serial_no int64 1 24.2k | cuda_source stringlengths 11 9.01M |
|---|---|
6,601 | extern "C" {
__global__ void writeTest(int* array){
for(int i = 0; i < 8; i++){
array[i] *= 2;
}
}
}
|
6,602 | #include <cuda_profiler_api.h>
#include <stdio.h>
int main()
{
cudaError_t cerr;
cerr = cudaProfilerStart();
if (cerr != cudaSuccess) {
fprintf(stdout, "Error with cudeProfilerStart with error %s\n", cudaGetErrorString(cerr));
}
const unsigned int N = 1048576;
const unsigned int bytes = N * sizeof(int);
int *h_a = (int*)malloc(bytes);
int *d_a;
cerr = cudaMalloc((int**)&d_a, bytes);
if (cerr != cudaSuccess) {
fprintf(stderr, "Error with cudaMalloc with error %s\n", cudaGetErrorString(cerr));
}
memset(h_a, 0, bytes);
cerr = cudaMemcpy(d_a, h_a, bytes, cudaMemcpyHostToDevice);
if (cerr != cudaSuccess) {
fprintf(stderr, "Error with Host to Device cudaMemcpy with error %s\n", cudaGetErrorString(cerr));
}
cerr = cudaMemcpy(h_a, d_a, bytes, cudaMemcpyDeviceToHost);
if (cerr != cudaSuccess) {
fprintf(stderr, "Error with Device to Host cudaMemcpy with error %s\n", cudaGetErrorString(cerr));
}
cerr = cudaFree(d_a);
if (cerr != cudaSuccess) {
fprintf(stderr, "Error with cudaFree with error %s\n", cudaGetErrorString(cerr));
}
cerr = cudaProfilerStop();
if (cerr != cudaSuccess) {
fprintf(stderr, "Error with cudaProfileStop with error %s\n", cudaGetErrorString(cerr));
}
return 0;
}
|
6,603 | #include <iostream>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <time.h>
__global__ void hello (int receive) {
int index = threadIdx.x;
printf("Hello from thread : %d with receive value : %d\n", index, receive);
}
int main(int argc, char **argv) {
printf("well\n");
hello<<<1,2>>>(5);
cudaDeviceSynchronize();
return 0;
} |
6,604 | #include "includes.h"
__global__ void FindMinMax(float *d_MinMax, float *d_Data, int width, int height)
{
__shared__ float minvals[128];
__shared__ float maxvals[128];
const int tx = threadIdx.x;
const int x = __mul24(blockIdx.x, 128) + tx;
const int y = __mul24(blockIdx.y, 16);
const int b = blockDim.x;
int p = __mul24(y, width) + x;
if (x<width) {
float val = d_Data[p];
minvals[tx] = val;
maxvals[tx] = val;
} else {
float val = d_Data[p-x];
minvals[tx] = val;
maxvals[tx] = val;
}
for (int ty=1;ty<16;ty++) {
p += width;
if (tx<width) {
float val = d_Data[p];
if (val<minvals[tx])
minvals[tx] = val;
if (val>maxvals[tx])
maxvals[tx] = val;
}
}
__syncthreads();
int mod = 1;
for (int d=1;d<b;d<<=1) {
if ((tx&mod)==0) {
if (minvals[tx+d]<minvals[tx+0])
minvals[tx+0] = minvals[tx+d];
if (maxvals[tx+d]>maxvals[tx+0])
maxvals[tx+0] = maxvals[tx+d];
}
mod = 2*mod + 1;
__syncthreads();
}
if (tx==0) {
int ptr = 2*(__mul24(gridDim.x,blockIdx.y) + blockIdx.x);
d_MinMax[ptr+0] = minvals[0];
d_MinMax[ptr+1] = maxvals[0];
}
__syncthreads();
} |
6,605 | __global__ void solution(float* img, float* xbar, int w, int h, int nc) {
int x = threadIdx.x + blockDim.x * blockIdx.x;
int y = threadIdx.y + blockDim.y * blockIdx.y;
if (x < w && y < h) {
int i;
for (int z = 0; z < nc; z++) {
i = x + w * y + w * h * z;
img[i] = xbar[i];
}
}
}
|
6,606 | #include "includes.h"
__global__ void update_population_fixed( unsigned int * pop , unsigned int rows , unsigned int cols , unsigned int * fixed ) {
} |
6,607 | #include <stdio.h>
#include <cuda.h>
#define HIST_LENGTH 256
#define THREADS_P_BLOCK 512
__global__ void histogram_kernel(int *data_d, int data_length, int *hist_d)
{
int index, offset;
/** Arreglo en memoria compartida **/
__shared__ int temp_hist[HIST_LENGTH];
/**Inicializacion en 0 **/
temp_hist[threadIdx.x] = 0;
__syncthreads();
index = threadIdx.x + blockIdx.x * blockDim.x;
/** Cada thread debe recorrer los datos segun el offset declarado **/
offset = blockDim.x * gridDim.x;
/** Llenado del histograma **/
while(index < data_length) {
atomicAdd( &temp_hist[data_d[index]], 1);
index += offset;
}
__syncthreads();
/** Traspaso a memoria global **/
atomicAdd(&(hist_d[threadIdx.x]), temp_hist[threadIdx.x]);
}
int main(int argc, char *argv[])
{
float elapsed_Time;
int i, blocks;
int data_length;
int hist_h[HIST_LENGTH], *data_h, *data_d, *hist_d;
cudaEvent_t start, stop;
/** Timers **/
cudaEventCreate(&start);
cudaEventCreate(&stop);
/** Input de datos **/
FILE *in_f = fopen(argv[1], "r");
fscanf(in_f, "%d", &data_length);
data_length *= data_length;
/** Declaracion dinamica del tamano del arreglo dependiendo de la matriz **/
data_h = (int *)malloc(data_length * sizeof(int));
for (i = 0; i < data_length && fscanf(in_f, "%d", &data_h[i]) == 1; ++i);
fclose(in_f);
cudaEventRecord(start, 0);
/** Alloc para la memoria en GPU **/
cudaMalloc((void **) &data_d, data_length * sizeof(int));
/** Se copian los datos del histograma a la memoria del dispositivo **/
cudaMemcpy(data_d, data_h, data_length * sizeof(int), cudaMemcpyHostToDevice);
/** Se reserva la memoria para el histograma en el dispositivo y se inicializa en 0 **/
cudaMalloc((void **) &hist_d, HIST_LENGTH * sizeof(int));
cudaMemset(hist_d, 0, HIST_LENGTH * sizeof(int));
/** Se analizo que los tiempos de ejecucion eran menores al enviar dos veces el numero de
** multiprocesadores presentes **/
cudaDeviceProp prop;
cudaGetDeviceProperties(&prop, 0);
blocks = prop.multiProcessorCount * 2;
/** 256 threads es el numero optimo para la ejecucion **/
histogram_kernel<<<blocks, 256>>>(data_d, data_length, hist_d);
/** Se traspasan los datos del histograma desde el dispositivo **/
cudaMemcpy(hist_h, hist_d, 256 * sizeof(int), cudaMemcpyDeviceToHost);
/** Se detiene el timer **/
cudaEventRecord(stop, 0);
cudaEventSynchronize(stop);
cudaEventElapsedTime(&elapsed_Time, start, stop);
cudaFree(data_d);
cudaFree(hist_d);
cudaEventDestroy(start);
cudaEventDestroy(stop);
/** Datos escritos en el archivo de salida **/
FILE *out = fopen("salida", "w");
for (i = 0; i < 256; i++)
{
if (i == 255)
fprintf(out, "%d", hist_h[i]);
else
fprintf(out, "%d\n", hist_h[i]);
}
fclose(out);
printf("Tiempo de ejecucion: %f ms\n", elapsed_Time);
return 0;
}
|
6,608 | #include <stdio.h>
#include <stdlib.h>
#define IN_SIZE 16
#define TH_X_BLK 8
//Since every thread is a comparator of two elements, every block sorts a list of TH_X_BLK*2 elements
//Refers to Mullapudi paper figure to understand the offsets
__global__ void bitonicMergeSort(int *in,int *out){
int x=threadIdx.x+blockIdx.x*blockDim.x;
__shared__ int tile[TH_X_BLK*2];
int tmp;
unsigned int dir,stage_oset,step_oset;
if(x<IN_SIZE/2){
tile[threadIdx.x]=in[x];
tile[threadIdx.x+TH_X_BLK]=in[x+TH_X_BLK];
}
for(unsigned int stage=1;stage<TH_X_BLK*2;stage*=2){
//direction determines if the thread swap up or down
dir=threadIdx.x%(stage*2);
//stage_oset determines the starting point from which the threads do comparisons
stage_oset=stage*(threadIdx.x/stage);
//step=...4,2,1. Powers of 2
for(unsigned int step=stage;step>0;step/=2){
//step_oset determines the starting point from which a thread do a comparison inside a stage
step_oset=step*((threadIdx.x%stage)/step);
__syncthreads();
//sort in ascending order
if(dir<stage){
if(tile[threadIdx.x+stage_oset+step_oset]>tile[threadIdx.x+stage_oset+step_oset+step]){
tmp=tile[threadIdx.x+stage_oset+step_oset];
tile[threadIdx.x+stage_oset+step_oset]=tile[threadIdx.x+stage_oset+step_oset+step];
tile[threadIdx.x+stage_oset+step_oset+step]=tmp;
}
}
//sort in descending order
else{
if(tile[threadIdx.x+stage_oset+step_oset]<tile[threadIdx.x+stage_oset+step_oset+step]){
tmp=tile[threadIdx.x+stage_oset+step_oset];
tile[threadIdx.x+stage_oset+step_oset]=tile[threadIdx.x+stage_oset+step_oset+step];
tile[threadIdx.x+stage_oset+step_oset+step]=tmp;
}
}
}
}
if(x<IN_SIZE/2){
out[x]=tile[threadIdx.x];
out[x+TH_X_BLK]=tile[threadIdx.x+TH_X_BLK];
}
}
int main(){
bool log=true;
int *in=(int*)malloc(IN_SIZE*sizeof(int)),
*out=(int*)malloc(IN_SIZE*sizeof(int)),
*gpu_in,*gpu_out;
for(int i=0;i<IN_SIZE;i++)
in[i]=rand()%5000;
if(log){
printf("Input array\n");
for(int i=0;i<IN_SIZE;i++)
printf("%d ",in[i]);
printf("\n");
}
cudaMalloc((void**)&gpu_in,IN_SIZE*sizeof(int));
cudaMalloc((void**)&gpu_out,IN_SIZE*sizeof(int));
cudaMemcpy(gpu_in,in,IN_SIZE*sizeof(int),cudaMemcpyHostToDevice);
bitonicMergeSort<<<(IN_SIZE/2+TH_X_BLK-1)/TH_X_BLK,TH_X_BLK>>>(gpu_in,gpu_out);
cudaMemcpy(out,gpu_out,IN_SIZE*sizeof(int),cudaMemcpyDeviceToHost);
if(log){
printf("Output array\n");
for(int i=0;i<IN_SIZE;i++)
printf("%d ",out[i]);
printf("\n");
}
cudaFree(gpu_in);
free(in);
} |
6,609 | #include<iostream>
using namespace std;
__global__ void multiply(int *ad,int *bd,int *cd,int n)
{
int row=blockIdx.x;
int sum=0;
for(int i=0;i<n;i++)
{
sum=sum+ad[row*n+i]*bd[i];
}
cd[row]=sum;
}
int main()
{
cout<<"Enter the size"<<endl;
int n;
cin>>n;
int a[n][n],b[n], c[n];
int size1=n*n*sizeof(int);
int size2=n*sizeof(int);
cudaEvent_t start,end;
cudaEventCreate(&start);
cudaEventCreate(&end);
cout<<"The intial matrices are"<<endl;
for(int i=0;i<n;i++)
{
for(int j=0;j<n;j++)
{
a[i][j]=3+i;
cout<<a[i][j]<<" ";
}
b[i]=2+i;
cout << b[i];
cout<<endl;
}
int *ad, *bd, *cd;
cudaMalloc(&ad,size1);
cudaMemcpy(ad,a,size1,cudaMemcpyHostToDevice);
cudaMalloc(&bd,size2);
cudaMemcpy(bd,b,size2,cudaMemcpyHostToDevice);
cudaMalloc(&cd,size2);
dim3 grid(n,1);
dim3 block(1,1);
cudaEventRecord(start);
multiply<<<grid,block>>>(ad,bd,cd,n);
cudaEventRecord(end);
cudaEventSynchronize(end);
float time=0;
cudaEventElapsedTime(&time,start,end);
cout<<"The time is "<<time<<endl;
cout<<"The multiplication is"<<endl;
cudaMemcpy(c,cd,size2,cudaMemcpyDeviceToHost);
for(int i=0;i<n;i++)
{
cout<<c[i]<<" ";
}
}
|
6,610 | #include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <stdio.h>
#define N 4096000
__global__ void mulKernel(int *a, int *c)
{
int tdx = blockIdx.x * blockDim.x + threadIdx.x;
if(tdx < N)
{
c[tdx] = a[tdx]*2;
}
}
int main(void)
{
int *a_h[2], *c_h[2];
//device memory pointers
int *a_d[2];
int *c_d[2];
cudaStream_t stream[2];
for (int i = 0; i < 2; ++i)
{
cudaStreamCreate(&stream[i]); //stream creation
//pinned memory allocation
cudaMallocHost((void**)&a_h[i], (N/2)*sizeof(int));
cudaMallocHost((void**)&c_h[i], (N/2)*sizeof(int));
//allocate device memory
cudaMalloc((void**)&a_d[i], (N/2)*sizeof(int));
//cudaMalloc((void**)&b_d[i], (N/2)*sizeof(int));
cudaMalloc((void**)&c_d[i], (N/2)*sizeof(int));
}
//load arrays with some numbers
for(int i=0; i<2; i++)
{
for(int ii=0; ii<N/2; ii++)
{
a_h[i][ii] = i*N/2+ii;
}
}
//CUDA events to measure time
cudaEvent_t start;
cudaEvent_t stop;
float elapsedTime;
//start timer
cudaEventCreate(&start);
cudaEventCreate(&stop);
cudaEventRecord(start, 0);
// grid and block size stuff
dim3 grid(N/32, N/32, 1);
dim3 block(32, 32, 1);
// stream 0
cudaMemcpyAsync(&a_d[0], &a_h[0], (N/2)*sizeof(int),
cudaMemcpyHostToDevice, stream[0]);
mulKernel <<< grid, block, 0, stream[0]>>>(a_d[0], c_d[0]);
cudaMemcpyAsync(&c_h[0], &c_d[0], (N/2)*sizeof(int),
cudaMemcpyDeviceToHost, stream[0]);
//stream 1
cudaMemcpyAsync(&a_d[1], &a_h[1], (N/2)*sizeof(int),
cudaMemcpyHostToDevice, stream[1]);
mulKernel <<<grid, block, 0, stream[1]>>>(a_d[1], c_d[1]);
cudaMemcpyAsync(&c_h[1], &c_d[1], (N/2)*sizeof(int),
cudaMemcpyDeviceToHost, stream[1]);
//stop timer
cudaEventRecord(stop, 0);
cudaEventSynchronize(stop);
cudaEventElapsedTime(&elapsedTime, start, stop);
//print out execution time
printf("Time to calculate results: %f ms.\n", elapsedTime);
cudaEventDestroy(start);
cudaEventDestroy(stop);
// Check results
for(int ii=0; ii<N/2; ii++)
{
if (2*a_h[0][ii] != c_h[0][ii] || 2*a_h[1][ii] != c_h[1][ii])
{
printf("Error: CPU and GPU result [%d] do not match\n", ii);
printf("CPU0:%d GPU0:%d\n", 2*a_h[0][ii], c_h[0][ii]);
printf("CPU1:%d GPU1:%d\n", 2*a_h[1][ii], c_h[1][ii]);
break;
}
}
for (int i = 0; i < 2; ++i)
{
cudaStreamDestroy(stream[i]);
//clean up
cudaFreeHost(a_h[i]);
cudaFreeHost(c_h[i]);
}
cudaDeviceReset();
return 0;
} |
6,611 | #define N 256
#include<stdlib.h>
__global__ void vecAdd(float *a, float *b, float *c){
c[threadIdx.x] = a[threadIdx.x]+b[threadIdx.x];
}
int main(void){
// number of bytes to alloc for arrays
size_t numBytes = N*sizeof(float);
// init host and device pointers
float *ha, *hb, *hc, *da, *db, *dc;
// alloc host memory/arrays (pagable memory)
ha = (float*)malloc(numBytes);
hb = (float*)malloc(numBytes);
hc = (float*)malloc(numBytes);
// mem alloc arrays on the GPU device
cudaMalloc(&da,numBytes);
cudaMalloc(&db,numBytes);
cudaMalloc(&dc,numBytes);
// copy host arrays to device
cudaMemcpy(da, ha, numBytes, cudaMemcpyHostToDevice);
cudaMemcpy(db, hb, numBytes, cudaMemcpyHostToDevice);
cudaMemcpy(dc, hc, numBytes, cudaMemcpyHostToDevice);
// launch configuration
dim3 gridSz (1,1,1), blockSz(N,1,1);
// launch CUDA kernel
vecAdd<<<gridSz,blockSz>>>(da,db,dc);
// wait for kernel to finish
cudaDeviceSynchronize();
// free host memory
free(ha); free(hb); free(hc);
// free device memory
cudaFree(da); cudaFree(db); cudaFree(dc);
}
|
6,612 | #include<stdio.h>
#include<stdlib.h>
#include<math.h>
#include<cuda.h>
__global__ void prime_generator(int *input,int *prime_list,int *total_input,int *seed)
{
printf("-------XXXXXX>>> %d\n",seed[0]);
int i= blockIdx.x * blockDim.x + threadIdx.x;
int primeno= prime_list[i];
int total=seed[0]*seed[0];
for(int k=seed[0];k<total;k++)
{
if(k%primeno==0)
{
input[k]=1;
}
}
}
int main()
{
int total_input=100000000;
int *input;
int n= 10 ;// seed prime list.
int calculate_upto=pow(n,2);
int *primelist;
input=(int *)malloc(total_input*sizeof(int));
primelist=(int *)malloc(total_input*sizeof(int));
memset(input,-1,total_input*sizeof(int));
for(int j=0;j<calculate_upto;j++)
{
input[j]=0;
}
for (int p=2; p*p<=n; p++)
{
if (input[p] == 0)
{
for (int i=p*2; i<=n; i += p)
input[i] = 1;
}
}
int i=0;
for (int p=2; p<=n; p++){
if (input[p]==0)
{
primelist[i]=p;
i++;
}
}
int *d_input;
int *d_prime_list;
int *h_pl;
int *d_total_input;
int *d_seed;
h_pl=(int *)malloc(total_input*sizeof(int));
cudaMalloc(&d_input,total_input*sizeof(int));
cudaMalloc(&d_prime_list,total_input*sizeof(int));
cudaMalloc(&d_total_input,sizeof(int));
cudaMalloc(&d_seed,sizeof(int));
cudaMemcpy(d_total_input,&total_input,sizeof(int),cudaMemcpyHostToDevice);
//cudaMemcpy(d_input,input,total_input*sizeof(int),cudaMemcpyHostToDevice);
while(n<=total_input){
printf("inside loop\n");
if(cudaMemcpy(d_input,input,total_input*sizeof(int),cudaMemcpyHostToDevice)!=cudaSuccess)
{
printf("not able to copy memory\n");
}
if(cudaMemcpy(d_prime_list,primelist,total_input*sizeof(int),cudaMemcpyHostToDevice) != cudaSuccess)
{
printf("not able to copy memory 2\n");
}
if(cudaMemcpy(d_seed,&n,sizeof(int),cudaMemcpyHostToDevice) != cudaSuccess)
{
printf(" not able to copy memory\n");
}
prime_generator<<<5,500>>>(d_input,d_prime_list,d_total_input,d_seed);
cudaError_t err = cudaGetLastError();
if (err != cudaSuccess)
printf("Error: %s\n", cudaGetErrorString(err));
/*
if(cudaMemcpy(h_pl,d_prime_list,total_input*sizeof(int),cudaMemcpyDeviceToHost)!=cudaSuccess)
{
printf("not able to copy memory!!\n");
}
*/
if(cudaMemcpy(input,d_input,total_input*sizeof(int),cudaMemcpyDeviceToHost)!=cudaSuccess)
{
printf(" hello not able to copy memory::\n");
}
printf("------------>> %d\n",i);
for(int p=n;p<total_input;p++)
{
// printf("%d ----> %d\n",p,input[p]);
if(input[p]==0){
primelist[i]=p;
i++;
}
}
for(int p=0;p<i;p++)
{
printf("%d\n",primelist[p]);
}
n=n*n;
printf("################ %d\n",n);
if(pow(n,2)>=total_input){
for(int m=n;m<total_input;m++) input[m]=0;
}
else
{
for(int m=n;m<pow(n,2);m++) input[m]=0;
}
}
/*for(int p=0;p<i;p++)
{
printf("%d\n",primelist[p]);
}*/
return 0;
}
|
6,613 | #include<stdio.h>
#include<stdlib.h>
#include<math.h>
/*
Strategy: every block reduce a part of the input of size: THREADS_PER_BLOCK. Partial values are then
reduced on the CPU
*/
#define INPUT_SIZE 65536
#define THREADS_PER_BLOCK 128
#define N_BLOCKS (INPUT_SIZE+THREADS_PER_BLOCK-1)/THREADS_PER_BLOCK
/*
Compute reduction with divergence
*/
__global__ void reductionWDiv(int *in,int *res){
int n=threadIdx.x+blockIdx.x*blockDim.x;
int i;
for(i=2;i<=THREADS_PER_BLOCK;i*=2){
/*
Divergence problem: iteration 0: thread 0,2,4...
iteration 1: thread 0,4,8...
...and so on...
Threads in the same warp will follow different paths
*/
if(threadIdx.x%i==0)
in[n]+=in[n+i/2];
/*
Synchronize threads in the block after every iteration
*/
__syncthreads();
}
if(threadIdx.x==0)
res[blockIdx.x]=in[n];
}
/*
Compute reduction without divergence
*/
__global__ void reductionWODivergence(int *in,int *res){
int n=threadIdx.x+blockIdx.x*blockDim.x;
int i;
for(i=2;i<=THREADS_PER_BLOCK;i*=2){
/*
This condition allows subsequent threads in the warp to be active together till the computation
is done
*/
if(threadIdx.x<THREADS_PER_BLOCK/i)
in[blockIdx.x*blockDim.x+threadIdx.x*i]+=in[blockIdx.x*blockDim.x+
threadIdx.x*i+
i/2];
__syncthreads();
}
if(threadIdx.x==0){
res[blockIdx.x]=in[n];
/*printf("Computed %d\n");*/
}
}
/*
Compute reduction without divergence and accessing subsequent memory cells.
This is the most efficient approach to the problem of reduction
*/
__global__ void reductionSubsMemory(int *in,int *res){
int n=threadIdx.x+blockIdx.x*blockDim.x;
int i;
for(i=2;i<=THREADS_PER_BLOCK;i*=2){
/*
This condition allows subsequent threads in the warp to be active together till the computation
is done
*/
if(threadIdx.x<THREADS_PER_BLOCK/i)
in[n]+=in[n+THREADS_PER_BLOCK/i];
__syncthreads();
}
if(threadIdx.x==0){
res[blockIdx.x]=in[n];
/*printf("Computed %d\n");*/
}
}
int main(){
bool log=false;
/*
Allocate cpu memory for input and array of partial results
*/
int *in=(int*)malloc(INPUT_SIZE*sizeof(int)),
*part=(int*)malloc(N_BLOCKS*sizeof(int));
/*
Declare pointers for GPU memory
*/
int *gpu_in,*gpu_part;
/*
Allocate global memory for GPU
*/
cudaMalloc((void**)&gpu_in,INPUT_SIZE*sizeof(int));
cudaMalloc((void**)&gpu_part,N_BLOCKS*sizeof(int));
/*
Fill input data
*/
int i;
for(i=0;i<INPUT_SIZE;i++){in[i]=2;}
if(log){
printf("Input array is:\n");
for(i=0;i<INPUT_SIZE;i++){printf("%d ",in[i]);}
printf("\n");
}
/*
Copy memory to device pointers
*/
cudaMemcpy(gpu_in,in,INPUT_SIZE*sizeof(int),cudaMemcpyHostToDevice);
/*
Invoke kernel
*/
reductionWDiv<<<N_BLOCKS,THREADS_PER_BLOCK>>>(gpu_in,gpu_part);
/*
Copy memory from device to host
*/
cudaMemcpy(part,gpu_part,N_BLOCKS*sizeof(int),cudaMemcpyDeviceToHost);
/*
Reduce array of partials to obtain the result of the reduction operation
*/
int res=0;
for(i=0;i<N_BLOCKS;i++){res+=part[i];}
if(log)
printf("The array is reduced with sum into %d\n",res);
if(log){
printf("Input array is:\n");
for(i=0;i<INPUT_SIZE;i++){printf("%d ",in[i]);}
printf("\n");
}
/*
Copy input from CPU to global device memory to avoid problems with values kept from the last call
*/
cudaMemcpy(gpu_in,in,INPUT_SIZE*sizeof(int),cudaMemcpyHostToDevice);
/*
Zero partial values array and copy to device
*/
for(i=0;i<N_BLOCKS;i++){part[i]=0;}
cudaMemcpy(gpu_part,part,N_BLOCKS*sizeof(int),cudaMemcpyHostToDevice);
/*
Invoke kernel
*/
reductionWODivergence<<<N_BLOCKS,THREADS_PER_BLOCK>>>(gpu_in,gpu_part);
/*
Copy memory from device to host
*/
cudaMemcpy(part,gpu_part,N_BLOCKS*sizeof(int),cudaMemcpyDeviceToHost);
res=0;
for(i=0;i<N_BLOCKS;i++){res+=part[i];}
if(log)
printf("The array is reduced with sum into %d\n",res);
if(log){
printf("Input array is:\n");
for(i=0;i<INPUT_SIZE;i++){printf("%d ",in[i]);}
printf("\n");
}
/*
Copy input from CPU to global device memory to avoid problems with values kept from the last call
*/
cudaMemcpy(gpu_in,in,INPUT_SIZE*sizeof(int),cudaMemcpyHostToDevice);
/*
Zero partial values array and copy to device
*/
for(i=0;i<N_BLOCKS;i++){part[i]=0;}
cudaMemcpy(gpu_part,part,N_BLOCKS*sizeof(int),cudaMemcpyHostToDevice);
/*
Invoke kernel
*/
reductionSubsMemory<<<N_BLOCKS,THREADS_PER_BLOCK>>>(gpu_in,gpu_part);
/*
Copy memory from device to host
*/
cudaMemcpy(part,gpu_part,N_BLOCKS*sizeof(int),cudaMemcpyDeviceToHost);
res=0;
for(i=0;i<N_BLOCKS;i++){res+=part[i];}
if(log)
printf("The array is reduced with sum into %d\n",res);
/*
Free GPU memory
*/
cudaFree(gpu_in);
cudaFree(gpu_part);
/*
Free CPU memory
*/
free(in);
free(part);
}
|
6,614 | /* Simple benchmark to test matrix addition */
#include <stdio.h>
#include <math.h>
#define ROWS 5000
#define COLS 5000
#define CUDA_BLOCK_X 128
#define CUDA_BLOCK_Y 1
#define CUDA_BLOCK_Z 1
__global__ void _auto_kernel_0(float A[5000][5000],float B[5000][5000],float C[5000][5000])
{
int thread_x_id;thread_x_id = blockIdx.x * blockDim.x + threadIdx.x;
int thread_y_id;thread_y_id = blockIdx.y * blockDim.y + threadIdx.y;
if (thread_x_id && thread_y_id)
if (thread_x_id <= 5000 && thread_y_id <= 5000) {
C[1 * thread_x_id + -1][1 * thread_y_id + -1] = A[1 * thread_x_id + -1][1 * thread_y_id + -1] + B[1 * thread_x_id + -1][1 * thread_y_id + -1];
}
}
int main()
{
int j_nom_4;
int i_nom_3;
int j_nom_2;
int i_nom_1;
int j;
int i;
/* Declare three arrays: C = A + B */
static float A[5000][5000];
static float B[5000][5000];
static float C[5000][5000];
/* Initialize */
for (i = 1; i <= 5000; i += 1) {
for (j = 1; j <= 5000; j += 1) {
A[1 * i + -1][1 * j + -1] = (sin((1 * i + -1 + (1 * j + -1))) * sin((1 * i + -1 + (1 * j + -1))));
B[1 * i + -1][1 * j + -1] = (cos((1 * i + -1 + (1 * j + -1))) * cos((1 * i + -1 + (1 * j + -1))));
}
}
{
/* Auto-generated code for call to _auto_kernel_0 */
typedef float _narray_A[5000];
_narray_A *d_A;
cudaMalloc((void **) &d_A, sizeof(float ) * 5000 * 5000);
cudaMemcpy(d_A, A, sizeof(float ) * 5000 * 5000, cudaMemcpyHostToDevice);
typedef float _narray_B[5000];
_narray_B *d_B;
cudaMalloc((void **) &d_B, sizeof(float ) * 5000 * 5000);
cudaMemcpy(d_B, B, sizeof(float ) * 5000 * 5000, cudaMemcpyHostToDevice);
typedef float _narray_C[5000];
_narray_C *d_C;
cudaMalloc((void **) &d_C, sizeof(float ) * 5000 * 5000);
cudaMemcpy(d_C, C, sizeof(float ) * 5000 * 5000, cudaMemcpyHostToDevice);
int CUDA_GRID_X;
CUDA_GRID_X = (5000 + CUDA_BLOCK_X - 1)/CUDA_BLOCK_X;
int CUDA_GRID_Y;
CUDA_GRID_Y = (5000 + CUDA_BLOCK_Y - 1)/CUDA_BLOCK_Y;
int CUDA_GRID_Z;
CUDA_GRID_Z = (1 + CUDA_BLOCK_Z - 1)/CUDA_BLOCK_Z;
const dim3 CUDA_blockSize(CUDA_BLOCK_X, CUDA_BLOCK_Y, CUDA_BLOCK_Z);
const dim3 CUDA_gridSize(CUDA_GRID_X, CUDA_GRID_Y, CUDA_GRID_Z);
_auto_kernel_0<<<CUDA_gridSize,CUDA_blockSize>>>(d_A, d_B, d_C);
cudaMemcpy(A, d_A, sizeof(float ) * 5000 * 5000, cudaMemcpyDeviceToHost);
cudaMemcpy(B, d_B, sizeof(float ) * 5000 * 5000, cudaMemcpyDeviceToHost);
cudaMemcpy(C, d_C, sizeof(float ) * 5000 * 5000, cudaMemcpyDeviceToHost);
}
/* Check the result */
double sum = 0;
for (i_nom_3 = 1; i_nom_3 <= 5000; i_nom_3 += 1) {
for (j_nom_4 = 1; j_nom_4 <= 5000; j_nom_4 += 1) {
sum += C[1 * i_nom_3 + -1][1 * j_nom_4 + -1];
}
}
/* Report the result */
double r = (double )5000;
double c = (double )5000;
printf("Result (Should be close to 1.00) : %f\n",sum / (r * c));
return 0;
}
|
6,615 | #include<iostream>
#include<ctime>
using namespace std;
#define Bits 20
#define BlockDimx 512
__device__ float out;
template<unsigned int BlockSize, typename T>
__global__ void add(T *in_data, unsigned int N) {
__shared__ T smem[BlockSize];
T tmp_val = T(0);
unsigned int idx = threadIdx.x + blockDim.x * blockIdx.x*4;
if (idx + blockDim.x * 3 < N) {
T a1 = in_data[idx];
T a2 = in_data[idx + blockDim.x];
T a3 = in_data[idx + blockDim.x * 2];
T a4 = in_data[idx + blockDim.x * 3];
tmp_val = a1 + a2 + a3 + a4;
}
smem[threadIdx.x] = tmp_val;
__syncthreads();
if(BlockSize >= 1024 && threadIdx.x < 512) {
smem[threadIdx.x] += smem[threadIdx.x + 512];
}
__syncthreads();
if(BlockSize >= 512 && threadIdx.x < 256) {
smem[threadIdx.x] += smem[threadIdx.x + 256];
}
__syncthreads();
if(BlockSize >= 256 && threadIdx.x < 128) {
smem[threadIdx.x] += smem[threadIdx.x + 128];
}
__syncthreads();
if(BlockSize >= 128 && threadIdx.x < 64) {
smem[threadIdx.x] += smem[threadIdx.x + 64];
}
__syncthreads();
if(threadIdx.x < 32) {
volatile T* vmem = smem;
vmem[threadIdx.x] += vmem[threadIdx.x + 32];
vmem[threadIdx.x] += vmem[threadIdx.x + 16];
vmem[threadIdx.x] += vmem[threadIdx.x + 8];
vmem[threadIdx.x] += vmem[threadIdx.x + 4];
vmem[threadIdx.x] += vmem[threadIdx.x + 2];
vmem[threadIdx.x] += vmem[threadIdx.x + 1];
}
if(threadIdx.x == 0) {
atomicAdd(&out, smem[0]);
}
}
int main(int argc ,char *argv[]) {
unsigned int N = 1 << Bits;
dim3 block(BlockDimx,1);
dim3 grid((N + BlockDimx -1) / BlockDimx / 4, 1);
float in[N], *in_dev, t = 0;
clock_t start ,end;
auto init = [](auto *in ,unsigned int size)->void {
for(int i = 0;i < size;++i) {
in[i] = 1;
}
};
init(in, N);
cudaMalloc((void**)&in_dev, sizeof(in));
cudaMemcpy(in_dev, in, sizeof(in), cudaMemcpyHostToDevice);
start = clock();
add<BlockDimx><<<grid, block>>>(in_dev, N);
cudaDeviceSynchronize();
end = clock();
float ans;
cudaMemcpyFromSymbol(&ans, out, sizeof(float));
cudaDeviceSynchronize();
printf("%f\n", ans);
printf("%d\n", block.x * grid.x * 4);
cout <<"gpu time: "<<end -start<<endl;
return 0;
}
|
6,616 | #include <stdio.h>
#define N 2250
#define T 512
__global__ void vecReverse(int *a, int *b, int *c){
int i = blockIdx.x * blockDim.x + threadIdx.x;
if (i < N){
if (i % 2 == 0){
c[i] = a[i] + b[i];
}else{
c[i] = a[i] - b[i];
}
}
}
int main(int argc, char *argv[]){
int size = N * sizeof(int);
int a[N], b[N], c[N], *devA, *devB, *devC;
int blocks;
//Compute the blocks in case that N % T != 0
if (N % T != 0){
blocks =(N+T-1) / T;
}else{
blocks = N/T;
}
srand(1234);
for (int i = 0; i < N; i++){
a[i] = rand() % 1000;
b[i] = rand() % 1000;
}
cudaMalloc((void**)&devA, size);
cudaMalloc((void**)&devB, size);
cudaMalloc((void**)&devC, size);
cudaMemcpy(devA, a, size, cudaMemcpyHostToDevice);
cudaMemcpy(devB, b, size, cudaMemcpyHostToDevice);
vecReverse<<<blocks,T>>>(devA,devB,devC);
cudaMemcpy(c, devC, size, cudaMemcpyDeviceToHost);
cudaFree(devA);
cudaFree(devB);
cudaFree(devC);
for (int i = 0; i < N; i++){
printf("%d ",c[i]);
}
printf("\n");
} |
6,617 | //============================================================================
// Name : HelloWorld_CUDA.cu
// Author :
// Version :
// Copyright : Your copyright notice
// Description : Hello World in CUDA
//============================================================================
/*
* Copyright 2008, Karen Hains, UWA . All rights reserved.
*
* NOTICE TO USER:
*
* This source code is subject to NVIDIA ownership rights under U.S. and
* international Copyright laws. Users and possessors of this source code
* are hereby granted a nonexclusive, royalty-free license to use this code
* in individual and commercial software.
*
* WE MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE
* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR
* IMPLIED WARRANTY OF ANY KIND.
*/
/* HelloWorld Project
* This project demonstrates the basics on how to setup
* an example GPU Computing application.*
* This file contains the CPU (host) code.
*/
// Host defines
#define NUM_THREADS 8
#define STR_SIZE 50
// Includes
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include <math.h>
#include <vector_types.h>
#include <cuda_runtime_api.h>
// GPU Kernels declarations
__global__ void HelloWorld_kernel(int size, char *gpu_odata);
//////////////////////
// Program main
//////////////////////
int main( int argc, char** argv)
{
// Host variables
int i,nBytes;
unsigned int num_threads;
char *cpu_odata;
char *string;
// GPU variables
char *gpu_odata;
int str_size;
// Initialize CPU variables and allocate required memory
num_threads = (unsigned int) NUM_THREADS;
nBytes = num_threads*STR_SIZE*sizeof(char);
// Allocate and initialize CPU output vector
string = (char *) malloc(STR_SIZE);
if(!string) {
printf("Cannot allocate string memory on CPU\n");
exit(-1);
}
cpu_odata = (char *) malloc(nBytes);
if(!cpu_odata) {
printf("Cannot allocate cpu_odata memory on CPU\n");
exit(-1);
}
// Allocate GPU (device) memory and variables
str_size = (int) STR_SIZE;
cudaMalloc( (void**) &gpu_odata, nBytes);
// Setup kernel execution parameters
dim3 grid(1,1,1);
dim3 threads(num_threads,1,1);
// Execute the kernel on the GPU
HelloWorld_kernel<<< grid, threads >>>(str_size, gpu_odata);
// Copy result from GPU to CPU
cudaMemcpy(cpu_odata,gpu_odata,nBytes,cudaMemcpyDeviceToHost);
// Output results is same as the expected solution
for(i=0;i<num_threads;i++) {
strncpy(string,&cpu_odata[i*STR_SIZE],STR_SIZE);
printf("From thread %d: %s\n",i,string);
}
//////////////////////////////////////////
// All done - clean up and exit
//////////////////////////////////////////
// Free up CPU memory
free(cpu_odata);
// Free up GPU memory
cudaFree(gpu_odata);
}
|
6,618 | #include <iostream>
#include <algorithm>
#include <stdio.h>
using namespace std;
#define BLOCK_SIZE 16
#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);
}
}
/*
Convolution
*/
__global__ void gpu_multABtoC(float *a,float *b, float *c, int m, int n, int k)
{
int row = blockIdx.y * blockDim.y + threadIdx.y;
int col = blockIdx.x * blockDim.x + threadIdx.x;
float sum = 0.0;
if( col < k && row < m)
{
for(int i = 0; i < n; i++)
{
sum += a[row * n + i] * b[i * k + col];
}
c[row * k + col] = sum;
}
}
extern "C"{
void matmul(float *C, float *A, float *B, int m, int n, int k)
{
// Allocate memory space on the device
float *dev_a, *dev_b, *dev_c;
cudaMalloc((void **) &dev_a, sizeof(float)*m*n);
cudaMalloc((void **) &dev_b, sizeof(float)*n*k);
cudaMalloc((void **) &dev_c, sizeof(float)*m*k);
// copy matrix A and B from host to device memory
cudaMemcpy(dev_a, A, sizeof(float)*m*n, cudaMemcpyHostToDevice);
cudaMemcpy(dev_b, B, sizeof(float)*n*k, cudaMemcpyHostToDevice);
cudaMemcpy(dev_c, C, sizeof(float)*m*k, cudaMemcpyHostToDevice);
unsigned int gridev_rows = (m + BLOCK_SIZE - 1) / BLOCK_SIZE;
unsigned int gridev_cols = (k + BLOCK_SIZE - 1) / BLOCK_SIZE;
dim3 dimGrid(gridev_cols, gridev_rows);
dim3 dimBlock(BLOCK_SIZE, BLOCK_SIZE);
// Launch kernel
gpu_multABtoC<<<dimGrid, dimBlock>>>(dev_a, dev_b, dev_c, m, n, k);
// Transefr results from device to host
cudaMemcpy(C, dev_c, sizeof(float)*m*k, cudaMemcpyDeviceToHost);
cudaDeviceSynchronize();
// free memory
cudaFree(dev_a);
cudaFree(dev_b);
cudaFree(dev_c);
}
} |
6,619 | #include <cuda.h>
#include <cmath>
__global__ void cuda_sort(int* arr_d,int* out, int* histogram_d, int size, int max_val);
__host__ void counting_sort(int arr[], int size, int max_val)
{
int* arr_d;
int* out;
int* histogram_d;
cudaMalloc((void**)&arr_d,size*sizeof(int));
cudaMalloc((void**)&out ,size*sizeof(int));
cudaMemcpy(arr_d,arr,size*sizeof(int),cudaMemcpyHostToDevice);
cudaMalloc((void**)&histogram_d,max_val*sizeof(int));
cudaMemset((void**)&histogram_d,0,max_val*sizeof(int));
if(size>1024) cuda_sort<<<ceil(size/1024),1024,size*sizeof(int)>>>(arr_d,out, histogram_d, size, max_val);
else cuda_sort<<<1,size,size*sizeof(int)>>>(arr_d,out, histogram_d, size, max_val);
// copy to host & finsh
cudaMemcpy(arr,out,size*sizeof(int),cudaMemcpyDeviceToHost);
cudaFree(arr_d); cudaFree(histogram_d);
cudaFree(out);
}
__global__ void cuda_sort(int* arr_d,int* out, int* histogram_d, int size, int max_val){
extern __shared__ int position[];
int i = threadIdx.x + blockDim.x * blockIdx.x;
//__device__ int histogram_d[max_val];
if(i<size){
atomicAdd(&histogram_d[arr_d[i]],1);
}
__syncthreads();
if(i<max_val){
if(i==0) position[0] = 0;
else {
position[i] = histogram_d[i-1];
for(int j=0; j<i-1; j++){
position[i] += histogram_d[j];
}
}
}
__syncthreads();
if(i<max_val){
for(int j=0; j<histogram_d[i]; j++){
out[position[i]+j] = i;
}
// memset(&arr_d[position[i]],i,sizeof(int)*histogram_d[i]);
}
// device code
}
|
6,620 | #include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <string>
#include <math.h>
#include <stdio.h>
#define numBlocks 12
#define numThreads 32
struct RSA_KEY
{
unsigned long p; // selected prime 1
unsigned long q; // selected prime 2
unsigned long n; // public - the modulus
unsigned long e; // public - for encryption
unsigned long d; // private - for decryption
};
// Function prototypes
RSA_KEY generate_RSA_key(unsigned long p, unsigned long q);
void print_RSA_key(RSA_KEY in_key);
void RSA_encode(
char *input,
size_t input_size,
unsigned long long *output,
size_t output_size,
unsigned long e,
unsigned long n);
void RSA_decode(
unsigned long long *input,
size_t input_size,
char *output,
size_t output_size,
unsigned long d,
unsigned long n);
int gcd(int a, int b);
int modulo(int a, int b, int n);
__device__ int is_prime(unsigned long input);
// RSA Cracking Kernel
__global__ void findPrime(unsigned long n, unsigned long roundedN)
{
// Round the input modulus to nearest power of 2
unsigned long rangeRounded = 2 << roundedN;
// Sanity dictates that both primes should be < half the modulus
unsigned long rangeTotal = rangeRounded / 2;
// Determine min & max range for this thread
unsigned long index = blockIdx.x * numThreads + threadIdx.x;
unsigned long rangeLow = rangeTotal / (numBlocks * numThreads) * index;
unsigned long rangeHigh = rangeTotal / (numBlocks * numThreads) * (index + 1) - 1;
//printf("Thread %d reporting in N:%d | %d to %d\n", index, n, rangeLow, rangeHigh);
// Loop through range and search for primes
unsigned long output = 0;
for (unsigned long myindex = rangeLow; myindex < rangeHigh; myindex++)
{
if (is_prime(myindex))
{
if (n % myindex == 0)
{
output = myindex;
printf("prime: %d\n", myindex);
}
}
}
// Debug Print
if (output != 0)
printf("B:%d T:%d I:%d Range: %8d to %8d of %8d RESULT: %d\n",
blockIdx.x, threadIdx.x, index, rangeLow, rangeHigh, rangeTotal, output);
}
int main()
{
// Message to encode
char secret_message[] = "The quick brown fox jumped over the lazy dog.";
printf("Message: %s\n\n",secret_message);
// Generate public & private key
printf("Generating key...\n");
RSA_KEY my_key;
unsigned long prime1 = 157;
unsigned long prime2 = 199;
my_key = generate_RSA_key(prime1, prime2);
print_RSA_key(my_key);
// Encode message using public key
printf("Encrypting message...\n");
unsigned long long ciphertext[50];
RSA_encode(secret_message, sizeof secret_message, ciphertext, sizeof ciphertext, my_key.e, my_key.n);
// Print the ciphertext
printf("Ciphertext : ");
for (int i = 0; i < sizeof(secret_message); i++)
{
if (i % 10 == 0) { printf("\n"); }
printf("%6d ", ciphertext[i]);
}
// Decrypt message using private key
printf("\n\nDecrypting using private key...\n");
char decrypt_message[50];
RSA_decode(ciphertext, sizeof ciphertext, decrypt_message, sizeof decrypt_message, my_key.d, my_key.n);
printf("Decrypted message: %s\n\n", decrypt_message);
// Attempt to bruteforce find the private key
findPrime <<< numBlocks, numThreads >>> (my_key.n, log2(my_key.n));
cudaDeviceSynchronize();
// Error checking
cudaError_t err = cudaGetLastError();
if (err != cudaSuccess)
printf("Error: %s\n", cudaGetErrorString(err));
//printf("%f\n", 31243 % 10239);
// Decrypt message using cracked key
printf("\nEnd Program\n");
}
RSA_KEY generate_RSA_key(unsigned long p, unsigned long q)
{
RSA_KEY ret_str;
//ret_str.p = 157; // TODO: hardcoded for now - needs random generation
//ret_str.q = 199; // TODO: hardcoded for now - needs random generation
ret_str.p = p;
ret_str.q = q;
// Calculate modulus
ret_str.n = ret_str.p * ret_str.q;
// Calculate totient
int totient = (ret_str.p - 1) * (ret_str.q - 1);
printf("Totient: %d\n", totient);
// Calculate public key exponent 'e'
int temp_e = 0;
while (true)
{
temp_e = rand() % totient + 1; // random int 1 < e < totient
if (gcd(temp_e, totient) == 1)
{
break;
}
}
ret_str.e = temp_e;
// Calculate private key exponent 'd'
int temp_d = 0;
int diff;
while (true)
{
temp_d++;
diff = (temp_d * ret_str.e) - 1;
if(diff % totient == 0)
{
break;
}
}
ret_str.d = temp_d;
return ret_str;
}
void print_RSA_key(RSA_KEY in_key)
{
printf("RSA Key: p = %d\n", in_key.p);
printf("RSA Key: q = %d\n", in_key.q);
printf("RSA Key: n = %d\n", in_key.n);
printf("RSA Key: e = %d\n", in_key.e);
printf("RSA Key: d = %d\n", in_key.d);
printf("\n");
}
// Greatest Common Denominator function
// Courtest of: https://codereview.stackexchange.com/a/39110
int gcd(int a, int b)
{
int x;
while (b)
{
x = a % b;
a = b;
b = x;
}
return a;
}
// RSA Message encoder
void RSA_encode(
char *input,
size_t input_size,
unsigned long long *output,
size_t output_size,
unsigned long e,
unsigned long n)
{
unsigned long long m,c;
//printf("e: %d n: %d\n", e, n);
// Convert message string to integer
for (int i = 0; i < input_size; i++)
{
m = (int)input[i]; //printf("m: %d ", m);
//p = pow(m, e); printf("p: %d\n", p);
//c = p % n;
c = modulo(m, e, n);
//printf("c: %d\n", c);
output[i] = c;
}
}
// RSA Message decoder
void RSA_decode(
unsigned long long *input,
size_t input_size,
char *output,
size_t output_size,
unsigned long d,
unsigned long n)
{
for (int i = 0; i < output_size; i++)
{
output[i] = modulo(input[i], d, n);
}
}
// Modulo Function for massive powers
// Courtest of: https://stackoverflow.com/a/36398956
int modulo(int a, int b, int n) {
long long x = 1, y = a;
while (b > 0) {
if (b % 2 == 1) {
x = (x*y) % n;
}
y = (y*y) % n; // squaring the base
b /= 2;
}
return x%n;
}
// Test if a number is prime
__device__ int is_prime(unsigned long input)
{
//if (input == 1)
//return 0;
for (unsigned long k = 2; k < input; k++)
{
if (input % k == 0)
{
return 0;
}
}
return 1;
} |
6,621 | #include<stdio.h>
#include<stdlib.h>
#include<time.h>
#define N 1000000 //Job size = 1M
#define M 128 // Varying Thread block size- 128, 256, 512, 1024
//add kernel
__global__ void add(int *a, int *b, int *c, int k)
{
int index = threadIdx.x+ blockIdx.x * blockDim.x;
if (index<k)
c[index] = a[index] + b[index];
}
//Random number generator function
void random_ints(int* x, int size)
{
int i;
for (i=0;i<size;i++) {
x[i]=rand()%N;
}
}
int main(void)
{
int *a, *b, *c;
int *d_a, *d_b, *d_c;
int size = N * sizeof(int);
//time start and stop
cudaEvent_t start, stop;
float time;
cudaEventCreate(&start);
cudaEventCreate(&stop);
//Allocate device memory
cudaMalloc((void **)&d_a, size);
cudaMalloc((void **)&d_b, size);
cudaMalloc((void **)&d_c, size);
//Allocate CPU memory
a = (int *)malloc(size); random_ints(a, N);
b = (int *)malloc(size); random_ints(b, N);
c = (int *)malloc(size);
cudaEventRecord( start, 0 );
//Copy CPU memory to GPU memory
cudaMemcpy(d_a, a, size, cudaMemcpyHostToDevice);
cudaMemcpy(d_b, b, size, cudaMemcpyHostToDevice);
//Call the add kernel
add<<<(N+M-1)/M,M>>>(d_a, d_b, d_c,N);
printf("GPU Execution Time = %f\n",time);
// Copy from device to host
cudaMemcpy(c, d_c, size, cudaMemcpyDeviceToHost);
cudaEventRecord( stop, 0 );
cudaEventSynchronize(stop);
cudaEventElapsedTime( &time, start, stop );
cudaEventDestroy( start );
cudaEventDestroy( stop );
printf("Execution Time = %f\n",time);
//Cleanup
free(a); free(b); free(c);
cudaFree(d_a); cudaFree(d_b); cudaFree(d_c);
return 0;
}
|
6,622 | /** Konstantin Burlachenko (burlachenkok@gmail.com)
* Console based application for enumerate installed NVIDIA GPU in the system and it's properties via CUDA runtime. Other usefull tool from NVIDIA is nvidia-smi
*/
#include <stdio.h>
#include <cuda_runtime_api.h>
#include <time.h>
#include <stdio.h>
#include <sys/time.h>
/** Reference
* http://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#compute-capabilities
* https://devblogs.nvidia.com/parallelforall/inside-pascal/
*/
int getSPcores(const cudaDeviceProp& devProp)
{
int cores = 0;
int mp = devProp.multiProcessorCount;
switch (devProp.major){
case 2: // Fermi
if (devProp.minor == 1)
{
cores = mp * 48;
}
else
{
cores = mp * 32;
}
break;
case 3: // Kepler
cores = mp * 192;
break;
case 5: // Maxwell
cores = mp * 128;
break;
case 6: // Pascal
if (devProp.minor == 1 || devProp.minor == 2)
cores = mp * 128;
else if (devProp.minor == 0)
cores = mp * 64;
else
printf("Unknown device type for get Scalara Processor count\n");
break;
case 7: // Volta (from this microarchitecture there are separate cores for FP32, FP64, INT operations)
if (devProp.minor == 0)
cores = mp * 64; // fp32 cores
else
printf("Unknown device type for get Scalara Processor count\n");
break;
case 8: // Ampere (in this microarchitecture there are separate cores for FP32, FP64, INT operations)
cores = mp * 64; // fp32 cores
break;
default:
printf("Unknown device type for get Scalara Processor count\n");
break;
}
return cores;
}
template <int iterations>
__global__ void sum(float *z, float *x, float *y)
{
int i = blockIdx.x * blockDim.x + threadIdx.x;
volatile float xi = x[i];
volatile float yi = y[i];
volatile float zi = 0;
for (int j = 0; j < iterations; ++j)
zi = xi + yi;
z[i] = zi;
}
void computeBenchmark(int N = 2*1024*1024*128 /*number of items*/)
{
float *x = 0, *y = 0, *z = 0, *d_x = 0, *d_y = 0, *d_z = 0;
x = (float*)malloc(N*sizeof(float));
y = (float*)malloc(N*sizeof(float));
z = (float*)malloc(N*sizeof(float));
cudaMalloc(&d_x, N*sizeof(float));
cudaMalloc(&d_y, N*sizeof(float));
cudaMalloc(&d_z, N*sizeof(float));
for (int i = 0; i < N; i++) {
x[i] = i * 1.0f;
y[i] = i * 2.0f;
}
// Args for cuda memcpy: dst, src, direction
cudaMemcpy(d_x, x, N*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(d_y, y, N*sizeof(float), cudaMemcpyHostToDevice);
const int BlockSize = 1024;
if(d_x == 0 || d_y == 0 || d_z == 0)
{
printf("Memory allocation for %.0lf MBytes [FAILED]\n", (3*N*sizeof(float))/(1024.0*1024.0));
exit(-1);
}
else
{
printf("Memory allocation for %.0lf MBytes [OK]\n", (3*N*sizeof(float))/(1024.0*1024.0));
}
cudaDeviceSynchronize();
timeval s; gettimeofday(&s, 0);
const int kIterations = 1000*10;
sum<kIterations><<<N/BlockSize, BlockSize>>>(d_z, d_x, d_y);
cudaDeviceSynchronize();
timeval e; gettimeofday(&e, 0);
double seconds = (e.tv_sec - s.tv_sec) + (e.tv_usec - s.tv_usec)/(1e+6);
seconds = seconds;
cudaMemcpy(z, d_z, N*sizeof(float), cudaMemcpyDeviceToHost);
double maxError = 0.0f;
for (int i = 0; i < N; i++)
{
maxError = abs(z[i] - (x[i] + y[i])) > maxError ? abs(z[i] - (x[i] + y[i])) : maxError;
}
printf(" Max error for summation benchmark: %lf\n", maxError);
printf(" Time for compute benchmark: %lf seconds\n", seconds);
printf(" Computational performance after optimization: %lf TFLOPs\n", (kIterations*double(N))*(1.0/*add*/)/(seconds*1e+12));
cudaFree(&d_x);
cudaFree(&d_y);
cudaFree(&d_z);
}
int main()
{
cudaDeviceProp deviceProp;
cudaError_t status;
int device_count = 0;
status = cudaGetDeviceCount(&device_count);
if (status != cudaSuccess) {
printf("cudaGetDeviceCount() failed: %s\n", cudaGetErrorString(status));
return -1;
}
printf("CUDA-capable devices: %i\n", device_count);
for (int device_index = 0; device_index < device_count; ++device_index)
{
cudaSetDevice(device_index); // cudaSetDevice does not cause host synchronization
status = cudaGetDeviceProperties(&deviceProp, device_index);
if (status != cudaSuccess)
{
printf("cudaGetDeviceProperties() for device %i failed: %s\n", device_index, cudaGetErrorString(status));
return -1;
}
printf("Device %d: \"%s\" %s \n", device_index, deviceProp.name, device_index == 0 ? "[DEFAULT]" : "");
int driverVersion = 0, runtimeVersion = 0;
cudaDriverGetVersion(&driverVersion);
cudaRuntimeGetVersion(&runtimeVersion);
printf(" CUDA Driver Version / Runtime Version %d.%d / %d.%d\n", driverVersion / 1000, (driverVersion % 100) / 10, runtimeVersion / 1000, (runtimeVersion % 100) / 10);
printf(" CUDA Capability Major/Minor version number: %d.%d\n", deviceProp.major, deviceProp.minor);
const char* arch_names[] = {"" /*0*/,
"" /*1*/,
"FERMI" /*2*/,
"KEPLER" /*3*/,
"" /*4*/,
"MAXWELL"/*5*/,
"PASCAL" /*6*/,
"VOLTA/TURING" /*7*/,
"AMPERE" /*8*/,
"Hooper" /*9*/};
if (deviceProp.major < sizeof(arch_names)/sizeof(arch_names[0]))
printf(" GPU Architecture: %s\n", arch_names[deviceProp.major]);
printf(" Total amount of global memory: %.2f GBytes (%llu bytes)\n", (float)deviceProp.totalGlobalMem / pow(1024.0, 3), (unsigned long long)deviceProp.totalGlobalMem);
printf(" GPU Clock rate: %.0f MHz (%0.2f GHz)\n", deviceProp.clockRate * 1e-3f, deviceProp.clockRate * 1e-6f);
printf(" Memory Clock rate: %.0f Mhz\n", deviceProp.memoryClockRate * 1e-3f);
printf(" Memory Bus Width: %d-bit\n", deviceProp.memoryBusWidth);
printf(" Number of multiprocessors on device: %d\n", deviceProp.multiProcessorCount);
printf(" Number of CUDA cores (ALU/FPU): %d\n", getSPcores(deviceProp));
if (deviceProp.l2CacheSize)
{
printf(" L2 Cache Size: %d bytes\n", deviceProp.l2CacheSize);
}
printf(" Max Texture Dimension Size (x,y,z) 1D=(%d), 2D=(%d,%d), 3D=(%d,%d,%d)\n", deviceProp.maxTexture1D, deviceProp.maxTexture2D[0], deviceProp.maxTexture2D[1],
deviceProp.maxTexture3D[0], deviceProp.maxTexture3D[1], deviceProp.maxTexture3D[2]);
printf(" Max Layered Texture Size (dim) x layers 1D=(%d) x %d, 2D=(%d,%d) x %d\n", deviceProp.maxTexture1DLayered[0], deviceProp.maxTexture1DLayered[1], deviceProp.maxTexture2DLayered[0],
deviceProp.maxTexture2DLayered[1], deviceProp.maxTexture2DLayered[2]);
printf(" Total amount of constant memory: %lu bytes\n", deviceProp.totalConstMem);
printf(" Warp size: %d\n", deviceProp.warpSize);
printf(" Total amount of shared memory per block: %lu bytes\n", deviceProp.sharedMemPerBlock);
printf(" Total number of registers available per block: %d\n", deviceProp.regsPerBlock);
printf(" Amount of 32bit registers available per block: %d\n", deviceProp.regsPerBlock);
printf(" Maximum number of threads per multiprocessor: %d\n", deviceProp.maxThreadsPerMultiProcessor);
printf(" Maximum number of threads per block: %d\n", deviceProp.maxThreadsPerBlock);
printf(" Maximum sizes of each dimension of a block: %d x %d x %d\n", deviceProp.maxThreadsDim[0], deviceProp.maxThreadsDim[1], deviceProp.maxThreadsDim[2]);
printf(" Maximum sizes of each dimension of a grid: %d x %d x %d\n", deviceProp.maxGridSize[0], deviceProp.maxGridSize[1], deviceProp.maxGridSize[2]);
printf(" Maximum memory pitch: %lu bytes\n", deviceProp.memPitch);
cudaSharedMemConfig bankCfg = cudaSharedMemBankSizeDefault;
cudaDeviceGetSharedMemConfig(&bankCfg);
const char* bankCfgStr = "";
switch(bankCfg)
{
case cudaSharedMemBankSizeDefault:
bankCfgStr = "cudaSharedMemBankSizeDefault";
break;
case cudaSharedMemBankSizeFourByte:
bankCfgStr = "cudaSharedMemBankSize 4 Byte";
break;
case cudaSharedMemBankSizeEightByte:
bankCfgStr = "cudaSharedMemBankSize 8 Byte";
break;
}
printf(" Bank size for shared memory: %s\n", bankCfgStr);
printf("\n");
printf(" Default Limits for GPU device\n");
size_t limitValue = 0;
if (cudaDeviceGetLimit ( &limitValue, cudaLimitStackSize) == cudaSuccess)
printf(" cudaLimitStackSize, stack size for each GPU thread: %zu bytes\n", limitValue);
if (cudaDeviceGetLimit ( &limitValue, cudaLimitPrintfFifoSize) == cudaSuccess)
printf(" cudaLimitPrintfFifoSize, size of the shared FIFO used by the GPU printf(): %zu KBytes\n", limitValue/1024);
if (cudaDeviceGetLimit ( &limitValue, cudaLimitMallocHeapSize) == cudaSuccess)
printf(" cudaLimitMallocHeapSize, size of the heap used by the GPU malloc() and free(): %zu KBytes\n", limitValue/1024);
// https://stackoverflow.com/questions/15055877/how-to-get-memory-bandwidth-from-memory-clock-memory-speed
const int kDDR3_PumpRate = 2; // For HBM1/HBM2 memory and GDDR3 memory
const int kDDR5_PumpRate = 4; // For GDDR5 memory
const int kDDR5X_PumpRate = 8; // For GDDR5X memory
double peakBandwidth_gb_sec = (double(deviceProp.memoryClockRate /*in KHz*/) * (deviceProp.memoryBusWidth / 8.0) * kDDR3_PumpRate) / 1e+6;
printf(" Estimated Peak Memory Bandwidth: %lf GB/second\n", peakBandwidth_gb_sec);
const int kInstructionPerCycle = 2; // https://devtalk.nvidia.com/default/topic/722525/cuda-programming-and-performance/how-to-calculate-theoretical-fp32-instructions-per-cycle-ipc-on-nvidia-gpu/
double peakPerformance_tflops = ( double(deviceProp.clockRate /*in KHz*/) * getSPcores(deviceProp) * kInstructionPerCycle) / 1e+9;
printf(" Estimated Peak Single Precision TFLOPS: %lf TFLOPS\n", peakPerformance_tflops);
printf(" Estimated Ratio of instruction:bytes: %lf\n", peakPerformance_tflops * 1000.0 / peakBandwidth_gb_sec);
printf("\n");
computeBenchmark();
printf("*******************************************************************************************************************\n");
printf(" NVIDIA GPU ARCHITECTURES: Fermi 2.* => Kepler 3.* => Maxwell 5.* => Pascal 6.* => Volta/Turing 7.* => Ampeter 8.* \n");
printf("*******************************************************************************************************************\n");
printf("\n");
/*
* Peak Bandwidth -- is theoretical memory bandwith
* Estimated Transfer bytes / (stop-start) -- is practical memory bandwidth
*
* Peak TFLOPS -- is theoretical computation bandwidth
* Estimated TLOPS for execution / (stop-start) -- is practical computation bandwidth
*
* "flops_sp" gives a count of floating point operations per kernel "nvprof --metrics flops_sp <path_to_binary>"
* "gld_throughput" gives read efficiency of the kernel in GB/second
*/
}
}
|
6,623 | #include "includes.h"
__global__ void updateStatistic ( const int nwl, const float *stt1, const float *q, const float *r, float *stt0 ) {
int i = threadIdx.x + blockDim.x * blockIdx.x;
if ( i < nwl ) {
stt0[i] = ( q[i] > r[i] ) * stt1[i] + ( q[i] < r[i] ) * stt0[i];
}
} |
6,624 | // RUN: %run_test hipify "%s" "%t" --skip-excluded-preprocessor-conditional-blocks %hipify_args -D__CUDA_API_VERSION_INTERNAL %clang_args
// CHECK: #include <hip/hip_runtime.h>
#include <cuda.h>
#include <stdio.h>
int main() {
printf("04. CUDA Driver API Defines synthetic test\n");
// CHECK: #define __HIPCC__;
#define __CUDACC__;
#if CUDA_VERSION > 7050
// CHECK: int DEVICE_CPU = hipCpuDeviceId;
// CHECK-NEXT: int DEVICE_INVALID = hipInvalidDeviceId;
int DEVICE_CPU = CU_DEVICE_CPU;
int DEVICE_INVALID = CU_DEVICE_INVALID;
#endif
// CHECK: int IPC_HANDLE_SIZE = HIP_IPC_HANDLE_SIZE;
int IPC_HANDLE_SIZE = CU_IPC_HANDLE_SIZE;
// CHECK: void* LAUNCH_PARAM_BUFFER_POINTER = HIP_LAUNCH_PARAM_BUFFER_POINTER;
// CHECK-NEXT: void* LAUNCH_PARAM_BUFFER_SIZE = HIP_LAUNCH_PARAM_BUFFER_SIZE;
// CHECK-NEXT: void* LAUNCH_PARAM_END = HIP_LAUNCH_PARAM_END;
void* LAUNCH_PARAM_BUFFER_POINTER = CU_LAUNCH_PARAM_BUFFER_POINTER;
void* LAUNCH_PARAM_BUFFER_SIZE = CU_LAUNCH_PARAM_BUFFER_SIZE;
void* LAUNCH_PARAM_END = CU_LAUNCH_PARAM_END;
// CHECK: int MEMHOSTALLOC_PORTABLE = hipHostMallocPortable;
// CHECK-NEXT: int MEMHOSTALLOC_DEVICEMAP = hipHostMallocMapped;
// CHECK-NEXT: int MEMHOSTALLOC_WRITECOMBINED = hipHostMallocWriteCombined;
// CHECK-NEXT: int MEMHOSTREGISTER_PORTABLE = hipHostRegisterPortable;
// CHECK-NEXT: int MEMHOSTREGISTER_DEVICEMAP = hipHostRegisterMapped;
int MEMHOSTALLOC_PORTABLE = CU_MEMHOSTALLOC_PORTABLE;
int MEMHOSTALLOC_DEVICEMAP = CU_MEMHOSTALLOC_DEVICEMAP;
int MEMHOSTALLOC_WRITECOMBINED = CU_MEMHOSTALLOC_WRITECOMBINED;
int MEMHOSTREGISTER_PORTABLE = CU_MEMHOSTREGISTER_PORTABLE;
int MEMHOSTREGISTER_DEVICEMAP = CU_MEMHOSTREGISTER_DEVICEMAP;
#if CUDA_VERSION > 7000
// CHECK: int MEMHOSTREGISTER_IOMEMORY = hipHostRegisterIoMemory;
int MEMHOSTREGISTER_IOMEMORY = CU_MEMHOSTREGISTER_IOMEMORY;
#endif
// CHECK: int TRSA_OVERRIDE_FORMAT = HIP_TRSA_OVERRIDE_FORMAT;
// CHECK-NEXT: int TRSF_NORMALIZED_COORDINATES = HIP_TRSF_NORMALIZED_COORDINATES;
// CHECK-NEXT: int TRSF_READ_AS_INTEGER = HIP_TRSF_READ_AS_INTEGER;
// CHECK-NEXT: int TRSF_SRGB = HIP_TRSF_SRGB;
int TRSA_OVERRIDE_FORMAT = CU_TRSA_OVERRIDE_FORMAT;
int TRSF_NORMALIZED_COORDINATES = CU_TRSF_NORMALIZED_COORDINATES;
int TRSF_READ_AS_INTEGER = CU_TRSF_READ_AS_INTEGER;
int TRSF_SRGB = CU_TRSF_SRGB;
// CHECK: int ARRAY3D_LAYERED = hipArrayLayered;
// CHECK-NEXT: int ARRAY3D_SURFACE_LDST = hipArraySurfaceLoadStore;
// CHECK-NEXT: int ARRAY3D_CUBEMAP = hipArrayCubemap;
// CHECK-NEXT: int ARRAY3D_TEXTURE_GATHER = hipArrayTextureGather;
int ARRAY3D_LAYERED = CUDA_ARRAY3D_LAYERED;
int ARRAY3D_SURFACE_LDST = CUDA_ARRAY3D_SURFACE_LDST;
int ARRAY3D_CUBEMAP = CUDA_ARRAY3D_CUBEMAP;
int ARRAY3D_TEXTURE_GATHER = CUDA_ARRAY3D_TEXTURE_GATHER;
#if CUDA_VERSION > 8000
// CHECK: int COOPERATIVE_LAUNCH_MULTI_DEVICE_NO_PRE_LAUNCH_SYNC = hipCooperativeLaunchMultiDeviceNoPreSync;
// CHECK-NEXT: int COOPERATIVE_LAUNCH_MULTI_DEVICE_NO_POST_LAUNCH_SYNC = hipCooperativeLaunchMultiDeviceNoPostSync;
int COOPERATIVE_LAUNCH_MULTI_DEVICE_NO_PRE_LAUNCH_SYNC = CUDA_COOPERATIVE_LAUNCH_MULTI_DEVICE_NO_PRE_LAUNCH_SYNC;
int COOPERATIVE_LAUNCH_MULTI_DEVICE_NO_POST_LAUNCH_SYNC = CUDA_COOPERATIVE_LAUNCH_MULTI_DEVICE_NO_POST_LAUNCH_SYNC;
#endif
// CHECK: hipStream_t STREAM_PER_THREAD = hipStreamPerThread;
CUstream STREAM_PER_THREAD = CU_STREAM_PER_THREAD;
return 0;
}
|
6,625 |
void CPUsaxy(float * x, float * y, float a, int N)
{
for (int i = 0; i < N; i++)
x[i] = a*x[i] + y[i];
}
|
6,626 | #include <iostream>
#include <vector>
__global__ void addMat( float * mA_d, float * mB_d, std::size_t w, std::size_t h )
{
auto x = blockDim.x * blockIdx.x + threadIdx.x;
auto y = blockDim.y * blockIdx.y + threadIdx.y;
if( x < w && y < h )
{
mA_d[ y * w + x ] += mB_d[ y * w + x ];
}
}
int main()
{
size_t const w = 100;
size_t const h = 100;
size_t const size = w * h;
std::vector< float > mA ( size );
std::vector< float > mB ( size );
std::vector< float > mC ( size );
float * mA_d = nullptr;
float * mB_d = nullptr;
std::fill( begin( mA), end( mA ), 1.0f );
std::fill( begin( mB), end( mB ), 1.0f );
cudaMalloc( &mA_d, size * sizeof( float ) );
cudaMalloc( &mB_d, size * sizeof( float ) );
cudaMemcpy( mA_d, mA.data(), size * sizeof( float ), cudaMemcpyHostToDevice );
cudaMemcpy( mB_d, mB.data(), size * sizeof( float ), cudaMemcpyHostToDevice );
dim3 block( 32, 32 );
dim3 grid( ( w - 1 ) / block.x + 1, ( h - 1 ) / block.y + 1 );
std::cout << grid.x << ' ' << grid.y << std::endl;
addMat<<< grid, block >>>( mA_d, mB_d, w, h );
cudaMemcpy( mC.data(), mA_d, size * sizeof( float ), cudaMemcpyDeviceToHost );
for( std::size_t j = 0 ; j < h ; ++j )
{
for( std::size_t i = 0 ; i < w ; ++i )
{
std::cout << mC[ j * w + i ] << ' ';
}
std::cout << std::endl;
}
return 0;
}
|
6,627 | #include "includes.h"
#define NUMBER_OF_BLOCKS 256
#define NUMBER_OF_THREADS 64
// ==========
// Macro taken from:
// https://stackoverflow.com/questions/14038589/what-is-the-canonical-way-to-check-for-errors-using-the-cuda-runtime-api
__device__ int getElementsPerUnit(int total, int number_of_units) {
int elements_per_unit = total / number_of_units;
double remains = total % number_of_units;
if(remains != 0) {
elements_per_unit += 1;
}
return elements_per_unit;
}
__global__ void cosineSimilarityKernel(double *dot_products, int a_size, int b_size, double *a_norms, double *b_norms, double *results) {
int a_vectors_per_block = getElementsPerUnit(a_size, gridDim.x);
int b_vectors_per_thread = getElementsPerUnit(b_size, blockDim.x);
int a_start = blockIdx.x * a_vectors_per_block;
int a_end = a_start + a_vectors_per_block;
if(a_end > a_size) {
a_end = a_size;
}
int b_start = threadIdx.x * b_vectors_per_thread;
int b_end = b_start + b_vectors_per_thread;
if(b_end > b_size) {
b_end = b_size;
}
for(int a_index = a_start; a_index < a_end; a_index++) {
for(int b_index = b_start; b_index < b_end; b_index++) {
results[a_index*b_size + b_index] = (double) dot_products[a_index*b_size + b_index] / (a_norms[a_index] * b_norms[b_index]);
}
}
} |
6,628 |
__global__ void child(float *z) {
z[threadIdx.x] += 1;
}
|
6,629 | #include "includes.h"
__global__ void scan(float * input, float * output, int len) {
//@@ Modify the body of this function to complete the functionality of
//@@ the scan on the device
//@@ You may need multiple kernel calls; write your kernels before this
//@@ function and call them from here
__shared__ float scan_array[BLOCK_SIZE];
int global_id = threadIdx.x + blockDim.x * blockIdx.x;
if (global_id < len)
scan_array[threadIdx.x] = input[global_id];
else
scan_array[threadIdx.x] = 0;
__syncthreads();
int stride = 1;
while (stride < BLOCK_SIZE) {
int index = (threadIdx.x + 1) * stride * 2 - 1;
if (index < BLOCK_SIZE)
scan_array[index] += scan_array[index - stride];
stride = stride << 1;
__syncthreads();
}
for(int stride = BLOCK_SIZE >> 1; stride > 0; stride = stride >> 1) {
__syncthreads();
int index = (threadIdx.x + 1) * stride * 2 - 1;
if (index + stride < BLOCK_SIZE)
scan_array[index + stride] += scan_array[index];
}
__syncthreads();
if (global_id < len)
output[global_id] = scan_array[threadIdx.x];
if (global_id < BLOCK_SIZE) {
__syncthreads();
for (int block_idx = 1; block_idx <= (len / BLOCK_SIZE) ; ++block_idx) {
float offset = output[block_idx * BLOCK_SIZE - 1];
if ((threadIdx.x + block_idx * blockDim.x) < len)
output[threadIdx.x + block_idx * blockDim.x] += offset;
__syncthreads();
}
}
} |
6,630 | /*Original source:
https://github.com/rasmusto/CUDA/blob/master/john_jacobi/jacobi.cu
There are even better versions there. TODO:
https://github.com/rasmusto/CUDA/blob/master/jacobi_final/1k_jacobi6.cu
*/
#include <stdio.h>
#include <stdlib.h>
#include <sys/time.h>
#include <math.h>
#include <cuda.h>
#include <cuda_runtime.h>
__global__ void
jacobikernel( float* a, float* newa, float* lchange, int n, int m, float w0, float w1, float w2 )
{
int ti = threadIdx.x;
int tj = threadIdx.y;
int i = blockIdx.x * blockDim.x + ti + 1;
int j = blockIdx.y * blockDim.y + tj + 2;
///* looks like arrays are linearized, so explicit address calculation is used to simulate 2-D access */
//// I think the linearization is wrong: it should be a[i*m + j -1] instead of a[j*m+i-1]
if((i < m-1) && (j < n))
{
newa[j*m+i] = w0*a[j*m+i] +
w1 * (a[j*m+i-1] + a[(j-1)*m+i] +
a[j*m+i+1] + a[(j+1)*m+i]) +
w2 * (a[(j-1)*m+i-1] + a[(j+1)*m+i-1] +
a[(j-1)*m+i+1] + a[(j+1)*m+i+1]);
}
__shared__ float mychange[256]; /* fixed 16x16 2-D thread block: each thread stores its own change into mychange[] */
// convert 2-D (ti,tj) into a 1-D ii , so 2-D block's computation results can fit into a 1-D array mychange[]
// Again, correct conversion should: ti*blockDim.y + tj
int ii = ti+blockDim.x*tj;
mychange[ii] = fabsf( newa[j*m+i] - a[j*m+i] );
__syncthreads();
// contiguous range reduction pattern: half folding and add
int nn = blockDim.x * blockDim.y; // total number of threads within the current thread block
while( (nn>>=1) > 0 ){
if( ii < nn )
mychange[ii] = fmaxf( mychange[ii], mychange[ii+nn] );
__syncthreads();
}
if( ii == 0 ) // thread (0,0) writes the block level reduction result (mychange) to grid level result variable: lchange[]
lchange[blockIdx.x + gridDim.x*blockIdx.y] = mychange[0];
}
__global__ void
reductionkernel( float* lchange, int n ) /* lchange[n] stores the local reduction results of each thread block*/
{
__shared__ float mychange[256]; /*Again, store lchange[] content into a shared memory buffer. 256 = 16x16, a 1-D thread block*/
float mych = 0.0f;
int ii = threadIdx.x, m;
if( ii < n )
mych = lchange[ii]; /* transfer to a register variable i when thread id < n*/
m = blockDim.x; /* total number of threads of this 1-D thread block*/
while( m <= n ){ /*handle the case when n > number of threads: fold them ?*/
mych = fmaxf( mych, lchange[ii+m] );
m += blockDim.x;
}
mychange[ii] = mych; /*now all elements are reduced into mych*/
__syncthreads();
int nn = blockDim.x; /*consider only the exact number of thread number of reduction variables*/
while( (nn>>=1) > 0 ){
if( ii < nn )
mychange[ii] = fmaxf(mychange[ii],mychange[ii+nn]);
__syncthreads();
}
if( ii == 0 )
lchange[0] = mychange[0];
}
static float sumtime;
extern "C" void initDevice(float *a, int n, int m, float** da, float** dnewa, float** lchange , int Thr, int rank)
{
size_t memsize;
int bx, by, gx, gy;
int devCount,devID;
cudaError_t cudareturn;
cudaGetDeviceCount(&devCount);
if(rank >= devCount)
{
printf("not enough devices!\n");
exit(-1);
}
cudareturn = cudaSetDevice(rank);
if (cudareturn == cudaErrorInvalidDevice)
{
perror("cudaSetDevice returned cudaErrorInvalidDevice");
exit(-1);
}else
cudaGetDevice(&devID);
bx = Thr;
by = Thr;
gx = (n-2)/bx + ((n-2)%bx == 0?0:1);
gy = (m-2)/by + ((m-2)%by == 0?0:1);
memsize = sizeof(float) * (n+2) * m;
cudaMalloc( da, memsize );
cudaMalloc( dnewa, memsize );
cudaMalloc( lchange, gx * gy * sizeof(float) );
cudaMemcpy( *da, a, memsize, cudaMemcpyHostToDevice );
cudaMemcpy( *dnewa, a, memsize, cudaMemcpyHostToDevice );
printf("DEV: %d %p %p at rank: %d\n",devID,*da, *dnewa, rank);
}
extern "C" void dumpFile(float *a, int rank, int m, int n, char* prefix)
{
FILE *initfile;
char name[15];
char buffer[5];
sprintf(buffer,"%d",rank);
strcpy(name, prefix);
strcat(name, buffer);
strcat(name, ".txt");
initfile = fopen(name,"w+");
for(int idxi = 0; idxi <= n+1; ++idxi)
{
fprintf(initfile, "row id = %d\n",idxi);
for(int idxj = 0; idxj < m; ++idxj)
{
fprintf(initfile," %f ", a[idxj + idxi*m]);
}
fprintf(initfile, "\n",idxi);
}
fclose(initfile);
}
extern "C" void JacobiGPU( float* a, int n, int m, float w0, float w1, float w2, float tol, int Thr, float *da, float *dnewa, float *lchange, float *sumtime, float *change)
{
int bx, by, gx, gy;
cudaEvent_t e1, e2;
int devID;
cudaGetDevice(&devID);
bx = Thr;
by = Thr;
gx = (m-2)/bx + ((m-2)%bx == 0?0:1);
gy = (n-2)/by + ((n-2)%by == 0?0:1);
// printf("\nGrids = %i and %i\n", gx, gy);
// sumtime = 0.0f;
cudaEventCreate( &e1 );
cudaEventCreate( &e2 );
dim3 block( bx, by );
dim3 grid( gx, gy );
cudaMemcpy( da, a, sizeof(float) * m, cudaMemcpyHostToDevice );
cudaMemcpy( (da+(n+1)*m), (a+(n+1)*m), sizeof(float) * m, cudaMemcpyHostToDevice );
float msec;
cudaEventRecord( e1 );
jacobikernel<<< grid, block >>>( da, dnewa, lchange, n, m, w0, w1, w2 );
reductionkernel<<< 1, bx*by >>>( lchange, gx*gy ); /* both levels of reduction happen on GPU */
cudaEventRecord( e2 );
cudaMemcpy( change, lchange, sizeof(float), cudaMemcpyDeviceToHost ); /* copy final reduction result to CPU */
cudaEventElapsedTime( &msec, e1, e2 );
*sumtime += msec;
cudaMemcpy( (a+m), (dnewa+m), sizeof(float) * m, cudaMemcpyDeviceToHost );
cudaMemcpy( (a+n*m), (dnewa+n*m), sizeof(float) * m, cudaMemcpyDeviceToHost );
cudaEventDestroy( e1 );
cudaEventDestroy( e2 );
}
extern "C" void cleanDevice(float *a, float *da, float *dnewa, float *lchange,int m, int n)
{
size_t memsize;
memsize = sizeof(float) * (n+2) * m;
cudaMemcpy( a, da, memsize, cudaMemcpyDeviceToHost );
cudaFree( da );
cudaFree( dnewa );
cudaFree( lchange );
}
|
6,631 | /*
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <stdio.h>
int main( void ) {
cudaDeviceProp prop;
int count;
cudaGetDeviceCount( &count ) ;
for (int i=0; i< count; i++) {
cudaGetDeviceProperties( &prop, i ) ;
printf( " --- General Information for device %d ---\n", i );
printf( "Name: %s\n", prop.name );
printf( "Compute capability: %d.%d\n", prop.major, prop.minor );
printf( "Clock rate: %d\n", prop.clockRate );
printf( "Device copy overlap: " );
if (prop.deviceOverlap)
printf( "Enabled\n" );
else
printf( "Disabled\n");
printf( "Kernel execution timeout : " );
if (prop.kernelExecTimeoutEnabled)
printf( "Enabled\n" );
else
printf( "Disabled\n" );
printf( " --- Memory Information for device %d ---\n", i );
printf( "Total global mem: %ld\n", prop.totalGlobalMem );
printf( "Total constant Mem: %ld\n", prop.totalConstMem );
printf( "Max mem pitch: %ld\n", prop.memPitch );
printf( "Texture Alignment: %ld\n", prop.textureAlignment );
printf( " --- MP Information for device %d ---\n", i );
printf( "Multiprocessor count: %d\n", prop.multiProcessorCount );
printf( "Shared mem per mp: %ld\n", prop.sharedMemPerBlock );
printf( "Registers per mp: %d\n", prop.regsPerBlock );
printf( "Threads in warp: %d\n", prop.warpSize );
printf( "Max threads per block: %d\n", prop.maxThreadsPerBlock );
printf( "Max thread dimensions: (%d, %d, %d)\n", prop.maxThreadsDim[0], prop.maxThreadsDim[1], prop.maxThreadsDim[2] );
printf( "Max grid dimensions: (%d, %d, %d)\n", prop.maxGridSize[0], prop.maxGridSize[1], prop.maxGridSize[2] );
printf( "\n" );
}
}
*/
/*
--- General Information for device 0 ---
Name: GeForce GTX 950M
Compute capability: 5.0
Clock rate: 1124000
Device copy overlap: Enabled
Kernel execution timeout : Enabled
--- Memory Information for device 0 ---
Total global mem: -2147483648
Total constant Mem: 65536
Max mem pitch: 2147483647
Texture Alignment: 512
--- MP Information for device 0 ---
Multiprocessor count: 5
Shared mem per mp: 49152
Registers per mp: 65536
Threads in warp: 32
Max threads per block: 1024
Max thread dimensions: (1024, 1024, 64)
Max grid dimensions: (2147483647, 65535, 65535)
Press any key to continue . . .
*/ |
6,632 | #include "includes.h"
__global__ void _kgauss64d(int nx, int ns, int nd, double *x, double *s, double *k, double g) {
int i, j, n, xj, sj;
double d, dd;
i = threadIdx.x + blockIdx.x * blockDim.x;
n = nx*ns;
while (i < n) {
xj = (i % nx)*nd;
sj = (i / nx)*nd;
dd = 0;
for (j = 0; j < nd; j++) {
d = x[xj++]-s[sj++];
dd += d*d;
}
k[i] = exp(-g * dd);
i += blockDim.x * gridDim.x;
}
} |
6,633 | //pass
//--blockDim=512 --gridDim=1 --warp-sync=32 --no-inline
#include <cuda.h>
#include <stdio.h>
#define N 32//128//512
__device__ static __attribute__((always_inline)) void scan_warp (int* A)
{
unsigned int tid = threadIdx.x;
unsigned int lane = tid % 32;
if (lane >= 1) A[tid] = A[tid - 1] + A[tid]; // this conditional is always true!!
if (lane >= 2) A[tid] = A[tid - 2] + A[tid];
if (lane >= 4) A[tid] = A[tid - 4] + A[tid];
if (lane >= 8) A[tid] = A[tid - 8] + A[tid];
if (lane >= 16) A[tid] = A[tid - 16] + A[tid];
}
__global__ void scan (int* A)
{
unsigned int tid = threadIdx.x;
unsigned int lane = tid % 32;
int temp [32] = {0};
scan_warp(A);
__syncthreads();
if (lane == 31) // ?????????
temp[tid / 32] = A[tid];
__syncthreads();
if (tid / 32 == 0)
scan_warp(temp);
__syncthreads();
A[tid] += temp[tid/32];
}
|
6,634 |
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <math.h>
#include <iostream>
using namespace std;
int num_samples;
int sample_dimension;
int num_clusters;
typedef struct
{
int width, height;
float *elements;
}Matrix;
typedef struct
{
int height;
int *elements;
}Label;
typedef struct
{
int num_loops;
Label labels;
Matrix cluster_centers;
}KmeansRecord;
__global__ void KmeansKernelFindClusters(Matrix, Matrix, Label, int *);
__global__ void kmeansKernelFindCenters(Matrix, Label);
Matrix createMatrix(const int height, const int width)
{
Matrix mat;
mat.width = width;
mat.height = height;
mat.elements = (float*)calloc(1ul * width * height, sizeof(float));
return mat;
}
void freeMatrix(Matrix mat)
{
free(mat.elements);
}
void freeLabel(Label lab)
{
free(lab.elements);
}
Matrix cloneMatrix(const Matrix &mat)
{
Matrix new_mat = createMatrix(mat.height, mat.width);
memcpy(new_mat.elements, mat.elements, sizeof(float) * new_mat.height * new_mat.width);
return new_mat;
}
__global__ void kmeansKernelFindCenters(Matrix samples, Label labels, Matrix cluster_centers)
{
int term = blockIdx.y * blockDim.y + threadIdx.y, term_count = 0;
int col = blockIdx.x * blockDim.x + threadIdx.x;
int n = samples.height, m = samples.width, k = cluster_centers.height;
float val = 0.0;
if (term >= k || col >= m)
return;
for (int i = 0; i < n; ++i)
{
int f = (labels.elements[i] == term);
term_count += f;
val += samples.elements[i * m + col] * f;
}
term_count = (term_count) ? term_count : 1;
cluster_centers.elements[term * m + col] = val / term_count;
}
__host__ __device__ float euclid_dist_square(Matrix samples, Matrix cluster_centers
, int sample, int cluster)
{
float ret = 0.0;
int num_samples = samples.height;
int sample_dimension = samples.width;
for (int i = 0; i < sample_dimension; ++i)
{
float current = samples.elements[sample_dimension * sample + i] - cluster_centers.elements[sample_dimension * cluster + i];
current *= current;
ret += current;
}
return ret;
}
__global__ void KmeansKernelFindClusters(Matrix samples, Matrix cluster_centers, Label new_labels)
{
int row = blockDim.x * blockIdx.x + threadIdx.x;
int num_clusters = cluster_centers.height;
if (row < samples.height)
{
int best = -1;
float dist, min_dist;
best = 0;
min_dist = euclid_dist_square(samples, cluster_centers, row, 0);
for(int i = 1; i < num_clusters; ++i)
{
dist = euclid_dist_square(samples, cluster_centers, row, i);
if (dist < min_dist)
{
min_dist = dist;
best = i;
}
}
new_labels.elements[row] = best;
}
}
float KmeansCalcualteSSE(Matrix samples, Matrix cluster_centers, Label labels)
{
int n = samples.height, m = samples.width, k = labels.height;
float sse = 0.0;
for (int i = 0; i < n; ++i)
{
for (int j = 0; j < m; ++j)
{
int label = labels.elements[i];
float diff = samples.elements[i * m + j] - cluster_centers.elements[label * m + j];
sse += diff * diff;
}
}
return sse;
}
KmeansRecord do_kmeans(const Matrix samples, const Matrix initial_centers, const int max_iter=1500)
{
const int block_size = 16;
int n = samples.height, m = samples.width, k = initial_centers.height;
// To prepare the calculation
Label labels;
labels.height = n;
size_t size = sizeof(float) * n;
labels.elements = (int*)malloc(size);
Label d_labels;
d_labels.height = n;
size = sizeof(float) * n;
cudaError_t err = cudaMalloc(&d_labels.elements, size);
// To copy samples and initial_centers to the device
Matrix d_samples;
d_samples.height = n; d_samples.width = m;
size = sizeof(float) * n * m;
err = cudaMalloc(&d_samples.elements, size);
if (err)
printf("fail to malloc samples: %s\n", cudaGetErrorString(err));
err = cudaMemcpy(d_samples.elements, samples.elements, size, cudaMemcpyHostToDevice);
if (err)
printf("fail to copy samples to device: %s\n", cudaGetErrorString(err));
Matrix centers;
centers.height = k; centers.width = m;
size = sizeof(float) * k * m;
centers.elements = (float*)malloc(size);
Matrix d_centers;
d_centers.height = k; d_centers.width = m;
size = sizeof(float) * k * m;
err = cudaMalloc(&d_centers.elements, size);
if (err)
printf("fail to malloc centers: %s\n", cudaGetErrorString(err));
err = cudaMemcpy(d_centers.elements, initial_centers.elements, size, cudaMemcpyHostToDevice);
if (err)
printf("fail to copy centers to device: %s\n", cudaGetErrorString(err));
dim3 dimBlock1(block_size * block_size);
dim3 dimGrid1((n + block_size * block_size - 1) / (block_size * block_size));
dim3 dimBlock2(block_size, block_size);
dim3 dimGrid2((m + block_size - 1) / block_size, (k + block_size - 1) / block_size);
KmeansRecord record;
record.cluster_centers.height = k;
record.cluster_centers.width = m;
record.cluster_centers.elements = (float*)malloc(sizeof(float) * k * m);
record.labels.height = n;
record.labels.elements = (int*)malloc(sizeof(int) * n);
float milliseconds = 0.0;
float before_sse = -1.0;
float this_sse = 0.0;
int total_loops = 0;
for (int i = 0; i < max_iter; ++i)
{
total_loops = i + 1;
cudaEvent_t start, stop;
float this_time = 0.0;
cudaEventCreate(&start);
cudaEventCreate(&stop);
cudaEventRecord(start);
KmeansKernelFindClusters<<<dimGrid1, dimBlock1>>>(d_samples, d_centers, d_labels);
cudaEventRecord(stop);
err = cudaEventSynchronize(stop);
cudaEventElapsedTime(&this_time, start, stop);
if (!err)
{
// printf("iteration %d: staget 1 done. Time: %.2f ms.\n ", i + 1, this_time);
milliseconds += this_time;
}
cudaEventRecord(start);
kmeansKernelFindCenters<<<dimGrid2, dimBlock2>>>(d_samples, d_labels, d_centers);
cudaEventRecord(stop);
err = cudaEventSynchronize(stop);
cudaEventElapsedTime(&this_time, start, stop);
if (!err)
{
// printf("iteration %d: staget 2 done. Time: %.2f ms.\n ", i + 1, this_time);
milliseconds += this_time;
}
cudaMemcpy(labels.elements, d_labels.elements, sizeof(int) * n, cudaMemcpyDeviceToHost);
cudaMemcpy(centers.elements, d_centers.elements, sizeof(float) * k * m, cudaMemcpyDeviceToHost);
this_sse = KmeansCalcualteSSE(samples, centers, labels);
if (!err)
{
/*
printf("SSE: %.2f\n", this_sse);
if (i > 0 && fabs(this_sse - before_sse) == 0)
{
printf("%d %.2f\n", i + 1, this_sse);
break;
}
*/
before_sse = this_sse;
}
else
{
printf("%s\n", cudaGetErrorString(err));
}
}
printf("total iteration: %d; total time: %.2f ms\n", total_loops, milliseconds);
memcpy(record.labels.elements, labels.elements, sizeof(int) * n);
memcpy(record.cluster_centers.elements, centers.elements, sizeof(float) * m * k);
freeMatrix(centers);
freeLabel(labels);
cudaFree(d_samples.elements);
cudaFree(d_centers.elements);
cudaFree(d_labels.elements);
return record;
}
Matrix read_samples(const char *path, int *k)
{
FILE* f = fopen(path, "rb");
int n, m;
fread(&n, 4, 1, f);
fread(&m, 4, 1, f);
fread(k, 4, 1, f);
Matrix samples = createMatrix(n, m);
fread(samples.elements, 4, 1ul * n * m, f);
return samples;
}
void save_record(KmeansRecord record)
{
}
int main(int argc, char **argv)
{
/*
if (argc < 2)
{
printf("usage: %s SOURCE\n", argv[0]);
return 0;
}
*/
// To read data
int k;
// Matrix samples = read_samples(argv[1], &k);
Matrix samples;
samples.height = 8192;
samples.width = 1024;
samples.elements = (float*)malloc(sizeof(float) * 8192 * 1024);
k = 50;
for (int i = 0; i < 8192; ++i)
{
for(int j = 0; j < 1024; ++j)
{
samples.elements[i * 1024 + j] = i;
}
}
Matrix initial_center = createMatrix(k, samples.width);
memcpy(initial_center.elements, samples.elements, sizeof(float) * k);
KmeansRecord record = do_kmeans(samples, initial_center);
save_record(record);
freeMatrix(samples);
freeMatrix(initial_center);
return 0;
}
|
6,635 | #include <stdio.h>
#include <stdlib.h>
#include <cuda.h>
#include <time.h>
#include <math.h>
__global__ void reduce(int *g_idata, int *g_odata){
extern __shared__ int sdata[];
unsigned int tid = threadIdx.x;
unsigned int i = blockIdx.x*blockDim.x + threadIdx.x;
sdata[tid] = g_idata[i];
__syncthreads();
for(unsigned int s = blockDim.x/2;s>0;s>>=1){
if(tid<s){
sdata[tid] += sdata[tid+s];
}
__syncthreads();
}
if(tid==0) g_odata[blockIdx.x] = sdata[0];
}
///////////////////////////////////////////////////////////////////////////
void init(int *A, int nA) {
for (int i=0; i<nA; i++){
A[i] = (int)rand() % 16;
}
}
int c_summation(int *A, int nA) {
int sum=A[0];
for (int i=1; i<nA; i++) {
sum += A[i];
}
return sum;
}
int main() {
int order = 18;
int nA = pow(2,order);
printf("Vector size: %d\n", nA);
size_t sizeA = nA*sizeof(int);
int c_sum,pr_sum;
int *A,*S;
A = (int*) malloc(sizeA);
S = (int*) malloc(sizeA);
srand(time(NULL));
init(A, nA);
int *dA =NULL;
int *dS =NULL;
cudaMalloc((void**)&dA,sizeA);
cudaMalloc((void**)&dS,sizeA);
cudaMemcpy(dA,A,sizeA,cudaMemcpyHostToDevice);
//cudaMemcpy(dS,S,sizeA,cudaMemcpyHostToDevice);
int B = 1024;
int G = (nA+B-1)/B;
int smemSize = B*sizeof(int);
float kernel_time;
cudaEvent_t start, stop;
cudaEventCreate(&start);
cudaEventCreate(&stop);
cudaEventRecord(start,0);
//////////////////////////// Start time record
reduce<<<G,B,smemSize>>>(dA,dS);
// for (unsigned int stride=B; stride < 1 ; stride >>= 1){
for(int problemsize=nA/B;problemsize > 1;problemsize/=B){
reduce<<<G,B,smemSize>>>(dS,dS);
}
cudaEventRecord(stop,0);
cudaEventSynchronize(stop);
cudaEventElapsedTime(&kernel_time,start,stop);
printf("GPU time = %lf ms\n", kernel_time);
cudaMemcpy(S,dS,sizeA,cudaMemcpyDeviceToHost);
pr_sum = S[0];
clock_t begin, end;
begin = clock();
c_sum = c_summation(A,nA);
end = clock();
double cpu_time = (double) (end-begin)/ CLOCKS_PER_SEC *1000.0f;
printf("CPU time = %lf ms\n", cpu_time);
printf("Parallel reduction sum result is: %d \n",pr_sum);
printf("c function sum result is: %d \n", c_sum);
free(A);
cudaFree(dA);
return 0;
}
|
6,636 | #include <iostream>
#include <chrono>
#include <cstdlib>
#include <cmath>
using namespace std;
using namespace std::chrono;
__global__ void reduce(float *g_idata, float *g_odata){
extern __shared__ float sdata[];
//each thread loads one element from global to shared mem
unsigned int tid = threadIdx.x;
unsigned int i = blockIdx.x * blockDim.x + threadIdx.x;
sdata[tid] = g_idata[i];
__syncthreads();
// do reduction in shared mem
for(unsigned int s = 1;s < blockDim.x; s *= 2){
if(tid % (2 * s) == 0){
sdata[tid] += sdata[tid + s];
}
__syncthreads();
}
// write result for this block to global mem
if (tid == 0) g_odata[blockIdx.x] = sdata[0];
}
__global__ void compute_difference_between_mean_and_elements(float *difference_array, float *original_array, float mean){
int tid = blockIdx.x * blockDim.x + threadIdx.x;
difference_array[tid] = (original_array[tid] - mean) * (original_array[tid] - mean);
}
void sum_CPU(float *host_input, float *host_output, unsigned int size){
host_output[0] = 0;
auto start = high_resolution_clock::now();
for(int i = 0;i < size;i ++){
host_output[0] += host_input[i];
}
auto stop = high_resolution_clock::now();
auto time_req = duration_cast<microseconds>(stop - start).count();
cout << endl << "Time required for CPU : " << time_req << " microseconds "<< endl;
cout << endl << " Sum from CPU : " << host_output[0] << endl;
}
void compute_sum_cpu(float *cpu_input, float *cpu_output, unsigned int n){
for(unsigned int i = 0;i < n;i ++){
cpu_output[0] += cpu_input[i];
}
}
int main(){
int maxThreads = 1024;
float *host_input, *host_output, *device_input, *device_output;
float *cpu_input, *cpu_output;
int n = 2 << 20;
size_t size = n * sizeof(int);
//CPU sum
cpu_input = (float *)malloc(size);
cpu_output = (float *)malloc(sizeof(int));
cpu_output[0] = 0;
for(unsigned int i = 0;i < n;i ++){
cpu_input[i] = rand()%10 ;
}
sum_CPU(cpu_input, cpu_output, n);
host_input = (float *)malloc(size);
for(int i = 0;i < n;i ++){
host_input[i] = cpu_input[i];
}
int blocks = n / maxThreads;
host_output = (float *)malloc(blocks * sizeof(int));
const dim3 block_size(maxThreads, 1, 1);
const dim3 grid_size(blocks, 1, 1);
cudaMalloc(&device_input, size);
cudaMalloc(&device_output, blocks * sizeof(int));
//copy reduce copy and sum
cudaMemcpy(device_input, host_input, size, cudaMemcpyHostToDevice);
reduce<<<grid_size, block_size, maxThreads * sizeof(float)>>>(device_input, device_output);
cudaMemcpy(host_output, device_output, blocks * sizeof(float), cudaMemcpyDeviceToHost);
for(int i = 1;i < blocks; i++){
host_output[0] += host_output[i];
}
cout << endl << " Sum from GPU : " << *host_output << endl;
float mean = float(host_output[0] / n);
cout << endl << " Mean of the array : " << mean << endl;
//Compute array of [(x1-mean)^2, (x2-mean)^2, (x3-mean)^2, ... ]
float *array_of_difference_between_mean_and_elements_device;
cudaMalloc(&array_of_difference_between_mean_and_elements_device, size);
compute_difference_between_mean_and_elements<<<grid_size, block_size>>>(array_of_difference_between_mean_and_elements_device, device_input, mean);
//Compute (x1-mean)^2 + (x2 - mean) ^ 2 + ...
float *output_array_for_sum_of_difference_between_elements, *output_array_for_sum_of_difference_between_elements_host;
output_array_for_sum_of_difference_between_elements_host = (float *)malloc(blocks * sizeof(int));//for host
cudaMalloc(&output_array_for_sum_of_difference_between_elements, blocks * sizeof(int));//for elements
reduce<<<grid_size, block_size, maxThreads * sizeof(int)>>>(array_of_difference_between_mean_and_elements_device, output_array_for_sum_of_difference_between_elements);
cudaMemcpy(output_array_for_sum_of_difference_between_elements_host, output_array_for_sum_of_difference_between_elements, blocks * sizeof(int), cudaMemcpyDeviceToHost);
for(int i = 1;i < blocks;i ++){
output_array_for_sum_of_difference_between_elements_host[0] += output_array_for_sum_of_difference_between_elements_host[i];
}
// Compute variance i.e ((x1 - mean)^2 + (x2 - mean)^2 ...) / n
output_array_for_sum_of_difference_between_elements_host[0] = output_array_for_sum_of_difference_between_elements_host[0] / n;
cout << endl << "Variance from GPU : " << output_array_for_sum_of_difference_between_elements_host[0] << endl;
//Compute square root of (x1 - mean) ^ 2 + (x2 - mean) ^ 2 ...
output_array_for_sum_of_difference_between_elements_host[0] = sqrt(output_array_for_sum_of_difference_between_elements_host[0]);
cout << endl << "Standard deviation from GPU : " << output_array_for_sum_of_difference_between_elements_host[0] << endl;
}
|
6,637 | #include "includes.h"
__global__ void gpu_imageSquare(float * out, const float * in, const int width, const int height) {
const int x = blockIdx.x*blockDim.x + threadIdx.x;
const int y = blockIdx.y*blockDim.y + threadIdx.y;
if (x >= width || y >= height) {
return;
}
int index = x + y*width;
out[index] = in[index]*in[index];
} |
6,638 | #define BLOCK_SIZE 32
#define DATALEN_PER_BLOCK (BLOCK_SIZE * 2)
#include <stdio.h>
__global__ void reductionKernel(float *a, float *r)
{
int blocksz = blockDim.x;
int tid = threadIdx.x;
int bid = blockIdx.x;
int i1 = DATALEN_PER_BLOCK * bid + tid;
int i2 = i1 + blocksz;
__shared__ float shared_data[BLOCK_SIZE];
shared_data[tid] = a[i1] + a[i2];
//shared_data[tid + blocksz] = a[i2];
__syncthreads();
for (int i = blocksz / 2; i > 0; i >>= 1) {
if (tid < i) {
shared_data[tid] += shared_data[tid + i];
}
__syncthreads();
}
r[bid] = shared_data[0];
}
float reduction(float *a, size_t len)
{
int data_len_per_block = DATALEN_PER_BLOCK;
float *dr = nullptr;
float *da = nullptr;
float *r = new float[data_len_per_block * sizeof(float)];
size_t tlen = len;
cudaMalloc(&da, sizeof(float) * len);
for (int k = 0; k < 20; k++) {
len = tlen;
cudaMemcpy(da, a, sizeof(float) * len, cudaMemcpyHostToDevice);
while (len > data_len_per_block) {
len /= data_len_per_block;
dim3 threads(BLOCK_SIZE);
dim3 grids(len);
reductionKernel<<<grids, threads>>>(da, da);
}
}
cudaMemcpy(r, da, len * sizeof(float), cudaMemcpyDeviceToHost);
if (len > 0) {
for (int i = 1; i < len; i++)
r[0] += r[i];
}
int rr = r[0];
cudaFree(&da);
cudaFree(&dr);
free(r);
return rr;
}
int main(int argc, char *argv[])
{
int len = 8192;
if (argc > 1)
len = atoi(argv[1]);
printf("len %d\n", len);
float *a = new float[len];
for (int i = 0; i < len; i++)
a[i] = 1.0;
float r = reduction(a, len);
printf("%.2f\n", r);
return 0;
}
|
6,639 | float h_A[]= {
0.8602840228518726, 0.5927379911329769, 0.5620306550391287, 0.9315992396584141, 0.9689356026821376, 0.5280311832795622, 0.9496028694352703, 0.5641880269821069, 0.6030538182680076, 0.7161847605921088, 0.5060945311802905, 0.886444777939754, 0.8781226800240409, 0.5916493031376786, 0.8904123175130663, 0.7858908969502676, 0.8670095687703278, 0.6702719943055491, 0.5722752580898081, 0.9989138426491808, 0.5079302819788281, 0.8545505203577692, 0.881285186698913, 0.9024712426423345, 0.9643400697417004, 0.7729521748014443, 0.9409005045384314, 0.7418364365076495, 0.9432199817085741, 0.7495265342890661, 0.8809060498289787, 0.9627042837394523, 0.570535420793068, 0.9814209015989703, 0.9751236308584689, 0.6746205589042932, 0.7436523690335364, 0.5225960780726773, 0.8400576070127777, 0.9536539181447442, 0.7610682868316863, 0.9700791189559959, 0.6542157126634931, 0.9615258010910317, 0.5881620851235232, 0.8255036398474047, 0.5171787290698973, 0.7024751761388948, 0.5693229120596256, 0.8258971317454344, 0.6853521401818525, 0.5218118650208736, 0.8333855805860315, 0.8778408770402235, 0.6399012315917749, 0.701134849908033, 0.9941338658358555, 0.6792944175670592, 0.6528182154455453, 0.866683345164937, 0.5114102779230462, 0.7684932343000271, 0.8655843847820717, 0.8867034845911396, 0.5667262123157396, 0.9097864990356299, 0.6039320157957546, 0.5891358267973679, 0.9103604123396528, 0.8298990303748011, 0.5507215209516652, 0.5926139894549219, 0.9750641681608931, 0.9682994273343464, 0.7848285151927183, 0.6369901914257143, 0.5712676368982605, 0.9182230287826997, 0.5400876092986648, 0.9470014480126976, 0.7993160552027836, 0.8326484790723048, 0.8085494645014568, 0.7681190294190764, 0.7143262977182433, 0.8504457617864781, 0.6049909609670145, 0.8928868105237509, 0.8745225156814072, 0.7064969793120577, 0.5743617093257782, 0.7853552434345943, 0.974016959573822, 0.534666210347204, 0.5578572849111747, 0.6627606826315317, 0.8008853550476285, 0.7509647912922053, 0.9555132681157366, 0.9184881392546884, 0.8294744679851532, 0.6419875580163812, 0.9053880802130616, 0.5786074529359735, 0.5456468769280681, 0.9498750552705606, 0.9345449524765033, 0.7387868142328379, 0.5077796813401644, 0.6658031285693387, 0.8097365624885975, 0.6506348770311576, 0.9898554574118077, 0.6218685003285596, 0.5584283872336847, 0.7408681129567999, 0.9302036435569752, 0.7321700940534508, 0.8596803172087487, 0.9247462693105648, 0.5767519396613052, 0.7633615697600328, 0.9636742981595803, 0.5637430579591463, 0.7809028943050258, 0.9625278440856536, 0.9663601684284473, 0.9719964146588878, 0.8450662560150319, 0.9763488236914826, 0.9532429380350123, 0.8922874314413677, 0.9619422007030596, 0.6890894889507312, 0.5988976658681933, 0.9310220629626575, 0.8693495136854766, 0.9205869295210837, 0.9153839428620898, 0.6649898883948884, 0.810346256558724, 0.7339949041364894, 0.8461382653182661, 0.8996331563756855, 0.8812092939242719, 0.5623393044947036, 0.8046545306300437, 0.5615280016492418, 0.8408437419167758, 0.8510980948847215, 0.5652208329757283, 0.7442092015343822, 0.6825825223714317, 0.7530954652097548, 0.699414464747076, 0.6750144115032524, 0.802304038491156, 0.5448876323226838, 0.6721142022637401, 0.719152467247973, 0.607253082200949, 0.5234350542011827, 0.8144797370945073, 0.805383194114955, 0.8272528777999919, 0.630518537159696, 0.9840935941479169, 0.855104096490652, 0.6356639928320637, 0.5593825708605942, 0.5537673022123799, 0.9470418189758258, 0.5225145360269733, 0.8950179176108937, 0.8052835517889092, 0.7592584511005289, 0.5421351915611764, 0.6478015715176444, 0.8604016260924662, 0.9237894005194072, 0.934027778383238, 0.5123656515043635, 0.9330005627632372, 0.8874789020033356, 0.6026206975981985, 0.6932484571132396, 0.6051086515073438, 0.9570363950750476, 0.9026684146852076, 0.8175705428912552, 0.6636555518594225, 0.7659972600395552, 0.6110246116330083, 0.9214590433003942, 0.7278679902337851, 0.6315774143093577, 0.5742015201758932, 0.7702784342491986, 0.7626665935919335, 0.59070771313335, 0.5187987286978658, 0.7532034118762354, 0.8564757996624205, 0.6773553560399885, 0.8299480891274953, 0.860229190730435, 0.5088810136420516, 0.9954359200694263, 0.8904972830925145, 0.7432078103106373, 0.9091616386193617, 0.6733614266555775, 0.9030341154241257, 0.5514518586123656, 0.8231864873332498, 0.8247706202327003, 0.6820458050123763, 0.5780290560935037, 0.7443746087480622, 0.8263658830566749, 0.8632618602750795, 0.9775802277517789, 0.6835142799575069, 0.8465310960463721, 0.9182290010637675, 0.9420918471209013, 0.5100242885570594, 0.623537553455366, 0.5208691705347157, 0.6985542050010116, 0.739098478801307, 0.7733098468791502, 0.6579970227500075, 0.8192172034668643, 0.7525330072826065, 0.924173834575941, 0.7447065942920502, 0.7994924792489981, 0.5835004427166025, 0.9918881069533017, 0.8088577156436139, 0.7297644868661394, 0.816915055384401, 0.5126461256373558, 0.9009563668892457, 0.53911933057668, 0.6051235520530966, 0.791766921272363, 0.8945827285260246, 0.7924963350395484, 0.6950218208460834, 0.6026993408541217, 0.5307570979479816, 0.5547343309261366, 0.7378258191453939, 0.5015006772343507, 0.9183853789123925, 0.964403048757771, 0.9301583593443258, 0.5100671149023839, 0.7137341454448293, 0.7490196929006674, 0.9280999414231902, 0.7810767613636775, 0.6086379650767315, 0.7529358184599482, 0.9933279186301547, 0.6023884499489125, 0.6836700721594067, 0.9278558185135325, 0.7784452675288531, 0.6570523003480327, 0.5941596889867218, 0.5827965138855439, 0.8740569096994842, 0.5079493601429845, 0.6110322293255095, 0.9314366581823679, 0.7256277415197141, 0.5613533368231347, 0.6601898149128863, 0.796403872842848, 0.8064378484367292, 0.8622846933785855, 0.5732139625007224, 0.8871119764107331, 0.8910240609682514, 0.5180112079367284, 0.7874203356707117, 0.9065487359888554, 0.9305771774909718, 0.6027495464075165, 0.8583237313430543, 0.5599243125487403, 0.8550777987285864, 0.8296337812655961, 0.5392518719961301, 0.7222106550575265, 0.8260297323839398, 0.8329625538945625, 0.5243866843896658, 0.6648411223934086, 0.9679207911871637, 0.6214447837907046, 0.5454886795950846, 0.7392506881131022, 0.6337187313110589, 0.9111411296135448, 0.7328805020206747, 0.8531575410654798, 0.9194530617651879, 0.6209699353993337, 0.8365760778187417, 0.857719853910212, 0.7451303462570018, 0.7682355821656481, 0.6397129066682976, 0.9103161196569316, 0.7679977275180598, 0.7589221903934775, 0.7577902701066965, 0.5785572457915031, 0.5784975568376687, 0.8753512768062555, 0.5443495189310319, 0.9797617915192874, 0.7347781047347439, 0.5383916246499152, 0.9848621717325644, 0.5004659819823916, 0.990732831322424, 0.6478518889643234, 0.5022444650625382, 0.6766381416970384, 0.5713572693084061, 0.6965743080594178, 0.9135868836369325, 0.6119284962390606, 0.8088585939149107, 0.6590952868124171, 0.891898053369993, 0.7888941336905556, 0.8442950789827306, 0.5245380755240334, 0.6496295069584148, 0.5756118921172407, 0.6662982933684105, 0.839334700889853, 0.7027688795648983, 0.857693041417589, 0.5601979621681139, 0.671189571718767, 0.7013155315506945, 0.5368772572732778, 0.9483481832736895, 0.5925570029653635, 0.9620151426707669, 0.6118003144520174, 0.7651410745197692, 0.541061103880895, 0.5186039644616073, 0.5422857641633374, 0.7140389978659816, 0.5823614786596827, 0.8655430314747845, 0.516094371367755, 0.5031988761977273, 0.7833172163216269, 0.7671384986184437, 0.8908406039563053, 0.9552229500195223, 0.6568807789865625, 0.60593176089758, 0.8194915039412956, 0.7270102349919926, 0.6209075674740374, 0.6763604118448687, 0.8784897495107669, 0.9861949363772786, 0.5639244664223588, 0.5603668854363977, 0.8662262820337072, 0.9451890201585614, 0.6462064010484979, 0.8362589653678172, 0.7871736886127374, 0.609667465031748, 0.8233355499902515, 0.5797696094043033, 0.8713409774711792, 0.8538209350832104, 0.9893641598559695, 0.9953451381040718, 0.6639290670381547, 0.717034131611339, 0.8023247340306918, 0.7190388863972648, 0.9525652262966777, 0.9512727464767293, 0.7795326275736056, 0.915327252026535, 0.8968319012100792, 0.7342673376413472, 0.6573911185727246, 0.6069500624013666, 0.8130087592320929, 0.7113530064156594, 0.6939929656698847, 0.7316049056679099, 0.5785317029463137, 0.9422549324588825, 0.6037851840773238, 0.8266159256583658, 0.9285482793088449, 0.842362166327768, 0.5923137559369602, 0.7644627675444542, 0.5556523883205413, 0.5669378029968084, 0.7396639468416948, 0.77363996848744, 0.6910666986105174, 0.7444727381755389, 0.7898048050074653, 0.7006227962088647, 0.9965998935499754, 0.7073880962201586, 0.8960022694074516, 0.7515530166211559, 0.8816389198639565, 0.9488008348133858, 0.8452107570788059, 0.6360727185892646, 0.6150442194195156, 0.712271857469322, 0.7081494250818688, 0.735627029216624, 0.514833889711213, 0.8444560504747728, 0.972352322464888, 0.5197333375193616, 0.5336436879150656, 0.844107124403179, 0.6311866266037178, 0.9421275147473649, 0.7689604569157003, 0.5137604394245412, 0.9886505869873998, 0.6084165132096541, 0.6835852911291528, 0.9024741207532869, 0.8889805535808804, 0.5760897781021601, 0.8236447515021501, 0.849016359485423, 0.6650585709622332, 0.5795462102301947, 0.5013950438705984, 0.736970791748675, 0.7548598115479197, 0.9916339725859649, 0.8350008208190041, 0.5025439471191859, 0.5117052060058145, 0.5014423804022268, 0.8684408600970608, 0.9813682327314456, 0.852969851059534, 0.611536729681033, 0.712094977975507, 0.6797120562428953, 0.7489498413617113, 0.9578303551546365, 0.9394726049031206, 0.7897680993284667, 0.9278925081313256, 0.5551526706281291, 0.6104508063627818, 0.9190979782226234, 0.9203029751291145, 0.9427376513498866, 0.8477698427188525, 0.6097838953952115, 0.9511220724560466, 0.6647876464689495, 0.8001047663096232, 0.5580669391737716, 0.5614119783391739, 0.7111615927582084, 0.8973966434242573, 0.693668705311337, 0.8982222294950436, 0.5107762939884087, 0.52460728828097, 0.5719318732755352, 0.5411247069292553, 0.6224173087994003, 0.8476074654417571, 0.5002458315159518, 0.7011616086683008, 0.9919153044400015, 0.6322199167766862, 0.7817723387774467, 0.7292377279393019, 0.7512096223905446, 0.6588525305726354, 0.6150560791960995, 0.8122161312382525, 0.797830615573455, 0.5836513615788287, 0.9129642838686867, 0.5712967230946234, 0.579989760104406, 0.7492992280035906, 0.7943661457269071, 0.8185812307247975, 0.7769916595601194, 0.695893747823948, 0.9520905057156546, 0.5528107819636321, 0.8670773988357674, 0.5023491893203289, 0.8735591416289099, 0.6141114362231069, 0.9314267994984124, 0.5650699955713474, 0.6692926874543788, 0.5046891033655148, 0.7443949399308596, 0.7353706965481697, 0.9748746693246737, 0.8680663085751594, 0.6556577062145637, 0.5701637331196928, 0.8269786458701054, 0.6592312603557858, 0.9732768866871067, 0.9312854119935927, 0.846684978042955, 0.6975180095083635, 0.5122413524056978, 0.8263954798156685, 0.5549286864744327, 0.5106197947360739, 0.9975806045809453, 0.6993914549238882, 0.5564238531025796, 0.8709363053126482, 0.7099401244690167, 0.9616152063557565, 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50000.0, 50000.0, 50000.0, 50000.0};
int h_B[]= {
0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66, 68, 70, 72, 74, 76, 78, 80, 82, 84, 86, 88, 90, 92, 94, 96, 98, 100, 102, 104, 106, 108, 110, 112, 114, 116, 118, 120, 122, 124, 126, 128, 130, 132, 134, 136, 138, 140, 142, 144, 146, 148, 150, 152, 154, 156, 158, 160, 162, 164, 166, 168, 170, 172, 174, 176, 178, 180, 182, 184, 186, 188, 190, 192, 194, 196, 198, 200, 202, 204, 206, 208, 210, 212, 214, 216, 218, 220, 222, 224, 226, 228, 230, 232, 234, 236, 238, 240, 242, 244, 246, 248, 250, 252, 254, 256, 258, 260, 262, 264, 266, 268, 270, 272, 274, 276, 278, 280, 282, 284, 286, 288, 290, 292, 294, 296, 298, 300, 302, 304, 306, 308, 310, 312, 314, 316, 318, 320, 322, 324, 326, 328, 330, 332, 334, 336, 338, 340, 342, 344, 346, 348, 350, 352, 354, 356, 358, 360, 362, 364, 366, 368, 370, 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, 476, 478, 480, 482, 484, 486, 488, 490, 492, 494, 496, 498, 500, 502, 504, 506, 508, 510, 512, 514, 516, 518, 520, 522, 524, 526, 528, 530, 532, 534, 536, 538, 540, 542, 544, 546, 548, 550, 552, 554, 556, 558, 560, 562, 564, 566, 568, 570, 572, 574, 576, 578, 580, 582, 584, 586, 588, 590, 592, 594, 596, 598, 600, 602, 604, 606, 608, 610, 612, 614, 616, 618, 620, 622, 624, 626, 628, 630, 632, 634, 636, 638, 640, 642, 644, 646, 648, 650, 652, 654, 656, 658, 660, 662, 664, 666, 668, 670, 672, 674, 676, 678, 680, 682, 684, 686, 688, 690, 692, 694, 696, 698, 700, 702, 704, 706, 708, 710, 712, 714, 716, 718, 720, 722, 724, 726, 728, 730, 732, 734, 736, 738, 740, 742, 744, 746, 748, 750, 752, 754, 756, 758, 760, 762, 764, 766, 768, 770, 772, 774, 776, 778, 780, 782, 784, 786, 788, 790, 792, 794, 796, 798, 800, 802, 804, 806, 808, 810, 812, 814, 816, 818, 820, 822, 824, 826, 828, 830, 832, 834, 836, 838, 840, 842, 844, 846, 848, 850, 852, 854, 856, 858, 860, 862, 864, 866, 868, 870, 872, 874, 876, 878, 880, 882, 884, 886, 888, 890, 892, 894, 896, 898, 900, 902, 904, 906, 908, 910, 912, 914, 916, 918, 920, 922, 924, 926, 928, 930, 932, 934, 936, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 962, 964, 966, 968, 970, 972, 974, 976, 978, 980, 982, 984, 986, 988, 990, 992, 994, 996, 998, 1000, 1002, 1004, 1006, 1008, 1010, 1012, 1014, 1016, 1018, 1020, 1022, 1024, 1026, 1028, 1030, 1032, 1034, 1036, 1038, 1040, 1042, 1044, 1046, 1048, 1050, 1052, 1054, 1056, 1058, 1060, 1062, 1064, 1066, 1068, 1070, 1072, 1074, 1076, 1078, 1080, 1082, 1084, 1086, 1088, 1090, 1092, 1094, 1096, 1098, 1100, 1102, 1104, 1106, 1108, 1110, 1112, 1114, 1116, 1118, 1120, 1122, 1124, 1126, 1128, 1130, 1132, 1134, 1136, 1138, 1140, 1142, 1144, 1146, 1148, 1150, 1152, 1154, 1156, 1158, 1160, 1162, 1164, 1166, 1168, 1170, 1172, 1174, 1176, 1178, 1180, 1182, 1184, 1186, 1188, 1190, 1192, 1194, 1196, 1198, 1200, 1202, 1204, 1206, 1208, 1210, 1212, 1214, 1216, 1218, 1220, 1222, 1224, 1226, 1228, 1230, 1232, 1234, 1236, 1238, 1240, 1242, 1244, 1246, 1248, 1250, 1252, 1254, 1256, 1258, 1260, 1262, 1264, 1266, 1268, 1270, 1272, 1274, 1276, 1278, 1280, 1282, 1284, 1286, 1288, 1290, 1292, 1294, 1296, 1298, 1300, 1302, 1304, 1306, 1308, 1310, 1312, 1314, 1316, 1318, 1320, 1322, 1324, 1326, 1328, 1330, 1332, 1334, 1336, 1338, 1340, 1342, 1344, 1346, 1348, 1350, 1352, 1354, 1356, 1358, 1360, 1362, 1364, 1366, 1368, 1370, 1372, 1374, 1376, 1378, 1380, 1382, 1384, 1386, 1388, 1390, 1392, 1394, 1396, 1398, 1400, 1402, 1404, 1406, 1408, 1410, 1412, 1414, 1416, 1418, 1420, 1422, 1424, 1426, 1428, 1430, 1432, 1434, 1436, 1438, 1440, 1442, 1444, 1446, 1448, 1450, 1452, 1454, 1456, 1458, 1460, 1462, 1464, 1466, 1468, 1470, 1472, 1474, 1476, 1478, 1480, 1482, 1484, 1486, 1488, 1490, 1492, 1494, 1496, 1498, 1500, 1502, 1504, 1506, 1508, 1510, 1512, 1514, 1516, 1518, 1520, 1522, 1524, 1526, 1528, 1530, 1532, 1534, 1536, 1538, 1540, 1542, 1544, 1546, 1548, 1550, 1552, 1554, 1556, 1558, 1560, 1562, 1564, 1566, 1568, 1570, 1572, 1574, 1576, 1578, 1580, 1582, 1584, 1586, 1588, 1590, 1592, 1594, 1596, 1598, 1600, 1602, 1604, 1606, 1608, 1610, 1612, 1614, 1616, 1618, 1620, 1622, 1624, 1626, 1628, 1630, 1632, 1634, 1636, 1638, 1640, 1642, 1644, 1646, 1648, 1650, 1652, 1654, 1656, 1658, 1660, 1662, 1664, 1666, 1668, 1670, 1672, 1674, 1676, 1678, 1680, 1682, 1684, 1686, 1688, 1690, 1692, 1694, 1696, 1698, 1700, 1702, 1704, 1706, 1708, 1710, 1712, 1714, 1716, 1718, 1720, 1722, 1724, 1726, 1728, 1730, 1732, 1734, 1736, 1738, 1740, 1742, 1744, 1746, 1748, 1750, 1752, 1754, 1756, 1758, 1760, 1762, 1764, 1766, 1768, 1770, 1772, 1774, 1776, 1778, 1780, 1782, 1784, 1786, 1788, 1790, 1792, 1794, 1796, 1798, 1800, 1802, 1804, 1806, 1808, 1810, 1812, 1814, 1816, 1818, 1820, 1822, 1824, 1826, 1828, 1830, 1832, 1834, 1836, 1838, 1840, 1842, 1844, 1846, 1848, 1850, 1852, 1854, 1856, 1858, 1860, 1862, 1864, 1866, 1868, 1870, 1872, 1874, 1876, 1878, 1880, 1882, 1884, 1886, 1888, 1890, 1892, 1894, 1896, 1898, 1900, 1902, 1904, 1906, 1908, 1910, 1912, 1914, 1916, 1918, 1920, 1922, 1924, 1926, 1928, 1930, 1932, 1934, 1936, 1938, 1940, 1942, 1944, 1946, 1948, 1950, 1952, 1954, 1956, 1958, 1960, 1962, 1964, 1966, 1968, 1970, 1972, 1974, 1976, 1978, 1980, 1982, 1984, 1986, 1988, 1990, 1992, 1994, 1996, 1998, 2000, 2002, 2004, 2006, 2008, 2010, 2013, 2015, 2017, 2019, 2021, 2023, 2025, 2027, 2029, 2031, 2033, 2035, 2037, 2039, 2042, 2044, 2046, 2048, 2050, 2052, 2054, 2056, 2058, 2060, 2062, 2064, 2066, 2068, 2070, 2072, 2074, 2076, 2078, 2080, 2082, 2084, 2086, 2088, 2090, 2092, 2094, 2096, 2098, 2100, 2102, 2104, 2106, 2108, 2110, 2112, 2114, 2116, 2118, 2120, 2122, 2124, 2126, 2128, 2130, 2132, 2134, 2136, 2138, 2140, 2142, 2144, 2146, 2148, 2150, 2152, 2154, 2156, 2158, 2160, 2162, 2164, 2166, 2168, 2170, 2172, 2174, 2176, 2178, 2180, 2182, 2184, 2186, 2188, 2190, 2192, 2194, 2196, 2198, 2200, 2202, 2204, 2206, 2208, 2210, 2212, 2214, 2216, 2218, 2220, 2222, 2224, 2226, 2228, 2230, 2232, 2234, 2236, 2238, 2240, 2242, 2244, 2246, 2248, 2250, 2252, 2254, 2256, 2258, 2260, 2262, 2264, 2266, 2268, 2271, 2273, 2275, 2277, 2280, 2282, 2284, 2286, 2288, 2290, 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, 2472, 2474, 2476, 2478, 2480, 2482, 2484, 2486, 2488, 2490, 2492, 2494, 2496, 2498, 2500, 2502, 2504, 2506, 2508, 2510, 2512, 2514, 2516, 2518, 2520, 2522, 2524, 2526, 2528, 2530, 2532, 2534, 2536, 2538, 2540, 2542, 2544, 2546, 2548, 2550, 2552, 2554, 2556, 2558, 2560, 2562, 2564, 2566, 2568, 2570, 2572, 2574, 2576, 2578, 2580, 2582, 2584, 2586, 2588, 2590, 2592, 2594, 2596, 2598, 2600, 2602, 2604, 2606, 2608, 2610, 2612, 2614, 2616, 2618, 2620, 2622, 2624, 2626, 2628, 2630, 2632, 2634, 2636, 2638, 2640, 2642, 2644, 2646, 2648, 2650, 2652, 2654, 2656, 2658, 2660, 2662, 2664, 2666, 2668, 2670, 2672, 2674, 2676, 2678, 2680, 2682, 2684, 2686, 2688, 2690, 2692, 2694, 2696, 2698, 2700, 2702, 2704, 2706, 2708, 2710, 2712, 2714, 2716, 2718, 2720, 2722, 2724, 2726, 2728, 2730, 2732, 2734, 2736, 2738, 2740, 2742, 2744, 2746, 2748, 2750, 2752, 2754, 2756, 2758, 2760, 2762, 2764, 2766, 2768, 2770, 2772, 2774, 2776, 2778, 2780, 2782, 2784, 2786, 2788, 2790, 2792, 2794, 2796, 2798, 2800, 2802, 2804, 2806, 2808, 2810, 2812, 2814, 2816, 2818, 2820, 2822, 2824, 2826, 2828, 2830, 2832, 2834, 2836, 2838, 2840, 2842, 2844, 2846, 2848, 2850, 2852, 2854, 2856, 2858, 2860, 2862, 2864, 2866, 2868, 2870, 2872, 2874, 2876, 2878, 2880, 2882, 2884, 2886, 2888, 2890, 2892, 2894, 2896, 2898, 2900, 2902, 2904, 2906, 2908, 2910, 2912, 2914, 2916, 2918, 2920, 2922, 2924, 2926, 2928, 2930, 2932, 2934, 2936, 2938, 2940, 2942, 2944, 2946, 2948, 2950, 2952, 2954, 2956, 2958, 2960, 2962, 2964, 2966, 2968, 2970, 2972, 2974, 2976, 2978, 2980, 2982, 2984, 2986, 2988, 2990, 2992, 2994, 2996, 2998, 3000, 3002, 3004, 3006, 3008, 3010, 3012, 3014, 3016, 3018, 3020, 3022, 3024, 3026, 3028, 3030, 3032, 3034, 3036, 3038, 3040, 3042, 3044, 3046, 3048, 3050, 3052, 3054, 3056, 3058, 3060, 3062, 3064, 3066, 3068, 3070, 3072, 3074, 3076, 3078, 3080, 3082, 3084, 3086, 3088, 3090, 3092, 3094, 3096, 3098, 3100, 3102, 3104, 3106, 3108, 3110, 3112, 3114, 3116, 3118, 3120, 3122, 3124, 3126, 3128, 3130, 3132, 3134, 3136, 3138, 3140, 3142, 3144, 3146, 3148, 3150, 3152, 3154, 3156, 3158, 3160, 3162, 3164, 3166, 3168, 3170, 3172, 3174, 3176, 3178, 3180, 3182, 3184, 3186, 3188, 3190, 3192, 3194, 3196, 3198, 3200, 3202, 3204, 3206, 3208, 3210, 3212, 3214, 3216, 3218, 3220, 3222, 3224, 3226, 3228, 3230, 3232, 3234, 3236, 3238, 3240, 3242, 3244, 3246, 3248, 3250, 3252, 3254, 3256, 3258, 3260, 3262, 3264, 3266, 3268, 3270, 3272, 3274, 3276, 3278, 3280, 3282, 3284, 3286, 3288, 3290, 3292, 3294, 3296, 3298, 3300, 3302, 3304, 3306, 3308, 3310, 3312, 3314, 3316, 3318, 3320, 3322, 3324, 3326, 3328, 3330, 3332, 3334, 3336, 3338, 3340, 3342, 3344, 3346, 3348, 3350, 3352, 3354, 3356, 3358, 3360, 3362, 3364, 3366, 3368, 3370, 3372, 3374, 3376, 3378, 3380, 3382, 3384, 3386, 3388, 3390, 3392, 3394, 3396, 3398, 3400, 3402, 3404, 3406, 3408, 3410, 3412, 3414, 3416, 3418, 3420, 3422, 3424, 3426, 3428, 3430, 3432, 3434, 3436, 3438, 3440, 3442, 3444, 3446, 3448, 3450, 3452, 3454, 3456, 3458, 3460, 3462, 3464, 3466, 3468, 3470, 3472, 3474, 3476, 3478, 3480, 3482, 3484, 3486, 3488, 3490, 3492, 3494, 3496, 3498, 3500, 3502, 3504, 3506, 3508, 3510, 3512, 3514, 3516, 3518, 3520, 3522, 3524, 3526, 3528, 3530, 3532, 3534, 3536, 3538, 3540, 3542, 3544, 3546, 3548, 3550, 3552, 3554, 3556, 3558, 3560, 3562, 3564, 3566, 3568, 3570, 3572, 3574, 3576, 3578, 3580, 3582, 3584, 3586, 3588, 3590, 3592, 3594, 3596, 3598, 3600, 3602, 3604, 3606, 3608, 3610, 3612, 3614, 3616, 3618, 3620, 3622, 3624, 3626, 3628, 3630, 3632, 3634, 3636, 3638, 3640, 3642, 3644, 3646, 3648, 3650, 3652, 3654, 3656, 3658, 3660, 3662, 3664, 3666, 3668, 3670, 3672, 3674, 3676, 3678, 3680, 3682, 3684, 3686, 3688, 3690, 3692, 3694, 3696, 3698, 3700, 3702, 3704, 3706, 3708, 3710, 3712, 3714, 3716, 3718, 3720, 3722, 3724, 3726, 3728, 3730, 3732, 3734, 3736, 3738, 3740, 3742, 3744, 3746, 3748, 3750, 3752, 3754, 3756, 3758, 3760, 3762, 3764, 3766, 3768, 3770, 3772, 3774, 3776, 3778, 3780, 3782, 3784, 3786, 3788, 3790, 3792, 3794, 3796, 3798, 3800, 3802, 3804, 3806, 3808, 3810, 3812, 3814, 3816, 3818, 3820, 3822, 3824, 3826, 3828, 3830, 3832, 3834, 3836, 3838, 3840, 3842, 3844, 3846, 3848, 3850, 3852, 3854, 3857, 3859, 3861, 3863, 3865, 3867, 3869, 3871, 3873, 3875, 3877, 3879, 3881, 3883, 3885, 3887, 3889, 3891, 3893, 3895, 3897, 3899, 3901, 3903, 3905, 3907, 3909, 3911, 3913, 3915, 3917, 3919, 3921, 3923, 3925, 3927, 3929, 3931, 3933, 3935, 3937, 3939, 3941, 3943, 3945, 3947, 3949, 3951, 3953, 3955, 3957, 3959, 3961, 3963, 3965, 3967, 3969, 3971, 3973, 3975, 3977, 3979, 3981, 3983, 3985, 3987, 3989, 3991, 3993, 3995, 3997, 3999, 4001, 4003, 4005, 4007, 4009, 4011, 4013, 4015, 4017, 4019, 4021, 4023, 4025, 4027, 4029, 4031, 4033, 4035, 4037, 4039, 4041, 4043, 4045, 4047, 4049, 4051, 4053, 4055, 4057, 4059, 4061, 4063, 4065, 4067, 4069, 4071, 4073, 4075, 4077, 4079, 4081, 4083, 4085, 4087, 4089, 4091, 4093, 4095, 4097, 4099, 4101, 4103, 4105, 4107, 4109, 4111, 4113, 4115, 4117, 4119, 4121, 4123, 4125, 4127, 4129, 4131, 4133, 4135, 4137, 4139, 4141, 4143, 4145, 4147, 4149, 4151, 4153, 4155, 4157, 4159, 4161, 4163, 4165, 4167, 4169, 4171, 4173, 4175, 4177, 4179, 4181, 4183, 4185, 4187, 4189, 4191, 4193, 4195, 4197, 4199, 4201, 4203, 4205, 4207, 4209, 4211, 4213, 4215, 4217, 4219, 4221, 4223, 4225, 4227, 4229, 4231, 4233, 4235, 4237, 4239, 4241, 4243, 4245, 4247, 4250, 4252, 4254, 4256, 4258, 4260, 4262, 4264, 4266, 4268, 4270, 4272, 4274, 4276, 4278, 4280, 4282, 4284, 4286, 4288, 4290, 4292, 4295, 4297, 4301, 4303, 4305, 4307, 4309, 4311, 4313, 4315, 4318, 4320, 4323, 4325, 4331, 4333, 4335, 4337, 4340, 4342, 4344, 4346, 4350, 4352, 4354, 4356, 4358, 4360, 4362, 4364, 4366, 4368, 4370, 4372, 4374, 4376, 4378, 4380, 4382, 4384, 4386, 4388, 4390, 4392, 4394, 4396, 4399, 4401, 4403, 4405, 4407, 4409, 4412, 4414, 4416, 4418, 4421, 4423, 4426, 4428, 4433, 4435, 4437, 4439, 4442, 4444, 4447, 4449, 4454, 4456, 4458, 4460, 4463, 4465, 4467, 4469, 4473, 4475, 4477, 4479, 4481, 4483, 4486, 4488, 4490, 4492, 4494, 4496, 4498, 4500, 4503, 4505, 4508, 4510, 4513, 4515, 4517, 4519, 4521, 4523, 4525, 4527, 4530, 4532, 4535, 4537, 4542, 4544, 4546, 4548, 4551, 4553, 4556, 4558, 4571, 4573, 4575, 4577, 4579, 4581, 4583, 4585, 4587, 4589, 4591, 4593, 4595, 4597, 4599, 4601, 4603, 4605, 4607, 4609, 4611, 4613, 4615, 4617, 4619, 4621, 4623, 4625, 4627, 4629, 4631, 4633, 4635, 4637, 4639, 4641, 4643, 4645, 4647, 4649, 4651, 4653, 4655, 4657, 4660, 4662, 4664, 4666, 4668, 4670, 4672, 4674, 4676, 4678, 4680, 4682, 4684, 4686, 4688, 4690, 4692, 4694, 4696, 4698, 4700, 4702, 4704, 4706, 4708, 4710, 4713, 4715, 4717, 4719, 4722, 4724, 4726, 4728, 4732, 4734, 4737, 4739, 4742, 4744, 4747, 4749, 4752, 4754, 4757, 4759, 4762, 4764, 4766, 4768, 4771, 4773, 4776, 4778, 4783, 4785, 4787, 4789, 4792, 4794, 4797, 4799, 4804, 4806, 4808, 4810, 4812, 4814, 4816, 4818, 4820, 4822, 4824, 4826, 4828, 4830, 4832, 4834, 4836, 4838, 4840, 4842, 4844, 4846, 4848, 4850, 4852, 4854, 4856, 4858, 4860, 4862, 4864, 4866, 4868, 4870, 4872, 4874, 4876, 4878, 4880, 4882, 4884, 4886, 4888, 4890, 4892, 4894, 4896, 4898, 4900, 4902, 4904, 4906, 4908, 4910, 4912, 4914, 4916, 4918, 4920, 4922, 4924, 4926, 4928, 4930, 4932, 4934, 4936, 4938, 4940, 4942, 4944, 4946, 4948, 4950, 4952, 4954, 4956, 4958, 4960, 4962, 4964, 4966, 4968, 4970, 4972, 4974, 4976, 4978, 4980, 4982, 4984, 4986, 4988, 4990, 4992, 4994, 4996, 4998, 5000, 5002, 5004, 5006, 5008, 5010, 5012, 5014, 5016, 5018, 5020, 5022, 5024, 5026, 5028, 5030, 5032, 5034, 5036, 5038, 5040, 5042, 5044, 5046, 5048, 5050, 5052, 5054, 5056, 5058, 5060, 5062, 5064, 5066, 5068, 5070, 5072, 5074, 5076, 5078, 5080, 5082, 5084, 5086, 5088, 5090, 5092, 5094, 5096, 5098, 5100, 5102, 5104, 5106, 5108, 5110, 5112, 5114, 5116, 5118, 5120, 5122, 5124, 5126, 5128, 5130, 5132, 5134, 5136, 5138, 5140, 5142, 5144, 5146, 5148, 5150, 5152, 5154, 5156, 5158, 5160, 5162, 5164, 5166, 5168, 5170, 5172, 5174, 5176, 5178, 5180, 5182, 5184, 5186, 5188, 5190, 5192, 5194, 5196, 5198, 5200, 5202, 5204, 5206, 5208, 5210, 5212, 5214, 5216, 5218, 5220, 5222, 5224, 5226, 5228, 5230, 5232, 5234, 5236, 5238, 5240, 5242, 5244, 5246, 5248, 5250, 5252, 5254, 5256, 5258, 5260, 5262, 5264, 5266, 5268, 5270, 5272, 5274, 5276, 5278, 5280, 5282, 5284, 5286, 5288, 5290, 5292, 5294, 5296, 5298, 5300, 5302, 5304, 5306, 5308, 5310, 5312, 5314, 5316, 5318, 5320, 5322, 5324, 5326, 5328, 5330, 5332, 5334, 5336, 5338, 5340, 5342, 5344, 5346, 5348, 5350, 5352, 5354, 5356, 5358, 5360, 5362, 5364, 5366, 5368, 5370, 5372, 5374, 5376, 5378, 5380, 5382, 5384, 5386, 5388, 5390, 5392, 5394, 5396, 5398, 5400, 5402, 5404, 5406, 5408, 5410, 5412, 5414, 5416, 5418, 5420, 5422, 5424, 5426, 5428, 5430, 5432, 5434, 5436, 5438, 5440, 5442, 5444, 5446, 5448, 5450, 5452, 5454, 5456, 5458, 5460, 5462, 5464, 5466, 5468, 5470, 5472, 5474, 5476, 5478, 5480, 5482, 5484, 5486, 5488, 5490, 5492, 5494, 5496, 5498, 5500, 5502, 5504, 5506, 5508, 5510, 5512, 5514, 5516, 5518, 5520, 5522, 5524, 5526, 5528, 5530, 5532, 5534, 5536, 5538, 5540, 5542, 5544, 5546, 5548, 5550, 5552, 5554, 5556, 5558, 5560, 5562, 5564, 5566, 5568, 5570, 5572, 5574, 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36055, 30553, 30552, 36067, 36606, 30564, 36074, 36607, 30573, 30572, 36608, 36609, 36611, 36612, 36613, 36614, 36615, 36617, 34379, 34380, 36618, 36619, 36620, 36621, 36623, 36625, 34408, 36628, 34412, 35419, 34418, 34419, 36632, 36633, 36634, 36636, 36638, 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, 36751, 36752, 36753, 36754, 36755, 36756, 36757, 36759, 36760, 36761, 36762, 36763, 36765, 36766, 36767, 36768, 36769, 36770, 36771, 36772, 36773, 36774, 36775, 36776, 36499, 36778, 36779, 36780, 36781, 36782, 36783, 36784, 36785, 36501, 36787, 36788, 36789, 36790, 36791, 36793, 36794, 36795, 36796, 36797, 36798, 36799, 36801, 35582, 36802, 36803, 35593, 36804, 36805, 36806, 36038, 36036, 36807, 36808, 36809, 35599, 36811, 36812, 36814, 36815, 36816, 35614, 36818, 35625, 36819, 36820, 36821, 36822, 36824, 36825, 36826, 36828, 36830, 36832, 36833, 36515, 36835, 36836, 36517, 36838, 36839, 36840, 36841, 36842, 36844, 36845, 36846, 36847, 36848, 36850, 35867, 36851, 36852, 36853, 36409, 36855, 36856, 36858, 36859, 36860, 36862, 36863, 36864, 36865, 36526, 36867, 36869, 36871, 36872, 36530, 36874, 36876, 36878, 36535, 36881, 36882, 36883, 36884, 36885, 36887, 36888, 36889, 36890, 36891, 36892, 36893, 36895, 36896, 36897, 36898, 36899, 36900, 36901, 36902, 36903, 36905, 36906, 36907, 36908, 36909, 36910, 36911, 36912, 36913, 36038, 36036, 36915, 36916, 36917, 36022, 36919, 36049, 36920, 36921, 36922, 36059, 36923, 36924, 36925, 36926, 35678, 36928, 36929, 36931, 36932, 36933, 36935, 36936, 36937, 36939, 36549, 36942, 36943, 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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, 37254, 37293, 37295, 37296, 37298, 37300, 37302, 37303, 37304, 37307, 36810, 37309, 37310, 37313, 36817, 37315, 37317, 37319, 37320, 37335, 37340, 37342, 37343, 37346, 36854, 37348, 37349, 37351, 37352, 37386, 37395, 37396, 37397, 37398, 37401, 36918, 37403, 37405, 37407, 37409, 37412, 36927, 37414, 37415, 37417, 37418, 37432, 37441, 37443, 36968, 37445, 37446, 37449, 37450, 37452, 37453, 37455, 37456, 37465, 37473, 37478, 37483, 37484, 37486, 37487, 37490, 37024, 37500, 37533, 37534, 37535, 37538, 37083, 37541, 37086, 37543, 37544, 37545, 37547, 37549, 37551, 37553, 37556, 37097, 37558, 37559, 37253, 37251, 37249, 37258, 37256, 36079, 37263, 37261, 37271, 37267, 37269, 37265, 36081, 37280, 37278, 37276, 37274, 36083, 37285, 37283, 36084, 37292, 37290, 37288, 36829, 36827, 36514, 37326, 36094, 37329, 36096, 37334, 37332, 37339, 37337, 37355, 35375, 37360, 36870, 36868, 36107, 36879, 36877, 36875, 35377, 37369, 37367, 36109, 37376, 37374, 37372, 36110, 37385, 37383, 37381, 37379, 37394, 37392, 37390, 37388, 36940, 36938, 36117, 37426, 37424, 36119, 37431, 37429, 36958, 36956, 36954, 36559, 37440, 37438, 37459, 36130, 37464, 37462, 37467, 36133, 37472, 37470, 37477, 37475, 37012, 37010, 36578, 37499, 37497, 37495, 37493, 37506, 37504, 37502, 36145, 37509, 35426, 37514, 37512, 36147, 37059, 37057, 37055, 35428, 37523, 37064, 37062, 36150, 37532, 37530, 37528, 37526, 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, 37294, 37636, 37638, 37306, 37312, 37647, 37341, 37345, 37663, 37400, 37668, 37670, 37411, 37442, 37448, 37482, 37695, 37489, 37699, 37537, 37540, 37707, 37710, 37712, 37555, 37718, 37719, 37720, 35341, 37721, 37722, 37723, 37724, 37725, 37726, 37727, 37728, 37729, 37730, 37731, 37732, 37733, 37734, 37735, 37736, 37737, 37738, 37739, 37740, 37741, 35346, 37644, 37650, 37742, 37743, 37744, 37745, 37746, 37747, 37748, 37749, 37750, 36097, 37751, 37752, 36098, 37658, 37660, 37753, 37754, 37755, 37756, 37757, 37758, 37759, 37760, 37761, 37762, 37763, 37764, 37765, 37766, 37767, 37768, 37769, 37770, 37771, 37772, 37773, 36111, 37774, 37775, 37776, 37777, 36112, 37675, 37677, 37778, 37779, 37780, 37781, 37782, 37783, 37784, 37785, 36120, 37786, 37787, 37788, 37789, 37790, 37791, 36123, 37683, 37687, 37689, 37792, 37793, 37794, 37795, 36131, 37796, 37797, 37798, 37799, 36134, 37800, 37801, 36135, 37802, 37803, 37804, 37805, 37806, 37807, 37808, 37809, 37810, 37811, 37812, 37813, 37814, 37815, 37816, 37817, 37818, 37819, 37820, 37821, 37822, 37823, 37824, 37825, 37826, 37827, 37828, 37829, 36151, 37702, 37717, 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, 37913, 37916, 37917, 37920, 37922, 37924, 37927, 37929, 37932, 37935, 37938, 37635, 37637, 37640, 37642, 37939, 37646, 37648, 37940, 37941, 37948, 37950, 37951, 37953, 37654, 37656, 37954, 37955, 37958, 37962, 37966, 37969, 37973, 37975, 37977, 37978, 37980, 37982, 37665, 37667, 37669, 37671, 37673, 37983, 37984, 37985, 37988, 37991, 37993, 37994, 37998, 38000, 37681, 38001, 37685, 38002, 38003, 38006, 38008, 38011, 38013, 38014, 38016, 38017, 37694, 37696, 37698, 38020, 38022, 36144, 38024, 38030, 38033, 38037, 38041, 38043, 38045, 38046, 37704, 37706, 37709, 37711, 37713, 37715, 38047, 37947, 37945, 37957, 38010, 38005, 38029, 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, 38144, 38147, 38150, 38153, 38155, 38156, 38157, 38158, 38160, 38161, 38168, 38169, 38172, 38173, 38175, 38176, 38179, 38182, 38183, 38184, 38185, 38186, 38193, 38196, 38198, 38208, 38209, 38210, 38211, 38213, 38214, 38216, 38217, 38218, 38222, 38223, 38224, 38225, 38226, 38227, 37934, 37919, 37107, 37110, 37943, 38167, 38229, 38165, 38230, 37114, 37113, 38231, 37968, 36627, 36626, 38192, 38195, 37990, 37987, 36630, 36629, 37120, 38204, 38232, 38019, 38233, 38202, 38206, 38234, 38032, 36639, 36631, 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, 38273, 37931, 38312, 38313, 38145, 38154, 38314, 37106, 37109, 37104, 37103, 37105, 38315, 37108, 38316, 38317, 38319, 38321, 38322, 37562, 37561, 37965, 37972, 37961, 38178, 38181, 38324, 37117, 37116, 38325, 37115, 38326, 36622, 37118, 37997, 38327, 38328, 38329, 38330, 38331, 38332, 38333, 37563, 37564, 38334, 38336, 38338, 38339, 37567, 37566, 37565, 38301, 38027, 38040, 38341, 38220, 38036, 36635, 38342, 37127, 37125, 37128, 38343, 37126, 37129, 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, 38401, 38404, 38405, 37926, 38407, 38408, 38409, 38410, 38411, 38413, 38414, 38318, 38419, 38420, 38417, 38421, 38422, 38423, 38424, 38425, 38427, 38428, 38430, 38432, 38433, 38434, 38436, 38442, 38443, 38439, 38444, 38445, 38446, 38448, 38449, 38450, 38451, 38452, 38453, 38455, 38456, 38457, 38459, 38460, 38461, 38463, 38464, 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, 38531, 38528, 38403, 38406, 38533, 38535, 38412, 38538, 38540, 38543, 38545, 38323, 38548, 38429, 38431, 38553, 38441, 38558, 38561, 38564, 38566, 38454, 38569, 38570, 38572, 38573, 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, 38530, 38657, 38659, 38661, 38663, 38542, 38665, 38667, 38668, 38670, 38671, 38557, 38673, 38674, 38675, 38677, 38678, 38680, 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, 38785, 38786, 38790, 38792, 38794, 38795, 38798, 38800, 38789, 38797, 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, 38917, 38920, 38915, 38919, 38913, 38921, 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, 39040, 39042, 39043, 39044, 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, 39168, 39169, 39171, 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, 39296, 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, 39424, 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[]= {
1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63, 65, 67, 69, 71, 73, 75, 77, 79, 81, 83, 85, 87, 89, 91, 93, 95, 97, 99, 101, 103, 105, 107, 109, 111, 113, 115, 117, 119, 121, 123, 125, 127, 129, 131, 133, 135, 137, 139, 141, 143, 145, 147, 149, 151, 153, 155, 157, 159, 161, 163, 165, 167, 169, 171, 173, 175, 177, 179, 181, 183, 185, 187, 189, 191, 193, 195, 197, 199, 201, 203, 205, 207, 209, 211, 213, 215, 217, 219, 221, 223, 225, 227, 229, 231, 233, 235, 237, 239, 241, 243, 245, 247, 249, 251, 253, 255, 257, 259, 261, 263, 265, 267, 269, 271, 273, 275, 277, 279, 281, 283, 285, 287, 289, 291, 293, 295, 297, 299, 301, 303, 305, 307, 309, 311, 313, 315, 317, 319, 321, 323, 325, 327, 329, 331, 333, 335, 337, 339, 341, 343, 345, 347, 349, 351, 353, 355, 357, 359, 361, 363, 365, 367, 369, 371, 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, 475, 477, 479, 481, 483, 485, 487, 489, 491, 493, 495, 497, 499, 501, 503, 505, 507, 509, 511, 513, 515, 517, 519, 521, 523, 525, 527, 529, 531, 533, 535, 537, 539, 541, 543, 545, 547, 549, 551, 553, 555, 557, 559, 561, 563, 565, 567, 569, 571, 573, 575, 577, 579, 581, 583, 585, 587, 589, 591, 593, 595, 597, 599, 601, 603, 605, 607, 609, 611, 613, 615, 617, 619, 621, 623, 625, 627, 629, 631, 633, 635, 637, 639, 641, 643, 645, 647, 649, 651, 653, 655, 657, 659, 661, 663, 665, 667, 669, 671, 673, 675, 677, 679, 681, 683, 685, 687, 689, 691, 693, 695, 697, 699, 701, 703, 705, 707, 709, 711, 713, 715, 717, 719, 721, 723, 725, 727, 729, 731, 733, 735, 737, 739, 741, 743, 745, 747, 749, 751, 753, 755, 757, 759, 761, 763, 765, 767, 769, 771, 773, 775, 777, 779, 781, 783, 785, 787, 789, 791, 793, 795, 797, 799, 801, 803, 805, 807, 809, 811, 813, 815, 817, 819, 821, 823, 825, 827, 829, 831, 833, 835, 837, 839, 841, 843, 845, 847, 849, 851, 853, 855, 857, 859, 861, 863, 865, 867, 869, 871, 873, 875, 877, 879, 881, 883, 885, 887, 889, 891, 893, 895, 897, 899, 901, 903, 905, 907, 909, 911, 913, 915, 917, 919, 921, 923, 925, 927, 929, 931, 933, 935, 937, 939, 941, 943, 945, 947, 949, 951, 953, 955, 957, 959, 961, 963, 965, 967, 969, 971, 973, 975, 977, 979, 981, 983, 985, 987, 989, 991, 993, 995, 997, 999, 1001, 1003, 1005, 1007, 1009, 1011, 1013, 1015, 1017, 1019, 1021, 1023, 1025, 1027, 1029, 1031, 1033, 1035, 1037, 1039, 1041, 1043, 1045, 1047, 1049, 1051, 1053, 1055, 1057, 1059, 1061, 1063, 1065, 1067, 1069, 1071, 1073, 1075, 1077, 1079, 1081, 1083, 1085, 1087, 1089, 1091, 1093, 1095, 1097, 1099, 1101, 1103, 1105, 1107, 1109, 1111, 1113, 1115, 1117, 1119, 1121, 1123, 1125, 1127, 1129, 1131, 1133, 1135, 1137, 1139, 1141, 1143, 1145, 1147, 1149, 1151, 1153, 1155, 1157, 1159, 1161, 1163, 1165, 1167, 1169, 1171, 1173, 1175, 1177, 1179, 1181, 1183, 1185, 1187, 1189, 1191, 1193, 1195, 1197, 1199, 1201, 1203, 1205, 1207, 1209, 1211, 1213, 1215, 1217, 1219, 1221, 1223, 1225, 1227, 1229, 1231, 1233, 1235, 1237, 1239, 1241, 1243, 1245, 1247, 1249, 1251, 1253, 1255, 1257, 1259, 1261, 1263, 1265, 1267, 1269, 1271, 1273, 1275, 1277, 1279, 1281, 1283, 1285, 1287, 1289, 1291, 1293, 1295, 1297, 1299, 1301, 1303, 1305, 1307, 1309, 1311, 1313, 1315, 1317, 1319, 1321, 1323, 1325, 1327, 1329, 1331, 1333, 1335, 1337, 1339, 1341, 1343, 1345, 1347, 1349, 1351, 1353, 1355, 1357, 1359, 1361, 1363, 1365, 1367, 1369, 1371, 1373, 1375, 1377, 1379, 1381, 1383, 1385, 1387, 1389, 1391, 1393, 1395, 1397, 1399, 1401, 1403, 1405, 1407, 1409, 1411, 1413, 1415, 1417, 1419, 1421, 1423, 1425, 1427, 1429, 1431, 1433, 1435, 1437, 1439, 1441, 1443, 1445, 1447, 1449, 1451, 1453, 1455, 1457, 1459, 1461, 1463, 1465, 1467, 1469, 1471, 1473, 1475, 1477, 1479, 1481, 1483, 1485, 1487, 1489, 1491, 1493, 1495, 1497, 1499, 1501, 1503, 1505, 1507, 1509, 1511, 1513, 1515, 1517, 1519, 1521, 1523, 1525, 1527, 1529, 1531, 1533, 1535, 1537, 1539, 1541, 1543, 1545, 1547, 1549, 1551, 1553, 1555, 1557, 1559, 1561, 1563, 1565, 1567, 1569, 1571, 1573, 1575, 1577, 1579, 1581, 1583, 1585, 1587, 1589, 1591, 1593, 1595, 1597, 1599, 1601, 1603, 1605, 1607, 1609, 1611, 1613, 1615, 1617, 1619, 1621, 1623, 1625, 1627, 1629, 1631, 1633, 1635, 1637, 1639, 1641, 1643, 1645, 1647, 1649, 1651, 1653, 1655, 1657, 1659, 1661, 1663, 1665, 1667, 1669, 1671, 1673, 1675, 1677, 1679, 1681, 1683, 1685, 1687, 1689, 1691, 1693, 1695, 1697, 1699, 1701, 1703, 1705, 1707, 1709, 1711, 1713, 1715, 1717, 1719, 1721, 1723, 1725, 1727, 1729, 1731, 1733, 1735, 1737, 1739, 1741, 1743, 1745, 1747, 1749, 1751, 1753, 1755, 1757, 1759, 1761, 1763, 1765, 1767, 1769, 1771, 1773, 1775, 1777, 1779, 1781, 1783, 1785, 1787, 1789, 1791, 1793, 1795, 1797, 1799, 1801, 1803, 1805, 1807, 1809, 1811, 1813, 1815, 1817, 1819, 1821, 1823, 1825, 1827, 1829, 1831, 1833, 1835, 1837, 1839, 1841, 1843, 1845, 1847, 1849, 1851, 1853, 1855, 1857, 1859, 1861, 1863, 1865, 1867, 1869, 1871, 1873, 1875, 1877, 1879, 1881, 1883, 1885, 1887, 1889, 1891, 1893, 1895, 1897, 1899, 1901, 1903, 1905, 1907, 1909, 1911, 1913, 1915, 1917, 1919, 1921, 1923, 1925, 1927, 1929, 1931, 1933, 1935, 1937, 1939, 1941, 1943, 1945, 1947, 1949, 1951, 1953, 1955, 1957, 1959, 1961, 1963, 1965, 1967, 1969, 1971, 1973, 1975, 1977, 1979, 1981, 1983, 1985, 1987, 1989, 1991, 1993, 1995, 1997, 1999, 2001, 2003, 2005, 2007, 2009, 2011, 2014, 2016, 2018, 2020, 2022, 2024, 2026, 2028, 2030, 2032, 2034, 2036, 2038, 2040, 2043, 2045, 2047, 2049, 2051, 2053, 2055, 2057, 2059, 2061, 2063, 2065, 2067, 2069, 2071, 2073, 2075, 2077, 2079, 2081, 2083, 2085, 2087, 2089, 2091, 2093, 2095, 2097, 2099, 2101, 2103, 2105, 2107, 2109, 2111, 2113, 2115, 2117, 2119, 2121, 2123, 2125, 2127, 2129, 2131, 2133, 2135, 2137, 2139, 2141, 2143, 2145, 2147, 2149, 2151, 2153, 2155, 2157, 2159, 2161, 2163, 2165, 2167, 2169, 2171, 2173, 2175, 2177, 2179, 2181, 2183, 2185, 2187, 2189, 2191, 2193, 2195, 2197, 2199, 2201, 2203, 2205, 2207, 2209, 2211, 2213, 2215, 2217, 2219, 2221, 2223, 2225, 2227, 2229, 2231, 2233, 2235, 2237, 2239, 2241, 2243, 2245, 2247, 2249, 2251, 2253, 2255, 2257, 2259, 2261, 2263, 2265, 2267, 2269, 2272, 2274, 2276, 2278, 2281, 2283, 2285, 2287, 2289, 2291, 2293, 2295, 2297, 2299, 2301, 2303, 2305, 2307, 2309, 2311, 2313, 2315, 2317, 2319, 2321, 2323, 2325, 2327, 2329, 2331, 2333, 2335, 2337, 2339, 2341, 2343, 2345, 2347, 2349, 2351, 2353, 2355, 2357, 2359, 2361, 2363, 2365, 2367, 2369, 2371, 2373, 2375, 2377, 2379, 2381, 2383, 2385, 2387, 2389, 2391, 2393, 2395, 2397, 2399, 2401, 2403, 2405, 2407, 2409, 2411, 2413, 2415, 2417, 2419, 2421, 2423, 2425, 2427, 2429, 2431, 2433, 2435, 2437, 2439, 2441, 2443, 2445, 2447, 2449, 2451, 2453, 2455, 2457, 2459, 2461, 2463, 2465, 2467, 2469, 2471, 2473, 2475, 2477, 2479, 2481, 2483, 2485, 2487, 2489, 2491, 2493, 2495, 2497, 2499, 2501, 2503, 2505, 2507, 2509, 2511, 2513, 2515, 2517, 2519, 2521, 2523, 2525, 2527, 2529, 2531, 2533, 2535, 2537, 2539, 2541, 2543, 2545, 2547, 2549, 2551, 2553, 2555, 2557, 2559, 2561, 2563, 2565, 2567, 2569, 2571, 2573, 2575, 2577, 2579, 2581, 2583, 2585, 2587, 2589, 2591, 2593, 2595, 2597, 2599, 2601, 2603, 2605, 2607, 2609, 2611, 2613, 2615, 2617, 2619, 2621, 2623, 2625, 2627, 2629, 2631, 2633, 2635, 2637, 2639, 2641, 2643, 2645, 2647, 2649, 2651, 2653, 2655, 2657, 2659, 2661, 2663, 2665, 2667, 2669, 2671, 2673, 2675, 2677, 2679, 2681, 2683, 2685, 2687, 2689, 2691, 2693, 2695, 2697, 2699, 2701, 2703, 2705, 2707, 2709, 2711, 2713, 2715, 2717, 2719, 2721, 2723, 2725, 2727, 2729, 2731, 2733, 2735, 2737, 2739, 2741, 2743, 2745, 2747, 2749, 2751, 2753, 2755, 2757, 2759, 2761, 2763, 2765, 2767, 2769, 2771, 2773, 2775, 2777, 2779, 2781, 2783, 2785, 2787, 2789, 2791, 2793, 2795, 2797, 2799, 2801, 2803, 2805, 2807, 2809, 2811, 2813, 2815, 2817, 2819, 2821, 2823, 2825, 2827, 2829, 2831, 2833, 2835, 2837, 2839, 2841, 2843, 2845, 2847, 2849, 2851, 2853, 2855, 2857, 2859, 2861, 2863, 2865, 2867, 2869, 2871, 2873, 2875, 2877, 2879, 2881, 2883, 2885, 2887, 2889, 2891, 2893, 2895, 2897, 2899, 2901, 2903, 2905, 2907, 2909, 2911, 2913, 2915, 2917, 2919, 2921, 2923, 2925, 2927, 2929, 2931, 2933, 2935, 2937, 2939, 2941, 2943, 2945, 2947, 2949, 2951, 2953, 2955, 2957, 2959, 2961, 2963, 2965, 2967, 2969, 2971, 2973, 2975, 2977, 2979, 2981, 2983, 2985, 2987, 2989, 2991, 2993, 2995, 2997, 2999, 3001, 3003, 3005, 3007, 3009, 3011, 3013, 3015, 3017, 3019, 3021, 3023, 3025, 3027, 3029, 3031, 3033, 3035, 3037, 3039, 3041, 3043, 3045, 3047, 3049, 3051, 3053, 3055, 3057, 3059, 3061, 3063, 3065, 3067, 3069, 3071, 3073, 3075, 3077, 3079, 3081, 3083, 3085, 3087, 3089, 3091, 3093, 3095, 3097, 3099, 3101, 3103, 3105, 3107, 3109, 3111, 3113, 3115, 3117, 3119, 3121, 3123, 3125, 3127, 3129, 3131, 3133, 3135, 3137, 3139, 3141, 3143, 3145, 3147, 3149, 3151, 3153, 3155, 3157, 3159, 3161, 3163, 3165, 3167, 3169, 3171, 3173, 3175, 3177, 3179, 3181, 3183, 3185, 3187, 3189, 3191, 3193, 3195, 3197, 3199, 3201, 3203, 3205, 3207, 3209, 3211, 3213, 3215, 3217, 3219, 3221, 3223, 3225, 3227, 3229, 3231, 3233, 3235, 3237, 3239, 3241, 3243, 3245, 3247, 3249, 3251, 3253, 3255, 3257, 3259, 3261, 3263, 3265, 3267, 3269, 3271, 3273, 3275, 3277, 3279, 3281, 3283, 3285, 3287, 3289, 3291, 3293, 3295, 3297, 3299, 3301, 3303, 3305, 3307, 3309, 3311, 3313, 3315, 3317, 3319, 3321, 3323, 3325, 3327, 3329, 3331, 3333, 3335, 3337, 3339, 3341, 3343, 3345, 3347, 3349, 3351, 3353, 3355, 3357, 3359, 3361, 3363, 3365, 3367, 3369, 3371, 3373, 3375, 3377, 3379, 3381, 3383, 3385, 3387, 3389, 3391, 3393, 3395, 3397, 3399, 3401, 3403, 3405, 3407, 3409, 3411, 3413, 3415, 3417, 3419, 3421, 3423, 3425, 3427, 3429, 3431, 3433, 3435, 3437, 3439, 3441, 3443, 3445, 3447, 3449, 3451, 3453, 3455, 3457, 3459, 3461, 3463, 3465, 3467, 3469, 3471, 3473, 3475, 3477, 3479, 3481, 3483, 3485, 3487, 3489, 3491, 3493, 3495, 3497, 3499, 3501, 3503, 3505, 3507, 3509, 3511, 3513, 3515, 3517, 3519, 3521, 3523, 3525, 3527, 3529, 3531, 3533, 3535, 3537, 3539, 3541, 3543, 3545, 3547, 3549, 3551, 3553, 3555, 3557, 3559, 3561, 3563, 3565, 3567, 3569, 3571, 3573, 3575, 3577, 3579, 3581, 3583, 3585, 3587, 3589, 3591, 3593, 3595, 3597, 3599, 3601, 3603, 3605, 3607, 3609, 3611, 3613, 3615, 3617, 3619, 3621, 3623, 3625, 3627, 3629, 3631, 3633, 3635, 3637, 3639, 3641, 3643, 3645, 3647, 3649, 3651, 3653, 3655, 3657, 3659, 3661, 3663, 3665, 3667, 3669, 3671, 3673, 3675, 3677, 3679, 3681, 3683, 3685, 3687, 3689, 3691, 3693, 3695, 3697, 3699, 3701, 3703, 3705, 3707, 3709, 3711, 3713, 3715, 3717, 3719, 3721, 3723, 3725, 3727, 3729, 3731, 3733, 3735, 3737, 3739, 3741, 3743, 3745, 3747, 3749, 3751, 3753, 3755, 3757, 3759, 3761, 3763, 3765, 3767, 3769, 3771, 3773, 3775, 3777, 3779, 3781, 3783, 3785, 3787, 3789, 3791, 3793, 3795, 3797, 3799, 3801, 3803, 3805, 3807, 3809, 3811, 3813, 3815, 3817, 3819, 3821, 3823, 3825, 3827, 3829, 3831, 3833, 3835, 3837, 3839, 3841, 3843, 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37466, 37468, 37471, 37469, 37476, 37474, 37480, 37479, 37481, 37498, 37496, 37494, 37492, 37505, 37503, 37501, 37507, 37508, 37510, 37513, 37511, 37515, 37518, 37517, 37516, 37519, 37522, 37521, 37520, 37524, 37531, 37529, 37527, 37525, 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, 37634, 37299, 37639, 37641, 37645, 37316, 37653, 37655, 37664, 37666, 37404, 37408, 37672, 37680, 37684, 37693, 37020, 37697, 37036, 37703, 37705, 37708, 37548, 37552, 37714, 14320, 14321, 14322, 37632, 14324, 14325, 14326, 14327, 14328, 14329, 14330, 14331, 14332, 14333, 14334, 14335, 14336, 14337, 14338, 14339, 14340, 14341, 14342, 14343, 14344, 37633, 37643, 37649, 14364, 14365, 14366, 14367, 14368, 14369, 14370, 14371, 14372, 37651, 14374, 14375, 37652, 37657, 37659, 14385, 14386, 14387, 14388, 14389, 14390, 14391, 14392, 14393, 14394, 14395, 14396, 14397, 14398, 14399, 14400, 14401, 14402, 14403, 14404, 14405, 37661, 14407, 14408, 14409, 14410, 37662, 37674, 37676, 14427, 14428, 14429, 14430, 14431, 14432, 14433, 14434, 37678, 14436, 14437, 14438, 14439, 14440, 14441, 37679, 37682, 37686, 37688, 14453, 14454, 14455, 14456, 37690, 14458, 14459, 14460, 14461, 37691, 14463, 14464, 37692, 14466, 14467, 14468, 14475, 14476, 14477, 14478, 14480, 14481, 14482, 14483, 14484, 14485, 14486, 14487, 14488, 14489, 14490, 14491, 14492, 14493, 14494, 14495, 14496, 14497, 14498, 14499, 14500, 37700, 37701, 37716, 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, 37914, 14323, 37918, 37921, 37923, 37925, 37928, 37930, 37933, 37936, 14345, 37888, 37889, 37890, 37891, 14355, 37892, 37893, 14362, 37942, 37949, 14373, 37952, 14376, 37894, 37895, 14381, 14383, 37959, 37963, 37967, 37970, 37974, 37976, 14406, 37979, 37981, 14411, 37896, 37897, 37898, 37899, 37900, 14423, 14425, 37986, 37989, 37992, 14435, 37995, 37999, 14442, 37901, 14445, 37902, 14449, 14451, 38007, 14457, 38012, 14462, 38015, 14465, 38018, 37903, 37904, 37905, 38021, 38023, 37906, 38025, 38031, 38034, 38038, 38042, 38044, 14501, 14502, 37907, 37908, 37909, 37910, 37911, 37912, 14517, 37946, 37944, 37956, 38009, 38004, 38028, 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, 37915, 38148, 38151, 37937, 14346, 14348, 14351, 14353, 14357, 14359, 14377, 14379, 37960, 37964, 37971, 38177, 38180, 14412, 14414, 14416, 14418, 14421, 37996, 14443, 14447, 14469, 14471, 14473, 38212, 14479, 38026, 38035, 38039, 38219, 14504, 14506, 14508, 14510, 14512, 14515, 38152, 38146, 38159, 38162, 38163, 38166, 14535, 38164, 14537, 38171, 38170, 14546, 38174, 38188, 38187, 38191, 38194, 38190, 38189, 38200, 38199, 38197, 38203, 14567, 38207, 14569, 38201, 38205, 14578, 38215, 38228, 38221, 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, 38149, 38274, 14520, 14521, 38272, 38275, 14525, 38279, 38281, 38277, 38276, 38278, 14531, 38280, 14533, 14534, 14536, 14538, 14539, 38283, 38282, 38285, 38286, 38284, 38287, 38288, 14548, 38292, 38290, 14551, 38289, 14553, 38291, 38293, 38294, 14557, 14558, 14559, 14560, 14561, 14562, 14563, 38295, 38296, 14566, 14568, 14570, 14571, 38299, 38298, 38297, 38300, 38302, 38304, 14579, 38305, 38303, 38309, 14583, 38308, 38306, 38310, 14587, 38307, 38311, 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, 14519, 14522, 14523, 38400, 14526, 14527, 14528, 14529, 14530, 14532, 38415, 38416, 14540, 14541, 38418, 14542, 14543, 14544, 14545, 14547, 14549, 14550, 14552, 14554, 14555, 14556, 38437, 14564, 14565, 38440, 38335, 38337, 38447, 14572, 14573, 14574, 14575, 14576, 14577, 14580, 14581, 14582, 14584, 14585, 14586, 14588, 14589, 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, 14524, 38402, 38529, 38532, 38534, 38536, 38537, 38539, 38541, 38544, 38546, 38547, 38549, 38550, 38551, 38435, 38555, 38559, 38562, 38565, 38340, 38567, 38458, 38571, 38462, 38574, 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, 38656, 38658, 38660, 38662, 38320, 38664, 38666, 38426, 38669, 38552, 38554, 38672, 38560, 38563, 38676, 38568, 38679, 38681, 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, 38784, 38787, 38791, 38793, 38438, 38556, 38799, 38801, 38788, 38796, 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, 38916, 14591, 38914, 38918, 38912, 14595, 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, 14590, 14592, 14593, 14594, 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, 39041, 39170, 39045, 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, 39297, 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, 39298, 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[]= {
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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 14720
#define SIZE_OF_AC 24960
__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[310*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];
__syncthreads();
for (int iter=0; iter< n_iter; iter++) {
R[i + 115*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 + 116*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 + 117*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 + 118*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 + 119*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 + 120*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 + 121*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 + 122*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 + 123*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 + 124*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 + 125*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 + 126*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 + 127*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 + 128*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 + 129*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 + 130*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 + 131*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 + 132*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 + 133*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 + 134*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 + 135*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 + 136*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 + 137*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 + 138*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 + 139*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 + 140*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 + 141*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 + 142*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 + 143*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 + 144*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 + 145*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 + 146*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 + 147*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 + 148*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 + 149*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 + 150*t] = Op[i + 35*t] ? R[B[i + 35*t]] * R[C[i + 35*t]] : R[B[i + 35*t]] + R[C[i + 35*t]];
__syncthreads();
R[i + 151*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 + 152*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 + 153*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 + 154*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 + 155*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 + 156*t] = Op[i + 41*t] ? R[B[i + 41*t]] * R[C[i + 41*t]] : R[B[i + 41*t]] + R[C[i + 41*t]];
R[i + 157*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 + 158*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 + 159*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 + 160*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 + 161*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 + 162*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 + 163*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 + 164*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 + 165*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 + 166*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 + 167*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 + 168*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 + 169*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 + 170*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 + 171*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]];
__syncthreads();
R[i + 172*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 + 173*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 + 174*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 + 175*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 + 176*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 + 177*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 + 178*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 + 179*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 + 180*t] = Op[i + 65*t] ? R[B[i + 65*t]] * R[C[i + 65*t]] : R[B[i + 65*t]] + R[C[i + 65*t]];
R[i + 181*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 + 182*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 + 183*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 + 184*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 + 185*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 + 186*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 + 187*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 + 188*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 + 189*t] = Op[i + 74*t] ? R[B[i + 74*t]] * R[C[i + 74*t]] : R[B[i + 74*t]] + R[C[i + 74*t]];
__syncthreads();
R[i + 190*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 + 191*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 + 192*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 + 193*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 + 194*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 + 195*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 + 196*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 + 197*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 + 198*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 + 199*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 + 200*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 + 201*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 + 202*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 + 203*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 + 204*t] = Op[i + 89*t] ? R[B[i + 89*t]] * R[C[i + 89*t]] : R[B[i + 89*t]] + R[C[i + 89*t]];
R[i + 205*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 + 206*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 + 207*t] = Op[i + 92*t] ? R[B[i + 92*t]] * R[C[i + 92*t]] : R[B[i + 92*t]] + R[C[i + 92*t]];
__syncthreads();
R[i + 208*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 + 209*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 + 210*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 + 211*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 + 212*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 + 213*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 + 214*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 + 215*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 + 216*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 + 217*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 + 218*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 + 219*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]];
__syncthreads();
R[i + 220*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 + 221*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 + 222*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 + 223*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 + 224*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 + 225*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 + 226*t] = Op[i + 111*t] ? R[B[i + 111*t]] * R[C[i + 111*t]] : R[B[i + 111*t]] + R[C[i + 111*t]];
R[i + 227*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 + 228*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 + 229*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 + 230*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 + 231*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 + 232*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 + 233*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 + 234*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 + 235*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 + 236*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 + 237*t] = Op[i + 122*t] ? R[B[i + 122*t]] * R[C[i + 122*t]] : R[B[i + 122*t]] + R[C[i + 122*t]];
R[i + 238*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]];
__syncthreads();
R[i + 239*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 + 240*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 + 241*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 + 242*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 + 243*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 + 244*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 + 245*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 + 246*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 + 247*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 + 248*t] = Op[i + 133*t] ? R[B[i + 133*t]] * R[C[i + 133*t]] : R[B[i + 133*t]] + R[C[i + 133*t]];
R[i + 249*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 + 250*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 + 251*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 + 252*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 + 253*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 + 254*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 + 255*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]];
__syncthreads();
R[i + 256*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 + 257*t] = Op[i + 142*t] ? R[B[i + 142*t]] * R[C[i + 142*t]] : R[B[i + 142*t]] + R[C[i + 142*t]];
R[i + 258*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 + 259*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 + 260*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 + 261*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 + 262*t] = Op[i + 147*t] ? R[B[i + 147*t]] * R[C[i + 147*t]] : R[B[i + 147*t]] + R[C[i + 147*t]];
R[i + 263*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]];
__syncthreads();
R[i + 264*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 + 265*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 + 266*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 + 267*t] = Op[i + 152*t] ? R[B[i + 152*t]] * R[C[i + 152*t]] : R[B[i + 152*t]] + R[C[i + 152*t]];
R[i + 268*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 + 269*t] = Op[i + 154*t] ? R[B[i + 154*t]] * R[C[i + 154*t]] : R[B[i + 154*t]] + R[C[i + 154*t]];
__syncthreads();
R[i + 270*t] = Op[i + 155*t] ? R[B[i + 155*t]] * R[C[i + 155*t]] : R[B[i + 155*t]] + R[C[i + 155*t]];
R[i + 271*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 + 272*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 + 273*t] = Op[i + 158*t] ? R[B[i + 158*t]] * R[C[i + 158*t]] : R[B[i + 158*t]] + R[C[i + 158*t]];
R[i + 274*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 + 275*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 + 276*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 + 277*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 + 278*t] = Op[i + 163*t] ? R[B[i + 163*t]] * R[C[i + 163*t]] : R[B[i + 163*t]] + R[C[i + 163*t]];
R[i + 279*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 + 280*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 + 281*t] = Op[i + 166*t] ? R[B[i + 166*t]] * R[C[i + 166*t]] : R[B[i + 166*t]] + R[C[i + 166*t]];
R[i + 282*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]];
__syncthreads();
R[i + 283*t] = Op[i + 168*t] ? R[B[i + 168*t]] * R[C[i + 168*t]] : R[B[i + 168*t]] + R[C[i + 168*t]];
R[i + 284*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 + 285*t] = Op[i + 170*t] ? R[B[i + 170*t]] * R[C[i + 170*t]] : R[B[i + 170*t]] + R[C[i + 170*t]];
R[i + 286*t] = Op[i + 171*t] ? R[B[i + 171*t]] * R[C[i + 171*t]] : R[B[i + 171*t]] + R[C[i + 171*t]];
__syncthreads();
R[i + 287*t] = Op[i + 172*t] ? R[B[i + 172*t]] * R[C[i + 172*t]] : R[B[i + 172*t]] + R[C[i + 172*t]];
R[i + 288*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 + 289*t] = Op[i + 174*t] ? R[B[i + 174*t]] * R[C[i + 174*t]] : R[B[i + 174*t]] + R[C[i + 174*t]];
R[i + 290*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 + 291*t] = Op[i + 176*t] ? R[B[i + 176*t]] * R[C[i + 176*t]] : R[B[i + 176*t]] + R[C[i + 176*t]];
R[i + 292*t] = Op[i + 177*t] ? R[B[i + 177*t]] * R[C[i + 177*t]] : R[B[i + 177*t]] + R[C[i + 177*t]];
R[i + 293*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 + 294*t] = Op[i + 179*t] ? R[B[i + 179*t]] * R[C[i + 179*t]] : R[B[i + 179*t]] + R[C[i + 179*t]];
R[i + 295*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 + 296*t] = Op[i + 181*t] ? R[B[i + 181*t]] * R[C[i + 181*t]] : R[B[i + 181*t]] + R[C[i + 181*t]];
R[i + 297*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 + 298*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 + 299*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 + 300*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 + 301*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 + 302*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 + 303*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 + 304*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 + 305*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 + 306*t] = Op[i + 191*t] ? R[B[i + 191*t]] * R[C[i + 191*t]] : R[B[i + 191*t]] + R[C[i + 191*t]];
__syncthreads();
R[i + 307*t] = Op[i + 192*t] ? R[B[i + 192*t]] * R[C[i + 192*t]] : R[B[i + 192*t]] + R[C[i + 192*t]];
__syncthreads();
R[i + 308*t] = Op[i + 193*t] ? R[B[i + 193*t]] * R[C[i + 193*t]] : R[B[i + 193*t]] + R[C[i + 193*t]];
__syncthreads();
R[i + 309*t] = Op[i + 194*t] ? R[B[i + 194*t]] * R[C[i + 194*t]] : R[B[i + 194*t]] + R[C[i + 194*t]];
if (i==0) { final += R[309*t]; }
__syncthreads();
}
if (i==0) { A[0]= final;}
}
|
6,640 | #include <cuda_runtime_api.h>
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include <time.h>
/***********************************************************************
**
Compile with:
nvcc -o CrackAZ99-With-Data-cuda CrackAZ99-With-Data-cuda.cu
To run program:
./CrackAZ99-With-Data-cuda
Dr Kevan Buckley, University of Wolverhampton, 2018
************************************************************************
******/
/****************************************************************************
This function returns 1 if the attempt at cracking the password is
identical to the plain text password string stored in the program.
Otherwise,it returns 0.
*****************************************************************************/
__device__ int is_a_match(char *attempt) {
char pwd[] = "ML4249";
char pwd1[] = "PE3462";
char pwd2[] = "UM4598";
char pwd3[] = "GT9859";
char *a = attempt;
char *p = pwd;
char *q = pwd1;
char *r = pwd2;
char *s = pwd3;
while(*a == *p) {
if(*a == '\0') {
return 1;
}
a++;
p++;
}
while(*a == *q) {
if(*a == '\0') {
return 1;
}
a++;
q++;
}
while(*a == *r) {
if(*a == '\0') {
return 1;
}
a++;
r++;
}
while(*a == *s) {
if(*a == '\0') {
return 1;
}
a++;
s++;
}
return 0;
}
/****************************************************************************
The kernel function run in 675 threads uses
nested loops to generate all possible passwords and test whether they match
the hidden password.
*****************************************************************************/
__global__ void crack() {
char alpha[26]= {'A','B','C','D','E','F','G','H','I','J','K','L','M','N','O','P','Q','R','S','T','U','V','W','X','Y','Z'};
char num[10] = {'0','1','2','3','4','5','6','7','8','9'};
char result[7];
result[6] = '\0';
int e, f, g, h;
for(e=0;e<=9;e++) {
for(f=0; f<=9; f++) {
for(g=0; g<=9; g++) {
for(h=0; h<=9; h++) {
result[0] = alpha[blockIdx.x];
result[1] = alpha[threadIdx.x];
result[2] = num[e];
result[3] = num[f];
result[4] = num[g];
result[5] = num[h];
if(is_a_match(result)) {
printf("password found: %s\n", result);
} else {
//printf("tried: %s\n", result);
}
}
}
}
}
}
int time_difference(struct timespec *start, struct timespec *finish,
long long int *difference) {
long long int ds = finish->tv_sec - start->tv_sec;
long long int dn = finish->tv_nsec - start->tv_nsec;
if(dn < 0 ) {
ds--;
dn += 1000000000;
}
*difference = ds * 1000000000 + dn;
return !(*difference > 0);
}
int main(int argc, char *argv[]) {
struct timespec start, finish
;
long long int time_elapsed;
clock_gettime(CLOCK_MONOTONIC, &start);
crack <<<26, 26>>>();
cudaThreadSynchronize();
clock_gettime(CLOCK_MONOTONIC, &finish);
time_difference(&start, &finish, &time_elapsed);
printf("Time elapsed was %lldns or %0.9lfs\n", time_elapsed,
(time_elapsed/1.0e9));
return 0;
}
|
6,641 | #include "includes.h"
__global__ void First_Initialize_Kernel(int size, unsigned int *randoms, int *bestSeen, int *origin)
{
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < size)
{
// Set the origin to be self
origin[idx] = idx;
// Set the bestSeen value to be random
bestSeen[idx] = randoms[idx] % 1000000;
}
} |
6,642 | #include "includes.h"
__global__ void sumaVector(float *v1, float *v2, float *res){
int index = blockIdx.x * blockDim.x + threadIdx.x;
if(index < TAM_V)
res[index] = v1[index] + v2[index];
} |
6,643 | #include<cuda_runtime.h>
#include<device_launch_parameters.h>
#include<stdlib.h>
#include<stdio.h>
#include<iostream>
#include<math.h>
#include<assert.h>
using namespace std;
__global__ void array_sum(int* v, int* r)
{
__shared__ int partial_sum[1024];
int tid = blockIdx.x*blockDim.x + threadIdx.x;
partial_sum[threadIdx.x] = v[tid];
__syncthreads();
for(int s = blockDim.x/2; s>0; s>>=1)
{
if(threadIdx.x<s)
{
partial_sum[threadIdx.x] += partial_sum[threadIdx.x+s];
}
__syncthreads();
}
if(threadIdx.x == 0)
{
r[blockIdx.x] = partial_sum[0];
}
}
int main(int argc, char const *argv[])
{
int n;
srand(0);
cout<<"Enter value for n"<<endl;
cin>>n;
int *v, *r;
int *d_v, *d_r;
v = (int*)malloc(n*sizeof(int));
r = (int*)malloc(n*sizeof(int));
cudaMalloc(&d_v, n*sizeof(int));
cudaMalloc(&d_r, n*sizeof(int));
for(int i=0;i<n;i++)
{
v[i] = rand()%100;
}
cudaMemcpy(d_v, v, n*sizeof(int), cudaMemcpyHostToDevice);
int num_threads = 256;
int num_blocks = n/num_threads;
array_sum<<<num_blocks, num_threads>>> (d_v, d_r);
array_sum<<<1, num_threads>>> (d_r, d_r);
cudaMemcpy(r, d_r, n*sizeof(int), cudaMemcpyDeviceToHost);
cout<<"Array Sum: "<<r[0]<<endl;
return 0;
} |
6,644 | #include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <time.h>
//// notes
// based on the examples given in the CUDE programming guide
// this one makes a list of gals, one list for ra and one for dec.
// it can then calcs the separation between gal pairs.
// note that it's not returning anythign from the calculation!
// just calculating how long each process takes.
//this version uses constant memory for the arrays of gals. Since I'm only
//using 128gals, this is fine. There's only 4k (I think) of const memory.
__constant__ float constRA[128];
__constant__ float constDEC[128];
//device code
__global__ void CalcSep(float* ra, float* dec, int ngals)
{
//does all the i's simultaneously - one for each thread
int ix = blockDim.x * blockIdx.x + threadIdx.x;
float sep=0;
// Do 1 ``column"
for(int ij=ix+1;ij<ngals;ij++)
{
sep = acos( sin(dec[ix])*sin(dec[ij]) + \
cos(dec[ix])*cos(dec[ij])*cos(fabs(ra[ix]-ra[ij])) );
}//loop over gals
}
//device code using csts
__global__ void CalcSepCst( int ngals)
{
//does all the i's simultaneously - one for each thread
int ix = blockDim.x * blockIdx.x + threadIdx.x;
float sep=0;
// Do 1 ``column"
for(int ij=ix+1;ij<ngals;ij++)
{
sep = acos( sin(constDEC[ix])*sin(constDEC[ij]) + \
cos(constDEC[ix])*cos(constDEC[ij])*cos(fabs(constRA[ix]-constRA[ij])) );
}//loop over gals
}
//Host code
int main()
{
int ngals = 128;
size_t sizeneededin = ngals * sizeof(float);
//allocate vectors in host memory
float* h_ra = (float*)malloc(sizeneededin);
float* h_dec = (float*)malloc(sizeneededin);
srand(time(0));
//initailise input vectors - place galaxies at rando coords between 0 and 1
for(int i=0;i<ngals;i++)
{
h_ra[i] = rand();
h_dec[i] = rand();
}
//Calculate separation in CPU and calculate time needed
clock_t teststart = clock();
float testsep=0;
for(int i=0;i<ngals;i++){
for(int j=i+1;j<ngals;j++){
testsep = acos( sin(h_dec[i])*sin(h_dec[j]) + cos(h_dec[i])*cos(h_dec[j])*cos(fabs(h_ra[i]-h_ra[j])) );
}
}
clock_t testend = clock();
float testelapsed = (float)(testend-teststart);
printf("elapsed time for CPU in ms: %f", testelapsed/CLOCKS_PER_SEC*1000);
printf("\n");
//allocate vectors in device memory
float* d_ra; float* d_dec;
cudaMalloc(&d_ra, sizeneededin);
cudaMalloc(&d_dec, sizeneededin);
//copy vectors from host to device memory
cudaMemcpy(d_ra, h_ra, sizeneededin, cudaMemcpyHostToDevice);
cudaMemcpy(d_dec, h_dec, sizeneededin, cudaMemcpyHostToDevice);
//invoke kernel
int threadsPerBlock = (ngals*ngals)/2; //256;
// Only need 1/2 as many threads
// int blocksPerGrid = (ngals/2 + threadsPerBlock -1) / threadsPerBlock; //???????
int blocksPerGrid = 1;
//set up the cuda timer.
cudaEvent_t cudastart, cudaend;
cudaEventCreate(&cudastart);
cudaEventCreate(&cudaend);
//record the start time
cudaEventRecord(cudastart,0);
//run the kernel!
CalcSep<<<blocksPerGrid, threadsPerBlock>>>(d_ra, d_dec, ngals);
//record the end time
cudaEventRecord(cudaend,0);
cudaEventSynchronize(cudaend);
//how long did the kernel take? this gives time in ms
float cudaelapsed=0;
cudaEventElapsedTime(&cudaelapsed, cudastart, cudaend);
printf("elapsed time for GPU in ms: %f",cudaelapsed);
printf("\n");
//delete memory
cudaEventDestroy(cudastart);
cudaEventDestroy(cudaend);
//////////////////////////////////////////////////
//now, do the same thing but with const memory
/////////////////////////////////////////////////
float cst_ra[ngals];
float cst_dec[ngals];
for(int i=0;i<ngals;i++){
cst_ra[i]=h_ra[i];
cst_dec[i] = h_dec[i];
}
cudaMemcpyToSymbol(constRA, cst_ra, sizeof(cst_ra));
cudaMemcpyToSymbol(constDEC, cst_dec, sizeof(cst_dec));
//set up the cuda timer.
cudaEvent_t cudastart2, cudaend2;
cudaEventCreate(&cudastart2);
cudaEventCreate(&cudaend2);
//record the start time
cudaEventRecord(cudastart2,0);
//run the kernel!
CalcSepCst<<<blocksPerGrid, threadsPerBlock>>>(ngals);
//record the end time
cudaEventRecord(cudaend2,0);
cudaEventSynchronize(cudaend2);
//how long did the kernel take? this gives time in ms
float cudaelapsed2=0;
cudaEventElapsedTime(&cudaelapsed2,cudastart2, cudaend2);
printf("elapsed time for GPU using constant memory in ms: %f",cudaelapsed2);
printf("\n");
//delete memory
cudaEventDestroy(cudastart2);
cudaEventDestroy(cudaend2);
//free device memory
cudaFree(d_ra); cudaFree(d_dec);
//free host memory
free(h_ra); free(h_dec);
}
|
6,645 | #include <stdio.h>
#include <stdlib.h>
#include <time.h>
#include <cuda_runtime.h>
#define THREADS_PER_BLOCK 1024
#define KERNEL_RUN_COUNT 100
__global__ void reduce_kernel(float *, const float *);
float reduce_cpu(float *, int);
int main(int argc, char **argv) {
const int ARRAY_LENGTH = 1<<20;
const int ARRAY_SIZE = ARRAY_LENGTH * sizeof(float);
// declare array to reduce
float h_in[ARRAY_LENGTH];
// populate array with random numbers
for(int i = 0; i < ARRAY_LENGTH; i++) {
// generate random float in [-1.0f, 1.0f]
h_in[i] = -1.0f + (float)rand()/((float)RAND_MAX/2.0f);
}
// declare GPU memory pointers
float * d_in, * d_intermediate, * d_out;
// allocate GPU memory
cudaMalloc((void **) &d_in, ARRAY_SIZE);
cudaMalloc((void **) &d_intermediate, ARRAY_SIZE);
cudaMalloc((void **) &d_out, sizeof(float));
// copy the input array to the GPU
cudaMemcpy(d_in, h_in, ARRAY_SIZE, cudaMemcpyHostToDevice);
cudaEvent_t start, stop;
cudaEventCreate(&start);
cudaEventCreate(&stop);
// launch the kernel
cudaEventRecord(start, 0);
int blocks = ARRAY_LENGTH / THREADS_PER_BLOCK;
for (int i = 0; i < KERNEL_RUN_COUNT; i++) {
//reduce elements in each block
reduce_kernel<<<blocks, THREADS_PER_BLOCK, THREADS_PER_BLOCK * sizeof(float)>>>(d_intermediate, d_in);
//reduce all blocks
reduce_kernel<<<1, blocks, blocks * sizeof(float)>>>(d_out, d_intermediate);
}
// copy back the sum from GPU
float h_out;
cudaMemcpy(&h_out, d_out, sizeof(float), cudaMemcpyDeviceToHost);
cudaEventRecord(stop, 0);
cudaEventSynchronize(stop);
float elapsedTimeGpu;
cudaEventElapsedTime(&elapsedTimeGpu, start, stop);
elapsedTimeGpu /= (float)KERNEL_RUN_COUNT;
// free GPU memory
cudaFree(d_in);
cudaFree(d_intermediate);
cudaFree(d_out);
// calculate reduced sum on CPU
cudaEventRecord(start, 0);
float sum = 0.0f;
sum = reduce_cpu(h_in, ARRAY_LENGTH);
cudaEventRecord(stop, 0);
cudaEventSynchronize(stop);
float elapsedTimeCpu;
cudaEventElapsedTime(&elapsedTimeCpu, start, stop);
cudaEventDestroy(start);
cudaEventDestroy(stop);
printf("GPU:\n\tProcessing time: %f sec\n\tSum: %f\n\n", elapsedTimeGpu, h_out);
printf("CPU:\n\tProcessing time: %f sec\n\tSum: %f\n\n", elapsedTimeCpu, sum);
printf("GPU/CPU speed ratio: %f\n", elapsedTimeCpu/elapsedTimeGpu);
return 0;
}
__global__ void reduce_kernel(float * d_out, const float * d_in) {
extern __shared__ float sdata[];
int id = threadIdx.x + blockDim.x * blockIdx.x;
int tid = threadIdx.x;
// copy one value per thread to shared memory
sdata[tid] = d_in[id];
__syncthreads();
// sum elements tree-wise in shared memory
for (unsigned int s = blockDim.x / 2; s > 0; s >>= 1) {
if (tid < s) {
sdata[tid] += sdata[tid + s];
}
__syncthreads();
}
if (tid == 0) {
d_out[blockIdx.x] = sdata[0];
}
}
float reduce_cpu(float * a, int size) {
float sum = 0;
for (int i = 0; i < size; i++) {
sum += a[i];
}
return sum;
}
|
6,646 | #include "includes.h"
__global__ void kSwapColumns(float* source, float* target, float* indices1, float* indices2, int cols, int width, int height){
const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
const unsigned int numThreads = blockDim.x * gridDim.x;
float temp;
unsigned int column, row, source_pos, target_pos;
for (unsigned int i = idx; i < height * cols; i += numThreads) {
column = i / height;
row = i % height;
source_pos = height * (int)indices1[column] + row;
target_pos = height * (int)indices2[column] + row;
temp = source[source_pos];
source[source_pos] = target[target_pos];
target[target_pos] = temp;
}
} |
6,647 | //
// TP: Lancer un kernel vide sur le GPU
//
#include <iostream>
__global__ void emptyKernel ();
int main(int argc, char** argv) {
cudaSetDevice(0);
emptyKernel<<<1,1>>>();
cudaDeviceSynchronize();
std::cout << "Hello, CUDA!" << std::endl;
return 0;
}
__global__ void emptyKernel(){
// Empty
}
|
6,648 | #include <iostream>
#include <math.h>
#include <unistd.h>
#include <memory>
#include <algorithm>
#include <array>
#include <numeric>
//const std::size_t N = 1 << 10;
//const std::size_t N = 1 << 8; // any higher of a shift and this will fail for a N*N*N matrix
const std::size_t N = 1 << 6; // any higher of a shift and this will fail for a N*N matrix
__global__
// increment all elements in parallel
void vec_inc(float* const c, const std::size_t n)
{
for (std::size_t x = threadIdx.x + (blockIdx.x * blockDim.x); x < n; x += (blockDim.x * gridDim.x))
{
for (std::size_t y = threadIdx.y + (blockIdx.y * blockDim.y); y < n; y += (blockDim.y * gridDim.y))
{
for (std::size_t z = threadIdx.z + (blockIdx.z * blockDim.z); z < n; z += (blockDim.z * gridDim.z))
{
++ c[(x * n * n) + (y * n) + z];
}
}
}
}
int main(void)
{
// grids and blocks are topologically laid out similar to the problem
// for 1D arrays, a grid size of (1,1,1) = 1D, and a block size of (N,1,1) = N, would be enough to fully cover the array
const dim3 grid_size(1, 1, 1);
const dim3 block_size(512, 1, 1);
std::size_t error_count = 0; // track errors found
float* d_a = nullptr; // handle on CUDA memory
//std::shared_ptr<std::array<float, N * N * N>> h_mat(new std::array<float, N * N * N>()); // matrix we want to do grid-strides
std::shared_ptr<std::array<float, N * N>> h_mat(new std::array<float, N * N>()); // matrix we want to do grid-strides
std::iota(h_mat->begin(), h_mat->end(), 0);
cudaStream_t stream;
if (nullptr == h_mat)
{
std::cout << __FUNCTION__ << " " << __LINE__ << std::endl;
return -__LINE__;
}
if (cudaSuccess != cudaStreamCreate(&stream))
{
std::cout << __LINE__ << std::endl;
return -__LINE__;
}
if (cudaSuccess != cudaMalloc(&d_a, h_mat->size() * sizeof(d_a[0])))
{
std::cout << __LINE__ << std::endl;
return -__LINE__;
}
if (cudaSuccess != cudaMemcpyAsync(d_a, h_mat->data(), h_mat->size() * sizeof(d_a[0]), cudaMemcpyHostToDevice, stream))
{
std::cout << __LINE__ << std::endl;
return -__LINE__;
}
vec_inc<<<grid_size, block_size, 0, stream>>>(d_a, N);
if (cudaSuccess != cudaMemcpyAsync(h_mat->data(), d_a, h_mat->size() * sizeof(d_a[0]), cudaMemcpyDeviceToHost, stream))
{
std::cout << __LINE__ << std::endl;
return -__LINE__;
}
if (cudaSuccess != cudaStreamSynchronize(stream))
{
std::cout << __LINE__ << std::endl;
return -__LINE__;
}
for (std::size_t i = 0; i < h_mat->size(); ++i)
{
if ((*h_mat)[i] != i + 1)
{
++ error_count;
}
}
std::cout << error_count << "/" << h_mat->size() << std::endl << std::flush;
cudaFree(d_a);
cudaStreamDestroy(stream);
return 0;
}
|
6,649 | #include "includes.h"
__global__ void Tri_k(float* a, float* b, float* c, float norm, int n)
{
// Identifies the thread working within a group
int tidx = threadIdx.x % n;
// Identifies the data concerned by the computations
int Qt = (threadIdx.x - tidx) / n;
// The global memory access index
int gb_index_x = Qt + blockIdx.x * (blockDim.x / n);
b[gb_index_x * n + tidx] = ((float)tidx + 1.0f) / (norm);
if (tidx > 0 && tidx < n - 1) {
a[gb_index_x * n + tidx] = ((float)tidx + 1.0f) / (norm * 3);
c[gb_index_x * n + tidx] = ((float)tidx + 1.0f) / (norm * 3);
}
else if (tidx == 0) {
a[gb_index_x * n + tidx] = 0.0f;
}
else {
c[gb_index_x * n + tidx] = 0.0f;
}
} |
6,650 | //
// Created by Zhen Peng on 10/17/19.
//
//#include "efanna2e/index_nsg.h"
#include <stdio.h>
#include "../include/index_cuda.cuh"
namespace efanna2e {
//__global__ void IndexNSG::SearchWithOptGraphOnCUDA(
// bool *d_switch)
//{
// int i = threadIdx.x;
// int j = threadIdx.y;
// int z = threadIdx.z;
//
// *switch = true;
//
// printf("i: %d j: %d z: %d\n", i, j, z);
//}
__global__ void cudaPrint(bool *d_switch)
{
int i = threadIdx.x;
int j = threadIdx.y;
int z = threadIdx.z;
*d_switch = true;
printf("i: %d j: %d z: %d\n", i, j, z);
}
} |
6,651 | #include "includes.h"
__global__ void CoalescedKernel(int *x, int *y, int *z, int *sum)
{
int idx = blockIdx.x * blockDim.x + threadIdx.x;
sum[idx] = 0;
sum[idx] += x[idx] * x[idx];
sum[idx] += y[idx] * y[idx];
sum[idx] += z[idx] * z[idx];
} |
6,652 | /*
* =====================================================================================
*
* Filename: cudatestfunc.cu
*
* Description:
*
* Version: 1.0
* Created: 2016年05月17日 00時26分44秒
* Revision: none
* Compiler: gcc
*
* Author: YOUR NAME (),
* Organization:
*
* =====================================================================================
*/
#include <cuda_runtime.h>
__global__ void test0(float* d_a)
{
int i = threadIdx.x;
d_a[i] *= 2.0f;
}
void cudatestfunc(float* h_a, int n)
{
float* d_a;
cudaMalloc((void**)&d_a, n * sizeof(float));
cudaMemcpy(d_a, h_a, n * sizeof(float), cudaMemcpyHostToDevice);
test0<<<1,n>>>(d_a);
cudaMemcpy(h_a, d_a, n * sizeof(float), cudaMemcpyDeviceToHost);
cudaFree(d_a);
}
/*
__host__ __device__ int getIndex(float x, float interval)
{
return (int)floorf(x / interval);
}
__host__ __device__ int* getIndex(const float* const x, const float* const interval, int* const xi)
{
xi[0] = getIndex(x[0], interval[0]);
xi[1] = getIndex(x[1], interval[1]);
xi[2] = getIndex(x[2], interval[2]);
xi[3] = getIndex(x[3], interval[3]);
return xi;
}
__host__ __device__ int sign(int x)
{
return (x > 0) ? 1 : ( (x < 0) ? -1 : 0 );
}
__host__ __device__ int* getDirection(const int* const from, const int* const to, int* const direction)
{
direction[0] = sign(to[0] - from[0]);
direction[1] = sign(to[1] - from[1]);
direction[2] = sign(to[2] - from[2]);
direction[3] = sign(to[3] - from[3]);
return direction;
}
__host__ __device__ bool equals(const int* const x0, const int* const x1)
{
bool ret = true;
ret = (x0[0] == x1[0]) ? ret : false;
ret = (x0[1] == x1[1]) ? ret : false;
ret = (x0[2] == x1[2]) ? ret : false;
ret = (x0[3] == x1[3]) ? ret : false;
return ret;
}
//交点を算出する
__host__ __device__ float* getCrossPoint
(
const float* const from,//線分の始点
const float* const to,//線分の終点
const int* const p_i,//セルの番号
const float* const interval,//セルの幅
const int* const dir_i,//線分の方向
int i,//交点を求める方向
float* const cross//算出する交点
)
{
//交点を求める平面の座標
float x = interval[i] * (p_i[i] + ((dir_i[i] + 1) / 2));
//交点の線分のパラメータ
float s = (x - from[i]) / (to[i] - from[i]);
//交点の算出
cross[0] = (to[0] - from[0]) * s + from[0];
cross[1] = (to[1] - from[1]) * s + from[1];
cross[2] = (to[2] - from[2]) * s + from[2];
cross[3] = (to[3] - from[3]) * s + from[3];
//i成分については誤差が出ないようにもともと求めていたxを使用する
cross[i] = x;
//交点を返却
return cross;
}
//交点が指定したセルの指定した方向の境界にいるかどうか判定する
__host__ __device__ bool atBound
(
const float* const cross,//交点
const int* const p_i,//セル番号
const float* const interval,//セルの幅
int i//方向
)
{
bool ret = true;
for(int j = 0; j < 4;j++)
{
if(j != i)
{
if((cross[i] < interval[j] * p_i[j]) || (interval[j] * (p_i[j] + 1) <= cross[i]))
{
ret = false;
}
}
else
{
if((cross[i] < interval[j] * (p_i[j] - 1)) || (interval[j] * (p_i[j] + 2) <= cross[i]))
{
ret = false;
}
}
}
return ret;
}
//線分が通過するセル境界に対し回数をカウントアップする
__host__ __device__ void countCrossing
(
const float* const from,//線分の始点
const float* const to,//線分の終点
int* const c,//カウンタ
int n,//カウンタの個数
const float* const interval//セルの幅
)
{
//fromの属するセル番号を取得
int from_i[4];
getIndex(from, interval, from_i);
//toの属するセル番号を取得
int to_i[4];
getIndex(to, interval, to_i);
//線分の各方向の向き
int direction_i[4];
getDirection(from_i, to_i, direction_i);
//どのセルにも進まない線分かチェック
if((direction_i[0] == 0) && (direction_i[1] == 0) && (direction_i[2] == 0) && (direction_i[3] == 0))
{
//同じセルにとどまっている線分なので集計なしで終了
return;
}
//ループ用のセル番号
int p_i[4] = {from_i[0], from_i[1], from_i[2], from_i[3]};
//fromからtoまでに通過するセルを求める
do
{
//次に通過するセル
int next_i[4] = {p_i[0], p_i[1], p_i[2], p_i[3]};
//p_iの次に通過するセルnext_iを求める
for(int i = 0; i < 4; i++)
{
//next_iが第i方向かどうかチェック
if(direction_i[i] == 0)
{
//この方向には進まないので別の方向をチェック
continue;
}
//toの位置に達していたらこれ以上は進まないので別の方向をチェック
if(p_i[i] == to_i[i])
{
//この方向には進まないので別の方向をチェック
continue;
}
//i方向と垂直な平面との交点を求める
float cross[4];
getCrossPoint(from, to, p_i, interval, direction_i, i, cross);
//現在のセルp_iの境界上の点であればnext_iを確定してbreakでforを抜ける
if(atBound(cross, p_i, interval, i))
{
next_i[i] += direction_i[i];
break;
}
}
//カウントアップを行う
//対象のカウンタのインデックス
int c_i = 0;//TODO カウンタのインデックスを算出する関数を作成する
for(int i = 0; i < 4; i++)
{
int c_dir_i = next_i[i] - p_i[i];
if(c_dir_i != 0)
{
c_i += 2 * i + ((c_dir_i > 0) ? 0 : 1);//向きに応じたカウンタのインデックス
break;
}
}
atomicAdd(&(c[c_i]),1);//カウンタにatomicに加算する <- __host__向けには使えない！！//TODO 修正する！
//p_iをnext_iに更新
p_i[0] = next_i[0];
p_i[1] = next_i[1];
p_i[2] = next_i[2];
p_i[3] = next_i[3];
}
while(!equals(p_i, to_i));//toのセルに到達したら終了
}
*/
|
6,653 | /*
Mmult application
Written by: Riccardo Fontanini
Start date: 3 May 2018
Note: This program is created to multiply 3 matrix
R O T A S
O P E R A
T E N E T
A R E P O
S A T O R
*/
#include <stdio.h>
#include <math.h>
#ifndef N
#define N 2048
#endif
#ifndef BLOCKDIM
#define BLOCKDIM 32
#endif
#ifndef NROWSTESLA
#define NROWSTESLA 2048
#endif
//start and end rows define which submatrix multiply
void __global__ shared_mmult_tesla(int *A, int *B, int *C, int startrow, int endrow) {
__shared__ int s_A [BLOCKDIM * BLOCKDIM];
__shared__ int s_B [BLOCKDIM * BLOCKDIM];
int value = 0;
int block_index = threadIdx.y * BLOCKDIM + threadIdx.x;
int xa, ya, xb, yb = 0;
ya = (startrow / BLOCKDIM) + blockIdx.y;
xb = blockIdx.x;
//check boundary
if(startrow + blockIdx.y * BLOCKDIM + threadIdx.y >= N || startrow + blockIdx.y * BLOCKDIM + threadIdx.y >= endrow || blockIdx.x * BLOCKDIM + threadIdx.x >= N)
return;
for(int i = 0; i < N/BLOCKDIM; i++) {
//copy global memory Axaya and Bxbyb to shared memory
xa = i;
yb = i;
*(s_A + block_index) = *(A + ( (ya * BLOCKDIM) + threadIdx.y ) * N + (threadIdx.x + xa * BLOCKDIM));
*(s_B + block_index) = *(B + ( (yb * BLOCKDIM) + threadIdx.y ) * N + (threadIdx.x + xb * BLOCKDIM));
__syncthreads();
for (int k = 0; k < BLOCKDIM; k++)
value += *(s_A + threadIdx.y * BLOCKDIM + k) * *(s_B + k * BLOCKDIM + threadIdx.x);
__syncthreads();
}
*(C + ( startrow + (blockIdx.y * BLOCKDIM) + threadIdx.y ) * N + (threadIdx.x + blockIdx.x * BLOCKDIM)) = value;
}
void __global__ shared_mmult_gtx(int *A, int *B, int *C, int startrow, int endrow) {
__shared__ int s_A [BLOCKDIM * BLOCKDIM];
__shared__ int s_B [BLOCKDIM * BLOCKDIM];
int value = 0;
int block_index = threadIdx.y * BLOCKDIM + threadIdx.x;
int xa, ya, xb, yb = 0;
ya = (startrow / BLOCKDIM) + blockIdx.y;
xb = blockIdx.x;
//check boundary
if(startrow + blockIdx.y * BLOCKDIM + threadIdx.y >= N || startrow + blockIdx.y * BLOCKDIM + threadIdx.y >= endrow || blockIdx.x * BLOCKDIM + threadIdx.x >= N)
return;
for(int i = 0; i < N/BLOCKDIM; i++) {
//copy global memory Axaya and Bxbyb to shared memory
xa = i;
yb = i;
*(s_A + block_index) = *(A + ( (ya * BLOCKDIM) + threadIdx.y ) * N + (threadIdx.x + xa * BLOCKDIM));
*(s_B + block_index) = *(B + ( (yb * BLOCKDIM) + threadIdx.y ) * N + (threadIdx.x + xb * BLOCKDIM));
__syncthreads();
for (int k = 0; k < BLOCKDIM; k++)
value += *(s_A + threadIdx.y * BLOCKDIM + k) * *(s_B + k * BLOCKDIM + threadIdx.x);
__syncthreads();
}
*(C + ( startrow + (blockIdx.y * BLOCKDIM) + threadIdx.y ) * N + (threadIdx.x + blockIdx.x * BLOCKDIM)) = value;
}
int main () {
//N: number of elements of matrix
int NN = N * N;
int *A = (int *) malloc(NN*sizeof(int));
int *B = (int *) malloc(NN*sizeof(int));
int *C = (int *) malloc(NN*sizeof(int));
int griddim = N / BLOCKDIM;
int *ptrA = A;
int *ptrB = B;
int *ptrC;
if( N % BLOCKDIM != 0)
griddim++;
int nDevices;
cudaGetDeviceCount(&nDevices);
fprintf (stderr, "Number of devices found: %d \n\n", nDevices);
cudaDeviceSynchronize();
for (int i = 0; i < N; i++)
for (int j = 0; j < N; j++, ptrA++, ptrB++) {
*ptrA = i * N + j;
*ptrB = 0;
if (i == j) *ptrB = 1;
}
fprintf (stderr, "La visualizzazione delle matrici è stata limitata a 10x10\n");
fprintf (stderr, "Matrix A\n");
ptrA = A;
for (int i = 0; i < 10; i++) {
ptrA = A + i * N;
for (int j = 0; j < 10; j++, ptrA++)
fprintf (stderr, "%2d ", *ptrA);
fprintf (stderr, "\n");
}
fprintf (stderr, "\n\n\n");
fprintf (stderr, "Matrix B\n");
ptrB = B;
for (int i = 0; i < 10; i++) {
ptrB = B + i * N;
for (int j = 0; j < 10; j++, ptrB++)
fprintf (stderr, "%2d ", *ptrB);
fprintf (stderr, "\n");
}
fprintf (stderr, "\n\n\n");
//pointer vector arrays
int *A_d[nDevices], *B_d[nDevices], *C_d[nDevices];
for (int i = 0; i<nDevices; i++) {
cudaSetDevice(i);
//allocate memory both GPU card and GPU
cudaMalloc (&(A_d[i]), NN * sizeof (int));
cudaMalloc (&(B_d[i]), NN * sizeof (int));
cudaMalloc (&(C_d[i]), NN * sizeof (int));
//copy memory from host to global memory of i-GPU card
//cudamemcopy is async
cudaMemcpy (A_d[i], A, NN * sizeof (int), cudaMemcpyHostToDevice);
cudaMemcpy (B_d[i], B, NN * sizeof (int), cudaMemcpyHostToDevice);
}
//sync to be sure device memory is ok
for (int i = 0; i<nDevices; i++) {
cudaSetDevice(i);
cudaDeviceSynchronize();
}
//kernel is not blocking, so kernels run parallel
cudaSetDevice(0);
dim3 blocksPerGridTESLA (N/BLOCKDIM, NROWSTESLA/BLOCKDIM);
dim3 threadsPerBlockTESLA (BLOCKDIM, BLOCKDIM);
shared_mmult_tesla <<< blocksPerGridTESLA, threadsPerBlockTESLA >>> (A_d[0], B_d[0], C_d[0], 0, NROWSTESLA);
cudaSetDevice(1);
dim3 blocksPerGridGTX (N/BLOCKDIM, (N - NROWSTESLA)/BLOCKDIM);
dim3 threadsPerBlockGTX (BLOCKDIM, BLOCKDIM);
shared_mmult_gtx <<< blocksPerGridGTX, threadsPerBlockGTX >>> (A_d[1], B_d[1], C_d[1], NROWSTESLA, N);
//join
for (int i = 0; i<nDevices; i++) {
cudaSetDevice(i);
cudaDeviceSynchronize();
}
cudaMemcpy (C, C_d[0], NROWSTESLA * N * sizeof (int), cudaMemcpyDeviceToHost);
cudaMemcpy ( (C + NROWSTESLA * N ), (C_d[1] + NROWSTESLA * N), (NN - NROWSTESLA * N ) * sizeof (int), cudaMemcpyDeviceToHost);
//check
fprintf (stderr, "Matrix C\n");
unsigned long diff = 0;
ptrC = C;
ptrA = A;
for (int i = 0; i < N; i++) {
for (int j = 0; j < N; j++, ptrC++, ptrA++) {
if( j < 10 && i < 10)
fprintf (stderr, "%2d ", *ptrC);
diff += abs( *ptrA - *ptrC );
}
if( i < 10 )
fprintf (stderr, "\n");
}
fprintf (stderr, "Differenza: %lu\n", diff);
free(A);
free(B);
for (int i = 0; i<nDevices; i++) {
cudaSetDevice(i);
cudaFree(A_d[i]);
cudaFree(B_d[i]);
cudaFree(C_d[i]);
}
return 0;
}
|
6,654 | #include "cuda_runtime.h"
#include "device_functions.h"
#include "device_launch_parameters.h"
#include <stdio.h>
#include <math.h>
extern "C" {
__global__ void EnergyOfExistingMolecule(
const int NTotal,
const float* x,
const float* y,
const float* z,
const int* types,
const float* sigma10x10,
const float* epsilon10x10,
const float* lengths,
const float cutoffFactor,
const int nthMolecule,
float* cacheEnergy
)
{
__shared__ float localCache[1024];
unsigned int threadId = blockDim.x * blockIdx.x + threadIdx.x;
const unsigned int cacheIndex = threadIdx.x;
const float iX = x[nthMolecule];
const float iY = y[nthMolecule];
const float iZ = z[nthMolecule];
const int iType = types[nthMolecule];
const float Lx = lengths[0];
const float Ly = lengths[1];
const float Lz = lengths[2];
float dx = 0.0, dy = 0.0, dz = 0.0, dr2 = 0.0, idr6 = 0.0, ljEnergy = 0.0;
int jType = -1;
float tempEnergy = 0;
const float cutoffFactor2 = cutoffFactor * cutoffFactor;
while (threadId < NTotal)
{
// Do not calculate the energy of the nthMolecule with itself
if (threadId == nthMolecule) {
threadId += blockDim.x * gridDim.x;
continue;
}
jType = types[threadId];
// Skip "empty" molecules, defined by a negative " types" value
if (jType < 0) {
threadId += blockDim.x * gridDim.x;
continue;
}
const float isigma2 = sigma10x10[iType + 10 * jType] * sigma10x10[iType + 10 * jType];
const float iepsilon = epsilon10x10[iType + 10 * jType];
dx = x[threadId] - iX;
dy = y[threadId] - iY;
dz = z[threadId] - iZ;
dx = dx - Lx * round(dx / Lx);
dx = dx - Ly * round(dy / Ly);
dx = dx - Lz * round(dz / Lz);
dr2 = (dx * dx + dy * dy + dz * dz);
dr2 /= isigma2;
if (dr2 <= cutoffFactor2) {
idr6 = 1.0 / (dr2 * dr2 * dr2);
ljEnergy = 4.0 * iepsilon * idr6 * (idr6 - 1.0);
tempEnergy += ljEnergy;
}
threadId += blockDim.x * gridDim.x;
}
localCache[cacheIndex] = tempEnergy;
__syncthreads();
int i = blockDim.x / 2;
while (i != 0)
{
if (cacheIndex < i)
localCache[cacheIndex] += localCache[cacheIndex + i];
__syncthreads();
i /= 2;
}
if (cacheIndex == 0)
cacheEnergy[blockIdx.x] = localCache[0];
}
__global__ void VectorSum(const int N, const float* v, float* sum)
{
__shared__ float chache[1024];
unsigned int tid = blockDim.x * blockIdx.x + threadIdx.x;
const unsigned int chacheindex = threadIdx.x;
float temp = 0;
while (tid < N)
{
temp += v[tid];
tid += blockDim.x * gridDim.x;
}
chache[chacheindex] = temp;
__syncthreads();
int i = blockDim.x / 2;
while (i != 0)
{
if (chacheindex < i)
chache[chacheindex] += chache[chacheindex + i];
__syncthreads();
i /= 2;
}
if (chacheindex == 0)
sum[blockIdx.x] = chache[0];
}
__global__ void VectorDotProduct (const int N, const float* V1, const float* V2, float* V3)
{
__shared__ float chache[1024];
float temp;
unsigned int tid = blockDim.x * blockIdx.x + threadIdx.x;
const unsigned int chacheindex = threadIdx.x;
while (tid < N)
{
temp += V1[tid] * V2[tid];
tid += blockDim.x * gridDim.x;
}
chache[chacheindex] = temp;
__syncthreads();
int i = blockDim.x / 2;
while (i != 0)
{
if (chacheindex < i)
chache[chacheindex] += chache[chacheindex + i];
__syncthreads();
i /= 2;
}
if (chacheindex == 0)
V3[blockIdx.x] = chache[0];
}
}
// Print device properties
void printDevProp(cudaDeviceProp devProp)
{
printf("Major revision number: %d\n", devProp.major);
printf("Minor revision number: %d\n", devProp.minor);
printf("Name: %s\n", devProp.name);
printf("Total global memory: %zu\n", devProp.totalGlobalMem);
printf("Total shared memory per block: %zu\n", devProp.sharedMemPerBlock);
printf("Total registers per block: %d\n", devProp.regsPerBlock);
printf("Warp size: %d\n", devProp.warpSize);
printf("Maximum memory pitch: %zu\n", devProp.memPitch);
printf("Maximum threads per block: %d\n", devProp.maxThreadsPerBlock);
for (int i = 1; i <= 3; ++i)
printf("Maximum dimension %d of block: %d\n", i, devProp.maxThreadsDim[i - 1]);
for (int i = 1; i <= 3; ++i)
printf("Maximum dimension %d of grid: %d\n", i, devProp.maxGridSize[i - 1]);
printf("Clock rate: %d\n", devProp.clockRate);
printf("Total constant memory: %zu\n", devProp.totalConstMem);
printf("Texture alignment: %zu\n", devProp.textureAlignment);
printf("Concurrent copy and execution: %s\n", (devProp.deviceOverlap ? "Yes" : "No"));
printf("Number of multiprocessors: %d\n", devProp.multiProcessorCount);
printf("Kernel execution timeout: %s\n", (devProp.kernelExecTimeoutEnabled ? "Yes" : "No"));
return;
}
int main()
{
// Number of CUDA devices
int devCount;
cudaGetDeviceCount(&devCount);
printf("CUDA Device Query...\n");
printf("There are %d CUDA devices.\n", devCount);
// Iterate through devices
for (int i = 0; i < devCount; ++i)
{
// Get device properties
printf("\nCUDA Device #%d\n", i);
cudaDeviceProp devProp;
cudaGetDeviceProperties(&devProp, i);
printDevProp(devProp);
}
printf("\nPress any key to exit...");
char c;
scanf("%c", &c);
return 0;
} |
6,655 | /***********************************************************************
To Compile:
/usr/local/cuda-10.0/bin/nvcc -arch=compute_52 -o file.out filename.cu
***********************************************************************/
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
#include <cuda_runtime.h>
void check_output(float *A, int dim) {
/* Print output for debug */
int i,j;
printf("\n");
for(i = 0; i < dim; i++) {
for(j = 0; j < dim; j++) {
printf("%3.4f ", A[i*dim + j]);
}
printf(";\n");
}
printf("\n");
}
__global__ void MyKernel(float *d_a,float *d_b,float *d_c,int dim){
// __shared__ float shared[1024];
float partial = 0.0;
int i = threadIdx.y + blockIdx.y * blockDim.y; //row i of c
int j = threadIdx.x + blockIdx.x * blockDim.x; //Column j of c
int k;
i = i*dim;
for(k = 0; k < dim; k++){
partial+=d_a[i+k] * d_b[k*dim+j];
}
d_c[i+j] = partial;
}
__global__ void MyKernel2(float *d_a,float *d_b,float *d_c,int dim){
extern __shared__ float s[]; // declear a single shared array.
float *a_tile = s; // Divide the shared array into two
float *b_tile = (float*)&a_tile[blockDim.x*blockDim.y];
float partial = 0.0;
int bx = blockIdx.x ; int by = blockIdx.y ;
int tx = threadIdx.x; int ty = threadIdx.y;
int i = by * blockDim.y + ty; //row i of c
int j = bx * blockDim.x + tx; //Column j of c
int k,m;
i = i * dim;
int y = ty * blockDim.y;
for(m = 0; m < dim/blockDim.x; m=m+blockDim.x) {
a_tile[y+tx] = d_a[i + (m+tx)]; /* load coalesced */
b_tile[y+tx] = d_b[(m+ty)*dim + j]; /* not coalesced */
__syncthreads();
for(k = 0; k < blockDim.x; ++k)
partial += a_tile[y+k] * b_tile[k*blockDim.y+tx]; /* A bank conflicts */
__syncthreads();
d_c[i+j] = partial;
}
}
__global__ void MyKernel3(float *d_a,float *d_b,float *d_cT,int dim){
extern __shared__ float s[]; // declear a single shared array.
float *a_tile = s; // Divide the shared array into two
float *bT_tile = (float*)&a_tile[blockDim.x*blockDim.y];
float partial = 0.0;
int bx = blockIdx.x ; int by = blockIdx.y ;
int tx = threadIdx.x; int ty = threadIdx.y;
int i = by * blockDim.y + ty; //row i of c
int j = bx * blockDim.x + tx; //Column j of c
int k,m;
i = i * dim;
int y = ty * blockDim.y;
for(m = 0; m < dim/blockDim.x; m=m+blockDim.x) {
a_tile[y+tx] = d_a[i + (m+tx)]; /* load coalesced */
bT_tile[y+tx] = d_b[i + (m+tx)]; /* load coalesced */
__syncthreads();
for(k = 0; k < blockDim.x; ++k)
partial += a_tile[ty+k*blockDim.x]*bT_tile[tx+k*blockDim.y]; /* No bank conflicts */
__syncthreads();
d_cT[i+j] = partial;
}
}
int main(int argc, char const *argv[]) {
// Initiailize matrix dimension
int dim = 1024,block_size = 32;
int i, grid_size;
if (argc > 1) {
dim = atoi(argv[1]);
block_size = atoi(argv[2]);
}
// declear host and device timer.
srand(3);
grid_size = dim / block_size;
dim3 Block(block_size,block_size);
dim3 Grid(grid_size,grid_size);
struct timespec start,finish;
int ntime, stime;
float tot_time=0.0;
// Populate matrice
float *a = (float*)malloc(sizeof(float)*dim*dim);
float *bT = (float*)malloc(sizeof(float)*dim*dim);
float *c = (float*)malloc(sizeof(float)*dim*dim);
float *d_a, *d_bT ,*d_c, limit=10.0; //d_bT for transposed
for(i = 0; i < dim*dim; i++){
a[i] = ((float)rand()/(float)(RAND_MAX)) * limit;
bT[i] = ((float)rand()/(float)(RAND_MAX)) * limit;
}
// Allocate device memeory.
cudaMalloc( (void**)&d_a, dim*dim*sizeof(float));
cudaMalloc( (void**)&d_bT, dim*dim*sizeof(float));
cudaMalloc( (void**)&d_c, dim*dim*sizeof(float));
// Initiailize timer & start recording.
clock_gettime(CLOCK_REALTIME, &start);
// Copy memory to device.
cudaMemcpy(d_a ,a ,dim*dim*sizeof(float),cudaMemcpyHostToDevice);
cudaMemcpy(d_bT,bT,dim*dim*sizeof(float),cudaMemcpyHostToDevice);
// Call CUDA kernel function.
MyKernel<<<Grid, Block>>>(d_a,d_bT,d_c,dim);
cudaMemcpy(c, d_c, sizeof(float)*dim*dim,cudaMemcpyDeviceToHost);
// Timer stop.
cudaDeviceSynchronize();
clock_gettime(CLOCK_REALTIME, &finish);
ntime = finish.tv_nsec - start.tv_nsec;
stime = (int)finish.tv_sec - (int) start.tv_sec;
tot_time = ntime*1.0E-9 + stime;
/* Print output for debug */
printf("kernel#1 Time elapsed: %f ms. matrix dimension: %d X %d\n",
tot_time*1.0E3,dim,dim);
// reset memory and timer.
cudaFree(d_c); cudaFree(d_bT); cudaFree(d_a);
/*----------------Tile method with bank conflicts:------------------------*/
// Allocate memory again:
cudaMalloc( (void**)&d_a, dim*dim*sizeof(float));
cudaMalloc( (void**)&d_bT, dim*dim*sizeof(float));
cudaMalloc( (void**)&d_c, dim*dim*sizeof(float));
// start timming.
clock_gettime(CLOCK_REALTIME, &start);
cudaMemcpy(d_a ,a ,dim*dim*sizeof(float),cudaMemcpyHostToDevice);
cudaMemcpy(d_bT,bT,dim*dim*sizeof(float),cudaMemcpyHostToDevice);
MyKernel2<<<Grid,Block,(2*Block.x*Block.y*sizeof(float))>>>(d_a,d_bT,d_c,dim);
cudaMemcpy(c, d_c, sizeof(float)*dim*dim,cudaMemcpyDeviceToHost);
// Timer stop.
cudaDeviceSynchronize();
clock_gettime(CLOCK_REALTIME, &finish);
ntime = finish.tv_nsec - start.tv_nsec;
stime = (int)finish.tv_sec - (int) start.tv_sec;
tot_time = ntime*1.0E-9 + stime;
/* Print output for debug */
printf("kernel#2 Time elapsed: %f ms. matrix dimension: %d X %d\n",
tot_time*1.0E3,dim,dim);
// reset memory and timer.
cudaFree(d_c); cudaFree(d_bT); cudaFree(d_a);
/*----------------Tile method with no bank conflicts:----------------------*/
// Allocate memory again:
cudaMalloc( (void**)&d_a, dim*dim*sizeof(float));
cudaMalloc( (void**)&d_bT, dim*dim*sizeof(float));
cudaMalloc( (void**)&d_c, dim*dim*sizeof(float));
// start timming.
clock_gettime(CLOCK_REALTIME, &start);
cudaMemcpy(d_a ,a ,dim*dim*sizeof(float),cudaMemcpyHostToDevice);
cudaMemcpy(d_bT,bT,dim*dim*sizeof(float),cudaMemcpyHostToDevice);
MyKernel3<<<Grid,Block,(2*Block.x*Block.y*sizeof(float))>>>(d_a,d_bT,d_c,dim);
cudaMemcpy(c, d_c, sizeof(float)*dim*dim,cudaMemcpyDeviceToHost);
// Timer stop.
cudaDeviceSynchronize();
clock_gettime(CLOCK_REALTIME, &finish);
ntime = finish.tv_nsec - start.tv_nsec;
stime = (int)finish.tv_sec - (int) start.tv_sec;
tot_time = ntime*1.0E-9 + stime;
/* Print output for debug */
printf("kernel#3 Time elapsed: %f ms. matrix dimension: %d X %d\n",
tot_time*1.0E3,dim,dim);
// reset memory and timer.
cudaFree(d_c); cudaFree(d_bT); cudaFree(d_a);
free(a); free(bT); free(c);
return 0;
}
|
6,656 | #include <cassert>
#include <cstdio>
#include <cstdlib>
#include <vector>
__global__ void add_kernel(double* result, double const* arr1,
double const* arr2, size_t const arr_size)
{
size_t const idx = blockIdx.x * blockDim.x + threadIdx.x;
size_t const stride = blockDim.x * gridDim.x;
for (size_t i = 0; i < arr_size; i += stride)
{
if (idx < arr_size)
{
result[idx] = arr1[idx] + arr2[idx];
}
}
}
std::vector<double> add(std::vector<double> inarr1, std::vector<double> inarr2)
{
assert(inarr1.size() == inarr2.size());
size_t const arr_size = inarr1.size();
cudaError_t error;
error = cudaGetLastError();
if (error != cudaSuccess)
{
std::printf("0 %s\n", cudaGetErrorString(error));
std::exit(1);
}
double* arr1;
double* arr2;
double* calc_result;
cudaMallocManaged(&arr1, arr_size * sizeof(double));
cudaMallocManaged(&arr2, arr_size * sizeof(double));
cudaMallocManaged(&calc_result, arr_size * sizeof(double));
std::copy(inarr1.begin(), inarr1.end(), arr1);
std::copy(inarr2.begin(), inarr2.end(), arr2);
size_t const block_size = 256;
size_t const num_blocks = (arr_size + block_size - 1) / block_size;
add_kernel<<<num_blocks, block_size>>>(calc_result, arr1, arr2, arr_size);
cudaDeviceSynchronize();
std::vector<double> result(calc_result, calc_result + arr_size);
cudaFree(arr1);
cudaFree(arr2);
cudaFree(calc_result);
return result;
}
|
6,657 | #include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include<stdio.h>
__global__ void unique_grid_id_calculation_2d(int* data)
{
int thread_id = threadIdx.x;
int block_offset = blockIdx.x * blockDim.x;
int row_offset = blockDim.x * gridDim.x * blockIdx.y;
int grid_id = row_offset + block_offset + thread_id;
printf("blockIdx.x: %d, blockIdx.y: %d, threadIdx.x: %d, grid ID: %d, - data : %d \n",
blockIdx.x, blockIdx.y, thread_id, grid_id, data[grid_id]);
}
/*
int main()
{
int array_size = 16;
int array_byte_size = sizeof(int) * array_size;
int h_data[] = { 23, 9, 4, 53, 65, 12, 1, 33, 87, 45, 23, 12, 342, 56, 44, 99 };
int* d_data;
cudaMalloc((void**)&d_data, array_byte_size);
cudaMemcpy(d_data, h_data, array_byte_size, cudaMemcpyHostToDevice);
dim3 block(4);
dim3 grid(2, 2);
unique_grid_id_calculation_2d<<<grid, block>>>(d_data);
cudaDeviceSynchronize();
cudaDeviceReset();
return 0;
}
*/ |
6,658 | #include "includes.h"
#define BOOL int
#define TRUE 1
#define FALSE 0
#define populationSize 128
#define chromosomeSize 10
#define maxGeneration 500
#define crossRate 0.8
#define mutationRate 0.01
#define eliteCount 0.05*populationSize
//typedef float float;
float LB[10] = {0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5}; //lower bound
float UB[10] = {5, 4, 5, 4, 5, 5, 5, 5, 5, 4}; //upper bound
float *a; //Tzaihe
float *aa; //yingliK
float *aaa; //Tyingli
int aRow;
int aaaRow;
float Dysum[9];
__device__ float c_LB[10]; //lower bound
__device__ float c_UB[10]; //upper bound
__device__ float *c_a; //Tzaihe
__device__ float *c_aa; //yingliK
__device__ float *c_aaa; //Tyingli
__device__ int c_aRow;
__device__ int c_aaaRow;
__device__ float c_Dysum[9];
float bestFitnessOfGen; //每一代的最优适应度
int bestIndexOfGen; //每一代的最优适应度位置
float aveFitnessOfGen[maxGeneration]; //每一代的平均最优适应度
float fval; //最终最优适应度
int G; //取得最终最优适应度的迭代次数
//BOOL elitism = TRUE; //是否精英选择
__global__ void selectPre(float *fitness, float *Fitness, float *tmpFitness, float *populationArray, float *tmpPopulationArray){
int idx = threadIdx.x;
Fitness[idx] = 1 / fitness[idx];
__syncthreads();
tmpFitness[idx] = fitness[idx];
__syncthreads();
for(int i = 0; i < chromosomeSize; i++){
tmpPopulationArray[idx * chromosomeSize + i] = populationArray[idx * chromosomeSize + i];
}
__syncthreads();
} |
6,659 | __device__ void warp_reduce(int *s_ptr, uint *s_idx)
{
int tidx = threadIdx.x;
for (int n = 16; n >= 1; n >>= 1) {
if (tidx < n) {
int val1, val2;
val1 = s_ptr[tidx];
val2 = s_ptr[tidx + n];
int idx1, idx2;
idx1 = s_idx[tidx];
idx2 = s_idx[tidx + n];
s_ptr[tidx] = idx2;
s_idx[tidx] = val2;
}
}
} |
6,660 | ////////////////////////////////////////////////////////////////////////////
//
// Copyright 1993-2015 NVIDIA Corporation. All rights reserved.
//
// Please refer to the NVIDIA end user license agreement (EULA) associated
// with this source code for terms and conditions that govern your use of
// this software. Any use, reproduction, disclosure, or distribution of
// this software and related documentation outside the terms of the EULA
// is strictly prohibited.
//
////////////////////////////////////////////////////////////////////////////
/* Template project which demonstrates the basics on how to setup a project
* example application.
* Host code.
*/
|
6,661 | #include "includes.h"
__global__ void inc(int *array, size_t n){
size_t idx = threadIdx.x+blockDim.x*blockIdx.x;
while (idx < n){
array[idx]++;
idx += blockDim.x*gridDim.x; // grid-stride loop
}
} |
6,662 | #include "includes.h"
__global__ void getMask(unsigned int *d_in, unsigned int *d_out, unsigned int in_size, unsigned int bit_shift, unsigned int One) {
unsigned int index = threadIdx.x + blockDim.x * blockIdx.x;
unsigned int bit = 0;
if (index < in_size) {
bit = d_in[index] & (1 << bit_shift);
bit = (bit > 0) ? 1 : 0;
d_out[index] = (One ? bit : 1 - bit);
}
} |
6,663 | #include "includes.h"
__global__ void cunn_OneVsAllMultiMarginCriterion_updateGradInput_kernel(float *gradInput, float *input, float *target, int nframe, int dim, int sizeaverage, float *positiveWeight)
{
// __shared__ float buffer[MULTIMARGIN_THREADS];
int k = blockIdx.x;
float *input_k = input + k*dim;
float *gradInput_k = gradInput + k*dim;
int target_k = ((int)target[k])-1;
float g = (sizeaverage ? 1./((float)dim) : 1.);
int i_start = threadIdx.x;
int i_end = dim;
int i_step = blockDim.x;
// buffer[threadIdx.x] = 0;
for (int i=i_start; i<i_end; i+=i_step)
{
float y = (i==target_k) ? 1.0 : -1.0;
float z = 1 - input_k[i]*y;
if(z > 0)
{
float weight = (i==target_k) ? positiveWeight[i] : 1.0;
float h = -y*g*weight;
gradInput_k[i] = h;
}
else
gradInput_k[i] = 0;
}
__syncthreads();
// reduce
//if (threadIdx.x == 0)
//{
// float gradInput_target_k = 0;
//for (int i=0; i<blockDim.x; i++)
// gradInput_target_k += buffer[i];
//gradInput_k[target_k] = gradInput_target_k;
//}
} |
6,664 | #include <stdio.h>
__global__ void hello_world(){
printf("Alix(SenPai) - With love from Kavar Shiraz, IRAN\n");
}
int main() {
hello_world<<<1,1>>>();
return 0;
}
|
6,665 | // includes
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
#include <cuda_runtime.h>
#define N 8
// MAIN: rutina principal ejecutada en el host
int main(int argc, char** argv)
{
// declaracion
float *hst_matriz;
float *dev_matriz;
float *hst1_matriz;
float *dev1_matriz;
// reserva en el host
hst_matriz = (float*)malloc( N*sizeof(float) );
hst1_matriz = (float*)malloc( N*sizeof(float) );
// reserva en el device
cudaMalloc( (void**)&dev_matriz, N*sizeof(float) );
cudaMalloc( (void**)&dev1_matriz, N*sizeof(float) );
// inicializacion de datos en el host
srand ( (int)time(NULL) );
for (int i=0; i<N; i++)
{
hst_matriz[i] = (float) rand() / RAND_MAX;
}
// visualizacion de datos en el host
printf("DATOS:\n");
for (int i=0; i<N; i++)
{
printf("A[%i] = %.2f\n", i, hst_matriz[i]);
}
// copia de datos CPU -> GPU
cudaMemcpy(dev_matriz, hst_matriz, N*sizeof(float), cudaMemcpyHostToDevice);
// copia de datos GPU -> GPU
cudaMemcpy(dev1_matriz, dev_matriz, N*sizeof(float), cudaMemcpyDeviceToDevice);
// copia de datos GPU -> CPU
cudaMemcpy(hst1_matriz, dev1_matriz, N*sizeof(float), cudaMemcpyDeviceToHost);
// visualizacion de datos en el host
printf("\nDATOS:\n");
for (int i=0; i<N; i++)
{
printf("A[%i] = %.2f\n", i, hst1_matriz[i]);
}
// salida
time_t fecha;
time(&fecha);
printf("***************************************************\n");
printf("Programa ejecutado el: %s\n", ctime(&fecha));
printf("<pulsa [INTRO] para finalizar>");
getchar();
return 0;
} |
6,666 |
#include <stdio.h>
#include <stdlib.h>
#include <stdio.h>
#include <math.h>
// CUDA Kernel
__global__ void matrixMul( float* C, float* A, float* B, int TM)
{
// chaque thread calcule C[i][j]
// Coordonnees absolues du thread : indices i j
int j = blockIdx.x * blockDim.x+ threadIdx.x;
int i = blockIdx.y * blockDim.y+ threadIdx.y;
float value = 0;
for (int k = 0; k < TM; k+= 8)
{
value += A[i * TM + k] * B[k * TM + j];
value += A[i * TM + k+1] * B[(k+1) * TM + j];
value += A[i * TM + k+2] * B[(k+2) * TM + j];
value += A[i * TM + k+3] * B[(k+3) * TM + j];
value += A[i * TM + k+4] * B[(k+4) * TM + j];
value += A[i * TM + k+5] * B[(k+5) * TM + j];
value += A[i * TM + k+6] * B[(k+6) * TM + j];
value += A[i * TM + k+7] * B[(k+7) * TM + j];
// value += A[i * TM + k+8] * B[(k+8) * TM + j];
// value += A[i * TM + k+9] * B[(k+9) * TM + j];
// value += A[i * TM + k+10] * B[(k+10) * TM + j];
// value += A[i * TM + k+11] * B[(k+11) * TM + j];
// value += A[i * TM + k+12] * B[(k+12) * TM + j];
// value += A[i * TM + k+13] * B[(k+13) * TM + j];
// value += A[i * TM + k+14] * B[(k+14) * TM + j];
// value += A[i * TM + k+15] * B[(k+15) * TM + j];
}
C[i * TM + j] = value;
}
/////////////////////////////////////////////////////////
// Program main
/////////////////////////////////////////////////////////
int main(int argc, char** argv) {
int i, j, TM, BLOCK_SIZE_X, BLOCK_SIZE_Y;
unsigned int M_size;
float *h_A, *h_B, *h_C;
float *d_A, *d_B, *d_C;
cudaError_t cerror;
float elapsedTime ;
cudaEvent_t start , stop ;
// Valeurs par defaut
TM=2048;
BLOCK_SIZE_X = 32;
BLOCK_SIZE_Y = 32;
// Possibilite de lire TM dans arg1, BLOCK_SIZE_X dans arg2 et BLOCK_SIZE_Y ans arg3
if (argc>1) {
TM=atoi(argv[1]);
}
if (argc>3) {
BLOCK_SIZE_X =atoi(argv[2]);
BLOCK_SIZE_Y =atoi(argv[3]);
}
// Verification de la bonne taille TM par rapport aux dimensions des blocs
if ((TM % BLOCK_SIZE_X) !=0) {
printf("Taille matrice non multiple de taille bloc X %d \n", BLOCK_SIZE_X);
exit(1);
}
if ((TM % BLOCK_SIZE_Y) !=0) {
printf("Taille matrice non multiple de taille bloc Y %d \n", BLOCK_SIZE_Y);
exit(1);
}
// Allocation memoire sur CPU
M_size = TM*TM*sizeof(float);
h_A = (float*) malloc(M_size);
h_B = (float*) malloc(M_size);
h_C = (float*) malloc(M_size);
// initialisation des matrices avec des valeurs permettant de verifier le resultat
for(i = 0; i < TM; i++){
for(j = 0; j < TM; j++){
h_A[i*TM+j] = 1.0;
h_B[i*TM+j] = 1.0;
h_C[i*TM+j] = 0.0;
if (i==j) {
h_A[i*TM+j]=(float) (i+1);
h_B[i*TM+j]=(float) (i+1);
}
}
}
// Allocation memoire sur GPU
cudaMalloc((void**) &d_A, M_size);
cudaMalloc((void**) &d_B, M_size);
cudaMalloc((void**) &d_C, M_size);
// Calcul du temps : top depart
cudaEventCreate (&start ) ;
cudaEventCreate (&stop ) ;
cudaEventRecord ( start , 0 ) ;
// copie des donnes CPU vers GPU
cudaMemcpy(d_A, h_A, M_size, cudaMemcpyHostToDevice);
cudaMemcpy(d_B, h_B, M_size, cudaMemcpyHostToDevice);
// choix de la structure : grille et blocs
dim3 threads(BLOCK_SIZE_X, BLOCK_SIZE_Y);
dim3 grid(TM / threads.x, TM / threads.y);
printf("bloc %d %d \n", BLOCK_SIZE_X, BLOCK_SIZE_Y);
printf("grille %d %d \n", TM / threads.x, TM / threads.y);
// Lancement des threads
matrixMul<<< grid, threads >>>(d_C, d_A, d_B, TM);
// En cas d'erreur
cerror=cudaGetLastError();
if ((int)cerror !=0) {
printf("Erreur appel kernel %d \n", (int) cerror);
exit(cerror);
}
// copie des resultats depuis le GPU
cudaMemcpy(h_C, d_C, M_size, cudaMemcpyDeviceToHost);
// Calcul du temps d'execution
cudaEventRecord ( stop , 0 ) ;
cudaEventSynchronize ( stop ) ;
cudaEventElapsedTime ( &elapsedTime , start , stop ) ;
cudaEventDestroy ( start ) ;
cudaEventDestroy ( stop ) ;
printf ( "Temps consomme: %f secondes\n" , elapsedTime / 1000.0 ) ;
// Verification des resultats
for(i = 0; i < TM; i++){
for(j = 0; j < TM; j++){
if ((i==j) && (h_C[i*TM+j] != (float)((i+1)*(i+1)+TM-1))) {
printf("Erreur i: %d j: %d %f\n", i, j, h_C[i*TM+j] ); exit(1);
}
else if ((i!=j) && (h_C[i*TM+j] != (float)(i+j+TM))){
printf("Erreur i: %d j: %d\n", i, j);
exit(1);
}
}
}
// liberation de la memoire
free(h_A);
free(h_B);
free(h_C);
cudaFree(d_A);
cudaFree(d_B);
cudaFree(d_C);
}
|
6,667 | #include "includes.h"
__global__ void histo_kernel(unsigned char *buffer1, long size1, unsigned int *histo1){
int i = threadIdx.x + blockDim.x * blockIdx.x;
int stride = blockDim.x * gridDim.x;
while (i < size1){
atomicAdd(&(histo1[buffer1[i]]),1);
i += stride;
}
} |
6,668 | #include <stdio.h>
#include <stdint.h>
#define CUDA_SAFE_CALL( err ) (safe_call(err, __LINE__))
typedef unsigned long long int LONG;
void safe_call(cudaError_t ret, int line)
{
if(ret!=cudaSuccess)
{
printf("Error at line %d : %s\n",line,cudaGetErrorString(ret));
exit(-1);
}
}
__global__
void kernel(double * A, LONG N)
{
LONG i = blockDim.x*blockIdx.x + threadIdx.x;
if(i < N)
A[i] = (double) i / threadIdx.x;
}
int main()
{
LONG l;
scanf("%llu",&l);
LONG N = (LONG)1 << l;;
printf("N=%llu Size=%f GB\n",N,N*sizeof(double)/(1024*1024*1024.0));
const LONG BLOCKSIZE = 1024;
const LONG NUMBLOCKS = (N + BLOCKSIZE - 1) / BLOCKSIZE;
CUDA_SAFE_CALL(cudaSetDevice(0));
/* UVA unpinned memory */
#if 0
printf("\nUVA unpinned memory allocation \n");
double* C_cpu;
double* D_gpu;
C_cpu = (double *) malloc(N * sizeof(double));
printf("Host memory allocated\n");
CUDA_SAFE_CALL(cudaMalloc((void **)&D_gpu, N * sizeof(double)));
printf("Device memory allocated\n");
CUDA_SAFE_CALL(cudaMemcpy((void *)D_gpu, (void *)C_cpu, N * sizeof(double), cudaMemcpyDefault));
printf("Memory copied to device\n");
kernel<<<NUMBLOCKS, BLOCKSIZE>>>(D_gpu,N);
CUDA_SAFE_CALL(cudaDeviceSynchronize());
CUDA_SAFE_CALL(cudaMemcpy((void *)C_cpu, (void *)D_gpu, N * sizeof(double), cudaMemcpyDefault));
printf("Memory copied to host\n");
free(C_cpu);
CUDA_SAFE_CALL(cudaFree(D_gpu));
#endif
/* UVA pinned memory */
#if 0
printf("\nUVA pinned memory allocation \n");
double* E_cpu;
CUDA_SAFE_CALL(cudaHostAlloc ((void **)&E_cpu, N * sizeof(double), cudaHostAllocMapped /*| cudaHostAllocPortable*/));
printf("Host memory allocated\n");
kernel<<<NUMBLOCKS, BLOCKSIZE>>>(E_cpu,N);
CUDA_SAFE_CALL(cudaDeviceSynchronize());
CUDA_SAFE_CALL(cudaFreeHost(E_cpu));
#endif
/* Unified memory */
#if 1
printf("\nUnified memory allocation \n");
double* F_cpu;
CUDA_SAFE_CALL(cudaMallocManaged((void **)&F_cpu, N * sizeof(double)));
printf("Host memory allocated\n");
kernel<<<NUMBLOCKS, BLOCKSIZE>>>(F_cpu,N);
CUDA_SAFE_CALL(cudaDeviceSynchronize());
CUDA_SAFE_CALL(cudaFree(F_cpu));
#endif
printf("\nExiting...\n");
return 0;
}
|
6,669 | #define blocksize 256
#define gridsize 32
__global__ void mul(const float* A,const float * B, float * C, int n)
{
int loop = n/(blocksize*gridsize);
for(int i =0;i<loop;i++){
*(C + i*(blocksize*gridsize) + (256 *blockIdx.x + threadIdx.x)) = \
*(C + i*(blocksize*gridsize) + (256 *blockIdx.x + threadIdx.x)) * \
*(C + i*(blocksize*gridsize) + (256 *blockIdx.x + threadIdx.x));
}
if((256 *blockIdx.x + threadIdx.x)<n%(blocksize*gridsize)){
*(C + loop*(blocksize*gridsize) + (256 *blockIdx.x + threadIdx.x)) = \
*(C + loop*(blocksize*gridsize) + (256 *blockIdx.x + threadIdx.x)) * \
*(C + loop*(blocksize*gridsize) + (256 *blockIdx.x + threadIdx.x));
}
}
void eltwise_mul(const float* A,const float * B, float * C, int n)
{
mul<<<gridsize, blocksize>>>(A, B, C, n);
}
|
6,670 | /*
* BC_PointwiseDotproduct.cu
*
* Created on: Feb 6, 2018
* Author: joseph
*/
#ifndef BC_POINTWISEDOTPRODUCT_CU_
#define BC_POINTWISEDOTPRODUCT_CU_
namespace BC {
#ifdef __CUDACC__
#define __BC_gcpu__ __host__ __device__
#define BLACKCAT_GPU_ENABLED
#else
#define __BC_gcpu__
#endif
template<class, class> class Matrix;
template<class, class> class Vector;
template<class, class> class expression;
template<class T, class functor_type>
struct pointwise_accessor : expression<T, pointwise_accessor<T, functor_type>> {
//Evaluates a single row*column operation of two tensors
functor_type lv;
functor_type rv;
T* evaluation;
};
template<class T, class ml>
Vector<T, ml> operator * (const Matrix<T, ml>&, const Vector<T, ml>&) {
}
}
#endif /* BC_POINTWISEDOTPRODUCT_CU_ */
|
6,671 | #include <cstdio>
__global__ void checkIndexKernel() {
int threadID = threadIdx.x + threadIdx.y * blockDim.x + threadIdx.z * blockDim.x * blockDim.y;
int blockID = blockIdx.x + blockIdx.y * gridDim.x + blockIdx.z * gridDim.x * gridDim.y;
int threadIDinGird = threadID + blockID * blockDim.x * blockDim.y * blockDim.z;
printf("thread id in grid: %2d; thread id in block: %2d (%d, %d, %d) in blockDim (%d, %d, %d); block id: %d (%d, %d, %d) in gridDim (%d, %d, %d)\n",
threadIDinGird,
threadID, threadIdx.x, threadIdx.y, threadIdx.z, blockDim.x, blockDim.y, blockDim.z,
blockID, blockIdx.x, blockIdx.y, blockIdx.z, gridDim.x, gridDim.y, gridDim.z);
}
int main() {
dim3 block_size(3, 2, 2);
dim3 grid_size(2);
checkIndexKernel<<<grid_size, block_size>>>();
cudaDeviceSynchronize();
return 0;
} |
6,672 | #include "includes.h"
#ifdef __cplusplus
extern "C" {
#endif
#ifdef __cplusplus
}
#endif
__global__ void kernel_compute(int* trainingSet, int* data, int* res, int setSize, int dataSize){
int diff, toAdd, computeId;
computeId = blockIdx.x * blockDim.x + threadIdx.x;
//__shared__ int set[784];
if(computeId < setSize){
diff = 0;
for(int i = 0; i < dataSize; i++){
toAdd = data[i] - trainingSet[computeId*784 + i];
diff += toAdd * toAdd;
}
res[computeId] = diff;
}
} |
6,673 | /***
* Ashutosh Dhar
* Department of Electrical and Computer Engineeing
* University of Illinois, Urbana-Champaign
*
*/
#include <cuda.h>
#include <iostream>
#include <cstdio>
#define THREADS_PER_SM 1
#define BLOCKS_PER_SM 1
#define ITERATIONS 64
#define L2_CACHE_SIZE 512*1024
#define DATA_SIZE (L2_CACHE_SIZE * ITERATIONS)
using namespace std;
__global__ void cache_latency(unsigned int *latency, float *data)
{
unsigned int start_t, stop_t;
float local;
int load=0;
for(int i=0; i<DATA_SIZE; i++)
{
start_t = clock();
local = data[load];
stop_t = clock();
__syncthreads();
data[load] = local+1;
latency[i] = start_t;
latency[i + ITERATIONS] = stop_t;
}
}
int main(int argc, char **argv)
{
float *data;
data = (float*) malloc(sizeof(float)*DATA_SIZE);
srand(12);
for(int i=0; i<DATA_SIZE; i++)
{
data[i] = 1.0*rand();
}
unsigned int *latency;
latency = (unsigned int*) malloc((sizeof(int)) * 2 * DATA_SIZE);
unsigned int *d_latency;
float *d_data;
cudaError_t errorFlag = cudaSuccess;
errorFlag = cudaMalloc((void**) &d_latency, (sizeof(unsigned int)*2*DATA_SIZE));
if(errorFlag != cudaSuccess)
{
fprintf(stderr, "Failed to alloc memory (error code %s)!\n", cudaGetErrorString(errorFlag));
exit(-1);
}
errorFlag = cudaMalloc((void**) &d_data, (sizeof(float)*DATA_SIZE));
if(errorFlag != cudaSuccess)
{
fprintf(stderr, "Failed to alloc memory (error code %s)!\n", cudaGetErrorString(errorFlag));
exit(-1);
}
errorFlag = cudaMemcpy(d_data, data, (sizeof(float)*DATA_SIZE), cudaMemcpyHostToDevice);
if(errorFlag != cudaSuccess)
{
fprintf(stderr, "Failed to copyback (error code %s)!\n", cudaGetErrorString(errorFlag));
exit(-1);
}
dim3 dimBlock(THREADS_PER_SM,1,1);
dim3 dimGrid(BLOCKS_PER_SM,1,1);
cache_latency<<<dimGrid,dimBlock>>>(d_latency,d_data);
cudaDeviceSynchronize();
errorFlag = cudaGetLastError();
if(errorFlag != cudaSuccess)
{
fprintf(stderr, "Failed to launch vectorAdd kernel (error code %s)!\n", cudaGetErrorString(errorFlag));
exit(-1);
}
errorFlag = cudaMemcpy(latency, d_latency, (sizeof(int)*2*DATA_SIZE), cudaMemcpyDeviceToHost);
if(errorFlag != cudaSuccess)
{
fprintf(stderr, "Failed to copyback (error code %s)!\n", cudaGetErrorString(errorFlag));
exit(-1);
}
cout<<"\nLatency\n";
for(int i=0; i<DATA_SIZE; i++)
{
cout<<latency[i+ITERATIONS] - latency[i]<<" ";
}
cout<<endl;
return 0;
}
|
6,674 | #include "includes.h"
//////////// Calculates weighting for assembling single element solution ///////////
// One weight is evaluated for each node
// Added back to global memory
__device__ void jacobi_iter( float *ue, float *up_glob, int *cells, float *temp1, int idx, int idy)
{
float ue_new;
int v;
int offset = 15*threadIdx.x;
/*
Le_shrd = &temp1[offset];
be_shrd = &temp1[offset + 9];
u_old = &temp1[offset + 12];
*/
v = cells[(idx*3) + idy];
ue_new = temp1[(offset + 9) + idy];
temp1[(offset + 12) + idy] = up_glob[v];
__syncthreads();
ue_new -= temp1[offset + (idy*3) + ((idy+1)%3) ] * temp1[(offset + 12) + (idy+1) % 3];
ue_new -= temp1[offset + (idy*3) + ((idy+2)%3) ] * temp1[(offset + 12) + (idy+2) % 3];
ue_new /= temp1[offset + (idy*3) + idy];
ue[(idx*3) + idy] = ue_new;
}
__device__ void elems_shared_cpy(float *Le, float *be, float *temp1, int idx, int idy){
int offset = 15*threadIdx.x;
// Le_shrd = &temp1[offset];
// be_shrd = &temp1[offset + 9];
temp1[(offset + 9) + idy] = be[(idx*3) + idy];
for(int i=0; i<3; i++){
temp1[offset + (idy*3) + i] = Le[(idx*9) + (idy*3) + i];
}
}
__global__ void local_sols( float *Le, float *be, float *ue, float *up_glob, int *cells, int num_cells)
{
int idx = blockIdx.x*blockDim.x + threadIdx.x;
int idy = blockIdx.y*blockDim.y + threadIdx.y;
extern __shared__ float temp1[];
if(idx < num_cells && idy < blockDim.y){
elems_shared_cpy(Le, be, temp1, idx, idy);
__syncthreads();
jacobi_iter(ue, up_glob, cells, temp1, idx, idy);
}
} |
6,675 |
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <iostream>
#include <random>
#include <string>
using namespace std;
float LIMIT_L = 1.f;
float LIMIT_R = 100.f;
random_device rd;
mt19937 mt(rd());
uniform_real_distribution<float> dist(LIMIT_L, LIMIT_R);
//Ctrl+ shift + space
//ctrl + k + ctrl + d
__global__
void vecAddkernel(float* A, float* B, float* C, int n) {
int i = blockDim.x * blockIdx.x + threadIdx.x;
if (i < n)
C[i] = A[i] + B[i];
}
void vecAdd(float* A, float* B, float* C, int n) {
int size = n * sizeof(float);
float* d_A, * d_B, * d_C;
cudaMalloc((void**)&d_A, size);
cudaMemcpy(d_A, A, size, cudaMemcpyHostToDevice);
cudaMalloc((void**)&d_B, size);
cudaMemcpy(d_B, B, size, cudaMemcpyHostToDevice);
cudaMalloc((void**)&d_C, size);
//Función kernel
vecAddkernel<<<ceil(n/256.0),256>>>(d_A, d_B, d_C,n);
cudaMemcpy(C, d_C, size, cudaMemcpyDeviceToHost);
//Print Vector Resultante
//Liberara memoria del device A,B,C
cudaFree(d_A); cudaFree(d_B); cudaFree(d_C);
}
void fillVector(float* Vect, int n) {
for (int i = 0; i < n; i++)
Vect[i] = dist(mt);
}
void printVector(float* Vect, int n, string nameVect = "") {
cout << endl;
if (nameVect != "")
cout << nameVect << " : ";
for (int i = 0; i < n; i++)
cout << Vect[i] << " ";
}
int main()
{
int n = 5;
float* A = new float[n];
float* B = new float[n];
float* C = new float[n];
fillVector(A, n);
fillVector(B, n);
printVector(A, n,"A");
printVector(B, n,"B");
vecAdd(A,B,C,n);
printVector(C, n, "C");
return 0;
}
|
6,676 | extern "C" __global__
void divElements(float * x, float * y, int n) {
int tid = blockIdx.x * blockDim.x + threadIdx.x;
if (tid < n) {
x[tid] /= y[tid];
}
}
extern "C" __global__
void elemMax(float * dst, float * src, int n) {
int tid = blockIdx.x * blockDim.x + threadIdx.x;
if (tid < n) {
dst[tid] = max(dst[tid], src[tid]);
}
}
extern "C" __global__
void expElements(float * x, int n) {
int tid = blockIdx.x * blockDim.x + threadIdx.x;
if (tid < n) {
x[tid] = expf(x[tid]);
}
}
extern "C" __global__
void logElements(float * x, int n) {
int tid = blockIdx.x * blockDim.x + threadIdx.x;
if (tid < n) {
x[tid] = logf(x[tid]);
}
}
extern "C" __global__
void tanhElements(float * x, int n) {
int tid = blockIdx.x * blockDim.x + threadIdx.x;
if (tid < n) {
x[tid] = tanhf(x[tid]);
}
}
extern "C" __global__
void sinElements(float * x, int n) {
int tid = blockIdx.x * blockDim.x + threadIdx.x;
if (tid < n) {
x[tid] = sinf(x[tid]);
}
}
extern "C" __global__
void sigmoidElements(float * x, int n) {
int tid = blockIdx.x * blockDim.x + threadIdx.x;
if (tid < n) {
x[tid] = (1 + tanhf(x[tid] / 2)) / 2;
}
}
extern "C" __global__
void clipPositive(float * x, int n) {
int tid = blockIdx.x * blockDim.x + threadIdx.x;
if (tid < n) {
x[tid] = fmaxf(0, x[tid]);
}
}
extern "C" __global__
void shiftRandUniform(float * x, int n) {
int tid = blockIdx.x * blockDim.x + threadIdx.x;
if (tid < n) {
if (x[tid] == 1.0f) {
x[tid] = 0;
}
}
}
extern "C" __global__
void uniformToBernoulli(float * x, int n) {
int tid = blockIdx.x * blockDim.x + threadIdx.x;
if (tid < n) {
if (x[tid] > 0.5) {
x[tid] = 1;
} else {
x[tid] = 0;
}
}
}
extern "C" __global__
void addRepeated(float * dest, float * source, int destLen, int sourceLen) {
int tid = blockIdx.x * blockDim.x + threadIdx.x;
if (tid < destLen) {
dest[tid] += source[tid % sourceLen];
}
}
extern "C" __global__
void addRepeatedPow2(float * dest, float * source, int destLen, int srcMask) {
int tid = blockIdx.x * blockDim.x + threadIdx.x;
if (tid < destLen) {
dest[tid] += source[tid & srcMask];
}
}
extern "C" __global__
void scaleRepeated(float * dest, float * source, int destLen, int sourceLen) {
int tid = blockIdx.x * blockDim.x + threadIdx.x;
if (tid < destLen) {
dest[tid] *= source[tid % sourceLen];
}
}
extern "C" __global__
void scaleRepeatedPow2(float * dest, float * source, int destLen, int srcMask) {
int tid = blockIdx.x * blockDim.x + threadIdx.x;
if (tid < destLen) {
dest[tid] *= source[tid & srcMask];
}
}
extern "C" __global__
void addScaler(float s, float * dest, int destLen) {
int tid = blockIdx.x * blockDim.x + threadIdx.x;
if (tid < destLen) {
dest[tid] += s;
}
}
extern "C" __global__
void setScaler(float s, float * dest, int destLen) {
int tid = blockIdx.x * blockDim.x + threadIdx.x;
if (tid < destLen) {
dest[tid] = s;
}
}
extern "C" __global__
void addChunks(float * dest, float * source, int destLen, int chunkSize) {
int tid = blockIdx.x * blockDim.x + threadIdx.x;
if (tid < destLen) {
dest[tid] += source[tid / chunkSize];
}
}
extern "C" __global__
void subChunks(float * dest, float * source, int destLen, int chunkSize) {
int tid = blockIdx.x * blockDim.x + threadIdx.x;
if (tid < destLen) {
dest[tid] -= source[tid / chunkSize];
}
}
extern "C" __global__
void lessThan(float s, float * v, int n) {
int tid = blockIdx.x * blockDim.x + threadIdx.x;
if (tid < n) {
if (v[tid] < s) {
v[tid] = 1;
} else {
v[tid] = 0;
}
}
}
extern "C" __global__
void greaterThan(float s, float * v, int n) {
int tid = blockIdx.x * blockDim.x + threadIdx.x;
if (tid < n) {
if (v[tid] > s) {
v[tid] = 1;
} else {
v[tid] = 0;
}
}
}
extern "C" __global__
void equalTo(float s, float * v, int n) {
int tid = blockIdx.x * blockDim.x + threadIdx.x;
if (tid < n) {
if (v[tid] == s) {
v[tid] = 1;
} else {
v[tid] = 0;
}
}
}
extern "C" __device__
float addLogPair(float x, float y) {
float m = max(x, y);
return logf(expf(x-m) + expf(y-m)) + m;
}
extern "C" __global__
void addLogs(float * dst, float * src, int rowSize) {
extern __shared__ float chunk[];
int rowIdx = blockIdx.y * blockDim.x + threadIdx.x;
if (rowIdx < rowSize) {
chunk[threadIdx.x] = src[rowIdx+rowSize*blockIdx.x];
}
__syncthreads();
for (int stride = (blockDim.x>>1); stride >= 1; stride >>= 1) {
if (threadIdx.x < stride && rowIdx+stride < rowSize) {
chunk[threadIdx.x] = addLogPair(chunk[threadIdx.x],
chunk[threadIdx.x+stride]);
}
__syncthreads();
}
if (threadIdx.x == 0) {
dst[blockIdx.y + blockIdx.x*gridDim.y] = chunk[0];
}
}
extern "C" __global__
void powScaler(float s, float * dest, int destLen) {
int tid = blockIdx.x * blockDim.x + threadIdx.x;
if (tid < destLen) {
dest[tid] = powf(dest[tid], s);
}
}
extern "C" __global__
void mapForward(float * dst, float * src, int * table, int tableSize) {
int tid = blockIdx.x * blockDim.x + threadIdx.x;
if (tid < tableSize) {
dst[tid] = src[table[tid]];
}
}
extern "C" __global__
void mapBackward(float * dst, float * src, int * table, int tableSize) {
int tid = blockIdx.x * blockDim.x + threadIdx.x;
if (tid < tableSize) {
atomicAdd(&dst[table[tid]], src[tid]);
}
}
extern "C" __global__
void mapMax(int * table, float * data, int rows, int cols) {
int tid = blockIdx.x * blockDim.x + threadIdx.x;
if (tid < rows) {
int base = tid * cols;
float * row = &data[base];
int maxIdx = 0;
float maxVal = row[0];
for (int i = 1; i < cols; ++i) {
if (row[i] > maxVal) {
maxVal = row[i];
maxIdx = i;
}
}
table[tid] = maxIdx + base;
}
}
|
6,677 | //pass
//--blockDim=64 --gridDim=64 --no-inline
#include "cuda.h"
__device__ void bar(float x) {
}
__global__ void foo(int* A) {
bar(A[0]);
}
|
6,678 | #include <stdio.h>
#include <assert.h>
#include <stdlib.h>
#include <sys/time.h>
#include <functional>
#include <iostream>
#include <climits>
#include<cuda.h>
#include <cuda_runtime.h>
#include <cuda_runtime_api.h>
#include <math.h>
using namespace std;
typedef unsigned int eid_t;
typedef long var;
#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 GS 1024
#define BS 1024
typedef struct {
var n;
var m;
var num_of_rows;
eid_t *rows;
eid_t *adj;
eid_t *num_edges;
eid_t *rlen;
} graph_t;
void free_graph(graph_t *g) {
if( g->adj != NULL )
free( g->adj );
if( g->num_edges != NULL )
free( g->num_edges );
}
static double timer() {
struct timeval tp;
gettimeofday(&tp, NULL);
return ((double) (tp.tv_sec) + tp.tv_usec * 1e-6);
}
/*********************** READ INPUT FILE ************************************************************/
int load_graph_from_file(char *filename, graph_t *g) {
FILE *infp = fopen(filename, "r");
if (infp == NULL) {
fprintf(stderr, "Error: could not open inputh file: %s.\n Exiting ...\n", filename);
exit(1);
}
fprintf(stdout, "Reading input file: %s\n", filename);
//double t0 = timer();
//Read N and M
fscanf(infp, "%ld %ld\n", &(g->n), &(g->m));
printf("N: %ld, M: %ld \n", g->n, g->m);
bool flag[g->n];
var m = 0;
//Allocate space
g->num_edges = (eid_t *) malloc((g->n + 1) * sizeof(eid_t));
assert(g->num_edges != NULL);
var i ;
for ( i=0; i<g->n + 1; i++) {
g->num_edges[i] = 0;
}
for ( i=0; i<g->n; i++) {
flag[i] = false;
}
eid_t u, v;
printf(" Checking1\n ");
while( fscanf(infp, "%u %u\n", &u, &v) != EOF ) {
if (u>v)
{ g->num_edges[u]++; flag[u] = true; m++; }
else if(u<v)
{ g->num_edges[v]++; flag[v] = true; m++;}
}
printf(" Checking2\n ");
g->rows = (eid_t *) malloc((g->n) * sizeof(eid_t));
g->num_of_rows = 0;
var k =0;
for (i = 0; i<g->n; i++)
{ if (flag[i] == true)
{ g->num_of_rows++;
g->rows[k] = i;
k++;
}
}
g->m = m;
/*cout<<"flag"<<endl;
for(long i = 0; i < g->n; i++)
{ cout<<flag[i] <<endl;
}
cout<<endl;
cout<<"g->rows"<<endl;
for(long i = 0; i < g->num_of_rows; i++)
{ cout<<g->rows[i] <<endl;
}
cout<<endl;
/* cout<<"num edges"<<endl;
for(long i = 0; i < m; i++)
{ cout<<g->num_edges[i] <<endl;
}
cout<<endl;
*/
fclose( infp );
/* if( m != g->m) {
printf("Reading error: file does not contain %ld edges.\n", g->m);
free( g->num_edges );
exit(1);
}
*/
m = 0;
eid_t *temp_num_edges = (eid_t *) malloc((g->n + 1) * sizeof(eid_t));
assert(temp_num_edges != NULL);
temp_num_edges[0] = 0;
for(i = 0; i < g->n; i++) {
m += g->num_edges[i];
temp_num_edges[i+1] = m;
}
//Allocate space for adj
g->adj = (eid_t *) malloc(m * sizeof(eid_t));
assert(g->adj != NULL);
for(i= 0; i < g->n+1; i++)
g->num_edges[i] = temp_num_edges[i];
/*
cout<<"g->num edges"<<endl;
for(long i = 0; i < g->n+1; i++)
{ cout<<g->num_edges[i] <<endl;
}
cout<<endl;
*/
g->rlen = (eid_t *) malloc((g->n) * sizeof(eid_t));
k =0;
for ( i = 0; i<g->n; i++)
{ if (flag[i] == true)
{ g->rlen[k] = g->num_edges[i+1] - g->num_edges[i];
}
else
g->rlen[k] = 0;
k++;
}
infp = fopen(filename, "r");
if (infp == NULL) {
fprintf(stderr, "Error: could not open input file: %s.\n Exiting ...\n", filename);
exit(1);
}
//Read N and M
fscanf(infp, "%ld %ld\n", &(g->n), &m);
for(i = 0; i < m; i++)
g->adj[i] = 0;
//Read the edges
while( fscanf(infp, "%u %u\n", &u, &v) != EOF ) {
if ( u > v )
{ g->adj[ temp_num_edges[u] ] = v;
temp_num_edges[u]++;
}
else if (u<v)
{
g->adj[ temp_num_edges[v] ] = u;
temp_num_edges[v]++;
}
}
fclose( infp );
/*
cout<<" g->adj"<<endl;
for(long i = 0; i < m; i++)
{ cout<<g->adj[i] <<endl;
}
cout<<" g->rlen"<<endl;
for(long i = 0; i < g->n; i++)
{ cout<<g->rlen[i] <<endl;
}
*/
//free( temp_num_edges );
return 0;
}
//***************************************************** CUDA KERNEL *****************************************************************
__global__ void support_compute(eid_t* roff, eid_t* rows, eid_t *cols, int* bitmap, eid_t* rlen, var M, var N, var m, int* supp, int k, bool* weak, bool* weak_vertices)
{
//printf ("Inside Kernel \n");
__shared__ int value[BS];
int tid = threadIdx.x;
eid_t io_s, io_e, io, jo_s, jo_e, jo, i;
int c;
for(var s = blockIdx.x; s<M; s += gridDim.x)
{
i = rows[s];
io_s = roff[i];
io_e = io_s + rlen[i];
for(io = io_s; io<io_e; io +=blockDim.x)
{
value[tid] = -1;
c= -1;
c = ((io+threadIdx.x)<io_e) ? (cols[io+threadIdx.x]): -1;
if (c>-1)
{ atomicOr((bitmap+(N*blockIdx.x)+c), 1);
value[tid] = c;
}
__syncthreads();
for(int t=0; t<blockDim.x; t++)
{
int j = value[t];
if(j == -1)
break;
var cnt = 0;
jo_s = roff[j];
jo_e = jo_s + rlen[j];
for(jo = jo_s + threadIdx.x; jo < jo_e; jo += blockDim.x)
{
eid_t k = cols[jo];
if (*(bitmap+(blockIdx.x*N)+k) == 1)
{ cnt++;
atomicAdd(supp + jo, 1);
eid_t a=0;
for( a =0; a <= rlen[i]; a++)
{ if (cols[io_s + a] == k)
break;
}
atomicAdd(supp+io_s+a, 1);
}
}
atomicAdd(supp+io+t, cnt);
}
}
atomicAnd((bitmap+(N*blockIdx.x))+c, 0);
}
//End of support computation
__syncthreads();
/*
if (threadIdx.x == 0 && blockIdx.x == 0)
{ printf("Support\n");
for( int i = 0; i<m; i++)
printf("%d \n", supp[i]);
}
*/
__shared__ int flag;
while(true)
{ //atomicAnd(point, 0);
//flag[threadIdx.x] = 0;
flag = 0;
for (int s = blockIdx.x; (s*blockDim.x + threadIdx.x)<m; s+= gridDim.x)
{
int i = s* blockDim.x + threadIdx.x;
//printf("it = %d, blockId = %d, threadId = %d, s = %d, i = %d, weak[i]= %d, supp[i] = %d\n", it, blockIdx.x , threadIdx.x, s, i, weak[i], supp[i]);
if (supp[i] < k-2 && weak[i] == 0)
{
weak[i] = 1;
supp[i] = -1;
flag = 1;
}
}
__syncthreads();
if(flag == 0)
break;
__syncthreads();
if(k>3)
{ for (int s = blockIdx.x; (s*blockDim.x + threadIdx.x)<m; s+= gridDim.x)
{
int i = s* blockDim.x + threadIdx.x;
if (weak[i] == 1)
{ int j=0;
long start = 0, end = N, mid;
while(start<end)
{ mid = (start+end)/2;
j = mid;
if(i+1 > roff[mid] ) start = mid+1;
else end = mid-1;
}
weak_vertices[j] = 1;
}
}
__syncthreads();
for (int s = blockIdx.x; (s*blockDim.x + threadIdx.x)<N; s+= gridDim.x)
{
int i = s* blockDim.x + threadIdx.x;
if (weak_vertices[i] == 1)
{ int j=0;
for ( j = 0; j<rlen[i]; j++)
{ int u = roff[i+j];
weak_vertices[cols[u]] = 1;
//printf("blockId = %d, threadId = %d, u = %d \n", blockIdx.x , threadIdx.x, cols[u]);
}
}
}
__syncthreads();
for(int s = blockIdx.x; s<M; s += gridDim.x)
{
i = rows[s];
io_s = roff[i];
io_e = io_s + rlen[i];
if(weak_vertices[i] == 1)
{
for(io = io_s; io<io_e; io +=blockDim.x)
{
value[tid] = -1;
c= -1;
c = ((io+threadIdx.x)<io_e) ? (cols[io+threadIdx.x]): -1;
if (c>-1)
{ atomicOr((bitmap+(N*blockIdx.x)+c), 1);
value[tid] = c;
}
__syncthreads();
for(int t=0; t<blockDim.x; t++)
{
int j = value[t];
if(j == -1)
break;
if (weak_vertices[j] == 1)
{
int cnt = 0;
jo_s = roff[j];
jo_e = jo_s + rlen[j];
for(jo = jo_s + threadIdx.x; jo < jo_e; jo += blockDim.x)
{
eid_t k = cols[jo];
if (*(bitmap+(blockIdx.x*N)+k) == 1)
{ cnt++;
eid_t a=0;
for( a =0; a<=rlen[i]; a++)
{ if (cols[io_s + a] == k)
break;
}
if(weak_vertices[k] == 1)
{
if( supp[jo] == -1 || supp[io_s+a] == -1 || supp[io+t] == -1)
{ if ( supp[jo] != -1) atomicSub(supp+jo, 1);
if ( supp[io_s+a] != -1) atomicSub(supp+io_s+a, 1);
if ( supp[io+t] != -1) atomicSub(supp+io+t, 1);
}
//printf("i = %d, j = %d, k = %d, f = %d\n", i, j, k,f);
}//if k end
}
}
//printf("i = %d, j = %d, cnt = %d, i,j = %d \n", i, j, cnt, io+t);
}//if j end
}
}
atomicAnd((bitmap+(N*blockIdx.x))+c, 0);
}//if i end
}//for end
__syncthreads();
} //(k > 3) end
if (k==3) break;
} // while end
}// cuda end
int main(int argc, char *argv[]) {
graph_t g;
int gs=GS;
int k = 3;
if( argc < 2 )
{
fprintf(stderr, "%s <Graph file>\n", argv[0]);
exit(1);
}
load_graph_from_file(argv[1], &g);
cout<<"File read complete"<<endl;
int *b;
b= (int *) malloc(g.m*sizeof(int));
for(int i=0;i<g.m;i++)
*(b + i ) = 0;
int *bm = (int *) malloc((g.n)*gs*sizeof(int));
for(int i=0;i<gs;i++)
{
for(int j=0;j<(g.n);j++)
{
*(bm + i*g.n +j)=0;
}
}
cout<<"g.num_of_rows = "<<g.num_of_rows<<endl;
int *supp;
eid_t *roff;
eid_t *r;
eid_t *col;
eid_t* rl;
int *bitmap;
bool *weak, *wh;
bool* weak_vertices;
wh= (bool *) malloc(g.m*sizeof(bool));
for(int i=0;i<g.m;i++)
*(wh + i ) = false;
cout<<" Malloc startng "<<endl;
gpuErrchk(cudaMalloc(&roff, (g.n + 1)*sizeof(eid_t)));
gpuErrchk(cudaMemcpy(roff, g.num_edges, (g.n + 1)*sizeof(eid_t), cudaMemcpyHostToDevice));
gpuErrchk(cudaMalloc(&r, (g.num_of_rows)*sizeof(eid_t)));
gpuErrchk(cudaMemcpy(r, g.rows, ( g.num_of_rows )*sizeof(eid_t), cudaMemcpyHostToDevice));
gpuErrchk(cudaMalloc(&col, (g.m)*sizeof(eid_t)));
gpuErrchk(cudaMemcpy(col, g.adj, (g.m)*sizeof(eid_t), cudaMemcpyHostToDevice));
gpuErrchk(cudaMalloc(&bitmap, (g.n)*gs*sizeof(int)));
gpuErrchk(cudaMemcpy(bitmap, bm ,(g.n)*gs*sizeof(int), cudaMemcpyHostToDevice));
gpuErrchk(cudaMalloc(&supp, g.m*sizeof(int)));
gpuErrchk(cudaMemcpy(supp, b, g.m*sizeof(int), cudaMemcpyHostToDevice));
gpuErrchk(cudaMalloc(&rl, g.n*sizeof(eid_t)));
gpuErrchk(cudaMemcpy(rl, g.rlen, g.n*sizeof(eid_t), cudaMemcpyHostToDevice));
gpuErrchk(cudaMalloc(&weak, g.m*sizeof(bool)));
gpuErrchk(cudaMemcpy(weak, wh, g.m*sizeof(bool), cudaMemcpyHostToDevice));
gpuErrchk(cudaMalloc(&weak_vertices, g.n*sizeof(bool)));
cout<<"sending into cuda"<<endl;
double t0 = timer();
support_compute<<<GS,BS>>>(roff, r, col, bitmap, rl, g.num_of_rows, g.n, g.m, supp, k, weak, weak_vertices);
gpuErrchk( cudaPeekAtLastError() );
gpuErrchk( cudaDeviceSynchronize() );
cout<<"Time: "<< timer() - t0<<" sec\n ";
gpuErrchk(cudaMemcpy(b, supp, g.m*sizeof(int), cudaMemcpyDeviceToHost));
gpuErrchk(cudaMemcpy(wh, weak, g.m*sizeof(bool), cudaMemcpyDeviceToHost));
cout<<"Return from Cuda"<<endl;
printf("Support\n");
for( var i = 0; i<g.m; i++)
printf("%d \n", b[i]);
printf("Weak\n");
for( int i = 0; i<g.m; i++)
printf("%d \n", wh[i]);
return 0;
}
|
6,679 | /**
* @file : constant_eg.cu
* @brief : Examples of using constant memory for CUDA
* @details : constant memory for CUDA examples
*
* @author : Ernest Yeung <ernestyalumni@gmail.com>
* @date : 20170103
* @ref : http://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#device-memory-specifiers
*
* https://www.paypal.com/cgi-bin/webscr?cmd=_donations&business=ernestsaveschristmas%2bpaypal%40gmail%2ecom&lc=US&item_name=ernestyalumni¤cy_code=USD&bn=PP%2dDonationsBF%3abtn_donateCC_LG%2egif%3aNonHosted
*
* which won't go through a 3rd. party such as indiegogo, kickstarter, patreon.
* Otherwise, I receive emails and messages on how all my (free) material on
* physics, math, and engineering have helped students with their studies,
* and I know what it's like to not have money as a student, but love physics
* (or math, sciences, etc.), so I am committed to keeping all my material
* open-source and free, whether or not
* sufficiently crowdfunded, under the open-source MIT license:
* feel free to copy, edit, paste, make your own versions, share, use as you wish.
* Just don't be an asshole and not give credit where credit is due.
* Peace out, never give up! -EY
*
* */
/*
* COMPILATION TIP
* nvcc constant_eg.cu -o constant_eg
*
* */
#include <iostream>
__constant__ float constData_global[256];
__device__ float devData;
__device__ float* devPointer;
int main(int argc, char* argv[]) {
float data_main[256];
/* "boilerplate" test values */
for (int idx=0; idx<256; idx++) {
data_main[idx] = ((float) idx+1);
}
cudaMemcpyToSymbol(constData_global, data_main, sizeof(data_main));
float data_main1[256];
for (int idx=0; idx < 256; idx++) { std::cout << data_main1[idx] << " "; }
cudaMemcpyFromSymbol(data_main1, constData_global, sizeof(data_main1) );
/* sanity check */
for (int idx=0; idx < 256; idx++) { std::cout << data_main1[idx] << " "; }
// __constant__ float constData_main[256]; // error: a "__constant__"
// variable declaration is not allowed inside a function body
// __device__ float devData; // error: a "__device__" variable declaration is not allowed inside a function body
float value = 3.14;
cudaMemcpyToSymbol(devData, &value, sizeof(float));
float *ptr;
cudaMalloc(&ptr, 256*sizeof(float));
cudaMemcpyToSymbol(devPointer, &ptr, sizeof(ptr));
}
|
6,680 | #include <cuda.h>
#include <iostream>
#include <sys/time.h>
#include <stdio.h>
using namespace std;
/* Bank conflict example
*/
// (*) also compare with: TILE_DIM = BLOCK_ROWS = 32
#define TILE_DIM 16
#define BLOCK_ROWS 16
__global__ void transposeCoalesced(double *odata, double *idata, int width, int height, int nreps)
{
// (*) Uncomment +1 to avoid bank conflicts
__shared__ float tile[TILE_DIM][TILE_DIM /* +1 */];
int xIndex = blockIdx.x * TILE_DIM + threadIdx.x;
int yIndex = blockIdx.y * TILE_DIM + threadIdx.y;
int index_in = xIndex + (yIndex)*width;
xIndex = blockIdx.y * TILE_DIM + threadIdx.x;
yIndex = blockIdx.x * TILE_DIM + threadIdx.y;
int index_out = xIndex + (yIndex)*height;
for (int r=0; r < nreps; r++) {
for (int i=0; i<TILE_DIM; i+=BLOCK_ROWS) {
tile[threadIdx.y+i][threadIdx.x] = idata[index_in+i*width];
}
__syncthreads();
for (int i=0; i<TILE_DIM; i+=BLOCK_ROWS) {
odata[index_out+i*height] = tile[threadIdx.x][threadIdx.y+i];
}
}
}
int main() {
// number of kernel calls
int nTranspose = 1;
// number of transposes inside kernel after copying to shared memory.
int nreps = 20;
time_t sTime = time(NULL);
struct timeval tt1, tt2;
int ms;
double fms;
int side = 2048;
int n = side*side;
double *data = (double*)malloc(2*n * sizeof(double));
for (int i=0; i<n; i++) {
data[i] = double(i);
data[i+n] = 0;
}
double *data_dev;
cudaMalloc((void**) &data_dev, 2 * n * sizeof(double));
dim3 grid(side/TILE_DIM,side/TILE_DIM,1);
dim3 threads(TILE_DIM,BLOCK_ROWS,1);
cudaMemcpy(data_dev, data, 2*n * sizeof(double), cudaMemcpyHostToDevice);
cudaThreadSynchronize();
gettimeofday( &tt1, NULL );
for (int i=0; i<nTranspose; i++)
transposeCoalesced <<< grid, threads >>>(data_dev+n, data_dev, side, side, nreps);
cudaThreadSynchronize();
gettimeofday( &tt2, NULL );
cudaMemcpy(data, data_dev + n, n * sizeof(double), cudaMemcpyDeviceToHost);
ms = (tt2.tv_sec - tt1.tv_sec);
ms = ms * 1000000 + (tt2.tv_usec - tt1.tv_usec);
fms = ((double)ms)/1000000.0;
cout << "Comp time = " << fms << endl;
cudaFree(data_dev);
cout << "data[145] = " << data[145] << endl;
free(data);
}
|
6,681 | extern "C"
{
// __device__ int fun5(int* argIntStar, float argFloat)
// {
// __shared__ int tab[123],temp,* ptr,arr[2];
// ptr=&tab[5];
// tab[5]=argIntStar[threadIdx.x];
// argIntStar[6]=*ptr;
// return 10;
// }
__device__ int fun1(int* argIntStar, float argFloat)
{
__shared__ int tab[123],temp,* ptr,arr[2];
ptr=&tab[5];
tab[5]=argIntStar[threadIdx.x];
argIntStar[6]=*ptr;
return 10;
}
__global__ void fun2(char argCharArray [],int argInt)
{
__shared__ char tab[12],a,b,* c;
__shared__ signed int*q[1] , w[2],*e;
argCharArray[threadIdx.x]=5;
}
__global__ void fun3()
{}
__device__ int*fun4(){ return 0;}
/*__device__ int fun1(int* argIntStar, float argFloat)
{
__shared__ int tab[123],temp,* ptr,arr[2];
ptr=&tab[5];/*
tab[5]=argIntStar[threadIdx.x];
argIntStar[6]=*ptr;
return 10;
}*/
__device__ void fun5(){
int /*asdf /* */a/**/=5; ///*/*asdf
}
} |
6,682 | #include <stdio.h>
__global__ void kernel() {
printf("Hello from block %d thread %d\n", blockIdx.x, threadIdx.x);
}
int main() {
kernel <<< 4, 6 >>>();
cudaDeviceSynchronize();
return 0;
}
|
6,683 | #include <stdlib.h>
#include <memory.h>
#include <stdio.h>
#include <time.h>
#include <stdint.h>
#include <cuda_runtime.h>
#include <cuda_runtime_api.h>
#include <curand_kernel.h>
#include <device_functions.h>
#define uint8 unsigned char
#define uint32 unsigned long int
#define SHA1_BLOCK_SIZE 20
typedef struct {
uint8 data[64];
uint32 datalen;
unsigned long long bitlen;
uint32 state[5];
uint32 k[4];
} CUDA_SHA1_CTX;
#ifndef ROTLEFT
#define ROTLEFT(a,b) (((a) << (b)) | ((a) >> (32-(b))))
#endif
/*********************** FUNCTION DEFINITIONS ***********************/
__device__ __host__ __forceinline__ void cuda_sha1_transform(CUDA_SHA1_CTX* ctx, const uint8 data[])
{
uint32 a, b, c, d, e, i, j, t, m[80];
for (i = 0, j = 0; i < 16; ++i, j += 4)
m[i] = (data[j] << 24) + (data[j + 1] << 16) + (data[j + 2] << 8) + (data[j + 3]);
for (; i < 80; ++i) {
m[i] = (m[i - 3] ^ m[i - 8] ^ m[i - 14] ^ m[i - 16]);
m[i] = (m[i] << 1) | (m[i] >> 31);
}
a = ctx->state[0];
b = ctx->state[1];
c = ctx->state[2];
d = ctx->state[3];
e = ctx->state[4];
for (i = 0; i < 20; ++i) {
t = ROTLEFT(a, 5) + ((b & c) ^ (~b & d)) + e + ctx->k[0] + m[i];
e = d;
d = c;
c = ROTLEFT(b, 30);
b = a;
a = t;
}
for (; i < 40; ++i) {
t = ROTLEFT(a, 5) + (b ^ c ^ d) + e + ctx->k[1] + m[i];
e = d;
d = c;
c = ROTLEFT(b, 30);
b = a;
a = t;
}
for (; i < 60; ++i) {
t = ROTLEFT(a, 5) + ((b & c) ^ (b & d) ^ (c & d)) + e + ctx->k[2] + m[i];
e = d;
d = c;
c = ROTLEFT(b, 30);
b = a;
a = t;
}
for (; i < 80; ++i) {
t = ROTLEFT(a, 5) + (b ^ c ^ d) + e + ctx->k[3] + m[i];
e = d;
d = c;
c = ROTLEFT(b, 30);
b = a;
a = t;
}
ctx->state[0] += a;
ctx->state[1] += b;
ctx->state[2] += c;
ctx->state[3] += d;
ctx->state[4] += e;
}
__device__ __host__ inline void cuda_sha1_init(CUDA_SHA1_CTX* ctx)
{
ctx->datalen = 0;
ctx->bitlen = 0;
ctx->state[0] = 0x67452301;
ctx->state[1] = 0xEFCDAB89;
ctx->state[2] = 0x98BADCFE;
ctx->state[3] = 0x10325476;
ctx->state[4] = 0xc3d2e1f0;
ctx->k[0] = 0x5a827999;
ctx->k[1] = 0x6ed9eba1;
ctx->k[2] = 0x8f1bbcdc;
ctx->k[3] = 0xca62c1d6;
}
__device__ __host__ inline void cuda_sha1_update(CUDA_SHA1_CTX* ctx, const uint8 data[], size_t len)
{
size_t i;
for (i = 0; i < len; ++i) {
ctx->data[ctx->datalen] = data[i];
ctx->datalen++;
if (ctx->datalen == 64) {
cuda_sha1_transform(ctx, ctx->data);
ctx->bitlen += 512;
ctx->datalen = 0;
}
}
}
__device__ __host__ inline void cuda_sha1_final(CUDA_SHA1_CTX* ctx, uint8 hash[])
{
uint32 i;
i = ctx->datalen;
// Pad whatever data is left in the buffer.
if (ctx->datalen < 56) {
ctx->data[i++] = 0x80;
while (i < 56)
ctx->data[i++] = 0x00;
}
else {
ctx->data[i++] = 0x80;
while (i < 64)
ctx->data[i++] = 0x00;
cuda_sha1_transform(ctx, ctx->data);
memset(ctx->data, 0, 56);
}
// Append to the padding the total message's length in bits and transform.
ctx->bitlen += ctx->datalen * 8;
ctx->data[63] = ctx->bitlen;
ctx->data[62] = ctx->bitlen >> 8;
ctx->data[61] = ctx->bitlen >> 16;
ctx->data[60] = ctx->bitlen >> 24;
ctx->data[59] = ctx->bitlen >> 32;
ctx->data[58] = ctx->bitlen >> 40;
ctx->data[57] = ctx->bitlen >> 48;
ctx->data[56] = ctx->bitlen >> 56;
cuda_sha1_transform(ctx, ctx->data);
// Since this implementation uses little endian byte ordering and MD uses big endian,
// reverse all the bytes when copying the final state to the output hash.
for (i = 0; i < 4; ++i) {
hash[i] = (ctx->state[0] >> (24 - i * 8)) & 0x000000ff;
hash[i + 4] = (ctx->state[1] >> (24 - i * 8)) & 0x000000ff;
hash[i + 8] = (ctx->state[2] >> (24 - i * 8)) & 0x000000ff;
hash[i + 12] = (ctx->state[3] >> (24 - i * 8)) & 0x000000ff;
hash[i + 16] = (ctx->state[4] >> (24 - i * 8)) & 0x000000ff;
}
}
__device__ __host__ inline void sha1new(uint8* msg, uint8 length, uint8 sha1[20]) {
CUDA_SHA1_CTX ctx;
cuda_sha1_init(&ctx);
cuda_sha1_update(&ctx, msg, length);
cuda_sha1_final(&ctx, sha1);
}
/*__global__ void kernel_sha1_hash(uint8* indata, uint32 inlen, uint8* outdata, uint32 n_batch)
{
uint32 thread = blockIdx.x * blockDim.x + threadIdx.x;
if (thread >= n_batch)
{
return;
}
uint8* in = indata + thread * inlen;
uint8* out = outdata + thread * SHA1_BLOCK_SIZE;
CUDA_SHA1_CTX ctx;
cuda_sha1_init(&ctx);
cuda_sha1_update(&ctx, in, inlen);
cuda_sha1_final(&ctx, out);
}
extern "C"
{
void mcm_cuda_sha1_hash_batch(uint8* in, uint32 inlen, uint8* out, uint32 n_batch)
{
uint8* cuda_indata;
uint8* cuda_outdata;
cudaMalloc(&cuda_indata, inlen * n_batch);
cudaMalloc(&cuda_outdata, SHA1_BLOCK_SIZE * n_batch);
cudaMemcpy(cuda_indata, in, inlen * n_batch, cudaMemcpyHostToDevice);
uint8 thread = 256;
uint8 block = (n_batch + thread - 1) / thread;
kernel_sha1_hash << < block, thread >> > (cuda_indata, inlen, cuda_outdata, n_batch);
cudaMemcpy(out, cuda_outdata, SHA1_BLOCK_SIZE * n_batch, cudaMemcpyDeviceToHost);
cudaDeviceSynchronize();
cudaError_t error = cudaGetLastError();
if (error != cudaSuccess) {
printf("Error cuda sha1 hash: %s \n", cudaGetErrorString(error));
}
cudaFree(cuda_indata);
cudaFree(cuda_outdata);
}
}
*/ |
6,684 | #include <stdio.h>
#include <iostream>
#include <math.h>
#define TPB 256
#define ARRAY_SIZE 10000
#define N (ARRAY_SIZE/TPB + 1)
using namespace std;
__global__ void saxpy(float *x, float *y, const int a)
{
const int i = blockIdx.x * blockDim.x + threadIdx.x;
if (i < ARRAY_SIZE) {
y[i] = a * x[i] + y[i];
}
}
int main()
{
float *x = NULL; // pointer to array of floats on host
float *y = NULL; // pointer to array of floats on host
float *result = NULL; // pointer to array that stores the results of SAXPY on the CPU
float *d_x = NULL; // pointer to array of floats on device
float *d_y = NULL; // pointer to array of floats on device
float *d_result = NULL; // pointer to array that stores the results of SAXPY on the GPU
int i = 0;
const int a = 3.0; // value of a in a
// Allocate memory for arrays on CPU
x = (float*)malloc(ARRAY_SIZE * sizeof(float));
y = (float*)malloc(ARRAY_SIZE * sizeof(float));
result = (float*)malloc(ARRAY_SIZE * sizeof(float));
d_result = (float*)malloc(ARRAY_SIZE * sizeof(float));
// Allocate memory for arrays on device
cudaMalloc(&d_x, ARRAY_SIZE * sizeof(float));
cudaMalloc(&d_y, ARRAY_SIZE * sizeof(float));
// Initialize with random values on host
for (int i = 0; i < ARRAY_SIZE; i++) {
x[i] = rand() % 1000;
y[i] = rand() % 1000;
}
// Copy random values to device
cudaMemcpy(d_x, x, ARRAY_SIZE * sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(d_y, y, ARRAY_SIZE * sizeof(float), cudaMemcpyHostToDevice);
printf("\nComputing SAXPY on the CPU...");
for (i = 0; i < ARRAY_SIZE; i++) {
result[i] = a * x[i] + y[i];
}
printf("Done!");
printf("\n\nComputing SAXPY on the GPU...");
saxpy<<<N, TPB>>>(d_x, d_y, a);
printf("Done!");
// comparing the results of the two versions
printf("\n\nComparing the output for each implementation...");
cudaMemcpy(d_result, d_y, ARRAY_SIZE * sizeof(float), cudaMemcpyDeviceToHost);
int flag_comparison = 0;
for (i = 0; i < ARRAY_SIZE; i++) {
if(abs(result[i] - d_result[i]) > 1)
{
flag_comparison = 1;
break;
}
}
if(flag_comparison == 0)
{
printf("Correct!");
}
else
{
printf("Incorrect!");
}
free(x);
free(y);
cudaFree(d_x);
cudaFree(d_y);
return 0;
}
|
6,685 | #include <stdio.h>
#include <stdlib.h>
// Device code
__global__ void VecAdd(int* A, int* B, int* C) {
int i = blockDim.x * blockIdx.x * threadIdx.x;
C[i] = A[i] + B[i];
}
// Host code
int main() {
int *h_A, *h_B, *h_C, *d_A, *d_B, *d_C;
int N = 4096;
size_t size = N * sizeof(int);
// Allocate input vectors h_A and h_B in host memory (CPU)
h_A = (int *) malloc(size);
h_B = (int *) malloc(size);
h_C = (int *) malloc(size);
// Initialise h_A and h_B here
for (int i = 0; i < N; i++) {
h_A[i] = i;
h_B[i] = i;
}
// Allocate Vectors in device memory (GPU)
cudaMalloc((void**) &d_A, size);
cudaMalloc((void**) &d_B, size);
cudaMalloc((void**) &d_C, size);
// Copy vectors from host memory to device memory
cudaMemcpy(d_A, h_A, size, cudaMemcpyHostToDevice);
cudaMemcpy(d_B, h_B, size, cudaMemcpyHostToDevice);
cudaMemcpy(d_C, h_C, size, cudaMemcpyHostToDevice);
// Invoke kernel
int threadsPerBlock = 256;
int blocksPerGrid = N / threadsPerBlock;
VecAdd<<<blocksPerGrid, threadsPerBlock>>>(d_A, d_B, d_C);
cudaMemcpy(h_C, d_C, size, cudaMemcpyDeviceToHost);
// Free host memory
free(h_A);
free(h_B);
free(h_C);
// Free device memory
cudaFree(d_A);
cudaFree(d_B);
cudaFree(d_C);
return 0;
} |
6,686 | #include <stdio.h>
using namespace std;
#define N 4
__global__ void mykernel(void) {}
__global__ void add(int *a, int *b, int *sum) {
int tid = blockIdx.x;
if(tid < N)
sum[tid] = a[tid] * b[tid];
}
int main() {
mykernel<<<1,1>>>();
int a[N],b[N],c[N];
int *d_a, *d_b, *d_c;
int sz = N * sizeof(int);
cudaMalloc((void **)&d_a, sz);
cudaMalloc((void **)&d_b, sz);
cudaMalloc((void **)&d_c, sz);
for(int i=0; i<N; i++) {
a[i] = -i;
b[i] = i * i;
}
cudaMemcpy(d_a, a, sz, cudaMemcpyHostToDevice);
cudaMemcpy(d_b, b, sz, cudaMemcpyHostToDevice);
add<<<N,1>>>(d_a, d_b, d_c);
cudaMemcpy(c, d_c, sz, cudaMemcpyDeviceToHost);
for(int i=0; i<N; i++) {
printf("%d + %d = %d\n", a[i], b[i], c[i]);
}
cudaFree(d_a);
cudaFree(d_b);
cudaFree(d_c);
return 0;
}
|
6,687 | /*
* cpp11Features.cc
*
* Created on: May 4, 2014
* Author: reid
*/
//Overriding Functions
//catch mistakes when overriding functions
struct B {
virtual void f(int);
};
struct D1: B {
void f(int) override;
}; // OK
/*
struct D2: B {
void f(long) override;
}; // error
*/
struct D3: B {
void f(long); //hiding intentional?
};
struct S {
S();
S(int); // => no implicit default constructor
virtual S& operator=(S const&) = default;
};
S::S() = default; // non-inline definition
template <typename T1, typename T2>
auto add(T1 first, T2 second) -> decltype(first + second){
decltype(5) myInt= 5; // C++11
decltype(3.14) myFloat= 3.14; // C++11
decltype(5) myFloat2= 3.14; // C++11
return first + second;
}
|
6,688 | #include <cuda_runtime.h>
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
void handleError(cudaError_t error, int lineno) {
if(error != cudaSuccess) {
printf("Error %s:%d\n", __FILE__, lineno);
printf("Code: %d, Reason: %s\n", error, cudaGetErrorString(error));
exit(EXIT_FAILURE);
}
}
void initializeData(float *iptr, int size) {
time_t t;
srand((unsigned int) time(&t));
for(int i = 0; i < size; i++) {
iptr[i] = (float)(rand() & 0xFF) / 10.0f;
}
}
__global__ void sumMatrix(float *A, float *B, float *C, int nx, int ny) {
unsigned int ix = threadIdx.x + blockIdx.x * blockDim.x;
unsigned int iy = threadIdx.y + blockIdx.y * blockDim.y;
unsigned int idx = ix + iy * nx;
if(ix < nx && iy < ny) {
C[idx] = A[idx] + B[idx];
}
}
int main(int argc, char *argv[]) {
int dev = 0;
cudaDeviceProp deviceProp;
handleError(cudaGetDeviceProperties(&deviceProp, dev), __LINE__);
handleError(cudaSetDevice(dev), __LINE__);
int nx = 1 << 11;
int ny = 1 << 11;
int nxy = nx * ny;
int nBytes = nxy * sizeof(float);
float *h_A, *h_B, *gpuRef;
h_A = (float *) malloc(nBytes);
h_B = (float *) malloc(nBytes);
gpuRef = (float *) malloc(nBytes);
memset(gpuRef, 0, nBytes);
initializeData(h_A, nxy);
initializeData(h_B, nxy);
float *d_A, *d_B, *d_C;
handleError(cudaMalloc((void **)&d_A, nBytes), __LINE__);
handleError(cudaMalloc((void **)&d_B, nBytes), __LINE__);
handleError(cudaMalloc((void **)&d_C, nBytes), __LINE__);
handleError(cudaMemcpy(d_A, h_A, nBytes, cudaMemcpyHostToDevice), __LINE__);
handleError(cudaMemcpy(d_B, h_B, nBytes, cudaMemcpyHostToDevice), __LINE__);
int dimx = 32;
int dimy = 32;
dim3 block(dimx, dimy);
dim3 grid((nx + block.x - 1)/ block.x, (ny + block.y - 1)/ block.y);
sumMatrix<<<grid, block>>>(d_A, d_B, d_C, nx, ny);
handleError(cudaMemcpy(gpuRef, d_C, nBytes, cudaMemcpyDeviceToHost), __LINE__);
for(int i = 0; i < nx; i++) {
for(int j = 0; j < ny; j++) {
printf("%.2f + %.2f = %.2f\n", h_A[i * nx + j], h_B[i * nx + j], gpuRef[i * nx + j]);
}
printf("\n");
}
cudaFree(d_A);
cudaFree(d_B);
cudaFree(d_C);
free(h_A);
free(h_B);
free(gpuRef);
cudaDeviceReset();
return 0;
}
|
6,689 | #include <stdio.h>
#include <cuda_runtime.h>
#define DEBUG 0
//will compute local histogram
//assuming passed pointers are adjusted for the thread
//bitpos is the lsb from which to consider numbits towards msb
__device__ void computeLocalHisto(int *localHisto, float *arrElem, int n,
int numBits, int bitpos) {
int i;
int numBuckets = 1 << numBits;
int mask = (1 << numBits) - 1;
int key;
for (i = 0; i < n; i++) {
key = (((int)arrElem[i]) >> bitpos) & mask;
localHisto[key]++;
}
}
__device__ void dispArr(int *arr, int n) {
int i;
//threadId with in a block, DMat doc to start with
int thId = threadIdx.x;
if (thId == 0) {
printf("\n");
for (i = 0; i < n; i++) {
printf(" %d ", arr[i]);
}
printf("\n");
}
}
//assuming sizeof int == sizeof float
__device__ void computeAtomicHisto(int *aggHisto, float *arrElem, int numElem,
int numBits, int bitpos) {
int i, j;
int numBuckets = 1 << numBits;
int mask = (1 << numBits) - 1;
int key;
void *vptr;
int *iptr;
//thread id within a block
int threadId = threadIdx.x;
//number of threads in block
int nThreads = blockDim.x;
for (i = threadId; i < numElem; i+=nThreads) {
vptr = (void*)(arrElem + i);
iptr = (int*)vptr;
key = ( (*iptr) >> bitpos) & mask;
atomicAdd(&(aggHisto[key]), 1);
}
}
//assuming sizeof int == sizeof float
__device__ void writeSortedVals(int *aggHisto, float *fromArr, float *toArr,
int numBits, int bitpos, int n) {
int i, key;
int mask = (1 << numBits) - 1;
void *vptr;
int *iptr;
for (i = 0; i < n; i++) {
vptr = (void*)(fromArr + i);
iptr = (int*)vptr;
key = ( (*iptr) >> bitpos) & mask;
if (DEBUG) {
printf("toArr[%d] = %f\n", aggHisto[key], fromArr[i]);
}
toArr[aggHisto[key]++] = fromArr[i];
}
}
__device__ void zeroedInt(int *arr, int count) {
int i;
//thread id within a block
int threadId = threadIdx.x;
//number of threads in block
int nThreads = blockDim.x;
for (i = threadId; i < count; i+=nThreads) {
arr[i] = 0;
}
}
//scan array arr of size n=nThreads, power of 2
__device__ void preSubScan(int *arr, int n, int prev) {
int i, d, ai, bi, offset, temp;
//threadId with in a block, DMat doc to start with
int thId = threadIdx.x;
//number of threads in blocks
int nThreads = blockDim.x;
d = 0;
offset = 1;
//build sum in place up the tree
for (d = n>>1; d > 0; d >>=1) {
__syncthreads();
if (thId < d) {
ai = offset*(2*thId+1) - 1;
bi = offset*(2*thId+2) - 1;
arr[bi] += arr[ai];
}
offset*=2;
}
//clear last element
if (thId == 0) {
arr[n-1] = 0;
}
//traverse down tree & build scan
for (int d = 1; d < n; d *=2) {
offset = offset >> 1;
__syncthreads();
if (thId < d) {
ai = offset*(2*thId + 1) - 1;
bi = offset*(2*thId + 2) - 1;
temp = arr[ai];
arr[ai] = arr[bi];
arr[bi] += temp;
}
}
for (i = thId; i < n; i+=nThreads) {
arr[i] += prev;
}
__syncthreads();
}
__device__ void d_dispFArr(float *arr, int n) {
int i;
//threadId with in a block, DMat doc to start with
int thId = threadIdx.x;
if (thId == 0) {
printf("\n");
for (i = 0; i < n; i++) {
printf(" %f ", arr[i]);
}
printf("\n");
}
}
//works efficiently for power of 2
__device__ void scan(int *arr, int n) {
int i, j, prev, next, temp;
//threadId with in a block, DMat doc to start with
int thId = threadIdx.x;
//number of threads in blocks
int nThreads = blockDim.x;
//divide the simpred into nThreads blocks,
//scan each block in parallel, with next iteration using results from prev blocks
prev = 0;
next = 0;
for (i = 0; i < n; i += nThreads) {
//dispArr(arr, n);
next = arr[i+nThreads-1];
if (n - i >= nThreads) {
preSubScan(arr + i, nThreads, (i>0?arr[i-1]:0) + prev);
} else {
//not power of 2 perform serial scan for others
//this will be last iteration of loop
if (thId == 0) {
for (j = i; j < n; j++) {
temp = prev + arr[j-1];
prev = arr[j];
arr[j] = temp;
}
}
}//end else
prev = next;
}//end for
__syncthreads();
}
//numbits means bits at a time
__global__ void radixSort(float *d_InArr, int n, int numBits) {
int i, j, elemPerThread;
int localHistoElemCount;
//get current block number
int blockId = blockIdx.x;
//thread id within a block
int threadId = threadIdx.x;
//number of threads in block
int nThreads = blockDim.x;
//global thread id
int globalThreadId = blockIdx.x * blockDim.x + threadIdx.x;
extern __shared__ int s[];
//shared mem space for aggregated histogram
int *aggHisto = s;
//shared mem space to copy array to be sorted
float *fromArr = (float*) &aggHisto[1<<numBits];
float *toArr = (float *) &fromArr[n];
float *tempSwap;
//bucket size
int bucketSize = 1 << numBits;
//initialize arrays in shared mem
for (i = threadId; i < n; i+=nThreads) {
fromArr[i] = d_InArr[i];
toArr[i] = 0;
}
if (threadId == 0 && DEBUG) {
printf("\n fromArray: ");
d_dispFArr(fromArr, n);
}
//for each numbits chunk do following
for (i = 0; i < sizeof(float)*8; i+=numBits) {
//reset histogram
zeroedInt(aggHisto, bucketSize);
if (threadId == 0 && DEBUG) {
printf("\n fromArray b4 histo : ");
d_dispFArr(fromArr, n);
}
//aggregate in histogram in shared mem
computeAtomicHisto(aggHisto, fromArr, n,
numBits, i);
if (threadId == 0 && DEBUG) {
printf("\naggHisto, bitpos:%d:", i);
dispArr(aggHisto, bucketSize);
printf("\n fromArray after histo : ");
d_dispFArr(fromArr, n);
}
//perform scan on aggHisto (assuming power of 2)
scan(aggHisto, bucketSize);
if (threadId == 0 && DEBUG) {
printf("\naggHisto after scan, bitpos:%d:", i);
dispArr(aggHisto, bucketSize);
}
__syncthreads();
if (threadId == 0) {
//copy values to correct output by a single thread
writeSortedVals(aggHisto, fromArr, toArr,
numBits, i, n);
}
__syncthreads();
if (threadId == 0 && DEBUG) {
printf("\n sorted: ");
d_dispFArr(toArr, n);
}
//toArr contains the sorted arr, for the next iteration point fromArr to this location
tempSwap = toArr;
toArr = fromArr;
fromArr = tempSwap;
}
//at this point fromAr will contain sorted arr in mem
//write this out to device in parallel
for (i = threadId; i < n; i+=nThreads) {
d_InArr[i] = fromArr[i];
}
}
void dispFArr(float *arr, int n) {
int i;
for (i = 0; i < n; i++) {
printf(" %f ", arr[i]);
}
}
int main(int argc, char *argv[]) {
float h_fArr[] = {0.1, 1.000, 0.5, 0.8, 0, 0.7, 0.8, 1.3, 0.098923, 2.5, 9.10, 0, 2};
int h_n = 13;
//float h_fArr[] = {0.1, 0.6, 0.4, 0.3, 0.8, 2.0};
//int h_n = 6;
float *d_fArr;
float *h_fSortedArr;
int i;
int numBits = 2;
printf("\n");
dispFArr(h_fArr, h_n);
//allocate mem on device
cudaMalloc((void **) &d_fArr, sizeof(float)*h_n);
//copy to device
cudaMemcpy((void *) d_fArr, (void *) h_fArr, sizeof(float)*h_n, cudaMemcpyHostToDevice);
//sort with 2 bits at a time
radixSort<<<1, 4, (sizeof(int)*(1<<numBits) + sizeof(float)*h_n*2)>>>(d_fArr, h_n, numBits);
//copy sorted back to host
cudaMemcpy((void *)h_fArr , (void *) d_fArr, sizeof(float)*h_n, cudaMemcpyDeviceToHost);
printf("\n");
dispFArr(h_fArr, h_n);
printf("\n");
}
|
6,690 | #include "includes.h"
__global__ void FindMaxMinPerGrid(int p_nGridSize, int p_nEigNum, float* p_devMax, float* p_devMin, float* p_devReduceMax, float* p_devReduceMin, int p_nMaxLevel)
{
__shared__ float MaxReduce[XBLOCK*(MAXEIGNUM - 1)];
__shared__ float MinReduce[XBLOCK*(MAXEIGNUM - 1)];
int taskPerTh = (p_nGridSize + XBLOCK - 1)/XBLOCK;
// First Assignment
if (threadIdx.x < p_nGridSize)
{
for (int i = 0; i < p_nEigNum - 1; i++)
{
MaxReduce[i*XBLOCK + threadIdx.x] = p_devMax[threadIdx.x + i * p_nGridSize];
MinReduce[i*XBLOCK + threadIdx.x] = p_devMin[threadIdx.x + i * p_nGridSize];
}
}
// First Reduction
for (int i = 1; i < taskPerTh; i++)
{
int curIndex = threadIdx.x + i * XBLOCK;
if (curIndex < p_nGridSize)
{
for (int j = 0; j < p_nEigNum - 1; j++)
{
if (MaxReduce[j*XBLOCK + threadIdx.x] < p_devMax[curIndex + j * p_nGridSize])
{
MaxReduce[j*XBLOCK + threadIdx.x] = p_devMax[curIndex + j * p_nGridSize];
}
if (MinReduce[j*XBLOCK + threadIdx.x] > p_devMin[curIndex + j * p_nGridSize])
{
MinReduce[j*XBLOCK + threadIdx.x] = p_devMin[curIndex + j * p_nGridSize];
}
}
}
}
__syncthreads();
//The Reductions Thereafter
int mask = 1;
for (int level = 0; level < p_nMaxLevel; level++)
{
if ((threadIdx.x & mask) == 0)
{
int index1 = threadIdx.x;
int index2 = (1 << level) + threadIdx.x;
if (index2 < p_nGridSize)
{
for (int i = 0; i < p_nEigNum - 1; i++)
{
if (MaxReduce[i*XBLOCK + index1] < MaxReduce[i*XBLOCK + index2])
{
MaxReduce[i*XBLOCK + index1] = MaxReduce[i*XBLOCK + index2];
}
if (MinReduce[i*XBLOCK + index1] > MinReduce[i*XBLOCK + index2])
{
MinReduce[i*XBLOCK + index1] = MinReduce[i*XBLOCK + index2];
}
}
}
}
__syncthreads();
mask = (mask<<1)|1;
}
//Write max and min into global memory
if (threadIdx.x == 0)
{
for (int i = 0; i < p_nEigNum - 1; i++)
{
p_devReduceMax[i] = MaxReduce[i*XBLOCK];
p_devReduceMin[i] = MinReduce[i*XBLOCK];
}
}
} |
6,691 | #include<stdio.h>
#include<stdlib.h>
__global__ void multiply_matrix(int* d_A, int* d_B, int* d_C, int width)
{
//Calculating the Row and Column
int row = blockIdx.x * blockDim.x + threadIdx.x;
int col = blockIdx.y * blockDim.y + threadIdx.y;
if(row<width && col<width)
{
int product_value = 0;
for(int k=0;k<width;k++)
{
product_value = product_value + d_A[row*width+k] * d_B[k*width+col];
}
d_C[row*width+col] = product_value;
}
}
int main()
{
//Initializing width of the square matrix
int width = 3;
//Initializing the Host matrices
int* h_A;
int* h_B;
int* h_C;
//Initializing the Device matrices
int* d_A;
int* d_B;
int* d_C;
size_t bytes = width*width*sizeof(int);
//Allocating Memory on Host Side
h_A = (int*)malloc(bytes);
h_B = (int*)malloc(bytes);
h_C = (int*)malloc(bytes);
//initializing the Host matrices with some values
for(int r=0;r<width;r++)
{
for(int c=0;c<width;c++)
{
h_A[r*width+c] = r*width+c;
h_B[r*width+c] = r*width+c;
}
}
printf("Matrix A: \n");
for(int r=0;r<width;r++)
{
for(int c=0;c<width;c++)
{
printf("%d ",h_A[r*width+c]);
}
}
printf("\nMatrix B: \n");
for(int r=0;r<width;r++)
{
for(int c=0;c<width;c++)
{
printf("%d ",h_B[r*width+c]);
}
}
//Allocating memory on Device side
cudaMalloc(&d_A,bytes);
cudaMalloc(&d_B,bytes);
cudaMalloc(&d_C,bytes);
//Copy data from Host to Device
cudaMemcpy(d_A,h_A,width,cudaMemcpyHostToDevice);
cudaMemcpy(d_B,h_B,width,cudaMemcpyHostToDevice);
dim3 blockSize(width, width);
dim3 gridSize(1);
multiply_matrix<<<gridSize,blockSize>>>(d_A,d_B,d_C,width);
//Copy data from Device back to Host
cudaMemcpy(h_C,d_C,width,cudaMemcpyDeviceToHost);
printf("\nCompleted Successfully!\n");
printf("Matrix C: \n");
for(int r=0;r<width;r++)
{
for(int c=0;c<width;c++)
{
printf("%d ",h_C[r*width+c]);
}
}
printf("\n");
//Memory clean-up Host
free(h_A);
free(h_B);
free(h_C);
//Memory clean-up Device
cudaFree(d_A);
cudaFree(d_B);
cudaFree(d_C);
return 0;
}
|
6,692 | #include "includes.h"
__global__ void kernal( void )
{
} |
6,693 | // https://forums.developer.nvidia.com/t/does-anybody-have-experience-on-cudahostregister-zero-copy-memory/22539/3
#include <stdio.h>
#include <sys/mman.h>
#define SIZE 10
#include <cuda.h>
// Kernel definition, see also section 4.2.3 of Nvidia Cuda Programming Guide
__global__ void vecAdd(float *A, float *B, float *C)
{
int i = threadIdx.x;
// A[i] = 0;
// B[i] = i;
C[i] = A[i] + B[i];
printf("Kernel: A[%d]=%f, B[%d]=%f, C[%d]=%f\n", i, A[i], i, B[i], i, C[i]);
}
void *map_alloc(size_t size)
{
return mmap(NULL, size, PROT_READ | PROT_WRITE,
MAP_PRIVATE | MAP_ANONYMOUS | MAP_LOCKED, -1, 0);
}
int main()
{
int N = SIZE;
// round up the size of the array to be a multiple of the page size
size_t memsize = ((SIZE * sizeof(float) + 4095) / 4096) * 4096;
cudaDeviceProp deviceProp;
// Get properties and verify device 0 supports mapped memory
cudaGetDeviceProperties(&deviceProp, 0);
if (!deviceProp.canMapHostMemory)
{
fprintf(stderr, "Device %d cannot map host memory!\n", 0);
exit(EXIT_FAILURE);
}
fprintf(stderr, "uni addr: %u\n", deviceProp.unifiedAddressing);
fprintf(stderr, "can use host pointer: %u\n",
deviceProp.canUseHostPointerForRegisteredMem);
// set the device flags for mapping host memory
cudaSetDeviceFlags(cudaDeviceMapHost);
float *A, *B, *C;
float *devPtrA, *devPtrB, *devPtrC;
// use valloc instead of malloc
A = (float *)map_alloc(memsize);
B = (float *)map_alloc(memsize);
C = (float *)map_alloc(memsize);
cudaHostRegister(A, memsize, cudaHostRegisterMapped);
cudaHostRegister(B, memsize, cudaHostRegisterMapped);
cudaHostRegister(C, memsize, cudaHostRegisterMapped);
for (int i = 0; i < SIZE; i++)
{
A[i] = B[i] = i;
}
cudaHostGetDevicePointer((void **)&devPtrA, (void *)A, 0);
fprintf(stderr, "%p =? %p\n", devPtrA, A);
{
cudaPointerAttributes attr;
cudaError_t rc = cudaPointerGetAttributes(&attr, (void *)A);
if (rc != cudaSuccess)
{
fprintf(stderr, "fail\n");
}
fprintf(stderr, "prop[%p]: dev %u, dptr %p, hptr %p\n", A, attr.device,
attr.devicePointer, attr.hostPointer);
}
{
cudaPointerAttributes attr;
cudaError_t rc = cudaPointerGetAttributes(&attr, (void *)devPtrA);
if (rc != cudaSuccess)
{
fprintf(stderr, "fail\n");
}
fprintf(stderr, "prop[%p]: dev %u, dptr %p, hptr %p\n", devPtrA,
attr.device, attr.devicePointer, attr.hostPointer);
}
cudaHostGetDevicePointer((void **)&devPtrB, (void *)B, 0);
cudaHostGetDevicePointer((void **)&devPtrC, (void *)C, 0);
vecAdd<<<1, N>>>(devPtrA, devPtrB, devPtrC);
cudaDeviceSynchronize();
for (int i = 0; i < SIZE; i++) printf("C[%d]=%f\n", i, C[i]);
cudaHostUnregister(A);
cudaHostUnregister(B);
cudaHostUnregister(C);
// free(A);
munmap(A, memsize);
munmap(B, memsize);
munmap(C, memsize);
}
|
6,694 | // SDSC Summer Institute 2018
// Andreas Goetz (agoetz@sdsc.edu)
// Hello World Program in CUDA C
//
// Notice this is identical to standard C
// All that changes is the file extension .c -> .cu
// This code can be compiled with the NVIDIA nvcc CUDA compiler
//
#include<stdio.h>
int main(void) {
printf("Hello World!\n");
return 0;
}
|
6,695 | #include "includes.h"
__global__ void SumaMatricesCU(int* A,int* B,int* C,int width){
int col=blockIdx.x*blockDim.x + threadIdx.x;//columnas
int row=blockIdx.y*blockDim.y + threadIdx.y;//filas
if((row<width)&&(col<width)){
C[row*width+col] = A[row*width+col]+B[row*width+col];
}
} |
6,696 | #include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <stdio.h>
#include <iostream>
#include <ctime>
//#define MAX_NUMBER 32768 // 32 * 1024
//#define MAX_NUMBER 65536 // 64 * 1024
//#define MAX_NUMBER 131072 // 128 * 1024
#define MAX_NUMBER (1024 * 1024 * 16) // 128 * 1024
typedef int cuda_int;
typedef int cpu_int;
const int device = 0; // 在Matebook 14上只有一个MX250
int deviceNum;
int maxBlockNumX;
int maxBlockNumY;
int maxBlockNumZ;
int maxThdPerBlock;
cudaDeviceProp deviceProp;
cuda_int *dev_a = NULL;
cuda_int *dev_b = NULL;
cuda_int *dev_c = NULL;
cpu_int a[MAX_NUMBER];
cpu_int b[MAX_NUMBER];
cpu_int c[MAX_NUMBER];
cudaError_t cudaEnvInit()
{
cudaError_t cudaStatus;
int value;
/* 在硬件平台上选择一个支持cuda的设备 */
cudaStatus = cudaGetDeviceCount(&deviceNum);
cudaStatus = cudaGetDeviceProperties(&deviceProp, 0);
cudaStatus = cudaSetDevice(device);
if (cudaStatus != cudaSuccess) {
std::cout << "cudaSetDevice failed!" << std::endl;
return cudaStatus;
}
cudaStatus = cudaDeviceGetAttribute(&value, cudaDevAttrMaxBlockDimX, device);\
maxBlockNumX = value;
cudaStatus = cudaDeviceGetAttribute(&value, cudaDevAttrMaxBlockDimY, device);
maxBlockNumY = value;
cudaStatus = cudaDeviceGetAttribute(&value, cudaDevAttrMaxBlockDimZ, device);
maxBlockNumZ = value;
cudaStatus = cudaMalloc((void **)&dev_a, MAX_NUMBER * sizeof(int));
if (cudaStatus != cudaSuccess) {
std::cout << "cudaMalloc dev_a failed!" << std::endl;
return cudaStatus;
}
cudaStatus = cudaMalloc((void **)&dev_b, MAX_NUMBER * sizeof(int));
if (cudaStatus != cudaSuccess) {
std::cout << "cudaMalloc dev_b failed!" << std::endl;
cudaFree(dev_a);
return cudaStatus;
}
cudaStatus = cudaMalloc((void **)&dev_c, MAX_NUMBER * sizeof(int));
if (cudaStatus != cudaSuccess) {
std::cout << "cudaMalloc dev_c failed!" << std::endl;
cudaFree(dev_a);
cudaFree(dev_b);
return cudaStatus;
}
maxThdPerBlock = deviceProp.maxThreadsPerBlock;
return cudaSuccess;
}
void cudaRelaseApp()
{
cudaFree(dev_a);
cudaFree(dev_b);
cudaFree(dev_c);
}
void cudaShowDevInfo()
{
std::cout << "CUDA device number: " << deviceNum << std::endl;
std::cout << "CUDA device name: " << deviceProp.name << std::endl;
std::cout << "CUDA device is " << (deviceProp.integrated == 1 ? "integrated" : "discreted") << std::endl;
std::cout << "Multiprocessor number: " << deviceProp.multiProcessorCount << std::endl;
std::cout << "register number of each Multiprocessor: " << deviceProp.regsPerMultiprocessor << std::endl;
std::cout << "Global L1 cache supported: " << (deviceProp.globalL1CacheSupported == 1 ? "Yes" : "No") << std::endl;
std::cout << "Local L1 cache supported: " << (deviceProp.localL1CacheSupported == 1 ? "Yes" : "No") << std::endl;
std::cout << "L2 cache size: " << deviceProp.l2CacheSize << std::endl;
std::cout << "warp size: " << deviceProp.warpSize << std::endl;
std::cout << "Max threads dimension: " << deviceProp.maxThreadsDim << std::endl;
std::cout << "Max threads per block: " << deviceProp.maxThreadsPerBlock << std::endl;
std::cout << "Max threads per multiprocessor: " << deviceProp.maxThreadsPerMultiProcessor << std::endl;
std::cout << "registers per block: " << deviceProp.regsPerBlock << std::endl;
std::cout << "Global memory available on device: " << (double)deviceProp.totalGlobalMem / 1024 / 1024 << "MB" << std::endl;
std::cout << "Max X blocks: " << maxBlockNumX << std::endl;
std::cout << "Max Y blocks: " << maxBlockNumY << std::endl;
std::cout << "Max Z blocks: " << maxBlockNumZ << std::endl;
std::cout << "Max threads per block: " << maxThdPerBlock << std::endl;
std::cout << "Clock rate: " << (double)deviceProp.clockRate / 1024 / 1024 << " GHz" << std::endl;
}
__global__ void kernel(int *C, const int *A, const int *B)
{
int i = blockIdx.x * blockDim.x + threadIdx.x;
C[i] = (A[i] + B[i]) * (A[i] - B[i]);
C[i] = 2 * (C[i] + i) / i;
C[i] = C[i] * C[i];
C[i] = C[i] + A[i] + B[i];
C[i] = C[i] / A[i];
C[i] = C[i] / B[i];
C[i] = C[i] * C[i];
C[i] = C[i] + A[i] + B[i];
C[i] = C[i] / A[i];
C[i] = C[i] / B[i];
C[i] = C[i] * C[i];
C[i] = C[i] + A[i] + B[i];
C[i] = C[i] / A[i];
C[i] = C[i] / B[i];
C[i] = C[i] * C[i];
C[i] = C[i] + A[i] + B[i];
C[i] = C[i] / A[i];
C[i] = C[i] / B[i];
}
int main()
{
for (int i = 0; i < MAX_NUMBER; i++) {
a[i] = 2;
b[i] = 1;
}
clock_t start_t, end_t;
cudaError_t res;
res = cudaEnvInit();
if (res != cudaSuccess)
return 0;
cudaShowDevInfo();
res = cudaMemcpy(dev_a, a, sizeof(cpu_int) * MAX_NUMBER, cudaMemcpyHostToDevice);
if (res != cudaSuccess)
std::cout << "cudaMemcpy a failed!" << std::endl;
res = cudaMemcpy(dev_b, b, sizeof(cpu_int) * MAX_NUMBER, cudaMemcpyHostToDevice);
if (res != cudaSuccess)
std::cout << "cudaMemcpy b failed!" << std::endl;
int usedBlockNum = maxBlockNumZ;
int usedThdPerBlock = maxThdPerBlock / 2;
int iter_lenth = usedBlockNum * usedThdPerBlock;
int iter_times = (MAX_NUMBER % iter_lenth == 0 ? MAX_NUMBER / iter_lenth : (MAX_NUMBER + iter_lenth) / iter_lenth);
std::cout << "iter_lenth: " << iter_lenth << std::endl;
std::cout << "iter_times: " << iter_times << std::endl;
start_t = clock();
for (int i = 0; i < iter_times; i++) {
kernel<<<usedBlockNum, usedThdPerBlock>>>(dev_c + iter_lenth * i, dev_a + iter_lenth * i, dev_b + iter_lenth * i);
res = cudaGetLastError();
if (res != cudaSuccess)
std::cout << "GetLastError failed!" << std::endl;
res = cudaDeviceSynchronize();
if (res != cudaSuccess)
std::cout << "DeviceSynchronize failed!" << std::endl;
if (i == iter_times - 1) {
int copy_len = (MAX_NUMBER % iter_lenth == 0 ? iter_lenth : MAX_NUMBER % iter_lenth);
res = cudaMemcpy(c + iter_lenth * i, dev_c + iter_lenth * i, sizeof(cpu_int) * copy_len, cudaMemcpyDeviceToHost);
}
else
res = cudaMemcpy(c + iter_lenth * i, dev_c + iter_lenth * i, sizeof(cpu_int) * iter_lenth, cudaMemcpyDeviceToHost);
if (res != cudaSuccess)
std::cout << "cudaMemcpy c failed!" << std::endl;
}
end_t = clock();
std::cout << "GPU used time: " << (double)(end_t - start_t) * 1000 / CLOCKS_PER_SEC << "ms" << std::endl;
start_t = clock();
for (int i = 1; i < MAX_NUMBER; i++) {
c[i] = (a[i] + b[i]) * (a[i] - b[i]);
c[i] = 2 * (c[i] + i) / i;
c[i] = c[i] * c[i];
c[i] = c[i] + a[i] + b[i];
c[i] = c[i] / a[i];
c[i] = c[i] / a[i];
c[i] = c[i] * c[i];
c[i] = c[i] + a[i] + b[i];
c[i] = c[i] / a[i];
c[i] = c[i] / a[i];
c[i] = c[i] * c[i];
c[i] = c[i] + a[i] + b[i];
c[i] = c[i] / a[i];
c[i] = c[i] / a[i];
c[i] = c[i] * c[i];
c[i] = c[i] + a[i] + b[i];
c[i] = c[i] / a[i];
c[i] = c[i] / a[i];
}
end_t = clock();
std::cout << "CPU used time: " << (double)(end_t - start_t) * 1000 / CLOCKS_PER_SEC << "ms" << std::endl;
cudaRelaseApp();
for (int i = 0; i < 100; i++)
std::cout << c[i] << ' ';
std::cout << std::endl;
system("pause");
return 0;
}
|
6,697 |
/* This is a automatically generated test. Do not modify */
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
__global__
void compute(float comp, int var_1,int var_2,int var_3,float var_4,float var_5,float var_6,float var_7,float var_8,float var_9,float var_10,float var_11,float var_12,float* var_13,float var_14,float var_15,float var_16,float* var_17,float var_18,float var_19,float var_20,float var_21,float var_22,float var_23,float var_24,float var_25,float var_26,float var_27,float* var_28,float var_29,float var_30) {
if (comp == (var_4 + -1.8522E-37f + var_5 - (var_6 / cosf((-1.2784E17f * (var_7 / tanhf(var_8 + -0.0f - var_9))))))) {
comp += (-1.1977E35f * var_10 / (+1.9911E-35f * (+1.2078E-36f * atanf((var_11 / +1.4141E-41f + var_12)))));
for (int i=0; i < var_1; ++i) {
var_13[i] = (var_14 / log10f(+1.0240E-35f));
comp += var_13[i] - var_15 + -0.0f;
comp += +1.4381E-41f / +1.2882E-36f * var_16 / -1.5557E-15f;
}
for (int i=0; i < var_2; ++i) {
var_17[i] = +0.0f;
comp = var_17[i] / (-1.5308E-36f - (-0.0f + var_18 + (+1.4126E-37f * var_19)));
float tmp_1 = var_20 * var_21 * +0.0f * (-0.0f / +1.7610E-37f / var_22);
comp = tmp_1 * var_23 / var_24 * (var_25 - (var_26 - var_27));
}
for (int i=0; i < var_3; ++i) {
var_28[i] = +0.0f;
comp += var_28[i] - +1.6726E34f - var_29 * (+1.3177E-37f + (-1.9295E36f / var_30));
}
}
printf("%.17g\n", comp);
}
float* initPointer(float v) {
float *ret = (float*) malloc(sizeof(float)*10);
for(int i=0; i < 10; ++i)
ret[i] = v;
return ret;
}
int main(int argc, char** argv) {
/* Program variables */
float tmp_1 = atof(argv[1]);
int tmp_2 = atoi(argv[2]);
int tmp_3 = atoi(argv[3]);
int tmp_4 = atoi(argv[4]);
float tmp_5 = atof(argv[5]);
float tmp_6 = atof(argv[6]);
float tmp_7 = atof(argv[7]);
float tmp_8 = atof(argv[8]);
float tmp_9 = atof(argv[9]);
float tmp_10 = atof(argv[10]);
float tmp_11 = atof(argv[11]);
float tmp_12 = atof(argv[12]);
float tmp_13 = atof(argv[13]);
float* tmp_14 = initPointer( atof(argv[14]) );
float tmp_15 = atof(argv[15]);
float tmp_16 = atof(argv[16]);
float tmp_17 = atof(argv[17]);
float* tmp_18 = initPointer( atof(argv[18]) );
float tmp_19 = atof(argv[19]);
float tmp_20 = atof(argv[20]);
float tmp_21 = atof(argv[21]);
float tmp_22 = atof(argv[22]);
float tmp_23 = atof(argv[23]);
float tmp_24 = atof(argv[24]);
float tmp_25 = atof(argv[25]);
float tmp_26 = atof(argv[26]);
float tmp_27 = atof(argv[27]);
float tmp_28 = atof(argv[28]);
float* tmp_29 = initPointer( atof(argv[29]) );
float tmp_30 = atof(argv[30]);
float tmp_31 = atof(argv[31]);
compute<<<1,1>>>(tmp_1,tmp_2,tmp_3,tmp_4,tmp_5,tmp_6,tmp_7,tmp_8,tmp_9,tmp_10,tmp_11,tmp_12,tmp_13,tmp_14,tmp_15,tmp_16,tmp_17,tmp_18,tmp_19,tmp_20,tmp_21,tmp_22,tmp_23,tmp_24,tmp_25,tmp_26,tmp_27,tmp_28,tmp_29,tmp_30,tmp_31);
cudaDeviceSynchronize();
return 0;
}
|
6,698 | #include <stdio.h>
#include <stdlib.h>
#include <cuda_runtime.h>
#define X 1
#define EMPTY 10
#define NO_WINNER 20
#define COLOR_RED "\x1b[31m"
#define COLOR_GREEN "\x1b[32m"
#define COLOR_YELLOW "\x1b[33m"
#define COLOR_BLUE "\x1b[34m"
#define COLOR_CYAN "\x1b[36m"
#define COLOR_RESET "\x1b[0m"
#define N 3
#define M N
typedef unsigned char symbol_t;
typedef struct board {
symbol_t m[N][M];
unsigned short n_empty;
} board_t;
typedef struct move {
unsigned short i, j;
} move_t;
typedef struct job_struct {
int alpha;
symbol_t symbol;
board_t board;
}Sjob;
board_t* create_board() {
int i, j;
board_t* board = (board_t*) malloc(sizeof(board_t));
for(i = 0; i < N; i++) {
for(j = 0; j < M; j++) {
board->m[i][j] = EMPTY;
}
}
board->n_empty = N * M;
return board;
}
__device__ __host__ void put_symbol(board_t* board, symbol_t symbol, move_t* move) {
board->m[move->i][move->j] = symbol;
board->n_empty --;
}
__device__ __host__ void clear_symbol(board_t* board, move_t* move) {
board->m[move->i][move->j] = EMPTY;
board->n_empty ++;
}
__device__ __host__ symbol_t winner(board_t* b) {
int i, j;
symbol_t sym;
int equal;
// check on lines
for(i = 0; i < N; i++) {
equal = 1;
sym = b->m[i][0];
if(sym != EMPTY) {
for(j = 1; j < M; j++) {
if(b->m[i][j] != sym) {
equal = 0;
break;
}
}
if(equal == 1) {
return sym;
}
}
}
// check on columns
for(i = 0; i < M; i++) {
equal = 1;
sym = b->m[0][i];
if(sym != EMPTY) {
for(j = 1; j < N; j++) {
if(b->m[j][i] != sym) {
equal = 0;
break;
}
}
if(equal == 1) {
return sym;
}
}
}
// main diagonal
equal = 1;
sym = b->m[0][0];
if(sym != EMPTY) {
for(i = 1; i < N; i++) {
if(b->m[i][i] != sym) {
equal = 0;
break;
}
}
if(equal == 1) {
return sym;
}
}
// secondary diagonal
equal = 1;
sym = b->m[0][M-1];
if(sym != EMPTY) {
for(i = 1; i < N; i++) {
if(b->m[i][M-i-1] != sym) {
equal = 0;
break;
}
}
if(equal == 1) {
return sym;
}
}
if(b->n_empty == 0) {
return NO_WINNER;
}
return EMPTY;
}
void print_board(board_t* board) {
int i, j;
for(i = 0; i < N; i++) {
printf("\t\t");
for(j = 0; j < M; j++) {
if(board->m[i][j] == X) {
printf(COLOR_YELLOW" X ");
} else if(board->m[i][j] == 0) {
printf(COLOR_YELLOW" O ");
} else {
printf(COLOR_YELLOW" - ");
}
if(j<M-1)
printf(COLOR_YELLOW"|");
}
printf(COLOR_YELLOW"\n\t\t -------------------\n");
}
}
void print_board_player(board_t* board) {
int i, j;
int qw=1;
for(i = 0; i < N; i++) {
printf("\t\t");
for(j = 0; j < M; j++) {
if(board->m[i][j] == X) {
printf(COLOR_YELLOW" X ");
} else if(board->m[i][j] == 0) {
printf(COLOR_YELLOW" Y ");
} else {
printf(COLOR_YELLOW" %d ",qw++);
}
if(j<M-1)
printf(COLOR_YELLOW"|");
}
printf(COLOR_YELLOW"\n\t\t -------------------\n");
}
}
__device__ __host__ move_t** get_all_possible_moves(board_t* board, symbol_t symbol, int* n) {
int i,j;
move_t** list = (move_t**) malloc(board->n_empty * sizeof(move_t*));
*n = 0;
for(i = 0; i < N; i++) {
for(j = 0; j < M; j++) {
if(board->m[i][j] == EMPTY) {
list[(*n)] = (move_t*) malloc(sizeof(move_t));
list[(*n)]->i = i;
list[(*n)]->j = j;
(*n) ++;
}
}
}
return list;
}
__device__ __host__ symbol_t other_symbol(symbol_t symbol) {
return 1 - symbol;
}
__device__ __host__ int get_score(board_t* board, int depth, symbol_t symbol) {
symbol_t result = winner(board);
if(result == symbol) {
return N * M + 10 - depth;
} else if(result != EMPTY && result != NO_WINNER) {
return -(N * M) - 10 + depth;
} else if(result == NO_WINNER) {
return 1;
}
return 0;
}
__device__ __host__ int move(board_t* board, symbol_t symbol, int depth, int alpha, int beta) {
int n, i;
move_t* max_move;
int score = get_score(board, depth, symbol);
if(score != 0) {
return score;
}
move_t** moves = get_all_possible_moves(board, symbol, &n);
for(i = 0; i < n; i++) {
put_symbol(board, symbol, moves[i]);
score = -move(board, other_symbol(symbol), depth + 1, -beta, -alpha);
clear_symbol(board, moves[i]);
if(score > alpha) {
alpha = score;
max_move = moves[i];
}
if(alpha >= beta) {
break;
}
}
for(i = 0; i < n; i++) {
free(moves[i]);
}
free(moves);
return alpha;
}
__global__ void GetScoreKernel(Sjob *a,int* sc) {
int ci = threadIdx.x;
sc[ci] = -move(&(a[ci].board), a[ci].symbol, 0, -9999, -(a[ci].alpha));
}
int main()
{
Sjob* d_jobs;
int * d_scores;
symbol_t result;
symbol_t current_symbol = X;
board_t* board = create_board();
int score;
// symbol_t done_symbol = 2;
int n, best_score_index, best_score;
move_t** moves;
int current_move[100];
struct job_struct* job = (Sjob *)malloc(sizeof(struct job_struct));
Sjob jobs[200];
while(1)
{
best_score = -9999;
for(int i=0;i<200;i++)
jobs[i].alpha = best_score;
if(current_symbol==0)
printf(COLOR_RESET"\t\tCPU to move \n");
else
printf(COLOR_RESET"\t\tPlayer to move \n");
moves = get_all_possible_moves(board, current_symbol, &n);
if((int) current_symbol==0)
{
if(n==0){printf(COLOR_RED"\t\tDraw\n No more Moves Left\n");exit(0);}
// pass one task to each available process
for(int i = 0; i < n; i++)
{
// printf("send move %i to %i\n", i, i + 1);
put_symbol(board, current_symbol, moves[i]);
jobs[i].board = *board;
jobs[i].symbol = other_symbol(current_symbol);
clear_symbol(board, moves[i]);
current_move[i+1] = i;
}
// if there are more moves to make than processes
cudaMalloc((void **)&d_jobs,100*sizeof(Sjob));
cudaMalloc((void **)&d_scores,100*sizeof(int));
cudaMemcpy(d_jobs,jobs,n*sizeof(Sjob),cudaMemcpyHostToDevice);
GetScoreKernel<<<1,n>>>(d_jobs,d_scores);
int scores[n];
cudaMemcpy(scores,d_scores,n*sizeof(int),cudaMemcpyDeviceToHost);
// wait for the rest of results
for(int i = 0; i < n; i++) {
if(scores[i] > best_score) {
best_score = scores[i];
best_score_index = i;
}
// printf("received score %i from %i\n", scores[i], i);
}
put_symbol(board, current_symbol, moves[best_score_index]);
print_board(board);
for(int i = 0; i < n; i++) {
free(moves[i]);
}
free(moves);
result = winner(board);
if(result != EMPTY) {
break;
}
}
else
{
int playMove;
print_board_player(board);
printf(COLOR_RESET"enter move accordingly ");
scanf("%d",&playMove);
put_symbol(board, current_symbol, moves[playMove-1]);
print_board(board);
}
current_symbol = 1 - current_symbol;
}
if(result==0)
{
printf(COLOR_GREEN"\t\tCPU Wins\n");
exit(0);
}
else
if(result==1)
{
printf(COLOR_GREEN"\t\tPlayer Wins\n");
exit(0);
}
}
|
6,699 | #include "./Dynamics.cuh"
#include "./Cells.cuh"
#include "./Constants.cuh"
#include <cuda.h>
#include <cuda_runtime.h>
//If this flag is set, normalize the phasefield gradient vector by 1+its norm. If not, don't normalize (/(1+0))
#if MEMBGRADIENTNORMALIZER > 0
#define PHAS_SQRT (4 + sqrt(PhGradX * PhGradX + PhGradY * PhGradY))
#else
#define PHAS_SQRT 0
#endif
//Offset gives the increment for saving results. +1 means 1 full (GPZX x GPZY, or 256x256) array of double values. Values for the individual steps of the midpoint rule: k1->1, k2->3, k3->4, k4->5
__global__ void ZeitDiff(int Startvalue, int Offset, double *Fila, double *NukA, double *NukI, double *PolX, double *PolY, double *Phas,
double *Area, double *FilaGradX, double *FilaGradY, double *PolaDiv, double *PhasGradX, double *PhasGradY,
double *FilaDiff, double *NukADiff, double *NukIDiff, double *PolXDiff, double *PolYDiff, double *PhasDiff, double *Curv,
SingleCell *Cells, int CellNum){
int ID = threadIdx.x + blockIdx.x * blockDim.x;
int IDZiel = ID + (Offset << 16) + Startvalue;
double PhDiff = (PHASEFIELDFLAG ? PhasDiff[ID] : 0), PhGradX = (PHASEFIELDFLAG ? PhasGradX[ID] : 0), PhGradY = (PHASEFIELDFLAG ? PhasGradY[ID] : 0);
ID = ID + Startvalue;
double FilaAlt = Fila[ID], NukAAlt = NukA[ID], NukIAlt = NukI[ID], PhasAlt = (PHASEFIELDFLAG ? Phas[ID] : 1), PolXAlt = PolX[ID], PolYAlt = PolY[ID];
double NukSummand = PhasAlt * (VAR_wd * (FilaAlt/* + VAR_Acti * (PolXAlt * PolXAlt + PolYAlt * PolYAlt)*/) * NukAAlt - NukIAlt * (1 + VAR_w0 * NukAAlt * NukAAlt));
ID = ID - Startvalue;
Fila[IDZiel] = /*VAR_DiAkt * (PhasAlt * FilaDiff[ID] - FilaAlt * PhDiff)*/ + PhasAlt * (VAR_alpha * NukAAlt - VAR_va * PolaDiv[ID] - VAR_kd * FilaAlt) - FilaAlt;
#if NUCLEATORDEGRADFLAG > 0
NukA[IDZiel] = (VAR_DiNa * (PhasAlt * NukADiff[ID] - NukAAlt * PhDiff) - NukSummand + (PhasAlt - 1) * NukAAlt);
NukI[IDZiel] = ( (PhasAlt * NukIDiff[ID] - NukIAlt * PhDiff) + NukSummand + (PhasAlt - 1) * NukIAlt + 15 * PhasAlt * (TotalNuk - *(Area+1) - *(Area+2)));
#else
NukA[IDZiel] = (VAR_DiNa * (PhasAlt * NukADiff[ID] - NukAAlt * PhDiff) - NukSummand);
NukI[IDZiel] = ( (PhasAlt * NukIDiff[ID] - NukIAlt * PhDiff) + NukSummand);
#endif
PolX[IDZiel] = /*VAR_DiAkt * (PhasAlt * PolXDiff[ID] - PolXAlt * PhDiff)*/ - PhasAlt * (VAR_kd * PolXAlt + VAR_va * FilaGradX[ID]) - PolXAlt;
PolY[IDZiel] = /*VAR_DiAkt * (PhasAlt * PolYDiff[ID] - PolYAlt * PhDiff)*/ - PhasAlt * (VAR_kd * PolYAlt + VAR_va * FilaGradY[ID]) - PolYAlt;
#if PHASEFIELDFLAG > 0
Phas[IDZiel] = (VAR_DiPh * PhDiff + VAR_kappa * PhasAlt * (1 - PhasAlt) * (PhasAlt - (0.5 + VAR_epsilon * (*Area - VAR_MeanVol) * CONST_Unit_Area))
- VAR_beta * (PolXAlt * PhGradX + PolYAlt * PhGradY) / (1 + PHAS_SQRT);
#endif
};
//SpeicherOffset gibt den Abstand des Endwertspeichers in Rastergrößen an. StartwertOffset gibt den negativen Abstand des Startwerts in Rastergrößen an.
__global__ void ZeitDiffMitEuler(int Startvalue, int SpeicherOffset, int StartwertOffset, double *Fila, double *NukA, double *NukI, double *PolX, double *PolY, double *Phas,
double *Area, double *FilaGradX, double *FilaGradY, double *PolaDiv, double *PhasGradX, double *PhasGradY,
double *FilaDiff, double *NukADiff, double *NukIDiff, double *PolXDiff, double *PolYDiff, double *PhasDiff, double *Curv,
SingleCell *Cells, int CellNum , double Schrittweite){
int ID = threadIdx.x + blockIdx.x * blockDim.x;
int IDZiel = (SpeicherOffset << 16) + ID + Startvalue;
int IDAlt = ID - (StartwertOffset << 16) + Startvalue;
double PhDiff = (PHASEFIELDFLAG ? PhasDiff[ID] : 0), PhGradX = (PHASEFIELDFLAG ? PhasGradX[ID] : 0), PhGradY = (PHASEFIELDFLAG ? PhasGradY[ID] : 0);
ID = ID + Startvalue;
double FilaAlt = Fila[ID], NukAAlt = NukA[ID], NukIAlt = NukI[ID], PhasAlt = (PHASEFIELDFLAG ? Phas[ID] : 1), PolXAlt = PolX[ID], PolYAlt = PolY[ID];
double NukSummand = PhasAlt * (VAR_wd * (FilaAlt/* + VAR_Acti * (PolXAlt * PolXAlt + PolYAlt * PolYAlt)*/) * NukAAlt - NukIAlt * (1 + VAR_w0 * NukAAlt * NukAAlt));
ID = ID - Startvalue;
Fila[IDZiel] = Fila[IDAlt] + Schrittweite * (/*VAR_DiAkt * (PhasAlt * FilaDiff[ID] - FilaAlt * PhDiff)*/ + PhasAlt * (VAR_alpha * NukAAlt - VAR_va * PolaDiv[ID] - VAR_kd * FilaAlt) - FilaAlt);
#if NUCLEATORDEGRADFLAG > 0
NukA[IDZiel] = NukA[IDAlt] + Schrittweite * (VAR_DiNa * (PhasAlt * NukADiff[ID] - NukAAlt * PhDiff) - NukSummand + (PhasAlt - 1) * NukAAlt);
NukI[IDZiel] = NukI[IDAlt] + Schrittweite * ( (PhasAlt * NukIDiff[ID] - NukIAlt * PhDiff) + NukSummand + (PhasAlt - 1) * NukIAlt + 15 * PhasAlt * (TotalNuk - *(Area+1) - *(Area+2)));
#else
NukA[IDZiel] = NukA[IDAlt] + Schrittweite * (VAR_DiNa * (PhasAlt * NukADiff[ID] - NukAAlt * PhDiff) - NukSummand);
NukI[IDZiel] = NukI[IDAlt] + Schrittweite * ( (PhasAlt * NukIDiff[ID] - NukIAlt * PhDiff) + NukSummand);
#endif
PolX[IDZiel] = PolX[IDAlt] + Schrittweite * (/*VAR_DiAkt * (PhasAlt * PolXDiff[ID] - PolXAlt * PhDiff)*/ - PhasAlt * (VAR_kd * PolXAlt + VAR_va * FilaGradX[ID]) - PolXAlt);
PolY[IDZiel] = PolY[IDAlt] + Schrittweite * (/*VAR_DiAkt * (PhasAlt * PolYDiff[ID] - PolYAlt * PhDiff)*/ - PhasAlt * (VAR_kd * PolYAlt + VAR_va * FilaGradY[ID]) - PolYAlt);
#if PHASEFIELDFLAG > 0
Phas[IDZiel] = Phas[IDAlt] + Schrittweite * (VAR_DiPh * PhDiff + VAR_kappa * PhasAlt * (1 - PhasAlt) * (PhasAlt - (0.5 + VAR_epsilon * (*Area - VAR_MeanVol) * CONST_Unit_Area))
- VAR_beta * (PolXAlt * PhGradX + PolYAlt * PhGradY) / (1 + PHAS_SQRT));
#endif
};
__global__ void Euler(int Offset, double Schrittweite, double *GPU_Fila, double *GPU_NukA, double *GPU_NukI, double *GPU_PolX, double *GPU_PolY, double *GPU_Phas){
int ID = threadIdx.x + blockIdx.x * blockDim.x;
int IDZiel = (Offset << 16) + ID, IDSchritt = (1 << 16) + ID;
// 1<<17 entspricht "2*RASTERGROESSE", also 2*256*256 = 2^17 und gibt den Offset zum Speichern an. In der Variable "Offset" ist der Speicherbereich des aktuellen f(c) für c_j+1 = c_j + dt * f(c_j) gespeichert
GPU_Fila[IDZiel] = GPU_Fila[ID] + Schrittweite * (GPU_Fila[IDSchritt]);
GPU_NukA[IDZiel] = GPU_NukA[ID] + Schrittweite * (GPU_NukA[IDSchritt]);
GPU_NukI[IDZiel] = GPU_NukI[ID] + Schrittweite * (GPU_NukI[IDSchritt]);
GPU_PolX[IDZiel] = GPU_PolX[ID] + Schrittweite * (GPU_PolX[IDSchritt]);
GPU_PolY[IDZiel] = GPU_PolY[ID] + Schrittweite * (GPU_PolY[IDSchritt]);
#if PHASEFIELDFLAG > 0
GPU_Phas[IDZiel] = GPU_Phas[ID] + Schrittweite * (GPU_Phas[IDSchritt]);
#endif
};
|
6,700 | extern "C"
__global__ void copyme (uchar4 *input, uchar4 *output,
const int width, const int height) {
int row = blockIdx.y * blockDim.y + threadIdx.y;
int col = blockIdx.x * blockDim.x + threadIdx.x;
if (row >= height || col >= width)
return;
uchar4 in = input[row * width + col];
uchar4 out;
out.x = in.w;
out.y = in.z;
out.z = in.y;
out.w = in.x;
// printf("I'm called! %d ", in.y);
output[row * width + col] = out;
}
|
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