594742118719564, 0.7230245711429681, 0.5167062106428816, 0.7300674534401822, 0.8350141125197552, 0.882012541519121, 0.5638807118988354, 0.682077072559901, 0.5146395999530735, 0.7303960550884288, 0.5613958723453474, 0.9664761573715046, 0.9400982928454455, 0.8175487459602373, 0.8942536611698881, 0.6779655097661768, 0.588274121240785, 0.9116304036809115, 0.6097252805689932, 0.785971725242335, 0.824088945179634, 0.8915189494997077, 0.6263759001512128, 0.9303168939537947, 0.7630623137555621, 0.5617486134257532, 0.6647053866427384, 0.8195119604783078, 0.5767484839660861, 0.5972580882252305, 0.9457776642491572, 0.7093100117794648, 0.7503348148914663, 0.5772005988059714, 0.6783582703884462, 0.9202966926412974, 0.8551718431661358, 0.9342683139583082, 0.6296882970476887, 0.6913648253727596, 0.5117997134915164, 0.6868535685682925, 0.5319609743345299, 0.7146306555062969, 0.8322946930525084, 0.888113546951495, 0.6590602005044548, 0.5920614673158332, 0.704054204043838, 0.5455701180442406, 0.9258885769052628, 0.6797729090454729, 0.6922999638584584, 0.9572701716497398, 0.87806940022058, 0.5280811538917445, 0.6019796893774914, 0.963689130928741, 0.9386869388417498, 0.8483676076203218, 0.7792041512535732, 0.5294051984911853, 0.9321078067878124, 0.6082343298517799, 0.9811233024558712, 0.7509850242680562, 0.7094138821774265, 0.6040600790353658, 0.8346879167623289, 0.7986387266290232, 0.9172431018606078, 0.9802981016625119, 0.6474662548295259, 0.8008184256923574, 0.6847506344804077, 0.7655153546646198, 0.9506252102246102, 0.9926248345457749, 0.5204269873583328, 0.7493569514572851, 0.9492020366971186, 0.6523541912205671, 0.8841574891366185, 0.5323415608930206, 0.818220811505691, 0.943136555907974, 0.8162730312895192, 0.935908262603203, 0.6840115063531789, 0.6069175007801948, 0.9557813350020045, 0.7029151060294543, 0.8752218834282897, 0.977501516087967, 0.791763342614144, 0.6053778614579438, 0.740938205468127, 0.6286644676604509, 0.9357501972808621, 0.6674106315768302, 0.5856497075189119, 0.8595766887236846, 0.8791912396251969, 0.5118020117328259, 0.6846057369896454, 0.9719248298270948, 0.7830763320312788, 0.812443627319053, 0.9377380565529794, 0.7038465783825628, 0.7085229760474904, 0.7191196697401199, 0.5577016700465968, 0.9568146999632456, 0.8452074970569807, 0.9871825842169616, 0.5673825406903736, 0.6368899519205016, 0.8238230603139087, 0.9641942175876879, 0.8139870096426347, 0.7490533457451586, 0.6603116665712551, 0.717599399275676, 0.6668273197166483, 0.7971722474793008, 0.8755998129979057, 0.6796125948873242, 0.6517522424389128, 0.9174116623712378, 0.8924920223818841, 0.6294981039144696, 0.6704254994598006, 0.6616895751937479, 0.9610187899715341, 0.9153597832074761, 0.8769402179699102, 0.7141690236787972, 0.6098759885282432, 0.5505782882920658, 0.9470987047373391, 0.8959947917992633, 0.5925831702146165, 0.81005396809396, 0.7050491484023316, 0.7953298377874376, 0.7313117550199786, 0.5829591537483925, 0.7625814634929926, 0.9326540097905576, 0.6017671123856034, 0.9622781639212756, 0.9705647952363794, 0.676583301500346, 0.5347121747193513, 0.5494555362384561, 0.717459660947703, 0.5809958479692001, 0.6628705780550811, 0.8719521170081713, 0.6550599895126363, 0.6392799723824676, 0.5797518726134623, 0.5033738326279747, 0.5712945649075447, 0.8297103775538508, 0.7384944353329456, 0.7959202464178772, 0.9934079490098513, 0.7720738679416173, 0.5861403011094641, 0.8904821426289673, 0.9751532171546808, 0.9036252511974614, 0.9343768721022617, 0.6578263175348066, 0.5532772385674052, 0.6626143085887388, 0.7606842210537139, 0.562727079325283, 0.6066481305011691, 0.5277167606193638, 0.812186158720081, 0.7534279815250188, 0.9630773236983086, 0.7170186755216444, 0.5902417299494132, 0.7566738669223376, 0.933767261558387, 0.8838418700396331, 0.7942311196499576, 0.5056840829942562, 0.5777054710467578, 0.7909333348779768, 0.7091824372526777, 0.8849784764911325, 0.6487640883830472, 0.8615138073819103, 0.602243095061846, 0.8970795071038671, 0.719233114007221, 0.9109242482160438, 0.5491545785448109, 0.5960646733300188, 0.5033309472917256, 0.761227074817893, 0.5846391987884396, 0.8448403882195767, 50000.0};
int h_B[]= {
3, 5, 7, 9, 11, 13, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66, 68, 70, 72, 74, 76, 78, 80, 82, 84, 86, 88, 90, 92, 94, 96, 98, 100, 102, 104, 106, 108, 110, 112, 114, 116, 118, 120, 122, 124, 126, 128, 130, 132, 134, 136, 138, 140, 142, 145, 147, 149, 151, 153, 155, 157, 159, 161, 163, 165, 167, 169, 171, 173, 175, 178, 180, 182, 184, 186, 188, 190, 192, 194, 196, 198, 200, 202, 204, 206, 208, 210, 212, 214, 216, 218, 220, 222, 224, 226, 228, 230, 232, 234, 236, 238, 240, 242, 244, 246, 248, 251, 253, 255, 257, 259, 261, 263, 265, 267, 269, 271, 273, 275, 277, 279, 281, 283, 285, 287, 289, 291, 293, 295, 297, 299, 301, 303, 305, 307, 309, 311, 313, 315, 317, 319, 321, 323, 325, 327, 329, 331, 333, 335, 337, 339, 341, 343, 345, 347, 349, 351, 353, 355, 357, 359, 361, 363, 365, 367, 369, 371, 373, 375, 377, 379, 381, 383, 385, 387, 389, 391, 393, 395, 397, 399, 401, 403, 405, 407, 409, 411, 413, 415, 417, 419, 421, 423, 425, 427, 429, 431, 433, 435, 437, 439, 441, 443, 445, 447, 449, 451, 453, 455, 457, 459, 461, 463, 465, 467, 469, 471, 473, 476, 478, 480, 482, 485, 487, 489, 491, 493, 495, 497, 499, 501, 503, 505, 507, 509, 511, 513, 515, 517, 519, 521, 523, 525, 527, 529, 531, 533, 535, 537, 539, 541, 543, 545, 547, 549, 551, 553, 555, 557, 559, 561, 563, 565, 567, 569, 571, 573, 575, 577, 579, 581, 583, 585, 587, 589, 591, 593, 595, 597, 599, 601, 603, 605, 607, 609, 611, 613, 615, 617, 619, 621, 623, 625, 627, 629, 631, 633, 635, 637, 639, 642, 644, 647, 649, 651, 653, 655, 657, 659, 661, 663, 665, 668, 670, 672, 674, 677, 679, 681, 683, 687, 689, 691, 693, 695, 697, 700, 702, 704, 706, 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, 768, 770, 773, 775, 778, 780, 782, 784, 786, 788, 790, 792, 795, 797, 800, 802, 805, 807, 809, 811, 813, 815, 817, 819, 822, 824, 827, 829, 831, 833, 836, 838, 840, 842, 846, 848, 850, 852, 854, 856, 858, 860, 862, 864, 867, 869, 872, 874, 877, 879, 881, 883, 885, 887, 890, 892, 894, 896, 898, 900, 903, 905, 908, 910, 913, 915, 918, 920, 923, 925, 928, 930, 933, 935, 938, 940, 942, 944, 946, 948, 951, 953, 955, 957, 959, 961, 964, 966, 968, 970, 972, 974, 977, 979, 982, 984, 987, 989, 992, 994, 996, 998, 1001, 1003, 1005, 1007, 1010, 1012, 1016, 1018, 1020, 1022, 1025, 1027, 1030, 1032, 1035, 1037, 1040, 1042, 1045, 1047, 1050, 1052, 1055, 1057, 1060, 1062, 1065, 1067, 1070, 1072, 1075, 1077, 1080, 1082, 1085, 1087, 1090, 1092, 1095, 1097, 1100, 1102, 1104, 1106, 1108, 1110, 1113, 1115, 1118, 1120, 1123, 1125, 1128, 1130, 1133, 1135, 1138, 1140, 1143, 1145, 1148, 1150, 1153, 1155, 1158, 1160, 1163, 1165, 1168, 1170, 1172, 1174, 1176, 1178, 1181, 1183, 1186, 1188, 1191, 1193, 1196, 1198, 1200, 1202, 1204, 1206, 1208, 1210, 1212, 1214, 1216, 1218, 1220, 1222, 1224, 1226, 1228, 1230, 1232, 1234, 1236, 1238, 1240, 1242, 1244, 1246, 1248, 1250, 1252, 1254, 1256, 1258, 1260, 1262, 1264, 1266, 1268, 1270, 1272, 1274, 1276, 1278, 1280, 1282, 1284, 1286, 1289, 1291, 1293, 1295, 1297, 1299, 1301, 1303, 1305, 1307, 1309, 1311, 1313, 1315, 1317, 1319, 1321, 1323, 1326, 1328, 1330, 1332, 1335, 1337, 1339, 1341, 1343, 1345, 1347, 1349, 1351, 1353, 1355, 1357, 1359, 1361, 1363, 1365, 1367, 1369, 1371, 1373, 1375, 1377, 1379, 1381, 1383, 1385, 1389, 1391, 1393, 1395, 1397, 1399, 1401, 1403, 1405, 1407, 1409, 1411, 1413, 1415, 1418, 1420, 1423, 1425, 1428, 1430, 1433, 1435, 1438, 1440, 1443, 1445, 1448, 1450, 1453, 1455, 1457, 1459, 1461, 1463, 1466, 1468, 1471, 1473, 1475, 1477, 1479, 1481, 1483, 1485, 1488, 1490, 1492, 1494, 1497, 1499, 1501, 1503, 1506, 1508, 1512, 1514, 1516, 1518, 1520, 1522, 1525, 1527, 1530, 1532, 1537, 1539, 1541, 1543, 1545, 1547, 1550, 1552, 1555, 1557, 1560, 1562, 1565, 1567, 1569, 1571, 1573, 1575, 1578, 1580, 1583, 1585, 1588, 1590, 1593, 1595, 1598, 1600, 1603, 1605, 1608, 1610, 1613, 1615, 1618, 1620, 1623, 1625, 1628, 1630, 1633, 1635, 1637, 1639, 1641, 1643, 1645, 1647, 1649, 1651, 1653, 1655, 1657, 1659, 1661, 1663, 1665, 1667, 1669, 1671, 1673, 1675, 1677, 1679, 1681, 1683, 1685, 1687, 1689, 1691, 1693, 1695, 1697, 1699, 1701, 1703, 1705, 1707, 1709, 1711, 1713, 1715, 1717, 1719, 1721, 1723, 1725, 1727, 1729, 1731, 1733, 1735, 1738, 1740, 1742, 1744, 1747, 1749, 1752, 1754, 1757, 1759, 1762, 1764, 1767, 1769, 1772, 1774, 1777, 1779, 1781, 1783, 1785, 1787, 1790, 1792, 1795, 1797, 1800, 1802, 1805, 1807, 1810, 1812, 1815, 1817, 1820, 1822, 1825, 1827, 1830, 1832, 1835, 1837, 1840, 1842, 1845, 1847, 1850, 1852, 1855, 1857, 1860, 1862, 1865, 1867, 1869, 1871, 1873, 1875, 1878, 1880, 1883, 1885, 1888, 1890, 1893, 1895, 1898, 1900, 1903, 1905, 1910, 1912, 1914, 1916, 1918, 1920, 1922, 1924, 1926, 1928, 1930, 1932, 1934, 1936, 1938, 1940, 1943, 1945, 1948, 1950, 1952, 1954, 1956, 1958, 1960, 1962, 1964, 1966, 1968, 1970, 1972, 1974, 1976, 1978, 1980, 1982, 1984, 1986, 1988, 1990, 1992, 1994, 1996, 1998, 2000, 2002, 2004, 2006, 2008, 2010, 2012, 2014, 2016, 2018, 2020, 2022, 2024, 2026, 2028, 2030, 2032, 2034, 2036, 2038, 2040, 2042, 2044, 2046, 2048, 2050, 2052, 2054, 2056, 2058, 2060, 2062, 2064, 2066, 2068, 2070, 2072, 2074, 2076, 2078, 2080, 2082, 2084, 2086, 2088, 2090, 2092, 2094, 2096, 2098, 2100, 2102, 2104, 2106, 2109, 2111, 2113, 2115, 2117, 2119, 2121, 2123, 2125, 2127, 2129, 2131, 2133, 2135, 2137, 2139, 2141, 2143, 2145, 2147, 2149, 2151, 2153, 2155, 2157, 2159, 2161, 2163, 2165, 2167, 2169, 2171, 2174, 2176, 2178, 2180, 2182, 2184, 2186, 2188, 2190, 2192, 2194, 2196, 2199, 2201, 2203, 2205, 2208, 2210, 2212, 2214, 2217, 2219, 2221, 2223, 2226, 2228, 2230, 2232, 2234, 2236, 2238, 2240, 2242, 2244, 2246, 2248, 2250, 2252, 2254, 2256, 2258, 2260, 2262, 2264, 2266, 2268, 2270, 2272, 2274, 2276, 2278, 2280, 2282, 2284, 2287, 2289, 2292, 2294, 2296, 2298, 2300, 2302, 2304, 2306, 2308, 2310, 2312, 2314, 2316, 2318, 2320, 2322, 2324, 2326, 2328, 2330, 2332, 2334, 2336, 2338, 2340, 2342, 2344, 2346, 2348, 2350, 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, 2440, 2442, 2444, 2446, 2448, 2450, 2452, 2454, 2456, 2458, 2460, 2462, 2464, 2466, 2468, 2470, 2473, 2475, 2477, 2479, 2481, 2483, 2485, 2487, 2489, 2491, 2493, 2495, 2497, 2499, 2501, 2503, 2505, 2507, 2509, 2511, 2513, 2515, 2517, 2519, 2521, 2523, 2525, 2527, 2529, 2531, 2533, 2535, 2537, 2539, 2541, 2543, 2545, 2547, 2549, 2551, 2553, 2555, 2557, 2559, 2562, 2564, 2566, 2568, 2571, 2573, 2575, 2577, 2579, 2581, 2583, 2585, 2587, 2589, 2592, 2594, 2596, 2598, 2601, 2603, 2605, 2607, 2610, 2612, 2615, 2617, 2621, 2623, 2625, 2627, 2629, 2631, 2633, 2635, 2637, 2639, 2641, 2643, 2645, 2647, 2649, 2651, 2653, 2655, 2658, 2660, 2662, 2664, 2667, 2669, 2672, 2674, 2677, 2679, 2682, 2684, 2687, 2689, 2691, 2693, 2695, 2697, 2700, 2702, 2705, 2707, 2710, 2712, 2715, 2717, 2720, 2722, 2725, 2727, 2729, 2731, 2733, 2735, 2738, 2740, 2743, 2745, 2748, 2750, 2753, 2755, 2758, 2760, 2763, 2765, 2768, 2770, 2773, 2775, 2778, 2780, 2783, 2785, 2788, 2790, 2793, 2795, 2798, 2800, 2803, 2805, 2808, 2810, 2813, 2815, 2817, 2819, 2821, 2823, 2826, 2828, 2831, 2833, 2836, 2838, 2841, 2843, 2846, 2848, 2851, 2853, 2856, 2858, 2861, 2863, 2865, 2867, 2869, 2871, 2874, 2876, 2879, 2881, 2884, 2886, 2889, 2891, 2894, 2896, 2899, 2901, 2904, 2906, 2909, 2911, 2914, 2916, 2919, 2921, 2924, 2926, 2929, 2931, 2934, 2936, 2939, 2941, 2944, 2946, 2949, 2951, 2954, 2956, 2959, 2961, 2964, 2966, 2969, 2971, 2974, 2976, 2979, 2981, 2984, 2986, 2989, 2991, 2994, 2996, 2999, 3001, 3004, 3006, 3009, 3011, 3013, 3015, 3017, 3019, 3022, 3024, 3027, 3029, 3032, 3034, 3037, 3039, 3041, 3043, 3046, 3048, 3051, 3053, 3059, 3061, 3063, 3065, 3067, 3069, 3072, 3074, 3077, 3079, 3082, 3084, 3087, 3089, 3092, 3094, 3097, 3099, 3101, 3103, 3105, 3107, 3109, 3111, 3113, 3115, 3117, 3119, 3122, 3124, 3127, 3129, 3132, 3134, 3137, 3139, 3142, 3144, 3147, 3149, 3155, 3157, 3159, 3161, 3163, 3165, 3168, 3170, 3172, 3174, 3176, 3178, 3181, 3183, 3186, 3188, 3194, 3196, 3199, 3201, 3204, 3206, 3208, 3210, 3213, 3215, 3217, 3219, 3224, 3226, 3228, 3230, 3232, 3234, 3236, 3238, 3241, 3243, 3245, 3247, 3249, 3251, 3254, 3256, 3259, 3261, 3264, 3266, 3269, 3271, 3273, 3275, 3277, 3279, 3281, 3283, 3285, 3287, 3290, 3292, 3295, 3297, 3300, 3302, 3305, 3307, 3310, 3312, 3315, 3317, 3320, 3322, 3325, 3327, 3330, 3332, 3334, 3336, 3338, 3340, 3342, 3344, 3346, 3348, 3351, 3353, 3356, 3358, 3360, 3362, 3364, 3366, 3368, 3370, 3372, 3374, 3376, 3378, 3380, 3382, 3385, 3387, 3389, 3391, 3393, 3395, 3398, 3400, 3403, 3405, 3407, 3409, 3411, 3413, 3416, 3418, 3421, 3423, 3426, 3428, 3431, 3433, 3436, 3438, 3441, 3443, 3446, 3448, 3451, 3453, 3456, 3458, 3460, 3462, 3464, 3466, 3468, 3470, 3472, 3474, 3476, 3478, 3480, 3482, 3484, 3486, 3488, 3490, 3492, 3494, 3497, 3499, 3501, 3503, 3505, 3507, 3510, 3512, 3515, 3517, 3519, 3521, 3523, 3525, 3528, 3530, 3533, 3535, 3538, 3540, 3543, 3545, 3547, 3549, 3551, 3553, 3556, 3558, 3561, 3563, 3566, 3568, 3571, 3573, 3576, 3578, 3582, 3584, 3586, 3588, 3593, 3595, 3598, 3600, 3603, 3605, 3608, 3610, 3613, 3615, 3617, 3619, 3622, 3624, 3627, 3629, 3641, 3643, 3646, 3648, 3651, 3653, 3656, 3658, 3661, 3663, 3665, 3667, 3669, 3671, 3674, 3676, 3679, 3681, 3684, 3686, 3689, 3691, 3694, 3696, 3699, 3701, 3703, 3705, 3708, 3710, 3713, 3715, 3721, 3723, 3725, 3727, 3729, 3731, 3734, 3736, 3739, 3741, 3744, 3746, 3749, 3751, 3753, 3755, 3757, 3759, 3762, 3764, 3767, 3769, 3772, 3774, 3777, 3779, 3781, 3783, 3785, 3787, 3790, 3792, 3795, 3797, 3800, 3802, 3805, 3807, 3810, 3812, 3815, 3817, 3820, 3822, 3825, 3827, 3829, 3831, 3834, 3836, 3839, 3841, 3847, 3849, 3852, 3854, 3857, 3859, 3862, 3864, 3867, 3869, 3872, 3874, 3877, 3879, 3882, 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4277, 4279, 4281, 4283, 4285, 4287, 4289, 4291, 4293, 4295, 4297, 4299, 4301, 4303, 4305, 4307, 4309, 4311, 4313, 4315, 4317, 4319, 4321, 4323, 4325, 4327, 4329, 4331, 4333, 4335, 4337, 4339, 4341, 4343, 4345, 4347, 4349, 4351, 4353, 4355, 4357, 4359, 4361, 4363, 4365, 4367, 4369, 4371, 4373, 4375, 4377, 4379, 4381, 4383, 4385, 4387, 4389, 4391, 4393, 4395, 4397, 4399, 4401, 4403, 4405, 4407, 4409, 4411, 4413, 4415, 4417, 4419, 4421, 4424, 4426, 4428, 4430, 4433, 4435, 4437, 4439, 4442, 4444, 4446, 4448, 4451, 4453, 4456, 4458, 4460, 4462, 4464, 4466, 4468, 4470, 4472, 4474, 4476, 4478, 4480, 4482, 4484, 4486, 4488, 4490, 4492, 4494, 4496, 4498, 4500, 4502, 4504, 4506, 4508, 4510, 4512, 4514, 4516, 4518, 4521, 4523, 4525, 4527, 4530, 4532, 4534, 4536, 4538, 4540, 4542, 4544, 4546, 4548, 4550, 4552, 4554, 4556, 4558, 4560, 4562, 4564, 4566, 4568, 4570, 4572, 4575, 4577, 4579, 4581, 4584, 4586, 4589, 4591, 4593, 4595, 4597, 4599, 4601, 4603, 4606, 4608, 4610, 4612, 4617, 4619, 4621, 4623, 4625, 4627, 4630, 4632, 4635, 4637, 4640, 4642, 4645, 4647, 4650, 4652, 4655, 4657, 4660, 4662, 4665, 4667, 4670, 4672, 4675, 4677, 4680, 4682, 4685, 4687, 4689, 4691, 4694, 4696, 4699, 4701, 4707, 4709, 4711, 4713, 4715, 4717, 4720, 4722, 4725, 4727, 4730, 4732, 4735, 4737, 4740, 4742, 4745, 4747, 4750, 4752, 4754, 4756, 4758, 4760, 4763, 4765, 4768, 4770, 4773, 4775, 4778, 4780, 4782, 4784, 4786, 4788, 4790, 4792, 4794, 4796, 4798, 4800, 4802, 4804, 4806, 4808, 4810, 4812, 4814, 4816, 4818, 4820, 4822, 4824, 4826, 4828, 4830, 4832, 4834, 4836, 4838, 4840, 4842, 4844, 4847, 4849, 4851, 4853, 4855, 4857, 4860, 4862, 4865, 4867, 4870, 4872, 4875, 4877, 4879, 4881, 4884, 4886, 4888, 4890, 4894, 4896, 4899, 4901, 4904, 4906, 4909, 4911, 4913, 4915, 4918, 4920, 4922, 4924, 4928, 4930, 4933, 4935, 4938, 4940, 4943, 4945, 4948, 4950, 4953, 4955, 4958, 4960, 4962, 4964, 4967, 4969, 4972, 4974, 4980, 4982, 4984, 4986, 4989, 4991, 4993, 4995, 4998, 5000, 5004, 5006, 5008, 5010, 5013, 5015, 5018, 5020, 5023, 5025, 5028, 5030, 5032, 5034, 5037, 5039, 5042, 5044, 5047, 5049, 5051, 5053, 5055, 5057, 5060, 5062, 5065, 5067, 5070, 5072, 5075, 5077, 5080, 5082, 5085, 5087, 5090, 5092, 5095, 5097, 5099, 5101, 5103, 5105, 5108, 5110, 5113, 5115, 5118, 5120, 5123, 5125, 5127, 5129, 5131, 5133, 5136, 5138, 5141, 5143, 5146, 5148, 5151, 5153, 5155, 5157, 5159, 5161, 5163, 5165, 5167, 5169, 5171, 5173, 5175, 5177, 5179, 5181, 5183, 5185, 5188, 5190, 5193, 5195, 5198, 5200, 5202, 5204, 5206, 5208, 5211, 5213, 5215, 5217, 5219, 5221, 5224, 5226, 5228, 5230, 5232, 5234, 5237, 5239, 5242, 5244, 5247, 5249, 5252, 5254, 5257, 5259, 5262, 5264, 5267, 5269, 5272, 5274, 5276, 5278, 5280, 5282, 5285, 5287, 5289, 5291, 5293, 5295, 5297, 5299, 5301, 5303, 5305, 5307, 5309, 5311, 5313, 5315, 5317, 5319, 5321, 5323, 5325, 5327, 5330, 5332, 5334, 5336, 5339, 5341, 5344, 5346, 5352, 5354, 5356, 5358, 5360, 5362, 5365, 5367, 5370, 5372, 5375, 5377, 5380, 5382, 5384, 5386, 5388, 5390, 5392, 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38251, 37622, 37620, 37936, 38179, 38179, 38198, 38254, 37622, 37620, 37936, 38256, 38258, 38262, 38264, 38266, 38270, 38274, 37658, 38279, 37660, 38284, 38288, 38261, 38290, 38269, 38291, 38273, 38278, 38283, 38292, 38287, 38293, 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, 38409, 38410, 38038, 38412, 38040, 38414, 38416, 38421, 38422, 38051, 38424, 38053, 38426, 38428, 38433, 38434, 38064, 38436, 38066, 38438, 38440, 38442, 38073, 38445, 38448, 38451, 38453, 38455, 38458, 38461, 38471, 38485, 38486, 38498, 38512, 38514, 38519, 38521, 38523, 38525, 38526, 38530, 38532, 38535, 38538, 38543, 38544, 38207, 38546, 38209, 38548, 38550, 38564, 38565, 38469, 38467, 38465, 38475, 38473, 38477, 38481, 38479, 38482, 38463, 38490, 38496, 38494, 38492, 38508, 38506, 38509, 38511, 38518, 38241, 37601, 38572, 38573, 38574, 38469, 38467, 38465, 38475, 38404, 38402, 38477, 38481, 38479, 38482, 38463, 38490, 38244, 37608, 38576, 38577, 38578, 37607, 38034, 38408, 38047, 38420, 38060, 38432, 38469, 38467, 38465, 38475, 38473, 38477, 38481, 38479, 38482, 38463, 38490, 38496, 38494, 38492, 38508, 38506, 38509, 38511, 38518, 37608, 38579, 38452, 38457, 38580, 38462, 38203, 38542, 38469, 38467, 38465, 38475, 38473, 38477, 38481, 38479, 38482, 38463, 38490, 38496, 38494, 38492, 38511, 38518, 38247, 37247, 38582, 38583, 38584, 38469, 38467, 38465, 38475, 38473, 38477, 38481, 38479, 38482, 38484, 38490, 38496, 38494, 38492, 38502, 38500, 38504, 38508, 38506, 38509, 38511, 38518, 38250, 37248, 38586, 38587, 38588, 38534, 38589, 38539, 38174, 38542, 38175, 38534, 38590, 38203, 38542, 38534, 38591, 38539, 38203, 38542, 38555, 38553, 38556, 38560, 38558, 38561, 38563, 38569, 38253, 37932, 38593, 38594, 38595, 38257, 38259, 38608, 38263, 38265, 38267, 38610, 38271, 38612, 38275, 37659, 38613, 38280, 37971, 38614, 38285, 38616, 38289, 35871, 35872, 35875, 35876, 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, 38037, 38050, 38063, 38687, 38688, 38690, 38691, 38206, 38708, 38709, 38710, 38711, 38712, 38686, 38713, 38714, 38715, 38716, 38717, 38718, 38719, 38720, 38721, 38722, 38723, 38689, 38724, 38725, 38726, 38727, 38728, 38730, 38731, 38243, 38734, 38735, 38736, 38686, 38737, 38738, 38739, 38740, 38741, 38742, 38743, 38744, 38745, 38747, 38748, 38246, 38751, 38752, 38753, 38661, 38659, 38662, 38754, 38755, 38668, 38666, 38669, 38756, 38757, 38675, 38673, 38676, 38758, 38759, 38760, 38686, 38761, 38762, 38763, 38764, 38765, 38766, 38767, 38768, 38769, 38770, 38771, 38689, 38772, 38773, 38774, 38775, 38776, 38777, 38071, 38447, 38678, 38680, 38681, 38779, 38088, 38780, 38683, 38684, 38685, 38782, 38783, 38784, 38706, 38704, 38707, 38785, 38786, 38787, 38686, 38788, 38789, 38790, 38791, 38792, 38793, 38794, 38795, 38796, 38797, 38798, 38689, 38799, 38800, 38802, 38803, 38249, 38806, 38807, 38808, 38686, 38809, 38810, 38811, 38812, 38813, 38814, 38815, 38816, 38817, 38818, 38819, 38689, 38820, 38821, 38822, 38823, 38824, 38825, 38826, 38827, 38829, 38830, 38252, 38185, 38833, 38698, 38692, 38693, 38694, 38695, 38835, 38836, 38837, 38706, 38704, 38702, 38707, 38838, 38706, 38704, 38702, 38185, 38839, 38698, 38699, 38700, 38841, 38842, 38706, 38704, 38702, 38707, 38185, 38843, 38698, 38699, 38700, 38845, 38846, 38847, 38706, 38704, 38707, 38848, 38849, 38850, 38851, 38852, 38853, 38854, 38855, 38857, 38858, 38255, 38861, 38862, 38864, 38865, 38866, 38868, 38870, 38871, 38873, 38874, 38876, 38878, 38879, 38880, 38881, 38882, 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, 38922, 38925, 38926, 38929, 38916, 38934, 38937, 38938, 38918, 38946, 38949, 38950, 38954, 38916, 38965, 38966, 38657, 38967, 38970, 38971, 38664, 38972, 38975, 38976, 38671, 38977, 38978, 38981, 38982, 38985, 38916, 38990, 38993, 38994, 38918, 39000, 39001, 39002, 39003, 39004, 39006, 39008, 39009, 39010, 39014, 39015, 38702, 39016, 39017, 39020, 39021, 39024, 38916, 39029, 39032, 38918, 39038, 39041, 39042, 39045, 38916, 39050, 39053, 39054, 39057, 38918, 39065, 39067, 39068, 39069, 39070, 39071, 39075, 39076, 39077, 39078, 39080, 39081, 39082, 39083, 39085, 39086, 39087, 39090, 39091, 39092, 39093, 39094, 39096, 39097, 39098, 39102, 39103, 38702, 39104, 39105, 39108, 38921, 38943, 38945, 38959, 38961, 38962, 39115, 39005, 39011, 38964, 38969, 38974, 39099, 39101, 38999, 39115, 39005, 39011, 39013, 39099, 39101, 39035, 39037, 39062, 39064, 39072, 39074, 39089, 39099, 39089, 39099, 39101, 39113, 39115, 39118, 39117, 39121, 39120, 39122, 35873, 39124, 35874, 39126, 39127, 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, 39168, 39172, 39173, 39176, 39177, 39179, 39181, 39184, 39182, 39188, 39186, 39192, 39190, 39194, 39198, 39199, 39202, 39204, 39007, 39214, 39212, 39216, 39220, 39221, 39223, 39224, 39228, 39229, 39233, 39066, 39240, 39244, 39084, 39251, 39095, 39261, 39259, 39265, 38931, 38928, 38940, 39056, 39266, 39267, 38956, 39268, 39269, 38987, 38984, 38996, 39056, 39270, 39271, 39272, 39207, 39273, 39211, 39274, 39275, 39276, 39277, 39258, 39278, 38987, 38984, 38996, 39056, 39279, 39280, 39281, 39207, 39282, 39211, 39283, 39284, 39258, 39285, 39026, 39023, 39059, 39056, 39286, 39287, 39047, 39044, 39059, 39056, 39288, 39289, 39290, 39239, 39237, 39291, 39292, 39293, 39250, 39294, 39295, 39258, 39296, 39110, 39107, 39297, 39298, 39299, 39300, 39301, 39302, 39303, 39304, 39305, 39306, 39307, 39308, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 38933, 38942, 38958, 39432, 39434, 39436, 38989, 38998, 39444, 39028, 39034, 39049, 39061, 39454, 39455, 39457, 39460, 39112, 39462, 39463, 39169, 39464, 39465, 39174, 39468, 38953, 39178, 39471, 39472, 39195, 39473, 39474, 39200, 39441, 39478, 39442, 39480, 39458, 39485, 39487, 39488, 39195, 39489, 39490, 39200, 39441, 39494, 39442, 39496, 39458, 39499, 39501, 39502, 39217, 39503, 39504, 39222, 39507, 39508, 39225, 39509, 39510, 39230, 39453, 39514, 39515, 39456, 39519, 39458, 39522, 39524, 39525, 39467, 39470, 39476, 39527, 39492, 39506, 39512, 39527, 39528, 39530, 39130, 39535, 39533, 39131, 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, 39572, 39570, 39552, 39575, 39573, 39553, 39577, 39578, 39554, 39581, 39579, 39558, 39584, 39582, 39559, 39585, 39477, 39587, 39479, 39185, 39189, 39193, 39589, 39484, 39262, 39593, 39591, 39558, 39596, 39594, 39559, 39597, 39493, 39599, 39495, 39215, 39601, 39498, 39262, 39605, 39603, 39561, 39608, 39606, 39562, 39611, 39609, 39563, 39614, 39612, 39564, 39615, 39513, 39243, 39254, 39618, 39518, 39254, 39620, 39521, 39262, 39622, 39569, 39624, 39625, 39626, 39627, 39628, 39629, 39630, 39631, 39634, 39635, 39129, 39636, 39128, 39637, 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, 39681, 39682, 39684, 39685, 39576, 39688, 39690, 39691, 39693, 39694, 39699, 39700, 39701, 39704, 39706, 39707, 39709, 39710, 39715, 39718, 39720, 39721, 39723, 39724, 39726, 39727, 39729, 39730, 39732, 39733, 39734, 39737, 39740, 39742, 39698, 39696, 39703, 39714, 39712, 39717, 39698, 39696, 39703, 39714, 39712, 39717, 39736, 39739, 39753, 39755, 39751, 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, 39812, 39842, 39843, 39820, 39819, 39818, 39844, 39821, 39811, 39809, 39835, 39845, 39846, 39826, 39847, 39827, 39817, 39815, 39836, 39837, 39841, 39848, 39849, 39820, 39819, 39818, 39850, 39821, 39825, 39823, 39851, 39852, 39826, 39853, 39827, 39831, 39829, 39835, 39833, 39836, 39837, 39838, 39854, 39839, 39855, 39840, 39841, 39856, 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, 39937, 39939, 39940, 39941, 39943, 39944, 39945, 39946, 39813, 39947, 39949, 39951, 39952, 39953, 39954, 39955, 39956, 39957, 39959, 39960, 39961, 39963, 39964, 39965, 39966, 39968, 39970, 39971, 39972, 39973, 39974, 39975, 39976, 39977, 39979, 39981, 39982, 39858, 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, 40065, 40069, 40072, 40076, 40082, 40086, 40091, 40093, 40068, 40075, 40074, 40099, 40098, 40097, 40079, 39746, 40085, 40090, 40089, 40099, 40098, 40097, 40096, 39750, 40101, 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, 40192, 40071, 40196, 40200, 39743, 40201, 40202, 39745, 40203, 40204, 40205, 40206, 40207, 40208, 39747, 40209, 40210, 39749, 39748, 40211, 40212, 40213, 40214, 40215, 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, 40320, 39744, 40324, 40325, 40327, 40328, 40330, 40322, 40334, 40335, 40337, 40338, 40339, 40341, 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, 40448, 40449, 40453, 40455, 40458, 40460, 40452, 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, 40323, 40577, 40333, 40332, 40582, 40343, 40580, 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, 40707, 40705, 40709, 40710, 40456, 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, 40833, 40832, 40836, 40834, 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, 40961, 40963, 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, 39756, 40216, 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, 41216, 41217, 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, 41344, 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};
int h_C[]= {
4, 6, 8, 10, 12, 14, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63, 65, 67, 69, 71, 73, 75, 77, 79, 81, 83, 85, 87, 89, 91, 93, 95, 97, 99, 101, 103, 105, 107, 109, 111, 113, 115, 117, 119, 121, 123, 125, 127, 129, 131, 133, 135, 137, 139, 141, 143, 146, 148, 150, 152, 154, 156, 158, 160, 162, 164, 166, 168, 170, 172, 174, 176, 179, 181, 183, 185, 187, 189, 191, 193, 195, 197, 199, 201, 203, 205, 207, 209, 211, 213, 215, 217, 219, 221, 223, 225, 227, 229, 231, 233, 235, 237, 239, 241, 243, 245, 247, 249, 252, 254, 256, 258, 260, 262, 264, 266, 268, 270, 272, 274, 276, 278, 280, 282, 284, 286, 288, 290, 292, 294, 296, 298, 300, 302, 304, 306, 308, 310, 312, 314, 316, 318, 320, 322, 324, 326, 328, 330, 332, 334, 336, 338, 340, 342, 344, 346, 348, 350, 352, 354, 356, 358, 360, 362, 364, 366, 368, 370, 372, 374, 376, 378, 380, 382, 384, 386, 388, 390, 392, 394, 396, 398, 400, 402, 404, 406, 408, 410, 412, 414, 416, 418, 420, 422, 424, 426, 428, 430, 432, 434, 436, 438, 440, 442, 444, 446, 448, 450, 452, 454, 456, 458, 460, 462, 464, 466, 468, 470, 472, 474, 477, 479, 481, 483, 486, 488, 490, 492, 494, 496, 498, 500, 502, 504, 506, 508, 510, 512, 514, 516, 518, 520, 522, 524, 526, 528, 530, 532, 534, 536, 538, 540, 542, 544, 546, 548, 550, 552, 554, 556, 558, 560, 562, 564, 566, 568, 570, 572, 574, 576, 578, 580, 582, 584, 586, 588, 590, 592, 594, 596, 598, 600, 602, 604, 606, 608, 610, 612, 614, 616, 618, 620, 622, 624, 626, 628, 630, 632, 634, 636, 638, 640, 643, 645, 648, 650, 652, 654, 656, 658, 660, 662, 664, 666, 669, 671, 673, 675, 678, 680, 682, 684, 688, 690, 692, 694, 696, 698, 701, 703, 705, 707, 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, 769, 771, 774, 776, 779, 781, 783, 785, 787, 789, 791, 793, 796, 798, 801, 803, 806, 808, 810, 812, 814, 816, 818, 820, 823, 825, 828, 830, 832, 834, 837, 839, 841, 843, 847, 849, 851, 853, 855, 857, 859, 861, 863, 865, 868, 870, 873, 875, 878, 880, 882, 884, 886, 888, 891, 893, 895, 897, 899, 901, 904, 906, 909, 911, 914, 916, 919, 921, 924, 926, 929, 931, 934, 936, 939, 941, 943, 945, 947, 949, 952, 954, 956, 958, 960, 962, 965, 967, 969, 971, 973, 975, 978, 980, 983, 985, 988, 990, 993, 995, 997, 999, 1002, 1004, 1006, 1008, 1011, 1013, 1017, 1019, 1021, 1023, 1026, 1028, 1031, 1033, 1036, 1038, 1041, 1043, 1046, 1048, 1051, 1053, 1056, 1058, 1061, 1063, 1066, 1068, 1071, 1073, 1076, 1078, 1081, 1083, 1086, 1088, 1091, 1093, 1096, 1098, 1101, 1103, 1105, 1107, 1109, 1111, 1114, 1116, 1119, 1121, 1124, 1126, 1129, 1131, 1134, 1136, 1139, 1141, 1144, 1146, 1149, 1151, 1154, 1156, 1159, 1161, 1164, 1166, 1169, 1171, 1173, 1175, 1177, 1179, 1182, 1184, 1187, 1189, 1192, 1194, 1197, 1199, 1201, 1203, 1205, 1207, 1209, 1211, 1213, 1215, 1217, 1219, 1221, 1223, 1225, 1227, 1229, 1231, 1233, 1235, 1237, 1239, 1241, 1243, 1245, 1247, 1249, 1251, 1253, 1255, 1257, 1259, 1261, 1263, 1265, 1267, 1269, 1271, 1273, 1275, 1277, 1279, 1281, 1283, 1285, 1287, 1290, 1292, 1294, 1296, 1298, 1300, 1302, 1304, 1306, 1308, 1310, 1312, 1314, 1316, 1318, 1320, 1322, 1324, 1327, 1329, 1331, 1333, 1336, 1338, 1340, 1342, 1344, 1346, 1348, 1350, 1352, 1354, 1356, 1358, 1360, 1362, 1364, 1366, 1368, 1370, 1372, 1374, 1376, 1378, 1380, 1382, 1384, 1386, 1390, 1392, 1394, 1396, 1398, 1400, 1402, 1404, 1406, 1408, 1410, 1412, 1414, 1416, 1419, 1421, 1424, 1426, 1429, 1431, 1434, 1436, 1439, 1441, 1444, 1446, 1449, 1451, 1454, 1456, 1458, 1460, 1462, 1464, 1467, 1469, 1472, 1474, 1476, 1478, 1480, 1482, 1484, 1486, 1489, 1491, 1493, 1495, 1498, 1500, 1502, 1504, 1507, 1509, 1513, 1515, 1517, 1519, 1521, 1523, 1526, 1528, 1531, 1533, 1538, 1540, 1542, 1544, 1546, 1548, 1551, 1553, 1556, 1558, 1561, 1563, 1566, 1568, 1570, 1572, 1574, 1576, 1579, 1581, 1584, 1586, 1589, 1591, 1594, 1596, 1599, 1601, 1604, 1606, 1609, 1611, 1614, 1616, 1619, 1621, 1624, 1626, 1629, 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1999, 2001, 2003, 2005, 2007, 2009, 2011, 2013, 2015, 2017, 2019, 2021, 2023, 2025, 2027, 2029, 2031, 2033, 2035, 2037, 2039, 2041, 2043, 2045, 2047, 2049, 2051, 2053, 2055, 2057, 2059, 2061, 2063, 2065, 2067, 2069, 2071, 2073, 2075, 2077, 2079, 2081, 2083, 2085, 2087, 2089, 2091, 2093, 2095, 2097, 2099, 2101, 2103, 2105, 2107, 2110, 2112, 2114, 2116, 2118, 2120, 2122, 2124, 2126, 2128, 2130, 2132, 2134, 2136, 2138, 2140, 2142, 2144, 2146, 2148, 2150, 2152, 2154, 2156, 2158, 2160, 2162, 2164, 2166, 2168, 2170, 2172, 2175, 2177, 2179, 2181, 2183, 2185, 2187, 2189, 2191, 2193, 2195, 2197, 2200, 2202, 2204, 2206, 2209, 2211, 2213, 2215, 2218, 2220, 2222, 2224, 2227, 2229, 2231, 2233, 2235, 2237, 2239, 2241, 2243, 2245, 2247, 2249, 2251, 2253, 2255, 2257, 2259, 2261, 2263, 2265, 2267, 2269, 2271, 2273, 2275, 2277, 2279, 2281, 2283, 2285, 2288, 2290, 2293, 2295, 2297, 2299, 2301, 2303, 2305, 2307, 2309, 2311, 2313, 2315, 2317, 2319, 2321, 2323, 2325, 2327, 2329, 2331, 2333, 2335, 2337, 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16636, 38239, 38238, 38240, 16713, 16715, 16719, 16730, 16732, 16736, 16749, 38276, 16762, 38281, 16775, 16871, 38260, 16911, 38268, 16918, 38272, 38277, 38282, 16933, 38286, 16968, 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, 16046, 16047, 38411, 16050, 38413, 16053, 38417, 16063, 16064, 38423, 16067, 38425, 16070, 38429, 16080, 16081, 38435, 16084, 38437, 16087, 38441, 16092, 38444, 38446, 38449, 16106, 16111, 38091, 38459, 16124, 16138, 38127, 38487, 16168, 38156, 38515, 38520, 16198, 38524, 16204, 16207, 16217, 38188, 38536, 16230, 16241, 16242, 38545, 16245, 38547, 16248, 38551, 38229, 38566, 38468, 38466, 38464, 38474, 38472, 38476, 38480, 38478, 38122, 38483, 38489, 38495, 38493, 38491, 38507, 38505, 38151, 38510, 38517, 38571, 38400, 16358, 16359, 16361, 38468, 38466, 38464, 38474, 38403, 38401, 38476, 38480, 38478, 38122, 38483, 38489, 38575, 38405, 16379, 16380, 16382, 38570, 38406, 38407, 38418, 38419, 38430, 38431, 38468, 38466, 38464, 38474, 38472, 38476, 38480, 38478, 38122, 38483, 38489, 38495, 38493, 38491, 38507, 38505, 38151, 38510, 38517, 38570, 16507, 38086, 38456, 16514, 38102, 38529, 38541, 38468, 38466, 38464, 38474, 38472, 38476, 38480, 38478, 38122, 38483, 38489, 38495, 38493, 38491, 38510, 38517, 38581, 38570, 16545, 16546, 16548, 38468, 38466, 38464, 38474, 38472, 38476, 38480, 38478, 38122, 38483, 38489, 38495, 38493, 38491, 38501, 38499, 38503, 38507, 38505, 38151, 38510, 38517, 38585, 38570, 16578, 16579, 16581, 38533, 16589, 38199, 38529, 38541, 38529, 38533, 16606, 38529, 38541, 38533, 16618, 38199, 38540, 38541, 38554, 38552, 38218, 38559, 38557, 38224, 38562, 38568, 38592, 38570, 16638, 16639, 16641, 38596, 38597, 16909, 38598, 38599, 38600, 16916, 38601, 16921, 38602, 38603, 16926, 38604, 38605, 16931, 38606, 16966, 38607, 38609, 38611, 38615, 38617, 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, 38656, 38663, 38670, 38128, 38488, 38513, 38516, 38701, 38230, 38567, 16332, 16333, 16334, 38470, 16336, 16337, 16338, 16339, 16340, 16341, 16342, 16343, 16345, 16346, 16347, 38497, 16349, 16350, 16351, 16352, 16353, 16357, 38732, 38733, 16362, 16363, 16364, 38470, 16366, 16367, 16368, 16369, 16370, 16371, 16372, 16373, 16374, 16378, 38749, 38750, 16383, 16460, 16461, 38660, 38658, 38415, 16466, 16467, 38667, 38665, 38427, 16472, 16473, 38674, 38672, 38439, 16478, 16479, 16480, 38470, 16482, 16483, 16484, 16485, 16486, 16487, 16488, 16489, 16491, 16492, 16493, 38497, 16495, 16496, 16497, 16498, 16499, 16501, 38677, 38679, 38443, 38079, 38450, 16508, 38682, 16510, 38454, 38095, 38460, 16515, 16516, 16517, 38705, 38703, 38549, 16522, 16523, 16524, 38470, 16526, 16527, 16528, 16529, 16530, 16531, 16532, 16533, 16535, 16536, 16537, 38497, 16539, 16540, 16544, 38804, 38805, 16549, 16550, 16551, 38470, 16553, 16554, 16555, 16556, 16557, 16558, 16559, 16560, 16562, 16563, 16564, 38497, 16566, 16567, 16568, 16569, 16570, 16571, 16572, 16573, 16577, 38831, 38832, 38697, 16583, 38531, 38165, 38168, 38522, 38173, 16590, 16591, 16592, 38528, 38527, 38696, 38549, 16597, 38528, 38527, 38696, 38697, 16602, 38531, 38192, 38537, 16607, 16608, 38184, 38183, 38182, 38549, 38697, 16614, 38531, 38192, 38537, 16619, 16620, 16621, 38705, 38703, 38549, 16626, 16627, 16628, 16629, 16630, 16631, 16632, 16633, 16637, 38859, 38860, 16907, 16908, 16910, 16914, 16915, 16917, 16922, 16923, 16927, 16928, 16932, 16967, 16985, 16988, 16994, 17008, 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, 38923, 16335, 38927, 38930, 38915, 38935, 16348, 38939, 38917, 38947, 16365, 38951, 38955, 38915, 16462, 16463, 38912, 16465, 16468, 16469, 38913, 16471, 16474, 16475, 38914, 16477, 38979, 16481, 38983, 38986, 38915, 38991, 16494, 38995, 38917, 16502, 16503, 16504, 16505, 16506, 16509, 16511, 16512, 16513, 16518, 16519, 38919, 16521, 39018, 16525, 39022, 39025, 38915, 39030, 16538, 38917, 39039, 16552, 39043, 39046, 38915, 39051, 16565, 39055, 39058, 38917, 16582, 16584, 16585, 16586, 16587, 16588, 16593, 16594, 16595, 16596, 16598, 16599, 16600, 16601, 16603, 16604, 16605, 16609, 16610, 16611, 16612, 16613, 16615, 16616, 16617, 16622, 16623, 38919, 16625, 39106, 39109, 38920, 38729, 38944, 38746, 38960, 38856, 39114, 38778, 38781, 38963, 38968, 38973, 38844, 39100, 38856, 39114, 38778, 38781, 39012, 38844, 39100, 38801, 39036, 38828, 39063, 38834, 39073, 39079, 38840, 39088, 38844, 39100, 38856, 39114, 38863, 39116, 38867, 39119, 38869, 39123, 38872, 39125, 38875, 38877, 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, 38924, 16344, 38936, 16354, 38948, 38952, 16375, 16464, 39183, 16470, 39187, 16476, 39191, 38980, 16490, 38992, 16500, 39205, 39209, 16520, 39213, 39019, 16534, 39031, 16541, 39040, 16561, 39052, 16574, 39235, 39241, 39245, 39248, 39252, 39256, 16624, 39260, 16634, 39171, 39170, 39175, 39231, 16695, 16696, 39180, 16701, 16702, 39197, 39196, 39201, 39231, 16711, 16712, 16787, 39206, 16790, 39210, 16792, 16794, 16796, 16799, 39257, 16801, 39197, 39196, 39201, 39231, 16811, 16812, 16814, 39206, 16817, 39210, 16819, 16822, 39257, 16824, 39219, 39218, 39232, 39231, 16834, 16835, 39227, 39226, 39232, 39231, 16844, 16845, 16847, 39238, 39236, 16850, 16852, 16855, 39249, 16857, 16860, 39257, 16862, 39264, 39263, 16867, 16868, 16983, 16984, 16986, 16987, 16989, 16990, 16991, 16992, 16993, 17007, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 39425, 39427, 39430, 39431, 39433, 39435, 39438, 39440, 39443, 39446, 39448, 39450, 39452, 39242, 39246, 39253, 39459, 39461, 16687, 16688, 39424, 16691, 16692, 39426, 16697, 39429, 39428, 16703, 16704, 39437, 16707, 16708, 39439, 39203, 16788, 39208, 16791, 39255, 16800, 16803, 16804, 39437, 16807, 16808, 39439, 39203, 16815, 39208, 16818, 39255, 16823, 16826, 16827, 39445, 16830, 16831, 39447, 16836, 16837, 39449, 16840, 16841, 39451, 39234, 16848, 16849, 39247, 16856, 39255, 16861, 16864, 16865, 39466, 39469, 39475, 39526, 39491, 39505, 39511, 39526, 39529, 39531, 39536, 39534, 39532, 39537, 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, 16689, 39571, 38932, 16693, 39574, 38941, 16698, 16699, 38957, 16705, 39580, 38988, 16709, 39583, 38997, 16786, 39586, 16789, 39588, 39555, 39556, 39557, 16798, 39590, 39568, 16805, 39592, 38988, 16809, 39595, 38997, 16813, 39598, 16816, 39600, 39560, 16821, 39602, 39568, 16828, 39604, 39027, 16832, 39607, 39033, 16838, 39610, 39048, 16842, 39613, 39060, 16846, 39616, 39565, 39566, 16854, 39619, 39567, 16859, 39621, 39568, 39623, 39111, 16891, 16894, 16902, 16906, 16945, 16953, 16956, 16965, 17012, 17013, 39633, 17015, 39632, 17020, 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, 39680, 16690, 39683, 16694, 39686, 16700, 39689, 16706, 39692, 16710, 16793, 16795, 16797, 16802, 39705, 16806, 39708, 16810, 16820, 16825, 39719, 16829, 39722, 16833, 39725, 16839, 39728, 16843, 39617, 16851, 16853, 16858, 16863, 16866, 39697, 39695, 39702, 39713, 39711, 39716, 39697, 39695, 39702, 39713, 39711, 39716, 39735, 39738, 17014, 17016, 39752, 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, 39687, 16882, 16883, 39483, 39482, 39481, 16887, 39486, 39810, 39808, 39834, 16895, 16896, 39497, 16898, 39500, 39816, 39814, 39731, 39516, 39741, 16936, 16937, 39483, 39482, 39481, 16941, 39486, 39824, 39822, 16946, 16947, 39497, 16949, 39500, 39830, 39828, 39834, 39832, 39731, 39516, 39517, 16960, 39520, 16962, 39523, 39741, 39754, 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, 39938, 16884, 16885, 16886, 16888, 16889, 16890, 16892, 39936, 39948, 16897, 16899, 16900, 16901, 16903, 16904, 16905, 39958, 16938, 16939, 16940, 16942, 16943, 16944, 39967, 16948, 16950, 16951, 16952, 16954, 16955, 16957, 16958, 16959, 16961, 16963, 16964, 39983, 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, 40066, 40070, 16893, 40077, 40083, 40087, 40092, 40094, 39942, 39950, 40073, 39980, 39978, 39978, 40078, 40080, 39962, 39969, 40088, 39980, 39978, 39978, 40095, 40100, 39857, 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, 40067, 40194, 40084, 16971, 40193, 16975, 16976, 40195, 16978, 16979, 16980, 16981, 16982, 16995, 40197, 16998, 16999, 40199, 40198, 17002, 17003, 17004, 17005, 17006, 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, 40064, 40321, 16974, 40326, 16977, 40329, 40331, 40081, 16997, 40336, 17000, 17001, 40340, 40342, 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, 16972, 16973, 40454, 16996, 40459, 40461, 40451, 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, 40576, 40450, 40579, 40578, 17010, 40581, 40457, 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, 17009, 40704, 17017, 17018, 40706, 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, 17011, 40708, 17019, 40835, 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, 40960, 40962, 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, 41089, 41088, 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, 17021, 17022, 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, 41345, 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};
bool h_Op[]= {
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
#define THREADS_PER_BLOCK 128
#define BLOCKS_PER_GRID 1
#define SIZE_OF_IN 17024
#define SIZE_OF_AC 24576
__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[325*THREADS_PER_BLOCK];
const int t= THREADS_PER_BLOCK;
__shared__ float final;
final=0;
R[i + 0*t] = A[i + 0*t];
R[i + 1*t] = A[i + 1*t];
R[i + 2*t] = A[i + 2*t];
R[i + 3*t] = A[i + 3*t];
R[i + 4*t] = A[i + 4*t];
R[i + 5*t] = A[i + 5*t];
R[i + 6*t] = A[i + 6*t];
R[i + 7*t] = A[i + 7*t];
R[i + 8*t] = A[i + 8*t];
R[i + 9*t] = A[i + 9*t];
R[i + 10*t] = A[i + 10*t];
R[i + 11*t] = A[i + 11*t];
R[i + 12*t] = A[i + 12*t];
R[i + 13*t] = A[i + 13*t];
R[i + 14*t] = A[i + 14*t];
R[i + 15*t] = A[i + 15*t];
R[i + 16*t] = A[i + 16*t];
R[i + 17*t] = A[i + 17*t];
R[i + 18*t] = A[i + 18*t];
R[i + 19*t] = A[i + 19*t];
R[i + 20*t] = A[i + 20*t];
R[i + 21*t] = A[i + 21*t];
R[i + 22*t] = A[i + 22*t];
R[i + 23*t] = A[i + 23*t];
R[i + 24*t] = A[i + 24*t];
R[i + 25*t] = A[i + 25*t];
R[i + 26*t] = A[i + 26*t];
R[i + 27*t] = A[i + 27*t];
R[i + 28*t] = A[i + 28*t];
R[i + 29*t] = A[i + 29*t];
R[i + 30*t] = A[i + 30*t];
R[i + 31*t] = A[i + 31*t];
R[i + 32*t] = A[i + 32*t];
R[i + 33*t] = A[i + 33*t];
R[i + 34*t] = A[i + 34*t];
R[i + 35*t] = A[i + 35*t];
R[i + 36*t] = A[i + 36*t];
R[i + 37*t] = A[i + 37*t];
R[i + 38*t] = A[i + 38*t];
R[i + 39*t] = A[i + 39*t];
R[i + 40*t] = A[i + 40*t];
R[i + 41*t] = A[i + 41*t];
R[i + 42*t] = A[i + 42*t];
R[i + 43*t] = A[i + 43*t];
R[i + 44*t] = A[i + 44*t];
R[i + 45*t] = A[i + 45*t];
R[i + 46*t] = A[i + 46*t];
R[i + 47*t] = A[i + 47*t];
R[i + 48*t] = A[i + 48*t];
R[i + 49*t] = A[i + 49*t];
R[i + 50*t] = A[i + 50*t];
R[i + 51*t] = A[i + 51*t];
R[i + 52*t] = A[i + 52*t];
R[i + 53*t] = A[i + 53*t];
R[i + 54*t] = A[i + 54*t];
R[i + 55*t] = A[i + 55*t];
R[i + 56*t] = A[i + 56*t];
R[i + 57*t] = A[i + 57*t];
R[i + 58*t] = A[i + 58*t];
R[i + 59*t] = A[i + 59*t];
R[i + 60*t] = A[i + 60*t];
R[i + 61*t] = A[i + 61*t];
R[i + 62*t] = A[i + 62*t];
R[i + 63*t] = A[i + 63*t];
R[i + 64*t] = A[i + 64*t];
R[i + 65*t] = A[i + 65*t];
R[i + 66*t] = A[i + 66*t];
R[i + 67*t] = A[i + 67*t];
R[i + 68*t] = A[i + 68*t];
R[i + 69*t] = A[i + 69*t];
R[i + 70*t] = A[i + 70*t];
R[i + 71*t] = A[i + 71*t];
R[i + 72*t] = A[i + 72*t];
R[i + 73*t] = A[i + 73*t];
R[i + 74*t] = A[i + 74*t];
R[i + 75*t] = A[i + 75*t];
R[i + 76*t] = A[i + 76*t];
R[i + 77*t] = A[i + 77*t];
R[i + 78*t] = A[i + 78*t];
R[i + 79*t] = A[i + 79*t];
R[i + 80*t] = A[i + 80*t];
R[i + 81*t] = A[i + 81*t];
R[i + 82*t] = A[i + 82*t];
R[i + 83*t] = A[i + 83*t];
R[i + 84*t] = A[i + 84*t];
R[i + 85*t] = A[i + 85*t];
R[i + 86*t] = A[i + 86*t];
R[i + 87*t] = A[i + 87*t];
R[i + 88*t] = A[i + 88*t];
R[i + 89*t] = A[i + 89*t];
R[i + 90*t] = A[i + 90*t];
R[i + 91*t] = A[i + 91*t];
R[i + 92*t] = A[i + 92*t];
R[i + 93*t] = A[i + 93*t];
R[i + 94*t] = A[i + 94*t];
R[i + 95*t] = A[i + 95*t];
R[i + 96*t] = A[i + 96*t];
R[i + 97*t] = A[i + 97*t];
R[i + 98*t] = A[i + 98*t];
R[i + 99*t] = A[i + 99*t];
R[i + 100*t] = A[i + 100*t];
R[i + 101*t] = A[i + 101*t];
R[i + 102*t] = A[i + 102*t];
R[i + 103*t] = A[i + 103*t];
R[i + 104*t] = A[i + 104*t];
R[i + 105*t] = A[i + 105*t];
R[i + 106*t] = A[i + 106*t];
R[i + 107*t] = A[i + 107*t];
R[i + 108*t] = A[i + 108*t];
R[i + 109*t] = A[i + 109*t];
R[i + 110*t] = A[i + 110*t];
R[i + 111*t] = A[i + 111*t];
R[i + 112*t] = A[i + 112*t];
R[i + 113*t] = A[i + 113*t];
R[i + 114*t] = A[i + 114*t];
R[i + 115*t] = A[i + 115*t];
R[i + 116*t] = A[i + 116*t];
R[i + 117*t] = A[i + 117*t];
R[i + 118*t] = A[i + 118*t];
R[i + 119*t] = A[i + 119*t];
R[i + 120*t] = A[i + 120*t];
R[i + 121*t] = A[i + 121*t];
R[i + 122*t] = A[i + 122*t];
R[i + 123*t] = A[i + 123*t];
R[i + 124*t] = A[i + 124*t];
R[i + 125*t] = A[i + 125*t];
R[i + 126*t] = A[i + 126*t];
R[i + 127*t] = A[i + 127*t];
R[i + 128*t] = A[i + 128*t];
R[i + 129*t] = A[i + 129*t];
R[i + 130*t] = A[i + 130*t];
R[i + 131*t] = A[i + 131*t];
R[i + 132*t] = A[i + 132*t];
__syncthreads();
for (int iter=0; iter< n_iter; iter++) {
R[i + 133*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 + 134*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 + 135*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 + 136*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 + 137*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 + 138*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 + 139*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 + 140*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 + 141*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 + 142*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 + 143*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 + 144*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 + 145*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 + 146*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 + 147*t] = Op[i + 14*t] ? R[B[i + 14*t]] * R[C[i + 14*t]] : R[B[i + 14*t]] + R[C[i + 14*t]];
R[i + 148*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 + 149*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 + 150*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 + 151*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 + 152*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 + 153*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 + 154*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 + 155*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 + 156*t] = Op[i + 23*t] ? R[B[i + 23*t]] * R[C[i + 23*t]] : R[B[i + 23*t]] + R[C[i + 23*t]];
R[i + 157*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 + 158*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 + 159*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 + 160*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 + 161*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 + 162*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 + 163*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 + 164*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 + 165*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 + 166*t] = Op[i + 33*t] ? R[B[i + 33*t]] * R[C[i + 33*t]] : R[B[i + 33*t]] + R[C[i + 33*t]];
R[i + 167*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 + 168*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 + 169*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 + 170*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 + 171*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 + 172*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 + 173*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 + 174*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]];
__syncthreads();
R[i + 175*t] = Op[i + 42*t] ? R[B[i + 42*t]] * R[C[i + 42*t]] : R[B[i + 42*t]] + R[C[i + 42*t]];
R[i + 176*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 + 177*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 + 178*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 + 179*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 + 180*t] = Op[i + 47*t] ? R[B[i + 47*t]] * R[C[i + 47*t]] : R[B[i + 47*t]] + R[C[i + 47*t]];
R[i + 181*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 + 182*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 + 183*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 + 184*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 + 185*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 + 186*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 + 187*t] = Op[i + 54*t] ? R[B[i + 54*t]] * R[C[i + 54*t]] : R[B[i + 54*t]] + R[C[i + 54*t]];
R[i + 188*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 + 189*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 + 190*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 + 191*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 + 192*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 + 193*t] = Op[i + 60*t] ? R[B[i + 60*t]] * R[C[i + 60*t]] : R[B[i + 60*t]] + R[C[i + 60*t]];
R[i + 194*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 + 195*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 + 196*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 + 197*t] = Op[i + 64*t] ? R[B[i + 64*t]] * R[C[i + 64*t]] : R[B[i + 64*t]] + R[C[i + 64*t]];
R[i + 198*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]];
__syncthreads();
R[i + 199*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 + 200*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 + 201*t] = Op[i + 68*t] ? R[B[i + 68*t]] * R[C[i + 68*t]] : R[B[i + 68*t]] + R[C[i + 68*t]];
R[i + 202*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 + 203*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 + 204*t] = Op[i + 71*t] ? R[B[i + 71*t]] * R[C[i + 71*t]] : R[B[i + 71*t]] + R[C[i + 71*t]];
R[i + 205*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 + 206*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 + 207*t] = Op[i + 74*t] ? R[B[i + 74*t]] * R[C[i + 74*t]] : R[B[i + 74*t]] + R[C[i + 74*t]];
R[i + 208*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 + 209*t] = Op[i + 76*t] ? R[B[i + 76*t]] * R[C[i + 76*t]] : R[B[i + 76*t]] + R[C[i + 76*t]];
R[i + 210*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 + 211*t] = Op[i + 78*t] ? R[B[i + 78*t]] * R[C[i + 78*t]] : R[B[i + 78*t]] + R[C[i + 78*t]];
R[i + 212*t] = Op[i + 79*t] ? R[B[i + 79*t]] * R[C[i + 79*t]] : R[B[i + 79*t]] + R[C[i + 79*t]];
R[i + 213*t] = Op[i + 80*t] ? R[B[i + 80*t]] * R[C[i + 80*t]] : R[B[i + 80*t]] + R[C[i + 80*t]];
R[i + 214*t] = Op[i + 81*t] ? R[B[i + 81*t]] * R[C[i + 81*t]] : R[B[i + 81*t]] + R[C[i + 81*t]];
R[i + 215*t] = Op[i + 82*t] ? R[B[i + 82*t]] * R[C[i + 82*t]] : R[B[i + 82*t]] + R[C[i + 82*t]];
R[i + 216*t] = Op[i + 83*t] ? R[B[i + 83*t]] * R[C[i + 83*t]] : R[B[i + 83*t]] + R[C[i + 83*t]];
R[i + 217*t] = Op[i + 84*t] ? R[B[i + 84*t]] * R[C[i + 84*t]] : R[B[i + 84*t]] + R[C[i + 84*t]];
R[i + 218*t] = Op[i + 85*t] ? R[B[i + 85*t]] * R[C[i + 85*t]] : R[B[i + 85*t]] + R[C[i + 85*t]];
R[i + 219*t] = Op[i + 86*t] ? R[B[i + 86*t]] * R[C[i + 86*t]] : R[B[i + 86*t]] + R[C[i + 86*t]];
R[i + 220*t] = Op[i + 87*t] ? R[B[i + 87*t]] * R[C[i + 87*t]] : R[B[i + 87*t]] + R[C[i + 87*t]];
R[i + 221*t] = Op[i + 88*t] ? R[B[i + 88*t]] * R[C[i + 88*t]] : R[B[i + 88*t]] + R[C[i + 88*t]];
R[i + 222*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 + 223*t] = Op[i + 90*t] ? R[B[i + 90*t]] * R[C[i + 90*t]] : R[B[i + 90*t]] + R[C[i + 90*t]];
R[i + 224*t] = Op[i + 91*t] ? R[B[i + 91*t]] * R[C[i + 91*t]] : R[B[i + 91*t]] + R[C[i + 91*t]];
R[i + 225*t] = Op[i + 92*t] ? R[B[i + 92*t]] * R[C[i + 92*t]] : R[B[i + 92*t]] + R[C[i + 92*t]];
R[i + 226*t] = Op[i + 93*t] ? R[B[i + 93*t]] * R[C[i + 93*t]] : R[B[i + 93*t]] + R[C[i + 93*t]];
R[i + 227*t] = Op[i + 94*t] ? R[B[i + 94*t]] * R[C[i + 94*t]] : R[B[i + 94*t]] + R[C[i + 94*t]];
R[i + 228*t] = Op[i + 95*t] ? R[B[i + 95*t]] * R[C[i + 95*t]] : R[B[i + 95*t]] + R[C[i + 95*t]];
R[i + 229*t] = Op[i + 96*t] ? R[B[i + 96*t]] * R[C[i + 96*t]] : R[B[i + 96*t]] + R[C[i + 96*t]];
R[i + 230*t] = Op[i + 97*t] ? R[B[i + 97*t]] * R[C[i + 97*t]] : R[B[i + 97*t]] + R[C[i + 97*t]];
R[i + 231*t] = Op[i + 98*t] ? R[B[i + 98*t]] * R[C[i + 98*t]] : R[B[i + 98*t]] + R[C[i + 98*t]];
R[i + 232*t] = Op[i + 99*t] ? R[B[i + 99*t]] * R[C[i + 99*t]] : R[B[i + 99*t]] + R[C[i + 99*t]];
R[i + 233*t] = Op[i + 100*t] ? R[B[i + 100*t]] * R[C[i + 100*t]] : R[B[i + 100*t]] + R[C[i + 100*t]];
R[i + 234*t] = Op[i + 101*t] ? R[B[i + 101*t]] * R[C[i + 101*t]] : R[B[i + 101*t]] + R[C[i + 101*t]];
R[i + 235*t] = Op[i + 102*t] ? R[B[i + 102*t]] * R[C[i + 102*t]] : R[B[i + 102*t]] + R[C[i + 102*t]];
R[i + 236*t] = Op[i + 103*t] ? R[B[i + 103*t]] * R[C[i + 103*t]] : R[B[i + 103*t]] + R[C[i + 103*t]];
R[i + 237*t] = Op[i + 104*t] ? R[B[i + 104*t]] * R[C[i + 104*t]] : R[B[i + 104*t]] + R[C[i + 104*t]];
R[i + 238*t] = Op[i + 105*t] ? R[B[i + 105*t]] * R[C[i + 105*t]] : R[B[i + 105*t]] + R[C[i + 105*t]];
R[i + 239*t] = Op[i + 106*t] ? R[B[i + 106*t]] * R[C[i + 106*t]] : R[B[i + 106*t]] + R[C[i + 106*t]];
R[i + 240*t] = Op[i + 107*t] ? R[B[i + 107*t]] * R[C[i + 107*t]] : R[B[i + 107*t]] + R[C[i + 107*t]];
R[i + 241*t] = Op[i + 108*t] ? R[B[i + 108*t]] * R[C[i + 108*t]] : R[B[i + 108*t]] + R[C[i + 108*t]];
R[i + 242*t] = Op[i + 109*t] ? R[B[i + 109*t]] * R[C[i + 109*t]] : R[B[i + 109*t]] + R[C[i + 109*t]];
R[i + 243*t] = Op[i + 110*t] ? R[B[i + 110*t]] * R[C[i + 110*t]] : R[B[i + 110*t]] + R[C[i + 110*t]];
R[i + 244*t] = Op[i + 111*t] ? R[B[i + 111*t]] * R[C[i + 111*t]] : R[B[i + 111*t]] + R[C[i + 111*t]];
__syncthreads();
R[i + 245*t] = Op[i + 112*t] ? R[B[i + 112*t]] * R[C[i + 112*t]] : R[B[i + 112*t]] + R[C[i + 112*t]];
R[i + 246*t] = Op[i + 113*t] ? R[B[i + 113*t]] * R[C[i + 113*t]] : R[B[i + 113*t]] + R[C[i + 113*t]];
R[i + 247*t] = Op[i + 114*t] ? R[B[i + 114*t]] * R[C[i + 114*t]] : R[B[i + 114*t]] + R[C[i + 114*t]];
R[i + 248*t] = Op[i + 115*t] ? R[B[i + 115*t]] * R[C[i + 115*t]] : R[B[i + 115*t]] + R[C[i + 115*t]];
R[i + 249*t] = Op[i + 116*t] ? R[B[i + 116*t]] * R[C[i + 116*t]] : R[B[i + 116*t]] + R[C[i + 116*t]];
R[i + 250*t] = Op[i + 117*t] ? R[B[i + 117*t]] * R[C[i + 117*t]] : R[B[i + 117*t]] + R[C[i + 117*t]];
R[i + 251*t] = Op[i + 118*t] ? R[B[i + 118*t]] * R[C[i + 118*t]] : R[B[i + 118*t]] + R[C[i + 118*t]];
R[i + 252*t] = Op[i + 119*t] ? R[B[i + 119*t]] * R[C[i + 119*t]] : R[B[i + 119*t]] + R[C[i + 119*t]];
R[i + 253*t] = Op[i + 120*t] ? R[B[i + 120*t]] * R[C[i + 120*t]] : R[B[i + 120*t]] + R[C[i + 120*t]];
R[i + 254*t] = Op[i + 121*t] ? R[B[i + 121*t]] * R[C[i + 121*t]] : R[B[i + 121*t]] + R[C[i + 121*t]];
R[i + 255*t] = Op[i + 122*t] ? R[B[i + 122*t]] * R[C[i + 122*t]] : R[B[i + 122*t]] + R[C[i + 122*t]];
__syncthreads();
R[i + 256*t] = Op[i + 123*t] ? R[B[i + 123*t]] * R[C[i + 123*t]] : R[B[i + 123*t]] + R[C[i + 123*t]];
R[i + 257*t] = Op[i + 124*t] ? R[B[i + 124*t]] * R[C[i + 124*t]] : R[B[i + 124*t]] + R[C[i + 124*t]];
R[i + 258*t] = Op[i + 125*t] ? R[B[i + 125*t]] * R[C[i + 125*t]] : R[B[i + 125*t]] + R[C[i + 125*t]];
R[i + 259*t] = Op[i + 126*t] ? R[B[i + 126*t]] * R[C[i + 126*t]] : R[B[i + 126*t]] + R[C[i + 126*t]];
R[i + 260*t] = Op[i + 127*t] ? R[B[i + 127*t]] * R[C[i + 127*t]] : R[B[i + 127*t]] + R[C[i + 127*t]];
R[i + 261*t] = Op[i + 128*t] ? R[B[i + 128*t]] * R[C[i + 128*t]] : R[B[i + 128*t]] + R[C[i + 128*t]];
R[i + 262*t] = Op[i + 129*t] ? R[B[i + 129*t]] * R[C[i + 129*t]] : R[B[i + 129*t]] + R[C[i + 129*t]];
R[i + 263*t] = Op[i + 130*t] ? R[B[i + 130*t]] * R[C[i + 130*t]] : R[B[i + 130*t]] + R[C[i + 130*t]];
R[i + 264*t] = Op[i + 131*t] ? R[B[i + 131*t]] * R[C[i + 131*t]] : R[B[i + 131*t]] + R[C[i + 131*t]];
R[i + 265*t] = Op[i + 132*t] ? R[B[i + 132*t]] * R[C[i + 132*t]] : R[B[i + 132*t]] + R[C[i + 132*t]];
R[i + 266*t] = Op[i + 133*t] ? R[B[i + 133*t]] * R[C[i + 133*t]] : R[B[i + 133*t]] + R[C[i + 133*t]];
__syncthreads();
R[i + 267*t] = Op[i + 134*t] ? R[B[i + 134*t]] * R[C[i + 134*t]] : R[B[i + 134*t]] + R[C[i + 134*t]];
R[i + 268*t] = Op[i + 135*t] ? R[B[i + 135*t]] * R[C[i + 135*t]] : R[B[i + 135*t]] + R[C[i + 135*t]];
R[i + 269*t] = Op[i + 136*t] ? R[B[i + 136*t]] * R[C[i + 136*t]] : R[B[i + 136*t]] + R[C[i + 136*t]];
R[i + 270*t] = Op[i + 137*t] ? R[B[i + 137*t]] * R[C[i + 137*t]] : R[B[i + 137*t]] + R[C[i + 137*t]];
R[i + 271*t] = Op[i + 138*t] ? R[B[i + 138*t]] * R[C[i + 138*t]] : R[B[i + 138*t]] + R[C[i + 138*t]];
R[i + 272*t] = Op[i + 139*t] ? R[B[i + 139*t]] * R[C[i + 139*t]] : R[B[i + 139*t]] + R[C[i + 139*t]];
R[i + 273*t] = Op[i + 140*t] ? R[B[i + 140*t]] * R[C[i + 140*t]] : R[B[i + 140*t]] + R[C[i + 140*t]];
R[i + 274*t] = Op[i + 141*t] ? R[B[i + 141*t]] * R[C[i + 141*t]] : R[B[i + 141*t]] + R[C[i + 141*t]];
R[i + 275*t] = Op[i + 142*t] ? R[B[i + 142*t]] * R[C[i + 142*t]] : R[B[i + 142*t]] + R[C[i + 142*t]];
__syncthreads();
R[i + 276*t] = Op[i + 143*t] ? R[B[i + 143*t]] * R[C[i + 143*t]] : R[B[i + 143*t]] + R[C[i + 143*t]];
R[i + 277*t] = Op[i + 144*t] ? R[B[i + 144*t]] * R[C[i + 144*t]] : R[B[i + 144*t]] + R[C[i + 144*t]];
R[i + 278*t] = Op[i + 145*t] ? R[B[i + 145*t]] * R[C[i + 145*t]] : R[B[i + 145*t]] + R[C[i + 145*t]];
R[i + 279*t] = Op[i + 146*t] ? R[B[i + 146*t]] * R[C[i + 146*t]] : R[B[i + 146*t]] + R[C[i + 146*t]];
R[i + 280*t] = Op[i + 147*t] ? R[B[i + 147*t]] * R[C[i + 147*t]] : R[B[i + 147*t]] + R[C[i + 147*t]];
__syncthreads();
R[i + 281*t] = Op[i + 148*t] ? R[B[i + 148*t]] * R[C[i + 148*t]] : R[B[i + 148*t]] + R[C[i + 148*t]];
R[i + 282*t] = Op[i + 149*t] ? R[B[i + 149*t]] * R[C[i + 149*t]] : R[B[i + 149*t]] + R[C[i + 149*t]];
R[i + 283*t] = Op[i + 150*t] ? R[B[i + 150*t]] * R[C[i + 150*t]] : R[B[i + 150*t]] + R[C[i + 150*t]];
R[i + 284*t] = Op[i + 151*t] ? R[B[i + 151*t]] * R[C[i + 151*t]] : R[B[i + 151*t]] + R[C[i + 151*t]];
R[i + 285*t] = Op[i + 152*t] ? R[B[i + 152*t]] * R[C[i + 152*t]] : R[B[i + 152*t]] + R[C[i + 152*t]];
__syncthreads();
R[i + 286*t] = Op[i + 153*t] ? R[B[i + 153*t]] * R[C[i + 153*t]] : R[B[i + 153*t]] + R[C[i + 153*t]];
R[i + 287*t] = Op[i + 154*t] ? R[B[i + 154*t]] * R[C[i + 154*t]] : R[B[i + 154*t]] + R[C[i + 154*t]];
R[i + 288*t] = Op[i + 155*t] ? R[B[i + 155*t]] * R[C[i + 155*t]] : R[B[i + 155*t]] + R[C[i + 155*t]];
__syncthreads();
R[i + 289*t] = Op[i + 156*t] ? R[B[i + 156*t]] * R[C[i + 156*t]] : R[B[i + 156*t]] + R[C[i + 156*t]];
R[i + 290*t] = Op[i + 157*t] ? R[B[i + 157*t]] * R[C[i + 157*t]] : R[B[i + 157*t]] + R[C[i + 157*t]];
R[i + 291*t] = Op[i + 158*t] ? R[B[i + 158*t]] * R[C[i + 158*t]] : R[B[i + 158*t]] + R[C[i + 158*t]];
__syncthreads();
R[i + 292*t] = Op[i + 159*t] ? R[B[i + 159*t]] * R[C[i + 159*t]] : R[B[i + 159*t]] + R[C[i + 159*t]];
R[i + 293*t] = Op[i + 160*t] ? R[B[i + 160*t]] * R[C[i + 160*t]] : R[B[i + 160*t]] + R[C[i + 160*t]];
R[i + 294*t] = Op[i + 161*t] ? R[B[i + 161*t]] * R[C[i + 161*t]] : R[B[i + 161*t]] + R[C[i + 161*t]];
__syncthreads();
R[i + 295*t] = Op[i + 162*t] ? R[B[i + 162*t]] * R[C[i + 162*t]] : R[B[i + 162*t]] + R[C[i + 162*t]];
R[i + 296*t] = Op[i + 163*t] ? R[B[i + 163*t]] * R[C[i + 163*t]] : R[B[i + 163*t]] + R[C[i + 163*t]];
__syncthreads();
R[i + 297*t] = Op[i + 164*t] ? R[B[i + 164*t]] * R[C[i + 164*t]] : R[B[i + 164*t]] + R[C[i + 164*t]];
R[i + 298*t] = Op[i + 165*t] ? R[B[i + 165*t]] * R[C[i + 165*t]] : R[B[i + 165*t]] + R[C[i + 165*t]];
R[i + 299*t] = Op[i + 166*t] ? R[B[i + 166*t]] * R[C[i + 166*t]] : R[B[i + 166*t]] + R[C[i + 166*t]];
__syncthreads();
R[i + 300*t] = Op[i + 167*t] ? R[B[i + 167*t]] * R[C[i + 167*t]] : R[B[i + 167*t]] + R[C[i + 167*t]];
R[i + 301*t] = Op[i + 168*t] ? R[B[i + 168*t]] * R[C[i + 168*t]] : R[B[i + 168*t]] + R[C[i + 168*t]];
__syncthreads();
R[i + 302*t] = Op[i + 169*t] ? R[B[i + 169*t]] * R[C[i + 169*t]] : R[B[i + 169*t]] + R[C[i + 169*t]];
R[i + 303*t] = Op[i + 170*t] ? R[B[i + 170*t]] * R[C[i + 170*t]] : R[B[i + 170*t]] + R[C[i + 170*t]];
__syncthreads();
R[i + 304*t] = Op[i + 171*t] ? R[B[i + 171*t]] * R[C[i + 171*t]] : R[B[i + 171*t]] + R[C[i + 171*t]];
R[i + 305*t] = Op[i + 172*t] ? R[B[i + 172*t]] * R[C[i + 172*t]] : R[B[i + 172*t]] + R[C[i + 172*t]];
__syncthreads();
R[i + 306*t] = Op[i + 173*t] ? R[B[i + 173*t]] * R[C[i + 173*t]] : R[B[i + 173*t]] + R[C[i + 173*t]];
R[i + 307*t] = Op[i + 174*t] ? R[B[i + 174*t]] * R[C[i + 174*t]] : R[B[i + 174*t]] + R[C[i + 174*t]];
__syncthreads();
R[i + 308*t] = Op[i + 175*t] ? R[B[i + 175*t]] * R[C[i + 175*t]] : R[B[i + 175*t]] + R[C[i + 175*t]];
__syncthreads();
R[i + 309*t] = Op[i + 176*t] ? R[B[i + 176*t]] * R[C[i + 176*t]] : R[B[i + 176*t]] + R[C[i + 176*t]];
__syncthreads();
R[i + 310*t] = Op[i + 177*t] ? R[B[i + 177*t]] * R[C[i + 177*t]] : R[B[i + 177*t]] + R[C[i + 177*t]];
__syncthreads();
R[i + 311*t] = Op[i + 178*t] ? R[B[i + 178*t]] * R[C[i + 178*t]] : R[B[i + 178*t]] + R[C[i + 178*t]];
__syncthreads();
R[i + 312*t] = Op[i + 179*t] ? R[B[i + 179*t]] * R[C[i + 179*t]] : R[B[i + 179*t]] + R[C[i + 179*t]];
__syncthreads();
R[i + 313*t] = Op[i + 180*t] ? R[B[i + 180*t]] * R[C[i + 180*t]] : R[B[i + 180*t]] + R[C[i + 180*t]];
__syncthreads();
R[i + 314*t] = Op[i + 181*t] ? R[B[i + 181*t]] * R[C[i + 181*t]] : R[B[i + 181*t]] + R[C[i + 181*t]];
__syncthreads();
R[i + 315*t] = Op[i + 182*t] ? R[B[i + 182*t]] * R[C[i + 182*t]] : R[B[i + 182*t]] + R[C[i + 182*t]];
__syncthreads();
R[i + 316*t] = Op[i + 183*t] ? R[B[i + 183*t]] * R[C[i + 183*t]] : R[B[i + 183*t]] + R[C[i + 183*t]];
__syncthreads();
R[i + 317*t] = Op[i + 184*t] ? R[B[i + 184*t]] * R[C[i + 184*t]] : R[B[i + 184*t]] + R[C[i + 184*t]];
__syncthreads();
R[i + 318*t] = Op[i + 185*t] ? R[B[i + 185*t]] * R[C[i + 185*t]] : R[B[i + 185*t]] + R[C[i + 185*t]];
__syncthreads();
R[i + 319*t] = Op[i + 186*t] ? R[B[i + 186*t]] * R[C[i + 186*t]] : R[B[i + 186*t]] + R[C[i + 186*t]];
__syncthreads();
R[i + 320*t] = Op[i + 187*t] ? R[B[i + 187*t]] * R[C[i + 187*t]] : R[B[i + 187*t]] + R[C[i + 187*t]];
__syncthreads();
R[i + 321*t] = Op[i + 188*t] ? R[B[i + 188*t]] * R[C[i + 188*t]] : R[B[i + 188*t]] + R[C[i + 188*t]];
__syncthreads();
R[i + 322*t] = Op[i + 189*t] ? R[B[i + 189*t]] * R[C[i + 189*t]] : R[B[i + 189*t]] + R[C[i + 189*t]];
__syncthreads();
R[i + 323*t] = Op[i + 190*t] ? R[B[i + 190*t]] * R[C[i + 190*t]] : R[B[i + 190*t]] + R[C[i + 190*t]];
__syncthreads();
R[i + 324*t] = Op[i + 191*t] ? R[B[i + 191*t]] * R[C[i + 191*t]] : R[B[i + 191*t]] + R[C[i + 191*t]];
if (i==0) { final += R[324*t]; }
__syncthreads();
}
if (i==0) { A[0]= final;}
}
|
11,543 | #include <time.h>
#include <stdio.h>
#include <stdlib.h>
#define RADIUS 30
#define NUM_ELEMENTS 1000000
#define NUM_THREADS_PER_BLOCK 32
static void handleError(cudaError_t err, const char *file, int line ) {
if (err != cudaSuccess) {
printf("%s in %s at line %d\n", cudaGetErrorString(err), file, line);
exit(EXIT_FAILURE);
}
}
#define cudaCheck( err ) (handleError(err, __FILE__, __LINE__ ))
__device__ __host__ int in_range(int idx) {
if (idx >= 0 && idx <= NUM_ELEMENTS -1)
return 1;
else
return 0;
}
__device__ __host__ void algo(int i, int *in, int *out) {
for(int j = 0; j <= RADIUS; j++) {
int idx1 = i - j;
if (in_range(idx1))
out[i] += in[idx1];
if (j == 0)
continue;
int idx2 = i + j;
if (!in_range(idx2))
out[i] += in[idx2];
}
}
__global__ void stencil_1d(int *in, int *out) {
int i = threadIdx.x + (blockIdx.x * blockDim.x);
// int all = blockDim.x * gridDim.x;
if (i == 0)
printf("ZERO\n");
if(i == NUM_ELEMENTS - 1)
printf("MAX\n");
algo(i, in, out);
}
void cpu_stencil_1d(int *in, int *out) {
for (int i = 0; i < NUM_ELEMENTS; i++) {
algo(i, in, out);
}
}
int compare(int * r1, int * r2) {
int diffs = 0;
for (int i = 0; i < NUM_ELEMENTS; i++) {
if (r1[i] != r2[i])
diffs++;
}
return diffs;
}
int main() {
//PUT YOUR CODE HERE - INPUT AND OUTPUT ARRAYS
cudaEvent_t start, stop;
cudaEventCreate(&start);
cudaEventCreate(&stop);
cudaEventRecord( start, 0 );
int *dev_in, *dev_out;
int BYTES_NUM = sizeof(int) * NUM_ELEMENTS;
int *host_in = (int*)malloc(BYTES_NUM);
int *host_out = (int*)malloc(BYTES_NUM);
int *from_dev_out = (int*)malloc(BYTES_NUM);
//PUT YOUR CODE HERE - DEVICE MEMORY ALLOCATION
cudaMalloc((void**)&dev_in, BYTES_NUM);
cudaMalloc((void**)&dev_out, BYTES_NUM);
//PUT YOUR CODE HERE - KERNEL EXECUTION
for(int i = 0; i < NUM_ELEMENTS; i++) {
host_in[i] = i;
}
cudaMemcpy(dev_in, host_in, BYTES_NUM, cudaMemcpyHostToDevice);
int num_blocks = NUM_ELEMENTS / NUM_THREADS_PER_BLOCK + 1;
stencil_1d<<<num_blocks, NUM_THREADS_PER_BLOCK>>>(dev_in, dev_out);
// blockDim.x,y,z gives the number of threads in a block, in the particular direction
// gridDim.x,y,z gives the number of blocks in a grid, in the particular direction
cudaCheck(cudaPeekAtLastError());
//PUT YOUR CODE HERE - COPY RESULT FROM DEVICE TO HOST
cudaMemcpy(from_dev_out, dev_out, BYTES_NUM, cudaMemcpyDeviceToHost);
cudaEventRecord(stop, 0);
cudaEventSynchronize(stop);
float elapsedTime;
cudaEventElapsedTime( &elapsedTime, start, stop);
printf("Total GPU execution time: %3.1f ms\n", elapsedTime);
cudaEventDestroy(start);
cudaEventDestroy(stop);
//PUT YOUR CODE HERE - FREE DEVICE MEMORY
cudaFree(dev_in);
cudaFree(dev_out);
struct timespec cpu_start, cpu_stop;
clock_gettime(CLOCK_PROCESS_CPUTIME_ID, &cpu_start);
cpu_stencil_1d(host_in, host_out);
clock_gettime(CLOCK_PROCESS_CPUTIME_ID, &cpu_stop);
double result = (cpu_stop.tv_sec - cpu_start.tv_sec) * 1e3 + (cpu_stop.tv_nsec - cpu_start.tv_nsec) / 1e6;
printf( "CPU execution time: %3.1f ms\n", result);
int diffs = compare(from_dev_out, host_out);
if (diffs != 0)
printf("BAD: diff: %d/%d\n", diffs, NUM_ELEMENTS);
else
puts("OK, 0 diffs!\n");
return 0;
}
|
11,544 |
#include <stdio.h>
#include <math.h>
#include <float.h>
#include <cuda.h>
#include <cuda_runtime.h>
#define GPU_MEMSIZE_GB 2
#define GLOBAL_MEM_USE_MB 773
#define MEM_USE_PER_THREAD_B 1280
#define MAX_XSIZE_POSSIBLE floor(((GPU_MEMSIZE_GB * 1000 - GLOBAL_MEM_USE_MB)*1000000)/MEM_USE_PER_THREAD_B)
//#define XSIZE 1201
//#define YSIZE 801
#define RADIUS 100
#define RADSTEP 1
#define ANGLESIZE 36
#define PI 3.14
//---------------------------Function declarations--------------------------------------------------------------------------//
__global__ void getMatrix(int* data,float* angle,float* anisotropy,float* azimuth,size_t XSIZE,size_t YSIZE);
//--------------------------------------------------------------------------------------------------------------------------//
//Current Usage:
//Global Memory: 773 MB
//Memory per Thread: 1.28 KiloBytes
// Thread Memory Usage =Total Threads * Memory Per Thread
// = 1001 * 601 * 1.28KB
// = 770.05 MB
__global__ void getMatrix(int* data,float* angle,float* anisotropy,float* azimuth,size_t XSIZE,size_t YSIZE)
{
//Actual computation
int xrad,yrad,xradOrtho1,yradOrtho1,xradOneEighty,yradOneEighty,valueOneEighty;
int valueOrtho1,valueOrtho2,xradOrtho2,yradOrtho2,i,j;
float variance[100];
float orientation[100];
float ortho[100];
float value,sum_value,avg_value;
float sum_valueOrtho,avg_valueOrtho;
sum_value = 0;
avg_value = 0;
sum_valueOrtho = 0;
avg_valueOrtho = 0;
int x = (blockIdx.x * blockDim.x) + threadIdx.x;
int y = (blockIdx.y * blockDim.y) + threadIdx.y;
if((y>(YSIZE - RADIUS - 1))||(y<(RADIUS))) return;
else if((x>(XSIZE - RADIUS - 1))||(x<(RADIUS))) return;
else
{
for(i=0;i<100;i++){
variance[i] = FLT_MAX;
ortho[i] = FLT_MAX;
orientation[i] = FLT_MAX;
}
//Flipped
for(i=0;i<ANGLESIZE;i++) {
//Initializing to 0 so that the sum is zero everytime it starts
sum_value = 0;
sum_valueOrtho = 0;
for(j = 0;j<RADIUS;j+=RADSTEP) {
//Computation for angle of interest
xrad = (int)lrintf(cosf(angle[i]) * (j+1) + x);
yrad = (int)lrintf(sinf(angle[i]) * (j+1) + y);
value = data[y * XSIZE + x] - data[yrad * XSIZE + xrad];
value = value * value;
//One eighty angle computation
xradOneEighty = (int)lrintf(cosf(angle[i]+PI) * (j+1) + x);
yradOneEighty = (int)lrintf(sinf(angle[i]+PI) * (j+1) + y);
valueOneEighty = data[y * XSIZE + x] - data[yradOneEighty * XSIZE + xradOneEighty];
valueOneEighty = valueOneEighty * valueOneEighty;
sum_value = sum_value + value + valueOneEighty;
avg_value = sum_value/(2*(j+1)); //the average variance from scale 1 to scale j
//Computation for values on angle orthogonal to angle of interest
xradOrtho1 = (int)lrintf(cosf(angle[i]+PI/2) * (j+1) + x);
yradOrtho1 = (int)lrintf(sinf(angle[i]+PI/2) * (j+1) + y);
valueOrtho1 = data[y * XSIZE + x] - data[yradOrtho1 * XSIZE + xradOrtho1];
valueOrtho1 = valueOrtho1 * valueOrtho1;
//One eighty ortho angle computation
xradOrtho2 = (int)lrintf(cosf(angle[i]+PI*3/2) * (j+1) + x);
yradOrtho2 = (int)lrintf(sinf(angle[i]+PI*3/2) * (j+1) + y);
valueOrtho2 = data[y * XSIZE + x] - data[yradOrtho2 * XSIZE + xradOrtho2];
valueOrtho2 = valueOrtho2 * valueOrtho2;
sum_valueOrtho = sum_valueOrtho + valueOrtho1 + valueOrtho2;
avg_valueOrtho = sum_valueOrtho/(2*j+1);
//Fail safe to ensure there is no nan or inf when taking anisotropy ratio, later on.
if(avg_value == 0) {
if((avg_valueOrtho < 1) && (avg_valueOrtho > 0)) {
avg_value = avg_valueOrtho;
}
else {
avg_value = 1;
}
}
if(avg_valueOrtho == 0) {
avg_valueOrtho = 1;
}
//Determine if the variance is minimum compared to others at scale j, if so record it and its angle i. If not, pass it
if(avg_value < variance[j]) {
variance[j] = avg_value;
orientation[j] = angle[i];
ortho[j] = avg_valueOrtho;
}
}
}
for(j=0;j<RADIUS;j+=RADSTEP){
anisotropy[y * XSIZE * RADIUS/RADSTEP + x * RADIUS/RADSTEP + j] = ortho[j]/variance[j];
azimuth[y * XSIZE * RADIUS/RADSTEP + x * RADIUS/RADSTEP + j] = orientation[j] * 180/PI;
}
}
}
//--------------------------------------END OF KERNEL-----------------------------------------------------------//
int main()
{
//Setting the output buffer to 500MB
size_t limit;
cudaDeviceSetLimit(cudaLimitPrintfFifoSize, 500 * 1024 * 1024);
cudaDeviceGetLimit(&limit,cudaLimitPrintfFifoSize);
//File declarations and opening them
FILE *datTxt1,*datTxt,*outputAnisotropy00,*outputAnisotropy09,*outputAnisotropy49,*outputAnisotropy99;
FILE *outputAzimuth00,*outputAzimuth09,*outputAzimuth49,*outputAzimuth99;
FILE * inpCheck;
inpCheck = fopen("inpCheck.txt","w");
if(inpCheck == NULL) {
perror("Cannot open dat.txt file");
return (-1);
}
datTxt1 = fopen("dat.txt","r");
if(datTxt1 == NULL) {
printf("Cannot open dat.txt file\n");
exit(1);
}
outputAnisotropy00 = fopen("outputDataAni00.txt","w");
outputAnisotropy09 = fopen("outputDataAni09.txt","w");
outputAnisotropy49 = fopen("outputDataAni49.txt","w");
outputAnisotropy99 = fopen("outputDataAni99.txt","w");
if((outputAnisotropy00 == NULL)||(outputAnisotropy09 == NULL)||(outputAnisotropy49 == NULL)||(outputAnisotropy99 == NULL)) {
perror("Cannot open Anisotropy file");
return (-1);
}
outputAzimuth00 = fopen("outputDataAzi00.txt","w");
outputAzimuth09 = fopen("outputDataAzi09.txt","w");
outputAzimuth49 = fopen("outputDataAzi49.txt","w");
outputAzimuth99 = fopen("outputDataAzi99.txt","w");
if((outputAzimuth00 == NULL)||(outputAzimuth09 == NULL)||(outputAzimuth49 == NULL)||(outputAzimuth99 == NULL)) {
perror("Cannot open Azimuth file");
return (-1);
}
//-----------Getting total rows and columns in the data file---------------------------------------------------------------------------------------------------//
int XSIZE,YSIZE;
XSIZE = 0;
YSIZE = 0;
int i,j;
//Counting number of columns(x)
char* max_line;
max_line = (char*)malloc(MAX_XSIZE_POSSIBLE);
memset(max_line,'\0',sizeof(max_line));
fgets(max_line,MAX_XSIZE_POSSIBLE,datTxt1)!=NULL;
while(*max_line)if(*max_line++ == ' ')++XSIZE;
XSIZE+=1;
//Counting number of rows(y)
do{
i = fgetc(datTxt1);
if(i == '\n') YSIZE++;
}while(i != EOF);
YSIZE+=1;
fclose(datTxt1);
datTxt = fopen("dat.txt","r");
if(datTxt == NULL) {
printf("Cannot open dat.txt file\n");
exit(1);
}
//-----------------------Checking if the data size fits the memory of the GPU----------------------------------------------------------------------------------------//
printf("(XSIZE,YSIZE):(%d,%d)\n",XSIZE,YSIZE);
//printf("Maximum size possible = %f\nTotal size of current data(XSIZE * YSIZE) = %zd\n",MAX_XSIZE_POSSIBLE,XSIZE * YSIZE);
//(MAX_XSIZE_POSSIBLE - XSIZE*YSIZE >0)? printf("There is enough memory for the computation\n"):printf("There is not enough memory and may result in incorrect results\n");
//--------------------------------------------------------------------------------------------------------------------------------------------------------------------//
int data[YSIZE][XSIZE];
//XSIZE ints in a row which are max of 5 digits
//with a space in the front and the back and space
//between each number
char *startPtr,*endPtr;
char line[(XSIZE-1) * 5 +2+(XSIZE-1)];
memset(line, '\0', sizeof(line));
int Value;
i = 0;
j = 0;
//Assuming each number in the data set has a max of 5 characters
char tempVal[5];
memset(tempVal,'\0',sizeof(tempVal));
printf("Working1\n");
while(fgets(line,(XSIZE-1) *5 + 2 + (XSIZE-1),datTxt)!=NULL) {
printf("Working2\n");
startPtr = line;
for(i=0;i<XSIZE;i++) {
Value = 0;
memset(tempVal,'\0',sizeof(tempVal));
if(i != (XSIZE - 1)) {
endPtr = strchr(startPtr,' ');
strncpy(tempVal,startPtr,endPtr-startPtr);
Value = atoi(tempVal);
data[j][i] = Value;
fprintf(inpCheck,"%d ",Value);
printf("%d\n",Value);
endPtr = endPtr + 1;
startPtr = endPtr;
}
else if(i == (XSIZE - 1)){
strcpy(tempVal,startPtr);
Value = atoi(tempVal);
data[j][i] = Value;
fprintf(inpCheck,"%d\n",Value);
}
}
j++;
}
//------------------------------------Matrix Declarations--------------------------------------------------------------------------------------------------------------//
float angle[ANGLESIZE];
for(int i=0;i<ANGLESIZE;i++) {
angle[i] = i * 5 * PI/180;
//printf("%d :: %f\n",i,angle[i]);
}
float* anisotropy;
anisotropy = (float*)malloc(YSIZE * XSIZE * RADIUS/RADSTEP * sizeof(float));
float *azimuth;
azimuth = (float*)malloc(YSIZE * XSIZE * RADIUS/RADSTEP * sizeof(float));
//anisotropy[0][0][99] = 834;
//--------------------------------------CUDA-------------------------------------------------------------------------------------------------------------------------//
int *data_ptr;
float *anisotropy_ptr,*azimuth_ptr,*angle_ptr;
/*
float *anisotropy_ptrH;
anisotropy_ptrH = &anisotropy[0][0][0];
float *azimuth_ptrH;
azimuth_ptrH = &azimuth[0][0][0];
*/
cudaMalloc((void**)&data_ptr,XSIZE * YSIZE * sizeof(int));
cudaMemcpy(data_ptr,data,XSIZE * YSIZE * sizeof(int),cudaMemcpyHostToDevice);
cudaMalloc((void**)&angle_ptr,ANGLESIZE * sizeof(float));
cudaMemcpy(angle_ptr,angle,ANGLESIZE * sizeof(float),cudaMemcpyHostToDevice);
cudaMalloc((void**)&anisotropy_ptr,YSIZE * XSIZE * RADIUS/RADSTEP * sizeof(float));
cudaMalloc((void**)&azimuth_ptr,YSIZE * XSIZE * RADIUS/RADSTEP * sizeof(float));
printf("Hello1\n");
dim3 gridSize(3,YSIZE,1);
dim3 blockSize(512,1,1);
printf("Hello2\n");
getMatrix<<<gridSize,blockSize>>>(data_ptr,angle_ptr,anisotropy_ptr,azimuth_ptr,XSIZE,YSIZE);
cudaDeviceSynchronize();
cudaError_t error = cudaGetLastError();
if(error != cudaSuccess)
{
printf("CUDA Error: %s\n", cudaGetErrorString(error));
// we can't recover from the error -- exit the program
return 0;
}
printf("Hello3\n");
cudaMemcpy(anisotropy,anisotropy_ptr,YSIZE * XSIZE * RADIUS/RADSTEP * sizeof(float),cudaMemcpyDeviceToHost);
cudaMemcpy(azimuth,azimuth_ptr,YSIZE * XSIZE * RADIUS/RADSTEP * sizeof(float),cudaMemcpyDeviceToHost);
printf("Hello4\n");
cudaFree(data_ptr);
cudaFree(angle_ptr);
cudaFree(azimuth_ptr);
cudaFree(anisotropy_ptr);
printf("Hello5\n");
//--------------------------------------------------------------------------------------------------------------------------------------------------------------------//
// Writing to files
for(j=0;j<YSIZE ;j++) {
for(i=0;i<XSIZE ;i++) {
if((j>(YSIZE - RADIUS - 1))||(j<(RADIUS))) continue;
if((i>(XSIZE - RADIUS - 1))||(i<(RADIUS))) continue;
if (i == (XSIZE - RADIUS - 1)) {
fprintf(outputAnisotropy00,"%f",anisotropy[j * XSIZE * RADIUS/RADSTEP + i * RADIUS/RADSTEP + 0]);
fprintf(outputAzimuth00,"%f",azimuth[j * XSIZE * RADIUS/RADSTEP + i * RADIUS/RADSTEP + 0]);
fprintf(outputAnisotropy00,"\n");
fprintf(outputAzimuth00,"\n");
fprintf(outputAnisotropy09,"%f",anisotropy[j * XSIZE * RADIUS/RADSTEP + i * RADIUS/RADSTEP + 9]);
fprintf(outputAzimuth09,"%f",azimuth[j * XSIZE * RADIUS/RADSTEP + i * RADIUS/RADSTEP + 9]);
fprintf(outputAnisotropy09,"\n");
fprintf(outputAzimuth09,"\n");
fprintf(outputAnisotropy49,"%f",anisotropy[j * XSIZE * RADIUS/RADSTEP + i * RADIUS/RADSTEP + 49]);
fprintf(outputAzimuth49,"%f",azimuth[j * XSIZE * RADIUS/RADSTEP + i * RADIUS/RADSTEP + 49]);
fprintf(outputAnisotropy49,"\n");
fprintf(outputAzimuth49,"\n");
fprintf(outputAnisotropy99,"%f",anisotropy[j * XSIZE * RADIUS/RADSTEP + i * RADIUS/RADSTEP + 99]);
fprintf(outputAzimuth99,"%f",azimuth[j * XSIZE * RADIUS/RADSTEP + i * RADIUS/RADSTEP + 99]);
fprintf(outputAnisotropy99,"\n");
fprintf(outputAzimuth99,"\n");
}
else {
fprintf(outputAnisotropy00,"%f",anisotropy[j * XSIZE * RADIUS/RADSTEP + i * RADIUS/RADSTEP + 0]);
fprintf(outputAzimuth00,"%f",azimuth[j * XSIZE * RADIUS/RADSTEP + i * RADIUS/RADSTEP + 0]);
fprintf(outputAnisotropy00,"\t");
fprintf(outputAzimuth00,"\t");
fprintf(outputAnisotropy09,"%f",anisotropy[j * XSIZE * RADIUS/RADSTEP + i * RADIUS/RADSTEP + 9]);
fprintf(outputAzimuth09,"%f",azimuth[j * XSIZE * RADIUS/RADSTEP + i * RADIUS/RADSTEP + 9]);
fprintf(outputAnisotropy09,"\t");
fprintf(outputAzimuth09,"\t");
fprintf(outputAnisotropy49,"%f",anisotropy[j * XSIZE * RADIUS/RADSTEP + i * RADIUS/RADSTEP + 49]);
fprintf(outputAzimuth49,"%f",azimuth[j * XSIZE * RADIUS/RADSTEP + i * RADIUS/RADSTEP + 49]);
fprintf(outputAnisotropy49,"\t");
fprintf(outputAzimuth49,"\t");
fprintf(outputAnisotropy99,"%f",anisotropy[j * XSIZE * RADIUS/RADSTEP + i * RADIUS/RADSTEP + 99]);
fprintf(outputAzimuth99,"%f",azimuth[j * XSIZE * RADIUS/RADSTEP + i * RADIUS/RADSTEP + 99]);
fprintf(outputAnisotropy99,"\t");
fprintf(outputAzimuth99,"\t");
}
}
}
fclose(datTxt);
fclose(inpCheck);
fclose(outputAnisotropy00);
fclose(outputAnisotropy09);
fclose(outputAnisotropy49);
fclose(outputAnisotropy99);
fclose(outputAzimuth00);
fclose(outputAzimuth09);
fclose(outputAzimuth49);
fclose(outputAzimuth99);
//free(max_line);
free(anisotropy);
free(azimuth);
size_t free_byte ;
size_t total_byte ;
cudaMemGetInfo( &free_byte, &total_byte );
double free_db = (double)free_byte;
double total_db = (double)total_byte;
double used_db = total_db - free_db;
printf("GPU memory usage: used = %f, free = %f MB, total = %f MB\n",used_db/1024.0/1024.0, free_db/1024.0/1024.0, total_db/1024.0/1024.0);
return 0;
}
|
11,545 | #include "gpuerrchk.cuh"
#include "real.h"
__global__ void ch9_strat2_kernel(char* buffer,unsigned int* histo, size_t inputsize){
unsigned int tid = threadIdx.x +blockIdx.x* blockDim.x;
for (int i=tid; i< inputsize; i+= blockDim.x*gridDim.x ){
int alphabet_position=buffer[i]-'a';
if (alphabet_position >=0 && alphabet_position < 26)
atomicAdd(&histo[alphabet_position/4], 1);
}
}
void ch9_strat2(char* buffer, unsigned int* histo,size_t inputsize){
ch9_strat2_kernel<<<1, 512 >>>(buffer,histo,inputsize);
gpuErrchk(cudaPeekAtLastError());
}
|
11,546 | #include "includes.h"
/*
* matrix multiplication C += A*B
* -> CUDA kernel
* (implementation adopted from Kirk&Hwu:
* "Programming Massively Parallel Processors, chapter 4)
* -> Features: none (basic tiled version, using only global memory)
*/
/*
* matrix multiplication C += A*B
* -> CUDA kernel
* (implementation adopted from Kirk&Hwu:
* "Programming Massively Parallel Processors, chapter 5)
* -> Features:
* - tiled matrix multiplication with use of shared memory
*/
/*
* matrix multiplication C += A*B
* -> CUDA kernel
* (implementation adopted from Kirk&Hwu:
* "Programming Massively Parallel Processors, chapter 5)
* -> Features:
* - tiled matrix multiplication with use of shared memory
* - coalesced memory access
* - overlapping loads of subsequent tile pairs (using registers & shared memory)
*/
__global__ void matrixMultKernel_overlap(float* Ad, float* Bd, float* Cd, int n)
{
__shared__ float A_shared[TILE_SIZE][TILE_SIZE];
__shared__ float B_shared[TILE_SIZE][TILE_SIZE];
int row = blockIdx.y*TILE_SIZE + threadIdx.y;
int column = blockIdx.x*TILE_SIZE + threadIdx.x;
if(row >= n || column >=n)
{
return;
}
float Celem = 0.0;
float reg_1 = *(Ad + row*n + threadIdx.x);
float reg_2 = *(Bd + threadIdx.y*n + column);
for(int m = 1;m<n/TILE_SIZE;m++)
{
A_shared[threadIdx.y][threadIdx.x] = reg_1;
B_shared[threadIdx.y][threadIdx.x] = reg_2;
__syncthreads();
reg_1 = *(Ad + row*n + m*TILE_SIZE + threadIdx.x);
reg_2 = *(Bd + (m*TILE_SIZE + threadIdx.y)*n + column);
for(int k = 0;k<TILE_SIZE;k++)
{
Celem += A_shared[threadIdx.y][k]*B_shared[k][threadIdx.x];
}
__syncthreads();
}
A_shared[threadIdx.y][threadIdx.x] = reg_1;
B_shared[threadIdx.y][threadIdx.x] = reg_2;
__syncthreads();
for(int k = 0;k<TILE_SIZE;k++)
{
Celem += A_shared[threadIdx.y][k]*B_shared[k][threadIdx.x];
}
__syncthreads();
*(Cd + row*n + column) = Celem;
} |
11,547 | #include <stdio.h>
#include <iostream>
#include <cuda.h>
#include <cuda_runtime.h>
class MyClass;
__global__ void kernel(int *a, unsigned int N);
class MyClass {
public:
MyClass(int len) {
length = len;
cudaMalloc((void **)&d_data, sizeof(int)*length);
cudaMemset((void *)d_data, 0, sizeof(int)*length);
};
~MyClass() {
cudaFree((void *)d_data);
printf("%s\n","cudafree" );
};
void run(dim3 grid,dim3 block) {
kernel<<<grid, block>>>(d_data, length);
};
void set(int* h_data)
{
cudaMemcpy(d_data,h_data,sizeof(int)*length,cudaMemcpyHostToDevice);
}
int* getData(void) {
return d_data;
};
int getLength(void)
{
return length;
}
void show(void)
{
int h_data[length];
cudaMemcpy(h_data, getData(), sizeof(int)*length, cudaMemcpyDeviceToHost);
for (int i=0; i<length; i++) {
std::cout << h_data[i] << " ";
}
std::cout << std::endl;
}
public:
int *d_data;
int length;
};
__global__ void kernel(int *a, unsigned int N)
{
const unsigned int i = blockIdx.x*blockDim.x+threadIdx.x;
if (i<N) {
a[i] += i;
}
}
__global__ void kernel1(int *a, unsigned int N)
{
const unsigned int i = blockIdx.x*blockDim.x+threadIdx.x;
if (i<N) {
a[i] += 2*i;
}
}
class MyClass1:public MyClass
{
public:
MyClass1(int len):MyClass(len){};
void run(dim3 grid,dim3 block)
{
kernel1<<<grid, block>>>(d_data, length);
};
};
int main(void) {
int arraySize = 20;
int* testArr = new int[arraySize];
for (int i = 0; i < arraySize; ++i)
{
testArr[i] = i;
}
// MyClass c(arraySize); //直接声明的对象是定义在栈上的，会被自动释放
// c.run();
// c.show();
dim3 grid(1);
dim3 block(arraySize);
MyClass1 *c = new MyClass1(arraySize);
c->set(testArr);
c->run(grid,block);
c->show();
delete c; //用指针指向new出来的对象是存放在堆上的，必须要手动delete对象，否则对象不会被释放掉。
}
|
11,548 | //
// Created by miguel on 11/03/2021.
//
#include "MatUtil.cuh"
#define BLOCK_SIZE 16
__global__ void matrixMultiplyKernel(const double *a, const double *b, double *c, unsigned int m, unsigned int n, unsigned int k) {
unsigned int row = blockIdx.y * blockDim.y + threadIdx.y;
unsigned int col = blockIdx.x * blockDim.x + threadIdx.x;
if (row < m && col < k) {
double aux = .0;
for (int i = 0; i < n; i++) {
aux += a[row * n + i] * b[i * k + col];
}
c[row * k + col] = aux;
__syncthreads();
}
}
__global__ void matrixTransposeKernel(const double *a, double *b, unsigned int rows, unsigned int cols) {
unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
unsigned int idy = blockIdx.y * blockDim.y + threadIdx.y;
if (idx < cols && idy < rows) {
unsigned int pos = idy * cols + idx;
unsigned int transposedPos = idx * rows + idy;
b[transposedPos] = a[pos];
}
}
void MatUtil::matrixMultiply(double *a, double *b, double *c, unsigned int m, unsigned int n, unsigned int k) {
dim3 dimGrid((k + BLOCK_SIZE - 1) / BLOCK_SIZE, (m + BLOCK_SIZE - 1) / BLOCK_SIZE);
dim3 dimBlock(BLOCK_SIZE, BLOCK_SIZE);
matrixMultiplyKernel<<<dimGrid, dimBlock>>>(a, b, c, m, n, k);
}
void MatUtil::matrixTranspose(double *a, double *b, unsigned int rows, unsigned int cols) {
unsigned int n = rows * cols;
dim3 dim_grid((n - 1) / BLOCK_SIZE + 1, (n - 1) / BLOCK_SIZE + 1, 1);
dim3 dim_block(BLOCK_SIZE, BLOCK_SIZE, 1);
matrixTransposeKernel<<<dim_grid, dim_block>>>(a, b, rows, cols);
}
|
11,549 | #include<stdio.h>
__global__ void swap(int *M, int mat_size)
{
int i = blockIdx.y * blockDim.y + threadIdx.y;
int j = blockIdx.x * blockDim.x + threadIdx.x;
int N = mat_size;
if((i < N) && (j < N) && (j%2 == 0) && (j != N - 1))
{
int tmp = M[i * mat_size + j];
M[i * mat_size + j] = M[i*mat_size + j + 1];
M[i * mat_size + j + 1] = tmp;
}
__syncthreads();
}
__global__ void reflect(int *M, int mat_size)
{
int i = blockIdx.y * blockDim.y + threadIdx.y;
int j = blockIdx.x * blockDim.x + threadIdx.x;
int N = mat_size;
if((i < N) && (j < N) && (i > j))
{
M[j*mat_size + i] = M[i*mat_size + j];
}
__syncthreads();
} |
11,550 | #include <cuda_runtime_api.h>
#include <iostream>
__global__ void HelloGPU()
{
// Print a simple message from the GPU
printf("Hello from the GPU!\n");
}
int main()
{
std::cout << "==== Sample 01 - Hello GPU ====\n" << std::endl;
// Expected output: 12x "Hello from the GPU!\n"
// Launch a kernel with 1 block that has 12 threads
HelloGPU<<<1, 12>>>();
/*
Synchronize with GPU to wait for printf to finish.
Results of printf are buffered and copied back to
the CPU for I/O after the kernel has finished.
*/
cudaDeviceSynchronize();
return 0;
}
/*
Exercises:
1) Change the message that is printed by the kernel
2) Write a different kernel (different name, different message)
3) Call the different kernels multiple times
*/ |
11,551 | #include <iostream>
#include <cuda.h>
#include <cuda_runtime.h>
#include <cstdlib>
#define BLOCK_SIZE 128
#define CHECK(call) \
{ \
const cudaError_t error = call; \
if (error != cudaSuccess) { \
fprintf(stderr, "Error: %s:%d, ", __FILE__, __LINE__); \
fprintf(stderr, "code: %d, reason: %s\n", error, \
cudaGetErrorString(error)); \
exit(1); \
} \
}
using namespace std;
__global__ void getMin(float *input, size_t len, float *output_val,
size_t *output_idx) {
__shared__ float smem_val[BLOCK_SIZE];
__shared__ size_t smem_idx[BLOCK_SIZE];
int tx = threadIdx.x;
size_t i = tx + blockIdx.x * BLOCK_SIZE * 8;
float min_val = INFINITY;
size_t min_idx = i;
if (i < len) {
float a1, a2, a3, a4, a5, a6, a7, a8;
a1 = input[i];
min_val = a1;
min_idx = i;
if ((i + BLOCK_SIZE) < len) {
a2 = input[i + BLOCK_SIZE];
if (a2 < min_val) {
min_val = a2;
min_idx = i + BLOCK_SIZE;
}
}
if ((i + 2 * BLOCK_SIZE) < len) {
a3 = input[i + 2 * BLOCK_SIZE];
if (a3 < min_val) {
min_val = a3;
min_idx = i + 2 * BLOCK_SIZE;
}
}
if (i + 3 * BLOCK_SIZE < len) {
a4 = input[i + 3 * BLOCK_SIZE];
if (a4 < min_val) {
min_val = a4;
min_idx = i + 3 * BLOCK_SIZE;
}
}
if (i + 4 * BLOCK_SIZE < len) {
a5 = input[i + 4 * BLOCK_SIZE];
if (a5 < min_val) {
min_val = a5;
min_idx = i + 4 * BLOCK_SIZE;
}
}
if (i + 5 * BLOCK_SIZE < len) {
a6 = input[i + 5 * BLOCK_SIZE];
if (a6 < min_val) {
min_val = a6;
min_idx = i + 5 * BLOCK_SIZE;
}
}
if (i + 6 * BLOCK_SIZE < len) {
a7 = input[i + 6 * BLOCK_SIZE];
if (a7 < min_val) {
min_val = a7;
min_idx = i + 6 * BLOCK_SIZE;
}
}
if (i + 7 * BLOCK_SIZE < len) {
a8 = input[i + 7 * BLOCK_SIZE];
if (a8 < min_val) {
min_val = a8;
min_idx = i + 7 * BLOCK_SIZE;
}
}
}
smem_val[tx] = min_val;
smem_idx[tx] = min_idx;
__syncthreads();
// in-place reduction in shared memory
if (blockDim.x >= 1024 && tx < 512) {
if (smem_val[tx + 512] < smem_val[tx]) {
smem_val[tx] = min_val = smem_val[tx + 512];
smem_idx[tx] = min_idx = smem_idx[tx + 512];
}
}
__syncthreads();
if (blockDim.x >= 512 && tx < 256) {
if (smem_val[tx + 256] < smem_val[tx]) {
smem_val[tx] = min_val = smem_val[tx + 256];
smem_idx[tx] = min_idx = smem_idx[tx + 256];
}
}
__syncthreads();
if (blockDim.x >= 256 && tx < 128) {
if (smem_val[tx + 128] < smem_val[tx]) {
smem_val[tx] = min_val = smem_val[tx + 128];
smem_idx[tx] = min_idx = smem_idx[tx + 128];
}
}
__syncthreads();
if (blockDim.x >= 128 && tx < 64) {
if (smem_val[tx + 64] < smem_val[tx]) {
smem_val[tx] = min_val = smem_val[tx + 64];
smem_idx[tx] = min_idx = smem_idx[tx + 64];
}
}
__syncthreads();
// unrolling warp
if (tx < 32) {
volatile float *vsmem_val = smem_val;
volatile size_t *vsmem_idx = smem_idx;
if (vsmem_val[tx + 32] < vsmem_val[tx]) {
vsmem_val[tx] = min_val = vsmem_val[tx + 32];
vsmem_idx[tx] = min_idx = vsmem_idx[tx + 32];
}
if (vsmem_val[tx + 16] < vsmem_val[tx]) {
vsmem_val[tx] = min_val = vsmem_val[tx + 16];
vsmem_idx[tx] = min_idx = vsmem_idx[tx + 16];
}
if (vsmem_val[tx + 8] < vsmem_val[tx]) {
vsmem_val[tx] = min_val = vsmem_val[tx + 8];
vsmem_idx[tx] = min_idx = vsmem_idx[tx + 8];
}
if (vsmem_val[tx + 4] < vsmem_val[tx]) {
vsmem_val[tx] = min_val = vsmem_val[tx + 4];
vsmem_idx[tx] = min_idx = vsmem_idx[tx + 4];
}
if (vsmem_val[tx + 2] < vsmem_val[tx]) {
vsmem_val[tx] = min_val = vsmem_val[tx + 2];
vsmem_idx[tx] = min_idx = vsmem_idx[tx + 2];
}
if (vsmem_val[tx + 1] < vsmem_val[tx]) {
vsmem_val[tx] = min_val = vsmem_val[tx + 1];
vsmem_idx[tx] = min_idx = vsmem_idx[tx + 1];
}
}
if (tx == 0) {
output_val[blockIdx.x] = min_val;
output_idx[blockIdx.x] = min_idx;
}
}
int main(int argc, char *argv[]) {
if (argc < 2 || argc > 2) {
cout << "Usage: " << argv[0] << " size\n";
return -1;
}
const size_t len = 1L << (atoi(argv[1]));
float *h_a = (float *)malloc(len * sizeof(float));
if (h_a == nullptr) {
cout << "Cannot allocate memory\n";
exit(-1);
}
srand(0);
clock_t begin = clock();
for (size_t i = 0; i < len; ++i) {
h_a[i] = rand() / (float)RAND_MAX;
}
cout << "Elapsed time: " << double(clock() - begin) / CLOCKS_PER_SEC * 1000
<< " ms\n";
begin = clock();
size_t len_out = ceil((float)len / (BLOCK_SIZE * 8));
float *h_val = (float *)malloc(sizeof(float) * len_out);
size_t *h_idx = (size_t *)malloc(sizeof(size_t) * len_out);
float *d_a;
float *d_val;
size_t *d_idx;
CHECK(cudaMalloc((void **)&d_a, sizeof(float) * len));
CHECK(cudaMalloc((void **)&d_val, sizeof(float) * len_out));
CHECK(cudaMalloc((void **)&d_idx, sizeof(size_t) * len_out));
CHECK(cudaMemcpy(d_a, h_a, sizeof(float) * len, cudaMemcpyHostToDevice));
getMin<<<len_out, BLOCK_SIZE>>>(d_a, len, d_val, d_idx);
CHECK(cudaMemcpy(h_val, d_val, sizeof(float) * len_out,
cudaMemcpyDeviceToHost));
CHECK(cudaMemcpy(h_idx, d_idx, sizeof(size_t) * len_out,
cudaMemcpyDeviceToHost));
CHECK(cudaDeviceSynchronize());
float val = h_val[0];
size_t idx = h_idx[0];
for (size_t i = 0; i < len_out; ++i) {
if (h_val[i] < val) {
val = h_val[i];
idx = h_idx[i];
}
}
cout << "Elapsed time: " << double(clock() - begin) / CLOCKS_PER_SEC * 1000
<< " ms\n";
cout << "Number of elements: " << len << ", min val: " << val
<< ", min idx: " << idx << "\n";
// Free device
CHECK(cudaFree(d_a));
CHECK(cudaFree(d_val));
CHECK(cudaFree(d_idx));
// Free host
free(h_a);
free(h_val);
free(h_idx);
return 0;
}
|
11,552 | #include<stdio.h>
#include<math.h>
#define N 2048
//Interleave addressing kernel version
__global__ void interleaved_reduce(int* d_in, int* d_out) {
int i = threadIdx.x;
int M = N/2;
__shared__ int sharedMem[N];
int id = blockIdx.x * blockDim.x + threadIdx.x;
sharedMem[i] = d_in[id];
__syncthreads();
for(int s = 1; s < N; s = s<<1) {
if(i < M) {
//printf("stride = %d, thread %d is active \n", s, i);
sharedMem[(2*s)*id] = sharedMem[(2*s)*id] + sharedMem[(2*s)*id+s];
}
__syncthreads();
M = M/2;
}
if(i == 0)
d_out[0] = sharedMem[0];
}
//Contiguous addressing kernel version
__global__ void contiguous_reduce(int* d_in, int* d_out){
int i = threadIdx.x;
int M = N/2;
__shared__ int sharedMem[N];
int id = blockIdx.x * blockDim.x + threadIdx.x;
sharedMem[i] = d_in[id];
__syncthreads();
for(int s = M; s > 0; s = s>>1) {
if(i < M) {
//printf("stride = %d, thread %d is active \n", s, i);
sharedMem[id] = sharedMem[id] + sharedMem[id+s];
}
__syncthreads();
M = M/2;
}
if(i == 0)
d_out[0] = sharedMem[0];
}
int main()
{
int h_in[N];
int h_out = 0;
for(int i = 0; i < N; i++)
h_in[i] = i+1;
int *d_in, *d_out;
cudaEvent_t start, stop;
cudaEventCreate(&start);
cudaEventCreate(&stop);
cudaMalloc((void**) &d_in, N*sizeof(int));
cudaMalloc((void**) &d_out, sizeof(int));
cudaMemcpy(d_in, &h_in, N*sizeof(int), cudaMemcpyHostToDevice);
cudaEventRecord(start);
//kernel call
//interleaved_reduce<<<1, 1024>>>(d_in, d_out);
contiguous_reduce<<<1, 1024>>>(d_in, d_out);
cudaEventRecord(stop);
cudaEventSynchronize(stop);
float milliseconds = 0;
cudaEventElapsedTime(&milliseconds, start, stop);
cudaMemcpy(&h_out, d_out, sizeof(int), cudaMemcpyDeviceToHost);
cudaFree(d_in);
cudaFree(d_out);
//printf("Output: %d\n", h_out);
printf("%f\n", milliseconds);
return -1;
}
|
11,553 | /*
* simple_hello.cu
* Copyright 1993-2010 NVIDIA Corporation.
* All right reserved
*/
#include <stdio.h>
#include <stdlib.h>
int main( void )
{
int deviceCount;
cudaGetDeviceCount( &deviceCount );
printf("Hello, NVIDIA DLI Workshop! You have %d devices\n", deviceCount );
return 0;
}
|
11,554 | #include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <stdio.h>
//#include <conio.h>
#include <stdlib.h>
#include <time.h>
#include <math.h>
#define THREADSX 32
#define THREADSY 32
#define cudaCheckError() { \
cudaError_t e = cudaGetLastError(); \
if (e != cudaSuccess) { \
printf("CUDA error %s:%d: %s\n", __FILE__, __LINE__, \
cudaGetErrorString(e)); \
exit(1); \
} \
}
__global__ void MatrixMulKernel(double *A_d, double *C_d, int m, int n) {
int row = blockIdx.y * blockDim.y + threadIdx.y;
int col = blockIdx.x * blockDim.x + threadIdx.x;
int k;
if ((row < n) && (col < n))
{
double sum = 0;
for (k = 0; k < m; k++)
{
sum += A_d[k*n + col] * A_d[k*n + row ];
}
C_d[row*n + col] = sum;
}
}
void MatrixMultiplication(double *a, double *mul, int m, int n) {
double BlockX;
double BlockY;
// int size = m * n * sizeof(double);
// printf("Size of A:%d\n",size);
// long int size2 = n * n * sizeof(double);
// printf("Size of Product:%ld\n",size2);
double *A_d, *C_d;
cudaMalloc((void**)&A_d, m * n * sizeof(double));
cudaCheckError();
cudaMemcpy(A_d, a, m * n * sizeof(double), cudaMemcpyHostToDevice);
cudaCheckError();
cudaMalloc((void**)&C_d, n * n * sizeof(double));
cudaCheckError();
//dim3 grid(1, 1);
//threadX = ceil(double(n / BLOCKX));
//threadY = ceil(double(n / BLOCKY));
BlockX = ceil((double)n / THREADSX);
BlockY = ceil((double)n / THREADSY);
if (THREADSX >= n)
{
BlockX = 1;
}
if (THREADSY >= n)
{
BlockY = 1;
}
dim3 grid(BlockX,BlockY);
dim3 block(THREADSX, THREADSY);
float time;
cudaEvent_t start, stop;
cudaEventCreate(&start);
cudaEventCreate(&stop);
cudaEventRecord(start, 0);
MatrixMulKernel <<< grid, block >>>(A_d, C_d, m, n);
cudaEventRecord(stop, 0);
cudaEventSynchronize(stop);
cudaEventElapsedTime(&time, start, stop);
printf("Time to generate :%f ms \n", time);
cudaCheckError();
cudaMemcpy(mul, C_d, n * n * sizeof(double), cudaMemcpyDeviceToHost);
cudaCheckError();
cudaFree(A_d);
cudaCheckError();
cudaFree(C_d);
cudaCheckError();
}
void CpuMatrixMultiplication(double *A, double *C, int m, int n){
double *mul_2;
mul_2 = (double*)malloc(sizeof(double)*n*n);
int i, j;
double diff = 0;
for (i = 0; i < n; i++)
{
for (j = 0; j < n; j++)
{
mul_2[i*n + j] = 0;
for (int k = 0; k < m; k++)
{
mul_2[i*n + j] = mul_2[i*n + j] + A[n*k + i] * A[k*n + j];
}
// printf("A[%d][%d]--->%lf", i, j, A[i*n + j]);
// printf("C[%d][%d]--->%lf\t", i, j, C[i*n + j]);
}
// printf("\n");
}
/* CALCULATE THE DIFFERENCE BETWEEN THE MATRIX CALCULATED FROM GPU AND THE MATRIX CALCULATED IN CPU*/
for (i = 0; i < n; i++)
{
for (j = 0; j < n; j++)
{
if (diff != 0)
{
break;
}
else
{
diff += C[i*n + j] - mul_2[i*n + j];
}
}
if (diff != 0)
{
break;
}
}
printf("diff --> %lf\n", diff);
free(mul_2);
}
int main(int argc,char* argv[]) {
int m, n, i, j;
//printf("Enter order of matrix A: ");
//scanf_s("%d%d", &m, &n);
if (argc == 3){
m = atoi(argv[1]);
n = atoi(argv[2]);
}
else{
printf("Error: Invalid number of arguments");
return 1;
}
double *A;
double *C;
time_t t;
A = (double*)malloc(sizeof(double)*m*n); // host memory for A
C = (double*)malloc(sizeof(double)*n*n); // host memory for C
srand((unsigned)time(&t));
for (i = 0; i < m; i++)
{
for (j = 0; j < n; j++)
{
// printf("Enter value of a[%d][%d]: ", i, j);
// scanf("%lf", &A[n*i + j]);
A[n*i + j] = ((double)rand() / (double)RAND_MAX);
// printf("A[%d][%d]-->%.4lf \t", i, j, A[n*i + j]);
}
// printf("\n");
}
printf("\tStart1\n" );
MatrixMultiplication(A,C, m, n);
// CpuMatrixMultiplication(A, C, m, n);
free(C);
free(A);
//_getch();
return 0;
}
|
11,555 | /********************************************************************************
* Trabalho 2: Programação Paralela para Processador Many-core (GPU) Usando CUDA
* Professora: Nahri Moreano
* Aluno: Ian Haranaka | RGA: 2018.1904.009-7
* Comando de compilação: nvcc solucao_inicial.cu -o solucao_inicial
********************************************************************************/
#include <iostream>
#include <fstream>
const int MAX_THREADS = 1024;
char *AlocaSequencia(int n);
__global__ void InicializaVetor(int n, int m, int *D);
__global__ void CalculaDistanciaEdicao(int n, int m, char *S, char *R, int *D);
int main(int argc, char *argv[])
{
std::ifstream entrada (argv[1]);
int n, m, resultado = 0;
char *h_S, *h_R;
// Leitura do arquivo de entrada
if (entrada.is_open())
{
entrada >> n >> m;
h_S = AlocaSequencia(n);
h_R = AlocaSequencia(m);
entrada >> &(h_S[1]) >> &(h_R[1]);
entrada.close();
}
else
{
std::cout << "Arquivo não encontrado!" << std::endl;
return -1;
}
int tam_vetor = (n+1) * (m+1);
// Aloca o vetor e as strings na memória global da GPU
int *d_D;
char *d_S, *d_R;
cudaMalloc(&d_D, tam_vetor * sizeof(int));
cudaMalloc(&d_S, (n+2) * sizeof(char));
cudaMalloc(&d_R, (m+2) * sizeof(char));
// Copia as strings para a memória global da GPU
cudaMemcpy(d_S, h_S, (n+2) * sizeof(char), cudaMemcpyHostToDevice);
cudaMemcpy(d_R, h_R, (m+2) * sizeof(char), cudaMemcpyHostToDevice);
// Invoca o kernel
int threads_por_bloco = n;
if (n > MAX_THREADS)
threads_por_bloco = MAX_THREADS;
InicializaVetor<<<1, 1>>>(n, m, d_D);
cudaDeviceSynchronize();
CalculaDistanciaEdicao<<<1, threads_por_bloco>>>(n, m, d_S, d_R, d_D);
cudaDeviceSynchronize();
cudaMemcpy(&resultado, &d_D[tam_vetor - 1], sizeof(int), cudaMemcpyDeviceToHost);
std::cout << resultado << std::endl;
// Libera o vetor e as strings
delete[] h_S;
delete[] h_R;
cudaFree(d_D);
cudaFree(d_S);
cudaFree(d_R);
return 0;
}
char *AlocaSequencia(int n)
{
char *seq = new (std::nothrow) char[n+2];
if (seq == nullptr)
std::cout << "Erro na alocação de memória!" << std::endl;
seq[0] = ' ';
return seq;
}
__global__ void InicializaVetor(int n, int m, int *D)
{
for (int i = 0, j = 0; i < (n+1) * (m+1); i += m+1, ++j)
D[i] = j;
for (int j = 0; j <= m; ++j)
D[j] = j;
}
__global__ void CalculaDistanciaEdicao(int n, int m, char *S, char *R, int *D)
{
int num_iteracoes = (n-1) / MAX_THREADS + 1;
int num_anti_diag, deslocamento;
int a, b, c, t, min;
int i, j;
for (int it = 0; it < num_iteracoes; ++it)
{
deslocamento = it * MAX_THREADS;
i = threadIdx.x + deslocamento + 1;
if (n - deslocamento > MAX_THREADS)
num_anti_diag = MAX_THREADS + m - 1;
else
num_anti_diag = n - deslocamento + m - 1;
for (int anti_diag = 2; anti_diag <= num_anti_diag + 1; anti_diag++)
{
j = anti_diag - (threadIdx.x + 1);
// Se é uma célula válida
if (i <= n && j > 0 && j <= m)
{
t = (S[i] != R[j] ? 1 : 0);
a = D[i*(m+1)+j-1] + 1;
b = D[(i-1)*(m+1)+j] + 1;
c = D[(i-1)*(m+1)+j-1] + t;
min = (a < b ? a : b);
min = (c < min ? c : min);
D[i*(m+1)+j] = min;
}
__syncthreads();
}
}
}
|
11,556 | #include "includes.h"
__global__ void sum(int *dest, int a, int b)
{
// Assuming a single thread, 1x1x1 block, 1x1 grid
*dest = a + b;
} |
11,557 | #include "includes.h"
__global__ void vecEps(float* a,const int N){
const int i = gridDim.x*blockDim.x*blockIdx.y + blockIdx.x*blockDim.x + threadIdx.x;
if(a[i] < EPS && i < N)
a[i] = EPS;
} |
11,558 | #include<stdio.h>
#include<math.h>
#define N 512
__global__ void inclusive_scan(int *d_in){
__shared__ int temp_in[N];
int i = threadIdx.x;
temp_in[i] = d_in[i];
__syncthreads();
for(unsigned int s = 1; s <= N-1; s <<= 1){
if((i >= s) && (i < N)) {
int a = temp_in[i];
int b = temp_in[i-s];
__syncthreads();
int c = a + b;
temp_in[i] = c;
}
__syncthreads();
}
d_in[i] = temp_in[i];
}
int main(){
int h_in[N];
int h_out[N];
for(int i=0; i < N; i++)
h_in[i] = 1;
int *d_in;
//int *d_out;
cudaMalloc((void**) &d_in, N*sizeof(int));
//cudaMalloc((void**) &d_out, N*sizeof(int));
cudaMemcpy(d_in, &h_in, N*sizeof(int), cudaMemcpyHostToDevice);
//Implementing kernel call
inclusive_scan<<<1, N>>>(d_in);
cudaMemcpy(&h_out, d_in, N*sizeof(int), cudaMemcpyDeviceToHost);
cudaFree(d_in);
//cudaFree(d_out);
for(int i=0; i<N; i++)
printf("out[%d] = %d\n", i, h_out[i]);
return -1;
} |
11,559 | /*
* BSD 2-Clause License
*
* Copyright (c) 2020, Alessandro Capotondi
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
* * Redistributions of source code must retain the above copyright notice, this
* list of conditions and the following disclaimer.
*
* * Redistributions in binary form must reproduce the above copyright notice,
* this list of conditions and the following disclaimer in the documentation
* and/or other materials provided with the distribution.
*
* 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.
*/
/**
* @file exercise1.c
* @author Alessandro Capotondi
* @date 27 Mar 2020
* @brief Exercise 1
*
* @see https://dolly.fim.unimore.it/2019/course/view.php?id=152
*/
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include <math.h>
#include <cuda_runtime.h>
/**
* @brief EX 1 - Launch CUDA kernel to "print" helloworld
*
* a) Detect global thread id. (tip: use threadIdx.x, blockDim.x, and blockIdx.x)
* b) Explore thread execution and schedule changing: N={8, 16, 32} and M={4,8,16}
*
* @return void
*/
__global__ void helloworld(void)
{
int gid = threadIdx.x + blockDim.x * blockIdx.x;
printf("Hello world, I am global thread %d (threadIdx=%d, blockIdx=%d, blockDim=%d)\n",
gid, threadIdx.x, blockIdx.x, blockDim.x);
}
#ifndef N
#define N 8
#endif
#ifndef BLOCK_SIZE
#define BLOCK_SIZE 4
#endif
int main(int argc, const char **argv)
{
helloworld<<<N / BLOCK_SIZE, BLOCK_SIZE>>>();
// CUDA exit -- needed to flush printf write buffer
cudaDeviceReset();
return 0;
}
|
11,560 | #include <cuda.h>
#include <cuda_runtime.h>
#include <iostream>
using namespace std;
struct Test {
int x;
int y;
};
__global__ void testfunc(Test t, int *z)
{
*z = t.x + t.y;
}
int main()
{
Test t;
t.x = 10;
t.y = 20;
int *z;
cudaMalloc((void **)&z, sizeof(int));
testfunc<<<1, 1>>>(t, z);
int hz;
cudaMemcpy(&hz, z, sizeof(int), cudaMemcpyDeviceToHost);
cout<<hz<<endl;
return 0;
}
|
11,561 | #include <time.h>
#include <iostream>
#include <math.h>
#include <vector>
using namespace std;
double Distance(vector<double> a, vector<double> b){
if(a.size() != b.size()){
cout<<"Error! Cannot do distance between vectors!"<<endl;
return -1;
}
double d = 0;
for(int i = 0; i < a.size(); i++){
d += (a[i] - b[i]) * (a[i] - b[i]);
}
return sqrt(d);
}
void FindBMU(vector<double> input, vector<vector<double> > v, int &x){
x = 0;
double dmin = Distance(input, v[0]);
double d = 100 * fabs(dmin);
for(int i = 0; i < v.size(); i++){
d = Distance(v[i], input);
if(d < dmin){
dmin = d;
x = i;
}
}
}
int main(int argc, char* argv[]){
int t_i = time(NULL);
srand(0);
int c = 1920;
vector<vector<double> > v(c);
for(int i = 0; i < v.size(); i++){
v[i].resize(c);
for(int j = 0; j < c; j++){
v[i][j] = rand();
}
}
int BMUx;
vector<double> training(c);
for(int l = 0; l < training.size(); l++){
training[l] = rand();
}
for(int k = 0; k < c; k++){
FindBMU(training, v, BMUx);
}
int t_f = time(NULL);
int total_time = t_f - t_i;
cout<<"Total Execution Time: "<<total_time<<endl;
}
|
11,562 | #define COALESCED_NUM 16
#define blockDimX 512
#define blockDimY 1
#define gridDimX (gridDim.x)
#define gridDimY (gridDim.y)
#define idx (blockIdx.x*blockDimX+threadIdx.x)
#define idy (blockIdx.y*blockDimY+threadIdx.y)
#define bidy (blockIdx.y)
#define bidx (blockIdx.x)
#define tidx (threadIdx.x)
#define tidy (threadIdx.y)
#define merger_y 1
#define coalesced_idy (bidy/(COALESCED_NUM/(merger_y*blockDimY))*COALESCED_NUM)
#define globalDimX 65536
#define globalDimY 1
__global__ void reduction(float * A, int size, int segSize)
{
#pragma gCompiler gValue segSize 262144
int k;
float sum;
int nidx;
__shared__ float shared_0[512];
nidx=((((tidx/16)*2048)+(idx&15))+((idx/512)*16));
float tmp_2;
float tmp_3;
float tmp_0;
float tmp_1;
sum=0;
for (k=0; k<size; k=(k+262144))
{
float r;
r=A[(nidx+k)];
sum+=r;
}
tmp_0=sum;
__syncthreads();
sum=0;
for (k=0; k<size; k=(k+262144))
{
float r;
r=A[((nidx+131072)+k)];
sum+=r;
}
tmp_1=sum;
__syncthreads();
float a;
float b;
float c;
a=tmp_0;
b=tmp_1;
c=(a+b);
tmp_2=c;
sum=0;
for (k=0; k<size; k=(k+262144))
{
float r;
r=A[((nidx+65536)+k)];
sum+=r;
}
tmp_0=sum;
__syncthreads();
sum=0;
for (k=0; k<size; k=(k+262144))
{
float r;
r=A[(((nidx+65536)+131072)+k)];
sum+=r;
}
tmp_1=sum;
__syncthreads();
a=tmp_0;
b=tmp_1;
c=(a+b);
tmp_3=c;
a=tmp_2;
b=tmp_3;
c=(a+b);
shared_0[(tidx+0)]=c;
__syncthreads();
if ((nidx<32768))
{
float a;
float b;
float c;
a=shared_0[(tidx+0)];
b=shared_0[(tidx+256)];
c=(a+b);
shared_0[(tidx+0)]=c;
}
__syncthreads();
if ((nidx<16384))
{
float a;
float b;
float c;
a=shared_0[(tidx+0)];
b=shared_0[(tidx+128)];
c=(a+b);
shared_0[(tidx+0)]=c;
}
__syncthreads();
if ((nidx<8192))
{
float a;
float b;
float c;
a=shared_0[(tidx+0)];
b=shared_0[(tidx+64)];
c=(a+b);
shared_0[(tidx+0)]=c;
}
__syncthreads();
if ((nidx<4096))
{
float a;
float b;
float c;
a=shared_0[(tidx+0)];
b=shared_0[(tidx+32)];
c=(a+b);
shared_0[(tidx+0)]=c;
}
__syncthreads();
if ((nidx<2048))
{
float a;
float b;
float c;
a=shared_0[(tidx+0)];
b=shared_0[(tidx+16)];
c=(a+b);
{
A[nidx]=c;
}
}
}
|
11,563 |
/* Daniel Diaz Giraldo
Restrictions
Mask = 5, Only works whit odd numbers and Mask size <= N _elements;
N_elements = defined by architecture from machine; (Femin-Maxwell....) in this case
i'm use a Kepler Arch; (the number of blocks that can support is around 2^31)
*/
#include <bits/stdc++.h>
#include <cuda.h>
#define N_elements 32
#define Mask_size 5
#define TILE_SIZE 1024
#define BLOCK_SIZE 1024
using namespace std;
__constant__ int Global_Mask[Mask_size];
//:::::::::::::::::::::::::::: Device Kernel Function ::::::::::::::::::::::::::::::
__global__ void convolution1d_tiles_constant_kernel(int *In, int *Out){
unsigned int index = blockIdx.x * blockDim.x + threadIdx.x; // Index 1d iterator.
__shared__ int Tile[TILE_SIZE + Mask_size - 1];
int n = Mask_size/2;
int halo_left_index = (blockIdx.x - 1 ) * blockDim.x + threadIdx.x;
if (threadIdx.x >= blockDim.x - n ){
Tile[threadIdx.x - (blockDim.x - n )] = (halo_left_index < 0) ? 0 : In[halo_left_index];
}
if(index<N_elements){Tile[n + threadIdx.x] = In[index];
}else{Tile[n + threadIdx.x] = 0;}
int halo_right_index = (blockIdx.x + 1 ) * blockDim.x + threadIdx.x;
if (threadIdx.x < n) {
Tile[n + blockDim.x + threadIdx.x]= (halo_right_index >= N_elements) ? 0 : In[halo_right_index];
}
__syncthreads();
int Value = 0;
for (unsigned int j = 0; j < Mask_size; j ++) {
Value += Tile[threadIdx.x + j] * Global_Mask[j];
}
Out[index] = Value;
}
__global__ void convolution1d_notile_noconstant_kernel(int *In, int *Out){
unsigned int index = blockIdx.x * blockDim.x + threadIdx.x; // Index 1d iterator.
int Value = 0;
int N_start_point = index - (Mask_size/2);
for ( int j = 0; j < Mask_size; j ++) {
if (N_start_point + j >= 0 && N_start_point + j < N_elements) {
Value += In[N_start_point + j] * Global_Mask[j];
}
}
Out[index] = Value;
}
__global__ void convolution1d_constant_simple_kernel(int *In, int *Out){
int i = blockIdx.x*blockDim.x + threadIdx.x;
__shared__ float N_ds[TILE_SIZE];
N_ds[threadIdx.x] = In[i];
__syncthreads();
int This_tile_start_point = blockIdx.x * blockDim.x;
int Next_tile_start_point = (blockIdx.x + 1) * blockDim.x;
int N_start_point = i - (Mask_size/2);
int Pvalue = 0;
for (int j = 0; j < Mask_size; j ++){
int N_index = N_start_point + j;
if (N_index >= 0 && N_index < N_elements){
if ((N_index >= This_tile_start_point) && (N_index < Next_tile_start_point)){
Pvalue += N_ds[threadIdx.x+j-(Mask_size/2)]*Global_Mask[j];
} else{ Pvalue += In[N_index] * Global_Mask[j]; }
}
}
Out[i] = Pvalue;
}
//:: Invocation Function
void d_convolution1d(int *In,int *Out,int *h_Mask,int op){
// Variables
int Size_of_bytes = N_elements * sizeof(int);
int *d_In, *d_Out;
float Blocksize=BLOCK_SIZE;
d_In = (int*)malloc(Size_of_bytes);
d_Out = (int*)malloc(Size_of_bytes);
// Memory Allocation in device
cudaMalloc((void**)&d_In,Size_of_bytes);
cudaMalloc((void**)&d_Out,Size_of_bytes);
// Memcpy Host to device
cudaMemcpy(d_In,In,Size_of_bytes,cudaMemcpyHostToDevice);
cudaMemcpy(d_Out,Out,Size_of_bytes,cudaMemcpyHostToDevice);
cudaMemcpyToSymbol(Global_Mask,h_Mask,Mask_size*sizeof(int)); // avoid cache coherence
// Thead logic and Kernel call
dim3 dimGrid(ceil(N_elements/Blocksize),1,1);
dim3 dimBlock(Blocksize,1,1);
if(op==1){
cout<<"convolution1d tile constant "<<endl;
convolution1d_tiles_constant_kernel<<<dimGrid,dimBlock>>>(d_In,d_Out);
}
if(op==2){
cout<<"convolution1d notile noconstant "<<endl;
convolution1d_notile_noconstant_kernel<<<dimGrid,dimBlock>>>(d_In,d_Out);
}
if (op==3) {
cout<<"convolution1d constant tile simple "<<endl;
convolution1d_constant_simple_kernel<<<dimGrid,dimBlock>>>(d_In,d_Out);
}
cudaDeviceSynchronize();
// save output result.
cudaMemcpy (Out,d_Out,Size_of_bytes,cudaMemcpyDeviceToHost);
// Free device memory
cudaFree(d_In);
cudaFree(d_Out);
//cudaFree(d_Mask);
}
//:::::::::::::::::::::::::::: Host Function ::::::::::::::::::::::::::::::
void h_Convolution_1d(int *In,int *Out, int *Mask){
for(int i=0;i<N_elements;i++){
int Gap=i-(Mask_size)/2; // asymmetric Gap (Left Right)
int Value=0;
for(int j=0;j<Mask_size;j++){
if(Gap+j >= 0 && j+Gap<N_elements){
Value+=In[Gap+j]*Mask[j];
}// end if
}// end for j
Out[i]=Value;
}// end for i
}
//:::::::::::::::::::::::::::: Rutinary Functions ::::::::::::::::::::::::::::::
void Fill_elements(int * VecIn1,int Value, int n){
for (int i = 0; i < n; i++) {
VecIn1[i]=Value;
}
}
void Show_vec(int *Vec,int Elements,char * Msg ){
cout<<Msg<<endl;
for (int i=0;i<Elements;i++){
if(i%10==0 && i!=0){
cout<<endl;
}
cout<<"["<<Vec[i]<<"] ";
}
cout<<endl;
}
int Check_op(int *A , int *B){
for(unsigned int i=0; i<N_elements;i++){
if(A[i]!=B[i]){
cout<<"Bad Work D:"<<endl;
return 0;
}
}
cout<<"Nice Work :D"<<endl;
return 1;
}
// :::::::::::::::::::::::::::::::::::Clock Function::::::::::::::::::::::::::::
double diffclock(clock_t clock1,clock_t clock2){
double diffticks=clock2-clock1;
double diffms=(diffticks)/(CLOCKS_PER_SEC/1); // /1000 mili
return diffms;
}
// :::::::::::::::::::::::::::::::::::::::Main::::::::::::::::::::::::::::::::::
int main(){
double T1,T2; // Time flags
clock_t start,end;// Time flags
int *VecIn1=(int*)malloc(N_elements*sizeof(int)); // Sequential and Parallel Vector Input
int *VecOut1=(int*)malloc(N_elements*sizeof(int)); // Sequential Vector Output
int *VecOut2=(int*)malloc(N_elements*sizeof(int)); // Parallel Vector Output
int *Mask=(int*)malloc(Mask_size*sizeof(int)); // Mask Vector;
Fill_elements(VecIn1,1,N_elements);
Fill_elements(Mask,1,Mask_size);
start = clock();
//Show_vec(VecIn1,N_elements,(char *)"Vector In");
//Show_vec(Mask,Mask_size,(char *)"Mask");
h_Convolution_1d(VecIn1,VecOut1,Mask);
end = clock();
T1=diffclock(start,end);
cout<<"Serial Result"<<" At "<<T1<<",Seconds"<<endl;
//Show_vec(VecOut1,N_elements,(char *)"Vector Out");
start = clock();
d_convolution1d(VecIn1,VecOut2,Mask,3); // 1 --> List of kernels
end = clock();
T2=diffclock(start,end);
cout<<"Parallel Result"<<" At "<<T2<<",Seconds"<<endl;
//Show_vec(VecOut2,N_elements,(char *)"Vector Out");
cout<<"Total acceleration "<<T1/T2<<"X"<<endl;
Check_op(VecOut1,VecOut2);
// Releasing Memory
free(VecIn1);
free(VecOut1);
free(VecOut2);
free(Mask);
return 0;
}
/*
1 - convolution1d tile constant
2 - convolution1d notile noconstant
3 - convolution1d constant tile simple
*/
|
11,564 | #include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <time.h>
#include <cuda.h>
typedef struct {
double _r;
double _g;
double _b;
double _m;
double _n;
} Point;
#define CUDA_CALL(x) {if((x) != cudaSuccess){ \
printf("CUDA error at %s:%d\n",__FILE__,__LINE__); \
printf(" %s\n", cudaGetErrorString(cudaGetLastError())); \
exit(EXIT_FAILURE);}}
//Leer las dimensiones de la imagen a x b pixeles
void readImageSize(FILE *ifp,int* K,int* a,int* b) {
fscanf(ifp,"%d\n",K);
printf("%d\n",*K);
fscanf(ifp,"%d\n",a);
printf("%d\n",*a);
fscanf(ifp,"%d\n",b);
printf("%d\n",*b);
}
//Leer archivo ifp y lo almacena en el struct
void readPoints(FILE* ifp,Point *points, int num_points) {
int i;
for(i=0;i<num_points;i++) {
fscanf(ifp,"%lf,%lf,%lf,%lf,%lf", &points[i]._r, &points[i]._g, &points[i]._b, &points[i]._m, &points[i]._n);
}
}
//Inicializar puntos aleatorios como los medios (k numero de clusters)
void initialize(Point* mean,int K, int num_points, Point* points) {
int i, a, p=2;
srand(time(NULL));
for(i=0;i<K;i++) {
a = num_points/p;
//printf("\n num_points: %d\n", num_points/p);
mean[i]._r = points[a]._r;
mean[i]._g = points[a]._g;
mean[i]._b = points[a]._b;
mean[i]._m = points[a]._m;
mean[i]._n = points[a]._n;
/*mean[i]._r=((double)(rand()%1000))/1000;
mean[i]._g=((double)(2*rand()%1000))/1000;
mean[i]._b=((double)(3*rand()%1000))/1000;
mean[i]._m=((double)(4*rand()%1000))/1000;
mean[i]._n=((double)(5*rand()%1000))/1000;*/
//printf("%lf,%lf,%lf,%lf,%lf\n",mean[i]._r,mean[i]._g,mean[i]._b,mean[i]._m,mean[i]._n);
p++;
/*mean[i]._r=((double)(rand()%1000))/1000;
mean[i]._g=((double)(2*rand()%1000))/1000;
mean[i]._b=((double)(3*rand()%1000))/1000;
mean[i]._m=((double)(4*rand()%1000))/1000;
mean[i]._n=((double)(5*rand()%1000))/1000;*/
}
}
//Todos los puntos sin clusters
void IntClusterMem(int *cluster, int num_points) {
int i;
for(i=0;i < num_points; i ++) {
cluster[i]=-1;
}
}
//Para calcular a que cluster pertenece el punto. (k numero de clusters)
__global__ void pointsCluster(int* after_cluster_d, Point* point_d,Point* Dmean,int K, int x, int y) {
int j, k, i;
j = blockIdx.x*blockDim.x+threadIdx.x;
k = blockIdx.y*blockDim.y+threadIdx.y;
int parent=0;
double dist=0;
int t = (k*(x)+j);
double minDist= sqrt((pow((point_d[t]._r-Dmean[0]._r),2)+pow((point_d[t]._g-Dmean[0]._g),2)+pow((point_d[t]._b-Dmean[0]._b),2)+pow((point_d[t]._m-Dmean[0]._m),2)+pow((point_d[t]._n-Dmean[0]._n),2)));
for(i=1;i<K;i++) {
dist = sqrt((pow((point_d[t]._r-Dmean[i]._r),2)+pow((point_d[t]._g-Dmean[i]._g),2)+pow((point_d[t]._b-Dmean[i]._b),2)+pow((point_d[t]._m-Dmean[i]._m),2)+pow((point_d[t]._n-Dmean[i]._n),2)));
if(minDist>=dist) {
parent=i;
minDist=dist;
}
}
after_cluster_d[t] = parent;
}
//Calcular nueva media
void calcNewMean(Point* points,int* cluster,Point* mean,int K,int num_points) {
Point* newMean=(Point*)malloc(sizeof(Point)*K);
int* members=(int*)malloc(sizeof(int)*(K));
int i;
for(i=0;i<K;i++) {
members[i]=0;
newMean[i]._r=0;
newMean[i]._g=0;
newMean[i]._b=0;
newMean[i]._m=0;
newMean[i]._n=0;
}
for(i=0;i<num_points;i++) {
members[cluster[i]]++;
newMean[cluster[i]]._r+=points[i]._r;
newMean[cluster[i]]._g+=points[i]._g;
newMean[cluster[i]]._b+=points[i]._b;
newMean[cluster[i]]._m+=points[i]._m;
newMean[cluster[i]]._n+=points[i]._n;
}
for(i=0;i<K;i++) {
if(members[i]!=0.0) {
newMean[i]._r/=members[i];
newMean[i]._g/=members[i];
newMean[i]._b/=members[i];
newMean[i]._m/=members[i];
newMean[i]._n/=members[i];
}
else {
newMean[i]._r=0;
newMean[i]._g=0;
newMean[i]._b=0;
newMean[i]._m=0;
newMean[i]._n=0;
}
}
for(i=0;i<K;i++) {
mean[i]._r=newMean[i]._r;
mean[i]._g=newMean[i]._g;
mean[i]._b=newMean[i]._b;
mean[i]._m=newMean[i]._m;
mean[i]._n=newMean[i]._n;
}
}
//Comprobamos la convergencia
//Comprueba que cada clúster de puntos permanece igual
int chkConvrg(int *before_clusters,int *after_cluster,int num_points, float tol) {
int i;
tol = num_points*tol;
for(i=0;i<num_points;i++) {
if(abs(before_clusters[i]-after_cluster[i])>tol) {
//check = abs(before_clusters[i]-after_cluster[i]);
//printf("check = %d, after_cluster[%d]=%d, before_clusters[%d]=%d\n",check,i,after_cluster[i],i,before_clusters[i]);
return -1;
}
}
return 0;
}
int main(int argc, char* argv[]) {
//Variables CPU
int K;
int num_points;
int * before_clusters;
int i;
int job_done=0;
int x,y,iter=0;
Point* mean;
Point* points;
int * after_cluster;
float tol;
//Variables GPU
Point* points_d;
Point* mean_d;
int * after_cluster_d;
int * before_cluster_d;
cudaEvent_t startinit, endinit, startmean, endmean, startcal, endcal, startindex, endindex;
cudaEvent_t start1, end1;
float timeinit, timemean, timecal, timeindex;
float time1;
//float totTime = 0;
tol = atof(argv[3]);
//iterations = atof(argv[3]);
//printf("Ingrese tolerancia: ");
//scanf("%f",&tol);
printf("Tolerancia = %f \n",tol);
cudaEventCreate(&start1);
cudaEventCreate(&end1);
cudaEventRecord(start1, 0);
//Leyendo archivo
FILE *ifp;
ifp=fopen(argv[1],"r");
readImageSize(ifp,&K,&x,&y);
K = atoi(argv[6]);
num_points = x*y;
int blockX=atoi(argv[4]);
int blockY=atoi(argv[5]);
//asignar memoria a la CPU
points=(Point*)malloc(sizeof(Point)*num_points);
readPoints(ifp,points,num_points);
fclose(ifp);
//printf("Entrada leida con exito \n");
before_clusters=(int*)malloc(sizeof(int)*num_points);
after_cluster=(int*)malloc(sizeof(int)*num_points);
mean=(Point*)malloc(sizeof(Point)*K);
//inicializando valores por defecto
initialize(mean,K, num_points, points);
IntClusterMem(before_clusters,num_points);
IntClusterMem(after_cluster,num_points);
CUDA_CALL(cudaMalloc((void**) &after_cluster_d, sizeof(int)*num_points));
CUDA_CALL(cudaMalloc((void**) &before_cluster_d, sizeof(int)*num_points));
CUDA_CALL(cudaMalloc((void**) &points_d, sizeof(Point)*num_points));
CUDA_CALL(cudaMalloc((void**) &mean_d, sizeof(Point)*K));
cudaEventCreate(&startinit);
cudaEventCreate(&endinit);
cudaEventRecord(startinit, 0);
//copiar los puntos al device
CUDA_CALL(cudaMemcpy(points_d, points, sizeof(Point)*num_points, cudaMemcpyHostToDevice));
CUDA_CALL(cudaMemcpy(after_cluster_d, after_cluster, sizeof(int)*num_points, cudaMemcpyHostToDevice));
cudaEventRecord(endinit, 0);
cudaEventSynchronize(endinit);
cudaEventElapsedTime(&timeinit, startinit, endinit);
int iter_max=0;
char *fcentroid;
if(K==10){
iter_max=4;
fcentroid="Centroid_10.txt";
}
else if(K==30){
iter_max=14;
fcentroid="Centroid_30.txt";
}
else if(K==50){
iter_max=63;
fcentroid="Centroid_50.txt";
}
else{
iter_max=87;
fcentroid="Centroid_100.txt";
}
while(1) {
cudaEventCreate(&startmean);
cudaEventCreate(&endmean);
cudaEventRecord(startmean, 0);
//copiar los centroides iniciales al device
CUDA_CALL(cudaMemcpy(mean_d, mean, sizeof(Point)*K, cudaMemcpyHostToDevice));
cudaEventRecord(endmean, 0);
cudaEventSynchronize(endmean);
cudaEventElapsedTime(&timemean, startmean, endmean);
//copia de memoria cuda
//CUDA_CALL(cudaMemcpy(after_cluster_d, after_cluster, sizeof(int)*num_points, cudaMemcpyHostToDevice));
//CUDA_CALL(cudaMemcpy(before_cluster_d, before_clusters, sizeof(int)*num_points, cudaMemcpyHostToDevice));
//CUDA_CALL(cudaMemcpy(x_d, &x, sizeof(int), cudaMemcpyHostToDevice));
//CUDA_CALL(cudaMemcpy(y_d, &y, sizeof(int), cudaMemcpyHostToDevice));
//CUDA_CALL(cudaMemcpy(K_d, &K, sizeof(int), cudaMemcpyHostToDevice));
cudaEventCreate(&startcal);
cudaEventCreate(&endcal);
cudaEventRecord(startcal, 0);
dim3 block(blockX, blockY);
dim3 grid((x+blockX-1)/blockX, (y+blockY-1)/blockY);
pointsCluster<<<grid,block>>>(after_cluster_d, points_d,mean_d,K,x,y);
cudaDeviceSynchronize();
cudaEventRecord(endcal, 0);
cudaEventSynchronize(endcal);
cudaEventElapsedTime(&timecal, startcal, endcal);
cudaEventCreate(&startindex);
cudaEventCreate(&endindex);
cudaEventRecord(startindex, 0);
CUDA_CALL(cudaMemcpy(after_cluster, after_cluster_d, sizeof(int)*num_points, cudaMemcpyDeviceToHost));
cudaEventRecord(endindex, 0);
cudaEventSynchronize(endindex);
cudaEventElapsedTime(&timeindex, startindex, endindex);
calcNewMean(points,after_cluster,mean,K,num_points);
//printf("Nuevos centroides son calculados!\n");
if(iter>iter_max) {
// printf("El algoritmo kmeans converge con = %d!\n",iter);
job_done=1;
}
else {
//printf("No converge!\n");
for(i=0;i<num_points;i++) {
//printf("1 after_cluster[%d]=%d, before_clusters[%d]=%d\n",i,after_cluster[i],i,before_clusters[i]);
before_clusters[i]=after_cluster[i];
//printf("after_cluster[%d]=%d, before_clusters[%d]=%d\n",i,after_cluster[i],i,before_clusters[i]);
}
}
if(job_done==1)
break;
++iter;
}
//Salida en archivos
FILE* ofp=fopen(argv[2],"w");
FILE* ofpc=fopen(fcentroid,"w");
fprintf(ofp,"%d\n",K);
fprintf(ofp,"%d\n",x);
fprintf(ofp,"%d\n",y);
for(i=0;i<K;i++)
fprintf(ofpc,"%.0f,%.0f,%.0f,%.0f,%.0f\n",mean[i]._r,mean[i]._g,mean[i]._b,mean[i]._m,mean[i]._n);
for(i=0;i<num_points;i++)
fprintf(ofp,"%.0f,%.0f,%.0f,%.0f,%.0f,%d\n",points[i]._r,points[i]._g,points[i]._b,points[i]._m,points[i]._n,after_cluster[i]+1);
fclose(ofp);
fclose(ofpc);
cudaEventRecord(end1, 0);
cudaEventSynchronize(end1);
cudaEventElapsedTime(&time1, start1, end1);
printf("Total Iteraciones = %d\n",iter-1);
printf("CUDA:Tiempo total transcurrido en la ejecucion. k= %d con tolerancia= %f en clusters : %f sec\n", K,tol,time1/1000);
//printf("Tiempo Total : %f\t sec\n",time1/1000);
CUDA_CALL(cudaFree(after_cluster_d));
CUDA_CALL(cudaFree(mean_d));
CUDA_CALL(cudaFree(points_d));
free(before_clusters);
free(mean);
free(points);
free(after_cluster);
return 0;
}
|
11,565 | #include "includes.h"
__global__ void mAddExternForce(float *w_dimX, float *w_dimY, float *f_dimX, float *f_dimY, float dt) {
int Idx = blockIdx.x * blockDim.x + threadIdx.x;
w_dimX[Idx] = -0.5*w_dimX[Idx];
w_dimY[Idx] = -0.5*w_dimY[Idx];
} |
11,566 | #include <stdlib.h>
#include <stdio.h>
#include <cuda_runtime.h>
#ifndef N
#define N (1024)
#endif
void fail(const char *message)
{
printf(message);
exit(EXIT_FAILURE);
}
__global__ void localToLocal(unsigned long long *d_time)
{
int arr1[N];
int arr2[N];
for (int i = 0; i < N; i++) {
arr1[i] = i * 2 + 1;
arr2[i] = i * 3 - 1;
}
unsigned long long startTime = clock();
for (int i = 0; i < N; i++) {
arr1[i] = arr2[i];
}
unsigned long long endTime = clock();
// Use the local array so the compiler doesn't optimize it away
arr1[0] = arr1[1];
*d_time = (endTime - startTime);
}
__global__ void globalToGlobal(int *d_v1, int *d_v2, unsigned long long *d_time)
{
unsigned long long startTime = clock();
for (int i = 0; i < N; i++) {
d_v1[i] = d_v2[i];
}
unsigned long long endTime = clock();
*d_time = (endTime - startTime);
}
__global__ void sharedToShared(unsigned long long *d_time)
{
__shared__ int shared1[N];
__shared__ int shared2[N];
for (int i = 0; i < N; i++) {
shared1[i] = i * 2 + 1;
shared2[i] = i * 3 - 1;
}
__syncthreads();
unsigned long long startTime = clock();
for (int i = 0; i < N; i++) {
shared1[i] = shared2[i];
}
unsigned long long endTime = clock();
// Used the shared array so the compiler doesn't optimize it away
shared1[0] = shared1[1];
*d_time = (endTime - startTime);
}
__global__ void localToGlobal(int *d_v, unsigned long long *d_time)
{
unsigned long long startTime = clock();
for (int i = 0; i < N; i++) {
d_v[i] = i;
}
unsigned long long endTime = clock();
*d_time = (endTime - startTime);
}
__global__ void globalToLocal(int *d_v, unsigned long long *d_time)
{
int arr[N];
for (int i = 0; i < N; i++) {
arr[i] = i * 2 + 1;
}
unsigned long long startTime = clock();
for (int i = 0; i < N; i++) {
arr[i] = d_v[i];
}
unsigned long long endTime = clock();
// Use the local array so the compiler doesn't optimize it away
arr[0] = arr[1];
*d_time = (endTime - startTime);
}
__global__ void sharedToGlobal(int *d_v, unsigned long long *d_time)
{
__shared__ int arr[N];
for (int i = 0; i < N; i++) {
arr[i] = i * 2 + 1;
}
__syncthreads();
unsigned long long startTime = clock();
for (int i = 0; i < N; i++) {
d_v[i] = arr[i];
}
unsigned long long endTime = clock();
*d_time = (endTime - startTime);
}
__global__ void globalToShared(int *d_v, unsigned long long *d_time)
{
__shared__ int arr[N];
for (int i = 0; i < N; i++) {
arr[i] = i * 2 + 1;
}
__syncthreads();
unsigned long long startTime = clock();
for (int i = 0; i < N; i++) {
arr[i] = d_v[i];
}
unsigned long long endTime = clock();
// Get rid of compiler warning
arr[0] = arr[1];
*d_time = (endTime - startTime);
}
__global__ void localToShared(unsigned long long *d_time)
{
__shared__ int arr[N];
for (int i = 0; i < N; i++) {
arr[i] = i * 2 + 1;
}
int localArr[N];
for (int i = 0; i < N; i++) {
arr[i] = i * 3 - 1;
}
__syncthreads();
unsigned long long startTime = clock();
for (int i = 0; i < N; i++) {
arr[i] = localArr[i];
}
unsigned long long endTime = clock();
// Get rid of compiler warning
arr[0] = arr[1];
*d_time = (endTime - startTime);
}
__global__ void sharedToLocal(unsigned long long *d_time)
{
__shared__ int arr[N];
for (int i = 0; i < N; i++) {
arr[i] = i * 2 + 1;
}
int localArr[N];
for (int i = 0; i < N; i++) {
arr[i] = i * 3 - 1;
}
__syncthreads();
unsigned long long startTime = clock();
for (int i = 0; i < N; i++) {
localArr[i] = arr[i];
}
unsigned long long endTime = clock();
// Use the local array so the compiler doesn't optimize it away
localArr[0] = localArr[1];
*d_time = (endTime - startTime);
}
int main()
{
/**
* Set up memory on device.
*/
int *d_globalToGlobal1 = NULL;
int *d_globalToGlobal2 = NULL;
int *d_localToGlobal = NULL;
int *d_globalToLocal = NULL;
int *d_sharedToGlobal = NULL;
int *d_globalToShared = NULL;
if (cudaMalloc((void **) &d_globalToGlobal1, N * sizeof(int)) != cudaSuccess)
fail("Failed to allocate space for 'd_globalToGlobal1'");
if (cudaMalloc((void **) &d_globalToGlobal2, N * sizeof(int)) != cudaSuccess)
fail("Failed to allocate space for 'd_globalToGlobal2'");
if (cudaMalloc((void **) &d_localToGlobal, N * sizeof(int)) != cudaSuccess)
fail("Failed to allocate space for 'd_localToGlobal'");
if (cudaMalloc((void **) &d_globalToLocal , N * sizeof(int)) != cudaSuccess)
fail("Failed to allocate space for 'd_globalToLocal'");
if (cudaMalloc((void **) &d_sharedToGlobal , N * sizeof(int)) != cudaSuccess)
fail("Failed to allocate space for 'd_sharedToGlobal'");
if (cudaMalloc((void **) &d_globalToShared , N * sizeof(int)) != cudaSuccess)
fail("Failed to allocate space for 'd_globalToShared'");
unsigned long long tLocalToLocal;
unsigned long long tGlobalToGlobal;
unsigned long long tSharedToShared;
unsigned long long tLocalToGLobal;
unsigned long long tglobalToLocal;
unsigned long long tsharedToGlobal;
unsigned long long tglobalToShared;
unsigned long long tlocalToShared;
unsigned long long tsharedToLocal;
unsigned long long *d_tLocalToLocal = NULL;
unsigned long long *d_tGlobalToGlobal = NULL;
unsigned long long *d_tSharedToShared = NULL;
unsigned long long *d_tLocalToGLobal = NULL;
unsigned long long *d_tglobalToLocal = NULL;
unsigned long long *d_tsharedToGlobal = NULL;
unsigned long long *d_tglobalToShared = NULL;
unsigned long long *d_tlocalToShared = NULL;
unsigned long long *d_tsharedToLocal = NULL;
if (cudaMalloc((void **) &d_tLocalToLocal , sizeof(unsigned long long)) != cudaSuccess)
fail("Failed to allocate space for 'd_tLocalToLocal'");
if (cudaMalloc((void **) &d_tGlobalToGlobal , sizeof(unsigned long long)) != cudaSuccess)
fail("Failed to allocate space for 'd_tGlobalToGlobal'");
if (cudaMalloc((void **) &d_tSharedToShared , sizeof(unsigned long long)) != cudaSuccess)
fail("Failed to allocate space for 'd_tSharedToShared'");
if (cudaMalloc((void **) &d_tLocalToGLobal , sizeof(unsigned long long)) != cudaSuccess)
fail("Failed to allocate space for 'd_tLocalToGLobal'");
if (cudaMalloc((void **) &d_tglobalToLocal , sizeof(unsigned long long)) != cudaSuccess)
fail("Failed to allocate space for 'd_tglobalToLocal'");
if (cudaMalloc((void **) &d_tsharedToGlobal , sizeof(unsigned long long)) != cudaSuccess)
fail("Failed to allocate space for 'd_tsharedToGlobal'");
if (cudaMalloc((void **) &d_tglobalToShared , sizeof(unsigned long long)) != cudaSuccess)
fail("Failed to allocate space for 'd_tglobalToShared'");
if (cudaMalloc((void **) &d_tlocalToShared , sizeof(unsigned long long)) != cudaSuccess)
fail("Failed to allocate space for 'd_tlocalToShared'");
if (cudaMalloc((void **) &d_tsharedToLocal , sizeof(unsigned long long)) != cudaSuccess)
fail("Failed to allocate space for 'd_tsharedToLocal'");
/**
* Execute kernels.
*/
int nBlocks = 32;
int nThreads = 128;
localToLocal<<<nBlocks, nThreads>>>(d_tLocalToLocal);
globalToGlobal<<<nBlocks, nThreads>>>(d_globalToGlobal1, d_globalToGlobal2, d_tGlobalToGlobal);
sharedToShared<<<nBlocks, nThreads>>>(d_tSharedToShared);
localToGlobal<<<nBlocks, nThreads>>>(d_localToGlobal, d_tLocalToGLobal);
globalToLocal<<<nBlocks, nThreads>>>(d_globalToLocal, d_tglobalToLocal);
sharedToGlobal<<<nBlocks, nThreads>>>(d_sharedToGlobal, d_tsharedToGlobal);
globalToShared<<<nBlocks, nThreads>>>(d_globalToShared, d_tglobalToShared);
localToShared<<<nBlocks, nThreads>>>(d_tlocalToShared);
sharedToLocal<<<nBlocks, nThreads>>>(d_tsharedToLocal);
/**
* Copy results back.
*/
if (cudaMemcpy(&tLocalToLocal, d_tLocalToLocal, sizeof(unsigned long long), cudaMemcpyDeviceToHost) != cudaSuccess)
fail("Failed to copy to tLocalToLocal");
if (cudaMemcpy(&tGlobalToGlobal, d_tGlobalToGlobal, sizeof(unsigned long long), cudaMemcpyDeviceToHost) != cudaSuccess)
fail("Failed to copy to tGlobalToGlobal");
if (cudaMemcpy(&tSharedToShared, d_tSharedToShared, sizeof(unsigned long long), cudaMemcpyDeviceToHost) != cudaSuccess)
fail("Failed to copy to tSharedToShared");
if (cudaMemcpy(&tLocalToGLobal, d_tLocalToGLobal, sizeof(unsigned long long), cudaMemcpyDeviceToHost) != cudaSuccess)
fail("Failed to copy to tLocalToGLobal");
if (cudaMemcpy(&tglobalToLocal, d_tglobalToLocal, sizeof(unsigned long long), cudaMemcpyDeviceToHost) != cudaSuccess)
fail("Failed to copy to tglobalToLocal");
if (cudaMemcpy(&tsharedToGlobal, d_tsharedToGlobal, sizeof(unsigned long long), cudaMemcpyDeviceToHost) != cudaSuccess)
fail("Failed to copy to tsharedToGlobal");
if (cudaMemcpy(&tglobalToShared, d_tglobalToShared, sizeof(unsigned long long), cudaMemcpyDeviceToHost) != cudaSuccess)
fail("Failed to copy to tglobalToShared");
if (cudaMemcpy(&tlocalToShared, d_tlocalToShared, sizeof(unsigned long long), cudaMemcpyDeviceToHost) != cudaSuccess)
fail("Failed to copy to tlocalToShared");
if (cudaMemcpy(&tsharedToLocal, d_tsharedToLocal, sizeof(unsigned long long), cudaMemcpyDeviceToHost) != cudaSuccess)
fail("Failed to copy to tsharedToLocal");
/**
* Print results.
*/
printf("Benchmark: moving ints between arrays\n");
printf("N = %d\n\n", N);
printf("Local to local:\t%llu cycles\t(%f)\n", tLocalToLocal, ((float) tLocalToLocal) / (float) N);
printf("Global to global:\t%llu cycles\t(%f)\n", tGlobalToGlobal, ((float) tGlobalToGlobal) / (float) N);
printf("Shared to shared:\t%llu cycles\t(%f)\n", tSharedToShared, ((float) tSharedToShared) / (float) N);
printf("Local to global:\t%llu cycles\t(%f)\n", tLocalToGLobal, ((float) tLocalToGLobal) / (float) N);
printf("Global to local:\t%llu cycles\t(%f)\n", tglobalToLocal, ((float) tglobalToLocal) / (float) N);
printf("Shared to global:\t%llu cycles\t(%f)\n", tsharedToGlobal, ((float) tsharedToGlobal) / (float) N);
printf("Global to shared:\t%llu cycles\t(%f)\n", tglobalToShared, ((float) tglobalToShared) / (float) N);
printf("Local to shared:\t%llu cycles\t(%f)\n", tlocalToShared, ((float) tlocalToShared) / (float) N);
printf("Shared to local:\t%llu cycles\t(%f)\n", tsharedToLocal, ((float) tsharedToLocal) / (float) N);
return 0;
} |
11,567 | #include "includes.h"
// helper for CUDA error handling
__global__ void getMeanImage( const double* images, double* meanImage, std::size_t imageNum, std::size_t pixelNum )
{
std::size_t col = blockIdx.x * blockDim.x + threadIdx.x;
if(col >= pixelNum)
{
return;
}
meanImage[col] = 0.0;
for(std::size_t row = 0; row < imageNum; ++row)
{
meanImage[col] += images[row*pixelNum + col];
}
meanImage[col] /= imageNum;
} |
11,568 | #include "includes.h"
__global__ void prefixSumBackward(float* arr,int step){
int bx = blockIdx.x;
int tx = threadIdx.x;
int BX = blockDim.x;
int i = bx*BX+tx;
int ii = i+1;
if(i >= n || ii > n/float(step)) return;
int temp = arr[ii*step-1];
arr[ii*step-1] += arr[ii*step-step/2-1];
arr[ii*step-step/2-1] = temp;
} |
11,569 | #include <iostream>
#include <stdio.h>
#define BLK_SIZE 128
__global__ void kernel(){
printf("Hello World!");
}
__global__ void print_kernel() {
printf("Hello from block %d, thread %d\n", blockIdx.x, threadIdx.x);
}
int main(){
print_kernel<<<10, 10>>>();
cudaDeviceSynchronize();
return 0;
}
|
11,570 | #include "includes.h"
__global__ void logicalkernel(bool *A, bool *B, int *neighbours, int order ,int degree) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if(idx <order){
for(int i=0 ; i < degree ; i++){
int n = neighbours[idx*degree + i ];
for(int j = 0; j < order; j++){
B[idx * order+ j] = B[idx*order+j] || A[n*order+j];
}
}
}
} |
11,571 |
// GPU kernel
__global__ void summation_kernel(int data_size, float * data_out)
{
unsigned int tid = threadIdx.x;
int id = blockIdx.x * blockDim.x + tid;
int data_chunk = data_size / (blockDim.x * gridDim.x);
float result = 0.0;
for(int i = data_chunk*(id+1); i >= data_chunk*id; i--)
result = i&1 ? result-1.0/(i+1) : result+1.0/(i+1);
data_out[id] = result;
for(int j = 2; j < blockDim.x; j *= 2)
if(tid < (blockDim.x/j))
data_out[id] += data_out[id+blockDim.x/j];
}
__global__ void reduce_grid(int data_size, int data_chunk, float * data_out)
{
unsigned int tid = threadIdx.x;
int id = blockIdx.x * blockDim.x + tid;
for(int j = 1; j < blockDim.x; j *= 2)
if(tid < (blockDim.x/j))
data_out[id*data_chunk] += data_out[(id+blockDim.x/j)*data_chunk];
} |
11,572 | #include "includes.h"
__global__ void predicate_kernel(float *d_predicates, float *d_input, int length)
{
int idx = blockDim.x * blockIdx.x + threadIdx.x;
if (idx >= length) return;
d_predicates[idx] = d_input[idx] > FLT_ZERO;
} |
11,573 | #include "ppm.h"
const int BLUR_SIZE=10;
//used to blur a 2d color ppm image
__global__
void blurKernel(int* in, int* out,int w,int h){
int curpix=blockIdx.x*blockDim.x+threadIdx.x;
int row=curpix / w;
int col=curpix % w;
if ( row>=h ) return;
int pixr=0;
int pixg=0;
int pixb=0;
int pixels=0;
for (int br=-BLUR_SIZE; br<=BLUR_SIZE;br++){
for (int bc=-BLUR_SIZE; bc<=BLUR_SIZE;bc++){
int currow=row+br;
int curcol=col+bc;
if(currow>=0 && currow<h && curcol>=0 && curcol <w ){
pixels++;
int pdex= 3*(currow*w+curcol);
pixr+=in[pdex];
pixg+=in[pdex+1];
pixb+=in[pdex+2];
}
}
}
int dex=3*(row*w+col);
out[dex]= round((float)pixr/pixels);
out[dex+1]= round((float)pixg/pixels);
out[dex+2]= round((float)pixb/pixels);
}
int main(){
ppm football("football.ppm");
int numpixels=football.height*football.width;
int size=3*numpixels;
int arsize=sizeof(int)*size;
std::cout <<"Size is: "<< size;
int* d_football_data;
int* d_bfootball_data;
cudaMalloc((void**)&d_football_data,arsize);
cudaMalloc((void**)&d_bfootball_data,arsize );
cudaMemcpy(d_football_data,football.data,arsize,cudaMemcpyHostToDevice);
cudaMemcpy(d_bfootball_data,football.data,arsize,cudaMemcpyHostToDevice);
blurKernel<<<ceil(numpixels/256) ,256>>>(d_football_data,d_bfootball_data,football.width,football.height);
ppm bfootball(football);
cudaMemcpy(bfootball.data,d_bfootball_data,arsize,cudaMemcpyDeviceToHost);
bfootball.write("bfootball.ppm");
cudaFree(d_football_data);
cudaFree(d_bfootball_data);
}
|
11,574 | /*
**********************************************
* CS314 Principles of Programming Languages *
* Fall 2020 *
**********************************************
*/
#include <stdio.h>
#include <stdlib.h>
__global__ void packGraph_gpu(int * newSrc, int * oldSrc, int * newDst, int * oldDst, int * newWeight, int * oldWeight, int * edgeMap, int numEdges) {
//Get current thread ID and total number of threads in grid
int tid = blockIdx.x * blockDim.x + threadIdx.x;
int num_threads = blockDim.x * gridDim.x;
for(int x = tid; x < numEdges; x += num_threads) {
int myMap = edgeMap[x];
int nextMap = edgeMap[x + 1];
if(myMap != nextMap) {
newSrc[myMap] = oldSrc[x];
newDst[myMap] = oldDst[x];
newWeight[myMap] = oldWeight[x];
}
}
}
|
11,575 | #include <stdio.h>
#include <assert.h>
#include <math.h>
#include <sys/time.h>
const int N=512; /* reduced to 512, because there is only one block, the max number of threads is 512 */
double rtclock()
{
struct timezone Tzp;
struct timeval Tp;
int stat;
stat = gettimeofday (&Tp, &Tzp);
if (stat != 0) printf("Error return from gettimeofday: %d",stat);
return(Tp.tv_sec + Tp.tv_usec*1.0e-6);
}
__global__ void matrixAdd(double *A, double *B, double *C, int N)
{
int i, j;
i = 0;
j = threadIdx.x;
if (j < N )
{
// The following statement is essentially: C[i][j] = A[i][j] + B[i][j]
C[i*N+j] = A[i*N+j] + B[i*N+j];
// Can also use the following
//C[j*N+i] = A[j*N+i] + B[j*N+i];
}
}
int main(int argc, char*argv[])
{
double A[1][N];
double *B; /* Program Stack Size is limited, so only Array A could be statically allocated (on the stack) */
double *C; /* B and C arrays are to be dynamically allocated on the heap, which has much larger space */
double *d_A, *d_B, *d_C;
double * gpu_C; /* stores the copy of d_C because CPU cannot access d_C directly
copy via cudaMemcpy Device (i.e. d_C) -> Host (i.e. gpu_C) */
int size = 1 * N * sizeof (double);
B = (double *) malloc (size);
C = (double *) malloc (size);
int THREAD_DIMX,THREAD_DIMY,BLOCK_DIMX,BLOCK_DIMY;
gpu_C= (double*) malloc (size);
/* allocate space for device copies */
cudaMalloc( (void **) &d_A, size );
cudaMalloc( (void **) &d_B, size );
cudaMalloc( (void **) &d_C, size);
for( int i = 0; i < 1; i++ )
for( int j = 0; j < N; j++ )
{
A[i][j] = 1.0;
B[i*N+j] = 2.0;
}
/* copy inputs to device */
cudaMemcpy( d_A, A, size, cudaMemcpyHostToDevice );
cudaMemcpy( d_B, B, size, cudaMemcpyHostToDevice );
/* launch the kernel on the GPU */
THREAD_DIMX = N;
THREAD_DIMY = 1;
BLOCK_DIMX = 1;
BLOCK_DIMY = 1;
dim3 dimGrid(BLOCK_DIMX,BLOCK_DIMY,1);
dim3 dimBlock(THREAD_DIMX,THREAD_DIMY,1);
double start_cpu = rtclock();
matrixAdd<<< dimGrid, dimBlock>>>( d_A, d_B, d_C, N);
cudaThreadSynchronize();
double end_cpu = rtclock();
printf("total time is %lf\n",(double)(end_cpu-start_cpu));
/* copy result back to host */
/* fix the parameters needed to copy data back to the host */
cudaMemcpy( gpu_C, d_C, size, cudaMemcpyDeviceToHost );
for (int i=0; i<1; i++)
for (int j=0; j<N; j++)
{
C[i*N+j] = A[i][j] + B[i*N+j];
if ( abs(C[i*N+j] - gpu_C[i*N+j]) > 1e-5 )
{
printf("CPU %f and GPU %f results do not match!\n", C[i*N+j], gpu_C[i*N+j]);
exit(-1);
}
}
/* clean up */
cudaFree( d_A );
cudaFree( d_B );
cudaFree( d_C );
free(gpu_C);
return 0;
} /* end main */
|
11,576 | #include <iostream>
#include <stdio.h>
__global__ void child_launch(int* counter) { atomicAdd(counter, 1); }
__device__ void syncBlock(volatile unsigned int* lock, int cnt)
{
atomicAdd((unsigned int*)lock, 1);
while(gridDim.x * cnt != *lock)
;
}
class BlockSyncer {
private:
bool dir;
volatile unsigned int* lock_var;
public:
__device__ BlockSyncer(volatile unsigned int* lock_var)
: dir(false)
, lock_var(lock_var){};
__device__ void sync()
{
if(threadIdx.x == 0 && threadIdx.y == 0 && threadIdx.z == 0) {
dir = !dir;
atomicAdd((unsigned int*)lock_var, dir ? 1 : -1);
while(gridDim.x * gridDim.y * gridDim.z * dir != *lock_var)
;
}
__syncthreads();
}
};
__global__ void test_kernel(volatile unsigned int* counter)
{
BlockSyncer blk(counter);
if(blockIdx.x == 0 && threadIdx.x == 0)
printf("counter = %d, gridDim.x = %d\n", *counter, gridDim.x);
blk.sync();
if(blockIdx.x == 0 && threadIdx.x == 0)
printf("counter = %d\n", *counter);
blk.sync();
if(blockIdx.x == 0 && threadIdx.x == 0)
printf("counter = %d\n", *counter);
}
int main()
{
unsigned int* counter;
cudaMalloc(&counter, sizeof(unsigned int));
unsigned int zero = 0;
cudaMemcpy(counter, &zero, sizeof(unsigned int), cudaMemcpyHostToDevice);
test_kernel<<<32, 256>>>(counter);
cudaDeviceSynchronize();
}
//__global__ void child_launch(int* data) { data[threadIdx.x] = data[threadIdx.x] + 1; }
//
//__global__ void parent_launch(int* data)
//{
// data[threadIdx.x] = threadIdx.x;
//
// __syncthreads();
//
// if(threadIdx.x == 0) {
// // child_launch<<<1, 256>>>(data);
// cudaDeviceSynchronize();
// }
//
// __syncthreads();
//}
//
// int main()
//{
// int* data;
// cudaMalloc(&data, sizeof(int) * 256);
// parent_launch<<<1, 256>>>(data);
//}
|
11,577 | #define _USE_MATH_DEFINES
#include <iostream>
#include <iomanip>
#include <fstream>
#include <string>
#include <cmath>
#include <float.h>
#include <cuda_profiler_api.h>
using namespace std;
#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);
}
}
__device__
void RGBtoHSV(double r, double g, double b, double &h, double &s, double &v) {
r = max(0., min(255.0, r));
g = max(0., min(255.0, g));
b = max(0., min(255.0, b));
r = r / 255.;
g = g / 255.;
b = b / 255.;
h = 0.0f;
v = max(r, max(g, b));
const float delta = v - min(r, min(g, b));
if (delta < FLT_MIN)
s = 0.0f;
else {
s = delta / v;
if (r >= v) {
h = (g - b) / delta;
if (h < 0.0f)
h += 6.0f;
} else if (g >= v)
h = 2.0f + (b - r) / delta;
else
h = 4.0f + (r - g) / delta;
}
h = max(0., min(6.0, h));
s = max(0., min(1.0, s));
v = max(0., min(1.0, v));
}
__device__
void HSVtoRGB(double h, double s, double v, double &r, double &g, double &b) {
h = max(0., min(6.0, h));
s = max(0., min(1.0, s));
v = max(0., min(1.0, v));
if (s < FLT_MIN)
r = g = b = v;
else {
const int i = (int) h;
const float f = h - i;
const float p = v * (1.0f - s);
if (i & 1) {
const float q = v * (1.0f - (s * f));
switch (i) {
case 1:
r = q;
g = v;
b = p;
break;
case 3:
r = p;
g = q;
b = v;
break;
default:
r = v;
g = p;
b = q;
break;
}
} else {
const float t = v * (1.0f - (s * (1.0f - f)));
switch (i) {
case 0:
r = v;
g = t;
b = p;
break;
case 2:
r = p;
g = v;
b = t;
break;
default:
r = t;
g = p;
b = v;
break;
}
}
}
r *= 255.;
g *= 255.;
b *= 255.;
r = max(0., min(255.0, r));
g = max(0., min(255.0, g));
b = max(0., min(255.0, b));
}
__device__
void RGBtoHSL(double r, double g, double b, double &h, double &s, double &l) {
r = max(0., min(255.0, r));
g = max(0., min(255.0, g));
b = max(0., min(255.0, b));
r = r / 255.;
g = g / 255.;
b = b / 255.;
const double maxRGB = max(r, max(g, b));
const double minRGB = min(r, min(g, b));
const double delta2 = maxRGB + minRGB;
l = delta2 * 0.5f;
const double delta = maxRGB - minRGB;
if (delta < DBL_MIN)
h = s = 0.0f;
else {
s = delta / (l > 0.5f ? 2.0f - delta2 : delta2);
if (r >= maxRGB) {
h = (g - b) / delta;
if (h < 0.0f)
h += 6.0f;
} else if (g >= maxRGB)
h = 2.0f + (b - r) / delta;
else
h = 4.0f + (r - g) / delta;
}
h = max(0., min(6.0, h));
s = max(0., min(1.0, s));
l = max(0., min(1.0, l));
}
__device__
void HSLtoRGB(double h, double s, double l, double &r, double &g, double &b) {
h = max(0., min(6.0, h));
s = max(0., min(1.0, s));
l = max(0., min(1.0, l));
if (s < DBL_MIN)
r = g = b = l;
else if (l < DBL_MIN)
r = g = b = 0.0f;
else {
const double q = l < 0.5f ? l * (1.0f + s) : l + s - l * s;
const double p = 2.0f * l - q;
double t[] = {h + 2.0f, h, h - 2.0f};
for (int i = 0; i < 3; ++i) {
double *color;
switch (i) {
case 0:
color = &r;
break;
case 1:
color = &g;
break;
case 2:
color = &b;
break;
}
if (t[i] < 0.0f)
t[i] += 6.0f;
else if (t[i] > 6.0f)
t[i] -= 6.0f;
if (t[i] < 1.0f)
*color = p + (q - p) * t[i];
else if (t[i] < 3.0f)
*color = q;
else if (t[i] < 4.0f)
*color = p + (q - p) * (4.0f - t[i]);
else
*color = p;
}
}
r *= 255.;
g *= 255.;
b *= 255.;
r = max(0., min(255.0, r));
g = max(0., min(255.0, g));
b = max(0., min(255.0, b));
}
__device__
void color_lighten(unsigned char &r, unsigned char &g, unsigned char &b, double quantity) {
double rD, gD, bD, h, s, l, v;
rD = r;
gD = g;
bD = b;
if (quantity > 1) {
RGBtoHSL(rD, gD, bD, h, s, l);
l *= quantity;
HSLtoRGB(h, s, l, rD, gD, bD);
} else if (quantity < 1) {
RGBtoHSV(rD, gD, bD, h, s, v);
v *= quantity;
HSVtoRGB(h, s, v, rD, gD, bD);
}
r = floor(rD);
g = floor(gD);
b = floor(bD);
}
__global__
void multibrot_kernel(
unsigned int unroll,
unsigned char *image,
int width, int height, double ratio,
int exponent, int iterations, double R, double eps,
unsigned char borderR, unsigned char borderG, unsigned char borderB, double borderThickness,
long normOrbitSkip, double normLightIntensity, double normLightAngle, double normLightHeight,
unsigned char bgR, unsigned char bgG, unsigned char bgB,
double kR, double kG, double kB, double kD,
unsigned char internalBorderR, unsigned char internalBorderG, unsigned char internalBorderB,
unsigned char internalCoreR, unsigned char internalCoreG, unsigned char internalCoreB,
double internalK,
double stripeDensity, double stripeLightIntensity,
double zoom, double posX, double posY
) {
unsigned int threadIndex = blockIdx.x * blockDim.x + threadIdx.x;
for (unsigned int unrollIndex = 0; unrollIndex < unroll; unrollIndex++) {
unsigned int currentIndex = threadIndex * unroll + unrollIndex;
if (currentIndex >= width * height) {
return;
}
//region Calculations
double c_r = (((currentIndex % width - 1) - width / 2.) / (width * zoom)) * ratio + posX;
double c_i = ((double) currentIndex / width - height / 2.) / (height * zoom) + posY;
double z_r = c_r;
double z_i = c_i;
double last_z_r = 0;
double last_z_i = 0;
double dz_r = 1.;
double dz_i = 0.;
double dc_r = 1.;
double dc_i = 0.;
double dzdz_r = 0.;
double dzdz_i = 0.;
double dcdc_r = 0.;
double dcdc_i = 0.;
double dcdz_r = 0.;
double dcdz_i = 0.;
double p = 1.;
double orbitCount = 0;
double V = 0;
long i;
for (i = 0; i < iterations; i++) {
double z2 = z_r * z_r + z_i * z_i;
if (z2 > R * R) {
V = log(z2) / p;
break;
}
if (eps > 0 && dz_r * dz_r + dz_i * dz_i < eps * eps) {
V = 0;
break;
}
double dzdz_r_temp = 2 * ((z_r * dzdz_r - z_i * dzdz_i) + (dz_r * dz_r - dz_i * dz_i));
dzdz_i = 2 * ((z_r * dzdz_i + z_i * dzdz_r) + (dz_r * dz_i + dz_i * dz_r));
dzdz_r = dzdz_r_temp;
double dcdc_r_temp = 2 * ((z_r * dcdc_r - z_i * dcdc_i) + (dc_r * dc_r - dc_i * dc_i));
dcdc_i = 2 * ((z_r * dcdc_i + z_i * dcdc_r) + (dc_r * dc_i + dc_i * dc_r));
dcdc_r = dcdc_r_temp;
double dcdz_r_temp = 2 * ((z_r * dcdz_r - z_i * dcdz_i) + (dz_r * dc_r - dz_i * dc_i));
dcdz_i = 2 * ((z_r * dcdz_i + z_i * dcdz_r) + (dc_r * dz_i + dc_i * dz_r));
dcdz_r = dcdz_r_temp;
double dz_r_temp = 2 * (z_r * dz_r - z_i * dz_i);
dz_i = 2 * (z_r * dz_i + z_i * dz_r);
dz_r = dz_r_temp;
double dc_r_temp = 2 * (z_r * dc_r - z_i * dc_i) + 1;
dc_i = 2 * (z_r * dc_i + z_i * dc_r);
dc_r = dc_r_temp;
p *= 2.;
if (i >= normOrbitSkip) {
orbitCount += 0.5 + 0.5 * sin(stripeDensity * atan2(last_z_i, last_z_r));
}
last_z_r = z_r;
last_z_i = z_i;
int esp = exponent;
if (esp != 0) {
if (esp < 0) {
esp = -esp;
double z_r_temp = z_r / (z_r * z_r + z_i * z_i);
z_i = -z_i / (z_r * z_r + z_i * z_i);
z_r = z_r_temp;
}
double z_esp_r = z_r;
double z_esp_i = z_i;
for (int e = 1; e < esp; e++) {
double z_esp_r_temp = (z_r * z_esp_r - z_i * z_esp_i);
z_esp_i = (z_esp_i * z_r + z_i * z_esp_r);
z_esp_r = z_esp_r_temp;
}
z_r = z_esp_r + c_r;
z_i = z_esp_i + c_i;
} else {
z_r = 1.0;
z_i = 0.0;
}
}
// endregion
if (V == 0) { // Inside!
//region Interior distance estimation
double u_r = 1 - dz_r;
double u_i = dz_i;
double v_r = (dc_r * u_r + dc_i * u_i) / (u_r * u_r + u_i * u_i);
double v_i = (dc_i * u_r - dc_r * u_i) / (u_r * u_r + u_i * u_i);
double l_r = (dzdz_r * v_r - dzdz_i * v_i);
double l_i = (dzdz_r * v_i + dzdz_i * v_r);
l_r += dcdz_r;
l_i += dcdz_i;
double d = (1 - (dz_r * dz_r + dz_i * dz_i)) / sqrt(l_r * l_r + l_i * l_i);
//endregion
// if(d < 50000) {
// image[currentIndex * 4] = internalCoreR;
// image[currentIndex * 4 + 1] = internalCoreG;
// image[currentIndex * 4 + 2] = internalCoreB;
// } else {
// image[currentIndex * 4] = internalBorderR;
// image[currentIndex * 4 + 1] = internalBorderG;
// image[currentIndex * 4 + 2] = internalBorderB;
// }
// if (d < 1) {
// image[currentIndex * 4] = 0;
// image[currentIndex * 4 + 1] = (int) max(0., min(255., (255. * tanh(d))));
// image[currentIndex * 4 + 1] = (unsigned char) (max(0., min(255., 0 + d * (255 - 0))));
// image[currentIndex * 4 + 2] = 0;
// } else {
// image[currentIndex * 4] = 0;
// image[currentIndex * 4 + 1] = 255;
// image[currentIndex * 4 + 2] = 0;
// }
double mix = internalK > 0 ? log(d) / internalK : 1;
if(mix < 0) {
mix = 0;
}
if (mix < 1) {
image[currentIndex * 4] = max(0., min(255., internalBorderR + mix * (internalCoreR - internalBorderR)));
image[currentIndex * 4 + 1] = max(0.,
min(255., internalBorderG + mix * (internalCoreG - internalBorderG)));
image[currentIndex * 4 + 2] = max(0.,
min(255., internalBorderB + mix * (internalCoreB - internalBorderB)));
} else {
image[currentIndex * 4] = internalCoreR;
image[currentIndex * 4 + 1] = internalCoreG;
image[currentIndex * 4 + 2] = internalCoreB;
}
} else { // Outside!
//region Exterior distance estimation
double rad = sqrt(z_r * z_r + z_i * z_i);
double d = rad * 2. * log(rad) / sqrt(dc_r * dc_r + dc_i * dc_i);
//endregion
unsigned char tempR = bgR;
unsigned char tempG = bgG;
unsigned char tempB = bgB;
//region Gradient Background Setup
if (kR > 0.01 || kR < -0.01) {
tempR = (unsigned char) (max(0., min(255., tempR + (255. * (1 - cos(log(V) / (kR))) / 2. / kD))));
}
if (kG > 0.01 || kG < -0.01) {
tempG = (unsigned char) (max(0., min(255., tempG + (255. * (1 - cos(log(V) / (kG))) / 2. / kD))));
}
if (kB > 0.01 || kB < -0.01) {
tempB = (unsigned char) (max(0., min(255., tempB + (255. * (1 - cos(log(V) / (kB))) / 2. / kD))));
}
//endregion
//region 3D Normal
if (normLightIntensity != 1) {
double vR = cos(normLightAngle * 2. * M_PI / 360.);
double vI = sin(normLightAngle * 2. * M_PI / 360.);
double lo = 0.5 * log(z_r * z_r + z_i * z_i);
double conjR = ((1. + lo) * (dc_r * dc_r - dc_i * dc_i) - (lo) * (z_r * dcdc_r - z_i * dcdc_i));
double conjI = ((1. + lo) * -(dc_r * dc_i + dc_i * dc_r) - (lo) * -(z_r * dcdc_i + z_i * dcdc_r));
double uR = (z_r * dc_r - z_i * dc_i);
double uI = (z_r * dc_i + z_i * dc_r);
double newUR = (uR * conjR - uI * conjI);
uI = (uR * conjI + uI * conjR);
uR = newUR;
newUR = uR / sqrt(uR * uR + uI * uI);
uI = uI / sqrt(uR * uR + uI * uI);
uR = newUR;
double t = uR * vR + uI * vI + normLightHeight;
t = t / (1. + normLightHeight);
if (t < 0) {
t = 0;
} else if (t > 1) {
t = 1;
}
unsigned char normLightR = tempR;
unsigned char normLightG = tempG;
unsigned char normLightB = tempB;
color_lighten(normLightR, normLightG, normLightB, normLightIntensity);
double normShadowIntensity = 1 + (1 - normLightIntensity);
unsigned char normShadowR = tempR;
unsigned char normShadowG = tempG;
unsigned char normShadowB = tempB;
color_lighten(normShadowR, normShadowG, normShadowB, normShadowIntensity);
tempR = (unsigned char) (normShadowR + (t * (normLightR - normShadowR)));
tempG = (unsigned char) (normShadowG + (t * (normLightG - normShadowG)));
tempB = (unsigned char) (normShadowB + (t * (normLightB - normShadowB)));
}
//endregion
//region Stripe Average Colouring
if (stripeLightIntensity != 1) {
double lastOrbit = 0.5 + 0.5 * sin(stripeDensity * atan2(last_z_i, last_z_r));
double smallCount = orbitCount - lastOrbit;
orbitCount /= (double) i;
smallCount /= (double) i - 1;
double frac = -1. + log10(2.0 * log(R * R)) / log10(2.) -
log10(0.5 * log(last_z_r * last_z_r + last_z_i * last_z_i)) / log10(2.);
double mix = frac * orbitCount + (1 - frac) * smallCount;
if (mix < 0) {
mix = 0;
} else if (mix > 1) {
mix = 1;
}
unsigned char stripeLightR = tempR;
unsigned char stripeLightG = tempG;
unsigned char stripeLightB = tempB;
color_lighten(stripeLightR, stripeLightG, stripeLightB, stripeLightIntensity);
double stripeShadowIntensity = 1 + (1 - stripeLightIntensity);
unsigned char stripeShadowR = tempR;
unsigned char stripeShadowG = tempG;
unsigned char stripeShadowB = tempB;
color_lighten(stripeShadowR, stripeShadowG, stripeShadowB, stripeShadowIntensity);
tempR = (unsigned char) (stripeShadowR + (mix * (stripeLightR - stripeShadowR)));
tempG = (unsigned char) (stripeShadowG + (mix * (stripeLightG - stripeShadowG)));
tempB = (unsigned char) (stripeShadowB + (mix * (stripeLightB - stripeShadowB)));
}
//endregion
//region Border
if (borderThickness > 0) {
double tBorder = d / borderThickness;
if (tBorder < 1) { // Border
tempR = (unsigned char) (borderR + tBorder * (tempR - borderR));
tempG = (unsigned char) (borderG + tBorder * (tempG - borderG));
tempB = (unsigned char) (borderB + tBorder * (tempB - borderB));
}
}
//endregion
image[currentIndex * 4] = tempR;
image[currentIndex * 4 + 1] = tempG;
image[currentIndex * 4 + 2] = tempB;
}
}
}
void multibrot(
unsigned int unroll,
unsigned int blockSize,
unsigned char *rgb,
int width, int height,
int exponent, int iterations, double R, double eps,
unsigned char borderR, unsigned char borderG, unsigned char borderB, double borderThickness,
long normOrbitSkip, double normLightIntensity, double normLightAngle, double normLightHeight,
unsigned char bgR, unsigned char bgG, unsigned char bgB,
double kR, double kG, double kB, double kD,
unsigned char internalBorderR, unsigned char internalBorderG, unsigned char internalBorderB,
unsigned char internalCoreR, unsigned char internalCoreG, unsigned char internalCoreB, double internalK,
double stripeDensity, double stripeLightIntensity,
double zoom, double posX, double posY) {
cudaProfilerStart();
//region Setup
cout << "Setting up..." << endl;
double ratio = (double) width / height;
unsigned int size = width * height;
unsigned char *imageHost;
imageHost = (unsigned char *) malloc(4 * size * sizeof(unsigned char));
unsigned char *imageDevice;
gpuErrchk(cudaMallocManaged(&imageDevice, 4 * size * sizeof(unsigned char)));
int suggestedBlockSize;
int minGridSize;
cudaOccupancyMaxPotentialBlockSize(&minGridSize, &suggestedBlockSize, multibrot_kernel, 0, 4 * size);
cout << "Suggested BlockSize: " << suggestedBlockSize << endl << "Min GridSize: " << minGridSize << endl;
int gridSize = (size + blockSize - 1) / blockSize / unroll;
cout << "BlockSize: " << blockSize << endl << "GridSize: " << gridSize << endl << "Unroll: " << unroll << endl;
cout << "Setup done!" << endl;
//endregion
//region Generation
cout << "Fractal generation in process..." << endl;
multibrot_kernel<<<gridSize, blockSize>>>(unroll,
imageDevice,
width, height, ratio,
exponent, iterations, R, eps,
borderR, borderG, borderB, borderThickness,
normOrbitSkip, normLightIntensity, normLightAngle, normLightHeight,
bgR, bgG, bgB,
kR, kG, kB, kD,
internalBorderR, internalBorderG, internalBorderB,
internalCoreR, internalCoreG, internalCoreB, internalK,
stripeDensity, stripeLightIntensity,
zoom, posX, posY);
gpuErrchk(cudaPeekAtLastError());
gpuErrchk(cudaDeviceSynchronize());
gpuErrchk(cudaMemcpy(imageHost, imageDevice, 4 * size * sizeof(unsigned char), cudaMemcpyDeviceToHost));
cout << "Generation done!" << endl;
int maxActiveBlocks;
cudaOccupancyMaxActiveBlocksPerMultiprocessor(&maxActiveBlocks, multibrot_kernel, blockSize, 0);
int device;
cudaDeviceProp props;
cudaGetDevice(&device);
cudaGetDeviceProperties(&props, device);
double occupancy = (maxActiveBlocks * blockSize / props.warpSize) /
(double) (props.maxThreadsPerMultiProcessor / props.warpSize);
cout << std::setprecision(4) << "Theoretical occupancy: " << occupancy << "%" << endl;
//endregion
for (int i = 0; i < size; i++) {
rgb[i * 3 + 2] = imageHost[i * 4];
rgb[i * 3 + 1] = imageHost[i * 4 + 1];
rgb[i * 3] = imageHost[i * 4 + 2];
}
//region Cleanup
free(imageHost);
cudaFree(imageDevice);
cudaDeviceReset();
//endregion
cudaProfilerStop();
}
|
11,578 | #include "includes.h"
/*
============================================================================
Name : Teste.cu
Author :
Version :
Copyright : Your copyright notice
Description : CUDA compute reciprocals
============================================================================
*/
static void CheckCudaErrorAux(const char *, unsigned, const char *,
cudaError_t);
#define CUDA_CHECK_RETURN(value) CheckCudaErrorAux(__FILE__,__LINE__, #value, value)
/**
* CUDA kernel that computes reciprocal values for a given vector
*/
/**
* Host function that copies the data and launches the work on GPU
*/
__global__ void reciprocalKernel(float *data, unsigned vectorSize) {
unsigned idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < vectorSize)
data[idx] = 1.0 / data[idx];
} |
11,579 | #include "includes.h"
__global__ void rgb2gray(float *grayImage, float *rgbImage, int channels, int width, int height) {
int x = threadIdx.x + blockIdx.x * blockDim.x;
int y = threadIdx.y + blockIdx.y * blockDim.y;
if (x < width && y < height) {
// get 1D coordinate for the grayscale image
int grayOffset = y * width + x;
// one can think of the RGB image having
// CHANNEL times columns than the gray scale image
int rgbOffset = grayOffset * channels;
float r = rgbImage[rgbOffset]; // red value for pixel
float g = rgbImage[rgbOffset + 1]; // green value for pixel
float b = rgbImage[rgbOffset + 2]; // blue value for pixel
// perform the rescaling and store it
// We multiply by floating point constants
grayImage[grayOffset] = 0.21f * r + 0.71f * g + 0.07f * b;
}
} |
11,580 | /******************************************************************************
* Author : Cody Rigby
* Description :
* CMT-Bone Kernel CUDA
* Last Revised : 02/09/19
******************************************************************************/
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
#include <math.h>
#include <cuda_profiler_api.h>
// Use GPU = 1 or run serially = 0
#define USE_GPU 0
/* -------------------------- Primary Parameters --------------------------- */
// Number of elements in each direction
// entire grid space is cubic
// of size (GRID_DIM , GRID_DIM , GRID_DIM)
// grid is always 3 dimensional
#ifndef GRID_DIM
#define GRID_DIM 4
#endif
// Size of each element (cubic), ternix dim
// (ELEMENT_SIZE,ELEMENT_SIZE,ELEMENT_SIZE)
#ifndef ELEMENT_SIZE
#define ELEMENT_SIZE 5
#endif
// Number of physics parameters tracked
// mass, energy and the three components of momentum
#define PHYSICAL_PARAMS 5
/* ------------------------- Secondary Parameters -------------------------- */
#define MPI_DTYPE MPI_DOUBLE
#define CARTESIAN_DIMENSIONS 3
/* ------------------------- Calculated Parameters ------------------------- */
// total number of elements in the grid
#define TOTAL_ELEMENTS (GRID_DIM * GRID_DIM * GRID_DIM)
// size of the face of a single element parameter
#define FACE_SIZE (ELEMENT_SIZE * ELEMENT_SIZE)
#define RK 3
/* ------------------------------------------------------------------------ */
/* -------------------------- Type Definitions ---------------------------- */
/* ------------------------------------------------------------------------ */
/* dtype: internal data storage type for calculations
ttype: type to use for representing time */
typedef double dtype;
typedef double ttype;
typedef struct {
int size;
dtype * V;
} vectortype, *vector;
typedef struct {
int rows;
int cols;
dtype ** M;
} matrixtype, *matrix;
typedef struct {
int rows;
int cols;
int layers;
dtype *** T;
} ternixtype, *ternix;
typedef struct {
ternix *B;
} elementtype, *element;
/* ------------------------- Enum for Kernel Operation ------------------------- */
enum kernel_op{dr,ds,dt};
__global__ void gpu_operation( double [ELEMENT_SIZE][ELEMENT_SIZE],
double[][ELEMENT_SIZE][ELEMENT_SIZE],
double[][ELEMENT_SIZE][ELEMENT_SIZE],
double[][ELEMENT_SIZE][ELEMENT_SIZE],
double[][ELEMENT_SIZE][ELEMENT_SIZE],
double[][ELEMENT_SIZE][ELEMENT_SIZE],
double[][ELEMENT_SIZE][ELEMENT_SIZE]
);
// insert these into the main routine
// const dim3 blockSize(ELEMENT_SIZE);
// const dim3 gridSize(ELEMENT_SIZE, ELEMENT_SIZE, ELEMENT_SIZE);
ttype tdiff(struct timespec a, struct timespec b)
/* Find the time difference. */
{
ttype dt = (( b.tv_sec - a.tv_sec ) + ( b.tv_nsec - a.tv_nsec ) / 1E9);
return dt;
}
struct timespec now(){
struct timespec t;
clock_gettime(CLOCK_REALTIME, &t);
return t;
}
/* ------------------------------------------------------------------------- */
/* -------------------------- Vector Functions ----------------------------- */
/* ------------------------------------------------------------------------- */
vector new_vector(int size)
/* Make a new 'vector' type and allocate memory for it. */
{
vector X = (vector)malloc(sizeof(vectortype));
X->size = size;
X->V = (dtype*)malloc(sizeof( dtype * ) * size);
return X;
}
void delete_vector(vector X)
/* Free up the memory allocated for the vector X. */
{
free(X->V);
free(X);
}
void random_fill_vector(vector X, dtype lower, dtype upper)
/* Fill a vector with random numbers over [lower, upper) */
{
int i;
for (i = 0; i < (X->size); i++) {
X->V[i] = ((dtype) rand() / (RAND_MAX)) * (upper - lower + 1) + lower;
}
}
vector new_random_vector(int size, dtype lower, dtype upper)
/* Return a newly-allocated random vector */
{
vector X = new_vector(size);
random_fill_vector(X, lower, upper);
return X;
}
/* ------------------------------------------------------------------------- */
/* -------------------------- Matrix Functions ----------------------------- */
/* ------------------------------------------------------------------------- */
matrix new_matrix(int rows, int cols)
/* Make a new 'matrix' type and allocate memory. Use: A->M[row][column]. */
{
int i;
matrix A = (matrix)malloc(sizeof(matrixtype));
A->rows = rows;
A->cols = cols;
A->M = (dtype**)malloc(sizeof( dtype * ) * rows);
for (i = 0; i < rows; i++) {
A->M[i] = (dtype*)malloc(sizeof( dtype * ) * cols);
}
return A;
}
void delete_matrix(matrix A)
/* Free up the memory allocated for the matrix A. */
{
int row;
for (row = 0; row<(A->rows); row++) { free(A->M[row]); }
free(A->M);
free(A);
}
void zero_matrix(matrix A)
/* Zero out the matrix A. */
{
int row, col;
for(row = 0; row<(A->rows); row++) {
for(col = 0; col<(A->cols); col++) {
A->M[row][col] = (dtype) 0;
}
}
}
void random_fill_matrix(matrix A, dtype lower, dtype upper)
/* Fill a matrix with random numbers over [lower, upper). */
{
int row, col;
for (row = 0; row < (A->rows); row++) {
for (col = 0; col < (A->cols); col++) {
A->M[row][col] = (dtype) rand() / RAND_MAX * (upper - lower + 1) + lower;
}
}
}
matrix new_random_matrix(int rows, int cols, dtype lower, dtype upper)
/* Return a newly-allocated random matrix. */
{
matrix A = new_matrix(rows, cols);
random_fill_matrix(A, lower, upper);
return A;
}
/* ------------------------------------------------------------------------- */
/* -------------------------- Ternix Functions ----------------------------- */
/* ------------------------------------------------------------------------- */
ternix new_ternix(int rows, int cols, int layers)
/*
Make a new 'ternix' type and allocate memory for it.
Access is done by: A->T[row][column][layer].
*/
{
int i, j;
ternix A = (ternix)malloc(sizeof(ternixtype));
A->rows = rows;
A->cols = cols;
A->layers = layers;
A->T = (dtype***)malloc( sizeof( dtype * ) * rows );
for (i = 0; i<rows; i++) {
A->T[i] = (dtype**)malloc( sizeof( dtype * ) * cols);
for (j = 0; j<cols; j++) {
A->T[i][j] = (dtype*)malloc( sizeof( dtype ) * layers);
}
}
return A;
}
void delete_ternix(ternix A)
/* Free up the memory allocated for the ternix A. */
{
int row, col;
for (row = 0; row<(A->rows); row++) {
for (col = 0; col<(A->cols); col++) {
free(A->T[row][col]);
}
free(A->T[row]);
}
free(A->T);
free(A);
}
void zero_ternix(ternix A)
/* Zero out the ternix A. */
{
int row, col, layer;
for(row = 0; row<(A->rows); row++) {
for(col = 0; col<(A->cols); col++) {
for(layer = 0; layer<(A->layers); layer++) {
A->T[row][col][layer] = (dtype) 0;
}
}
}
}
ternix new_zero_ternix(int rows, int cols, int layers)
/* Return a newly-allocated zeroed ternix. */
{
ternix A = new_ternix(rows, cols, layers);
zero_ternix(A);
return A;
}
void random_fill_ternix(ternix A, dtype lower, dtype upper)
/* Fill a ternix with random numbers over [lower, upper). */
{
int row, col, layer;
for (row = 0; row<(A->rows); row++) {
for (col = 0; col<(A->cols); col++) {
for(layer = 0; layer<(A->layers); layer++) {
A->T[row][col][layer] = ((dtype) rand() / (RAND_MAX)) *
(upper - lower + 1) + lower;
}
}
}
}
ternix new_random_ternix(int rows, int cols, int layers,
dtype lower, dtype upper)
/* Return a random newly-allocated ternix. */
{
ternix A = new_ternix(rows, cols, layers);
random_fill_ternix(A, lower, upper);
return A;
}
/* ------------------------------------------------------------------------- */
/* -------------------------- Element Functions ---------------------------- */
/* ------------------------------------------------------------------------- */
element new_random_element(dtype lower, dtype upper)
/* Return an element with PHYSICAL_PARAMTERS blocks of ELEMENT_SIZE,
randomly filled with ternices over [lower, upper). */
// elements have multiple ternices
{
int i;
element A = (element)malloc(sizeof(elementtype));
A->B = (ternix *)malloc(sizeof( ternix * ) * PHYSICAL_PARAMS);
for (i = 0; i < PHYSICAL_PARAMS; i++) {
A->B[i] = new_random_ternix( ELEMENT_SIZE,
ELEMENT_SIZE,
ELEMENT_SIZE,
lower, upper );
}
return A;
}
element new_zero_element()
/* Return an element with PHYSICAL_PARAMTERS blocks of ELEMENT_SIZE. */
{
int i;
element A = (element)malloc( sizeof(elementtype) );
A->B = (ternix *)malloc( sizeof( ternix * ) * PHYSICAL_PARAMS );
for (i = 0; i < PHYSICAL_PARAMS; i++) {
A->B[i] = new_zero_ternix(ELEMENT_SIZE, ELEMENT_SIZE, ELEMENT_SIZE);
}
return A;
}
void delete_element(element A)
/* Frees up the memory allocated for the element A. */
{
int i;
for (i = 0; i < PHYSICAL_PARAMS; i++) { delete_ternix( A->B[i] ); }
free(A->B);
free(A);
}
/* ------------------------------------------------------------------------- */
/* ------------------------ Faked CMT-Nek Operations ----------------------- */
/* ------------------------------------------------------------------------- */
void operation_dr(matrix A, ternix B, ternix C)
/* Perform the R axis derivative operation, with kernel A and result C. */
{
zero_ternix(C);
int k, j, i, g;
for (k = 0; k < ELEMENT_SIZE; k++) {
for (j = 0; j < ELEMENT_SIZE; j++) {
for (i = 0; i < ELEMENT_SIZE; i++) {
for (g = 0; g < ELEMENT_SIZE; g++) {
C->T[i][j][k] += A->M[i][g] * B->T[g][j][k]; } } } }
}
void operation_ds(matrix A, ternix B, ternix C)
/* Perform the S axis derivative operation, with kernel A and result C. */
{
zero_ternix(C);
int k, j, i, g;
for (k = 0; k < ELEMENT_SIZE; k++) {
for (j = 0; j < ELEMENT_SIZE; j++) {
for (i = 0; i < ELEMENT_SIZE; i++) {
for (g = 0; g < ELEMENT_SIZE; g++) {
C->T[i][j][k] += A->M[j][g] * B->T[i][g][k]; } } } }
}
void operation_dt(matrix A, ternix B, ternix C)
/* Perform the T axis derivative operation, with kernel A and result C. */
{
zero_ternix(C);
int k, j, i, g;
for (k = 0; k < ELEMENT_SIZE; k++) {
for (j = 0; j < ELEMENT_SIZE; j++) {
for (i = 0; i < ELEMENT_SIZE; i++) {
for (g = 0; g < ELEMENT_SIZE; g++) {
C->T[i][j][k] += A->M[k][g] * B->T[i][j][g]; } } } }
}
void operation_conv(ternix Q, ternix *RX, ternix Hx, ternix Hy, ternix Hz,
ternix Ur, ternix Us, ternix Ut)
/* Given Q, produce UR, US, and UT by faked transformation. HX, HY, and HZ
are temporary space. RX is the list of transformation ternices. */
{
/* Generate three random constants. */
dtype a = ((dtype) rand() / (RAND_MAX));
dtype d = ((dtype) rand() / (RAND_MAX));
dtype c = ((dtype) rand() / (RAND_MAX));
/* indexing variables */
// i,j,k are for indexing through the grid
// r is for 'stages' (idk what this means but it makes the whole computation stage loop three times)
// b is for indexing throught the physical paramaters of the individual elements
int k, j, i;
/* First, make HX, HY, and HZ, which are used in the next step. */
for (k = 0; k < ELEMENT_SIZE; k++) {
for (j = 0; j < ELEMENT_SIZE; j++) {
for (i = 0; i < ELEMENT_SIZE; i++) {
Hx->T[i][j][k] = a * Q->T[i][j][k];
Hy->T[i][j][k] = d * Q->T[i][j][k];
Hz->T[i][j][k] = c * Q->T[i][j][k];
}
}
}
/* Then, produce our outputs using HX, HY, HZ, and RX. */
for (k = 0; k < ELEMENT_SIZE; k++) {
for (j = 0; j < ELEMENT_SIZE; j++) {
for (i = 0; i < ELEMENT_SIZE; i++) {
/* Generate UR. */
Ur->T[i][j][k] = ( RX[0]->T[i][j][k] * Hx->T[i][j][k] +
RX[1]->T[i][j][k] * Hy->T[i][j][k] +
RX[2]->T[i][j][k] * Hz->T[i][j][k] );
/* Generate US. */
Us->T[i][j][k] = ( RX[3]->T[i][j][k] * Hx->T[i][j][k] +
RX[4]->T[i][j][k] * Hy->T[i][j][k] +
RX[5]->T[i][j][k] * Hz->T[i][j][k] );
/* Generate UT. */
Ut->T[i][j][k] = ( RX[6]->T[i][j][k] * Hx->T[i][j][k] +
RX[7]->T[i][j][k] * Hy->T[i][j][k] +
RX[8]->T[i][j][k] * Hz->T[i][j][k] );
}
}
}
}
void operation_sum(ternix X, ternix Y, ternix Z, ternix R)
/* Add three ternices together and put the result in R. */
{
int k, j, i;
for (k = 0; k < ELEMENT_SIZE; k++) {
for (j = 0; j < ELEMENT_SIZE; j++) {
for (i = 0; i < ELEMENT_SIZE; i++) {
R->T[i][j][k] = X->T[i][j][k] + Y->T[i][j][k] + Z->T[i][j][k];
}
}
}
}
void operation_rk(ternix Q, ternix R)
/* Perform a faked Runge Kutta stage (no previous stage information used). */
{
int k, j, i;
for (k = 0; k < ELEMENT_SIZE; k++) {
for (j = 0; j < ELEMENT_SIZE; j++) {
for (i = 0; i < ELEMENT_SIZE; i++) {
Q->T[i][j][k] = ( R->T[i][j][k] * 0.5 +
R->T[i][j][k] * 0.25 +
Q->T[i][j][k] * 0.5 );
}
}
}
}
/* ------------------------------------------------------------------------- */
/* ------------------------ Kernel GPU Operation --------------------------- */
/* ------------------------------------------------------------------------- */
void kernel_operation( matrix kernel, double * kernel_flat, double * kernel_flat_dev,
ternix Ur, ternix Us, ternix Ut,
ternix Vr, ternix Vs, ternix Vt,
double * Ur_flat, double * Us_flat, double * Ut_flat,
double * Vr_flat, double * Vs_flat, double * Vt_flat,
double * Ur_flat_dev, double * Us_flat_dev, double * Ut_flat_dev,
double * Vr_flat_dev, double * Vs_flat_dev, double * Vt_flat_dev)
{
/*---------------------- time and other variables -------------------*/
//float setup_t,kernel_t,cleanup_t;
int n, nn;
//struct timespec tA, tB;
size_t element_size = sizeof(double)*ELEMENT_SIZE*ELEMENT_SIZE*ELEMENT_SIZE;
size_t kernel_size = sizeof(double)*ELEMENT_SIZE*ELEMENT_SIZE;
/*---------------------- copy over input ternices -------------------*/
//tA = now();
n=0;
nn=0;
for(int i=0; i<ELEMENT_SIZE;i++){
for(int j=0; j<ELEMENT_SIZE;j++){
kernel_flat[nn++] = kernel->M[i][j];
for(int k=0; k<ELEMENT_SIZE;k++){
Ur_flat[n] = Ur->T[i][j][k];
Us_flat[n] = Us->T[i][j][k];
Ut_flat[n] = Ut->T[i][j][k];
n++;
}
}
}
/*---------------------- cuda copy the inputs ---------------------*/
cudaError_t err = cudaMemcpy(kernel_flat_dev, kernel_flat, kernel_size, cudaMemcpyHostToDevice);
err = cudaMemcpy(Ur_flat_dev, Ur_flat, element_size, cudaMemcpyHostToDevice);
err = cudaMemcpy(Us_flat_dev, Us_flat, element_size, cudaMemcpyHostToDevice);
err = cudaMemcpy(Ut_flat_dev, Ut_flat, element_size, cudaMemcpyHostToDevice);
/* ---------------------- set up cuda dimensions ------------------*/
const dim3 blockSize(ELEMENT_SIZE);
const dim3 gridSize(ELEMENT_SIZE, ELEMENT_SIZE, ELEMENT_SIZE);
/* ---------------------- CUDA Function Time ------------------*/
//tA = now();
gpu_operation<<<blockSize,gridSize>>>( (double(*)[ELEMENT_SIZE])&kernel_flat_dev[0],
(double(*)[ELEMENT_SIZE][ELEMENT_SIZE])&Ur_flat_dev[0],
(double(*)[ELEMENT_SIZE][ELEMENT_SIZE])&Us_flat_dev[0],
(double(*)[ELEMENT_SIZE][ELEMENT_SIZE])&Ut_flat_dev[0],
(double(*)[ELEMENT_SIZE][ELEMENT_SIZE])&Vr_flat_dev[0],
(double(*)[ELEMENT_SIZE][ELEMENT_SIZE])&Vs_flat_dev[0],
(double(*)[ELEMENT_SIZE][ELEMENT_SIZE])&Vt_flat_dev[0]);
cudaError_t errk = cudaDeviceSynchronize();
//tB = now();
//kernel_t = tdiff(tA, tB);
// printf( "cuda function duration: %.8f seconds \n", tdiff(tA, tB));
/* ---------------------- Copy over resulting matrices ------------------*/
//err = cudaMemcpy(Vr_flat, Vr_flat_dev, element_size, cudaMemcpyDeviceToHost);
//err = cudaMemcpy(Vs_flat, Vs_flat_dev, element_size, cudaMemcpyDeviceToHost);
//err = cudaMemcpy(Vt_flat, Vt_flat_dev, element_size, cudaMemcpyDeviceToHost);
// tA = now();
nn=0;
for(int i=0; i<ELEMENT_SIZE;i++){
for(int j=0; j<ELEMENT_SIZE;j++){
for(int k=0; k<ELEMENT_SIZE;k++){
Vr->T[i][j][k]=Vr_flat[0];
Vs->T[i][j][k]=Vs_flat[nn];
Vt->T[i][j][k]=Vt_flat[nn];
nn++;
}
}
}
/* ---------------------- don't free the memory yet ------------------*/
// cudaFree(kernel_flat_dev);
// cudaFree(element_in_flat_dev);
// cudaFree(element_out_flat_dev);
/* ---------------------- display the time ------------------*/
//float total_t = (setup_t+cleanup_t+kernel_t);
//printf( "total : %.8f, setup : %.8f%, kernel : %.8f%, cleanup : %.8f% \n", total_t, setup_t/total_t, kernel_t/total_t, cleanup_t/total_t);
}
/* ------------------------------------------------------------------------- */
/* ---------------------------- CUDA Functions ------------------------------ */
/* ------------------------------------------------------------------------- */
__global__ void gpu_operation( double k[ELEMENT_SIZE][ELEMENT_SIZE],
double Ur[][ELEMENT_SIZE][ELEMENT_SIZE],
double Us[][ELEMENT_SIZE][ELEMENT_SIZE],
double Ut[][ELEMENT_SIZE][ELEMENT_SIZE],
double Vr[][ELEMENT_SIZE][ELEMENT_SIZE],
double Vs[][ELEMENT_SIZE][ELEMENT_SIZE],
double Vt[][ELEMENT_SIZE][ELEMENT_SIZE])
{
/*----------------------retreive a row from the input ternix ----------------------------*/
int blockId = blockIdx.x + blockIdx.y * gridDim.x + gridDim.x * gridDim.y * blockIdx.z;
int threadId = blockId * blockDim.x + threadIdx.x;
/* ----------------------- shared memory setup -----------------------------*/
__shared__ double Ur_partial_sums[ELEMENT_SIZE];
__shared__ double Us_partial_sums[ELEMENT_SIZE];
__shared__ double Ut_partial_sums[ELEMENT_SIZE];
__shared__ double kernel[ELEMENT_SIZE][ELEMENT_SIZE];
for(int g=0;g<ELEMENT_SIZE;g++)
kernel[threadId][g] = k[threadId][g];
__syncthreads();
/*-------------------------------------- Calculate the partial sums ---------------------------------*/
Ur_partial_sums[threadId] = 0.0;
Us_partial_sums[threadId] = 0.0;
Ut_partial_sums[threadId] = 0.0;
for(int g=0;g<ELEMENT_SIZE;g++){
Ur_partial_sums[threadId] += kernel[threadId][g]*Ur[g][blockIdx.y][blockIdx.z];
Us_partial_sums[threadId] += kernel[threadId][g]*Us[blockIdx.x][g][blockIdx.z];
Ut_partial_sums[threadId] += kernel[threadId][g]*Ut[blockIdx.x][blockIdx.y][g];
}
__syncthreads();
/*-------------------------------------- Accumulate the partial sums ---------------------------------*/
Vr[blockIdx.x][blockIdx.y][blockIdx.z] = 0.0;
Vt[blockIdx.x][blockIdx.y][blockIdx.z] = 0.0;
Vs[blockIdx.x][blockIdx.y][blockIdx.z] = 0.0;
if(threadId == 0){
for(int g=0;g<ELEMENT_SIZE;g++){
Vr[blockIdx.x][blockIdx.y][blockIdx.z]+=Ur_partial_sums[g];
Vt[blockIdx.x][blockIdx.y][blockIdx.z]+=Ut_partial_sums[g];
Vs[blockIdx.x][blockIdx.y][blockIdx.z]+=Us_partial_sums[g];
}
}
// done
}
/* ------------------------------------------------------------------------- */
/* ---------------------------- Main Function ------------------------------ */
/* ------------------------------------------------------------------------- */
int main (int argc, char *argv[])
{
// /* ------------------------------ Dimensions Setup --------------------------- */
// char *a = argv[1];
// char *b = argv[2];
// int GRID_DIM = atoi(a);
// int ELEMENT_SIZE = atoi(b);
/* ------------------------------ Memory Setup --------------------------- */
struct timespec tA, tB, tS, tE;
// begin the time measurements
tA = now();
srand( 11 );
// Index variables: i,j,k for grid indexing
// r for stages (i dont know what that means)
// b for the physical parameters of an element
int i, j, k, r, b;
// create the grid space as a ternix of elements
element elements_Q[ GRID_DIM ][ GRID_DIM ][ GRID_DIM ];
// create the grid space for the output
// elements_R and elements_Q are seperate for now
// that could change in the future
element elements_R[ GRID_DIM ][ GRID_DIM ][ GRID_DIM ];
// generate elements in the grid
for (i = 0; i < GRID_DIM; i++) {
for (j = 0; j < GRID_DIM; j++){
for (k = 0; k < GRID_DIM; k++){
elements_Q[i][j][k] = new_random_element(0, 10);
elements_R[i][j][k] = new_zero_element();
}
}
}
// The same kernel is used for everything
// kernel used on all elements and is the same size as a element ternix face
// the ternix's in an element have faces the same size as this matrix
matrix kernel = new_random_matrix(ELEMENT_SIZE, ELEMENT_SIZE, -10, 10);
// The same transformation ternix (RX) is used for all elements.
// This is an approximation, there should be one for each element.
ternix RX[9];
// fill of the intermediate ternix
for (i = 0; i < 9; i++) {
RX[i] = new_random_ternix(ELEMENT_SIZE, ELEMENT_SIZE, ELEMENT_SIZE, -1, 1);
}
// Intermediate 3D structures: used in conv operation
ternix Hx = new_zero_ternix(ELEMENT_SIZE, ELEMENT_SIZE, ELEMENT_SIZE);
ternix Hy = new_zero_ternix(ELEMENT_SIZE, ELEMENT_SIZE, ELEMENT_SIZE);
ternix Hz = new_zero_ternix(ELEMENT_SIZE, ELEMENT_SIZE, ELEMENT_SIZE);
// Intermediate 3D structures: outputs of conv operation
ternix Ur = new_zero_ternix(ELEMENT_SIZE, ELEMENT_SIZE, ELEMENT_SIZE);
ternix Us = new_zero_ternix(ELEMENT_SIZE, ELEMENT_SIZE, ELEMENT_SIZE);
ternix Ut = new_zero_ternix(ELEMENT_SIZE, ELEMENT_SIZE, ELEMENT_SIZE);
// Intermediate 3D structures: outputs of derivative operations
ternix Vr = new_zero_ternix(ELEMENT_SIZE, ELEMENT_SIZE, ELEMENT_SIZE);
ternix Vs = new_zero_ternix(ELEMENT_SIZE, ELEMENT_SIZE, ELEMENT_SIZE);
ternix Vt = new_zero_ternix(ELEMENT_SIZE, ELEMENT_SIZE, ELEMENT_SIZE);
/* ------------------------------- GPU mem setup ----------------------------- */
double *kernel_flat, *kernel_flat_dev, *Ur_flat, *Us_flat, *Ut_flat, *Vr_flat, *Vs_flat, *Vt_flat, *Ur_flat_dev, *Us_flat_dev, *Ut_flat_dev, *Vr_flat_dev, *Vs_flat_dev, *Vt_flat_dev;
if (USE_GPU) {
size_t element_size = sizeof(double)*ELEMENT_SIZE*ELEMENT_SIZE*ELEMENT_SIZE;
size_t kernel_size = sizeof(double)*ELEMENT_SIZE*ELEMENT_SIZE;
// allocate flattened host memory
kernel_flat = (double*)malloc(kernel_size);
Ur_flat = (double*)malloc(element_size);
Us_flat = (double*)malloc(element_size);
Ut_flat = (double*)malloc(element_size);
Vr_flat = (double*)malloc(element_size);
Vs_flat = (double*)malloc(element_size);
Vt_flat = (double*)malloc(element_size);
// allocate memory for 'flattened' kernel in cuda
cudaError_t err = cudaMalloc((void**)&kernel_flat_dev, kernel_size);
err = cudaMalloc((void**)&Ur_flat_dev, element_size);
err = cudaMalloc((void**)&Us_flat_dev, element_size);
err = cudaMalloc((void**)&Ut_flat_dev, element_size);
err = cudaMalloc((void**)&Vr_flat_dev, element_size);
err = cudaMalloc((void**)&Vs_flat_dev, element_size);
err = cudaMalloc((void**)&Vt_flat_dev, element_size);
}
// report the time it took to allocate the memory for the above objects
tB = now();
printf( "HOST MESSAGE : Memory Allocation took, %.8f seconds \n",tdiff(tA, tB));
/* ----------------------------------------------------------------------- */
/* ------------------------------- Main Loop ----------------------------- */
/* ----------------------------------------------------------------------- */
// TIME MEASUREMENT VARIABLES
//int flag = 0;
float cuda_t = 0.0;
float serial_t = 0.0;
tS = now(); // start time for whole loop
// For each of the three 'stages':
for (r = 0; r < RK; r++) {
/* --------------------------- Serial Compute (A) --------------------------- */
for ( i = 0; i < GRID_DIM; i++ ) {
for ( j = 0; j < GRID_DIM; j++ ) {
for ( k = 0; k < GRID_DIM; k++ ) {
// For each block in the element:
for ( b = 0; b < PHYSICAL_PARAMS; b++ ) {
// Generate Ur, Us, and Ut.
// take the curently indexed ternix from the current element
// make the corresponding transformations to generate Ur, Us, and Ut
// which are the input ternix data to the three kernels.
operation_conv(elements_Q[i][j][k]->B[b], RX, Hx, Hy, Hz, Ur, Us, Ut);
/* --------------------------- CUDA Kernels Begin --------------------------- */
if(USE_GPU){
tA = now();
kernel_operation( kernel, kernel_flat, kernel_flat_dev,
Ur, Us, Ut, Vr, Vs, Vt,
Ur_flat, Us_flat, Ut_flat,
Vr_flat, Vs_flat, Vt_flat,
Ur_flat_dev, Us_flat_dev, Ut_flat_dev,
Vr_flat_dev, Vs_flat_dev, Vt_flat_dev);
tB = now();
cuda_t += tdiff(tA,tB);
//printf( "CUDA Step : grid index [%d, %d, %d] duration: %.8f seconds \n", i,j,k, tdiff(tA, tB));
}
else{
tA = now();
operation_dr(kernel, Ur, Vr);
operation_ds(kernel, Us, Vs);
operation_dt(kernel, Ut, Vt);
tB = now();
serial_t += tdiff(tA,tB);
//printf( "SERIAL Step : grid index [%d, %d, %d] duration: %.8f seconds \n", i,j,k, tdiff(tA, tB));
}
/* --------------------------- CUDA Kernels End --------------------------- */
/* Add Vr, Vs, and Vt to make R. */
// elements_R is the output.
operation_sum( Vr, Vs, Vt, elements_R[i][j][k]->B[b] );
}
}
}
}
if (USE_GPU){
cudaFree(kernel_flat_dev);
cudaFree(Ur_flat_dev);
cudaFree(Us_flat_dev);
cudaFree(Ut_flat_dev);
cudaFree(Vr_flat_dev);
cudaFree(Vs_flat_dev);
cudaFree(Vt_flat_dev);
}
/* --------------------------- Location of Communication (C) ------------------------------- */
// This section might need to approximate the surface integral calculations
/* --------------------------- Compute (B) --------------------------- */
/* For each element owned by this rank: */
for ( i = 0; i < GRID_DIM; i++ ) {
for ( j = 0; j < GRID_DIM; j++ ) {
for ( k = 0; k < GRID_DIM; k++ ) {
/* For each block in the element: */
for ( b = 0; b < PHYSICAL_PARAMS; b++ ) {
// Perform a fake Runge Kutta stage (without R from the last stage)
// to obtain a new value of Q.
operation_rk(elements_R[i][j][k]->B[b], elements_Q[i][j][k]->B[b]);
}
}
}
}
// tB = now(); printf( "Step : %d duration: %.8f seconds \n",r,tdiff(tA, tB));
} // for each stage (RK) ...
tE = now(); // time at end of loop
// final report
if (USE_GPU) {
printf("CUDA kernel avg duration: %.8f seconds \n", cuda_t/( GRID_DIM * GRID_DIM * GRID_DIM * PHYSICAL_PARAMS * RK));
printf("CUDA kernel total duration: %.8f seconds \n", cuda_t);
}
else {
printf( "SERIAL kernel avg duration: %.8f seconds \n", serial_t/( GRID_DIM * GRID_DIM * GRID_DIM * PHYSICAL_PARAMS * RK));
printf("SERIAL FINAL total duration: %.8f seconds \n", serial_t);
}
printf("Total kernel iterations: %d \n", ( GRID_DIM * GRID_DIM * GRID_DIM * PHYSICAL_PARAMS * RK));
printf("Total time for grid dim %d and element dim %d : %f \n",GRID_DIM,ELEMENT_SIZE, tdiff(tS, tE));
/* ----------------------------------------------------------------------- */
/* -------------------------------- Cleanup ------------------------------ */
/* ----------------------------------------------------------------------- */
tA = now();
for ( i = 0; i < GRID_DIM; i++ ) {
for ( j = 0; j < GRID_DIM; j++ ) {
for ( k = 0; k < GRID_DIM; k++ ) {
delete_element(elements_Q[i][j][k]);
delete_element(elements_R[i][j][k]);
}
}
}
delete_matrix(kernel);
for (i = 0; i < 9; i++) {
delete_ternix(RX[i]);
}
delete_ternix(Hx);
delete_ternix(Hy);
delete_ternix(Hz);
delete_ternix(Ur);
delete_ternix(Us);
delete_ternix(Ut);
delete_ternix(Vr);
delete_ternix(Vs);
delete_ternix(Vt);
tB = now();
printf("Cleanup: %.8f seconds \n", tdiff(tA, tB));
return 0;
}
|
11,581 | #include <stdio.h>
#include <stdlib.h>
#define ARRAY_SIZE 10000
#define TBB 256
__global__ void saxpyGPU(int a, int n, float *x, float *y){
int i = blockIdx.x*blockDim.x + threadIdx.x;
if (i<n) y[i] = a*x[i] + y[i];
}
void saxpyCPU(int a, float *x, float *y){
for(int i=0;i<ARRAY_SIZE;i++){
y[i] = a*x[i] + y[i];
}
}
void random_array(float *x){
for(int i=0;i<ARRAY_SIZE;i++){
x[i]=rand()%100;
}
}
void print_array(float *x){
for(int i=0;i<ARRAY_SIZE;i++){
printf("%f;", x[i]);
}
}
int main(int argc, char **argv){
int grid = (ARRAY_SIZE+(TBB-(ARRAY_SIZE%TBB)))/TBB;
int a = rand()%100;
float *x = (float *)malloc(sizeof(float) * ARRAY_SIZE);
float *w = (float *)malloc(sizeof(float) * ARRAY_SIZE) ;
random_array(x);
memcpy(w, x, sizeof(float) * ARRAY_SIZE);
float *y = (float *)malloc(sizeof(float) * ARRAY_SIZE);
float *z = (float *)malloc(sizeof(float) * ARRAY_SIZE);
random_array(y);
memcpy(z, y, sizeof(float) * ARRAY_SIZE);
float *d_x = NULL;
float *d_y = NULL;
cudaMalloc(&d_x, ARRAY_SIZE * sizeof(float));
cudaMalloc(&d_y, ARRAY_SIZE * sizeof(float));
cudaMemcpy(d_x, x, ARRAY_SIZE * sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(d_y, y, ARRAY_SIZE * sizeof(float), cudaMemcpyHostToDevice);
saxpyGPU<<<grid,TBB>>>(a, ARRAY_SIZE, d_x, d_y);
cudaMemcpy(x, d_x, ARRAY_SIZE * sizeof(float), cudaMemcpyDeviceToHost);
cudaMemcpy(y, d_y, ARRAY_SIZE * sizeof(float), cudaMemcpyDeviceToHost);
printf("Computing SAXPY on the GPU... Done!\n");
saxpyCPU(a, w, z);
printf("Computing SAXPY on the CPU... Done!\n");
printf("Comparing the output for each implementation...\n");
int n = memcmp(y, z, sizeof(float) * ARRAY_SIZE);
if (n==0){
printf("Correct!\n");
}
else{
printf("Not Correct!\n");
}
free(x);
free(y);
free(w);
free(z);
cudaFree(d_x);
cudaFree(d_y);
return 0;
} |
11,582 | #include <stdio.h>
#include <stdlib.h>
// CUDA runtime
#include <cuda_runtime.h> // =========== Handles all the CUDA Syntax
#include <device_launch_parameters.h> // ====== Handles device parameters (threadIdx.x, blockIdx.x)
__global__ void myKernel(void) {
printf("Hello World!\n");
}
int main(void) {
myKernel <<<1, 1>>> ();
return 0;
} |
11,583 | #include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
__global__ void revStr(char *str, int *length)
{
int i = threadIdx.x;
int len = *length;
int temp = str[i];
str[i] = str[len - i - 1];
str[len - i - 1] = temp;
}
int main()
{
char *str = (char *) calloc(BUFSIZ, sizeof(char)), *dStr;
printf("Enter the string\n");
scanf("%[^\n]%*c", str);
int len = strlen(str), *dLen;
cudaMalloc(&dStr, len);
cudaMalloc(&dLen, sizeof(int));
cudaMemcpy(dStr, str, len, cudaMemcpyHostToDevice);
cudaMemcpy(dLen, &len, sizeof(int), cudaMemcpyHostToDevice);
revStr<<<1, len / 2>>>(dStr, dLen);
cudaMemcpy(str, dStr, len, cudaMemcpyDeviceToHost);
printf("The reversed string:\n%s", str);
cudaFree(dStr);
}
|
11,584 | /* Two kernels, no shared memory, manual laplacian, 2D malloc */
#include <stdio.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);
}
}
#define M(a, z, x) (*((float *) ((char *)a + z * model_pitch) + x))
#define WF(a, b, z, x) (*((float *) ((char *)a + b * nz * model_pitch + (z) * model_pitch) + x))
#define SA(a, b, s, t) (*((float *) ((char *)a + b * ns * source_amplitude_pitch + s * source_amplitude_pitch) + t))
#define SL(a, b, s) (*((int *) ((char *)a + b * sources_loc_pitch) + s))
__constant__ float fd_d[3];
size_t model_pitch_h;
// Device code
__global__ void step_d(const float *const model,
float *wfc,
float *wfp,
const int nb, const int nz, const int nx,
const size_t model_pitch)
{
int x = blockDim.x * blockIdx.x + threadIdx.x;
int z = blockDim.y * blockIdx.y + threadIdx.y;
int b = blockDim.z * blockIdx.z + threadIdx.z;
float lap;
bool in_domain = (x > 1) && (x < nx - 2)
&& (z > 1) && (z < nz - 2)
&& (b < nb);
if (in_domain)
{
/* Laplacian */
lap = (fd_d[0] * WF(wfc, b, z, x) +
fd_d[1] *
(WF(wfc, b, z, x + 1) +
WF(wfc, b, z, x - 1) +
WF(wfc, b, z + 1, x) +
WF(wfc, b, z - 1, x)) +
fd_d[2] *
(WF(wfc, b, z, x + 2) +
WF(wfc, b, z, x - 2) +
WF(wfc, b, z + 2, x) +
WF(wfc, b, z - 2, x)));
/* Main evolution equation */
WF(wfp, b, z, x) = M(model, z, x) * lap + 2 * WF(wfc, b, z, x)
- WF(wfp, b, z, x);
}
}
__global__ void add_sources_d(const float *const model,
float *wfp,
const float *const source_amplitude,
const int *const sources_z,
const int *const sources_x,
const int nz, const int nx,
const int nt, const int ns, const int it,
const size_t model_pitch, const size_t source_amplitude_pitch,
const size_t sources_loc_pitch)
{
int x = threadIdx.x;
int b = blockIdx.x;
int sz = SL(sources_z, b, x);
int sx = SL(sources_x, b, x);
WF(wfp, b, sz, sx) += SA(source_amplitude, b, x, it) * M(model, sz, sx);
}
// Host code
extern "C"
void setup(int nb, int nz, int nx, float dx, float *model_h,
float **model_d, float **wfc_d, float **wfp_d)
{
float fd[3] = {
-10.0f / 2 / (dx * dx),
4.0f / 3 / (dx * dx),
-1.0f / 12 / (dx * dx)
};
gpuErrchk(cudaMemcpyToSymbol(fd_d, fd, 3*sizeof(float)));
gpuErrchk(cudaMallocPitch(model_d, &model_pitch_h, nx * sizeof(float),
nz));
gpuErrchk(cudaMemcpy2D(*model_d, model_pitch_h, model_h,
nx * sizeof(float), nx * sizeof(float),
nz, cudaMemcpyHostToDevice));
gpuErrchk(cudaMallocPitch(wfc_d, &model_pitch_h, nx * sizeof(float),
nb * nz));
gpuErrchk(cudaMemset2D(*wfc_d, model_pitch_h, 0, nx * sizeof(float),
nb * nz));
gpuErrchk(cudaMallocPitch(wfp_d, &model_pitch_h, nx * sizeof(float),
nb * nz));
gpuErrchk(cudaMemset2D(*wfp_d, model_pitch_h, 0, nx * sizeof(float),
nb * nz));
}
extern "C"
void step(int nb, int nz, int nx, int nt, int ns,
float *model_d, float *wfc_d, float *wfp_d,
float *source_amplitude_h,
int *sources_z_h, int *sources_x_h, float *wfc_h)
{
size_t source_amplitude_pitch;
size_t sources_loc_pitch;
float *source_amplitude_d;
gpuErrchk(cudaMallocPitch(&source_amplitude_d,
&source_amplitude_pitch,
nt * sizeof(float), nb * ns));
gpuErrchk(cudaMemcpy2D(source_amplitude_d, source_amplitude_pitch,
source_amplitude_h,
nt * sizeof(float), nt * sizeof(float),
nb * ns, cudaMemcpyHostToDevice));
int *sources_z_d;
gpuErrchk(cudaMallocPitch(&sources_z_d, &sources_loc_pitch,
ns * sizeof(int), nb));
gpuErrchk(cudaMemcpy2D(sources_z_d, sources_loc_pitch, sources_z_h,
ns * sizeof(int), ns * sizeof(int),
nb, cudaMemcpyHostToDevice));
int *sources_x_d;
gpuErrchk(cudaMallocPitch(&sources_x_d, &sources_loc_pitch,
ns * sizeof(int), nb));
gpuErrchk(cudaMemcpy2D(sources_x_d, sources_loc_pitch, sources_x_h,
ns * sizeof(int), ns * sizeof(int),
nb, cudaMemcpyHostToDevice));
dim3 dimBlock(32, 32, 1);
int gridx = (nx + dimBlock.x - 1) / dimBlock.x;
int gridz = (nz + dimBlock.y - 1) / dimBlock.y;
int gridb = (nb + dimBlock.z - 1) / dimBlock.z;
dim3 dimGrid(gridx, gridz, gridb);
int it;
float *tmp;
for (it = 0; it < nt; it++)
{
step_d<<<dimGrid, dimBlock>>>(model_d, wfc_d, wfp_d,
nb, nz, nx, model_pitch_h);
gpuErrchk( cudaPeekAtLastError() );
add_sources_d<<<nb, ns>>>(model_d, wfp_d,
source_amplitude_d, sources_z_d, sources_x_d,
nz, nx, nt, ns, it, model_pitch_h,
source_amplitude_pitch, sources_loc_pitch);
gpuErrchk( cudaPeekAtLastError() );
tmp = wfc_d;
wfc_d = wfp_d;
wfp_d = tmp;
}
gpuErrchk(cudaMemcpy2D(wfc_h, nx * sizeof(float), wfc_d,
model_pitch_h, nx * sizeof(float),
nb * nz, cudaMemcpyDeviceToHost));
gpuErrchk(cudaFree(source_amplitude_d));
gpuErrchk(cudaFree(sources_z_d));
gpuErrchk(cudaFree(sources_x_d));
}
extern "C"
void finalise(float *model_d, float *wfc_d, float *wfp_d)
{
gpuErrchk(cudaFree(model_d));
gpuErrchk(cudaFree(wfc_d));
gpuErrchk(cudaFree(wfp_d));
}
|
11,585 | #include <stdio.h>
#include <stdlib.h>
#include <cuda_runtime.h>
void printMat(int *mat, int row, int column){
int i, j;
printf("\t");
for(j=0; j<column; j++)
printf("<%02d>\t", j);
printf("\n");
for(i=0; i<row; i++){
printf("<%02d>\t", i);
for(j=0; j<column; j++)
printf("%2d\t", mat[i*column+j]);
printf("\n");
}
}
int main(){
int *a, *b, *c, *ac;
int i, j;
size_t ac_pitch;
a = (int*)malloc(sizeof(int)*10*10);
b = (int*)malloc(sizeof(int)*10*10);
c = (int*)malloc(sizeof(int)*10*10);
for(i=0; i<10; i++)
for(j=0; j<10; j++){
a[i*10+j] = i*10+j;
b[i*10+j] = -1;
}
cudaMallocPitch((void**) &ac, &ac_pitch, sizeof(int)*10, 10);
printf("ac_pitch: %d\nsizeof(int)*10: %ld\n", ac_pitch, sizeof(int)*10);
printf("\nOri data>>\n");
printMat((int*)a, 10, 10);
//partial copy to device
cudaMemcpy2D(ac, ac_pitch, a, sizeof(int)*10, sizeof(int)*10, 10, cudaMemcpyHostToDevice);
//fully copy to host
cudaMemcpy2D(b, sizeof(int)*10, ac, ac_pitch, sizeof(int)*10, 10, cudaMemcpyDeviceToHost);
printf("\nDevice data>>\n");
printMat((int*)b, 10, 10);
//partial copy to host
cudaMemcpy2D(c, sizeof(int)*10, ac, 36, sizeof(int)*10, 10, cudaMemcpyDeviceToHost);
printf("\npartial Copy>>\n");
printMat((int*)c, 10, 10);
return 0;
}
|
11,586 | // includes, system
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <iostream>
// includes, project
#include <cuda_runtime.h>
#include <cufft.h>
#include <cufftXt.h>
// Complex data type
typedef float2 Complex;
static __host__ inline float ComplexAbs(Complex);
//This is the number of data points and stuff
#define N 50
constexpr double Pi = 3.14159265358979323846;
//this is the function we're transforming
float f(float t)
{
return sin(2*Pi*t);
}
////////////////////////////////////////////////////////////////////////////////
// Program main
////////////////////////////////////////////////////////////////////////////////
int main(int argc, char** argv) {
//memory for the function we're transforming
Complex* h_fvalues = reinterpret_cast<Complex*>(malloc(sizeof(Complex) * N));
for (unsigned int i = 0; i < N; i++) //initializing
{
h_fvalues[i].x = f(i * 1.0 / N);
h_fvalues[i].y = 0;
}
//device memory for the signal
Complex* d_fvalues;
cudaMalloc(reinterpret_cast<void**>(&d_fvalues), sizeof(Complex)*N);
//copy host memory to device
cudaMemcpy(d_fvalues, h_fvalues, sizeof(Complex)*N, cudaMemcpyHostToDevice);
//setting up the plan
cufftHandle plan;
cufftPlan1d(&plan, sizeof(Complex)*N, CUFFT_C2C,1);
//execute plan. This transforms the signal in place.
cufftExecC2C(plan, d_fvalues, d_fvalues, CUFFT_FORWARD);
//copying the results back onto the host
Complex* h_Fvalues = reinterpret_cast<Complex*>(malloc(sizeof(Complex) * N));
cudaMemcpy(h_Fvalues, d_fvalues, sizeof(Complex) * N, cudaMemcpyDeviceToHost);
for (unsigned int i = 0; i < N; i++)
{
std::cout << "i: " << i << " Re(F): " << h_Fvalues[i].x/N << " Im(F): " << h_Fvalues[i].y/100 << " |F|: " << ComplexAbs(h_Fvalues[i])/100 << std::endl;
}
free(h_fvalues);
cudaFree(d_fvalues);
free(h_Fvalues);
return 0;
}
//Complex absolute value
static __host__ inline float ComplexAbs(Complex a)
{
return sqrt(a.x * a.x + a.y * a.y);
}
|
11,587 | #include "includes.h"
__global__ void scatter_kernel(int *x_coors, int *y_coors, float *pfe_output, float *scattered_feature, const int max_num_pillars_, const int grid_x_size, const int grid_y_size) {
int i_pillar = blockIdx.x;
int i_feature = threadIdx.x;
int x_ind = x_coors[i_pillar];
int y_ind = y_coors[i_pillar];
float feature = pfe_output[i_feature * max_num_pillars_ + i_pillar];
scattered_feature[i_feature * grid_y_size * grid_x_size +
y_ind * grid_x_size + x_ind] = feature;
} |
11,588 | #include "includes.h"
__global__ void add2(int a, int b, int *sum)
{
*sum = *sum + a + b;
} |
11,589 | /*
============================================================================
Filename : implementation.cu
Author : Lucien Michaël Iseli, Loris Pilotto
SCIPER : 274999, 262651
============================================================================
*/
#include <iostream>
#include <iomanip>
#include <sys/time.h>
#include <cuda_runtime.h>
#include <math.h>
using namespace std;
// CPU Baseline
void array_process(double *input, double *output, int length, int iterations)
{
double *temp;
for(int n=0; n<(int) iterations; n++)
{
for(int i=1; i<length-1; i++)
{
for(int j=1; j<length-1; j++)
{
output[(i)*(length)+(j)] = (input[(i-1)*(length)+(j-1)] +
input[(i-1)*(length)+(j)] +
input[(i-1)*(length)+(j+1)] +
input[(i)*(length)+(j-1)] +
input[(i)*(length)+(j)] +
input[(i)*(length)+(j+1)] +
input[(i+1)*(length)+(j-1)] +
input[(i+1)*(length)+(j)] +
input[(i+1)*(length)+(j+1)] ) / 9;
}
}
output[(length/2-1)*length+(length/2-1)] = 1000;
output[(length/2)*length+(length/2-1)] = 1000;
output[(length/2-1)*length+(length/2)] = 1000;
output[(length/2)*length+(length/2)] = 1000;
temp = input;
input = output;
output = temp;
}
}
__global__
void init_gpu(double* gpu_input, double* gpu_output, int length){
gpu_input[(length/2-1)*length+(length/2-1)] = 1000;
gpu_input[(length/2)*length+(length/2-1)] = 1000;
gpu_input[(length/2-1)*length+(length/2)] = 1000;
gpu_input[(length/2)*length+(length/2)] = 1000;
gpu_output[(length/2-1)*length+(length/2-1)] = 1000;
gpu_output[(length/2)*length+(length/2-1)] = 1000;
gpu_output[(length/2-1)*length+(length/2)] = 1000;
gpu_output[(length/2)*length+(length/2)] = 1000;
}
__global__
void compute_gpu(double* gpu_input, double* gpu_output, int length){
int x_glob = blockIdx.x + 1;
int y_glob = threadIdx.y + 1;
if(x_glob == length/2-1 && (y_glob == length/2-1 || y_glob == length/2) ||
x_glob == length/2 && (y_glob == length/2-1 || y_glob == length/2))
return;
gpu_output[(x_glob)*(length)+(y_glob)] = (gpu_input[(x_glob-1)*(length)+(y_glob-1)] +
gpu_input[(x_glob-1)*(length)+(y_glob)] +
gpu_input[(x_glob-1)*(length)+(y_glob+1)] +
gpu_input[(x_glob)*(length)+(y_glob-1)] +
gpu_input[(x_glob)*(length)+(y_glob)] +
gpu_input[(x_glob)*(length)+(y_glob+1)] +
gpu_input[(x_glob+1)*(length)+(y_glob-1)] +
gpu_input[(x_glob+1)*(length)+(y_glob)] +
gpu_input[(x_glob+1)*(length)+(y_glob+1)] ) /9;
}
// GPU Optimized function
void GPU_array_process(double *input, double *output, int length, int iterations)
{
//Cuda events for calculating elapsed time
cudaEvent_t cpy_H2D_start, cpy_H2D_end, comp_start, comp_end, cpy_D2H_start, cpy_D2H_end;
cudaEventCreate(&cpy_H2D_start);
cudaEventCreate(&cpy_H2D_end);
cudaEventCreate(&cpy_D2H_start);
cudaEventCreate(&cpy_D2H_end);
cudaEventCreate(&comp_start);
cudaEventCreate(&comp_end);
/* Preprocessing goes here */
size_t SIZE = length * length * sizeof(double);
double* gpu_input;
double* gpu_output;
double* temp;
cudaMalloc((void**) &gpu_input, SIZE);
cudaMalloc((void**) &gpu_output, SIZE);
cudaMemset(gpu_output, 0, SIZE);
cudaMemset(gpu_input, 0, SIZE);
/* End preprocessing */
cudaEventRecord(cpy_H2D_start);
/* Copying array from host to device goes here */
init_gpu <<< 1, 1 >>> (gpu_input, gpu_output, length);
/* End copy array */
cudaEventRecord(cpy_H2D_end);
cudaEventSynchronize(cpy_H2D_end);
//Copy array from host to device
cudaEventRecord(comp_start);
/* GPU calculation goes here */
dim3 thrsPerBlock(1,length-2);
dim3 nBlks(length-2,1);
for(int n = 0; n <(int)iterations; n++){
compute_gpu <<< nBlks, thrsPerBlock >>> (gpu_input, gpu_output, length);
temp = gpu_input;
gpu_input = gpu_output;
gpu_output = temp;
}
/* End GPU calculation */
cudaEventRecord(comp_end);
cudaEventSynchronize(comp_end);
cudaEventRecord(cpy_D2H_start);
/* Copying array from device to host goes here */
cudaMemcpy((void*)output,
(void*)gpu_output,
SIZE,
cudaMemcpyDeviceToHost);
/* End copy array */
cudaEventRecord(cpy_D2H_end);
cudaEventSynchronize(cpy_D2H_end);
/* Postprocessing goes here */
cudaFree((void**) &gpu_input);
cudaFree((void**) &gpu_output);
float time;
cudaEventElapsedTime(&time, cpy_H2D_start, cpy_H2D_end);
cout<<"Host to Device MemCpy takes "<<setprecision(4)<<time/1000<<"s"<<endl;
cudaEventElapsedTime(&time, comp_start, comp_end);
cout<<"Computation takes "<<setprecision(4)<<time/1000<<"s"<<endl;
cudaEventElapsedTime(&time, cpy_D2H_start, cpy_D2H_end);
cout<<"Device to Host MemCpy takes "<<setprecision(4)<<time/1000<<"s"<<endl;
}
|
11,590 | #include "includes.h"
__global__ void rgb2yuv_kernel(int img_size, unsigned char *img_r, unsigned char *img_g, unsigned char *img_b, unsigned char *img_y, unsigned char *img_u, unsigned char *img_v) {
int i = threadIdx.x + blockDim.x * blockIdx.x;
if(i < img_size){
int r, g, b;
r = img_r[i];
g = img_g[i];
b = img_b[i];
img_y[i] = (unsigned char)( 0.299*r + 0.587*g + 0.114*b);
img_u[i] = (unsigned char)(-0.169*r - 0.331*g + 0.499*b + 128);
img_v[i] = (unsigned char)( 0.499*r - 0.418*g - 0.0813*b + 128);
}
} |
11,591 | #include <string>
#include <iostream>
#include <sstream>
#include <fstream>
#include "stdio.h"
#include "stdlib.h"
#include "math.h"
#include "time.h"
#define Nx 510 // Dimension X
#define Ny 510 // Dimension Y
#define Nz 510 // Dimension Z
//Mapping function
#define STRIDE ((Ny+2)*(Nz+2))
#define WIDTH (Nz+2)
#define pos(x,y,z) (STRIDE*(x)+WIDTH*(y)+(z))
#define LENMAX 256
#define REAL double
////////////////////////////
// CPU I/O function
////////////////////////////
void Init(REAL *P1,REAL *U1);
void WriteFields(REAL *P,REAL *U);
////////////////////////////////////////////////
// Main CPU program //
////////////////////////////////////////////////
int main(int argc, char **argv)
{
size_t SizeGrid = (Nx+2)*(Ny+2)*(Nz+2);
REAL *h_Psi=(REAL*)malloc(SizeGrid*sizeof(REAL)) ;
REAL *h_U=(REAL*)malloc(SizeGrid*sizeof(REAL)) ;
clock_t begin=clock();
printf("Initializing...\n");
Init(h_Psi,h_U);
WriteFields(h_Psi,h_U);
clock_t end=clock();
REAL CompTime=(end-begin)/CLOCKS_PER_SEC;
printf("\n\nThe time of writing binary files (2 GB) is %d s\n", int(CompTime));
free(h_Psi) ;
free(h_U) ;
return EXIT_SUCCESS;
}
/////////////////////////////////////////////////////////////////////////////////
/////////////////////////////////// Initialization //////////////////////////////
/////////////////////////////////////////////////////////////////////////////////
void Init(REAL *P1,REAL *U1)
{
REAL r=((REAL) rand() / (RAND_MAX));
for (int i=0;i<Nx+2;i++) {
for (int j=0;j<Ny+2;j++) {
for (int k=0;k<Nz+2;k++) {
P1[pos(i,j,k)]=r;
U1[pos(i,j,k)]=r*r;
}
}
}
}
/////////////////////////////////////////////////////////////////////////////////
//////////////////////////////////// Output /////////////////////////////////////
/////////////////////////////////////////////////////////////////////////////////
void WriteFields(REAL *P,REAL *U)
{
//================================================
// Output dat file
//================================================
char FileName1[256];
FILE *OutFile1;
sprintf(FileName1,"File1.dat");
OutFile1=fopen(FileName1,"w");
for(int i=0; i<Nx+2; i++) {
for(int j=0; j<Ny+2; j++) {
for(int k=0; k<Nz+2; k++) {
REAL d = P[pos(i,j,k)];
fwrite((char*)&d,sizeof(REAL),1,OutFile1);
}
}
}
fclose(OutFile1);
printf("Written File1.dat \n");
char FileName2[256];
FILE *OutFile2;
sprintf(FileName2,"File2.dat");
OutFile2=fopen(FileName2,"w");
for(int i=0; i<Nx+2; i++) {
for(int j=0; j<Ny+2; j++) {
for(int k=0; k<Nz+2; k++) {
REAL d = U[pos(i,j,k)];
fwrite((char*)&d,sizeof(REAL),1,OutFile2);
}
}
}
fclose(OutFile2);
printf("Written File2.dat \n");
} |
11,592 | // Matrix addition, CPU version
// gcc matrix_cpu.c -o matrix_cpu -std=c99
/*
answers
N = 2**6 = 64 is the turning point in gpu v cpu
block size 16*16 gave the best performance for us
coalescing
on N=2**10
Time 0.4686
Time 0.1143
*/
#include <stdio.h>
__global__
void add_matrix(float *a, float *b, float *c, int N)
{
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
int index = x + y*N;
if (index < N*N) // allow allocating more threads than elements
c[index] = a[index] + b[index];
}
void add_matrix_cpu(float *a, float *b, float *c, int N)
{
for (int y=0; y < N; y++)
for (int x=0; y < N; x++){
int index = x + y*N;
c[index] = a[index] + b[index];
}
}
int main()
{
const int N = 1<<10;
const int blockSize = 16;
const int size = N*N*sizeof(float);
float t;
float *a, *ad;
float *b, *bd;
float *c, *cd;
a = new float[N*N];
b = new float[N*N];
c = new float[N*N];
cudaMalloc( (void**)&ad, size );
cudaMalloc( (void**)&bd, size );
cudaMalloc( (void**)&cd, size );
cudaEvent_t event0, event1;
cudaEventCreate(&event0);
cudaEventCreate(&event1);
for (int i = 0; i < N; i++)
for (int j = 0; j < N; j++)
{
a[i+j*N] = 10 + i;
b[i+j*N] = (float)j / N;
}
cudaMemcpy( ad, a, size, cudaMemcpyHostToDevice );
cudaMemcpy( bd, b, size, cudaMemcpyHostToDevice );
dim3 dimBlock( blockSize, blockSize );
dim3 dimGrid( N/blockSize, N/blockSize );
cudaEventRecord(event0, 0);
add_matrix<<< dimGrid, dimBlock >>>(ad, bd, cd, N);
cudaEventRecord(event1, 0);
cudaMemcpy( c, cd, size, cudaMemcpyDeviceToHost );
cudaEventSynchronize(event0);
cudaEventSynchronize(event1);
cudaEventElapsedTime(&t, event0, event1);
for (int i = 0; i < N; i++)
{
for (int j = 0; j < N; j++)
{
if (i==N-1 && N-32 < j)printf("%0.2f ", c[i+j*N]);
}
//printf("\n");
}
printf("\n");
printf("Time %0.4f\n", t);
}
|
11,593 | #include <stdio.h>
#include "cuda.h"
__global__ void computeFrame(char *in, char *out, int width, int height)
{
int g_x = blockDim.x * blockIdx.x + threadIdx.x;
int g_y = blockDim.y * blockIdx.y + threadIdx.y;
if(g_x < width && g_y < height) {
g_x += 1; g_y += 1; //Offset by one since there is a border buffer
int sum = 0;
sum += in[(g_x + 1) + (g_y + 1) * (width + 2)];
sum += in[(g_x + 0) + (g_y + 1) * (width + 2)];
sum += in[(g_x - 1) + (g_y + 1) * (width + 2)];
sum += in[(g_x + 1) + (g_y + 0) * (width + 2)];
sum += in[(g_x - 1) + (g_y + 0) * (width + 2)];
sum += in[(g_x + 1) + (g_y - 1) * (width + 2)];
sum += in[(g_x + 0) + (g_y - 1) * (width + 2)];
sum += in[(g_x - 1) + (g_y - 1) * (width + 2)];
if(in[g_x + g_y * (width + 2)]) {
out[g_x + g_y * (width + 2)] = sum == 2 || sum == 3;
} else {
out[g_x + g_y * (width + 2)] = sum == 3;
}
}
}
__global__ void computeFrame2(char *in, char *out, int width, int height)
{
extern __shared__ char s_in[];
int g_x = blockDim.x * blockIdx.x + threadIdx.x;
int g_y = blockDim.y * blockIdx.y + threadIdx.y;
int t_x = threadIdx.x;
int t_y = threadIdx.y;
if(g_x < width && g_y < height) {
g_x += 1; g_y += 1; t_x += 1; t_y += 1; //Offset by one since there is a border buffer
//Base Cell
s_in[t_x + t_y * (blockDim.x + 2)] = in[g_x + g_y * (width + 2)];
//Upper Cell
if(t_y == 1) {
s_in[t_x + (t_y - 1) * (blockDim.x + 2)] = in[g_x + (g_y - 1) * (width + 2)];
}
//Lower Cell
if(t_y == blockDim.y || g_y == height) {
s_in[t_x + (t_y + 1) * (blockDim.x + 2)] = in[g_x + (g_y + 1) * (width + 2)];
}
//Left Cell
if(t_x == 1) {
s_in[(t_x - 1) + t_y * (blockDim.x + 2)] = in[(g_x - 1) + g_y * (width + 2)];
}
//Rigth Cell
if(t_x == blockDim.x || g_x == width) {
s_in[(t_x + 1) + t_y * (blockDim.x + 2)] = in[(g_x + 1) + g_y * (width + 2)];
}
//Upper-Left Corner
if(t_x == 1 && t_y == 1) {
s_in[(t_x - 1) + (t_y - 1) * (blockDim.x + 2)] = in[(g_x - 1) + (g_y - 1) * (width + 2)];
}
//Lower-Left Corner
if(t_x == 1 && ((t_y == blockDim.y || g_y == height))) {
s_in[(t_x - 1) + (t_y + 1) * (blockDim.x + 2)] = in[(g_x - 1) + (g_y + 1) * (width + 2)];
}
//Upper-Right Corner
if((t_x == blockDim.x || g_x == width) && t_y == 1) {
s_in[(t_x + 1) + (t_y - 1) * (blockDim.x + 2)] = in[(g_x + 1) + (g_y - 1) * (width + 2)];
}
//Lower-Right Corner
if((t_x == blockDim.x || g_x == width) && (t_y == blockDim.y || g_y == height)) {
s_in[(t_x + 1) + (t_y + 1) * (blockDim.x + 2)] = in[(g_x + 1) + (g_y + 1) * (width + 2)];
}
__syncthreads();
int sum = 0;
sum += s_in[(t_x + 1) + (t_y + 1) * (blockDim.x + 2)];
sum += s_in[(t_x + 0) + (t_y + 1) * (blockDim.x + 2)];
sum += s_in[(t_x - 1) + (t_y + 1) * (blockDim.x + 2)];
sum += s_in[(t_x + 1) + (t_y + 0) * (blockDim.x + 2)];
sum += s_in[(t_x - 1) + (t_y + 0) * (blockDim.x + 2)];
sum += s_in[(t_x + 1) + (t_y - 1) * (blockDim.x + 2)];
sum += s_in[(t_x + 0) + (t_y - 1) * (blockDim.x + 2)];
sum += s_in[(t_x - 1) + (t_y - 1) * (blockDim.x + 2)];
if(s_in[t_x + t_y * (blockDim.x + 2)]) {
out[g_x + g_y * (width + 2)] = sum == 2 || sum == 3;
} else {
out[g_x + g_y * (width + 2)] = sum == 3;
}
}
} |
11,594 | #include <stdio.h>
#include <string.h>
#include <math.h>
#include <time.h>
#include <cuda.h>
#include <cuda_runtime.h>
#define BLOCK_SIZE 8
extern "C" void deconvolve (int N1, int N2, double *uIni, double *srcImg, int itertime, double h, double lambda, double delta, double epsilon, double * dataNow);
__global__ void nablaIni_kernel(int N1, int N2, double *nablaU){
int i=blockIdx.x*blockDim.x+threadIdx.x;
int j=blockIdx.y*blockDim.y+threadIdx.y;
if (i<N1&&j<N2){
nablaU[i*N2+j]=0;
}
}
__global__ void normNabla_kernel(int N1, int N2, double *u, double h, double * nablaU){
double ex, ey;
int i=blockIdx.x*blockDim.x+threadIdx.x;
int j=blockIdx.y*blockDim.y+threadIdx.y;
if (i<N1-1&&j<N2-1&&i>0&&j>0){
ex=(u[(i+1)*N2+j]-u[(i-1)*N2+j])/2/h;
ey=(u[i*N2+j+1]-u[i*N2+j-1])/2/h;
nablaU[i*N2+j]=sqrt(ex*ex+ey*ey);
}
}
__global__ void normMinus_kernel(int N1, int N2, double * a, double * b, double * result){
int i=blockIdx.x*blockDim.x+threadIdx.x;
int j=blockIdx.y*blockDim.y+threadIdx.y;
if (i<N1&&j<N2){
result[i*N2+j]=abs(a[i*N2+j]-b[i*N2+j]);
}
}
__global__ void fcal_kernel(int N1, int N2, double *srcImg, double *f, double *nablaU, double *uMinusf, double lambda, double delta, double epsilon){
int i=blockIdx.x*blockDim.x+threadIdx.x;
int j=blockIdx.y*blockDim.y+threadIdx.y;
if (i<N1&&j<N2){
f[i*N2+j]=lambda*srcImg[i*N2+j]/sqrt(uMinusf[i*N2+j]*uMinusf[i*N2+j]+delta)*sqrt(nablaU[i*N2+j]*nablaU[i*N2+j]+epsilon);
}
}
__global__ void deblur_kernel(int N1, int N2, double *u, double *srcImg, double *deblurU, double *f, double *nablaU, double *uMinusf, double h, double lambda, double delta, double epsilon){
int i=blockIdx.x*blockDim.x+threadIdx.x;
int j=blockIdx.y*blockDim.y+threadIdx.y;
if (i<N1-1&&j<N2-1&&i>0&&j>0){
deblurU[i*N2+j]=(h*h*f[i*N2+j]+u[(i-1)*N2+j]+u[i*N2+j-1]+u[(i+1)*N2+j]+u[i*N2+j+1])/(4+lambda*sqrt(nablaU[i*N2+j]*nablaU[i*N2+j]+epsilon)*h*h/sqrt(uMinusf[i*N2+j]*uMinusf[i*N2+j]+delta));
if (deblurU[i*N2+j]>255){deblurU[i*N2+j]=255;}
if (deblurU[i*N2+j]<0){deblurU[i*N2+j]=0;}
}
}
__global__ void deblur_kernel(int N1, int N2, double *nabla){
int i=blockIdx.x*blockDim.x+threadIdx.x;
int j=blockIdx.y*blockDim.y+threadIdx.y;
if(i<N1&&j<N2){
nabla[i*N2+j]=0;
}
}
double residual(int N1, int N2, double* u, double* f) {
double residual_norm_sq = 0.0;
for (int i = 0; i < N1 * N2; i++) {
residual_norm_sq += (u[i] - f[i]) * (u[i] - f[i]);
}
return sqrt(residual_norm_sq);
}
void deconvolve (int N1, int N2, double *uIni, double *srcImg, int itertime, double h, double lambda, double delta, double epsilon, double * u) {
dim3 dimBlock(BLOCK_SIZE, BLOCK_SIZE);
dim3 dimGrid(N1/BLOCK_SIZE+1,N2/BLOCK_SIZE+1);
double *u_d,*u_prev_d, *f, *nablaU, *uMinusf;
double *srcImg_d;
cudaMalloc(&u_d,N1*N2*sizeof(double));
cudaMalloc(&u_prev_d,N1*N2*sizeof(double));
cudaMalloc(&f,N1*N2*sizeof(double));
cudaMalloc(&nablaU,N1*N2*sizeof(double));
cudaMalloc(&uMinusf,N1*N2*sizeof(double));
cudaMalloc(&srcImg_d, N1*N2*sizeof(double));
cudaMemcpy(srcImg_d, srcImg, N1*N2*sizeof(double), cudaMemcpyHostToDevice);
cudaMemcpy(u_d,uIni,N1*N2*sizeof(double),cudaMemcpyHostToDevice);
cudaMemcpy(u_prev_d,uIni,N1*N2*sizeof(double),cudaMemcpyHostToDevice);
clock_t t;
t=clock();
printf("Initial residule: %f\n", residual(N1,N2, uIni, srcImg));
printf("time= %f seconds\n", ((float)t)/CLOCKS_PER_SEC);
for (int i=0; i<itertime;i++){
//printf("itertime=%d\n",i);
nablaIni_kernel<<<dimGrid,dimBlock>>>(N1,N2,nablaU);
normNabla_kernel<<<dimGrid,dimBlock>>>(N1, N2, u_d, h, nablaU);
normMinus_kernel<<<dimGrid,dimBlock>>>(N1, N2, u_d, srcImg_d, uMinusf);
fcal_kernel<<<dimGrid,dimBlock>>>(N1,N2,srcImg_d,f,nablaU,uMinusf,lambda, delta,epsilon);
deblur_kernel<<<dimGrid,dimBlock>>>(N1,N2, u_prev_d,srcImg_d,u_d,f, nablaU, uMinusf, h, lambda, delta, epsilon);
cudaMemcpy(u_prev_d,u_d,N1*N2*sizeof(double),cudaMemcpyDeviceToDevice);
}
t=clock()-t;
double tt=((float)t)/CLOCKS_PER_SEC;
printf("time= %f seconds\n", tt);
printf("GPU Bandwidth = %f GB/s\n",25*N1*N2*sizeof(double)/tt/1e9);
cudaMemcpy(u,u_prev_d,N1*N2*sizeof(double),cudaMemcpyDeviceToHost);
//cudaMemcpy(u, srcImg_d, N1*N2*sizeof(double), cudaMemcpyDeviceToHost);
printf("Final residule: %f\n", residual(N1,N2, u, srcImg));
cudaFree(u_d);
cudaFree(u_prev_d);
cudaFree(f);
cudaFree(nablaU);
cudaFree(uMinusf);
cudaFree(srcImg_d);
}
|
11,595 | #include "includes.h"
///////////////////////////////////////////////////////////////////////////////
//Round a / b to nearest higher integer value
__global__ void updateHeightmapKernel(float* heightMap, float2* ht, unsigned int width){
unsigned int x = blockIdx.x*blockDim.x + threadIdx.x;
unsigned int y = blockIdx.y*blockDim.y + threadIdx.y;
unsigned int i = y*width+x;
float sign_correction = ((x + y) & 0x01) ? -1.0f : 1.0f;
heightMap[i] = ht[i].x * sign_correction;
} |
11,596 | #include<stdlib.h>
#include<stdio.h>
#include <cuda_runtime.h>
#define TILE_WIDTH 16
#define seed 13
__global__ void matrixMul(float *dev_A, float *dev_B, float *dev_C, int
matrixWitdh)
{
__shared__ float A_tile[TILE_WIDTH][TILE_WIDTH];
__shared__ float B_tile[TILE_WIDTH][TILE_WIDTH];
int bx = blockIdx.x;
int by = blockIdx.y;
int tx = threadIdx.x;
int ty = threadIdx.y;
int row = by * TILE_WIDTH + ty;
int col = bx * TILE_WIDTH + tx;
float partial = 0.0;
int m;
for( m=0 ; m < matrixWitdh/TILE_WIDTH; m++){
A_tile[ty][tx] = dev_A[row * matrixWitdh + (m * TILE_WIDTH + tx)];
B_tile[ty][tx] = dev_B[col + (m * TILE_WIDTH + ty) * matrixWitdh];
__syncthreads();
int k;
for(k=0; k< TILE_WIDTH; k++)
partial += A_tile[ty][k] * B_tile[k][tx];
__syncthreads();
dev_C[row * matrixWitdh + col] = partial;
}
}
int main(int argc, char **argv){
srand(seed);
if(argc != 2){
printf("Usage /lab4_4 <matrixWitdh>");
return 1;
}
int matrixWitdh = atoi(argv[1]);
float *h_A = (float*) malloc(matrixWitdh * matrixWitdh * sizeof(float));
float *h_B = (float*) malloc(matrixWitdh * matrixWitdh * sizeof(float));
float *h_C = (float*) malloc(matrixWitdh * matrixWitdh * sizeof(float));
int i,j;
for(i=0;i<matrixWitdh;i++){
for(j=0;j<matrixWitdh;j++){
h_A[i * matrixWitdh + j] = (float)rand()/((float)RAND_MAX/10.0);
h_B[i * matrixWitdh + j] = (float)rand()/((float)RAND_MAX/10.0);
}
}
float *d_A, *d_B, *d_C;
cudaMalloc((void**) &d_A, matrixWitdh * matrixWitdh * sizeof(float));
cudaMalloc((void**) &d_B, matrixWitdh * matrixWitdh * sizeof(float));
cudaMalloc((void**) &d_C, matrixWitdh * matrixWitdh * sizeof(float));
dim3 dimBlock(TILE_WIDTH, TILE_WIDTH, 2);
dim3 dimGrid(matrixWitdh/TILE_WIDTH, matrixWitdh/TILE_WIDTH, 1);
float elapsedTime;
cudaEvent_t start, stop;
cudaEventCreate(&start);
cudaEventCreate(&stop);
cudaEventRecord(start, 0);
cudaMemcpy(d_A, h_A, matrixWitdh * matrixWitdh * sizeof(float),
cudaMemcpyHostToDevice);
cudaMemcpy(d_B, h_B, matrixWitdh * matrixWitdh * sizeof(float),
cudaMemcpyHostToDevice);
matrixMul<<< dimGrid, dimBlock >>>(d_A, d_B, d_C, matrixWitdh);
cudaMemcpy(h_C, d_C, matrixWitdh* matrixWitdh * sizeof(float),
cudaMemcpyDeviceToHost);
cudaEventRecord(stop, 0);
cudaEventSynchronize(stop);
cudaEventElapsedTime(&elapsedTime, start, stop);
cudaEventDestroy(start);
cudaEventDestroy(stop);
printf("For tiled version, the elapsed time is %.4f(ms).\n", elapsedTime);
cudaFree(d_A);
cudaFree(d_B);
cudaFree(d_C);
free(h_A);
free(h_B);
free(h_C);
return 0;
}
|
11,597 | #include "includes.h"
__device__ void EncodeValuesInternal(float value, float& origin, float& dir, float& output, int squaredMode)
{
if (squaredMode == 1)
{
// origin part: o * (1 - t)^2
output = (1 - fabs(value)) * (1 - fabs(value)) * origin;
// direction part: dir * (-t^2 + 2*t)
output += (-value * value + 2 * fabs(value)) * dir;
}
else
{
// origin part: o * (1 - t)
output = (1 - fabs(value)) * origin;
// direction part: dir * t
output += fabs(value) * dir;
}
}
__global__ void EncodeValues(float* values, int numOfValues, float* output, int symbolSize, int squaredMode, float* dirX, float* dirY, float* negDirX, float* negDirY, float* originX, float* originY)
{
int threadId = blockDim.x*blockIdx.y*gridDim.x //rows preceeding current row in grid
+ blockDim.x*blockIdx.x //blocks preceeding current block
+ threadIdx.x;
__shared__ float s_values[2];
if (threadIdx.x < 2)
{
//clamp to (-1, 1) if square mode is used
if (squaredMode == 1)
{
s_values[threadIdx.x] = fmaxf(fminf(values[threadIdx.x], 1), -1);
}
else
{
s_values[threadIdx.x] = values[threadIdx.x];
}
}
__syncthreads();
if (threadId >= symbolSize)
return;
// X dim
float* dir = (s_values[0] > 0) ? dirX : negDirX;
EncodeValuesInternal(s_values[0], originX[threadId], dir[threadId], output[threadId], squaredMode);
// Y dim
if (numOfValues > 1)
{
dir = (s_values[1] > 0) ? dirY : negDirY;
EncodeValuesInternal(s_values[1], originY[threadId], dir[threadId], output[threadId], squaredMode);
}
} |
11,598 | extern "C"
__global__ void distance(int nu_g, int nt_g, int nk_g, int *u, int *t, int *d0, int *d1, int *d2)
{
// allocate local copies of this variable for a faster access
int nu, nt, nd, nk;
nu = nu_g; nt = nt_g; nk = nk_g;
// local indexes and temp variables
int k, l, max_l, tmp_min;
int *tmp_d;
for(k = 2 ; k <= nk ; k++){
tmp_d = d0;
d0 = d1;
d1 = d2;
d2 = tmp_d;
l = (k > nu ? (k - nu) : 0) + threadIdx.x;
max_l = k > nt ? nt : k;
for(; l <= max_l ; l += blockDim.x){
if (l == 0){
d2[0] = d1[0] + 1;
} else if (l == k){
d2[l] = d1[l-1] + 1;
} else {
tmp_min = d1[l-1] > d1[l] ? (d1[l] + 1) : (d1[l-1] + 1);
if (u[k-l-1] != t[l-1])
d2[l] = tmp_min < (d0[l-1] + 1) ? tmp_min : (d0[l-1] + 1);
else
d2[l] = tmp_min < d0[l-1] ? tmp_min : d0[l-1];
}
}
__syncthreads();
}
}
|
11,599 | __global__ void transpose(float **inputdata, float **outputdata, int width, int height)
{
__shared__ float block[32][32]; // alloc static shared memory, blockDim.x = 32, blockDim.y = 32
// read matrix to shared memory
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
if ((x >= width) || (y >= height)) {
return;
}
int i = threadIdx.x, j = threadIdx.y;
block[j][i] = inputdata[x][y];
__syncthreads();
outputdata[x][y] = block[i][j];
}
|
11,600 | /*********************************************************************
* Copyright © 2011-2012,
* Marwan Abdellah: <abdellah.marwan@gmail.com>
*
* This library is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation.
* This library is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser General Public License for more details.
* You should have received a copy of the GNU Lesser General Public
* License along with this library; if not, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston,
* MA 02110-1301, USA.
********************************************************************/
#include <curand.h>
#include <curand_kernel.h>
__global__
void initSeed_Kernel(curandState *randState)
{
// Thread index
int index = blockIdx.x * blockDim.x + threadIdx.x;
// Initialization
curand_init(1337, index, 0, &randState[index]);
}
/*!
* CUDA : This kernel fills an input vector - or 1D array - with random
* sequence of numbers of length N.
*
* @param devArray
* Input device vector - or 1D array - to the kernel.
*
* @param N
* Vector length.
*
* @author
* Marwan Abdellah <abdellah.marwan@gmail.com>
*
* @date
* Created: April, 2011.
* @date
* Last Update: September, 2012.
*
* @note
* Minimum CUDA version 3.2.
* @note
* Minimum Device Compute Capability 1.0.
*/
template <typename T>
__global__
void Fill_1D_Array_RND_Kernel(T* devArray, int N, curandState* randState)
{
// Thread index @X
int x_threadIdx = threadIdx.x;
// Block size @X
int x_blockDim = blockDim.x;
// Block index @X
int x_blockIdx = blockIdx.x;
// Thread flat index
int index = ((x_blockIdx * x_blockDim) + x_threadIdx);
#ifdef VEC_CHECK
if (index < N)
devArray[index] = (T) (float) curand_uniform(&randState[index]);
#else
devArray[index] = (T) (float) curand_uniform(&randState[index]);
#endif
}
|
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