serial_no int64 1 24.2k | cuda_source stringlengths 11 9.01M |
|---|---|
23,701 | //pass
//--blockDim=64 --gridDim=64 --no-inline
#include "cuda.h"
__global__ void foo() {
int a;
int* local_ptr;
local_ptr = &a;
*local_ptr = 0;
}
|
23,702 | /***************************************************************************
*cr
*cr (C) Copyright 2010 The Board of Trustees of the
*cr University of Illinois
*cr All Rights Reserved
*cr
***************************************************************************/
#include "common.h"
__global__ void block2D_reg_tiling(float c0,float c1,float *A0,float *Anext, int nx, int ny, int nz)
{
int i = blockIdx.x*blockDim.x+threadIdx.x;
int j = blockIdx.y*blockDim.y+threadIdx.y;
float bottom=A0[Index3D (nx, ny, i, j, 0)] ;
float current=A0[Index3D (nx, ny, i, j, 1)] ;
if( i>0 && j>0 &&(i<nx-1) &&(j<ny-1) )
{
for(int k=1;k<nz-1;k++)
{
float top =A0[Index3D (nx, ny, i, j, k+1)] ;
Anext[Index3D (nx, ny, i, j, k)] =
(top +
bottom +
A0[Index3D (nx, ny, i, j + 1, k)] +
A0[Index3D (nx, ny, i, j - 1, k)] +
A0[Index3D (nx, ny, i + 1, j, k)] +
A0[Index3D (nx, ny, i - 1, j, k)])*c1
- current*c0;
bottom=current;
current=top;
}
}
}
|
23,703 | #include "includes.h"
__global__ void morph(float* output, float* input1, float* input2, float ampCoeff, float freqCoeff, int length) {
int i = threadIdx.x + blockIdx.x * blockDim.x;
int j = i<<1;
if (j < length) {
output[j] = input1[j]*(1.0-ampCoeff) + input2[j]*(ampCoeff);
output[j+1] = input1[j+1]*(1.0-freqCoeff) + input2[j+1]*(freqCoeff);
}
} |
23,704 | #include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <limits.h>
#define OUTPUT_FILE_NAME_B "q1b.txt"
#define OUTPUT_FILE_NAME_MIN "q1a.txt"
#define NUM_THREADS_A 32
#define NUM_BLOCKS_A 2
#define NUM_THREADS_B 32
#define NUM_BLOCKS_B 2
// int* fileToArray(char file1[], int* n){
// FILE* fptr = fopen(file1, "r");
// char* str = (char*) malloc(sizeof(char)*2048);
// int token;
// fscanf(fptr, "%d,", n);
// int* array;
// //int* array = malloc(sizeof(int)*(*n));
// cudaMallocManaged(&array, sizeof(int)*(*n));
// for(int i = 0; i < *n; i++){
// fscanf(fptr, "%d,", &token);
// array[i] = token;
// }
// fclose(fptr);
// return array;
// }
int* fileToArray(char file1[], int* n){
FILE* fptr = fopen(file1, "r");
char* str = (char*) malloc(sizeof(char)*2048);
int token;
int count = 0;
while (fscanf(fptr, "%d, ", &token) != EOF) {
//("%dth token: %d\n", count, token);
count++;
}
*n = count;
//printf("total number of elements: %d\n", *n);
int* array;
cudaMallocManaged(&array, sizeof(int)*(*n));
rewind(fptr);
for(int i = 0; i < *n; i++){
fscanf(fptr, "%d, ", &token);
array[i] = token;
}
fclose(fptr);
return array;
}
__global__
void lastDigit(int* array, int* result, int n) {
int index = blockIdx.x * blockDim.x + threadIdx.x;
int stride = blockDim.x * gridDim.x;
for (int i = index; i < n; i += stride) {
result[i] = array[i] % 10;
}
}
__global__
void min(int* array, int n){
int index = blockIdx.x * blockDim.x + threadIdx.x;
int stride = blockDim.x * gridDim.x;
int currentMin = INT_MAX;
for(int i = index; i < n; i += stride){
if(array[i] < currentMin){
currentMin = array[i];
}
}
array[index] = currentMin;
}
int computeMin(int* array, int n){
min<<<NUM_BLOCKS_A, NUM_THREADS_A>>>(array, n);
cudaDeviceSynchronize();
int minNum = INT_MAX;
for(int i = 0; i < NUM_THREADS_A; i++){
if(array[i] < minNum){
minNum = array[i];
}
}
return minNum;
}
void computeLastDigit(int* array, int n) {
int* result;
cudaMallocManaged(&result, sizeof(int)*(n));
lastDigit<<<NUM_BLOCKS_B, NUM_THREADS_B>>>(array, result, n);
cudaDeviceSynchronize();
// for (int i = 0; i < 10; i++) {
// printf("array[%d]: %d, result[%d]: %d\n", i, array[i], i, result[i]);
// }
FILE *output = fopen(OUTPUT_FILE_NAME_B, "w");
if(output == NULL) printf("failed to open file %s\n", OUTPUT_FILE_NAME_B);
fprintf(output, "%d", result[0]);
for(int i = 1; i < n ; i++) {
fprintf(output, ", %d", result[i]);
}
fclose(output);
}
int main(int argc, char* argv[]){
int n;
int* array = fileToArray("inp.txt", &n);
//printf("Number of elements in array: %d\n", n);
// for (int i = 0; i < n; i++) {
// printf("%d, ", array[i]);
// }
/*for(int i = 0; i < 10; i++){
printf("%d\n", array[i]);
}*/
computeLastDigit(array, n);
int min = computeMin(array, n);
FILE *output = fopen(OUTPUT_FILE_NAME_MIN, "w");
if(output == NULL) printf("failed to open file %s\n", OUTPUT_FILE_NAME_MIN);
fprintf(output, "%d", min);
fclose(output);
cudaFree(array);
}
|
23,705 | #include "includes.h"
extern "C" {
}
__global__ void Vector_Addition(int *a, int *b, int *c)
{
int tid = blockIdx.x;
if (tid < 100)
c[tid] = a[tid] + b[tid];
} |
23,706 | #include "includes.h"
#define BIN_WIDTH 0.25
#define BLOCK_DIM 256
#define COVERAGE 180
#define LINE_LENGTH 30
#define BINS_TOTAL (COVERAGE * (int)(1 / BIN_WIDTH))
typedef struct Galaxy
{
float declination;
float declination_cos;
float declination_sin;
float right_ascension;
} Galaxy;
__global__ void measure_galaxy_distribution(int *DD_histogram, int *DR_histogram, int *RR_histogram, float *distribution, int n)
{
int index = blockIdx.x * blockDim.x + threadIdx.x;
int stride = blockDim.x * gridDim.x;
for (int i = index; i < n; i += stride)
{
if (RR_histogram[i] == 0)
continue;
distribution[i] = (DD_histogram[i] - 2.0f * DR_histogram[i] + RR_histogram[i]) / RR_histogram[i];
}
} |
23,707 | #include "includes.h"
__global__ void VecAdd(double* A,double* B,double* C)
{
// extern __shared__ float sdata[];
int i=threadIdx.x;
C[i]=A[i]+B[i];
} |
23,708 | //Three Dimensional Poisson solver using NVIDIA CUDA
//Author: Arkavo Hait, 2021
#include <stdio.h>
#include <iostream>
#include <cmath>
#include <time.h>
#include <chrono>
#include <string>
#include <limits.h>
#include <fstream>
using namespace std;
//Box struct
struct BOX
{
int X;
int Y;
int Z;
};
//X double derivative
__global__ void DDX(double* R, double* C,int X,int Y,int Z,double dx)
{
int idx = threadIdx.x + blockIdx.x*blockDim.x;
if(idx<((Y-2)*(Z-2)))
{
int idy = idx / (Y-2) ;
int idz = idx % (Y-2) ;
int index = idy*X + idz*X*Y + X+X*Y;
for(int i=1;i<X-1;i++)
{
*(R+index+i) = (*(C+index+1+i) + *(C+index-1+i) - 2* *(C+index+i))/(dx*dx);
}
}
}
//Y double derivative
__global__ void DDY(double* R, double* C,int X,int Y,int Z, double dy)
{
int idx = threadIdx.x + blockIdx.x*blockDim.x;
if(idx<((Y-2)*(Z-2))){
int idy = idx / (Y-2) ;
int idz = idx % (Y-2) ;
int index = idy*X + idz*X*Y + X+X*Y;
for(int i=1;i<Y-1;i++)
{
*(R+index+i*X) = (*(C+index+X*(i+1)) + *(C+index+X*(i-1)) - 2* *(C+index+i*X))/(dy*dy);
}}
}
//Z double derivative
__global__ void DDZ(double* R, double* C,int X,int Y,int Z, double dz)
{
int idx = threadIdx.x + blockIdx.x*blockDim.x;
if(idx<((Y-2)*(Z-2))){
int idy = idx / (Y-2) ;
int idz = idx % (Y-2) ;
int index = idy*X + idz*X*Y + X+X*Y;
for(int i=0;i<Z-1;i++)
{
*(R+index+i*X*Y) = (*(C+index+X*Y*(i+1)) + *(C+index+X*Y*(i-1))- 2* *(C+index+i*X*Y))/(dz*dz);
}}
}
//parallel function to update matrices
__global__ void ASSIGN(double* R, double* C,int X,int Y,int Z)
{
int idx = threadIdx.x + blockIdx.x*blockDim.x;
if(idx<((Y-2)*(Z-2))){
int idy = idx / (Y-2) ;
int idz = idx % (Y-2) ;
int index = idy*X + idz*X*Y + X+X*Y;
for(int i=1;i<X-1;i++)
{
*(R+index+i) = *(C+index+i);
}}
}
//parallel function to add two matrices
__global__ void ADD(double* R,double* C,double dt,int X,int Y,int Z)
{
int idx = threadIdx.x + blockIdx.x*blockDim.x;
if(idx<((Y-2)*(Z-2))){
int idy = idx / (Y-2) ;
int idz = idx % (Y-2) ;
int index = idy*X + idz*X*Y + X+X*Y;
for(int i=0;i<X;i++)
{
*(R+index+i) += (*(C+index+i) * dt);
}}
}
//parallel function to compare two matrices, outputting a maximum difference bteween elements
__global__ void COMPARE(double* R, double* C, double* OUT_H,int X,int Y,int Z)
{
int idx = threadIdx.x + blockIdx.x*blockDim.x;
if(idx<((Y-2)*(Z-2))){
int idy = idx / (Y-2) ;
int idz = idx % (Y-2) ;
int index = idy*X + idz*X*Y + X+X*Y;
for(int i=1;i<X-1;i++)
{
if(abs(*(R+index+i)-*(C+index+i))>= *OUT_H)
{
*OUT_H = abs(*(R+index+i) - *(C+index+i));
}
}}
}
//reset function, use it to reset any pointer
__global__ void RESET_CTR(double* C)
{
*C = 0;
}
//display function, use to disaply any array [deprecated for future use]
void display(double* DATA,int X,int Y,int Z)
{
for(int k=0;k<Z;k++)
{for(int j=0;j<Y;j++)
{for(int i=0;i<X;i++)
{printf("%.2lf ",*(DATA+i+X*j+X*Z*k));}
printf("\n");}printf("\n");}
}
//main fxn, will fix with args after
int main(int argc, char* argv[])
{
std::ofstream file;
file.open("data.csv",ios::app);
//file<<"Threads, X, Y, Z, Total, Time\n";
//DECLARE YOUR VARIABLES HERE
struct BOX grid;
int threads;
//std::cout<<argc<<"\n";
if(argc==2)
{
threads = stoi(argv[1]);
grid.X=10;
grid.Y=10;
grid.Z=10;
}
if(argc>2)
{
threads = stoi(argv[1]);
grid.X = stoi(argv[2]);
grid.Y = stoi(argv[3]);
grid.Z = stoi(argv[4]);
}
std::cout<<"Threads: "<<threads<<"\n";
const int X = grid.X;
const int Y = grid.Y;
const int Z = grid.Z;
int blocks = 1 + (Y-2) * (Z-2) / threads;
std::cout<<"Blocks: "<<blocks<<"\n";
//step for double derivatives
double step = 0.001;
//tolerence
double tol = 0.00001;
std::cout<<"\nX"<<X<<" Y"<<Y<<" Z"<<Z<<" Total Capacity⇒"<<X*Y*Z<<endl;
unsigned long long SIZE_0 = ((int)sizeof(double))*X*Y*Z;
double* DATA_H;
double* DATA_F;
DATA_H = (double*)malloc(SIZE_0);
DATA_F = (double*)malloc(SIZE_0);
//Impose Boundary conditions here
for(int i=0;i<X;i++)
{for(int j=0;j<Y;j++)
{for(int k=0;k<Z;k++)
{
if(i==0||i==(X-1)||j==0||j==(Y-1)||k==0||k==(Z-1))
*(DATA_H+i+j*X+k*X*Y) = 5;
else
*(DATA_H+i+j*X+k*X*Y) = 0;
}}}
//error counters
double* CC;
double CCD = 0;
//DATA pointer for device state 0
double* DATA_ORIGINAL;
//DATA pointer for device state 1
double* DATA_NEXT;
//Derivative results pointers
double* DDX_D;
double* DDY_D;
double* DDZ_D;
//array size for device
//allocating space for arrays
cudaMalloc((void**)&DATA_ORIGINAL,SIZE_0);
cudaMalloc((void**)&DATA_NEXT,SIZE_0);
cudaMalloc((void**)&DDX_D,SIZE_0);
cudaMalloc((void**)&DDY_D,SIZE_0);
cudaMalloc((void**)&DDZ_D,SIZE_0);
cudaMalloc(&CC,(int)sizeof(double));
//token counter
CCD = 10.;
//copy data state0, state1
cudaMemcpy(DATA_ORIGINAL,DATA_H,SIZE_0,cudaMemcpyHostToDevice);
cudaMemcpy(DATA_NEXT,DATA_H,SIZE_0,cudaMemcpyHostToDevice);
//counter
int ct = 0;
auto hst_st = std::chrono::high_resolution_clock::now();
//run while tolerence > differences
while(CCD>tol)
{
//reset difference every loop
RESET_CTR <<<1,1>>> (CC);
//run derivatives
DDY <<<blocks,threads>>> (DDY_D,DATA_ORIGINAL,X,Y,Z,10.);
DDZ <<<blocks,threads>>> (DDZ_D,DATA_ORIGINAL,X,Y,Z,10.);
DDX <<<blocks,threads>>> (DDX_D,DATA_ORIGINAL,X,Y,Z,10.); //add into state 1
ADD <<<blocks,threads>>> (DATA_NEXT,DDX_D,step,X,Y,Z);
ADD <<<blocks,threads>>> (DATA_NEXT,DDY_D,step,X,Y,Z);
ADD <<<blocks,threads>>> (DATA_NEXT,DDZ_D,step,X,Y,Z);
//compare state1 state 0
COMPARE<<<blocks,threads>>>(DATA_ORIGINAL,DATA_NEXT,CC,X,Y,Z);
//copy back max error
cudaMemcpy(&CCD,CC,sizeof(double),cudaMemcpyDeviceToHost);
//make state 1 as state0
ASSIGN <<<blocks,threads>>> (DATA_ORIGINAL,DATA_NEXT,X,Y,Z);
//update counter
ct += 1;
//information every 1000 loops because of visibility
if(ct%1000==0)
{
cudaMemcpy(&CCD,CC,sizeof(double),cudaMemcpyDeviceToHost);
printf("%d loops %0.6lf max error\r",ct,CCD);
}
}
//copy back final array
cudaMemcpy(DATA_F,DATA_ORIGINAL,SIZE_0,cudaMemcpyDeviceToHost);
auto hst_en = std::chrono::high_resolution_clock::now();
std::chrono::duration<float> duration = hst_en-hst_st;
std::cout<<"\nDuration: "<<duration.count()<<"\n";
//final print statement
std::cout<<"\n\nConverged in "<<ct-1<<" loops\n\n";
//file<<"Threads, X, Y, Z, Total, Time\n";
file<<threads<<","<<X<<","<<Y<<","<<Z<<","<<X*Y*Z<<","<<duration.count()<<"\n";
file.close();
//display optional
//display(DATA_F,X,Y,Z);
//free pointers
cudaFree(DATA_ORIGINAL);
cudaFree(DATA_NEXT);
cudaFree(DDX_D);
cudaFree(DDY_D);
cudaFree(DDZ_D);
return 0;
} |
23,709 | /*
Test to check basic functioning of Tic Tac Toe interface
Rahul Kejriwal
CS14B023
*/
#include <iostream>
#include "../GameInterfaces/TicTacToe.cu"
using namespace std;
__global__ void test_kernel(){
GameState *initial_stage = new TicTacToeState;
initial_stage = initial_stage->makeMove(0);
initial_stage = initial_stage->makeMove(4);
initial_stage = initial_stage->makeMove(8);
initial_stage->printState();
}
void test_function(){
GameState *initial_stage = new TicTacToeState;
initial_stage = initial_stage->makeMove(0);
initial_stage = initial_stage->makeMove(4);
initial_stage = initial_stage->makeMove(8);
initial_stage->printState();
}
int main(){
cout << "Checking TicTacToe interface" << endl
<< "----------------------------" << endl;
cout << "Using Kernel: " << endl;
test_kernel<<<1,1>>>();
cudaDeviceSynchronize();
cout << "Using function: " << endl;
test_function();
cout << endl;
return 0;
} |
23,710 | #include "includes.h"
//-----------------------------------------
// Autor: Farias
// Data : January 2012
// Goal : Image treatment
//-----------------------------------------
/***************************************************************************************************
Includes
***************************************************************************************************/
/***************************************************************************************************
Defines
***************************************************************************************************/
#define ELEM(i,j,DIMX_) (i+(j)*(DIMX_))
#define BLOCK_SIZE 16
/***************************************************************************************************
Functions
***************************************************************************************************/
using namespace std;
/**************************************************************************************************/
__global__ void filter1( int width, int height, unsigned char *src, unsigned char *dest ) {
int i = threadIdx.x + blockIdx.x*blockDim.x;
int j = threadIdx.y + blockIdx.y*blockDim.y;
int aux, idx;
if(i > 0 && j > 0 && i < width - 1 && j < height - 1) {
for (int k = 0; k < 3; ++k)
{
aux = 0;
idx = 3*ELEM( i, j, width );
aux += 4*src[ idx+k ];
idx = 3*ELEM( i-1, j, width );
aux+= 2*src[ idx+k ];
idx = 3*ELEM( i, j-1, width );
aux+= 2*src[ idx+k ];
idx = 3*ELEM( i+1, j, width );
aux+= 2*src[ idx+k ];
idx = 3*ELEM( i, j+1, width );
aux+= 2*src[ idx+k ];
aux /= 12;
idx = 3*ELEM( i, j, width );
dest[ idx+k ] = (unsigned char)aux;
}
}
} |
23,711 | #include <cuda_runtime.h>
#define THREADS 128
__shared__ int smem[THREADS];
__global__ void sumKernel(int *data_in, int *sum_out) {
int tx = threadIdx.x;
smem[tx] = data_in[tx] + tx;
if (tx == 0) {
*sum_out = 0;
for (int i = 0; i < THREADS; ++i)
*sum_out += smem[i];
}
}
int main(int argc, char **argv){
int *data_in = NULL;
int *sum_out = NULL;
cudaMalloc((void**)&data_in, sizeof(int) * THREADS);
cudaMalloc((void**)&sum_out, sizeof(int));
cudaMemset(data_in, 0, sizeof(int) * THREADS);
sumKernel<<<1, THREADS>>>(data_in, sum_out);
cudaDeviceSynchronize();
cudaFree(data_in);
cudaFree(sum_out);
return 0;
} |
23,712 | #include <thrust/device_vector.h>
#include <thrust/sort.h>
#include <thrust/copy.h>
#include <thrust/functional.h>
#include <iostream>
using namespace std;
#define N 6
int main()
{
int A[N] = {1,4,2,8,5,7};
char B[N] = {'b', 'a', 'h', 'j', 'd', 'c'};
thrust::device_vector<int> D(N);
thrust::device_vector<char> E(N);
thrust::copy(A, A+N, D.begin());
thrust::copy(B, B+N, E.begin());
// thrust::sort(D.begin(), D.end());
// thrust::stable_sort(D.begin(), D.end(), thrust::greater<int>());
// thrust::sort_by_key(D.begin(), D.end(), E.begin());
thrust::sort_by_key(D.begin(), D.end(), E.begin(), thrust::greater<int>());
thrust::copy(D.begin(), D.end(), ostream_iterator<int>(cout, " "));
cout << endl;
thrust::copy(E.begin(), E.end(), ostream_iterator<char>(cout, " "));
cout << endl;
return 0;
}
|
23,713 | #include "includes.h"
__global__ void vecDiv(float* a,float* b,float* c,const int N){
const int i = blockIdx.x*blockDim.x + threadIdx.x;
if(i<N)
c[i] = __fdividef(a[i],b[i]);
//c[i] = a[i]/b[i];
} |
23,714 | #include <stdio.h>
__device__ void sleep0(void *p_kernel_time, int clockRate)
{
//This method will sleep for clockRate*kernel_time many clock ticks
// which is equivalent to sleeping for kernel_time milliseconds
int kernel_time = *((int *) p_kernel_time);
int finish_clock;
int start_time;
for(int temp=0; temp<kernel_time; temp++){
start_time = clock();
finish_clock = start_time + clockRate;
bool wrapped = finish_clock < start_time;
while( clock() < finish_clock || wrapped) wrapped = clock()>0 && wrapped;
}
}
|
23,715 | float h_A[]= {
0.8629824282246745, 0.9809381081792694, 0.6033353234247322, 0.9301837183870358, 0.7606145634335923, 0.7102338082874134, 0.9100867887912554, 0.6131782755390371, 0.7682624667626302, 0.9225130643212059, 0.7881034643389964, 0.7683203381565156, 0.6808946040344683, 0.6069419355000365, 0.7423359762916779, 0.6901629584839581, 0.8522004350623015, 0.9325077181733225, 0.6122006268661258, 0.8523308871138681, 0.5363998227195224, 0.6953557214148984, 0.8104346687914366, 0.5708599848231695, 0.8441393216322519, 0.7745268576526735, 0.9232788219438748, 0.8170173887338226, 0.8194340039054868, 0.993225335311058, 0.5845293366751197, 0.7729578749675967, 0.5528634962476274, 0.6191168983755757, 0.806070899295337, 0.7850257129423912, 0.805446023496612, 0.8221561236303285, 0.6119821853315428, 0.9250407030594562, 0.7427796848419423, 0.799627062830455, 0.5088968450880349, 0.8833069728142495, 0.6108826364374431, 0.9937578325683258, 0.6078751462672842, 0.7724530044069235, 0.8212218827908762, 0.6678749074295842, 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0.7264980523328242, 0.5601601284776812, 0.9386859020239791, 0.5254084887777128, 0.6741470720174763, 0.7572298541868279, 0.7947928354397956, 0.8322881324021492, 0.737368250287181, 0.8933100107394065, 0.6997420278723485, 0.8948982931763783, 0.766269099834062, 0.7288861920737068, 0.8110974418261296, 0.9372323079106707, 0.695602269927708, 0.7714017726424152, 0.7372789820090498, 0.5181003844273293, 0.7216724798366565, 0.639541316803881, 0.9172351522861945, 0.5899784210069787, 0.7673844951559479, 0.801804879225422, 0.9849615599801655, 0.9292889014689811, 0.7484033711151683, 0.9553525908657866, 0.5675866914030461, 0.8418450448697405, 0.821669353026261, 0.7270152168103942, 0.5890024900932758, 0.5887588103711574, 0.9200913675812302, 0.648710418237818, 0.6386416518586835, 0.935212142381878, 0.708900595629415, 0.5659028027573514, 0.5684212010130001, 0.8408449137074931, 0.8280751235367539, 0.5378460480917615, 0.5827898579640163, 0.6054343229285732, 0.6116362710903155, 0.9235544123714123, 0.6805398872025764, 0.8244439589772091, 0.9753171106131983, 0.5709239722213503, 0.6127384378123395, 0.774925738451316, 0.6647608780315761, 0.9003598563067841, 0.5481490782518057, 0.6779661997123907, 0.5590204008464594, 0.9250363732973343, 0.8629506137664363, 0.6097310874235933, 0.6570506076386329, 0.5064511029508053, 0.7507423981430483, 0.666549717233401, 0.643916418238292, 0.7107548689369532, 0.8102727403140195, 0.9144342470060309, 0.7650428075554088, 0.7131850264861479, 0.6145074760128109, 0.5189661420696822, 0.9823498205840695, 0.9489481720308643, 0.9350763403271576, 0.7126703160678607, 0.9847736357451573, 0.9692127354175875, 0.9645777740160353, 0.5201922488538938, 0.8771755347457217, 0.5957798880764233, 0.6532030418285507, 0.760751203070237, 0.8588827787786644, 0.9389159060683047, 0.6308062555748208, 0.9886413942558865, 0.6508951915065994, 0.8169632458760347, 0.9207040764213388, 0.5488610668014061, 0.7216624119834136, 0.735933846031094, 0.6432989575872622, 0.6567212576748351, 0.7074222401477906, 0.9143375605763284, 0.9367237648499672, 0.82767799999576, 0.652310199676249, 0.6605750373873791, 0.674417390773324, 0.8085610114099718, 0.8163220133858993, 0.7568256862581448, 0.66713479827225, 0.9503370586298416, 0.6796478857174046, 0.6618729128217635, 0.9794309823773168, 0.5633997663230141, 0.5016201641366044, 0.9044003782483203, 0.6752957626141836, 0.6482899872415393, 0.8064307784393792, 0.6171785758087339, 0.5731032331850936, 0.9137743584688769, 0.765679531242093, 0.7736833719173686, 0.8588828772040824, 0.6410982078171621, 0.5751907124350241, 0.9914943229884255, 0.6795000639639599, 0.50225789938107, 0.7425974668255855, 0.7317555750229836, 0.6847481991629824, 0.5582439696368334, 0.862383692242127, 0.8101494484831, 0.6263425016800921, 0.9134263128508529, 0.6649725752876074, 0.6311583893101815, 0.832245223958931, 0.9247823308142202, 0.9423271170387763, 0.8301088116623505, 0.866526453032876, 0.6173022069568773, 0.963658960240473, 0.6105040055137942, 0.663119862372685, 0.5246959702898955, 0.6277690267377722, 0.5003138766023614, 0.829944945912586, 0.9762609653455402, 0.5319641230523346, 0.922127792108859, 0.9489791385012613, 0.8767857620423876, 0.8667384105041323, 0.7263202109917661, 0.9589291486803115, 0.8776100202864728, 0.7319244289296803, 0.7698733839283396, 0.8592891097597387, 0.9027103692790112, 0.5371996740075607, 0.9005287368359642, 0.7270390126251952, 0.9594437709727908, 0.7565235897537409, 0.6367739584602041, 0.731477901148002, 0.5910465294869314, 0.6811604603924075, 0.9262637711925064, 0.6901609656815879, 0.5224620844821176, 0.9986951937499164, 0.5853057404734049, 0.8260482413543739, 0.7425357227979034, 0.8860416111333052, 0.5896395494021638, 0.9507386373663762, 0.9931477115727076, 0.8297359273476401, 0.5286704804558254, 0.6036771472518166, 0.9121205538111035, 0.6731077502013435, 0.6264200923209284, 0.9460899440504159, 0.832071137293249, 0.8438166777168198, 0.6413669883710718, 0.8933331717509823, 0.9308213840963562, 0.9997378636441248, 0.9254154650784756, 0.9881188975530424, 0.5823256660490812, 0.7067152478482412, 0.9570671517423637, 0.6036314775138262, 0.9100539927481357, 0.8373298907586908, 0.56225989269032, 0.8030422167744484, 0.8416934433122787, 0.5554399070510012, 0.7855966181043625, 0.7379403613171174, 0.9674899386866764, 0.5890787936026731, 0.502395038333405, 0.9285648448127728, 0.8788879693597724, 0.9173218812083022, 0.815931534588503, 0.8025495335401898, 0.9112984140984784, 0.6609953381217968, 0.9029255165895813, 0.8224610232858744, 0.7024631856865517, 0.7623942583761647, 0.7241951519285564, 0.751705571299476, 0.961031781273143, 0.9408009545983966, 0.6935707947662937, 0.5301119519433386, 0.6039877720200408, 0.9871341559926416, 0.7586042389652594, 0.9410916340766529, 0.5462060552253896, 0.9330708312708927, 0.9968208393359086, 0.5040683586336483, 0.915559784734882, 0.5543359419621572, 0.7177099940297518, 0.7748036811814907, 0.75926877812107, 0.5832970738372243, 0.8429408479853988, 0.573196457059312, 0.8623981849579478, 0.7864286386685917, 0.8767379292353883, 0.704923663282196, 0.7727481201784621, 0.5334702424886312, 0.5999599364803674, 0.6056107533641232, 0.6381652491954868, 0.670590118511769, 0.6513601127607883, 0.6994697476946515, 0.7904907122788716, 0.9609873568549793, 0.8507687719624519, 0.6293089950227, 0.6057430156030366, 0.8633390308847928, 0.8674058672625807, 0.8671060746556318, 0.8904457150918037, 0.8705262098086585, 0.922858336431073, 0.6412902552817636, 0.9538820905969241, 0.9879143507921381, 0.5552448301070231, 0.6522504517518475, 0.7099075487929495, 0.8101696874920293, 0.6197450701547381, 0.8258268731092946, 0.5075762884619162, 0.6856514962909231, 0.7931613326304221, 0.6228231183634271, 0.5016724498137224, 0.5552334951403228, 0.7005347648872317, 0.8860395954715782, 0.5239142984131586, 0.761907601466003, 0.7914469526585934, 0.6558839420050719, 0.8488111110224383, 0.8362905194552473, 0.5890339137137939, 0.9541202056256073, 0.9901749658460849, 0.5651396688800052, 0.6890066409941966, 0.8390150108957475, 0.5545192999490529, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0};
int h_B[]= {
0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66, 68, 70, 72, 74, 76, 78, 80, 82, 84, 86, 88, 90, 92, 94, 96, 98, 100, 102, 104, 106, 108, 110, 112, 114, 116, 118, 120, 122, 124, 126, 128, 130, 132, 134, 136, 138, 140, 142, 144, 146, 148, 150, 152, 154, 156, 158, 160, 162, 164, 166, 168, 170, 172, 174, 176, 178, 180, 182, 184, 186, 188, 190, 192, 194, 196, 198, 200, 202, 204, 206, 208, 210, 212, 214, 216, 218, 220, 222, 224, 226, 228, 230, 232, 234, 236, 238, 240, 242, 244, 246, 248, 250, 252, 254, 256, 258, 260, 262, 264, 266, 268, 270, 272, 274, 276, 278, 280, 282, 284, 286, 288, 290, 292, 294, 296, 298, 300, 302, 304, 306, 308, 310, 312, 314, 316, 318, 320, 322, 324, 326, 328, 330, 332, 334, 336, 338, 340, 342, 344, 346, 348, 350, 352, 354, 356, 358, 360, 362, 364, 366, 368, 370, 374, 376, 378, 380, 382, 384, 386, 388, 390, 392, 394, 396, 398, 400, 402, 404, 406, 408, 410, 412, 414, 416, 418, 420, 422, 424, 426, 428, 430, 432, 434, 436, 438, 440, 442, 444, 446, 448, 450, 452, 454, 456, 458, 460, 462, 464, 466, 468, 470, 472, 474, 476, 478, 480, 482, 484, 486, 488, 490, 492, 494, 496, 498, 500, 502, 504, 506, 508, 510, 512, 514, 516, 518, 520, 522, 524, 526, 528, 530, 532, 534, 536, 538, 540, 542, 544, 546, 548, 550, 552, 554, 556, 558, 560, 562, 564, 566, 568, 570, 572, 574, 576, 578, 580, 582, 584, 586, 588, 590, 592, 594, 596, 598, 600, 602, 604, 606, 608, 610, 612, 614, 616, 618, 620, 622, 624, 626, 628, 630, 632, 634, 636, 638, 640, 642, 644, 646, 648, 650, 652, 654, 656, 658, 660, 662, 664, 666, 668, 670, 672, 674, 676, 678, 680, 682, 684, 686, 688, 690, 692, 694, 696, 698, 700, 702, 704, 706, 708, 710, 712, 714, 716, 718, 720, 722, 724, 726, 728, 730, 732, 734, 736, 738, 740, 742, 744, 746, 748, 750, 752, 754, 756, 758, 760, 762, 764, 766, 768, 770, 772, 774, 776, 778, 780, 782, 784, 786, 788, 790, 792, 794, 796, 798, 800, 802, 804, 806, 808, 810, 812, 814, 816, 818, 820, 822, 824, 826, 828, 830, 832, 834, 836, 838, 840, 842, 844, 846, 848, 850, 852, 854, 856, 858, 860, 862, 864, 866, 868, 870, 872, 874, 876, 878, 880, 882, 884, 886, 888, 890, 892, 894, 896, 898, 900, 902, 904, 906, 908, 910, 912, 914, 916, 918, 920, 922, 924, 926, 928, 930, 932, 934, 936, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 962, 964, 966, 968, 970, 972, 974, 976, 978, 980, 982, 984, 986, 988, 990, 992, 994, 996, 998, 1000, 1002, 1004, 1006, 1008, 1010, 1012, 1014, 1016, 1018, 1020, 1022, 1024, 1026, 1028, 1030, 1032, 1034, 1036, 1038, 1040, 1042, 1044, 1046, 1048, 1050, 1052, 1054, 1056, 1058, 1060, 1062, 1064, 1066, 1068, 1070, 1072, 1074, 1076, 1078, 1080, 1082, 1084, 1086, 1088, 1090, 1092, 1094, 1096, 1098, 1100, 1102, 1104, 1106, 1108, 1110, 1112, 1114, 1116, 1118, 1120, 1122, 1124, 1126, 1128, 1130, 1132, 1134, 1136, 1138, 1140, 1142, 1144, 1146, 1148, 1150, 1152, 1154, 1156, 1158, 1160, 1162, 1164, 1166, 1168, 1170, 1172, 1174, 1176, 1178, 1180, 1182, 1184, 1186, 1188, 1190, 1192, 1194, 1196, 1198, 1200, 1202, 1204, 1206, 1208, 1210, 1212, 1214, 1216, 1218, 1220, 1222, 1224, 1226, 1228, 1230, 1232, 1234, 1236, 1238, 1240, 1242, 1244, 1246, 1248, 1250, 1252, 1254, 1256, 1258, 1260, 1262, 1264, 1266, 1268, 1270, 1272, 1274, 1276, 1278, 1280, 1282, 1284, 1286, 1288, 1290, 1292, 1294, 1296, 1298, 1300, 1302, 1304, 1306, 1308, 1310, 1312, 1314, 1316, 1318, 1320, 1322, 1324, 1326, 1328, 1330, 1332, 1334, 1336, 1338, 1340, 1342, 1344, 1346, 1348, 1350, 1352, 1354, 1356, 1358, 1360, 1362, 1364, 1366, 1368, 1370, 1372, 1374, 1376, 1378, 1380, 1382, 1384, 1386, 1388, 1390, 1392, 1394, 1396, 1398, 1400, 1402, 1404, 1406, 1408, 1410, 1412, 1414, 1416, 1418, 1420, 1422, 1424, 1426, 1428, 1430, 1432, 1434, 1436, 1438, 1440, 1442, 1444, 1446, 1448, 1450, 1452, 1454, 1456, 1458, 1460, 1462, 1464, 1466, 1468, 1470, 1472, 1474, 1476, 1478, 1480, 1482, 1484, 1486, 1488, 1490, 1492, 1494, 1496, 1498, 1500, 1502, 1504, 1506, 1508, 1510, 1512, 1514, 1516, 1518, 1520, 1522, 1524, 1526, 1528, 1530, 1532, 1534, 1536, 1538, 1540, 1542, 1544, 1546, 1548, 1550, 1552, 1554, 1556, 1558, 1560, 1562, 1564, 1566, 1568, 1570, 1572, 1574, 1576, 1578, 1580, 1582, 1584, 1586, 1588, 1590, 1592, 1594, 1596, 1598, 1600, 1602, 1604, 1606, 1608, 1610, 1612, 1614, 1616, 1618, 1620, 1622, 1624, 1626, 1628, 1630, 1632, 1634, 1636, 1638, 1640, 1642, 1644, 1646, 1648, 1650, 1652, 1654, 1656, 1658, 1660, 1662, 1664, 1666, 1668, 1670, 1672, 1674, 1676, 1678, 1680, 1682, 1684, 1686, 1688, 1690, 1692, 1694, 1696, 1698, 1700, 1702, 1704, 1706, 1708, 1710, 1712, 1714, 1716, 1718, 1720, 1722, 1724, 1726, 1728, 1730, 1732, 1734, 1736, 1738, 1740, 1742, 1744, 1746, 1748, 1750, 1752, 1754, 1756, 1758, 1760, 1762, 1764, 1766, 1768, 1770, 1772, 1774, 1776, 1778, 1780, 1782, 1784, 1786, 1788, 1790, 1792, 1794, 1796, 1798, 1800, 1802, 1804, 1806, 1808, 1810, 1812, 1814, 1816, 1818, 1820, 1822, 1824, 1826, 1828, 1830, 1832, 1834, 1836, 1838, 1840, 1842, 1844, 1846, 1848, 1850, 1852, 1854, 1856, 1858, 1860, 1862, 1864, 1866, 1868, 1870, 1872, 1874, 1876, 1878, 1880, 1882, 1884, 1886, 1888, 1890, 1892, 1894, 1896, 1898, 1900, 1902, 1904, 1906, 1908, 1910, 1912, 1914, 1916, 1918, 1920, 1922, 1924, 1926, 1928, 1930, 1932, 1934, 1936, 1938, 1940, 1942, 1944, 1946, 1948, 1950, 1952, 1954, 1956, 1958, 1960, 1962, 1964, 1966, 1968, 1970, 1972, 1974, 1976, 1978, 1980, 1982, 1984, 1986, 1988, 1990, 1992, 1994, 1996, 1998, 2000, 2002, 2004, 2006, 2008, 2010, 2012, 2014, 2016, 2018, 2020, 2022, 2024, 2026, 2028, 2030, 2032, 2034, 2036, 2038, 2040, 2042, 2044, 2046, 2048, 2050, 2052, 2054, 2056, 2058, 2060, 2062, 2064, 2066, 2068, 2070, 2072, 2074, 2076, 2078, 2080, 2082, 2084, 2086, 2088, 2090, 2092, 2094, 2096, 2098, 2100, 2102, 2104, 2106, 2108, 2110, 2112, 2114, 2116, 2118, 2120, 2122, 2124, 2126, 2128, 2130, 2132, 2134, 2136, 2138, 2140, 2142, 2144, 2146, 2148, 2150, 2152, 2154, 2156, 2158, 2160, 2162, 2164, 2166, 2168, 2170, 2172, 2174, 2176, 2178, 2180, 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, 2270, 2272, 2274, 2276, 2278, 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, 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22685, 22687, 22688, 22689, 22690, 22692, 22693, 22694, 22695, 22696, 22697, 22698, 22699, 22700, 22701, 22702, 22703, 22706, 22707, 22708, 22709, 22710, 22711, 22712, 22713, 22714, 22717, 22718, 22719, 22720, 22721, 22722, 22723, 22724, 22725, 22726, 22727, 22728, 22729, 22730, 22731, 22732, 22733, 22735, 22736, 22737, 22738, 22739, 22740, 22741, 22742, 22744, 22746, 22747, 22748, 22749, 22750, 22751, 22752, 22753, 22754, 22755, 22756, 22757, 22758, 22759, 22760, 22761, 22762, 22763, 22765, 22766, 22767, 22768, 22769, 22770, 22771, 22772, 22774, 22776, 22777, 22778, 22779, 22780, 22781, 22782, 22783, 22784, 22785, 22786, 22787, 22788, 22789, 22790, 22791, 22792, 22793, 22794, 22795, 22796, 22797, 22798, 22799, 22800, 22801, 22802, 22803, 22804, 22805, 22806, 22807, 22808, 22809, 22810, 22811, 22812, 22813, 22814, 22815, 22816, 22817, 22818, 22819, 22820, 22821, 22822, 22823, 22824, 22825, 22826, 22827, 22828, 22829, 22830, 22831, 22832, 22834, 22835, 22836, 22837, 22838, 22839, 22840, 22841, 22842, 22843, 22844, 22845, 22846, 22847, 22848, 22849, 22850, 22851, 22852, 22853, 22854, 22855, 22856, 22857, 22858, 22859, 22860, 22861, 22862, 22863, 22864, 22866, 22867, 22868, 22869, 22870, 22871, 22872, 22875, 22876, 22877, 22878, 22879, 22881, 22882, 22883, 22884, 22885, 22886, 22887, 22888, 22889, 22891, 22892, 22893, 22894, 22895, 22896, 22897, 22899, 22900, 22902, 22903, 22904, 22906, 22907, 22909, 22910, 22911, 22912, 22913, 22915, 22916, 22917, 22918, 22919, 22920, 22921, 22922, 22923, 22924, 22926, 22927, 22928, 22929, 22930, 22931, 22934, 22935, 22936, 22939, 22940, 22941, 22943, 22944, 22945, 22948, 22949, 22950, 22951, 22952, 22953, 22954, 22956, 22958, 22959, 22960, 22961, 22962, 22963, 22966, 22967, 22968, 22969, 22970, 22971, 22972, 22973, 22974, 22976, 22977, 22979, 22981, 22982, 22984, 22985, 22986, 22987, 22989, 22990, 22992, 22994, 22996, 22997, 22999, 23000, 23001, 23003, 23004, 23005, 23006, 23007, 23009, 23010, 23011, 23012, 23013, 23014, 23015, 23016, 23017, 23018, 23019, 23020, 23021, 23022, 23023, 23024, 23025, 23026, 23027, 23029, 23031, 23033, 23035, 23036, 23037, 23038, 23041, 23043, 23046, 23048, 23049, 23050, 23051, 23052, 23054, 23056, 23058, 23060, 23063, 23065, 23068, 23070, 23073, 23075, 23076, 23077, 23078, 23079, 23080, 23081, 23082, 23084, 23086, 23088, 23090, 23093, 23095, 23096, 23097, 23098, 23099, 23101, 23103, 23105, 23107, 23109, 23110, 23111, 23112, 23115, 23117, 23118, 22978, 23123, 23128, 22993, 23131, 23132, 23133, 23135, 23137, 23139, 23141, 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, 23168, 23170, 23174, 23177, 23179, 23182, 23185, 23187, 23189, 23191, 23194, 23198, 23202, 23204, 23206, 23209, 23212, 23214, 23216, 23219, 23222, 23226, 23228, 23231, 23235, 23245, 23248, 23251, 23254, 23255, 23257, 23260, 23264, 23266, 23268, 23274, 23277, 23280, 23282, 23284, 23287, 23290, 23293, 23294, 23296, 23305, 23308, 23311, 23313, 23317, 23318, 23324, 23326, 23328, 23330, 23333, 23335, 23339, 23343, 23347, 23349, 23352, 23353, 23356, 23357, 23359, 23363, 23364, 23370, 23372, 23374, 23376, 23379, 23381, 23383, 23386, 23389, 23391, 23393, 23402, 23405, 23407, 23411, 23413, 23422, 23424, 23427, 23429, 23433, 23435, 23441, 23443, 23446, 23449, 23452, 23454, 23457, 23459, 23461, 23465, 23467, 23470, 23472, 23474, 23476, 23479, 23482, 23484, 23487, 23490, 23493, 23496, 23497, 23499, 23506, 23508, 23509, 23512, 23514, 23518, 23521, 23524, 23527, 23529, 23530, 23536, 23538, 23542, 23544, 23547, 23550, 23552, 23555, 23559, 23563, 23566, 23569, 23572, 23573, 23575, 23577, 23582, 23583, 23585, 23588, 23591, 23595, 23598, 23600, 23601, 23604, 23607, 23610, 23613, 23614, 23616, 23618, 23620, 23622, 23623, 23626, 23629, 23632, 23635, 23636, 23638, 23640, 23645, 23646, 23648, 23651, 23654, 23660, 23663, 23666, 23669, 23672, 23675, 23677, 23679, 23682, 23684, 23686, 23687, 23692, 23695, 23698, 23701, 23703, 23707, 23709, 23715, 23718, 23720, 23722, 23726, 23728, 23738, 23740, 23742, 23745, 23748, 23751, 23754, 23756, 23757, 23763, 23766, 23769, 23772, 23774, 23776, 23779, 23781, 23783, 23784, 23789, 23792, 23795, 23798, 23800, 23806, 23810, 23813, 23815, 23818, 23820, 23822, 23828, 23829, 23835, 23837, 23840, 23843, 23845, 23849, 23850, 23854, 23857, 23859, 23861, 23866, 23869, 23872, 23874, 23878, 23882, 23884, 23889, 23892, 23895, 23897, 23903, 23905, 23910, 23913, 23916, 23923, 23925, 23927, 23930, 23934, 23936, 23938, 23941, 23945, 23948, 23951, 23953, 23956, 23961, 23964, 23969, 23972, 23975, 23978, 23980, 23983, 23985, 23987, 23990, 23995, 23998, 24003, 24006, 24009, 24012, 24014, 24017, 24019, 24023, 24026, 24029, 24032, 24035, 24038, 24041, 24044, 24049, 24052, 24057, 24060, 24063, 24066, 24068, 24071, 24074, 24077, 24078, 24080, 24087, 24089, 24090, 24092, 24095, 24098, 24101, 24102, 24104, 24111, 24113, 24114, 24116, 24119, 24122, 24128, 24131, 24134, 24137, 24140, 24144, 24146, 24150, 24152, 24154, 24158, 24161, 24164, 24168, 24170, 24174, 24177, 24183, 24186, 24189, 24193, 24195, 24198, 24199, 24201, 24202, 24208, 24211, 24214, 24217, 24219, 24221, 24224, 24226, 24228, 24229, 24236, 24240, 24244, 24246, 24249, 24251, 24254, 24255, 24261, 24263, 24264, 24267, 24269, 24273, 24275, 24278, 24281, 24283, 24285, 24286, 24290, 24294, 24296, 24300, 24302, 24305, 24308, 24310, 24312, 24313, 24317, 24321, 24323, 24325, 24328, 24331, 24333, 24336, 24339, 24342, 24344, 24347, 24350, 24353, 24355, 24358, 24361, 24365, 24367, 24370, 24372, 24374, 24376, 24378, 24380, 24382, 24387, 24389, 24391, 24393, 24395, 23562, 23303, 23176, 24401, 24405, 24407, 24409, 24412, 24417, 24422, 24426, 24021, 24056, 24430, 24441, 24445, 24447, 24259, 23239, 23244, 23242, 23240, 23273, 23271, 23269, 24259, 23562, 23303, 23301, 23316, 23323, 23321, 23338, 23342, 23362, 23368, 23397, 23395, 23401, 23399, 23410, 23418, 23416, 21723, 21721, 23432, 23439, 23437, 24450, 24452, 24456, 24458, 23504, 23502, 23517, 21856, 21854, 21852, 23541, 23562, 24473, 24478, 24480, 24482, 23581, 23644, 23658, 23691, 23706, 22066, 22064, 22062, 23731, 22092, 22089, 21025, 22098, 22096, 22094, 22130, 22128, 22126, 22162, 22160, 23803, 22181, 22190, 22188, 22186, 23827, 23825, 23833, 23848, 23864, 23881, 23887, 23902, 23900, 23908, 23922, 23920, 24486, 24488, 24490, 24493, 23960, 23968, 23994, 24002, 24021, 24056, 24499, 24048, 24056, 24085, 24083, 24109, 24107, 24126, 24149, 22584, 22582, 22580, 24173, 22615, 22613, 22611, 22647, 22645, 22643, 24235, 22679, 22677, 24259, 24272, 24293, 24299, 24320, 24509, 24511, 24513, 24516, 24519, 24522, 24525, 24528, 24530, 24385, 24532, 24463, 24466, 24469, 24472, 24538, 24397, 24541, 24436, 24399, 24398, 24543, 24400, 24545, 24547, 24416, 24421, 24425, 24429, 24553, 24435, 24433, 24555, 24436, 24440, 24438, 24557, 24559, 24562, 24564, 24461, 24463, 24464, 24466, 24467, 24469, 24470, 24472, 24570, 24572, 24574, 24578, 24581, 24485, 24584, 24586, 24587, 24588, 24498, 24496, 24589, 24504, 24502, 24590, 24591, 24508, 24506, 24592, 24596, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 24644, 24648, 24650, 24651, 24654, 24655, 24660, 24662, 24663, 24667, 24670, 24671, 24675, 24676, 24681, 24684, 24685, 24686, 24692, 24694, 24700, 24702, 24707, 24716, 24722, 24731, 24732, 24733, 24735, 24736, 24738, 24740, 24741, 24744, 24745, 24746, 24748, 24749, 24750, 24756, 24768, 24770, 24774, 24775, 24776, 24783, 24784, 24789, 24790, 24791, 24792, 24799, 24800, 24801, 24802, 24809, 24810, 24813, 24820, 24834, 24852, 24862, 24864, 24869, 24871, 24872, 24876, 24881, 24883, 24888, 24890, 24894, 24897, 24899, 24900, 24901, 24903, 24904, 24905, 24906, 24908, 24909, 24910, 24911, 24912, 24913, 24916, 24917, 24919, 24920, 24921, 24922, 24923, 24924, 24927, 24928, 24930, 24931, 24933, 24934, 24936, 24937, 24938, 24939, 24940, 24941, 24944, 24945, 24946, 24953, 24954, 24955, 24962, 24963, 24966, 24969, 24977, 24984, 24997, 25001, 25002, 25008, 25011, 25015, 25017, 25025, 25027, 25032, 25034, 25035, 25037, 25038, 25039, 25041, 25042, 25043, 25045, 25046, 25047, 25049, 25050, 25052, 25054, 24641, 23289, 23286, 23175, 25062, 25063, 25064, 25065, 25066, 25069, 25070, 25071, 25072, 24647, 23184, 25073, 25074, 24065, 24384, 23952, 24022, 24384, 25076, 24653, 24657, 24253, 25005, 25009, 25079, 23221, 23218, 25080, 23237, 25081, 25082, 25083, 23250, 23247, 24674, 24673, 25084, 25085, 25086, 24678, 24253, 25005, 25009, 25087, 23289, 23286, 23300, 25088, 25089, 25090, 24688, 25091, 24690, 25092, 25093, 23325, 24696, 25094, 23340, 23345, 25095, 24699, 24705, 25096, 25097, 24708, 24710, 23378, 23385, 24713, 23388, 25098, 25099, 25100, 25101, 23404, 24721, 25102, 25103, 25104, 24724, 25105, 25106, 23426, 24727, 25107, 24729, 24730, 25108, 25109, 24753, 24754, 25114, 25115, 24758, 25116, 23523, 23520, 23526, 23532, 24763, 25117, 25118, 25119, 24766, 25120, 23549, 23557, 25121, 24773, 25122, 24779, 25126, 23579, 24782, 23593, 24787, 24786, 24795, 24797, 24805, 25127, 23642, 24808, 23656, 24812, 25128, 23671, 23668, 23674, 24818, 23681, 23689, 25129, 23697, 23694, 23700, 24828, 25130, 23711, 22056, 25131, 25132, 25133, 24833, 23717, 24836, 25134, 25135, 25136, 25137, 25138, 25139, 25140, 24838, 23744, 23750, 23747, 23753, 23759, 24844, 25141, 25142, 25143, 23768, 23765, 23771, 24850, 23778, 23786, 25144, 25145, 23794, 23791, 23797, 24860, 25146, 24861, 25147, 25148, 25149, 25150, 24867, 25151, 25152, 25153, 24870, 24874, 25154, 24877, 24878, 24880, 25155, 23871, 25156, 24885, 24887, 25157, 23894, 25158, 25159, 24893, 25160, 23915, 23918, 25161, 25162, 23952, 24065, 24384, 25167, 25168, 23977, 25169, 25170, 24011, 24031, 24040, 24031, 24040, 25171, 25172, 24022, 24031, 24040, 25174, 25175, 24065, 24949, 24950, 25176, 25177, 24952, 24958, 24959, 25178, 25179, 24961, 24124, 24965, 25180, 24139, 24136, 24971, 25181, 24974, 22575, 25182, 25183, 25184, 24163, 24160, 24979, 25185, 24179, 24176, 25186, 25187, 25188, 24188, 24185, 24986, 24987, 24204, 24989, 25189, 25190, 25191, 24213, 24210, 24216, 24995, 24223, 24231, 25192, 25193, 25194, 25004, 24253, 25005, 25009, 25195, 25013, 25196, 24280, 24288, 25197, 25021, 25023, 25198, 24307, 24315, 25199, 25031, 25202, 24384, 25204, 25206, 25209, 24454, 25057, 24460, 25058, 25211, 25059, 25212, 25060, 25213, 25061, 25214, 25215, 25216, 25164, 25218, 25219, 25220, 25222, 25166, 24404, 24411, 25225, 25226, 25227, 25228, 25075, 25230, 25231, 25233, 25234, 25235, 25077, 25237, 25123, 25078, 25110, 24454, 25112, 24460, 25240, 25241, 25242, 25243, 25244, 25245, 25246, 25247, 25205, 25207, 25123, 25125, 25200, 25201, 25203, 25205, 25207, 25252, 25253, 25164, 25165, 25166, 25258, 25259, 25173, 25261, 25262, 25265, 25266, 25200, 25201, 25203, 25205, 25207, 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, 25423, 25424, 25425, 23172, 25320, 23173, 25426, 24642, 25427, 25280, 25436, 25437, 25381, 25383, 25385, 25440, 25419, 22833, 25422, 25441, 25359, 25442, 25385, 25443, 25419, 22833, 25422, 25444, 23201, 23197, 25446, 25285, 25447, 25448, 25449, 25401, 25450, 25402, 25452, 25453, 23225, 25287, 23234, 25455, 25456, 25459, 25460, 24668, 25290, 23263, 25461, 25462, 25463, 25293, 25466, 25467, 25468, 25401, 25469, 25402, 25471, 25472, 24682, 25320, 23299, 25473, 21583, 25474, 25297, 25477, 25479, 25480, 25482, 25299, 25483, 25485, 25486, 25488, 24703, 24701, 25489, 25302, 25492, 25493, 25494, 25495, 25496, 25497, 24718, 25498, 25500, 25502, 25503, 21714, 25505, 25507, 25510, 25511, 25513, 25514, 25306, 24734, 25309, 23464, 25306, 24734, 25309, 23464, 25311, 21782, 25313, 21791, 25422, 23486, 25311, 21782, 25313, 21791, 25422, 23486, 24751, 21804, 21800, 25517, 25518, 25319, 25521, 25523, 25524, 25525, 25526, 25527, 25528, 25531, 25320, 25533, 25534, 23554, 25536, 25408, 25036, 24777, 21895, 21891, 25538, 25540, 25541, 25326, 25542, 25543, 25544, 25327, 24793, 21943, 21939, 25545, 25546, 25331, 24803, 21969, 21965, 25547, 25549, 25550, 25336, 25551, 25552, 25337, 25554, 25555, 25556, 25557, 25558, 25559, 24822, 25561, 25562, 25563, 25564, 25566, 25567, 25568, 25571, 25572, 23725, 25573, 25575, 25578, 25581, 25582, 25583, 25584, 25585, 25586, 25587, 25588, 25591, 25592, 25593, 25594, 25595, 25596, 24854, 25597, 25599, 25600, 25601, 25602, 25604, 25606, 24865, 24863, 25609, 25610, 25343, 25613, 25345, 25614, 25346, 25616, 25617, 25618, 25347, 25620, 23876, 25622, 25623, 25349, 25625, 22290, 25626, 25628, 25351, 25630, 25631, 25632, 25353, 23933, 25356, 23944, 25359, 25634, 25385, 25635, 25419, 22833, 25422, 25636, 25361, 25363, 25365, 25639, 25366, 22389, 25369, 25371, 25373, 25642, 25374, 22428, 25377, 25643, 25379, 25644, 25377, 25645, 25379, 25646, 25381, 25383, 25385, 25649, 25419, 22833, 25377, 25650, 25379, 25651, 25381, 25383, 25385, 25654, 25419, 22833, 24947, 22487, 22483, 25655, 25656, 25659, 24956, 22515, 22511, 25660, 25661, 25664, 25393, 25665, 25666, 25394, 25668, 25669, 24143, 25670, 25672, 25673, 25674, 25677, 25678, 24167, 25679, 25681, 25682, 25683, 25686, 25687, 24192, 25688, 25689, 25690, 25691, 25692, 25695, 25696, 25697, 25698, 25699, 25700, 24999, 25701, 24243, 24239, 25704, 25705, 25706, 25401, 25707, 25402, 25709, 25403, 25711, 25712, 25019, 25714, 25715, 25405, 25717, 25718, 25029, 25720, 25408, 25036, 25411, 25040, 25414, 25044, 25417, 24364, 25419, 22833, 25422, 25722, 25726, 25727, 25728, 25729, 25731, 25733, 25735, 25739, 25741, 25744, 25430, 25745, 25431, 25746, 25432, 25433, 25434, 25435, 25751, 25752, 25755, 25445, 25757, 25759, 25760, 25761, 25762, 25763, 25764, 25537, 25773, 25724, 25774, 25775, 25776, 25777, 25778, 25721, 25779, 25723, 25780, 25724, 25781, 25784, 25785, 25786, 25787, 25789, 25790, 25792, 25794, 25795, 25721, 25796, 25723, 25797, 25724, 25798, 25768, 25766, 25772, 25770, 40, 41, 42, 43, 44, 45, 46, 47, 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26337, 26341, 25910, 26346, 26344, 26350, 26348, 25925, 26572, 26573, 25930, 25933, 26574, 26575, 26358, 25943, 26576, 25945, 26577, 26578, 25950, 26579, 26367, 26365, 26371, 26369, 26375, 26373, 26377, 26381, 26379, 26383, 26153, 26488, 25985, 26580, 26391, 26048, 26581, 26426, 26582, 26180, 26509, 26507, 25991, 26583, 26525, 26523, 26217, 26396, 26153, 26488, 26586, 26490, 26492, 26587, 26494, 26496, 26588, 26498, 26500, 26589, 26180, 26509, 26507, 26196, 26590, 26202, 26591, 26517, 26519, 26521, 26525, 26523, 26217, 26002, 26004, 26403, 26407, 26019, 26021, 26414, 26595, 26026, 26596, 26033, 26419, 26597, 26421, 26423, 26048, 26598, 26426, 26599, 26054, 26600, 26062, 26431, 26601, 26434, 26073, 26602, 26436, 26079, 26603, 26439, 26084, 26604, 26086, 26089, 26605, 26606, 26446, 26444, 26450, 26099, 26097, 26103, 26456, 26107, 25638, 25637, 26461, 26113, 25641, 25640, 26117, 26119, 26121, 26123, 26470, 26127, 25648, 25647, 26131, 26133, 26477, 26137, 25653, 25652, 26153, 26488, 26607, 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26764, 26765, 26766, 26768, 26770, 26503, 26068, 26772, 26773, 26775, 26776, 26778, 26779, 26781, 26782, 26785, 26786, 26787, 26788, 26789, 26790, 26791, 26792, 26793, 26794, 25886, 26795, 26796, 26797, 26798, 26309, 26799, 26800, 26801, 26802, 26803, 26352, 26804, 26805, 26807, 26808, 25936, 26811, 26812, 26814, 26815, 26817, 25952, 26693, 26819, 26820, 26821, 26822, 26823, 26824, 26825, 26826, 26827, 26828, 26829, 26830, 26831, 26832, 26833, 26834, 26835, 26837, 26838, 26839, 26841, 26843, 26844, 26846, 26503, 26848, 26849, 26068, 26851, 26852, 26854, 26855, 26857, 26858, 26860, 26861, 26864, 26865, 26866, 26867, 26868, 26869, 26870, 26871, 26872, 26873, 26874, 26875, 26876, 26877, 26878, 25886, 26879, 26880, 26881, 26882, 26883, 26884, 26885, 26886, 26887, 26888, 26889, 26890, 26891, 26352, 26892, 26893, 26895, 26896, 25936, 26899, 26900, 26902, 26903, 26905, 25952, 26693, 26907, 26908, 26909, 26910, 26911, 26912, 26913, 26914, 26915, 26916, 26388, 26917, 26918, 26919, 26921, 26922, 26924, 26926, 26503, 26188, 26927, 26928, 26929, 26394, 26931, 26932, 26933, 26934, 26145, 26151, 26935, 26936, 26938, 26939, 26941, 26942, 26944, 26945, 26947, 26503, 26188, 26948, 26949, 26950, 26512, 26952, 26515, 26954, 26955, 26956, 26957, 26958, 26959, 26960, 26961, 26962, 26963, 26964, 26965, 26966, 26968, 25560, 26970, 26971, 26973, 26974, 26975, 26977, 26979, 26059, 26981, 26982, 26068, 26984, 26985, 26987, 26988, 26990, 26991, 26993, 26994, 26997, 26998, 26999, 27000, 27001, 27002, 27003, 27004, 27005, 27006, 27007, 27008, 27009, 27010, 27011, 27012, 27013, 27014, 27015, 27016, 27017, 27018, 27019, 27020, 27021, 27022, 27023, 27024, 26145, 26151, 27025, 27026, 27028, 27029, 27031, 27032, 27034, 27035, 27037, 26503, 26188, 27038, 27039, 27040, 26512, 27042, 26515, 27044, 27045, 27046, 27047, 27048, 27049, 27052, 27054, 27059, 27060, 27061, 27062, 27063, 27064, 27067, 27073, 27074, 27077, 27078, 27079, 27086, 27087, 27088, 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, 27143, 27144, 27146, 27149, 27153, 27155, 27157, 27161, 27162, 26731, 26733, 27168, 27169, 27170, 27172, 27174, 27177, 26011, 26000, 26017, 26757, 26760, 26763, 27193, 26769, 27196, 27197, 26771, 26774, 27201, 27203, 27205, 27207, 27211, 27216, 27217, 27219, 27221, 27223, 27225, 27227, 27231, 27232, 26810, 27234, 27238, 27239, 27240, 27242, 27244, 27247, 26011, 26017, 26000, 26836, 27259, 26842, 26845, 27264, 26847, 27267, 26850, 26853, 27271, 27273, 27275, 27276, 27278, 27281, 27285, 27291, 27292, 27294, 27297, 27301, 27303, 27305, 27309, 27310, 26898, 27312, 27316, 27317, 27318, 27320, 27322, 27325, 25979, 27328, 27331, 27333, 26925, 27336, 27337, 27338, 27340, 27341, 27342, 26143, 27346, 26149, 27347, 26937, 26940, 26943, 26946, 27357, 27358, 27359, 27361, 27362, 27363, 27364, 27368, 26000, 26011, 26017, 26967, 27379, 26969, 26972, 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27663, 27151, 27148, 27524, 27526, 27529, 27527, 27677, 27678, 27165, 25208, 25210, 27679, 24534, 27680, 24536, 27681, 27682, 27194, 27545, 27188, 27190, 27192, 27548, 27547, 26784, 27204, 27202, 27683, 27684, 27685, 27557, 27558, 27560, 27563, 27561, 27686, 27687, 27235, 25223, 25224, 27688, 24549, 27690, 24551, 27692, 27693, 27578, 27266, 27262, 27258, 27260, 27582, 27581, 26863, 27274, 27272, 27694, 27695, 27696, 27697, 27299, 27296, 27594, 27596, 27599, 27597, 27698, 27699, 27313, 25238, 25239, 27700, 24566, 27701, 24568, 27702, 27612, 27334, 27332, 27614, 27616, 27703, 25248, 27705, 27706, 27626, 27355, 27353, 27351, 27628, 27632, 27630, 27707, 27708, 27709, 27710, 25251, 27712, 27713, 27381, 27389, 27385, 27638, 27383, 27643, 27647, 27646, 26996, 27397, 27395, 25782, 27714, 27715, 27716, 27717, 27718, 27719, 27720, 27721, 27669, 27436, 27434, 27432, 27671, 27675, 27673, 27722, 27723, 27724, 27725, 25268, 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, 27521, 27806, 27807, 27808, 27809, 27810, 27811, 27532, 27814, 27815, 27816, 27818, 27820, 27186, 27182, 27185, 27823, 27824, 27825, 27826, 27827, 27828, 27829, 27830, 27831, 27832, 25217, 25221, 27555, 27836, 27837, 27838, 27839, 27840, 27566, 27843, 27844, 27845, 27847, 27849, 27256, 27255, 27254, 27852, 27853, 27854, 27855, 27856, 27857, 27858, 27859, 27860, 27861, 25229, 25232, 25236, 27591, 27866, 27867, 27868, 27869, 27870, 27871, 27602, 27874, 27875, 27876, 27878, 27880, 27608, 27882, 27883, 27884, 27885, 27886, 27888, 27621, 27619, 27891, 27892, 27893, 27894, 27895, 27896, 27897, 27899, 27902, 27374, 27371, 27375, 27903, 27905, 27906, 27907, 27908, 27909, 27910, 27911, 27912, 27913, 27914, 27915, 27916, 25254, 25256, 25255, 27918, 25260, 25263, 27664, 27662, 27925, 27926, 27927, 27928, 27929, 27930, 27931, 27933, 27936, 27056, 27068, 27075, 27076, 27085, 61, 62, 63, 27968, 27969, 27973, 27975, 27817, 27819, 27981, 27982, 27983, 27984, 27986, 27989, 27991, 27994, 27995, 27996, 28000, 28002, 27846, 27848, 28008, 28009, 28010, 28011, 28013, 28016, 28018, 28021, 28022, 28023, 28024, 28025, 28029, 28031, 27877, 27879, 28037, 28038, 27887, 28044, 28045, 28046, 28048, 28051, 27901, 28055, 28056, 28057, 28059, 28061, 28063, 28065, 28067, 28071, 28072, 28073, 28075, 28076, 28077, 28078, 28079, 28081, 28084, 27935, 27972, 27051, 27050, 28088, 27999, 27455, 27454, 28089, 28028, 27070, 27069, 28042, 28090, 27463, 28091, 27080, 27466, 28092, 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, 28098, 28099, 27821, 28103, 28105, 28108, 27834, 27835, 28112, 28113, 28116, 28117, 28119, 28122, 27862, 27863, 27864, 28128, 28129, 27881, 28133, 27890, 28137, 28141, 28144, 28148, 27917, 28150, 28152, 28153, 27924, 28156, 28160, 28097, 27453, 27452, 28161, 28162, 28164, 27997, 27691, 27689, 28165, 28166, 28168, 28127, 27072, 27071, 28169, 28170, 28171, 27704, 28139, 27711, 28173, 28175, 28158, 27726, 28176, 59, 60, 61, 62, 63, 28225, 28226, 28228, 28233, 28234, 28236, 28242, 28245, 28247, 28248, 28074, 28252, 28253, 28254, 28257, 28258, 28259, 28260, 28229, 27058, 27057, 28263, 28264, 28265, 28266, 28237, 27460, 27066, 27065, 28269, 28270, 28271, 28272, 28244, 28275, 28276, 28277, 28249, 27081, 28280, 28281, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 28289, 28292, 28296, 28288, 28303, 28306, 28307, 28308, 28291, 28310, 28313, 28314, 28315, 28316, 28294, 28318, 28321, 28172, 28246, 28324, 28325, 28326, 27083, 27084, 27082, 28255, 28328, 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, 28355, 28356, 28290, 28358, 28360, 28361, 28293, 28167, 28364, 28366, 28367, 28274, 28370, 28371, 28297, 28374, 28375, 28376, 28377, 28378, 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, 28416, 28418, 28419, 28420, 28422, 28423, 28425, 28323, 28430, 28373, 28432, 28327, 28369, 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, 28480, 28357, 28483, 28362, 28486, 28372, 28489, 28492, 28429, 28435, 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, 28550, 28426, 28482, 28421, 28417, 28552, 28553, 28485, 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, 28608, 28609, 28610, 28611, 28612, 28615, 28613, 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, 28672, 28551, 28675, 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, 28736, 28738, 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, 28800, 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, 28864, 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, 28928, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63};
int h_C[]= {
1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63, 65, 67, 69, 71, 73, 75, 77, 79, 81, 83, 85, 87, 89, 91, 93, 95, 97, 99, 101, 103, 105, 107, 109, 111, 113, 115, 117, 119, 121, 123, 125, 127, 129, 131, 133, 135, 137, 139, 141, 143, 145, 147, 149, 151, 153, 155, 157, 159, 161, 163, 165, 167, 169, 171, 173, 175, 177, 179, 181, 183, 185, 187, 189, 191, 193, 195, 197, 199, 201, 203, 205, 207, 209, 211, 213, 215, 217, 219, 221, 223, 225, 227, 229, 231, 233, 235, 237, 239, 241, 243, 245, 247, 249, 251, 253, 255, 257, 259, 261, 263, 265, 267, 269, 271, 273, 275, 277, 279, 281, 283, 285, 287, 289, 291, 293, 295, 297, 299, 301, 303, 305, 307, 309, 311, 313, 315, 317, 319, 321, 323, 325, 327, 329, 331, 333, 335, 337, 339, 341, 343, 345, 347, 349, 351, 353, 355, 357, 359, 361, 363, 365, 367, 369, 371, 375, 377, 379, 381, 383, 385, 387, 389, 391, 393, 395, 397, 399, 401, 403, 405, 407, 409, 411, 413, 415, 417, 419, 421, 423, 425, 427, 429, 431, 433, 435, 437, 439, 441, 443, 445, 447, 449, 451, 453, 455, 457, 459, 461, 463, 465, 467, 469, 471, 473, 475, 477, 479, 481, 483, 485, 487, 489, 491, 493, 495, 497, 499, 501, 503, 505, 507, 509, 511, 513, 515, 517, 519, 521, 523, 525, 527, 529, 531, 533, 535, 537, 539, 541, 543, 545, 547, 549, 551, 553, 555, 557, 559, 561, 563, 565, 567, 569, 571, 573, 575, 577, 579, 581, 583, 585, 587, 589, 591, 593, 595, 597, 599, 601, 603, 605, 607, 609, 611, 613, 615, 617, 619, 621, 623, 625, 627, 629, 631, 633, 635, 637, 639, 641, 643, 645, 647, 649, 651, 653, 655, 657, 659, 661, 663, 665, 667, 669, 671, 673, 675, 677, 679, 681, 683, 685, 687, 689, 691, 693, 695, 697, 699, 701, 703, 705, 707, 709, 711, 713, 715, 717, 719, 721, 723, 725, 727, 729, 731, 733, 735, 737, 739, 741, 743, 745, 747, 749, 751, 753, 755, 757, 759, 761, 763, 765, 767, 769, 771, 773, 775, 777, 779, 781, 783, 785, 787, 789, 791, 793, 795, 797, 799, 801, 803, 805, 807, 809, 811, 813, 815, 817, 819, 821, 823, 825, 827, 829, 831, 833, 835, 837, 839, 841, 843, 845, 847, 849, 851, 853, 855, 857, 859, 861, 863, 865, 867, 869, 871, 873, 875, 877, 879, 881, 883, 885, 887, 889, 891, 893, 895, 897, 899, 901, 903, 905, 907, 909, 911, 913, 915, 917, 919, 921, 923, 925, 927, 929, 931, 933, 935, 937, 939, 941, 943, 945, 947, 949, 951, 953, 955, 957, 959, 961, 963, 965, 967, 969, 971, 973, 975, 977, 979, 981, 983, 985, 987, 989, 991, 993, 995, 997, 999, 1001, 1003, 1005, 1007, 1009, 1011, 1013, 1015, 1017, 1019, 1021, 1023, 1025, 1027, 1029, 1031, 1033, 1035, 1037, 1039, 1041, 1043, 1045, 1047, 1049, 1051, 1053, 1055, 1057, 1059, 1061, 1063, 1065, 1067, 1069, 1071, 1073, 1075, 1077, 1079, 1081, 1083, 1085, 1087, 1089, 1091, 1093, 1095, 1097, 1099, 1101, 1103, 1105, 1107, 1109, 1111, 1113, 1115, 1117, 1119, 1121, 1123, 1125, 1127, 1129, 1131, 1133, 1135, 1137, 1139, 1141, 1143, 1145, 1147, 1149, 1151, 1153, 1155, 1157, 1159, 1161, 1163, 1165, 1167, 1169, 1171, 1173, 1175, 1177, 1179, 1181, 1183, 1185, 1187, 1189, 1191, 1193, 1195, 1197, 1199, 1201, 1203, 1205, 1207, 1209, 1211, 1213, 1215, 1217, 1219, 1221, 1223, 1225, 1227, 1229, 1231, 1233, 1235, 1237, 1239, 1241, 1243, 1245, 1247, 1249, 1251, 1253, 1255, 1257, 1259, 1261, 1263, 1265, 1267, 1269, 1271, 1273, 1275, 1277, 1279, 1281, 1283, 1285, 1287, 1289, 1291, 1293, 1295, 1297, 1299, 1301, 1303, 1305, 1307, 1309, 1311, 1313, 1315, 1317, 1319, 1321, 1323, 1325, 1327, 1329, 1331, 1333, 1335, 1337, 1339, 1341, 1343, 1345, 1347, 1349, 1351, 1353, 1355, 1357, 1359, 1361, 1363, 1365, 1367, 1369, 1371, 1373, 1375, 1377, 1379, 1381, 1383, 1385, 1387, 1389, 1391, 1393, 1395, 1397, 1399, 1401, 1403, 1405, 1407, 1409, 1411, 1413, 1415, 1417, 1419, 1421, 1423, 1425, 1427, 1429, 1431, 1433, 1435, 1437, 1439, 1441, 1443, 1445, 1447, 1449, 1451, 1453, 1455, 1457, 1459, 1461, 1463, 1465, 1467, 1469, 1471, 1473, 1475, 1477, 1479, 1481, 1483, 1485, 1487, 1489, 1491, 1493, 1495, 1497, 1499, 1501, 1503, 1505, 1507, 1509, 1511, 1513, 1515, 1517, 1519, 1521, 1523, 1525, 1527, 1529, 1531, 1533, 1535, 1537, 1539, 1541, 1543, 1545, 1547, 1549, 1551, 1553, 1555, 1557, 1559, 1561, 1563, 1565, 1567, 1569, 1571, 1573, 1575, 1577, 1579, 1581, 1583, 1585, 1587, 1589, 1591, 1593, 1595, 1597, 1599, 1601, 1603, 1605, 1607, 1609, 1611, 1613, 1615, 1617, 1619, 1621, 1623, 1625, 1627, 1629, 1631, 1633, 1635, 1637, 1639, 1641, 1643, 1645, 1647, 1649, 1651, 1653, 1655, 1657, 1659, 1661, 1663, 1665, 1667, 1669, 1671, 1673, 1675, 1677, 1679, 1681, 1683, 1685, 1687, 1689, 1691, 1693, 1695, 1697, 1699, 1701, 1703, 1705, 1707, 1709, 1711, 1713, 1715, 1717, 1719, 1721, 1723, 1725, 1727, 1729, 1731, 1733, 1735, 1737, 1739, 1741, 1743, 1745, 1747, 1749, 1751, 1753, 1755, 1757, 1759, 1761, 1763, 1765, 1767, 1769, 1771, 1773, 1775, 1777, 1779, 1781, 1783, 1785, 1787, 1789, 1791, 1793, 1795, 1797, 1799, 1801, 1803, 1805, 1807, 1809, 1811, 1813, 1815, 1817, 1819, 1821, 1823, 1825, 1827, 1829, 1831, 1833, 1835, 1837, 1839, 1841, 1843, 1845, 1847, 1849, 1851, 1853, 1855, 1857, 1859, 1861, 1863, 1865, 1867, 1869, 1871, 1873, 1875, 1877, 1879, 1881, 1883, 1885, 1887, 1889, 1891, 1893, 1895, 1897, 1899, 1901, 1903, 1905, 1907, 1909, 1911, 1913, 1915, 1917, 1919, 1921, 1923, 1925, 1927, 1929, 1931, 1933, 1935, 1937, 1939, 1941, 1943, 1945, 1947, 1949, 1951, 1953, 1955, 1957, 1959, 1961, 1963, 1965, 1967, 1969, 1971, 1973, 1975, 1977, 1979, 1981, 1983, 1985, 1987, 1989, 1991, 1993, 1995, 1997, 1999, 2001, 2003, 2005, 2007, 2009, 2011, 2013, 2015, 2017, 2019, 2021, 2023, 2025, 2027, 2029, 2031, 2033, 2035, 2037, 2039, 2041, 2043, 2045, 2047, 2049, 2051, 2053, 2055, 2057, 2059, 2061, 2063, 2065, 2067, 2069, 2071, 2073, 2075, 2077, 2079, 2081, 2083, 2085, 2087, 2089, 2091, 2093, 2095, 2097, 2099, 2101, 2103, 2105, 2107, 2109, 2111, 2113, 2115, 2117, 2119, 2121, 2123, 2125, 2127, 2129, 2131, 2133, 2135, 2137, 2139, 2141, 2143, 2145, 2147, 2149, 2151, 2153, 2155, 2157, 2159, 2161, 2163, 2165, 2167, 2169, 2171, 2173, 2175, 2177, 2179, 2181, 2183, 2185, 2187, 2189, 2191, 2193, 2195, 2197, 2199, 2201, 2203, 2205, 2207, 2209, 2211, 2213, 2215, 2217, 2219, 2221, 2223, 2225, 2227, 2229, 2231, 2233, 2235, 2237, 2239, 2241, 2243, 2245, 2247, 2249, 2251, 2253, 2255, 2257, 2259, 2261, 2263, 2265, 2267, 2269, 2271, 2273, 2275, 2277, 2279, 2281, 2283, 2285, 2287, 2289, 2291, 2293, 2295, 2297, 2299, 2301, 2303, 2305, 2307, 2309, 2311, 2313, 2315, 2317, 2319, 2321, 2323, 2325, 2327, 2329, 2331, 2333, 2335, 2337, 2339, 2341, 2343, 2345, 2347, 2349, 2351, 2353, 2355, 2357, 2359, 2361, 2363, 2365, 2367, 2369, 2371, 2373, 2375, 2377, 2379, 2381, 2383, 2385, 2387, 2389, 2391, 2393, 2395, 2397, 2399, 2401, 2403, 2405, 2407, 2409, 2411, 2413, 2415, 2417, 2419, 2421, 2423, 2425, 2427, 2429, 2431, 2433, 2435, 2437, 2439, 2441, 2443, 2445, 2447, 2449, 2451, 2453, 2455, 2457, 2459, 2461, 2463, 2465, 2467, 2469, 2471, 2473, 2475, 2477, 2479, 2481, 2483, 2485, 2487, 2489, 2491, 2493, 2495, 2497, 2499, 2501, 2503, 2505, 2507, 2509, 2511, 2513, 2515, 2517, 2519, 2521, 2523, 2525, 2527, 2529, 2531, 2533, 2535, 2537, 2539, 2541, 2543, 2545, 2547, 2549, 2551, 2553, 2555, 2557, 2559, 2561, 2563, 2565, 2567, 2569, 2571, 2573, 2575, 2577, 2579, 2581, 2583, 2585, 2587, 2589, 2591, 2593, 2595, 2597, 2599, 2601, 2603, 2605, 2607, 2609, 2611, 2613, 2615, 2617, 2619, 2621, 2623, 2625, 2627, 2629, 2631, 2633, 2635, 2637, 2639, 2641, 2643, 2645, 2647, 2649, 2651, 2653, 2655, 2657, 2659, 2661, 2663, 2665, 2667, 2669, 2671, 2673, 2675, 2677, 2679, 2681, 2683, 2685, 2687, 2689, 2691, 2693, 2695, 2697, 2699, 2701, 2703, 2705, 2707, 2709, 2711, 2713, 2715, 2717, 2719, 2721, 2723, 2725, 2727, 2729, 2731, 2733, 2735, 2737, 2739, 2741, 2743, 2745, 2747, 2749, 2751, 2753, 2755, 2757, 2759, 2761, 2763, 2765, 2767, 2769, 2771, 2773, 2775, 2777, 2779, 2781, 2783, 2785, 2787, 2789, 2791, 2793, 2795, 2797, 2799, 2801, 2803, 2805, 2807, 2809, 2811, 2813, 2815, 2817, 2819, 2821, 2823, 2825, 2827, 2829, 2831, 2833, 2835, 2837, 2839, 2841, 2843, 2845, 2847, 2849, 2851, 2853, 2855, 2857, 2859, 2861, 2863, 2865, 2867, 2869, 2871, 2873, 2875, 2877, 2879, 2881, 2883, 2885, 2887, 2889, 2891, 2893, 2895, 2897, 2899, 2901, 2903, 2905, 2907, 2909, 2911, 2913, 2915, 2917, 2919, 2921, 2923, 2925, 2927, 2929, 2931, 2933, 2935, 2937, 2939, 2941, 2943, 2945, 2947, 2949, 2951, 2953, 2955, 2957, 2959, 2961, 2963, 2965, 2967, 2969, 2971, 2973, 2975, 2977, 2979, 2981, 2983, 2985, 2987, 2989, 2991, 2993, 2995, 2997, 2999, 3001, 3003, 3005, 3007, 3009, 3011, 3013, 3015, 3017, 3019, 3021, 3023, 3025, 3027, 3029, 3031, 3033, 3035, 3037, 3039, 3041, 3043, 3045, 3047, 3049, 3051, 3053, 3055, 3057, 3059, 3061, 3063, 3065, 3067, 3069, 3071, 3073, 3075, 3077, 3079, 3081, 3083, 3085, 3087, 3089, 3091, 3093, 3095, 3097, 3099, 3101, 3103, 3105, 3107, 3109, 3111, 3113, 3115, 3117, 3119, 3121, 3123, 3125, 3127, 3129, 3131, 3133, 3135, 3137, 3139, 3141, 3143, 3145, 3147, 3149, 3151, 3153, 3155, 3157, 3159, 3161, 3163, 3165, 3167, 3169, 3171, 3173, 3175, 3177, 3179, 3181, 3183, 3185, 3187, 3189, 3191, 3193, 3195, 3197, 3199, 3201, 3203, 3205, 3207, 3209, 3211, 3213, 3215, 3217, 3219, 3221, 3223, 3225, 3227, 3229, 3231, 3233, 3235, 3237, 3239, 3241, 3243, 3245, 3247, 3249, 3251, 3253, 3257, 3259, 3261, 3263, 3265, 3267, 3269, 3271, 3273, 3275, 3277, 3279, 3281, 3283, 3285, 3287, 3289, 3291, 3293, 3295, 3297, 3299, 3301, 3303, 3305, 3307, 3309, 3311, 3313, 3315, 3317, 3319, 3321, 3323, 3325, 3327, 3329, 3331, 3333, 3335, 3337, 3339, 3341, 3343, 3345, 3347, 3349, 3351, 3353, 3355, 3357, 3359, 3361, 3363, 3365, 3367, 3369, 3371, 3373, 3375, 3377, 3379, 3381, 3383, 3385, 3387, 3389, 3391, 3393, 3395, 3397, 3399, 3401, 3403, 3405, 3407, 3409, 3411, 3413, 3415, 3417, 3419, 3421, 3423, 3425, 3427, 3429, 3431, 3433, 3435, 3437, 3439, 3441, 3443, 3445, 3447, 3449, 3451, 3453, 3455, 3457, 3459, 3461, 3463, 3465, 3467, 3469, 3471, 3473, 3475, 3477, 3479, 3481, 3483, 3485, 3487, 3489, 3491, 3493, 3495, 3497, 3499, 3501, 3503, 3505, 3507, 3509, 3511, 3514, 3516, 3518, 3520, 3522, 3524, 3526, 3528, 3530, 3532, 3534, 3536, 3538, 3540, 3542, 3544, 3546, 3548, 3550, 3552, 3554, 3556, 3558, 3560, 3563, 3565, 3567, 3569, 3571, 3573, 3576, 3578, 3580, 3582, 3585, 3587, 3590, 3592, 3597, 3599, 3608, 3610, 3612, 3614, 3617, 3619, 3622, 3624, 3630, 3632, 3634, 3636, 3639, 3641, 3644, 3646, 3652, 3654, 3656, 3658, 3660, 3662, 3664, 3666, 3668, 3670, 3672, 3674, 3676, 3678, 3680, 3682, 3684, 3686, 3688, 3690, 3693, 3695, 3697, 3699, 3702, 3704, 3706, 3708, 3710, 3712, 3714, 3716, 3718, 3720, 3722, 3724, 3726, 3728, 3731, 3733, 3735, 3737, 3739, 3741, 3743, 3745, 3747, 3749, 3751, 3753, 3755, 3757, 3759, 3761, 3763, 3765, 3767, 3769, 3771, 3773, 3775, 3777, 3779, 3781, 3783, 3785, 3788, 3790, 3793, 3795, 3798, 3800, 3803, 3805, 3808, 3810, 3812, 3814, 3817, 3819, 3822, 3824, 3829, 3831, 3834, 3836, 3840, 3842, 3845, 3847, 3850, 3852, 3854, 3856, 3859, 3861, 3863, 3865, 3869, 3871, 3874, 3876, 3879, 3881, 3884, 3886, 3888, 3890, 3892, 3894, 3896, 3898, 3900, 3902, 3904, 3906, 3908, 3910, 3913, 3915, 3917, 3919, 3921, 3923, 3925, 3927, 3929, 3931, 3933, 3935, 3937, 3939, 3941, 3943, 3945, 3947, 3949, 3951, 3953, 3955, 3957, 3959, 3961, 3963, 3965, 3967, 3969, 3971, 3973, 3975, 3978, 3980, 3982, 3984, 3986, 3988, 3990, 3992, 3995, 3997, 4000, 4002, 4008, 4010, 4012, 4014, 4017, 4019, 4021, 4023, 4026, 4028, 4031, 4033, 4038, 4040, 4042, 4044, 4047, 4049, 4052, 4054, 4060, 4062, 4068, 4070, 4073, 4075, 4077, 4079, 4081, 4083, 4085, 4087, 4089, 4091, 4093, 4095, 4097, 4099, 4101, 4103, 4108, 4110, 4112, 4114, 4116, 4118, 4121, 4123, 4126, 4128, 4134, 4136, 4141, 4143, 4146, 4148, 4150, 4152, 4154, 4156, 4158, 4160, 4162, 4164, 4166, 4168, 4174, 4176, 4179, 4181, 4184, 4186, 4189, 4191, 4197, 4199, 4201, 4203, 4205, 4207, 4209, 4211, 4213, 4215, 4217, 4219, 4221, 4223, 4225, 4227, 4229, 4231, 4233, 4235, 4237, 4239, 4241, 4243, 4245, 4247, 4249, 4251, 4253, 4255, 4257, 4259, 4261, 4263, 4265, 4267, 4269, 4271, 4273, 4275, 4277, 4279, 4281, 4283, 4285, 4287, 4289, 4291, 4293, 4295, 4297, 4299, 4301, 4303, 4305, 4307, 4309, 4311, 4313, 4315, 4317, 4319, 4321, 4323, 4325, 4327, 4329, 4331, 4333, 4335, 4337, 4339, 4341, 4343, 4345, 4347, 4349, 4351, 4353, 4355, 4357, 4359, 4361, 4363, 4365, 4367, 4369, 4371, 4373, 4375, 4377, 4379, 4381, 4383, 4386, 4388, 4390, 4392, 4394, 4396, 4398, 4400, 4402, 4404, 4406, 4408, 4410, 4412, 4414, 4416, 4418, 4420, 4422, 4424, 4426, 4428, 4430, 4432, 4434, 4436, 4438, 4440, 4442, 4444, 4447, 4449, 4451, 4453, 4456, 4458, 4460, 4462, 4465, 4467, 4469, 4471, 4473, 4475, 4477, 4479, 4482, 4484, 4486, 4488, 4491, 4493, 4496, 4498, 4503, 4505, 4507, 4509, 4511, 4513, 4515, 4517, 4520, 4522, 4524, 4526, 4529, 4531, 4534, 4536, 4541, 4543, 4545, 4547, 4549, 4551, 4554, 4556, 4559, 4561, 4564, 4566, 4569, 4571, 4574, 4576, 4579, 4581, 4584, 4586, 4005, 4005, 4591, 4593, 4595, 4597, 4599, 4601, 4603, 4605, 4607, 4609, 372, 372, 4613, 4615, 4617, 4619, 4621, 4623, 4625, 4627, 4629, 4631, 4633, 4635, 4637, 4639, 4641, 4643, 4645, 4647, 4649, 4651, 4653, 4655, 4657, 4659, 4661, 4663, 4665, 4667, 4682, 4684, 4686, 4688, 4690, 4692, 4694, 4696, 4698, 4700, 4702, 4704, 4706, 4708, 4710, 4712, 4714, 4716, 4718, 4720, 4722, 4724, 4726, 4728, 4730, 4732, 4734, 4736, 4738, 4740, 3993, 3993, 4005, 4005, 4035, 4035, 4005, 4005, 3993, 3993, 4776, 4778, 4780, 4782, 4784, 4786, 4788, 4790, 4792, 4794, 4796, 4798, 4800, 4802, 4804, 4806, 3561, 3561, 3561, 3561, 3561, 3561, 3603, 3603, 3603, 3603, 3603, 3603, 3615, 3627, 3637, 3637, 3649, 3649, 3615, 3627, 3637, 3637, 3649, 3649, 3976, 3976, 3993, 3993, 4194, 4194, 4005, 4005, 3976, 3976, 3976, 3976, 4005, 4005, 4057, 4057, 3512, 3512, 372, 372, 3512, 3512, 373, 373, 3627, 3627, 3637, 3637, 3649, 3649, 3976, 3976, 3976, 3976, 3976, 3976, 3993, 3993, 4057, 4057, 4045, 4065, 4065, 4065, 4065, 3512, 3512, 372, 372, 3512, 3512, 373, 373, 3512, 3512, 3615, 3615, 3627, 3627, 3832, 3837, 3583, 3594, 3627, 3627, 3615, 3615, 3649, 3649, 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27321, 27323, 27326, 27136, 10389, 26920, 26923, 27335, 10400, 10401, 27339, 26930, 10406, 27343, 27140, 10425, 27141, 10427, 27350, 27352, 27354, 27356, 10441, 10442, 27360, 26951, 10447, 26953, 10450, 27369, 27137, 27138, 27139, 27378, 10489, 27380, 27382, 26976, 27386, 10501, 27388, 10505, 27391, 27393, 26989, 26992, 26995, 27400, 27402, 27406, 27408, 27410, 27412, 27418, 27420, 27424, 27426, 27140, 10588, 27141, 10590, 27431, 27433, 27435, 27437, 10604, 10605, 27441, 27041, 10610, 27043, 10613, 27450, 27158, 27166, 27176, 27179, 27180, 27183, 27206, 27210, 27214, 27228, 27236, 27246, 27457, 27249, 27459, 27250, 27252, 27280, 27284, 27288, 27289, 27306, 27314, 27324, 27327, 27329, 27344, 27462, 27345, 27348, 27365, 27367, 27366, 27370, 27465, 27372, 27376, 27404, 27414, 27413, 27416, 27415, 27422, 27421, 27429, 27446, 27448, 27447, 27451, 27468, 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, 27520, 27531, 10106, 10110, 10112, 27209, 27213, 27554, 27565, 10242, 10244, 10246, 27587, 27283, 27287, 27590, 27601, 10388, 10424, 10426, 10477, 10481, 10483, 27403, 27654, 27656, 27658, 27660, 10587, 10589, 27523, 27522, 27152, 27525, 27528, 27160, 10647, 10649, 27530, 27533, 27534, 10655, 27535, 10659, 27536, 10663, 10667, 27543, 27544, 27540, 27541, 27542, 27199, 27546, 27551, 27550, 27549, 10678, 10680, 10683, 27556, 27222, 27559, 27562, 27230, 10692, 10694, 27564, 27567, 27568, 10700, 27569, 10704, 27570, 10709, 10712, 27577, 27579, 27576, 27574, 27575, 27269, 27580, 27585, 27584, 27583, 10723, 10726, 10729, 10732, 27593, 27592, 27300, 27595, 27598, 27308, 10741, 10743, 27600, 27603, 27604, 10749, 27605, 10753, 27606, 10757, 27611, 27610, 27609, 27613, 27615, 10764, 27617, 10768, 10770, 27625, 27624, 27623, 27622, 27627, 27631, 27629, 10780, 10782, 10783, 10785, 27633, 10791, 10792, 27639, 27644, 27641, 27637, 27640, 27642, 27392, 27645, 27650, 27649, 27648, 27651, 10807, 10810, 10811, 10816, 10817, 10820, 10821, 10823, 27668, 27667, 27666, 27665, 27670, 27674, 27672, 10833, 10835, 10836, 10838, 27676, 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, 27776, 10641, 10642, 10643, 10644, 10645, 10646, 27777, 10650, 10651, 10653, 10656, 10660, 27780, 27778, 27779, 10668, 10669, 10670, 10671, 10672, 10673, 10674, 10675, 10676, 10677, 27781, 27782, 27783, 10687, 10688, 10689, 10690, 10691, 27784, 10695, 10696, 10698, 10701, 10705, 27787, 27786, 27785, 10713, 10714, 10715, 10716, 10717, 10718, 10719, 10720, 10721, 10722, 27788, 27789, 27790, 27791, 10735, 10736, 10737, 10738, 10739, 10740, 27792, 10744, 10745, 10747, 10750, 10754, 27793, 10759, 10760, 10761, 10762, 10763, 10765, 27795, 27794, 10773, 10774, 10775, 10776, 10777, 10778, 10779, 27900, 10786, 27797, 27796, 27798, 27904, 10794, 10795, 10796, 10797, 10798, 10799, 10800, 10801, 10802, 10803, 10804, 10805, 27799, 27801, 27800, 27919, 27802, 27803, 27805, 27804, 10826, 10827, 10828, 10829, 10830, 10831, 10832, 27934, 10839, 27833, 27865, 27889, 27898, 27932, 61, 62, 63, 10640, 27970, 27974, 10648, 27979, 27980, 10664, 10665, 10666, 27985, 27987, 27990, 27992, 10681, 10684, 10686, 28001, 10693, 28006, 28007, 10708, 10710, 10711, 28012, 28014, 28017, 28019, 10724, 10727, 10730, 10734, 28026, 28030, 10742, 28035, 28036, 10758, 28039, 28043, 10771, 10772, 28047, 28049, 28052, 28054, 10789, 10790, 10793, 28060, 28062, 28064, 28066, 28068, 10808, 10812, 10813, 10815, 10819, 10824, 10825, 28080, 28082, 28085, 28087, 27971, 27978, 27977, 10853, 27998, 28005, 28004, 10863, 28027, 28034, 28033, 28041, 10876, 28053, 10882, 28070, 28086, 10894, 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, 27812, 27813, 28102, 28104, 28106, 27993, 28109, 28110, 27841, 27842, 27850, 28118, 28120, 28020, 28123, 28124, 28125, 27872, 27873, 28132, 28040, 28135, 28138, 28142, 28145, 28069, 28149, 28151, 27920, 27922, 28154, 28157, 10843, 28096, 28101, 28100, 10847, 10848, 10855, 28111, 28115, 28114, 10859, 10860, 10868, 28126, 28131, 28130, 10872, 10873, 10874, 28134, 28050, 28140, 10881, 10888, 28083, 28159, 10893, 59, 60, 61, 62, 63, 27976, 28227, 27988, 28003, 28235, 28015, 28032, 28136, 28058, 28146, 28251, 27921, 27923, 28155, 10844, 10845, 10846, 28261, 28107, 28231, 28230, 10856, 10857, 10858, 28267, 28121, 28240, 28239, 28238, 10869, 10870, 10871, 28273, 28243, 10877, 10878, 10880, 28147, 28250, 10890, 10892, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 27822, 27851, 28143, 28224, 28304, 10849, 10851, 10852, 28232, 28311, 10861, 10864, 10865, 10866, 28241, 28319, 10875, 28322, 28295, 28278, 10883, 10885, 28299, 28300, 28298, 28301, 28282, 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, 10842, 28305, 28352, 28359, 10854, 28312, 28353, 28363, 28365, 10867, 28320, 28368, 10879, 28174, 28354, 10886, 10887, 10889, 10891, 28177, 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, 28256, 10850, 28163, 28262, 10862, 28424, 28268, 28428, 10884, 28431, 28279, 28434, 28427, 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, 28302, 28481, 28309, 28484, 28317, 28488, 28490, 10897, 28487, 28491, 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, 28433, 28548, 28545, 28546, 28544, 10901, 10902, 28547, 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, 28549, 10896, 10898, 10899, 10900, 10903, 28614, 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, 10895, 28674, 28676, 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, 28673, 28678, 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, 28737, 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, 28801, 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, 28677, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63};
bool h_Op[]= {
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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#define THREADS_PER_BLOCK 64
#define BLOCKS_PER_GRID 1
#define SIZE_OF_IN 10944
#define SIZE_OF_AC 18112
__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[454*THREADS_PER_BLOCK];
const int t= THREADS_PER_BLOCK;
__shared__ float final;
final=0;
R[i + 0*t] = A[i + 0*t];
R[i + 1*t] = A[i + 1*t];
R[i + 2*t] = A[i + 2*t];
R[i + 3*t] = A[i + 3*t];
R[i + 4*t] = A[i + 4*t];
R[i + 5*t] = A[i + 5*t];
R[i + 6*t] = A[i + 6*t];
R[i + 7*t] = A[i + 7*t];
R[i + 8*t] = A[i + 8*t];
R[i + 9*t] = A[i + 9*t];
R[i + 10*t] = A[i + 10*t];
R[i + 11*t] = A[i + 11*t];
R[i + 12*t] = A[i + 12*t];
R[i + 13*t] = A[i + 13*t];
R[i + 14*t] = A[i + 14*t];
R[i + 15*t] = A[i + 15*t];
R[i + 16*t] = A[i + 16*t];
R[i + 17*t] = A[i + 17*t];
R[i + 18*t] = A[i + 18*t];
R[i + 19*t] = A[i + 19*t];
R[i + 20*t] = A[i + 20*t];
R[i + 21*t] = A[i + 21*t];
R[i + 22*t] = A[i + 22*t];
R[i + 23*t] = A[i + 23*t];
R[i + 24*t] = A[i + 24*t];
R[i + 25*t] = A[i + 25*t];
R[i + 26*t] = A[i + 26*t];
R[i + 27*t] = A[i + 27*t];
R[i + 28*t] = A[i + 28*t];
R[i + 29*t] = A[i + 29*t];
R[i + 30*t] = A[i + 30*t];
R[i + 31*t] = A[i + 31*t];
R[i + 32*t] = A[i + 32*t];
R[i + 33*t] = A[i + 33*t];
R[i + 34*t] = A[i + 34*t];
R[i + 35*t] = A[i + 35*t];
R[i + 36*t] = A[i + 36*t];
R[i + 37*t] = A[i + 37*t];
R[i + 38*t] = A[i + 38*t];
R[i + 39*t] = A[i + 39*t];
R[i + 40*t] = A[i + 40*t];
R[i + 41*t] = A[i + 41*t];
R[i + 42*t] = A[i + 42*t];
R[i + 43*t] = A[i + 43*t];
R[i + 44*t] = A[i + 44*t];
R[i + 45*t] = A[i + 45*t];
R[i + 46*t] = A[i + 46*t];
R[i + 47*t] = A[i + 47*t];
R[i + 48*t] = A[i + 48*t];
R[i + 49*t] = A[i + 49*t];
R[i + 50*t] = A[i + 50*t];
R[i + 51*t] = A[i + 51*t];
R[i + 52*t] = A[i + 52*t];
R[i + 53*t] = A[i + 53*t];
R[i + 54*t] = A[i + 54*t];
R[i + 55*t] = A[i + 55*t];
R[i + 56*t] = A[i + 56*t];
R[i + 57*t] = A[i + 57*t];
R[i + 58*t] = A[i + 58*t];
R[i + 59*t] = A[i + 59*t];
R[i + 60*t] = A[i + 60*t];
R[i + 61*t] = A[i + 61*t];
R[i + 62*t] = A[i + 62*t];
R[i + 63*t] = A[i + 63*t];
R[i + 64*t] = A[i + 64*t];
R[i + 65*t] = A[i + 65*t];
R[i + 66*t] = A[i + 66*t];
R[i + 67*t] = A[i + 67*t];
R[i + 68*t] = A[i + 68*t];
R[i + 69*t] = A[i + 69*t];
R[i + 70*t] = A[i + 70*t];
R[i + 71*t] = A[i + 71*t];
R[i + 72*t] = A[i + 72*t];
R[i + 73*t] = A[i + 73*t];
R[i + 74*t] = A[i + 74*t];
R[i + 75*t] = A[i + 75*t];
R[i + 76*t] = A[i + 76*t];
R[i + 77*t] = A[i + 77*t];
R[i + 78*t] = A[i + 78*t];
R[i + 79*t] = A[i + 79*t];
R[i + 80*t] = A[i + 80*t];
R[i + 81*t] = A[i + 81*t];
R[i + 82*t] = A[i + 82*t];
R[i + 83*t] = A[i + 83*t];
R[i + 84*t] = A[i + 84*t];
R[i + 85*t] = A[i + 85*t];
R[i + 86*t] = A[i + 86*t];
R[i + 87*t] = A[i + 87*t];
R[i + 88*t] = A[i + 88*t];
R[i + 89*t] = A[i + 89*t];
R[i + 90*t] = A[i + 90*t];
R[i + 91*t] = A[i + 91*t];
R[i + 92*t] = A[i + 92*t];
R[i + 93*t] = A[i + 93*t];
R[i + 94*t] = A[i + 94*t];
R[i + 95*t] = A[i + 95*t];
R[i + 96*t] = A[i + 96*t];
R[i + 97*t] = A[i + 97*t];
R[i + 98*t] = A[i + 98*t];
R[i + 99*t] = A[i + 99*t];
R[i + 100*t] = A[i + 100*t];
R[i + 101*t] = A[i + 101*t];
R[i + 102*t] = A[i + 102*t];
R[i + 103*t] = A[i + 103*t];
R[i + 104*t] = A[i + 104*t];
R[i + 105*t] = A[i + 105*t];
R[i + 106*t] = A[i + 106*t];
R[i + 107*t] = A[i + 107*t];
R[i + 108*t] = A[i + 108*t];
R[i + 109*t] = A[i + 109*t];
R[i + 110*t] = A[i + 110*t];
R[i + 111*t] = A[i + 111*t];
R[i + 112*t] = A[i + 112*t];
R[i + 113*t] = A[i + 113*t];
R[i + 114*t] = A[i + 114*t];
R[i + 115*t] = A[i + 115*t];
R[i + 116*t] = A[i + 116*t];
R[i + 117*t] = A[i + 117*t];
R[i + 118*t] = A[i + 118*t];
R[i + 119*t] = A[i + 119*t];
R[i + 120*t] = A[i + 120*t];
R[i + 121*t] = A[i + 121*t];
R[i + 122*t] = A[i + 122*t];
R[i + 123*t] = A[i + 123*t];
R[i + 124*t] = A[i + 124*t];
R[i + 125*t] = A[i + 125*t];
R[i + 126*t] = A[i + 126*t];
R[i + 127*t] = A[i + 127*t];
R[i + 128*t] = A[i + 128*t];
R[i + 129*t] = A[i + 129*t];
R[i + 130*t] = A[i + 130*t];
R[i + 131*t] = A[i + 131*t];
R[i + 132*t] = A[i + 132*t];
R[i + 133*t] = A[i + 133*t];
R[i + 134*t] = A[i + 134*t];
R[i + 135*t] = A[i + 135*t];
R[i + 136*t] = A[i + 136*t];
R[i + 137*t] = A[i + 137*t];
R[i + 138*t] = A[i + 138*t];
R[i + 139*t] = A[i + 139*t];
R[i + 140*t] = A[i + 140*t];
R[i + 141*t] = A[i + 141*t];
R[i + 142*t] = A[i + 142*t];
R[i + 143*t] = A[i + 143*t];
R[i + 144*t] = A[i + 144*t];
R[i + 145*t] = A[i + 145*t];
R[i + 146*t] = A[i + 146*t];
R[i + 147*t] = A[i + 147*t];
R[i + 148*t] = A[i + 148*t];
R[i + 149*t] = A[i + 149*t];
R[i + 150*t] = A[i + 150*t];
R[i + 151*t] = A[i + 151*t];
R[i + 152*t] = A[i + 152*t];
R[i + 153*t] = A[i + 153*t];
R[i + 154*t] = A[i + 154*t];
R[i + 155*t] = A[i + 155*t];
R[i + 156*t] = A[i + 156*t];
R[i + 157*t] = A[i + 157*t];
R[i + 158*t] = A[i + 158*t];
R[i + 159*t] = A[i + 159*t];
R[i + 160*t] = A[i + 160*t];
R[i + 161*t] = A[i + 161*t];
R[i + 162*t] = A[i + 162*t];
R[i + 163*t] = A[i + 163*t];
R[i + 164*t] = A[i + 164*t];
R[i + 165*t] = A[i + 165*t];
R[i + 166*t] = A[i + 166*t];
R[i + 167*t] = A[i + 167*t];
R[i + 168*t] = A[i + 168*t];
R[i + 169*t] = A[i + 169*t];
R[i + 170*t] = A[i + 170*t];
__syncthreads();
for (int iter=0; iter< n_iter; iter++) {
R[i + 171*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 + 172*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 + 173*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 + 174*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 + 175*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 + 176*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 + 177*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 + 178*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 + 179*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 + 180*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 + 181*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 + 182*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 + 183*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 + 184*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 + 185*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 + 186*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 + 187*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 + 188*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 + 189*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 + 190*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 + 191*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 + 192*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 + 193*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 + 194*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 + 195*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 + 196*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 + 197*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 + 198*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 + 199*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 + 200*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 + 201*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 + 202*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 + 203*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 + 204*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 + 205*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 + 206*t] = Op[i + 35*t] ? R[B[i + 35*t]] * R[C[i + 35*t]] : R[B[i + 35*t]] + R[C[i + 35*t]];
R[i + 207*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 + 208*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 + 209*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 + 210*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 + 211*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 + 212*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 + 213*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 + 214*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 + 215*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 + 216*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 + 217*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 + 218*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 + 219*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 + 220*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 + 221*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 + 222*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 + 223*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 + 224*t] = Op[i + 53*t] ? R[B[i + 53*t]] * R[C[i + 53*t]] : R[B[i + 53*t]] + R[C[i + 53*t]];
__syncthreads();
R[i + 225*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 + 226*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 + 227*t] = Op[i + 56*t] ? R[B[i + 56*t]] * R[C[i + 56*t]] : R[B[i + 56*t]] + R[C[i + 56*t]];
R[i + 228*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 + 229*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 + 230*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 + 231*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 + 232*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 + 233*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 + 234*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 + 235*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 + 236*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 + 237*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 + 238*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 + 239*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 + 240*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 + 241*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 + 242*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 + 243*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 + 244*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 + 245*t] = Op[i + 74*t] ? R[B[i + 74*t]] * R[C[i + 74*t]] : R[B[i + 74*t]] + R[C[i + 74*t]];
R[i + 246*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 + 247*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 + 248*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 + 249*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 + 250*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 + 251*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 + 252*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 + 253*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 + 254*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 + 255*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 + 256*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 + 257*t] = Op[i + 86*t] ? R[B[i + 86*t]] * R[C[i + 86*t]] : R[B[i + 86*t]] + R[C[i + 86*t]];
__syncthreads();
R[i + 258*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 + 259*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 + 260*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 + 261*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 + 262*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 + 263*t] = Op[i + 92*t] ? R[B[i + 92*t]] * R[C[i + 92*t]] : R[B[i + 92*t]] + R[C[i + 92*t]];
R[i + 264*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 + 265*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 + 266*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 + 267*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 + 268*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 + 269*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 + 270*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 + 271*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 + 272*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 + 273*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 + 274*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 + 275*t] = Op[i + 104*t] ? R[B[i + 104*t]] * R[C[i + 104*t]] : R[B[i + 104*t]] + R[C[i + 104*t]];
R[i + 276*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 + 277*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 + 278*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 + 279*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 + 280*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 + 281*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 + 282*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 + 283*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 + 284*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 + 285*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 + 286*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 + 287*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 + 288*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]];
__syncthreads();
R[i + 289*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 + 290*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 + 291*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 + 292*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 + 293*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 + 294*t] = Op[i + 123*t] ? R[B[i + 123*t]] * R[C[i + 123*t]] : R[B[i + 123*t]] + R[C[i + 123*t]];
R[i + 295*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 + 296*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 + 297*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 + 298*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 + 299*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 + 300*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 + 301*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 + 302*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 + 303*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 + 304*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 + 305*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 + 306*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 + 307*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 + 308*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 + 309*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 + 310*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 + 311*t] = Op[i + 140*t] ? R[B[i + 140*t]] * R[C[i + 140*t]] : R[B[i + 140*t]] + R[C[i + 140*t]];
R[i + 312*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 + 313*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 + 314*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 + 315*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 + 316*t] = Op[i + 145*t] ? R[B[i + 145*t]] * R[C[i + 145*t]] : R[B[i + 145*t]] + R[C[i + 145*t]];
__syncthreads();
R[i + 317*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 + 318*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 + 319*t] = Op[i + 148*t] ? R[B[i + 148*t]] * R[C[i + 148*t]] : R[B[i + 148*t]] + R[C[i + 148*t]];
R[i + 320*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 + 321*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 + 322*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 + 323*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 + 324*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 + 325*t] = Op[i + 154*t] ? R[B[i + 154*t]] * R[C[i + 154*t]] : R[B[i + 154*t]] + R[C[i + 154*t]];
R[i + 326*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 + 327*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 + 328*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 + 329*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 + 330*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 + 331*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 + 332*t] = Op[i + 161*t] ? R[B[i + 161*t]] * R[C[i + 161*t]] : R[B[i + 161*t]] + R[C[i + 161*t]];
R[i + 333*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 + 334*t] = Op[i + 163*t] ? R[B[i + 163*t]] * R[C[i + 163*t]] : R[B[i + 163*t]] + R[C[i + 163*t]];
__syncthreads();
R[i + 335*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 + 336*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 + 337*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 + 338*t] = Op[i + 167*t] ? R[B[i + 167*t]] * R[C[i + 167*t]] : R[B[i + 167*t]] + R[C[i + 167*t]];
R[i + 339*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 + 340*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 + 341*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 + 342*t] = Op[i + 171*t] ? R[B[i + 171*t]] * R[C[i + 171*t]] : R[B[i + 171*t]] + R[C[i + 171*t]];
R[i + 343*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 + 344*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 + 345*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 + 346*t] = Op[i + 175*t] ? R[B[i + 175*t]] * R[C[i + 175*t]] : R[B[i + 175*t]] + R[C[i + 175*t]];
R[i + 347*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 + 348*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 + 349*t] = Op[i + 178*t] ? R[B[i + 178*t]] * R[C[i + 178*t]] : R[B[i + 178*t]] + R[C[i + 178*t]];
R[i + 350*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 + 351*t] = Op[i + 180*t] ? R[B[i + 180*t]] * R[C[i + 180*t]] : R[B[i + 180*t]] + R[C[i + 180*t]];
R[i + 352*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 + 353*t] = Op[i + 182*t] ? R[B[i + 182*t]] * R[C[i + 182*t]] : R[B[i + 182*t]] + R[C[i + 182*t]];
R[i + 354*t] = Op[i + 183*t] ? R[B[i + 183*t]] * R[C[i + 183*t]] : R[B[i + 183*t]] + R[C[i + 183*t]];
R[i + 355*t] = Op[i + 184*t] ? R[B[i + 184*t]] * R[C[i + 184*t]] : R[B[i + 184*t]] + R[C[i + 184*t]];
R[i + 356*t] = Op[i + 185*t] ? R[B[i + 185*t]] * R[C[i + 185*t]] : R[B[i + 185*t]] + R[C[i + 185*t]];
R[i + 357*t] = Op[i + 186*t] ? R[B[i + 186*t]] * R[C[i + 186*t]] : R[B[i + 186*t]] + R[C[i + 186*t]];
R[i + 358*t] = Op[i + 187*t] ? R[B[i + 187*t]] * R[C[i + 187*t]] : R[B[i + 187*t]] + R[C[i + 187*t]];
R[i + 359*t] = Op[i + 188*t] ? R[B[i + 188*t]] * R[C[i + 188*t]] : R[B[i + 188*t]] + R[C[i + 188*t]];
R[i + 360*t] = Op[i + 189*t] ? R[B[i + 189*t]] * R[C[i + 189*t]] : R[B[i + 189*t]] + R[C[i + 189*t]];
R[i + 361*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 + 362*t] = Op[i + 191*t] ? R[B[i + 191*t]] * R[C[i + 191*t]] : R[B[i + 191*t]] + R[C[i + 191*t]];
R[i + 363*t] = Op[i + 192*t] ? R[B[i + 192*t]] * R[C[i + 192*t]] : R[B[i + 192*t]] + R[C[i + 192*t]];
R[i + 364*t] = Op[i + 193*t] ? R[B[i + 193*t]] * R[C[i + 193*t]] : R[B[i + 193*t]] + R[C[i + 193*t]];
R[i + 365*t] = Op[i + 194*t] ? R[B[i + 194*t]] * R[C[i + 194*t]] : R[B[i + 194*t]] + R[C[i + 194*t]];
R[i + 366*t] = Op[i + 195*t] ? R[B[i + 195*t]] * R[C[i + 195*t]] : R[B[i + 195*t]] + R[C[i + 195*t]];
R[i + 367*t] = Op[i + 196*t] ? R[B[i + 196*t]] * R[C[i + 196*t]] : R[B[i + 196*t]] + R[C[i + 196*t]];
R[i + 368*t] = Op[i + 197*t] ? R[B[i + 197*t]] * R[C[i + 197*t]] : R[B[i + 197*t]] + R[C[i + 197*t]];
R[i + 369*t] = Op[i + 198*t] ? R[B[i + 198*t]] * R[C[i + 198*t]] : R[B[i + 198*t]] + R[C[i + 198*t]];
R[i + 370*t] = Op[i + 199*t] ? R[B[i + 199*t]] * R[C[i + 199*t]] : R[B[i + 199*t]] + R[C[i + 199*t]];
R[i + 371*t] = Op[i + 200*t] ? R[B[i + 200*t]] * R[C[i + 200*t]] : R[B[i + 200*t]] + R[C[i + 200*t]];
R[i + 372*t] = Op[i + 201*t] ? R[B[i + 201*t]] * R[C[i + 201*t]] : R[B[i + 201*t]] + R[C[i + 201*t]];
R[i + 373*t] = Op[i + 202*t] ? R[B[i + 202*t]] * R[C[i + 202*t]] : R[B[i + 202*t]] + R[C[i + 202*t]];
R[i + 374*t] = Op[i + 203*t] ? R[B[i + 203*t]] * R[C[i + 203*t]] : R[B[i + 203*t]] + R[C[i + 203*t]];
R[i + 375*t] = Op[i + 204*t] ? R[B[i + 204*t]] * R[C[i + 204*t]] : R[B[i + 204*t]] + R[C[i + 204*t]];
R[i + 376*t] = Op[i + 205*t] ? R[B[i + 205*t]] * R[C[i + 205*t]] : R[B[i + 205*t]] + R[C[i + 205*t]];
R[i + 377*t] = Op[i + 206*t] ? R[B[i + 206*t]] * R[C[i + 206*t]] : R[B[i + 206*t]] + R[C[i + 206*t]];
R[i + 378*t] = Op[i + 207*t] ? R[B[i + 207*t]] * R[C[i + 207*t]] : R[B[i + 207*t]] + R[C[i + 207*t]];
R[i + 379*t] = Op[i + 208*t] ? R[B[i + 208*t]] * R[C[i + 208*t]] : R[B[i + 208*t]] + R[C[i + 208*t]];
R[i + 380*t] = Op[i + 209*t] ? R[B[i + 209*t]] * R[C[i + 209*t]] : R[B[i + 209*t]] + R[C[i + 209*t]];
R[i + 381*t] = Op[i + 210*t] ? R[B[i + 210*t]] * R[C[i + 210*t]] : R[B[i + 210*t]] + R[C[i + 210*t]];
R[i + 382*t] = Op[i + 211*t] ? R[B[i + 211*t]] * R[C[i + 211*t]] : R[B[i + 211*t]] + R[C[i + 211*t]];
R[i + 383*t] = Op[i + 212*t] ? R[B[i + 212*t]] * R[C[i + 212*t]] : R[B[i + 212*t]] + R[C[i + 212*t]];
R[i + 384*t] = Op[i + 213*t] ? R[B[i + 213*t]] * R[C[i + 213*t]] : R[B[i + 213*t]] + R[C[i + 213*t]];
__syncthreads();
R[i + 385*t] = Op[i + 214*t] ? R[B[i + 214*t]] * R[C[i + 214*t]] : R[B[i + 214*t]] + R[C[i + 214*t]];
R[i + 386*t] = Op[i + 215*t] ? R[B[i + 215*t]] * R[C[i + 215*t]] : R[B[i + 215*t]] + R[C[i + 215*t]];
R[i + 387*t] = Op[i + 216*t] ? R[B[i + 216*t]] * R[C[i + 216*t]] : R[B[i + 216*t]] + R[C[i + 216*t]];
R[i + 388*t] = Op[i + 217*t] ? R[B[i + 217*t]] * R[C[i + 217*t]] : R[B[i + 217*t]] + R[C[i + 217*t]];
R[i + 389*t] = Op[i + 218*t] ? R[B[i + 218*t]] * R[C[i + 218*t]] : R[B[i + 218*t]] + R[C[i + 218*t]];
R[i + 390*t] = Op[i + 219*t] ? R[B[i + 219*t]] * R[C[i + 219*t]] : R[B[i + 219*t]] + R[C[i + 219*t]];
R[i + 391*t] = Op[i + 220*t] ? R[B[i + 220*t]] * R[C[i + 220*t]] : R[B[i + 220*t]] + R[C[i + 220*t]];
R[i + 392*t] = Op[i + 221*t] ? R[B[i + 221*t]] * R[C[i + 221*t]] : R[B[i + 221*t]] + R[C[i + 221*t]];
R[i + 393*t] = Op[i + 222*t] ? R[B[i + 222*t]] * R[C[i + 222*t]] : R[B[i + 222*t]] + R[C[i + 222*t]];
R[i + 394*t] = Op[i + 223*t] ? R[B[i + 223*t]] * R[C[i + 223*t]] : R[B[i + 223*t]] + R[C[i + 223*t]];
__syncthreads();
R[i + 395*t] = Op[i + 224*t] ? R[B[i + 224*t]] * R[C[i + 224*t]] : R[B[i + 224*t]] + R[C[i + 224*t]];
R[i + 396*t] = Op[i + 225*t] ? R[B[i + 225*t]] * R[C[i + 225*t]] : R[B[i + 225*t]] + R[C[i + 225*t]];
R[i + 397*t] = Op[i + 226*t] ? R[B[i + 226*t]] * R[C[i + 226*t]] : R[B[i + 226*t]] + R[C[i + 226*t]];
R[i + 398*t] = Op[i + 227*t] ? R[B[i + 227*t]] * R[C[i + 227*t]] : R[B[i + 227*t]] + R[C[i + 227*t]];
R[i + 399*t] = Op[i + 228*t] ? R[B[i + 228*t]] * R[C[i + 228*t]] : R[B[i + 228*t]] + R[C[i + 228*t]];
R[i + 400*t] = Op[i + 229*t] ? R[B[i + 229*t]] * R[C[i + 229*t]] : R[B[i + 229*t]] + R[C[i + 229*t]];
R[i + 401*t] = Op[i + 230*t] ? R[B[i + 230*t]] * R[C[i + 230*t]] : R[B[i + 230*t]] + R[C[i + 230*t]];
R[i + 402*t] = Op[i + 231*t] ? R[B[i + 231*t]] * R[C[i + 231*t]] : R[B[i + 231*t]] + R[C[i + 231*t]];
R[i + 403*t] = Op[i + 232*t] ? R[B[i + 232*t]] * R[C[i + 232*t]] : R[B[i + 232*t]] + R[C[i + 232*t]];
__syncthreads();
R[i + 404*t] = Op[i + 233*t] ? R[B[i + 233*t]] * R[C[i + 233*t]] : R[B[i + 233*t]] + R[C[i + 233*t]];
R[i + 405*t] = Op[i + 234*t] ? R[B[i + 234*t]] * R[C[i + 234*t]] : R[B[i + 234*t]] + R[C[i + 234*t]];
R[i + 406*t] = Op[i + 235*t] ? R[B[i + 235*t]] * R[C[i + 235*t]] : R[B[i + 235*t]] + R[C[i + 235*t]];
R[i + 407*t] = Op[i + 236*t] ? R[B[i + 236*t]] * R[C[i + 236*t]] : R[B[i + 236*t]] + R[C[i + 236*t]];
R[i + 408*t] = Op[i + 237*t] ? R[B[i + 237*t]] * R[C[i + 237*t]] : R[B[i + 237*t]] + R[C[i + 237*t]];
R[i + 409*t] = Op[i + 238*t] ? R[B[i + 238*t]] * R[C[i + 238*t]] : R[B[i + 238*t]] + R[C[i + 238*t]];
R[i + 410*t] = Op[i + 239*t] ? R[B[i + 239*t]] * R[C[i + 239*t]] : R[B[i + 239*t]] + R[C[i + 239*t]];
__syncthreads();
R[i + 411*t] = Op[i + 240*t] ? R[B[i + 240*t]] * R[C[i + 240*t]] : R[B[i + 240*t]] + R[C[i + 240*t]];
R[i + 412*t] = Op[i + 241*t] ? R[B[i + 241*t]] * R[C[i + 241*t]] : R[B[i + 241*t]] + R[C[i + 241*t]];
R[i + 413*t] = Op[i + 242*t] ? R[B[i + 242*t]] * R[C[i + 242*t]] : R[B[i + 242*t]] + R[C[i + 242*t]];
R[i + 414*t] = Op[i + 243*t] ? R[B[i + 243*t]] * R[C[i + 243*t]] : R[B[i + 243*t]] + R[C[i + 243*t]];
R[i + 415*t] = Op[i + 244*t] ? R[B[i + 244*t]] * R[C[i + 244*t]] : R[B[i + 244*t]] + R[C[i + 244*t]];
R[i + 416*t] = Op[i + 245*t] ? R[B[i + 245*t]] * R[C[i + 245*t]] : R[B[i + 245*t]] + R[C[i + 245*t]];
__syncthreads();
R[i + 417*t] = Op[i + 246*t] ? R[B[i + 246*t]] * R[C[i + 246*t]] : R[B[i + 246*t]] + R[C[i + 246*t]];
R[i + 418*t] = Op[i + 247*t] ? R[B[i + 247*t]] * R[C[i + 247*t]] : R[B[i + 247*t]] + R[C[i + 247*t]];
R[i + 419*t] = Op[i + 248*t] ? R[B[i + 248*t]] * R[C[i + 248*t]] : R[B[i + 248*t]] + R[C[i + 248*t]];
R[i + 420*t] = Op[i + 249*t] ? R[B[i + 249*t]] * R[C[i + 249*t]] : R[B[i + 249*t]] + R[C[i + 249*t]];
R[i + 421*t] = Op[i + 250*t] ? R[B[i + 250*t]] * R[C[i + 250*t]] : R[B[i + 250*t]] + R[C[i + 250*t]];
R[i + 422*t] = Op[i + 251*t] ? R[B[i + 251*t]] * R[C[i + 251*t]] : R[B[i + 251*t]] + R[C[i + 251*t]];
R[i + 423*t] = Op[i + 252*t] ? R[B[i + 252*t]] * R[C[i + 252*t]] : R[B[i + 252*t]] + R[C[i + 252*t]];
__syncthreads();
R[i + 424*t] = Op[i + 253*t] ? R[B[i + 253*t]] * R[C[i + 253*t]] : R[B[i + 253*t]] + R[C[i + 253*t]];
R[i + 425*t] = Op[i + 254*t] ? R[B[i + 254*t]] * R[C[i + 254*t]] : R[B[i + 254*t]] + R[C[i + 254*t]];
R[i + 426*t] = Op[i + 255*t] ? R[B[i + 255*t]] * R[C[i + 255*t]] : R[B[i + 255*t]] + R[C[i + 255*t]];
R[i + 427*t] = Op[i + 256*t] ? R[B[i + 256*t]] * R[C[i + 256*t]] : R[B[i + 256*t]] + R[C[i + 256*t]];
R[i + 428*t] = Op[i + 257*t] ? R[B[i + 257*t]] * R[C[i + 257*t]] : R[B[i + 257*t]] + R[C[i + 257*t]];
R[i + 429*t] = Op[i + 258*t] ? R[B[i + 258*t]] * R[C[i + 258*t]] : R[B[i + 258*t]] + R[C[i + 258*t]];
__syncthreads();
R[i + 430*t] = Op[i + 259*t] ? R[B[i + 259*t]] * R[C[i + 259*t]] : R[B[i + 259*t]] + R[C[i + 259*t]];
R[i + 431*t] = Op[i + 260*t] ? R[B[i + 260*t]] * R[C[i + 260*t]] : R[B[i + 260*t]] + R[C[i + 260*t]];
R[i + 432*t] = Op[i + 261*t] ? R[B[i + 261*t]] * R[C[i + 261*t]] : R[B[i + 261*t]] + R[C[i + 261*t]];
R[i + 433*t] = Op[i + 262*t] ? R[B[i + 262*t]] * R[C[i + 262*t]] : R[B[i + 262*t]] + R[C[i + 262*t]];
__syncthreads();
R[i + 434*t] = Op[i + 263*t] ? R[B[i + 263*t]] * R[C[i + 263*t]] : R[B[i + 263*t]] + R[C[i + 263*t]];
R[i + 435*t] = Op[i + 264*t] ? R[B[i + 264*t]] * R[C[i + 264*t]] : R[B[i + 264*t]] + R[C[i + 264*t]];
R[i + 436*t] = Op[i + 265*t] ? R[B[i + 265*t]] * R[C[i + 265*t]] : R[B[i + 265*t]] + R[C[i + 265*t]];
__syncthreads();
R[i + 437*t] = Op[i + 266*t] ? R[B[i + 266*t]] * R[C[i + 266*t]] : R[B[i + 266*t]] + R[C[i + 266*t]];
R[i + 438*t] = Op[i + 267*t] ? R[B[i + 267*t]] * R[C[i + 267*t]] : R[B[i + 267*t]] + R[C[i + 267*t]];
__syncthreads();
R[i + 439*t] = Op[i + 268*t] ? R[B[i + 268*t]] * R[C[i + 268*t]] : R[B[i + 268*t]] + R[C[i + 268*t]];
R[i + 440*t] = Op[i + 269*t] ? R[B[i + 269*t]] * R[C[i + 269*t]] : R[B[i + 269*t]] + R[C[i + 269*t]];
__syncthreads();
R[i + 441*t] = Op[i + 270*t] ? R[B[i + 270*t]] * R[C[i + 270*t]] : R[B[i + 270*t]] + R[C[i + 270*t]];
__syncthreads();
R[i + 442*t] = Op[i + 271*t] ? R[B[i + 271*t]] * R[C[i + 271*t]] : R[B[i + 271*t]] + R[C[i + 271*t]];
__syncthreads();
R[i + 443*t] = Op[i + 272*t] ? R[B[i + 272*t]] * R[C[i + 272*t]] : R[B[i + 272*t]] + R[C[i + 272*t]];
__syncthreads();
R[i + 444*t] = Op[i + 273*t] ? R[B[i + 273*t]] * R[C[i + 273*t]] : R[B[i + 273*t]] + R[C[i + 273*t]];
__syncthreads();
R[i + 445*t] = Op[i + 274*t] ? R[B[i + 274*t]] * R[C[i + 274*t]] : R[B[i + 274*t]] + R[C[i + 274*t]];
__syncthreads();
R[i + 446*t] = Op[i + 275*t] ? R[B[i + 275*t]] * R[C[i + 275*t]] : R[B[i + 275*t]] + R[C[i + 275*t]];
__syncthreads();
R[i + 447*t] = Op[i + 276*t] ? R[B[i + 276*t]] * R[C[i + 276*t]] : R[B[i + 276*t]] + R[C[i + 276*t]];
__syncthreads();
R[i + 448*t] = Op[i + 277*t] ? R[B[i + 277*t]] * R[C[i + 277*t]] : R[B[i + 277*t]] + R[C[i + 277*t]];
__syncthreads();
R[i + 449*t] = Op[i + 278*t] ? R[B[i + 278*t]] * R[C[i + 278*t]] : R[B[i + 278*t]] + R[C[i + 278*t]];
__syncthreads();
R[i + 450*t] = Op[i + 279*t] ? R[B[i + 279*t]] * R[C[i + 279*t]] : R[B[i + 279*t]] + R[C[i + 279*t]];
__syncthreads();
R[i + 451*t] = Op[i + 280*t] ? R[B[i + 280*t]] * R[C[i + 280*t]] : R[B[i + 280*t]] + R[C[i + 280*t]];
__syncthreads();
R[i + 452*t] = Op[i + 281*t] ? R[B[i + 281*t]] * R[C[i + 281*t]] : R[B[i + 281*t]] + R[C[i + 281*t]];
__syncthreads();
R[i + 453*t] = Op[i + 282*t] ? R[B[i + 282*t]] * R[C[i + 282*t]] : R[B[i + 282*t]] + R[C[i + 282*t]];
if (i==0) { final += R[453*t]; }
__syncthreads();
}
if (i==0) { A[0]= final;}
}
|
23,716 | #include <cuda.h>
#include <stdio.h>
#include <iostream>
#include <time.h>
//programma per il calcolo matrice matrice, usando il primo approccio delle slide ma modificato da me per non avere race condition
//si moltiplica una matrice a size lxm con b size mxn, ottenendo c size lxn
//un elemento, tra gli lxn, di c, di indice (i,j) è ottenuto come prod scalare tra la riga i-esima di a e la colonna j-esima di back
//quindi possiamo assegnare un blocco di thread a ciascuna degli elementi di c, e i thread di quel blocco si occuperanno di calcolare il prodotto scalare
//tra la iesima riga e jesima colonna, quindi ogni blocco effettuerà m prodotti e m-1 somme. Il blocco assegnato sarà quello con indice riga blocco i e indice col j
//a seconda di quanti sono i thread del blocco, e di quanto vale m, ci sarà un map 1 cella di a e b, 1 thread, o più celle di a e b, 1 thread.
//per evitare la race condition, i thread non aggiornano direttamente c(i,j) ma ciascun thread scrive in un suo elemento di un buffer shared memory
//e solo il thread di indice 0 nel blocco alla fine somma tutti gli elementi nello shared buffer, e trascrive la somma in c(i,j)
using namespace std;
//input: l,m,n, (e quindi il totale di blocchi nella griglia sarà lxn), t (totale di thread per blocco)
//la griglia sarà 2d (in modo tale da mappare gli indici riga e colonna dei blocchi con le celle di c) mentre ciascun blocco sarà monodimensionale
//in quanto i thread di un blocco devono essere mappati su riga a-colonna b.
//t deve essere <=m e per semplicità m%t deve essere uguale a 0
__host__ void allocaEInizializzaMatrice(int **res,int m,int n){
*res=new int[m*n];
for(int i=0;i<m;i++)
for(int j=0;j<n;j++)
*((*res)+i*n+j)=1+rand()%10;
}
__host__ void stampaMatrice(int *a,int m,int n){
cout<<"--------------------------------------"<<endl;
for(int i=0;i<m;i++){
for(int j=0;j<n;j++)
cout<<a[i*n+j]<<" ";
cout<<endl;
}
}
__host__ void matMatCPU(int *a,int *b,int *res,int l,int m,int n){
for(int i=0;i<l;i++)
for(int j=0;j<n;j++){
int v=0;
for(int k=0;k<m;k++)
v+=a[i*m+k] * b[k*n+j];
res[i*n+j]=v;
}
}
__global__ void matMatGPU(int *a,int *b,int *c,int l,int m,int n){
//ogni elemento della matrice risultato c viene assegnata ad un blocco
extern __shared__ int buffer[]; //questo verrà allocato per il numero di thread in un blocco
int stride = m / blockDim.x; // lo stride è la porzione della riga/colonna (assegnata al blocco) che viene assegnata a ciascun thread del blocco
buffer[threadIdx.x] = 0;
for(int i=0;i<stride;i++){
if(threadIdx.x * stride + i < m)
buffer[threadIdx.x]+= (a[blockIdx.x * m + (threadIdx.x * stride +i)] * b[(threadIdx.x*stride +i)*n + blockIdx.y ]);
else break;
}
__syncthreads();
if(threadIdx.x == 0){
c[blockIdx.x * n + blockIdx.y] = 0;
for(int i=0;i<blockDim.x;i++)
c[blockIdx.x * n + blockIdx.y]+=buffer[i];
}
}
int main(int argc,char *argv[]){
srand((unsigned int)time(NULL));
int l,m,n,t;
dim3 sizeGriglia,sizeBlocco;
if(argc!=5){
l=10;
m=5;
n=4;
t=5;
}
else {
sscanf(argv[1],"%d",&l);
sscanf(argv[2],"%d",&m);
sscanf(argv[3],"%d",&n);
sscanf(argv[4],"%d",&t);
}
if(m%t != 0 || t>m){
cout<<"wrong usage\n";
exit(1);
}
sizeGriglia.x = l;
sizeGriglia.y = n; //ogni blocco della griglia un elemento della matrice risultato c (size lxn)
sizeBlocco.x = t; //blocco monodimensionale
int *ha,*hb,*hc;
allocaEInizializzaMatrice(&ha,l,m);
allocaEInizializzaMatrice(&hb,m,n);
hc=new int[l*n];
stampaMatrice(ha,l,m);
stampaMatrice(hb,m,n);
matMatCPU(ha,hb,hc,l,m,n);
stampaMatrice(hc,l,n);
int *da,*db,*dc;
cudaMalloc(&da,l*m*sizeof(int));
cudaMalloc(&db,m*n*sizeof(int));
cudaMalloc(&dc,l*n*sizeof(int));
cudaMemset(dc,0,l*n*sizeof(int));
cudaMemcpy(da,ha,l*m*sizeof(int),cudaMemcpyHostToDevice);
cudaMemcpy(db,hb,m*n*sizeof(int),cudaMemcpyHostToDevice);
matMatGPU<<<sizeGriglia,sizeBlocco,sizeof(int)*sizeBlocco.x >>>(da,db,dc,l,m,n);
int *copia=new int[l*n];
cudaMemcpy(copia,dc,l*n*sizeof(int),cudaMemcpyDeviceToHost);
stampaMatrice(dc,l,n);
return 0;
}
|
23,717 | #include "cudaCode.cuh"
__device__ int x_error, y_error;
__device__ int errorFlag = 0;
__device__ int errorCode;
__device__ void nanException(int x, int y, int e) {
errorFlag = 1;
errorCode = e;
x_error = x;
y_error = y;
asm("trap;");
}
__global__ void calcRhoGPU(float* data, float* rhos) {
int tid = blockDim.x*blockIdx.x+threadIdx.x + yOffset[0];
if (tid < gridIter[0]) {
float result = 0;
for(int i = 0; i<q; i++) {
result += data[i*nNodes[0]+tid];
}
rhos[tid] = result;
}
}
__global__ void calcVelGPU(float* data, float* vels, float* usqrs, float* rhos) {
int tid = blockDim.x*blockIdx.x+threadIdx.x + yOffset[0];
if (tid < gridIter[0]) {
int thisnNodes = nNodes[0];
float ux;
float uy;
float uxPlus;
float uyPlus;
float uxMinus;
float uyMinus;
uxPlus = data[1*thisnNodes+tid] + data[5*thisnNodes+tid] + data[8*thisnNodes+tid];
uyPlus = data[2*thisnNodes+tid] + data[5*thisnNodes+tid] + data[6*thisnNodes+tid];
uxMinus = data[3*thisnNodes+tid] + data[6*thisnNodes+tid] + data[7*thisnNodes+tid];
uyMinus = data[4*thisnNodes+tid] + data[7*thisnNodes+tid] + data[8*thisnNodes+tid];
ux = (uxPlus - uxMinus)/rhos[tid];
uy = (uyPlus - uyMinus)/rhos[tid];
vels[tid] = ux;
vels[thisnNodes+tid] = uy;
usqrs[tid] = ux*ux + uy*uy;
}
}
__global__ void calcEquilibriumGPU(float* equis, float* rhos, float* vels, float* usqrs) {
int tid = blockDim.x*blockIdx.x+threadIdx.x + yOffset[0];
if (tid < gridIter[0]) {
int thisnNodes = nNodes[0];
float ux = vels[tid];
float uy = vels[thisnNodes+tid];
float uSqr = usqrs[tid];
float rho = rhos[tid];
for (int i=0; i<q; i++) {
float velDot = velCol0[i]*ux + velCol1[i]*uy;
float result = rho * weights[i] * (1.0 + 3.0*velDot + 4.5*velDot*velDot - 1.5*uSqr);
equis[i*thisnNodes+tid] = result;
}
}
}
__global__ void BGKCollideGPU(float* data, float* equis, int* nodeMap) {
int tid = blockIdx.x*blockDim.x + threadIdx.x + yOffset[0];
if (tid < gridIter[0]) {
if (nodeMap[tid] == 0) {
int thisnNodes = nNodes[0];
for (int i = 0; i<q; i++) {
data[i*thisnNodes+tid] *= (1.0 - omega[0]);
data[i*thisnNodes+tid] += omega[0]*equis[i*thisnNodes+tid];
}
}
}
}
__global__ void bounceBackGPU(float* data, int* nodeMap) {
int tid = blockIdx.x*blockDim.x + threadIdx.x + yOffset[0];
if (tid<gridIter[0]) {
int thisnNodes = nNodes[0];
float oldVels[q];
if (nodeMap[tid] == 1) {
for (int i = 0; i<q; i++) {
float tmpResult = data[i*thisnNodes+tid];
// if (tmpResult != tmpResult) {
// nanException(tid%lx, tid/lx, 3);
// }
oldVels[i] = tmpResult;
}
for (int i = 0; i<q; i++) {
data[i*thisnNodes+tid] = oldVels[bounceBackMap[i]];
}
}
}
}
__global__ void streamGPU(float* outData, float* inData) {
int tid = blockIdx.x*blockDim.x + threadIdx.x + yOffset[0];
//Avoid streaming the special first and last column.
//Probably best avoided by just no-oping those threads.
if (tid < gridIter[0]) {
int thisnNodes = nNodes[0];
int thisLx = lx[0];
int x = tid % thisLx;
if (x != 0 || x != thisLx-1) {
for (int i = 0; i<q; i++) {
float tmpResult = inData[i*thisnNodes + tid];
int dst = i*thisnNodes + tid + velColInt1[i]*thisLx + velColInt0[i];
outData[dst] = tmpResult;
}
}
}
}
__global__ void streamInGPU(float* outData, float* inData, float* inFlow) {
int tid = blockIdx.x*blockDim.x + threadIdx.x;
if (tid < gridIter[0]) {
int thisnNodes = nNodes[0];
int thisLx = lx[0];
int thisLy = ly[0];
int node = tid*thisLx + yOffset[0];
int y = tid + 1;
outData[5*thisnNodes+node] = inFlow[(thisLy+2)*5+y];
outData[1*thisnNodes+node] = inFlow[(thisLy+2)*1+y];
outData[8*thisnNodes+node] = inFlow[(thisLy+2)*8+y];
outData[2*thisnNodes+node] = inData[2*thisnNodes+node-thisLx];
outData[4*thisnNodes+node] = inData[4*thisnNodes+node+thisLx];
outData[5*thisnNodes+node+1] = inData[5*thisnNodes+node-thisLx];
outData[1*thisnNodes+node+1] = inData[1*thisnNodes+node];
outData[8*thisnNodes+node+1] = inData[8*thisnNodes+node+thisLx];
}
}
__global__ void streamOutGPU(float* outData, float* inData, float* inFlow) {
int tid = blockIdx.x*blockDim.x + threadIdx.x;
if (tid < gridIter[0]) {
int thisnNodes = nNodes[0];
int thisLx = lx[0];
int thisLy = ly[0];
int node = tid*thisLx + yOffset[0] + thisLx-1;
int y = node / thisLx;
outData[6*thisnNodes+node] = inFlow[(thisLy+2)*6+y];
outData[3*thisnNodes+node] = inFlow[(thisLy+2)*3+y];
outData[7*thisnNodes+node] = inFlow[(thisLy+2)*7+y];
outData[2*thisnNodes+node] = inData[2*thisnNodes+node-thisLx];
outData[4*thisnNodes+node] = inData[4*thisnNodes+node+thisLx];
outData[6*thisnNodes+node-1] = inData[6*thisnNodes+node-thisLx];
outData[3*thisnNodes+node-1] = inData[3*thisnNodes+node];
outData[7*thisnNodes+node-1] = inData[7*thisnNodes+node+thisLx];
}
} |
23,718 |
#include <stdlib.h>
#include <stdio.h>
#include <stdlib.h>
#include <sys/time.h>
__global__ void sim_cell_onelife(int* d_cell, int m, int n) {
__shared__ int s_arr[100];
int neighbor = 0;
int r = threadIdx.x % m;
int c = threadIdx.x / m;
int cell = d_cell[r + c * m];
int row = r - 1;
int col = c - 1;
s_arr[r + c * m] = d_cell[r + c * m];
__syncthreads();
for (int i = row; i < row + 3; i++) {
for (int j = col; j < col + 3; j++) {
if (i >= 0 && j >= 0) {
if (i < m && j < n) {
if (i != r || j != c) {
if (s_arr[i + j * m] == 1) {
neighbor += 1;
}
}
}
}
}
}
if (cell == 1 && neighbor <= 1) {
cell = 0;
} else if (cell == 0 && neighbor == 2) {
cell = 1;
} else if (cell == 0 && neighbor == 3){
cell = 1;
} else if (cell == 1 && neighbor >= 4) {
cell = 0;
}
__syncthreads();
d_cell[r + c * m] = cell;
}
int main(void) {
int *cell;
int *d_cell;
int t_num;
int size;
int m, n, k;
struct timeval gpu_start, gpu_end;
// m x n
printf("Enter M: \n");
scanf("%d", &m);
printf("Enter N: \n");
scanf("%d", &n);
printf("Enter K: \n");
scanf("%d", &k);
t_num = n * m;
size = t_num * sizeof(int);
cell = (int *)malloc(size);
printf("-------BEFORE LIFE CYCLE--------\n");
for (int i = 0; i < m; ++i) {
for (int j = 0; j < n; ++j) {
cell[i + j * m] = rand() % (1 - 0 + 1) + 0;
}
}
for (int i = 0; i < m; ++i) {
for (int j = 0; j < n; ++j) {
printf("%d ", cell[i + j * m]);
}
printf("\n");
}
printf("-------AFTER %d LIFE CYCLES-------\n", k);
cudaMalloc((void **)&d_cell, size);
cudaMemcpy(d_cell, cell, size, cudaMemcpyHostToDevice);
gettimeofday(&gpu_start, NULL);
for (int i = 0; i < k; i++) {
sim_cell_onelife<<<1,t_num>>>(d_cell, m, n);
}
gettimeofday(&gpu_end, NULL);
memset(cell, 0, size);
cudaMemcpy(cell, d_cell, size, cudaMemcpyDeviceToHost);
for (int i = 0; i < m; ++i) {
for (int j = 0; j < n; ++j) {
printf("%d ", cell[i + j * m]);
}
printf("\n");
}
free(cell);
cudaFree(d_cell);
printf("GPU run time: %ld\n",(gpu_end.tv_usec - gpu_start.tv_usec));
} |
23,719 | #include "includes.h"
#define getPos(a,k) (((a)>>(k-1))&1)
extern "C" {
}
__global__ void replace(int * input_T, int * output_T, int * prefix_T, int * prefix_helper_T, int n, int k, int blockPower) {
for(int i = 0; i<blockPower; i++) {
int oldpos = threadIdx.x + 1024*blockIdx.x + i*1024*gridDim.x;
if(oldpos >= n) return ;
int newpos = prefix_T[oldpos] + prefix_helper_T[blockIdx.x + i*gridDim.x];
if(getPos(input_T[oldpos],k) == 0) {
newpos = oldpos - newpos;
} else {
newpos = prefix_helper_T[(n+1023)/1024] + newpos - 1;
}
output_T[newpos] = input_T[oldpos];
}
} |
23,720 | #include <bits/stdc++.h>
#include <cuda.h>
#include <curand.h>
#include <curand_kernel.h>
#define MAXEDGES 3000000
using namespace std;
using namespace std::chrono;
//sample comment2
int *edge_array,*edge_array_parent,*vertex_array,*vertex_array_parent,*start_interval,*end_interval;
bool *is_leaf;
int counter=0;
__global__ void BFS2(int* off,int* edge,int* current,int* size,int N,int E,volatile int* c_arr,int* c_size,int* dist,volatile int* D_start_interval,volatile int* D_end_interval,volatile int* mutexs){
int id = blockIdx.x*blockDim.x+threadIdx.x;
if(id < *size){
int node = current[id];
int start = off[node];
int end = off[node+1];
while(start<end){
int child = edge[start];
bool isSet = false;
do
{
//printf("hmm\n");
if (isSet = atomicCAS((int *)(mutexs+child), 0, 1) == 0)
{
bool flag=false;
if(D_start_interval[child]>D_start_interval[node]){
D_start_interval[child]=D_start_interval[node];
flag=true;
}
if(D_end_interval[child]<D_end_interval[node]){
D_end_interval[child]=D_end_interval[node];
flag=true;
}
if (flag){
int index = atomicAdd(c_size,1);
c_arr[index]= child;
}
}
if (isSet) //if acquired the lock then release it
{
mutexs[child] = 0;
}
}while (!isSet);
start++;
}
}
}
__global__ void BFS(int* off,int* edge,int* current,int* size,int N,int E,volatile int* c_arr,int* c_size,int* dist,volatile int* D_start_interval,volatile int* D_end_interval,volatile int* mutexs){
int id = blockIdx.x*blockDim.x+threadIdx.x;
if(id < *size){
int node = current[id];
int start = off[node];
int end = off[node+1];
while(start<end){
int child = edge[start];
bool isSet = false;
do
{
//printf("hmm\n");
if (isSet = atomicCAS((int *)(mutexs+child), 0, 1) == 0)
{
bool flag=false;
if(D_start_interval[child]==0 && D_end_interval[child]==0){
D_start_interval[child]=D_start_interval[node];
D_end_interval[child]=D_end_interval[node];
}
else{
if(D_start_interval[child]>D_start_interval[node]){
D_start_interval[child]=D_start_interval[node];
flag=true;
}
if(D_end_interval[child]<D_end_interval[node]){
D_end_interval[child]=D_end_interval[node];
flag=true;
}
}
if ( dist[child] < 0 || flag){
dist[child] = 0;
int index = atomicAdd(c_size,1);
c_arr[index]= child;
}
}
if (isSet) //if acquired the lock then release it
{
mutexs[child] = 0;
}
}while (!isSet);
start++;
}
}
}
void CSR(unordered_map<int,vector<int> > &m,int *vertex_array,int *edge_array, int nodes){
int curr_index=0;
for(int i=0;i<nodes;i++){
int num_of_edges=m[i].size();
vertex_array[i]=curr_index;
for(int j=0;j<num_of_edges;j++){
edge_array[curr_index+j]=m[i][j];
}
curr_index+=num_of_edges;
}
vertex_array[nodes]=curr_index;
}
void find_leaf(unordered_map<int,vector<int> >&m,int nodes){
for(int i=0;i<nodes;i++){
if(m[i].size()==0){
is_leaf[i]=true;
}
}
}
void init(int nodes,int edges){
edge_array=new int[MAXEDGES];
vertex_array=new int[nodes+1];
edge_array_parent=new int[MAXEDGES];
vertex_array_parent=new int[nodes+1];
start_interval=new int[nodes];
end_interval=new int[nodes];
is_leaf=new bool[nodes];
for(int i=0;i<nodes;i++){
is_leaf[i]=false;
start_interval[i]=0;
end_interval[i]=0;
}
}
int main(){
int nodes,edges,root;
srand(time(NULL));
unordered_map<int,vector<int> >m,m2;
cin>>nodes>>edges;
int u,v;
for(int i=0;i<edges;i++){
cin>>u>>v;
m[u].push_back(v);
m2[v].push_back(u);
}
cin>>root;
init(nodes,edges);
CSR(m,vertex_array,edge_array,nodes);
CSR(m2,vertex_array_parent,edge_array_parent,nodes);
find_leaf(m,nodes);
int* H_current_node = (int*)malloc(sizeof(int)*MAXEDGES);
int counter=0;
for(int i=0;i<nodes;i++){
if(is_leaf[i]){
H_current_node[counter]=i;
counter++;
start_interval[i]=counter;
end_interval[i]=counter;
//cout<<i<<endl;
}
}
/*for(int i=0;i<counter;i++){
printf("%d ",H_current_node[i]);
}*/
int* H_c_size = (int*)malloc(sizeof(int));
*H_c_size = counter;
int* H_visited = (int*)malloc(sizeof(int)*nodes);
memset(H_visited,-1,sizeof(int)*nodes);
int* H_mutexs = (int*)malloc(sizeof(int)*nodes);
memset(H_mutexs,0,sizeof(int)*nodes);
for(int i=0;i<nodes;i++){
if(is_leaf[i]){
H_visited[i]=0;
}
//printf("%d\n",H_mutexs[i]);
}
/*for(int i=0;i<nodes;i++){
printf("%d, %d\n",i,H_visited[i]);
}
printf("sdfbdsjfbsjd\n");*/
int* a0 = (int*)malloc(sizeof(int));
*a0=0;
int* a1 = (int*)malloc(sizeof(int));
*a1=counter;
int* D_offset;
int* D_edges;
int* D_visited;
int* D_current_node1;
int* D_c_size1;
int* D_current_node2;
int* D_mutexs;
int* D_start_interval;
int* D_end_interval;
int* D_c_size2;
cudaMalloc(&D_offset,sizeof(int)*(nodes+1));
cudaMalloc(&D_visited,sizeof(int)*nodes);
cudaMalloc(&D_edges,sizeof(int)*MAXEDGES);
cudaMalloc(&D_current_node1,sizeof(int)*MAXEDGES);
cudaMalloc(&D_c_size1,sizeof(int));
cudaMalloc(&D_current_node2,sizeof(int)*MAXEDGES);
cudaMalloc(&D_c_size2,sizeof(int));
cudaMalloc(&D_mutexs,sizeof(int)*nodes);
cudaMalloc(&D_start_interval,sizeof(int)*nodes);
cudaMalloc(&D_end_interval,sizeof(int)*nodes);
cudaMemcpy(D_offset,vertex_array_parent,sizeof(int)*(nodes+1),cudaMemcpyHostToDevice);
cudaMemcpy(D_edges,edge_array_parent,sizeof(int)*MAXEDGES,cudaMemcpyHostToDevice);
cudaMemcpy(D_current_node1,H_current_node,sizeof(int)*MAXEDGES,cudaMemcpyHostToDevice);
cudaMemcpy(D_visited,H_visited,sizeof(int)*nodes,cudaMemcpyHostToDevice);
cudaMemcpy(D_c_size1,a1,sizeof(int),cudaMemcpyHostToDevice);
cudaMemcpy(D_c_size2,a0,sizeof(int),cudaMemcpyHostToDevice);
cudaMemcpy(D_mutexs,H_mutexs,sizeof(int)*nodes,cudaMemcpyHostToDevice);
cudaMemcpy(D_start_interval,start_interval,sizeof(int)*nodes,cudaMemcpyHostToDevice);
cudaMemcpy(D_end_interval,end_interval,sizeof(int)*nodes,cudaMemcpyHostToDevice);
int i=1;
while(*H_c_size>0){
int numThreads = 512;
int numBlocks = (*H_c_size+numThreads-1)/numThreads;
if(i%2==1){
//use array 1
//printf("hmm\n");
BFS<<<numBlocks,numThreads>>>(D_offset,D_edges,D_current_node1,D_c_size1,nodes,edges,D_current_node2,D_c_size2,D_visited,D_start_interval,D_end_interval,D_mutexs);
cudaMemcpy(H_c_size,D_c_size2, sizeof(int),cudaMemcpyDeviceToHost);
// reset the index
cudaMemcpy(D_c_size1,a0,sizeof(int),cudaMemcpyHostToDevice);
}
else{
//use array 2
BFS<<<numBlocks,numThreads>>>(D_offset,D_edges,D_current_node2,D_c_size2,nodes,edges,D_current_node1,D_c_size1,D_visited,D_start_interval,D_end_interval,D_mutexs);
cudaMemcpy(H_c_size,D_c_size1, sizeof(int),cudaMemcpyDeviceToHost);
//reset index
cudaMemcpy(D_c_size2,a0,sizeof(int),cudaMemcpyHostToDevice);
}
i++;
}
cudaMemcpy(H_visited,D_visited, sizeof(int)*nodes,cudaMemcpyDeviceToHost);
for(int j=nodes-1;j>=0;j--){
//printf("%d %d %d %d\n",i,H_visited[i],start_interval[i],end_interval[i]);
if(H_visited[j]==-1){
//printf("hmm\n");
H_current_node[0]=j;
*a1=1;
*H_c_size=1;
cudaMemcpy(D_current_node1,H_current_node,sizeof(int)*MAXEDGES,cudaMemcpyHostToDevice); // OPTIMIZATION POSSIBLE HERE. INSTEAD OF COPYING ENTIRE ARRAY JUST COPY FIRST ELEMENT
cudaMemcpy(D_c_size1,a1,sizeof(int),cudaMemcpyHostToDevice);
cudaMemcpy(D_c_size2,a0,sizeof(int),cudaMemcpyHostToDevice);
counter++;
cudaMemcpy(&D_start_interval[j], &counter, sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(&D_end_interval[j], &counter, sizeof(int), cudaMemcpyHostToDevice);
i=1;
while(*H_c_size>0){
//printf("%d %d\n",*H_c_size,j);
int numThreads = 512;
int numBlocks = (*H_c_size+numThreads-1)/numThreads;
if(i%2==1){
//use array 1
//printf("hmm\n");
BFS<<<numBlocks,numThreads>>>(D_offset,D_edges,D_current_node1,D_c_size1,nodes,edges,D_current_node2,D_c_size2,D_visited,D_start_interval,D_end_interval,D_mutexs);
cudaMemcpy(H_c_size,D_c_size2, sizeof(int),cudaMemcpyDeviceToHost);
// reset the index
cudaMemcpy(D_c_size1,a0,sizeof(int),cudaMemcpyHostToDevice);
}
else{
//use array 2
BFS<<<numBlocks,numThreads>>>(D_offset,D_edges,D_current_node2,D_c_size2,nodes,edges,D_current_node1,D_c_size1,D_visited,D_start_interval,D_end_interval,D_mutexs);
cudaMemcpy(H_c_size,D_c_size1, sizeof(int),cudaMemcpyDeviceToHost);
//reset index
cudaMemcpy(D_c_size2,a0,sizeof(int),cudaMemcpyHostToDevice);
}
i++;
}
cudaMemcpy(H_visited,D_visited,sizeof(int)*nodes,cudaMemcpyDeviceToHost);
}
}
cudaMemcpy(start_interval,D_start_interval,sizeof(int)*nodes,cudaMemcpyDeviceToHost);
cudaMemcpy(end_interval,D_end_interval,sizeof(int)*nodes,cudaMemcpyDeviceToHost);
int batch_size=512;
int *addition_x = new int[batch_size];
int *addition_y = new int[batch_size];
for(int i=0;i<batch_size;i++){
int st = rand()%nodes;
int en = rand()%nodes;
if(st < en){
addition_x[i]=st;
addition_y[i]=en;
}
else{
i--;
}
}
for(int i=0;i<batch_size;i++){
m[addition_x[i]].push_back(addition_y[i]);
m2[addition_y[i]].push_back(addition_x[i]);
edges++;
//printf("%d %d %d\n",i,addition_x[i],addition_y[i]);
start_interval[addition_x[i]]=min(start_interval[addition_x[i]],start_interval[addition_y[i]]);
end_interval[addition_x[i]]=max(end_interval[addition_x[i]],end_interval[addition_y[i]]);
}
for(int i=0;i<batch_size;i++){
H_current_node[i] = addition_x[i];
}
*H_c_size = batch_size;
memset(H_visited,-1,sizeof(int)*nodes);
memset(H_mutexs,0,sizeof(int)*nodes);
for(int i=0;i<batch_size;i++){
H_visited[addition_x[i]]=0;
//printf("%d\n",H_mutexs[i]);
}
/*for(int i=0;i<nodes;i++){
printf("%d, %d\n",i,H_visited[i]);
}
printf("sdfbdsjfbsjd\n");*/
*a0=0;
*a1=batch_size;
cudaMemcpy(D_offset,vertex_array_parent,sizeof(int)*(nodes+1),cudaMemcpyHostToDevice);
cudaMemcpy(D_edges,edge_array_parent,sizeof(int)*MAXEDGES,cudaMemcpyHostToDevice);
cudaMemcpy(D_current_node1,H_current_node,sizeof(int)*MAXEDGES,cudaMemcpyHostToDevice);
cudaMemcpy(D_visited,H_visited,sizeof(int)*nodes,cudaMemcpyHostToDevice);
cudaMemcpy(D_c_size1,a1,sizeof(int),cudaMemcpyHostToDevice);
cudaMemcpy(D_c_size2,a0,sizeof(int),cudaMemcpyHostToDevice);
cudaMemcpy(D_mutexs,H_mutexs,sizeof(int)*nodes,cudaMemcpyHostToDevice);
cudaMemcpy(D_start_interval,start_interval,sizeof(int)*nodes,cudaMemcpyHostToDevice);
cudaMemcpy(D_end_interval,end_interval,sizeof(int)*nodes,cudaMemcpyHostToDevice);
i=1;
auto start_t = high_resolution_clock::now();
while(*H_c_size>0){
int numThreads = 512;
int numBlocks = (*H_c_size+numThreads-1)/numThreads;
if(i%2==1){
//use array 1
//printf("hmm\n");
BFS2<<<numBlocks,numThreads>>>(D_offset,D_edges,D_current_node1,D_c_size1,nodes,edges,D_current_node2,D_c_size2,D_visited,D_start_interval,D_end_interval,D_mutexs);
cudaMemcpy(H_c_size,D_c_size2, sizeof(int),cudaMemcpyDeviceToHost);
// reset the index
cudaMemcpy(D_c_size1,a0,sizeof(int),cudaMemcpyHostToDevice);
}
else{
//use array 2
BFS2<<<numBlocks,numThreads>>>(D_offset,D_edges,D_current_node2,D_c_size2,nodes,edges,D_current_node1,D_c_size1,D_visited,D_start_interval,D_end_interval,D_mutexs);
cudaMemcpy(H_c_size,D_c_size1, sizeof(int),cudaMemcpyDeviceToHost);
//reset index
cudaMemcpy(D_c_size2,a0,sizeof(int),cudaMemcpyHostToDevice);
}
i++;
}
auto stop_t = high_resolution_clock::now();
auto duration = duration_cast<microseconds>(stop_t - start_t);
cout<<"Time taken : "<<duration.count()<<endl;
printf("--------------------------------------------------\n\n");
cudaMemcpy(start_interval,D_start_interval,sizeof(int)*nodes,cudaMemcpyDeviceToHost);
cudaMemcpy(end_interval,D_end_interval,sizeof(int)*nodes,cudaMemcpyDeviceToHost);
//for(int i=nodes-1;i>=0;i--){
// printf("%d %d %d %d\n",i,H_visited[i],start_interval[i],end_interval[i]);
// }
CSR(m,vertex_array,edge_array,nodes);
CSR(m2,vertex_array_parent,edge_array_parent,nodes);
find_leaf(m,nodes);
return 0;
}
|
23,721 | /******************************************************************************
*cr
*cr (C) Copyright 2010 The Board of Trustees of the
*cr University of Illinois
*cr All Rights Reserved
*cr
******************************************************************************/
#define BLOCK_SIZE 512
#define SIMPLE
__global__ void reduction(float *out, float *in, unsigned size)
{
/********************************************************************
Load a segment of the input vector into shared memory
Traverse the reduction tree
Write the computed sum to the output vector at the correct index
********************************************************************/
#ifdef SIMPLE
__shared__ float in_s[2*BLOCK_SIZE];
int idx = 2 * blockIdx.x * blockDim.x + threadIdx.x;
in_s[threadIdx.x] = ((idx < size)? in[idx]: 0.0f);
in_s[threadIdx.x+BLOCK_SIZE] = ((idx + BLOCK_SIZE < size)? in[idx+BLOCK_SIZE]: 0.0f);
for(int stride = 1; stride < BLOCK_SIZE<<1; stride <<= 1) {
__syncthreads();
if(threadIdx.x % stride == 0)
in_s[2*threadIdx.x] += in_s[2*threadIdx.x + stride];
}
#else
// INSERT KERNEL CODE HERE
#endif
if(threadIdx.x == 0)
out[blockIdx.x] = in_s[0];
}
|
23,722 | #include "./fitnessTransform.cuh"
FitnessTransform::FitnessTransform(int dataSize) : dataSize(dataSize) {}
__device__ float FitnessTransform::operator() (const float f) const {
return f / dataSize;
}
|
23,723 | #include<stdio.h>
//Vector Size
#define N 32
//Device Function
__global__ void add(int* a, int* b, int* c)
{
//perfrom single addition
c[threadIdx.x] = a[threadIdx.x] + b[threadIdx.x];
//store result in c
}
//Generate N random integers, store in a
void random_ints(int* a)
{
int i;
for(i=0; i < N; i++)
{
a[i] = rand() % 10;
printf("%02d ", a[i]);
}
printf("\n");
}
int main(void)
{
//Host Arrays
int *a, *b, *c;
//Device Arrays
int *d_a, *d_b, *d_c;
//Total mem size
int size = N * sizeof(int);
//Allocate device mem
cudaMalloc((void **) &d_a, size);
cudaMalloc((void **) &d_b, size);
cudaMalloc((void **) &d_c, size);
a = (int *)malloc(size); random_ints(a);
b = (int *)malloc(size); random_ints(b);
//Allocate and populate a,b
c = (int *)malloc(size);
//Allocate c
cudaMemcpy(d_a, a, size, cudaMemcpyHostToDevice);
cudaMemcpy(d_b, b, size, cudaMemcpyHostToDevice);
//Copy a and b to device memory, store in d_a and d_b
//Execute in 1 block, N threads
add<<<1,N>>>(d_a, d_b, d_c);
//Copy result back from device
cudaMemcpy(c, d_c, size, cudaMemcpyDeviceToHost);
for(int i=0; i < N; i++)
{
printf("%02d ", c[i]);
}
printf("\n");
//--Free Memory--//
free(a);
free(b);
free(c);
cudaFree(d_a);
cudaFree(d_b);
cudaFree(d_c);
//---------------//
return 0;
}
|
23,724 | // This is the REAL "hello world" for CUDA!
// It takes the string "Hello ", prints it, then passes it to CUDA
// with an array of offsets. Then the offsets are added in parallel
// to produce the string "World!"
// By Ingemar Ragnemalm 2010
// nvcc hello-world.cu -L /usr/local/cuda/lib -lcudart -o hello-world
#include <stdio.h>
#define gpuErrchk(ans) { gpuAssert((ans), __FILE__, __func__, __LINE__, false); }
inline void gpuAssert(cudaError_t code, const char *file, const char *func, int line, bool abort=true)
{
if (code != cudaSuccess)
{
fprintf(stderr,"\e[1;31mGPUassert: %s %s %s %d\n\e[1;0m", cudaGetErrorString(code), file, func, line);
if (abort) exit(code);
}
}
const int N = 16;
const int blocksize = 16;
__global__
void hello(char *a, int *b)
{
// Calculate "world"
a[threadIdx.x] += b[threadIdx.x];
// Say where we are working from
int i = threadIdx.x + blockDim.x * blockIdx.x;
printf("Hello thread, world from the device number %i \n", i);
}
int main()
{
char a[N] = "Hello \0\0\0\0\0\0";
int b[N] = {15, 10, 6, 0, -11, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
char *ad;
int *bd;
const int csize = N*sizeof(char);
const int isize = N*sizeof(int);
printf("%s", a);
gpuErrchk( cudaMalloc( (void**)&ad, csize ) );
gpuErrchk( cudaMalloc( (void**)&bd, isize ) );
gpuErrchk( cudaMemcpy( ad, a, csize, cudaMemcpyHostToDevice ) );
gpuErrchk( cudaMemcpy( bd, b, isize, cudaMemcpyHostToDevice ) );
dim3 dimBlock( blocksize, 1 );
dim3 dimGrid( 1, 1 );
// launch a kernel with dimBlock thread groups, and dimGrid blocks per thread.
hello<<<dimGrid, dimBlock>>>(ad, bd);
gpuErrchk( cudaPeekAtLastError() );
gpuErrchk( cudaDeviceSynchronize() );
gpuErrchk( cudaMemcpy( a, ad, csize, cudaMemcpyDeviceToHost ) );
gpuErrchk( cudaFree( ad ) );
gpuErrchk( cudaFree( bd ) );
printf("%s\n", a);
return EXIT_SUCCESS;
}
|
23,725 | #include <iostream>
#include <fstream>
#include <sstream>
#include <string>
#include <vector>
#include <algorithm>
#define NUM_THREADS 256
/*********************
* GLOBAL VARIABLES *
*********************/
float k;
float starting_temp;
int dimension;
int timesteps;
int width, height, depth;
int num_fixed_points;
std::vector<float> fixed_points;
/*******************
* HOST FUNCTIONS *
/******************/
void read_file(std::string file_name); // Reads configuration file
float* alloc_host_mem(int width, int height, int depth, float temp); // Allocates space for a "simulated" array on the host
float* alloc_device_mem(int width, int height, int depth); // Allocates space for a "simulated" array on the device
void print_grid(float* grid, int width, int height, int depth); // Prints the grid to a csv file
void set_fixed_temps_2D(std::vector<float> fixed_points, int num_fixed_points, float* grid, int width); // Sets the fixed temp blocks in the grid (2D)
void set_fixed_temps_3D(std::vector<float> fixed_points, int num_fixed_points, float* grid, int width, int height); // Sets the fixed temp blocks in the grid (3D)
/*********************
* DEVICE FUNCTIONS *
/********************/
__global__ void update_grid_2D(float* d_old_grid, float* d_new_grid, int width, int height, float k); // Updates 2D grid (one timestep)
__global__ void update_grid_3D(float* d_old_grid, float* d_new_grid, int width, int height, int depth, float k); // Updates 3D grid (one timestep)
/*********************
****** MAIN *******
/********************/
int main(int argc, char** argv) {
// Read in the file to get the configurations
read_file(argv[1]);
// Allocate space for grids
float* grid = alloc_host_mem(width, height, depth, starting_temp); // HOST MEMORY (set blocks to default temp)
float* d_new_grid = alloc_device_mem(width, height, depth); // DEVICE MEMORY
float* d_old_grid = alloc_device_mem(width, height, depth); // DEVICE MEMORY
// Size var for size of array
int size = width*height*depth*sizeof(float);
// Determine number of blocks to launch
int NUM_BLOCKS = (int)((width*height*depth)/NUM_THREADS) + 1;
// 2D timestep loop
if (dimension == 2) {
// Set the fixed temps (first time)
set_fixed_temps_2D(fixed_points, num_fixed_points, grid, width);
/*** Timestep Loop ***/
for (int i = 0; i < timesteps; ++i) {
// Copy host grid to device
cudaMemcpy(d_old_grid, grid, size, cudaMemcpyHostToDevice);
// Call device function (updates grid for this timestep)
update_grid_2D<<<NUM_BLOCKS, NUM_THREADS>>>(d_old_grid, d_new_grid, width, height, k);
// Copy device grid to host
cudaMemcpy(grid, d_new_grid, size, cudaMemcpyDeviceToHost);
// Set the fixed temps (reset every timestep)
set_fixed_temps_2D(fixed_points, num_fixed_points, grid, width);
}
}
// 3D timestep loop
if (dimension == 3) {
// Set the fixed temps (reset every timestep)
set_fixed_temps_3D(fixed_points, num_fixed_points, grid, width, height);
/*** Timestep Loop ***/
for (int i = 0; i < timesteps; ++i) {
// Copy host grid to device
cudaMemcpy(d_old_grid, grid, size, cudaMemcpyHostToDevice);
// Call device function (updates grid for this timestep)
update_grid_3D<<<NUM_BLOCKS, NUM_THREADS>>>(d_old_grid, d_new_grid, width, height, depth, k);
// Copy device grid to host
cudaMemcpy(grid, d_new_grid, size, cudaMemcpyDeviceToHost);
// Set the fixed temps (reset every timestep)
set_fixed_temps_3D(fixed_points, num_fixed_points, grid, width, height);
}
}
// Print out the results (to the output file)
print_grid(grid, width, height, depth);
// Free up that memory
free(grid);
cudaFree(d_old_grid);
cudaFree(d_new_grid);
return 0;
}
/*******************
* HOST FUNCTIONS *
/******************/
// Read in the configuration file and populate global variables
void read_file(std::string file_name) {
// Use std namespace for function calls (string, etc...)
using namespace std;
// Open a file
ifstream fh(file_name);
// find the first line ignore comments/empty lines
string line;
getline(fh, line);
line.erase(remove(line.begin(), line.end(), ' '), line.end());
while (line[0] == '#' || line.empty()) { getline(fh, line); line.erase(remove(line.begin(), line.end(), ' '), line.end()); }
// get rid of possible whitespaces in the line
line.erase(remove(line.begin(), line.end(), ' '), line.end());
// Set the dimension (2 or 3)
if (line.compare("2D") == 0)
dimension = 2;
else
dimension = 3;
//Loop through 5 more times to get the remaing arguments
for (int i = 0; i < 5; ++i) {
// Find the next line that is not a comment or empty
getline(fh, line);
// get rid of possible whitespaces in the line
line.erase(remove(line.begin(), line.end(), ' '), line.end());
while (line[0] == '#' || line.empty()) { getline(fh, line); line.erase(remove(line.begin(), line.end(), ' '), line.end());}
// Use these for tokenizing line 3 and 5
stringstream data(line);
vector<string> tokens;
string tok;
switch (i)
{
case 0:
k = stof(line);
break;
case 1:
timesteps = stoi(line);
break;
case 2:
while(getline(data, tok, ',')) {
tokens.push_back(tok);
}
// Set width and height to the tokens
width = stoi(tokens[0]);
height = stoi(tokens[1]);
if (dimension == 3) { depth = stoi(tokens[2]); }
else { depth = 1; }
break;
case 3:
starting_temp = stof(line);
break;
case 4:
num_fixed_points++;
while(getline(data, tok, ',')) {
fixed_points.push_back(stof(tok));
}
while(getline(fh, line)) {
if (line[0] != '#' && !line.empty()) {
stringstream tmp(line);
num_fixed_points++;
while (getline(tmp, tok, ',')) {
fixed_points.push_back(stof(tok));
}
}
}
break;
}
}
fh.close();
}
// Allocates space for a "simulated" array on the host
float* alloc_host_mem(int width, int height, int depth, float temp) {
float* grid = (float*)calloc(width*height*depth, sizeof(float));
for (int i = 0; i < width*height*depth; ++i)
grid[i] = temp;
return grid;
}
// Allocates space for a "simulated" array on the device
float* alloc_device_mem(int width, int height, int depth) {
float* d_grid;
cudaMalloc((void**)&d_grid, width*height*depth*sizeof(float));
return d_grid;
}
// Print the grid to the output file
void print_grid(float* grid, int width, int height, int depth) {
// Open a file
//int size = width*height;
std::ofstream fh("heatOutput.csv");
for (int d = 0; d < depth; ++d) {
for (int i = 0; i < height; ++i) {
for (int j = 0; j < width; ++j) {
fh << grid[d*width*height + i*width + j];
// Print a comma if youre not the last element
if (j != width - 1)
fh << ", ";
}
//Move to a new line each row
fh << "\n";
}
// Put a blank line in between each slice
fh << "\n\n";
}
fh.close();
}
// Take a grid and vector of fixed points and fill in the fixed temps (2D)
void set_fixed_temps_2D(std::vector<float> fixed_points, int num_fixed_points, float* grid, int width) {
// Fill in fixed temp spots
for (int i = 0; i < num_fixed_points; ++i) {
// Extract the current fixed point
int f_x = fixed_points[i*5 + 0];
int f_y = fixed_points[i*5 + 1];
int f_width = fixed_points[i*5 + 2];
int f_height = fixed_points[i*5 + 3];
int f_temp = fixed_points[i*5 + 4];
for (int j = 0; j < f_height; ++j) {
for (int k = 0; k < f_width; ++k) {
grid[(f_y+j)*width + f_x + k] = f_temp;
}
}
}
}
// Take a grid and vector of fixed points and fill in the fixed temps (3D)
void set_fixed_temps_3D(std::vector<float> fixed_points, int num_fixed_points, float* grid, int width, int height) {
// Fill in fixed temp spots
for (int i = 0; i < num_fixed_points; ++i) {
// Extract the current fixed point
int f_x = fixed_points[i*7 + 0];
int f_y = fixed_points[i*7 + 1];
int f_z = fixed_points[i*7 + 2];
int f_width = fixed_points[i*7 + 3];
int f_height = fixed_points[i*7 + 4];
int f_depth = fixed_points[i*7 + 5];
int f_temp = fixed_points[i*7 + 6];
for (int d = 0; d < f_depth; ++d) {
for (int j = 0; j < f_height; ++j) {
for (int k = 0; k < f_width; ++k) {
grid[(f_z+d)*width*height + (f_y+j)*width + (f_x + k)] = f_temp; // (f_z+d) + (f_y+j)*depth + (f_x + k)*height*depth
}
}
}
}
}
/*********************
* DEVICE FUNCTIONS *
/********************/
// Temp diffusion function that updates the 2D grid each timestep
__global__ void update_grid_2D(float* d_old_grid, float* d_new_grid, int width, int height, float k) {
// Get the current index in the device array
int index = threadIdx.x + blockIdx.x * blockDim.x;
// Make sure the index is within the array (blockDim is not always a perfect multiple)
if (index < width * height) {
// Extract the (x,y) cooridnates to check for edge cases
int x, y;
x = index % width;
y = (int)(index/width);
// Values for calculating next temp
float T_old, T_top, T_bottom, T_left, T_right;
T_old = d_old_grid[index];
// Get T_left
if (x != 0)
T_left = d_old_grid[index - 1];
else
T_left = T_old;
// Get T_top
if (y != 0)
T_top = d_old_grid[index - width] ;
else
T_top = T_old;
// Get T_right
if (x != width - 1)
T_right = d_old_grid[index + 1];
else
T_right = T_old;
// Get T_bottom
if (y != height - 1)
T_bottom = d_old_grid[index + width];
else
T_bottom = T_old;
// Update d_new_grid with new value
d_new_grid[index] = T_old + k*(T_top + T_bottom + T_left + T_right - 4*T_old);
}
}
// Temp diffusion function that updates the 3D grid each timestep
__global__ void update_grid_3D(float* d_old_grid, float* d_new_grid, int width, int height, int depth, float k) {
// Get the current index in the device array
int index = threadIdx.x + blockIdx.x * blockDim.x;
// Make sure the index is within the array (blockDim is not always a perfect multiple)
if (index < width*height*depth) {
// Extract the (x,y,z) cooridnates to check for edge cases
int x, y, z;
z = (int)(index/(width*height));
y = (int)((index-width*height*z)/width);
x = (index-width*height*z) % width;
// Values for calculating next temp
float T_old, T_top, T_bottom, T_left, T_right, T_front, T_back;
T_old = d_old_grid[index];
// Get T_left
if (x != 0)
T_left = d_old_grid[index - 1];
else
T_left = T_old;
// Get T_top
if (y != 0)
T_top = d_old_grid[index - width] ;
else
T_top = T_old;
// Get T_right
if (x != width - 1)
T_right = d_old_grid[index + 1];
else
T_right = T_old;
// Get T_bottom
if (y != height - 1)
T_bottom = d_old_grid[index + width];
else
T_bottom = T_old;
// Get T_front
if (z != 0)
T_front = d_old_grid[index - width*height];
else
T_front = T_old;
// Get T_back
if (z != depth - 1)
T_back = d_old_grid[index + width*height];
else
T_back = T_old;
// Update d_new_grid with new value
d_new_grid[index] = T_old + k*(T_top + T_bottom + T_left + T_right + T_front + T_back - 6*T_old);
}
} |
23,726 | /******************************************************************************
*cr
*cr (C) Copyright 2010 The Board of Trustees of the
*cr University of Illinois
*cr All Rights Reserved
*cr
******************************************************************************/
__global__ void vecAddKernelUnshared(float* A, float* B, float* C, int n) {
/* Multiplicaciçón de matrices sin memoria compartida */
// Índices de la matriz
int i = threadIdx.y + blockDim.y * blockIdx.y;
int j = threadIdx.x + blockDim.x * blockIdx.x;
float Csub = 0.0;
/* loop que itera fila * columna de A y B respectivamente */
if ( i < n && j < n){
for (int k = 0; k < n; k++)
{
Csub += A[i*n + k] * B[k*n + j];
}
C[i*n+j] = Csub;
}
}
|
23,727 | //Elapsed Real Time for input-4.txt: 0.603, 0.606, 0.599
//Elapsed Real Time for input-5.txt: 1.419, 1.391, 1.408
#include <stdio.h>
#include <stdbool.h>
#include <cuda_runtime.h>
// Representation for a 2D point with integer coordinates.
typedef struct {
// Coordinates of the point.
int x, y;
} Point;
// Return the squared distance between point i and point j in the ptList.
__device__ double distSquared(Point *devPtList, int i, int j ) {
double dx = (double)devPtList[ i ].x - (double)devPtList[ j ].x;
double dy = (double)devPtList[ i ].y - (double)devPtList[ j ].y;
return dx * dx + dy * dy;
}
/**
* This function finds the largest of 3 numbers.
*@param p0 number 1
*@param p1 number 2
*@param p2 number 3
*@return the largest of the 3 parameters
*/
__device__ double findLargest( double p0, double p1, double p2){
if ( p0 >= p1 && p0 >= p2 ){
return p0;
} else if (p1 >= p2 && p1 >= p0) {
return p1;
} else
return p2;
}
/**
* This function finds the smallest of 3 numbers.
*@param p0 number 1
*@param p1 number 2
*@param p2 number 3
*@return the smallest of the 3 parameters
*/
__device__ double findSmallest( double p0, double p1, double p2){
if ( p0 <= p1 && p0 <= p2 ){
return p0;
}else if( p1 <= p2 && p1 <= p0){
return p1;
}else
return p2;
}
/**
* This function determines whether the 3 points are within an acceptable range of each other
* in order to qualify as points of a "semilateral" triangle. That range being +/- 10%.
*@param d0 distance b/w point 1 and point 2
*@param d1 distance b/w point 2 and point 3
*@param d2 distance b/w point 3 and point 1
*@return true/false: true if the difference is acceptable. false if not.
*/
__device__ bool check( double d0, double d1, double d2 ){
double largest = findLargest( d0, d1, d2 );
double smallest = findSmallest( d0, d1, d2 );
double diff = (smallest * .10);
// printf("Largest: %.2f, Smallest: %.2f, pts[%.2f, %.2f, %.2f]\n", largest, smallest, d0, d1, d2);
if ( ( largest - smallest ) < diff ){
return true;
} else
return false;
}
__global__ void countTriangles( int *devCountList, Point *devPtList, int n ) {
//for(int k = 0; k < n; k++)
// printf("%d, %d\n", devPtList[k].x, devPtList[k].y);
// Unique index for this worker, it's the index of the first point
// in any triangles we're supposed to find.
int a = blockDim.x * blockIdx.x + threadIdx.x;
int total = 0;
// Make sure I actually have something to work on.
if ( a < n ) {
//If the index is above 2, then we can check the points.
double p0, p1, p2;
//The loop starts comparing : arr[a], arr[i], arr[j]
//So it effectively compares a to all following indicies.
for( int i = a + 1; i < n - 1; i++ ){
for( int j = i + 1; j < n; j++ ){
p0 = (double)distSquared(devPtList, a, i);
p0 = sqrt(p0);
p1 = (double)distSquared(devPtList, i, j);
p1 = sqrt(p1);
p2 = (double)distSquared(devPtList, a, j);
p2 = sqrt(p2);
if ( check( p0, p1, p2 ) )
total++;
}
}
//printf("a:%d, pts[%.2f, %.2f, %.2f]\n", a, p0, p1, p2);
}
devCountList[a] = total;
}
// List of all points from the input
Point *ptList;
int *countList;
// Number of points in the input.
int ptCount = 0;
// Read the list of all points
void readPoints() {
// Use a resizable array, increasing capacity as we read
int capacity = 10;
ptList = (Point *) malloc( capacity * sizeof( Point ) );
// Read points until we can't read any more.
int x, y;
while ( scanf( "%d%d", &x, &y ) == 2 ) {
// Grow the point list if needed.
if ( ptCount >= capacity ) {
capacity *= 2;
ptList = (Point *) realloc( ptList, capacity * sizeof( Point ) );
}
// Add this new point to the end of the list.
ptList[ ptCount ].x = x;
ptList[ ptCount ].y = y;
ptCount += 1;
}
}
// General function to report a failure and exit.
static void fail( char const *message ) {
fprintf( stderr, "%s\n", message );
exit( 1 );
}
int main( ) {
readPoints();
//for(int k = 0; k < ptCount; k++)
// printf("%d, %d\n", ptList[k].x, ptList[k].y);
// ...
countList = (int *)malloc( ptCount * sizeof(int) );
//
//
Point *devPtList = NULL;
if ( cudaMalloc((void **)&devPtList, ptCount * sizeof(Point) ) != cudaSuccess )
fail( "Failed to allocate space for lenght list on device" );
//copy list to device from host
if ( cudaMemcpy( devPtList, ptList, ptCount * sizeof(Point),
cudaMemcpyHostToDevice) != cudaSuccess )
fail( "Can't copy list to device from host" );
int *devCountList = NULL;
if ( cudaMalloc((void **)&devCountList, ptCount * sizeof(int) ) != cudaSuccess )
fail( "Failed to allocate space for lenght list on device" );
// Block and grid dimensions.
int threadsPerBlock = 250;
// Round up.
int blocksPerGrid = ( ptCount + threadsPerBlock - 1 ) / threadsPerBlock;
// Run our kernel on these block/grid dimensions
countTriangles<<<blocksPerGrid, threadsPerBlock>>>( devCountList, devPtList, ptCount );
if ( cudaGetLastError() != cudaSuccess )
fail( "Failure in CUDA kernel execution." );
// copy list from device to host
if ( cudaMemcpy( ptList, devPtList, ptCount * sizeof(Point),
cudaMemcpyDeviceToHost) != cudaSuccess )
fail( "Can't copy list from device to host" );
// copy list from device to host
if ( cudaMemcpy( countList, devCountList, ptCount * sizeof(int),
cudaMemcpyDeviceToHost) != cudaSuccess )
fail( "Can't copy list from device to host" );
// Print out a sample of the resulting values.
int sum = 0;
for ( int i = 0; i < ptCount; i++ ){
sum += countList[ i ];
}
printf("Triangles: %d\n", sum);
//
cudaFree( devCountList );
cudaFree( devPtList );
//
free( ptList );
free( countList );
cudaDeviceReset();
}
|
23,728 | #include <stdio.h>
#include <cuda_runtime.h>
// CUDA check error
#define cuda_check_error(msg) \
{ \
cudaError_t err = cudaGetLastError(); \
if( cudaSuccess != err) { \
fprintf(stderr, "[GPUJPEG] [Error] %s (line %i): %s: %s.\n", \
__FILE__, __LINE__, msg, cudaGetErrorString( err) ); \
exit(-1); \
} \
} \
__global__
void get_value(int* index, int* value)
{
int x[3];
for ( int i = 0; i < 3; i++ )
x[i] = 55;
*value = x[*index];
}
int main()
{
int* d_index;
int* d_value;
cudaMalloc((void**)&d_index, sizeof(int));
cudaMalloc((void**)&d_value, sizeof(int));
cuda_check_error("Alloc failed");
int index = 0;
int value = 0;
cudaMemcpy(d_index, &index, sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(d_value, &value, sizeof(int), cudaMemcpyHostToDevice);
cuda_check_error("Init failed");
get_value<<<1, 1>>>(d_index, d_value);
cudaThreadSynchronize();
cuda_check_error("Kernel failed");
cudaMemcpy(&index, d_index, sizeof(int), cudaMemcpyDeviceToHost);
cudaMemcpy(&value, d_value, sizeof(int), cudaMemcpyDeviceToHost);
cuda_check_error("Copy failed");
printf("index = %d\n", index);
printf("value = %d\n", value);
return 0;
}
|
23,729 | //STL
#include <iostream>
#include <vector>
#include <time.h>
#include <algorithm>
using std::cout; using std::endl; using namespace std;
unsigned i;
const unsigned N = 2048;
unsigned gpuThr = 256;
unsigned gpuBl = N / gpuThr;
std::vector < float > inputVec( N );
//=========================== gpu ===========================
__device__ float d_x[ N ], d_Xfp32[ N ], d_ix[ N ];
__constant__ unsigned d_N[ 1 ];
__constant__ float d_median[ 1 ];
__global__ void printKernel()
{
unsigned resNo = 10;
for ( unsigned i = 0; i < resNo; i++ )
printf( "d_x[%i] + d_median[ 0 ]: %f\n", i, d_x[ i ] + d_median[ 0 ] );
for ( unsigned i = 0; i < resNo; i++ )
printf( "d_Xfp32[%i]: %.6f\n", i, d_Xfp32[ i ] );
for ( unsigned i = 0; i < resNo; i++ )
printf( "d_ix[%i]: %f\n", i, d_ix[ i ] );
}
__global__ void idctKernelFloat()
{
unsigned ind = blockIdx.x * blockDim.x + threadIdx.x;
float constVal = ( float( ind ) + 0.5f ) * 3.14159265f / float( N );
float sqrConst = sqrtf( 2.0f / float( N ) );
float tmpX = sqrtf( 1.0f / float( N ) ) * d_Xfp32[ 0 ];
float accDC = 0.0f, tmpx = 0.0f;
for ( unsigned k = 1; k < N; k++ )
{
tmpx = d_Xfp32[ k ];
tmpX += tmpx * sqrConst * __cosf( constVal * ( float( k ) ) );
accDC += tmpx;
}
d_ix[ ind ] = tmpX + d_median[ 0 ];
}
__global__ void dctKernelFloat()
{
unsigned ind = blockIdx.x * blockDim.x + threadIdx.x;
float constVal = float( ind ) * 3.14159265f / float( N );
float sqrConst = sqrtf( 2.0f / float( N ) );
float tmpX = 0.0f, accDC = 0.0f, tmpx = 0.0f;
for ( unsigned i = 0; i < N; i++ )
{
tmpx = d_x[ i ];
tmpX += sqrConst * tmpx * __cosf( constVal * ( float( i ) + 0.5f ) );
accDC += tmpx;
}
d_Xfp32[ ind ] = tmpX;
d_Xfp32[ 0 ] = accDC / sqrtf( float( N ) );
}
__global__ void dataMedianPreprocess()
{
unsigned ind = blockIdx.x * blockDim.x + threadIdx.x;
d_x[ ind ] -= d_median[ 0 ];
}
int main( int argc, char* argv[] )
{
for(i=0;i<(unsigned)inputVec.size();i++)inputVec[i]=0.1f*i;
inputVec[ 3 ] = 0.05f;
vector < float > sortVec( inputVec ); sort( sortVec.begin(), sortVec.end() );
float vecMedian = sortVec[ sortVec.size() / 2 ];
cout << "vector median: " << vecMedian << endl;
cudaMemcpyToSymbol( d_x, &inputVec[ 0 ], sizeof( float ) * ( unsigned )inputVec.size() );
cudaMemcpyToSymbol( d_N, &N, sizeof( unsigned ) );
cudaMemcpyToSymbol( d_median, &vecMedian, sizeof( float ) );
dataMedianPreprocess<<< gpuBl, gpuThr >>>();
clock_t t = clock();
dctKernelFloat<<< gpuBl, gpuThr >>>();
cudaDeviceSynchronize();
cout << "CPU clocks float accumulator: " << double( clock() - t ) << endl;
t = clock();
idctKernelFloat<<< gpuBl, gpuThr >>>();
cudaDeviceSynchronize();
cout << "CPU clocks idct float accumulator: " << double( clock() - t ) << endl;
printKernel<<< 1, 1 >>>();
cudaFree( d_x );
cudaFree( d_ix );
cudaFree( d_median );
cudaFree( d_Xfp32 );
cudaFree( d_N );
cudaDeviceSynchronize();
cudaDeviceReset();
return 0;
}
//P.S. Slightly better results for extracting median value from input data.
|
23,730 | #include "RELU_Activation_Kernel.cuh"
__global__ void Leaky_RELU_Kernel(float* tensor, int tensorSize)
{
int threadIndex = (blockIdx.x + blockIdx.y * gridDim.x) * blockDim.x + threadIdx.x;
if (threadIndex < tensorSize)
{
if (tensor[threadIndex] > 0)
{
tensor[threadIndex] = tensor[threadIndex];
}
else
{
tensor[threadIndex] = tensor[threadIndex] * 0.1f;
}
}
}
__global__ void RELU_Kernel(float* tensor, int tensorSize)
{
int threadIndex = (blockIdx.x + blockIdx.y * gridDim.x) * blockDim.x + threadIdx.x;
if (threadIndex < tensorSize)
{
tensor[threadIndex] = tensor[threadIndex] * (tensor[threadIndex] > 0);
}
}
void LeakyRELUActivation_GPU(float* tensor, int tensorSize) {
//vedno je deljivo z 512 (4)
Leaky_RELU_Kernel << <tensorSize / 512, 512 >> > (tensor, tensorSize);
}
void RELUActivation_GPU(float* tensor, int tensorSize) {
//vedno je deljivo z 512 (4)
RELU_Kernel << <tensorSize / 512, 512 >> > (tensor, tensorSize);
} |
23,731 | #include "includes.h"
/* kernel.cu */
__global__ void AddVector( int vecSize, const float* vecA, const float* vecB, float* vecC)
{
int i = blockDim.x * blockIdx.x + threadIdx.x;
if (i < vecSize)
vecC[i] = vecA[i] + vecB[i];
} |
23,732 | #include "includes.h"
__global__ void add(long* in, long* out, int offset, int n){
int gid = threadIdx.x + blockIdx.x * blockDim.x;
if(gid >= n) return ;
out[gid] = in[gid];
if(gid >= offset)
out[gid] += in[gid-offset];
} |
23,733 | #include <stdio.h>
#include <stdlib.h>
#define EPS2 0.00000001
#define N 100
// Global variables
__device__ float4 globalX[N];
__device__ float4 globalV[N];
__device__ float4 globalA[N];
// Function prototypes
__global__ void calculate_forces(float4 globalX[N], float4 globalA[N]);
__device__ float3 tile_calculation(float4 myPosition, float3 accel);
__device__ float3 bodyBodyInteraction(float4 bi, float4 bj, float3 ai);
// Kernel definitions
__global__ void calculate_forces(float4 globalX[N], float4 globalA[N])
{
// A shared memory buffer to store the body positions.
__shared__ float4 shPosition[N];
float4 myPosition;
int i, tile;
float3 acc = {0.0f, 0.0f, 0.0f};
// Global thread ID (represent the unique body index in the simulation)
int gtid = blockIdx.x * blockDim.x + threadIdx.x;
// This is the position of the body we are computing the acceleration for.
myPosition = globalX[gtid];
for (i = 0, tile = 0; i < N; i += blockDim.x, tile++)
{
int idx = tile * blockDim.x + threadIdx.x;
// Each thread in the block participates in populating the shared memory buffer.
shPosition[threadIdx.x] = globalX[idx];
// Synchronize guarantees all thread in the block have updated the shared memory
// buffer.
__syncthreads();
acc = tile_calculation(myPosition, acc);
// Synchronize again to make sure all threads have used the shared memory
// buffer before we overwrite the values for the next tile.
__syncthreads();
}
// Save the total acceleration in global memory for the integration step.
float4 acc4 = {acc.x, acc.y, acc.z, 0.0f};
globalA[gtid] = acc4;
}
// tile_calculation
__device__ float3 tile_calculation(float4 myPosition, float3 accel)
{
int i;
__shared__ float4 shPosition[N];
for (i = 0; i < blockDim.x; i++)
{
accel = bodyBodyInteraction(myPosition, shPosition[i], accel);
}
return accel;
}
// Body Interaction
__device__ float3 bodyBodyInteraction(float4 bi, float4 bj, float3 ai)
{
float3 r;
// r_ij [3 FLOPS]
r.x = bj.x - bi.x;
r.y = bj.y - bi.y;
r.z = bj.z - bi.z;
// distSqr = dot(r_ij, r_ij) + EPS^2 [6 FLOPS]
float distSqr = r.x * r.x + r.y * r.y + r.z * r.z + EPS2;
// invDistCube =1/distSqr^(3/2) [4 FLOPS (2 mul, 1 sqrt, 1 inv)]
float distSixth = distSqr * distSqr * distSqr;
float invDistCube = 1.0f/sqrtf(distSixth);
// s = m_j * invDistCube [1 FLOP]
float s = bj.w * invDistCube;
// a_i = a_i + s * r_ij [6 FLOPS]
ai.x += r.x * s;
ai.y += r.y * s;
ai.z += r.z * s;
return ai;
}
// Main method
int main(int argc, char* argv[])
{
//float4* h_A = (float*)malloc(size);
return 0;
} |
23,734 | #include <stdio.h>
#include <stdlib.h>
#include <iostream>
#include <cuda_runtime.h>
__global__ void matrixMulKernel(float *C, float *A, float *B, int width, int height){
int tx = blockIdx.x * blockDim.x + threadIdx.x;
int ty = blockIdx.y * blockDim.y + threadIdx.y;
if(tx >= width || ty >= height)
return;
float sum = 0;
for(int i=0; i<width; ++i){
sum += A[ty * width + i] * B[i * width + tx];
}
C[ty * width + tx] = sum;
}
void constantInit(float *data, int size, float val){
for (int i = 0; i < size; ++i){
//data[i] = val;
data[i] = rand() * 1.0 / (RAND_MAX);
}
}
void matrixMul(){
unsigned int width = 2;
unsigned int height = 2;
unsigned int size = width * height * sizeof(float);
float *h_A = (float*)malloc(size);
float *h_B = (float*)malloc(size);
float *h_C = (float*)malloc(size);
// Initialize host memory
const float valB = 0.01f;
constantInit(h_A, width*height, 1.0f);
constantInit(h_B, width*height, valB);
float *d_A, *d_B, *d_C;
cudaMalloc((void**)&d_A, size);
cudaMalloc((void**)&d_B, size);
cudaMalloc((void**)&d_C, size);
//copy host memory to device
cudaMemcpy(d_A, h_A, size, cudaMemcpyHostToDevice);
cudaMemcpy(d_B, h_B, size, cudaMemcpyHostToDevice);
//config dims
dim3 block(16, 16);
// dim3 grid(width / block.x, height / block.y);
dim3 grid(1, 1);
// Excute the kernel
matrixMulKernel<<<grid, block>>>(d_C, d_A, d_B, width, height);
// Copy the memory from device to host
cudaMemcpy(h_C, d_C, size, cudaMemcpyDeviceToHost);
printf("Checking computed result for correctness: \n");
for(int i = 0; i < width * height; i++){
printf("%f ", h_A[i]);
}
printf("\n");
for(int i = 0; i < width * height; i++){
printf("%f ", h_B[i]);
}
printf("\n");
for(int i = 0; i < width * height; i++){
printf("%f ", h_C[i]);
}
printf("\n");
bool correct = true;
// test relative error by the formula
// |<x, y>_cpu - <x,y>_gpu|/<|x|, |y|> < eps
double eps = 1.e-6 ; // machine zero
for (int i = 0; i < width*height; i++){
double abs_err = fabs(h_C[i] - (width * valB));
double dot_length = width;
double abs_val = fabs(h_C[i]);
double rel_err = abs_err/abs_val/dot_length ;
if (rel_err > eps)
{
printf("Error! Matrix[%05d]=%.8f, ref=%.8f error term is > %E\n", i, h_C[i], (float)(width*height), eps);
correct = false;
}
}
printf("%s\n", correct ? "Result = PASS" : "Result = FAIL");
// Free
free(h_A);
free(h_B);
free(h_C);
cudaFree(d_A);
cudaFree(d_B);
cudaFree(d_C);
}
int main(){
matrixMul();
} |
23,735 | #include "includes.h"
__global__ void divScalarMatrix(double *dMatrix, double *dScalar, int dSize){
int tid = threadIdx.x + blockIdx.x * blockDim.x;
while (tid < dSize) {
dMatrix[tid] = dMatrix[tid]/dScalar[0];
tid += blockDim.x * gridDim.x;
}
} |
23,736 | __host__ __device__ inline bool is_I_up(float a, float y, float Cp, float Cn) {
return (y > 0 && a < Cp) || (y < 0 && a > 0);
}
__host__ __device__ inline bool is_I_low(float a, float y, float Cp, float Cn) {
return (y > 0 && a > 0) || (y < 0 && a < Cn);
}
__host__ __device__ inline bool is_free(float a, float y, float Cp, float Cn) {
return a > 0 && (y > 0 ? a < Cp : a < Cn);
}
__host__ __device__ inline bool min_t(float a, float y) {
return a > y ? y : a;
}
__host__ __device__ inline bool max_t(float a, float y) {
return a > y ? a : y;
}
__device__ int get_block_min_t(const float *values, int *index) {
int tid = threadIdx.x;
index[tid] = tid;
__syncthreads();
//block size is always the power of 2
for (int offset = blockDim.x / 2; offset > 0; offset >>= 1) {
if (tid < offset) {
if (values[index[tid + offset]] < values[index[tid]]) {
index[tid] = index[tid + offset];
}
}
__syncthreads();
}
return index[0];
}
__global__ void
c_smo_solve_kernel(const int *label, float *f_val, float *alpha, float *alpha_diff,
const int *working_set, int ws_size,
float Cp, float Cn, const float *k_mat_rows, const float *k_mat_diag, int row_len,
float eps,
float *diff, int max_t_iter) {
//"row_len" equals to the number of instances in the original training dataset.
//allocate shared memory
__shared__ int shared_mem[256];
int *f_idx2reduce = shared_mem; //temporary memory for reduction
float *f_val2reduce = (float *) &shared_mem[ws_size]; //f values used for reduction.
float *alpha_i_diff = (float *) &shared_mem[ws_size + ws_size * sizeof(float) / sizeof(int)]; //delta alpha_i
float *alpha_j_diff = &alpha_i_diff[1];
float *kd = (float *) &alpha_j_diff[1]; // diagonal elements for kernel matrix
//index, f value and alpha for each instance
int tid = threadIdx.x;
int wsi = working_set[tid];
kd[tid] = k_mat_diag[wsi];
float y = label[wsi];
float f = f_val[wsi];
float a = alpha[wsi];
float aold = a;
__syncthreads();
float local_eps;
int numOfIter = 0;
while (1) {
//select fUp and fLow
if (is_I_up(a, y, Cp, Cn))
f_val2reduce[tid] = f;
else
f_val2reduce[tid] = INFINITY;
int i = get_block_min_t(f_val2reduce, f_idx2reduce);
float up_value = f_val2reduce[i];
float kIwsI = k_mat_rows[row_len * i + wsi];//K[i, wsi]
__syncthreads();
if (is_I_low(a, y, Cp, Cn))
f_val2reduce[tid] = -f;
else
f_val2reduce[tid] = INFINITY;
int j1 = get_block_min_t(f_val2reduce, f_idx2reduce);
float low_value = -f_val2reduce[j1];
float local_diff = low_value - up_value;
if (numOfIter == 0) {
local_eps = max_t(eps, 0.1f * local_diff);
if (tid == 0) {
diff[0] = local_diff;
}
}
if (numOfIter > max_t_iter || local_diff < local_eps) {
alpha[wsi] = a;
alpha_diff[tid] = -(a - aold) * y;
diff[1] = numOfIter;
break;
}
__syncthreads();
//select j2 using second order heuristic
if (-up_value > -f && (is_I_low(a, y, Cp, Cn))) {
float aIJ = kd[i] + kd[tid] - 2 * kIwsI;
float bIJ = -up_value + f;
f_val2reduce[tid] = (-bIJ * bIJ / aIJ);
} else
f_val2reduce[tid] = INFINITY;
int j2 = get_block_min_t(f_val2reduce, f_idx2reduce);
//update alpha
if (tid == i)
*alpha_i_diff = y > 0 ? Cp - a : a;
if (tid == j2)
*alpha_j_diff = min_t(y > 0 ? a : Cn - a, (-up_value + f) / (kd[i] + kd[j2] - 2 * kIwsI));
__syncthreads();
float l = min_t(*alpha_i_diff, *alpha_j_diff);
if (tid == i)
a += l * y;
if (tid == j2)
a -= l * y;
//update f
float kJ2wsI = k_mat_rows[row_len * j2 + wsi];//K[J2, wsi]
f -= l * (kJ2wsI - kIwsI);
numOfIter++;
}
} |
23,737 | // Rishabh Agarwal - 18JE0676
#include <bits/stdc++.h>
#include <cuda.h>
using namespace std;
// kernel functions
// single precision addition
__global__ void MatrixAddFloatKernel(float *temp_ha, float *temp_hb, float *temp_hd, int n, int m) {
int i = blockDim.x * blockIdx.x + threadIdx.x;
if(i < n*m) {
temp_hd[i] = temp_ha[i] + temp_hb[i];
}
}
__global__ void MatrixAddFloatKernel(double *temp_had, double *temp_hbd, double *temp_hdd, int n, int m) {
int i = blockDim.x * blockIdx.x + threadIdx.x;
if(i < n*m) {
temp_hdd[i] = temp_had[i] + temp_hbd[i];
}
}
// main()
int main() {
int n, m;
cout << "Enter n: ";
cin >> n;
cout << "\nEnter m: ";
cin >> m;
float totalf = (float)n*m;
double totald = (double)n*m;
// Single point precision matrix addition
cout << "Single point precision matrix addition\n\n";
// allocating memories
float *ha, *hb, *hc, *temp_ha, *temp_hb, *temp_hd, *hd;
ha = (float*)malloc(n*m * sizeof(float));
hb = (float*)malloc(n*m * sizeof(float));
hc = (float*)malloc(n*m * sizeof(float));
hd = (float*)malloc(n*m * sizeof(float));
cudaMalloc((void**)&temp_ha, n*m * sizeof(float));
cudaMalloc((void**)&temp_hb, n*m * sizeof(float));
cudaMalloc((void**)&temp_hd, n*m * sizeof(float));
// assigning values
for(int i=0; i<n*m; i++) {
ha[i] = ((float)i + totalf) / totalf;
hb[i] = ((float)i*i + totalf) / totalf;
hc[i] = ha[i] + hb[i];
}
// copying memory from host to device
cudaMemcpy(temp_ha, ha, n*m*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(temp_hb, hb, n*m*sizeof(float), cudaMemcpyHostToDevice);
// calling kernel
int blockSize = 1024;
int gridSize = (int)ceil((float)n/blockSize);
MatrixAddFloatKernel<<<gridSize, blockSize>>>(temp_ha, temp_hb, temp_hd, n, m);
// copying memory to host
cudaMemcpy(hd, temp_hd, n*m*sizeof(float), cudaMemcpyDeviceToHost);
cudaFree(temp_ha);
cudaFree(temp_hb);
cudaFree(temp_hd);
cout << "Result matrix after addition of single point precision numbers in host:\n";
int t = 0;
for(int i=0; i<n; i++) {
for(int j=0; j<m; j++) {
cout << hc[t] << " ";
t++;
}
cout << "\n";
}
cout << "\nResult matrix after addition of single point precision numbers in device:\n";
t = 0;
for(int i=0; i<n; i++) {
for(int j=0; j<m; j++) {
cout << hd[t] << " ";
t++;
}
cout << "\n";
}
t = 0;
bool ans = true;
for(int i=0; i<n; i++) {
for(int j=0; j<m; j++) {
if(hc[t] != hd[t]) {
ans = false;
break;
}
t++;
}
}
if(ans) {
cout << "\n Matrices are equal\n";
} else {
cout << "\n Matrices are unequal\n";
}
// Double point precision matrix addition
cout << "\n\nDouble point precision matrix addition\n\n";
// allocating memories
double *had, *hbd, *hcd, *temp_had, *temp_hbd, *temp_hdd, *hdd;
had = (double*)malloc(n*m * sizeof(double));
hbd = (double*)malloc(n*m * sizeof(double));
hcd = (double*)malloc(n*m * sizeof(double));
hdd = (double*)malloc(n*m * sizeof(double));
cudaMalloc((void**)&temp_had, n*m * sizeof(double));
cudaMalloc((void**)&temp_hbd, n*m * sizeof(double));
cudaMalloc((void**)&temp_hdd, n*m * sizeof(double));
// assigning values
for(int i=0; i<n*m; i++) {
had[i] = ((double)i + totald) / totald;
hbd[i] = ((double)i*i + totald) / totald;
hcd[i] = had[i] + hbd[i];
}
// copying memory from host to device
cudaMemcpy(temp_had, had, n*m*sizeof(double), cudaMemcpyHostToDevice);
cudaMemcpy(temp_hbd, hbd, n*m*sizeof(double), cudaMemcpyHostToDevice);
// calling kernel
blockSize = 1024;
gridSize = (int)ceil((double)n/blockSize);
MatrixAddFloatKernel<<<gridSize, blockSize>>>(temp_had, temp_hbd, temp_hdd, n, m);
// copying memory to host
cudaMemcpy(hdd, temp_hdd, n*m*sizeof(double), cudaMemcpyDeviceToHost);
cudaFree(temp_had);
cudaFree(temp_hbd);
cudaFree(temp_hdd);
cout << "Result matrix after addition of double point precision numbers in host:\n";
t = 0;
for(int i=0; i<n; i++) {
for(int j=0; j<m; j++) {
cout << hcd[t] << " ";
t++;
}
cout << "\n";
}
cout << "\nResult matrix after addition of double point precision numbers in device:\n";
t = 0;
for(int i=0; i<n; i++) {
for(int j=0; j<m; j++) {
cout << hdd[t] << " ";
t++;
}
cout << "\n";
}
t = 0;
ans = true;
for(int i=0; i<n; i++) {
for(int j=0; j<m; j++) {
if(hcd[t] != hdd[t]) {
ans = false;
break;
}
t++;
}
}
if(ans) {
cout << "\n Matrices are equal\n";
} else {
cout << "\n Matrices are unequal\n";
}
return 0;
}
|
23,738 | #include <stdio.h>
#define N 512
void random_ints(int* a, int n)
{
int i;
for (i = 0; i < n; ++i) {
a[i] = rand() %5000;
}
}
// each parallel invocation of add() is referred to as a block : the set of blocks is referred to as a grid
__global__ void add(int *a, int *b, int *c)
{
// each block handles a different element of the array
// on the device, each block can execute in parallel
// use blockIdx.x to access block index
c[blockIdx.x] = a[blockIdx.x] + b[blockIdx.x];
}
int main(void)
{
// host copies of a, b, c
int *a, *b, *c;
// device copies of a, b, c
int *d_a, *d_b, *d_c;
int size = N * sizeof(int);
// we need to allocate memory on the GPU
// allocate space for device copies of a, b, c
cudaMalloc((void **)&d_a, size);
cudaMalloc((void **)&d_b, size);
cudaMalloc((void **)&d_c, size);
// allocate space for host copies of a, b, c and setup input values
a = (int *)malloc(size);
random_ints(a, N);
b = (int *)malloc(size);
random_ints(b, N);
c = (int *)malloc(size);
// copy inputs to device
cudaMemcpy(d_a, &a, size, cudaMemcpyHostToDevice);
cudaMemcpy(d_b, &b, size, cudaMemcpyHostToDevice);
// launch add() kernel on the GPU with N blocks
add<<<N,1>>>(d_a, d_b, d_c);
// copy result back to host
cudaMemcpy(c, d_c, size, cudaMemcpyDeviceToHost);
// don't forget to free the memory
free(a);
free(b);
free(c);
cudaFree(d_a);
cudaFree(d_b);
cudaFree(d_c);
// check error
printf("CUDA error: %s\n", cudaGetErrorString(cudaGetLastError()));
return 0;
} |
23,739 | //Complete 3SAT solver using CUDA
// Parallel Processing course assignment
// Author : Gourab Saha
//Contact : 9051110501
// To compile : nvcc sat.cu
#include<stdio.h>
#include<cuda.h>
#include<math.h>
#include <stdlib.h>
#include <set>
#include <iostream>
#include <map>
#include <time.h>
#define NO_OF_VARIABLE 25
#define NO_OF_CLAUSE 20
#define CLAUSE_SIZE 3
#define MAX_THREAD_PER_BLOCK 512
int ind;
using namespace std;
void generate_random_cnf( int *,int);
void reset (int *status,int *new_status);
void find_sequence(int *sequence,int temp);
int find_status(int start,int end,int* sat_cnf2,int *sequence,int *status,int *new_status);
int find_one(int *final_result,int size);
void print_details(int i,int j,int k,int index,int *sat_cnf2);
__global__ void sat_Kernel1 (int *sat_cnf2_device,int *result_h1_device,int iteration)
{
int sequence[5];
int temp,i,j,new1,val,start,end;
int status[25];
unsigned int index = blockIdx.x * blockDim.x + threadIdx.x;
result_h1_device[index]=1;
temp=index;
for(i=4;i>=0;i--)
{
sequence[i]=temp%3;
temp=temp/3;
}
for(i=0;i<25;i++)
{
status[i]=2;
}
start=iteration *5;
end=start+4;
for(i=start,j=0;i<end;i++,j++)
{
new1=sat_cnf2_device[i*6+sequence[j]*2];
val=sat_cnf2_device[i*6+sequence[j]*2+1];
if(status[new1]+val==1)
{
result_h1_device[index]=0;
break;
}
else
{
status[new1]=val;
}
}
__syncthreads();
}
__global__ void sat_Kernel2 (int * sat_cnf2_device,int *result_h1_device,int *status_device,int *final_result_device)
{
int sequence[5];
int temp,i,j,new1,val,start,end;
int status[25];
int status2[25];
int flag;
unsigned int index = blockIdx.x * blockDim.x + threadIdx.x;
temp= result_h1_device[index];
for(i=4;i>=0;i--)
{
sequence[i]=temp%3;
temp=temp/3;
}
for(i=0;i<25;i++)
{
status[i]=status_device[i];
status2[i]=2;
}
start=15;
end=19;
for(i=start,j=0;i<end;i++,j++)
{
new1=sat_cnf2_device[i*6+sequence[j]*2];
val=sat_cnf2_device[i*6+sequence[j]*2+1];
status2[new1]=val;
}
flag=1;
for(i=0;i<25;i++)
{
if((status[i]+status2[i])==1)
{
flag=0;
break;
}
}
final_result_device[index]=flag;
__syncthreads();
}
int main(void)
{
int *sat_cnf,*sat_cnf2,*result_h1;
int size,size2,no_of_iteration;
int i,j,k,z1,*result_h1_device,*status_device;
size_t array_size,block_size,num_blocks;
int *sat_cnf2_device,*final_result_device;
int count[4];
int status[25];
int *final_result;
int p1,p2,p3,flag,final_flag;
int sequence[5],new_status_h[25],new_status_i[25],new_status_j[25],new_status_k[25];
size=NO_OF_CLAUSE *NO_OF_VARIABLE;
size2=NO_OF_CLAUSE * CLAUSE_SIZE * 2 ;
sat_cnf=(int*) malloc(size * sizeof(int));
sat_cnf2=(int*) malloc(size2 * sizeof(int));
generate_random_cnf( sat_cnf,size);
printf("\nRandom generated boolean expression with 25 variables and 20 clasues(with 3 literals) in Conjunctive normal form\n\n");
for(i=0;i<NO_OF_CLAUSE;i++)
{
printf("(");
z1=0;
for(j=0;j<NO_OF_VARIABLE;j++)
{
if(sat_cnf[i*NO_OF_VARIABLE+j]==1)
{
printf("x%d",j);
sat_cnf2[i*CLAUSE_SIZE * 2+2*z1]=j;
sat_cnf2[i*CLAUSE_SIZE * 2+2*z1+1]=1;
z1++;
}
if(sat_cnf[i*NO_OF_VARIABLE+j]==0)
{
printf("~x%d",j);
sat_cnf2[i*CLAUSE_SIZE * 2+2*z1]=j;
sat_cnf2[i*CLAUSE_SIZE * 2+2*z1+1]=0;
z1++;
}
if(z1<CLAUSE_SIZE && sat_cnf[i*NO_OF_VARIABLE+j]!=2)
printf("+");
}
printf(")");
if(i<NO_OF_CLAUSE-1)
printf(" * ");
}
array_size=NO_OF_CLAUSE * CLAUSE_SIZE * 2 * sizeof(int) ;
cudaMalloc((void **) &sat_cnf2_device,array_size);
cudaMemcpy(sat_cnf2_device,sat_cnf2,array_size,cudaMemcpyHostToDevice);
block_size=243;
num_blocks=1;
no_of_iteration=4;
size= no_of_iteration*243;
result_h1=(int*) malloc(size * sizeof(int));
array_size=243*sizeof(int);
cudaMalloc((void **) &result_h1_device,array_size);
array_size=no_of_iteration*243*sizeof(int);
cudaMalloc((void **) &result_h1_device,array_size);
for(i=0;i<no_of_iteration;i++)
{
sat_Kernel1<<<1,block_size>>>(sat_cnf2_device,result_h1_device,i);
array_size=243*sizeof(int);
cudaMemcpy(&result_h1[i*243],result_h1_device,array_size,cudaMemcpyDeviceToHost);
}
for(i=0;i<no_of_iteration;i++)
{
count[i]=0;
for(j=0;j<243;j++)
{
if(result_h1[i*243+j]==1)
{
result_h1[i*243+count[i]]=j;
count[i]++;
}
}
}
final_result=(int *)malloc(count[3]*sizeof(int));
block_size=count[3];
num_blocks=1;
dim3 dimBlock(block_size);
dim3 dimGrid(num_blocks);
array_size=25*sizeof(int);
cudaMalloc((void **) &status_device,array_size);
array_size=count[3]*sizeof(int);
cudaMalloc((void **) &final_result_device,array_size);
array_size=243*sizeof(int);
cudaMemcpy(result_h1_device,&result_h1[729],array_size,cudaMemcpyHostToDevice);
final_flag=0;
for(i=0;i<25;i++)
{
new_status_h[i]=2;
}
for(i=0;i<count[0];i++)
{
if(final_flag==1)
{
i--;
break;
}
reset(status,new_status_h);
p1=result_h1[i];
find_sequence(sequence,p1);
flag=find_status(0,4,sat_cnf2,sequence,status,new_status_i);
if(flag==0)
continue;
for(j=0;j<count[1];j++)
{
if(final_flag==1)
{
j--;
break;
}
reset(status,new_status_i);
p2=result_h1[243+j];
find_sequence(sequence,p2);
flag=find_status(5,9,sat_cnf2,sequence,status,new_status_j);
if(flag==0)
continue;
for(k=0;k<count[2];k++)
{
reset(status,new_status_j);
p3=result_h1[2*243+k];
find_sequence(sequence,p3);
flag=find_status(10,14,sat_cnf2,sequence,status,new_status_k);
if(flag==0)
continue;
else
{
reset(status,new_status_k);
array_size=25*sizeof(int);
cudaMemcpy(status_device,status,array_size,cudaMemcpyHostToDevice);
sat_Kernel2<<<dimGrid,dimBlock>>>(sat_cnf2,result_h1_device,status_device,final_result_device);
array_size=count[3]*sizeof(int);
cudaMemcpy(final_result,final_result_device,array_size,cudaMemcpyDeviceToHost);
printf("\n\n");
final_flag=find_one(final_result,count[3]);
if(final_flag==1)
break;
}
}
}
}
printf("\n\n");
if(final_flag==1)
{
printf(" \nThe CNF is satisfiable with the following assignment \n\n");
print_details(i,j,k,ind,sat_cnf2);
}
else
{
printf(" \nThe CNF is not satisfiable !! \n\n");
}
/*
for(i=0;i<no_of_iteration;i++)
{
printf("\nno of 1s :%d\n",count[i]);
for(j=0;j<243;j++)
{
printf("%d ",result_h1[i*243+j]);
}
printf("\n");
}
printf("\n");
for(i=0;i<NO_OF_CLAUSE;i++)
{
for(j=0;j<6;j=j+2)
{
printf("x%d",sat_cnf2[i*6+j]);
if(sat_cnf2[i*6+j+1]==0)
printf("'");
printf("+");
}
printf("\n");
}
*/
cudaFree(sat_cnf2_device);
cudaFree(final_result_device);
cudaFree(result_h1_device);
cudaFree(status_device);
free(sat_cnf);
free(sat_cnf2);
free(result_h1);
free(final_result);
return 1;
}
void generate_random_cnf( int *sat_cnf,int size)
{
int i,j,flag,k;
int a[CLAUSE_SIZE];
for(i=0;i<size;i++)
{
sat_cnf[i]=2;
}
for(i=0;i<NO_OF_CLAUSE;i++)
{
for(j=0;j<CLAUSE_SIZE;j++)
{
flag=1;
while(flag)
{
a[j]=rand()%NO_OF_VARIABLE;
flag=0;
for(k=j-1;k>=0;k--)
{
if(a[j]==a[k])
{
flag=1;
break;
}
}
}
if(rand()%2==0)
sat_cnf[i*NO_OF_VARIABLE+a[j]]=0;
else
sat_cnf[i*NO_OF_VARIABLE+a[j]]=1;
}
}
}
void reset (int *status ,int *new_status)
{
int i;
for(i=0;i<25;i++)
{
status[i]=new_status[i];
}
}
void find_sequence(int *sequence,int temp)
{
int i;
for(i=4;i>=0;i--)
{
sequence[i]=temp%3;
temp=temp/3;
}
}
int find_status(int start,int end,int* sat_cnf2,int *sequence,int *status,int *new_status)
{
int i,j,new1,val;
for(i=0;i<25;i++)
new_status[i]=status[i];
for(i=start,j=0;i<end;i++,j++)
{
new1=sat_cnf2[i*6+sequence[j]*2];
val=sat_cnf2[i*6+sequence[j]*2+1];
if(new_status[new1]+val==1)
{
return 0;
}
else
{
new_status[new1]=val;
}
}
return 1;
}
int find_one(int *final_result,int size)
{
int i,flag=0;
for(i=0;i<size;i++)
{
if(final_result[i]==1)
{
flag=1;
ind=i;
break;
}
}
return flag;
}
void print_details(int i,int j,int k,int index,int *sat_cnf2)
{
int sequence[5];
int temp,x,new1,y,val;
int status[25];
for(x=0;x<25;x++)
status[x]=2;
temp=i;
for(x=4;x>=0;x--)
{
sequence[x]=temp%3;
temp=temp/3;
}
for(x=0,y=0;x<4;x++,y++)
{
new1=sat_cnf2[x*6+sequence[y]*2];
val=sat_cnf2[x*6+sequence[y]*2+1];
status[new1]=val;
}
temp=j;
for(x=4;x>=0;x--)
{
sequence[x]=temp%3;
temp=temp/3;
}
for(x=5,y=0;x<9;x++,y++)
{
new1=sat_cnf2[x*6+sequence[y]*2];
val=sat_cnf2[x*6+sequence[y]*2+1];
status[new1]=val;
}
temp=k;
for(x=4;x>=0;x--)
{
sequence[x]=temp%3;
temp=temp/3;
}
for(x=10,y=0;x<14;x++,y++)
{
new1=sat_cnf2[x*6+sequence[y]*2];
val=sat_cnf2[x*6+sequence[y]*2+1];
status[new1]=val;
}
temp=i;
for(x=4;x>=0;x--)
{
sequence[x]=temp%3;
temp=temp/3;
}
for(x=15,y=0;x<19;x++,y++)
{
new1=sat_cnf2[x*6+sequence[y]*2];
val=sat_cnf2[x*6+sequence[y]*2+1];
status[new1]=val;
}
for(x=0;x<25;x++)
{
if(status[x]==2)
printf("\nx%d : Dont Care(0 or 1)",x);
else
printf("\nx%d : %d",x,status[x]);
}
printf("\n\n\n");
}
|
23,740 | /*
* SPDX-FileCopyrightText: Copyright (c) 1993-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: Apache-2.0
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include <iostream>
#include <cuda_runtime_api.h>
#include <cuda_fp16.h>
namespace nvinfer1
{
namespace plugin
{
const int PILLARS_PER_BLOCK = 64;
const int PILLAR_FEATURE_SIZE = 64;
template <typename Element>
__global__ void scatterBEV_kernel(const Element *pillar_features_data,
const unsigned int *coords_data, const unsigned int *params_data,
unsigned int featureX, unsigned int featureY,
Element *spatial_feature_data)
{
int pillar_idx = blockIdx.x * PILLARS_PER_BLOCK + threadIdx.x;
int valid_pillars_inBlock = PILLARS_PER_BLOCK;
const int num_pillars = params_data[0];
int valid_blocks = (num_pillars+PILLARS_PER_BLOCK-1)/PILLARS_PER_BLOCK;
if(blockIdx.x >= valid_blocks) return;
if(blockIdx.x == (valid_blocks-1)) {
valid_pillars_inBlock = num_pillars % PILLARS_PER_BLOCK;
}
valid_pillars_inBlock = (valid_pillars_inBlock==0) ? PILLARS_PER_BLOCK : valid_pillars_inBlock;
__shared__ Element pillarSM[PILLARS_PER_BLOCK][PILLAR_FEATURE_SIZE]; //pillar*64
for (int i = 0; i < valid_pillars_inBlock; i++)
{
pillarSM[i][threadIdx.x] = pillar_features_data[ (blockIdx.x * PILLARS_PER_BLOCK +i)*PILLAR_FEATURE_SIZE + threadIdx.x];
}
__syncthreads();
if(pillar_idx >= num_pillars) return;
int4 coord = ((const int4 *)coords_data)[pillar_idx];
int x = coord.w;
int y = coord.z;
for (int i = 0; i < PILLAR_FEATURE_SIZE; i++)
{
spatial_feature_data[i*featureY*featureX + y*featureX + x] = pillarSM[threadIdx.x][i];
}
}
template <typename Element>
int pillarScatterKernelLaunch(
int batch_size,
int max_pillar_num,
int num_features,
const Element *pillar_features_data,
const unsigned int *coords_data,
const unsigned int *params_data,
unsigned int featureX, unsigned int featureY,
Element *spatial_feature_data,
cudaStream_t stream)
{
dim3 blocks( (featureX*featureY+PILLARS_PER_BLOCK-1)/PILLARS_PER_BLOCK);
dim3 threads(PILLARS_PER_BLOCK);
for (int b = 0; b < batch_size; b++) {
scatterBEV_kernel<Element><<<blocks, threads, 0, stream>>>
(pillar_features_data + b*max_pillar_num*num_features,
coords_data + b*max_pillar_num*4,
params_data + b,
featureX,
featureY,
spatial_feature_data + b*num_features*featureX*featureY
);
auto err = cudaGetLastError();
if (cudaSuccess != err) {
fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
return -1;
}
}
return 0;
}
template int pillarScatterKernelLaunch<half>(
int batch_size,
int max_pillar_num,
int num_features,
const half *pillar_features_data,
const unsigned int *coords_data,
const unsigned int *params_data,
unsigned int featureX, unsigned int featureY,
half *spatial_feature_data,
cudaStream_t stream);
template int pillarScatterKernelLaunch<float>(
int batch_size,
int max_pillar_num,
int num_features,
const float *pillar_features_data,
const unsigned int *coords_data,
const unsigned int *params_data,
unsigned int featureX, unsigned int featureY,
float *spatial_feature_data,
cudaStream_t stream);
} // namespace plugin
} // namespace nvinfer1
|
23,741 | #include <stdio.h>
#include <stdlib.h>
#include <time.h>
#include <math.h>
#define BLOCK_DIM 32
#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);
}
}
__global__ void matrixAdd (int *a, int *b, int *c);
// Random
void swap_int(int* a, int* b){ int tmp = *a; *a=*b; *b=tmp; }
int rand_int_hi_lo(int upper, int lower){
return((rand() % (upper-lower+1)) + lower);
}
int rand_int(int a,int b)
{
if (b > a) swap_int(&a,&b);
return rand_int_hi_lo(a,b);
}
// End of Random
int* createMatrix (int row, int column) {
return (int*) malloc(sizeof(int) * row * column);
}
void generateValue_Matrix(int* Matrix, int rowsize, int columnsize, char options, int predata[20][20]){
for (int inrow = 0; inrow < rowsize; inrow++) {
for (int incolumn = 0; incolumn < columnsize; incolumn++) {
switch (options) {
case 's':
*(Matrix+inrow+(incolumn*rowsize)) = 0;
break;
case 'p':
*(Matrix+inrow+(incolumn*rowsize)) = predata[inrow][incolumn];
break;
case 'r':
*(Matrix+inrow+(incolumn*rowsize)) = rand_int(1,9);
break;
}
}
}
}
void printMatrixM(int* Matrix, int rowsize, int columnsize) {
for (int inrow = 0; inrow < rowsize; inrow++) {
for (int incolumn = 0; incolumn < columnsize; incolumn++) {
printf("%d ", *(Matrix+inrow+(incolumn*rowsize)));
}
printf("\n");
}
}
__global__ void multiplication_Matrix (int *matrixA, int *matrixB, int *matrixC, int matrixA_rowsize, int matrixA_columnsize, int matrixB_rowsize, int matrixB_columnsize) {
int matrixAnswer_rowsize = matrixA_rowsize;
int matrixAnswer_columnsize = matrixB_columnsize;
int row = blockIdx.x * blockDim.x + threadIdx.x;
int col = blockIdx.y * blockDim.y + threadIdx.y;
if (row < matrixAnswer_rowsize && col < matrixAnswer_columnsize) {
int answer = 0;
for (int incolumn_MatrixA = 0; incolumn_MatrixA < matrixA_columnsize; incolumn_MatrixA++) {
answer += *(matrixA+row+(incolumn_MatrixA*matrixA_rowsize)) * *(matrixB+incolumn_MatrixA+(col*matrixB_rowsize));
}
*(matrixC+row+(col*matrixAnswer_rowsize)) = answer;
}
}
int main() {
time_t timestart = time(NULL);
int matrixA_rowsize = 10000;
int matrixA_columnsize = 10000;
int matrixB_rowsize = 10000;
int matrixB_columnsize = 10000;
int matrixC_rowsize = matrixA_rowsize;
int matrixC_columnsize = matrixB_columnsize;
int* MatrixA = createMatrix(matrixA_rowsize,matrixA_columnsize);
int* MatrixB = createMatrix(matrixB_rowsize,matrixB_columnsize);
int* MatrixC = createMatrix(matrixC_rowsize,matrixC_columnsize);
//int mA[20][20] = {{2,1,1,1,1},{1,1,1,1,1},{1,1,1,1,1},{1,1,1,1,1},{1,1,1,1,1}};
//int mB[20][20] = {{1,1,1,1,1},{1,1,1,1,1},{1,1,1,1,1},{1,1,1,1,1},{1,1,1,1,5}};
int mA[20][20] = {{3,4,2}};
int mB[20][20] = {{13,9,7,15},{8,7,4,6},{6,4,0,3}};
generateValue_Matrix(MatrixA,matrixA_rowsize,matrixA_columnsize,'r', mA);
generateValue_Matrix(MatrixB,matrixB_rowsize,matrixB_columnsize,'r', mB);
//printMatrixM(MatrixA,matrixA_rowsize, matrixA_columnsize);
//printMatrixM(MatrixB,matrixB_rowsize, matrixB_columnsize);
printf("Generate value completed!\n");
int *dev_MatrixA, *dev_MatrixB, *dev_MatrixC;
int size_MatrixA = matrixA_rowsize * matrixA_columnsize * sizeof(int);
int size_MatrixB = matrixB_rowsize * matrixB_columnsize * sizeof(int);
int size_MatrixC = matrixC_rowsize * matrixC_columnsize * sizeof(int);
cudaMalloc((void**)&dev_MatrixA, size_MatrixA);
cudaMalloc((void**)&dev_MatrixB, size_MatrixB);
cudaMalloc((void**)&dev_MatrixC, size_MatrixC);
cudaMemcpy (dev_MatrixA, MatrixA, size_MatrixA, cudaMemcpyHostToDevice);
cudaMemcpy (dev_MatrixB, MatrixB, size_MatrixB, cudaMemcpyHostToDevice);
dim3 dimBlock (BLOCK_DIM, BLOCK_DIM);
dim3 dimGrid ((int)ceil((matrixC_rowsize*1.0)/dimBlock.x),(int)ceil((matrixC_columnsize*1.0)/dimBlock.y));
printf("thread per block is : %d\n", BLOCK_DIM*BLOCK_DIM);
printf("block per grid is : %d , %d\n", (int)ceil((matrixC_rowsize*1.0)/dimBlock.x),(int)ceil((matrixC_columnsize*1.0)/dimBlock.y));
multiplication_Matrix<<<dimGrid, dimBlock>>>(dev_MatrixA, dev_MatrixB, dev_MatrixC, matrixA_rowsize, matrixA_columnsize, matrixB_rowsize, matrixB_columnsize);
cudaDeviceSynchronize();
gpuErrchk( cudaPeekAtLastError() );
gpuErrchk( cudaDeviceSynchronize() );
//cudaMemcpy (MatrixA, dev_MatrixA, size, cudaMemcpyDeviceToHost);
//cudaMemcpy (MatrixB, dev_MatrixB, size, cudaMemcpyDeviceToHost);
//cudaMemcpy (MatrixC, dev_MatrixC, size_MatrixC, cudaMemcpyDeviceToHost);
cudaFree(dev_MatrixA); cudaFree(dev_MatrixB); cudaFree(dev_MatrixC);
printf("---------------------------\n");
//printMatrixM(MatrixA,matrixA_rowsize, matrixA_columnsize);
//printMatrixM(MatrixB,matrixB_rowsize, matrixB_columnsize);
//printMatrixM(MatrixC,matrixC_rowsize, matrixC_columnsize);
printf("Calulate completed\n");
printf("\nestimate using time : %.5f\n", (double)(time(NULL) - timestart));
}
__global__ void matrixAdd (int *a, int *b, int *c) {
//int row = blockIdx.x * blockDim.x + threadIdx.x;
//int col = blockIdx.y * blockDim.y + threadIdx.y;
//int index = row + col * N;
//if (row < N && col < N) {
//*(a+index) = 5;
//*(b+index) = 5;
// c[index] = a[index] + b[index];
//}
} |
23,742 | #include "includes.h"
__global__ void multiplyKernel(float* Z, float* A, float* B, int size){
int id = blockDim.x * blockIdx.x + threadIdx.x;
if(id < size){
Z[id] = A[id] * B[id];
}
} |
23,743 | #include "includes.h"
#define max(a, b) ((a > b)?a:b)
#define THREADSPERDIM 16
#define FALSE 0
#define TRUE !FALSE
// mX has order rows x cols
// vectY has length rows
// mX has order rows x cols
// vectY has length rows
__global__ void ftest(int diagFlag, int p, int rows, int colsx, int colsy, int rCols, int unrCols, float * obs, int obsDim, float * rCoeffs, int rCoeffsDim, float * unrCoeffs, int unrCoeffsDim, float * rdata, int rdataDim, float * unrdata, int unrdataDim, float * dfStats) // float * dpValues)
{
int
j = blockIdx.x * THREADSPERDIM + threadIdx.x,
i = blockIdx.y * THREADSPERDIM + threadIdx.y,
idx = i*colsx + j, k, m;
float
kobs, fp = (float) p, frows = (float) rows,
rSsq, unrSsq,
rEst, unrEst,
score = 0.f,
* tObs, * tRCoeffs, * tUnrCoeffs,
* tRdata, * tUnrdata;
if((i >= colsy) || (j >= colsx)) return;
if((!diagFlag) && (i == j)) {
dfStats[idx] = 0.f;
// dpValues[idx] = 0.f;
return;
}
tObs = obs + (i*colsx+j)*obsDim;
tRCoeffs = rCoeffs + i*rCoeffsDim;
tRdata = rdata + i*rdataDim;
tUnrCoeffs = unrCoeffs + (i*colsx+j)*unrCoeffsDim;
tUnrdata = unrdata + (i*colsx+j)*unrdataDim;
rSsq = unrSsq = 0.f;
for(k = 0; k < rows; k++) {
unrEst = rEst = 0.f;
kobs = tObs[k];
for(m = 0; m < rCols; m++)
rEst += tRCoeffs[m] * tRdata[k+m*rows];
for(m = 0; m < unrCols; m++)
unrEst += tUnrCoeffs[m] * tUnrdata[k+m*rows];
rSsq += (kobs - rEst) * (kobs - rEst);
unrSsq += (kobs - unrEst) * (kobs - unrEst);
}
score = ((rSsq - unrSsq)*(frows-2.f*fp-1.f)) / (fp*unrSsq);
if(!isfinite(score))
score = 0.f;
dfStats[idx] = score;
} |
23,744 | #include <stdio.h>
#include <assert.h>
#include <stdlib.h>
#include <string.h>
#include <time.h>
#include <cuda.h>
#define MAX 1000
#define DISPLAY 1
__global__ void histoKernel(int*, int, int*);
__global__ void reduceKernel(int*, int*, int);
static double CLOCK();
int main(int argc, char* argv[]) {
double start, end; // Timing
// Host
int *data, data_sz;
int class_cnt;
int class_sz;
int node_cnt; // To compare with Q1.
int *result;
data_sz = atoi(argv[1]);
class_cnt = atoi(argv[2]);
node_cnt = atoi(argv[3]);
class_sz = MAX/class_cnt;
data = (int*) malloc(sizeof(int) * data_sz);
result = (int*) malloc(sizeof(int) * class_cnt);
for (int i=0; i<data_sz; ++i)
data[i] = rand() % MAX;
// Device
int* data_device;
int* result_extd_device; // extended
int* result_device;
cudaMalloc(&data_device, sizeof(int) * data_sz);
cudaMalloc(&result_extd_device, sizeof(int) * class_cnt * node_cnt);
cudaMalloc(&result_device, sizeof(int) * class_cnt);
cudaMemcpy(data_device, data, sizeof(int) * data_sz,
cudaMemcpyHostToDevice);
// Actual computation
start = CLOCK();
cudaMemset(result_extd_device, 0, sizeof(int) * class_cnt * node_cnt);
histoKernel<<<node_cnt, class_cnt>>>(data_device, data_sz,
result_extd_device);
// Reduction
cudaMemset(result_device, 0, sizeof(int) * class_cnt);
reduceKernel<<<1, class_cnt>>>(result_extd_device, result_device, node_cnt);
cudaDeviceSynchronize();
end = CLOCK();
cudaMemcpy(result, result_device, sizeof(int) * class_cnt,
cudaMemcpyDeviceToHost);
printf("[CUDA] Histogramming took %.3f milliseconds.\n", end-start);
#ifdef DISPLAY
for (int i=0; i<class_cnt; ++i) {
printf("[%3d - %4d[: %d\n", i*class_sz, (i+1)*class_sz,
result[i]);
}
#endif // DISPLAY
return 0;
}
__global__ void
histoKernel(int* data, int data_sz, int* result) {
int i, j;
int left_bound, right_bound;
int class_sz;
int chunk_sz;
chunk_sz = data_sz/blockDim.x;
class_sz = MAX/blockDim.x;
for (i = blockIdx.x; i < blockDim.x; i += gridDim.x) {
left_bound = i * chunk_sz;
right_bound = (i + 1) * chunk_sz;
for (j = left_bound; j < right_bound; ++j) {
if ((data[j] >= threadIdx.x*class_sz) &&
(data[j] < (threadIdx.x+1)*class_sz)) {
result[blockIdx.x * blockDim.x + threadIdx.x] += 1;
}
}
}
}
__global__ void
reduceKernel(int* input, int* output, int node_cnt) {
for (int i=0; i<node_cnt; ++i) {
output[threadIdx.x] += input[blockDim.x * i + threadIdx.x];
}
}
double CLOCK() {
struct timespec t = {0, 0};
clock_gettime(CLOCK_MONOTONIC, &t);
return (double) (t.tv_sec*1.0e3 + t.tv_nsec*1.0e-6);
}
|
23,745 | #include <stdio.h>
#include <stdlib.h>
#include <assert.h>
#include <time.h>
#include <math.h>
#include <string.h>
#define EPSILON 1E-9
#define BLOCK_SIZE_F 512 //Work with blocks of 512 threads due to double precision - shared memory
#define BLOCK_SIZE_VP 1024 //Work with blocks of 512 threads due to double precision - shared memory
//#define STREAMS 4
/*Declarando as structs de particula e forca*/
struct stCoord{
double x,
y,
z;
};
typedef struct stCoord tpCoord;
struct stParticle
{
tpCoord p,
v,
f;
};
typedef struct stParticle tpParticle;
//--------------------------------------------------------------------------------------------------------
__device__ double distance( double* dx,
double* dy,
double* dz,
const tpParticle A,
const tpParticle B){
double x = A.p.x - B.p.x;
double y = A.p.y - B.p.y;
double z = A.p.z - B.p.z;
*dx = x; *dy = y; *dz = z;
x *= x; y *= y; z *= z;
return 1.0 / sqrt((double)x + y + z + EPSILON);
}
__global__ void particleParticleForces_k(tpParticle *p1, tpParticle *p2, const double dt, const int streams){
extern __shared__ tpParticle subParticles[];
int i = blockDim.x * blockIdx.x + threadIdx.x;
tpParticle p = p1[i];
// __shared__ tpParticle subParticles[BLOCK_SIZE];
for (int blk = 0; blk < gridDim.x * streams; blk++){
subParticles[threadIdx.x] = p2[ blockDim.x * blk + threadIdx.x];
__syncthreads();
for (int j = 0; j < blockDim.x; j++){
double dx = 0.0f, dy = 0.0f, dz = 0.0f;
double d = distance(&dx, &dy, &dz, p, subParticles[j]);
p.f.x += dx * d;
p.f.y += dy * d;
p.f.z += dz * d;
}//end-for (int j = 0; j < blockDim.x; j++){
__syncthreads();
}//end-for (int blk = 0; blk < gridDim.x; blk++){
p1[i] = p;
}
__global__ void particleParticleVelocityPosition_k(tpParticle *particles, const double dt){
int i = blockDim.x * blockIdx.x + threadIdx.x;
particles[i].v.x += dt * particles[i].f.x;
particles[i].v.y += dt * particles[i].f.y;
particles[i].v.z += dt * particles[i].f.z;
particles[i].p.x += dt * particles[i].v.x;
particles[i].p.y += dt * particles[i].v.y;
particles[i].p.z += dt * particles[i].v.z;
}
void particleParticle (cudaStream_t *streams , int nstreams, tpParticle *h_particles, int nParticles, int timesteps, double dt){
int threadsF = 0,
blocksF = 0,
threadsVP = 0,
blocksVP = 0,
sPart = 0;
tpParticle *d_particles;
sPart = nParticles / nstreams;
threadsF = BLOCK_SIZE_F;
blocksF = sPart / BLOCK_SIZE_F;
threadsVP = BLOCK_SIZE_VP;
blocksVP = sPart / BLOCK_SIZE_VP;
if (sPart < BLOCK_SIZE_F){
blocksF = 1;
threadsF = sPart;
}
if (sPart < BLOCK_SIZE_VP){
blocksVP = 1;
threadsVP = sPart;
}
assert(cudaMalloc((void**) &d_particles, nParticles * sizeof(tpParticle)) == cudaSuccess);
assert(cudaMemcpy(d_particles, h_particles, nParticles * sizeof(tpParticle), cudaMemcpyHostToDevice) == cudaSuccess);
assert( ((sPart % threadsF) == 0) && ((sPart % threadsVP) == 0) && ((nParticles % nstreams) == 0) );
//fprintf(stdout, "\n B(%d) T(%d) \n", blocks, threads);
//fprintf(stdout, "Shared memory allocated %d\n", threads * sizeof(tpParticle));
for (int t = 0; t < timesteps; t++){
for (int i = 0; i < nstreams; i++){
int offset = sPart * i;
particleParticleForces_k<<<blocksF, threadsF, threadsF * sizeof(tpParticle), streams[i]>>>(d_particles+offset, d_particles, dt, nstreams);
particleParticleVelocityPosition_k<<<blocksVP, threadsVP, 0, streams[i]>>>(d_particles+offset, dt);
}
/*
for (int i = 0; i < STREAMS; i++){
assert( cudaStreamSynchronize(streams[i]) == cudaSuccess );
}
for (int i = 0; i < STREAMS; i++){
int offset = sPart * i;
}
*/
for (int i = 0; i < nstreams; i++){
assert( cudaStreamSynchronize(streams[i]) == cudaSuccess );
}
}//end-for (int t = 0; t < timesteps; t++){
assert(cudaMemcpy(h_particles, d_particles, nParticles * sizeof(tpParticle), cudaMemcpyDeviceToHost) == cudaSuccess);
cudaFree(d_particles);
}
//--------------------------------------------------------------------------------------------------------
void printLog(tpParticle *particles, int nParticles, int timestep);
void initialCondition(tpParticle *particles, int nParticles);
int main (int ac, char **av){
int timesteps = atoi(av[1]),
nParticles = atoi(av[2]),
flagSave = atoi(av[3]),
nstreams = atoi(av[4]);
cudaStream_t *streams = (cudaStream_t *) malloc (nstreams * sizeof(cudaStream_t));
double dt = 0.00001f;
tpParticle *h_Particles = NULL;
fprintf(stdout, "\nParcile system particle to particle \n");
fprintf(stdout, "Memory used %lu bytes \n", nParticles * sizeof(tpParticle));
assert(cudaDeviceReset()== cudaSuccess);
assert(cudaMallocHost((void**) (&h_Particles), nParticles * sizeof(tpParticle)) == cudaSuccess);
assert(h_Particles != NULL);
for (int i = 0; i < nstreams; i++)
assert(cudaStreamCreate(&streams[i]) == cudaSuccess);
initialCondition(h_Particles, nParticles);
particleParticle(streams, nstreams, h_Particles, nParticles, timesteps, dt);
if (flagSave == 1)
printLog(h_Particles, nParticles, timesteps);
for (int i = 0; i < nstreams; i++)
assert(cudaStreamDestroy(streams[i]) == cudaSuccess);
cudaFreeHost(h_Particles);
}
void printLog(tpParticle *particles, int nParticles, int timestep){
char fileName[128];
sprintf(fileName, "%s-%d-log.bin", __FILE__, timestep);
fprintf(stdout, "Saving file [%s] ", fileName); fflush(stdout);
FILE *ptr = fopen(fileName, "wb+");
//fwrite ((const void*)particles , sizeof(tpParticle), nParticles, ptr);
for(int i = 0; i < nParticles; i++)
fprintf(ptr, "%d \t %.10f %.10f %.10f \t %.10f %.10f %.10f \t %.10f %.10f %.10f \n", i, particles[i].p.x, particles[i].p.y, particles[i].p.z, particles[i].v.x, particles[i].v.y, particles[i].v.z, particles[i].f.x, particles[i].f.y, particles[i].f.z);
fclose(ptr);
fprintf(stdout, "[OK]\n"); fflush(stdout);
}
void initialCondition(tpParticle *particles, int nParticles){
srand(42);
memset(particles, 0x00, nParticles * sizeof(tpParticle));
for (int i = 0; i < nParticles ; i++){
particles[i].p.x = 2.0 * (rand() / (double)RAND_MAX) - 1.0;
particles[i].p.y = 2.0 * (rand() / (double)RAND_MAX) - 1.0;
particles[i].p.z = 2.0 * (rand() / (double)RAND_MAX) - 1.0;
}
}
|
23,746 | #include <iostream>
#include <stdio.h>
#include <math.h>
// CUDA kernel to add elements
__global__ void add(int N, float *x)
{
int i = blockIdx.x * blockDim.x + threadIdx.x;
if (i<N)
x[i] = x[i] *2;
}
int main(void)
{
int N = 1<<20;
float *x;
// Allocate Unified Memory -- accessible from CPU or GPU
cudaMallocManaged(&x, N*sizeof(float));
// initialize x array on the host
for (int i = 0; i < N; i++) {
x[i] = 1.0f;
}
// Launch kernel on 1M elements on the GPU
int threadsPerBlock = 256;
int blocksPerGrid = (N + threadsPerBlock - 1) / threadsPerBlock;
cudaSetDevice(0);
cudaDeviceProp deviceProp;
cudaGetDeviceProperties(&deviceProp, 0);
if( (unsigned long long) (N*sizeof(float)) >= (unsigned long long)deviceProp.totalGlobalMem) {
fprintf(stderr, "Memory overload!\n");
exit(EXIT_FAILURE);
}
if( threadsPerBlock >= deviceProp.maxThreadsPerBlock){
fprintf(stderr, "Threads overload!\n");
exit(EXIT_FAILURE);
}
if( blocksPerGrid >= deviceProp.maxGridSize[0]){
fprintf(stderr, "Grid overload!\n");
exit(EXIT_FAILURE);
}
add<<<threadsPerBlock, blocksPerGrid >>>(N, x);
// Wait for GPU to finish before accessing on host
cudaDeviceSynchronize();
// Free memory
cudaFree(x);
return 0;
}
|
23,747 |
#include <thrust/reduce.h>
#include <thrust/iterator/counting_iterator.h>
int main()
{
thrust::counting_iterator<int> first(10);
thrust::counting_iterator<int> last = first + 3;
std::cout << first[0] << std::endl; // returns 10
std::cout << first[1] << std::endl; // returns 11
std::cout << first[100] << std::endl; // returns 110
// sum of [first, last)
float result = thrust::reduce(first, last); // returns 33 (i.e. 10 + 11 + 12)
std::cout << result << std::endl;
return 0;
}
|
23,748 | #include "includes.h"
__global__ void addRows(double *matrix, int *d_i){
int i=*d_i;
int n=blockDim.x+i;
int id= n*(blockIdx.x+i+1) + threadIdx.x+i;
__shared__ double multiplier;
if(threadIdx.x==0){
multiplier=matrix[n*(blockIdx.x+1+i)+i]/matrix[n*i+i];
}
__syncthreads();
matrix[id]-=matrix[n*i+threadIdx.x+i]*multiplier;
} |
23,749 | #include "includes.h"
__global__ void subDiffuseKernel(float *data, int x, int y, float pressure)
{
data[NX * x + y] -= pressure;
} |
23,750 | /*******************************************
*
* Program for 2D/3D array allocations of CUDA
* a :zhonghy
* date:2018-4-16
*
********************************************/
//#include "cuda.h"
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include "device_functions.h"
#include <stdio.h>
#include <iostream>
//access type of global variables
__constant__ float constData[256];
__device__ float devData;
__device__ float *devPointer;
//Device code, access for 2D array, will be padded
//pitch is actual size of row after padded? inerval across each rows
__global__ void MyKernel2D(float *devPtr, size_t pitch, int width, int height)
{
for(int r = 0; r < height; r++)
{
float *row = (float*)((char*)devPtr + pitch); //first pointer of row
for(int c = 0; c < width; c++)
{
float element = row[c];
}
}
}
//Device code, access for 3D array, will be padded
__global__ void MyKernel3D(cudaPitchedPtr devPitchedPtr,
int width, int height, int depth)
{
char *devPtr = (char *)devPitchedPtr.ptr; //add type transfrom?
size_t pitch = devPitchedPtr.pitch;
size_t slicePitch = pitch * height;
for(int z = 0; z < depth; ++z)
{
float *slice = (float*)(devPtr + z * slicePitch);
for(int y = 0; y < height; ++y)
{
float *row = (float*)(devPtr + y * pitch); //poir to calucate first pointer
for(int x = 0; x < width; ++x)
{
float element = row[x];
}
}
}
}
int main(int argc, char *argv[])
{
//CUDA allocate width x height 2D array
int width = 64, height = 64;
float *devPtr;
size_t pitch;
cudaMallocPitch(&devPtr, &pitch, //cudaMallocPitch()
width * sizeof(float), height);
MyKernel2D<<<100, 512>>>(devPtr, pitch, width, height);
std::cout << "pitch of 2D array: " << pitch << std::endl;
std::cout << "width of 2D array is :" << width << std::endl;
cudaFree(devPtr);
//CUDA allocate witdth x height x depth 3D array
int depth = 64;
cudaExtent extent = make_cudaExtent(width * sizeof(float), height, depth);
cudaPitchedPtr devPitchedPtr;
cudaMalloc3D(&devPitchedPtr, extent);
MyKernel3D<<<100, 512>>>(devPitchedPtr, width, height, depth);
std::cout << std::endl;
std::cout << "pitch of 2D array: " << devPitchedPtr.pitch << std::endl;
std::cout << "width of 2D array is :" << devPitchedPtr.xsize << std::endl;
std::cout << "y of 2D array is :" << devPitchedPtr.ysize << std::endl;
cudaFree(devPitchedPtr.ptr);
//access type of global variables
//__constant__ float constData[256];
float data[256];
cudaMemcpyToSymbol(constData, data, sizeof(data));
cudaMemcpyFromSymbol(data, constData, sizeof(data));
//__device__ float devData;
float value = 3.14f;
cudaMemcpyToSymbol(&devData, &value, sizeof(float)); //wrong in document
//__device__ float *devPointer;
float *ptr;
cudaMalloc(&ptr, 256 * sizeof(float));
cudaMemcpyToSymbol(devPointer, ptr, sizeof(ptr)); //it is like reference?
return 0;
} |
23,751 | extern "C" {
__global__ void rgb2yiq(float3 * src,float3 * dst,int imgWidth,int imgHeight){
int xIndex = threadIdx.x + blockIdx.x * blockDim.x;
int yIndex = threadIdx.y + blockIdx.y * blockDim.y;
if (xIndex < imgWidth && yIndex < imgHeight)
{
float3 rgb = src[yIndex * imgWidth + xIndex];
dst[yIndex * imgWidth + xIndex].x = 0.299 * rgb.x + 0.587 * rgb.y + 0.114 * rgb.z;
dst[yIndex * imgWidth + xIndex].y = 0.5950059 * rgb.x - 0.27455667 * rgb.y - 0.32134392 * rgb.z;
dst[yIndex * imgWidth + xIndex].z = 0.21153661 * rgb.x - 0.52273617 * rgb.y + 0.31119955 * rgb.z;
}
}
__global__ void yiq2rgb(float3 * src,float3 * dst,int imgWidth,int imgHeight){
int xIndex = threadIdx.x + blockIdx.x * blockDim.x;
int yIndex = threadIdx.y + blockIdx.y * blockDim.y;
if (xIndex < imgWidth && yIndex < imgHeight)
{
float3 rgb = src[yIndex * imgWidth + xIndex];
dst[yIndex * imgWidth + xIndex].x = 1.00000001 * rgb.x + 0.95598634 * rgb.y + 0.6208248 * rgb.z;
dst[yIndex * imgWidth + xIndex].y = 0.9999999 * rgb.x - 0.27201283 * rgb.y - 0.64720424 * rgb.z;
dst[yIndex * imgWidth + xIndex].z = 1.00000002 * rgb.x - 1.10674021 * rgb.y + 1.70423049 * rgb.z;
}
}
__global__ void rgb2yuv(float3*src,float3 *dst,int imgWidth,int imgHeight){
int xIndex = threadIdx.x + blockIdx.x * blockDim.x;
int yIndex = threadIdx.y + blockIdx.y * blockDim.y;
if (xIndex < imgWidth && yIndex < imgHeight)
{
float3 rgb = src[yIndex * imgWidth + xIndex];
dst[yIndex * imgWidth + xIndex].x = 0.299 * rgb.x + 0.587 * rgb.y + 0.114 * rgb.z;
dst[yIndex * imgWidth + xIndex].y = -0.14714119 * rgb.x - 0.28886916 * rgb.y + 0.43601035 * rgb.z;
dst[yIndex * imgWidth + xIndex].z = 0.61497538 * rgb.x - 0.51496512 * rgb.y - 0.10001026 * rgb.z;
}
}
__global__ void yuv2rgb(float3*src,float3 *dst,int imgWidth,int imgHeight){
int xIndex = threadIdx.x + blockIdx.x * blockDim.x;
int yIndex = threadIdx.y + blockIdx.y * blockDim.y;
if (xIndex < imgWidth && yIndex < imgHeight)
{
float3 rgb = src[yIndex * imgWidth + xIndex];
dst[yIndex * imgWidth + xIndex].x = 1.0 * rgb.x + 0.0 * rgb.y + 1.13988303 * rgb.z;
dst[yIndex * imgWidth + xIndex].y = 1.0 * rgb.x - 0.395 * rgb.y - 0.58062185 * rgb.z;
dst[yIndex * imgWidth + xIndex].z = 1.0 * rgb.x + 2.03206185 * rgb.y - 0.00000000 * rgb.z;
}
}
__global__ void rgb2gray_avg(float3*src,float*dst,int imgWidth,int imgHeight){
int xIndex = threadIdx.x + blockIdx.x * blockDim.x;
int yIndex = threadIdx.y + blockIdx.y * blockDim.y;
if (xIndex < imgWidth && yIndex < imgHeight)
{
float3 rgb = src[yIndex * imgWidth + xIndex];
dst[yIndex * imgWidth + xIndex] = (rgb.x + rgb.y + rgb.z)/3.0;
}
}
__global__ void rgb2gray(float3*src,float*dst,int imgWidth,int imgHeight){
int xIndex = threadIdx.x + blockIdx.x * blockDim.x;
int yIndex = threadIdx.y + blockIdx.y * blockDim.y;
if (xIndex < imgWidth && yIndex < imgHeight)
{
float3 rgb = src[yIndex * imgWidth + xIndex];
dst[yIndex * imgWidth + xIndex] = 0.299 * rgb.x + 0.587 * rgb.y + 0.114 * rgb.z;
}
}
__global__ void rgb2YCbCr(float3 * src,float3 * dst,int imgWidth,int imgHeight){
int xIndex = threadIdx.x + blockIdx.x * blockDim.x;
int yIndex = threadIdx.y + blockIdx.y * blockDim.y;
const float YCbCrYRF = 0.299; // RGB转YCbCr的系数(浮点类型）
const float YCbCrYGF = 0.587;
const float YCbCrYBF = 0.114;
const float YCbCrCbRF = -0.168736;
const float YCbCrCbGF = -0.331264;
const float YCbCrCbBF = 0.500000;
const float YCbCrCrRF = 0.500000;
const float YCbCrCrGF = -0.418688;
const float YCbCrCrBF = -0.081312;
const float RGBRYF = 1.00000 ; // YCbCr转RGB的系数(浮点类型）
const float RGBRCbF = 0.0000;
const float RGBRCrF = 1.40200;
const float RGBGYF = 1.00000 ;
const float RGBGCbF = -0.34414;
const float RGBGCrF = -0.71414;
const float RGBBYF = 1.00000 ;
const float RGBBCbF = 1.77200;
const float RGBBCrF = 0.00000 ;
const int Shift = 20;
const int HalfShiftValue = 1 << (Shift - 1);
const int YCbCrYRI = (int)((YCbCrYRF * (1 << Shift) + 0.5)); // RGB转YCbCr的系数(整数类型）
const int YCbCrYGI = (int)((YCbCrYGF * (1 << Shift) + 0.5));
const int YCbCrYBI = (int)((YCbCrYBF * (1 << Shift) + 0.5));
const int YCbCrCbRI = (int)((YCbCrCbRF * (1 << Shift) + 0.5));
const int YCbCrCbGI = (int)((YCbCrCbGF * (1 << Shift) + 0.5));
const int YCbCrCbBI = (int)((YCbCrCbBF * (1 << Shift) + 0.5));
const int YCbCrCrRI = (int)((YCbCrCrRF * (1 << Shift) + 0.5));
const int YCbCrCrGI = (int)((YCbCrCrGF * (1 << Shift) + 0.5));
const int YCbCrCrBI = (int)((YCbCrCrBF * (1 << Shift) + 0.5));
/*
const int RGBRYI = (int)((RGBRYF * (1 << Shift) + 0.5)) ; // YCbCr转RGB的系数(整数类型）
const int RGBRCbI = (int)((RGBRCbF * (1 << Shift) + 0.5));
const int RGBRCrI = (int)((RGBRCrF * (1 << Shift) + 0.5));
const int RGBGYI = (int)((RGBGYF * (1 << Shift) + 0.5));
const int RGBGCbI = (int)((RGBGCbF * (1 << Shift) + 0.5));
const int RGBGCrI = (int)((RGBGCrF * (1 << Shift) + 0.5));
const int RGBBYI = (int)((RGBBYF * (1 << Shift) + 0.5));
const int RGBBCbI = (int)((RGBBCbF * (1 << Shift) + 0.5));
const int RGBBCrI = (int)((RGBBCrF * (1 << Shift) + 0.5)) ;
*/
if (xIndex < imgWidth && yIndex < imgHeight)
{
float3 rgb = src[yIndex * imgWidth + xIndex];
int Red = rgb.x;
int Green = rgb.y;
int Blue = rgb.z;
float Y = (float)((YCbCrYRI * Red + YCbCrYGI * Green + YCbCrYBI * Blue + HalfShiftValue) >> Shift);
float Cb = (float)( 128 + ( (YCbCrCbRI * Red + YCbCrCbGI * Green + YCbCrCbBI * Blue + HalfShiftValue) >> Shift));
float Cr = (float)(128+( (YCbCrCrRI * Red + YCbCrCrGI * Green + YCbCrCrBI * Blue + HalfShiftValue) >> Shift));
dst[yIndex * imgWidth + xIndex].x = Y;
dst[yIndex * imgWidth + xIndex].y = Cb;
dst[yIndex * imgWidth + xIndex].z = Cr;
}
}
__global__ void YCrCb2RGB(float3*src,float3*dst,int imgWidth,int imgHeight){
int xIndex = threadIdx.x + blockIdx.x * blockDim.x;
int yIndex = threadIdx.y + blockIdx.y * blockDim.y;
const float YCbCrYRF = 0.299; // RGB转YCbCr的系数(浮点类型）
const float YCbCrYGF = 0.587;
const float YCbCrYBF = 0.114;
const float YCbCrCbRF = -0.168736;
const float YCbCrCbGF = -0.331264;
const float YCbCrCbBF = 0.500000;
const float YCbCrCrRF = 0.500000;
const float YCbCrCrGF = -0.418688;
const float YCbCrCrBF = -0.081312;
const float RGBRYF = 1.00000 ; // YCbCr转RGB的系数(浮点类型）
const float RGBRCbF = 0.0000;
const float RGBRCrF = 1.40200;
const float RGBGYF = 1.00000 ;
const float RGBGCbF = -0.34414;
const float RGBGCrF = -0.71414;
const float RGBBYF = 1.00000 ;
const float RGBBCbF = 1.77200;
const float RGBBCrF = 0.00000 ;
const int Shift = 20;
const int HalfShiftValue = 1 << (Shift - 1);
const int YCbCrYRI = (int)((YCbCrYRF * (1 << Shift) + 0.5)); // RGB转YCbCr的系数(整数类型）
const int YCbCrYGI = (int)((YCbCrYGF * (1 << Shift) + 0.5));
const int YCbCrYBI = (int)((YCbCrYBF * (1 << Shift) + 0.5));
const int YCbCrCbRI = (int)((YCbCrCbRF * (1 << Shift) + 0.5));
const int YCbCrCbGI = (int)((YCbCrCbGF * (1 << Shift) + 0.5));
const int YCbCrCbBI = (int)((YCbCrCbBF * (1 << Shift) + 0.5));
const int YCbCrCrRI = (int)((YCbCrCrRF * (1 << Shift) + 0.5));
const int YCbCrCrGI = (int)((YCbCrCrGF * (1 << Shift) + 0.5));
const int YCbCrCrBI = (int)((YCbCrCrBF * (1 << Shift) + 0.5));
const int RGBRYI = (int)((RGBRYF * (1 << Shift) + 0.5)) ; // YCbCr转RGB的系数(整数类型）
const int RGBRCbI = (int)((RGBRCbF * (1 << Shift) + 0.5));
const int RGBRCrI = (int)((RGBRCrF * (1 << Shift) + 0.5));
const int RGBGYI = (int)((RGBGYF * (1 << Shift) + 0.5));
const int RGBGCbI = (int)((RGBGCbF * (1 << Shift) + 0.5));
const int RGBGCrI = (int)((RGBGCrF * (1 << Shift) + 0.5));
const int RGBBYI = (int)((RGBBYF * (1 << Shift) + 0.5));
const int RGBBCbI = (int)((RGBBCbF * (1 << Shift) + 0.5));
const int RGBBCrI = (int)((RGBBCrF * (1 << Shift) + 0.5)) ;
if (xIndex < imgWidth && yIndex < imgHeight)
{
float3 rgb = src[yIndex * imgWidth + xIndex];
int Y = rgb.x;
int Cb = rgb.y - 128;
int Cr = rgb.z - 128;
float Red = Y + ((RGBRCrI * Cr + HalfShiftValue) >> Shift);
float Green = Y + ((RGBGCbI * Cb + RGBGCrI * Cr+ HalfShiftValue) >> Shift);
float Blue = Y + ((RGBBCbI * Cb + HalfShiftValue) >> Shift);
dst[yIndex * imgWidth + xIndex].x = Red;
dst[yIndex * imgWidth + xIndex].y = Green;
dst[yIndex * imgWidth + xIndex].z = Blue;
}
}
} |
23,752 | /**
* APPROXIMATE PATTERN MATCHING
*
* INF560
*/
#include <string.h>
#include <stdio.h>
#include <stdlib.h>
#include <fcntl.h>
#include <unistd.h>
#include <sys/time.h>
#include <cuda_runtime.h>
#define APM_DEBUG 0
char *
read_input_file( char * filename, int * size )
{
char * buf ;
off_t fsize;
int fd = 0 ;
int n_bytes = 1 ;
/* Open the text file */
fd = open( filename, O_RDONLY ) ;
if ( fd == -1 )
{
fprintf( stderr, "Unable to open the text file <%s>\n", filename ) ;
return NULL ;
}
/* Get the number of characters in the textfile */
fsize = lseek(fd, 0, SEEK_END);
if ( fsize == -1 )
{
fprintf( stderr, "Unable to lseek to the end\n" ) ;
return NULL ;
}
#if APM_DEBUG
printf( "File length: %lld\n", fsize ) ;
#endif
/* Go back to the beginning of the input file */
if ( lseek(fd, 0, SEEK_SET) == -1 )
{
fprintf( stderr, "Unable to lseek to start\n" ) ;
return NULL ;
}
/* Allocate data to copy the target text */
buf = (char *)malloc( fsize * sizeof ( char ) ) ;
if ( buf == NULL )
{
fprintf( stderr, "Unable to allocate %lld byte(s) for main array\n",
fsize ) ;
return NULL ;
}
n_bytes = read( fd, buf, fsize ) ;
if ( n_bytes != fsize )
{
fprintf( stderr,
"Unable to copy %lld byte(s) from text file (%d byte(s) copied)\n",
fsize, n_bytes) ;
return NULL ;
}
#if APM_DEBUG
printf( "Number of read bytes: %d\n", n_bytes ) ;
#endif
*size = n_bytes ;
close( fd ) ;
return buf ;
}
#define MIN3(a, b, c) ((a) < (b) ? ((a) < (c) ? (a) : (c)) : ((b) < (c) ? (b) : (c)))
int levenshtein(char *s1, char *s2, int len, int * column) {
unsigned int x, y, lastdiag, olddiag;
for (y = 1; y <= len; y++)
{
column[y] = y;
}
for (x = 1; x <= len; x++) {
column[0] = x;
lastdiag = x-1 ;
for (y = 1; y <= len; y++) {
olddiag = column[y];
column[y] = MIN3(
column[y] + 1,
column[y-1] + 1,
lastdiag + (s1[y-1] == s2[x-1] ? 0 : 1)
);
lastdiag = olddiag;
}
}
return(column[len]);
}
__global__ void compMatches(char* pattern,char * buf, int n_bytes, int size_pattern, int approx_factor, int * resultsth){
int distance = 0 ;
int size ;
int i = threadIdx.x + blockIdx.x*blockDim.x;
int n_th = gridDim.x*blockDim.x;
resultsth[i] = 0;
int * column = (int *)malloc( (size_pattern+1) * sizeof( int ) ) ;
if(i < n_bytes){
for(int j = i; j < n_bytes; j += n_th){
size = size_pattern ;
if ( n_bytes - j < size_pattern )
{
size = n_bytes - j ;
}
char * s1 = pattern;
char * s2 = &buf[j];
int len = size;
unsigned int x, y, lastdiag, olddiag;
for (y = 1; y <= len; y++)
{
column[y] = y;
}
for (x = 1; x <= len; x++) {
column[0] = x;
lastdiag = x-1 ;
for (y = 1; y <= len; y++) {
olddiag = column[y];
column[y] = MIN3(
column[y] + 1,
column[y-1] + 1,
lastdiag + (s1[y-1] == s2[x-1] ? 0 : 1)
);
lastdiag = olddiag;
}
}
distance = column[len];
if ( distance <= approx_factor ) {
resultsth[i]++ ;
}
}
}
}
void checkGpuMem()
{
float free_m,total_m,used_m;
size_t free_t,total_t;
cudaMemGetInfo(&free_t,&total_t);
free_m =(uint)free_t/1048576.0 ;
total_m=(uint)total_t/1048576.0;
used_m=total_m-free_m;
printf ( " mem free %d .... %f MB mem total %d....%f MB mem used %f MB\n",free_t,free_m,total_t,total_m,used_m);
}
int
main( int argc, char ** argv )
{
char ** pattern ;
char * filename ;
int approx_factor = 0 ;
int nb_patterns = 0 ;
char * buf ;
struct timeval t1, t2;
double duration ;
int n_bytes ;
int * n_matches ;
/* Check number of arguments */
if ( argc < 4 )
{
printf( "Usage: %s approximation_factor "
"dna_database pattern1 pattern2 ...\n",
argv[0] ) ;
return 1 ;
}
/* Get the distance factor */
approx_factor = atoi( argv[1] ) ;
/* Grab the filename containing the target text */
filename = argv[2] ;
/* Get the number of patterns that the user wants to search for */
nb_patterns = argc - 3 ;
/* Fill the pattern array */
pattern = (char **)malloc( nb_patterns * sizeof( char * ) ) ;
if ( pattern == NULL )
{
fprintf( stderr,
"Unable to allocate array of pattern of size %d\n",
nb_patterns ) ;
return 1 ;
}
checkGpuMem();
/* Grab the patterns */
for (int i = 0 ; i < nb_patterns ; i++ )
{
int l ;
l = strlen(argv[i+3]) ;
if ( l <= 0 )
{
fprintf( stderr, "Error while parsing argument %d\n", i+3 ) ;
return 1 ;
}
pattern[i] = (char *)malloc( (l+1) * sizeof( char ) ) ;
if ( pattern[i] == NULL )
{
fprintf( stderr, "Unable to allocate string of size %d\n", l ) ;
return 1 ;
}
strncpy( pattern[i], argv[i+3], (l+1) ) ;
}
printf( "Approximate Pattern Mathing: "
"looking for %d pattern(s) in file %s w/ distance of %d\n",
nb_patterns, filename, approx_factor ) ;
buf = read_input_file( filename, &n_bytes ) ;
if ( buf == NULL )
{
return 1 ;
}
cudaError_t err;
char * cbuf;
err = cudaMalloc((void **)&cbuf, n_bytes* sizeof(char));
if( err != cudaSuccess) {
printf("Error !");
exit(1);
}
err = cudaMemcpy(cbuf, buf,n_bytes* sizeof(char) ,cudaMemcpyHostToDevice);
if( err != cudaSuccess) {
printf("Error !");
exit(1);
}
int nblock = 100;
int nth_b = 1024;
int nth = nblock*nth_b;
int * results_th;
err = cudaMalloc((void **)&results_th, nth* sizeof(int));
if( err != cudaSuccess) {
printf("Error !");
exit(1);
}
/* Allocate the array of matches */
n_matches = (int *)malloc( nb_patterns * sizeof( int ) ) ;
if ( n_matches == NULL )
{
fprintf( stderr, "Error: unable to allocate memory for %ldB\n",
nb_patterns * sizeof( int ) ) ;
return 1 ;
}
/*****
* BEGIN MAIN LOOP
******/
/* Timer start */
gettimeofday(&t1, NULL);
/* Check each pattern one by one */
for (int i = 0 ; i < nb_patterns ; i++ )
{
int size_pattern = strlen(pattern[i]) ;
/* Initialize the number of matches to 0 */
n_matches[i] = 0 ;
char * cpattern;
err = cudaMalloc((void **)&cpattern, size_pattern* sizeof(char));
if( err != cudaSuccess) {
printf("Error !");
exit(1);
}
err= cudaMemcpy(cpattern,pattern[i], size_pattern* sizeof(char), cudaMemcpyHostToDevice);
if( err != cudaSuccess) {
printf("Error !");
exit(1);
}
compMatches<<<nblock, nth_b>>>(cpattern,cbuf,n_bytes,size_pattern,approx_factor,results_th);
err = cudaGetLastError();
if( err != cudaSuccess) {
printf("Error ! in kernel");
exit(1);
}
int * results;
results = (int *)malloc(nth* sizeof(int));
err = cudaMemcpy(results,results_th, nth* sizeof(int), cudaMemcpyDeviceToHost);
if( err != cudaSuccess) {
printf("Error ! in copying results %s", cudaGetErrorString(err));
exit(1);
}
for(int j = 0; j < nth && j < n_bytes; j++){
n_matches[i] += results[j];
}
}
/* Timer stop */
gettimeofday(&t2, NULL);
duration = (t2.tv_sec -t1.tv_sec)+((t2.tv_usec-t1.tv_usec)/1e6);
printf( "APM done in %lf s\n", duration ) ;
/*****
* END MAIN LOOP
******/
for ( int i = 0 ; i < nb_patterns ; i++ )
{
printf( "Number of matches for pattern <%s>: %d\n",
pattern[i], n_matches[i] ) ;
}
return 0 ;
}
|
23,753 | #include <stdio.h>
#include <iostream>
#include <vector>
__global__
void sum(int n, double *x, double *y, double *z)
{
int i = blockIdx.x*blockDim.x + threadIdx.x;
if(i<n){
z[i]=x[i]+y[i];
printf("check from function i= %d %f + %f = %f\n",i,x[i],y[i],z[i]);
}
//blockIdx variabile di tipo dim3, contiene l'indice del blocco
//threadIdx variabile di tipo dim3, contiene l'indice del thread
}
int main(void ) {
const int Nfix=10;//fixed dimension
double *a[Nfix],*b[Nfix],*c[Nfix]; //for the host
double *da[Nfix],*db[Nfix],*dc[Nfix]; //for the device
int Nvar[Nfix]; //dynamic dimension
//then I allocate the memory
for(int i=0;i<Nfix;i++){
Nvar[i]=3*(i+1);
a[i]=(double*)malloc(Nvar[i]*sizeof(double));
b[i]=(double*)malloc(Nvar[i]*sizeof(double));
c[i]=(double*)malloc(Nvar[i]*sizeof(double));
for(int k=0;k<Nvar[i];k++){
a[i][k]=k+(i+1);
b[i][k]=k+2*(i+1);
}
//and for the device
cudaMalloc(&da[i],Nvar[i]*sizeof(double));
cudaMalloc(&db[i],Nvar[i]*sizeof(double));
cudaMalloc(&dc[i],Nvar[i]*sizeof(double));
//the I copy the memory
cudaMemcpy(da[i],a[i],Nvar[i]*sizeof(double),cudaMemcpyHostToDevice);
cudaMemcpy(db[i],b[i],Nvar[i]*sizeof(double),cudaMemcpyHostToDevice);
sum<<<1,Nvar[i]>>>(Nvar[i],da[i],db[i],dc[i]);
cudaMemcpy(c[i],dc[i],Nvar[i]*sizeof(double),cudaMemcpyDeviceToHost);
for(int k=0;k<Nvar[i];k++)std::cout<<"i= "<<k<<" "<<a[i][k]<<" + "<<b[i][k]<<" = "<<c[i][k]<<std::endl;
cudaFree(da[i]);
cudaFree(db[i]);
cudaFree(dc[i]);
free(a[i]);
free(b[i]);
free(c[i]);
}
}
|
23,754 | // from the following URL
// https://www.sharcnet.ca/help/index.php/CUDA_tips_and_tricks
/*
* this program is a simple test of the binary tree recommended reduction
* algorithm for CUDA
*
*/
#include <iostream>
#define TOTAL_SIZE 100000
#define nTPB 256
#define BLOCK_SIZE 64 // MUST BE A POWER OF 2!!
//#define NUM_THREADS 1 // this is now "BLOCK_SIZE"
#define NUM_BLOCKS 1
#define LENGTH_LOOKUP 240
__global__ void sumMaxMinKernel( float* inputAtoms, float* answers ) {
// Reduction (min/max/avr/sum), valid only when blockDim.x is a power of two:
int thread2;
float temp;
__shared__ float min[BLOCK_SIZE], max[BLOCK_SIZE], avg[BLOCK_SIZE], sum[BLOCK_SIZE];
// printf("inputting %f %d\n", inputAtoms[threadIdx.x], threadIdx.x);
// import info
min[threadIdx.x] = inputAtoms[threadIdx.x];
max[threadIdx.x] = inputAtoms[threadIdx.x];
avg[threadIdx.x] = inputAtoms[threadIdx.x];
sum[threadIdx.x] = inputAtoms[threadIdx.x];
__syncthreads();
int nTotalThreads = blockDim.x; // Total number of active threads
while (nTotalThreads > 1) {
int halfPoint = (nTotalThreads >> 1); // divide by two
// only the first half of the threads will be active.
if (threadIdx.x < halfPoint) {
// printf("thread pair %d %d\n", threadIdx.x, threadIdx.x + halfPoint);
thread2 = threadIdx.x + halfPoint;
// Get the shared value stored by another thread
// min reduction
temp = min[thread2];
if (temp < min[threadIdx.x])
min[threadIdx.x] = temp;
// max reduction
temp = max[thread2];
if (temp > max[threadIdx.x])
max[threadIdx.x] = temp;
// sum reduction
// printf("sum %f %f\n", sum[threadIdx.x], sum[thread2]);
sum[threadIdx.x] += sum[thread2];
// average reduction
avg[threadIdx.x] += avg[thread2];
avg[threadIdx.x] *= 0.5f;
}
__syncthreads();
// Reducing the binary tree size by two:
nTotalThreads = halfPoint;
}
// export results
if (threadIdx.x == 0) {
answers[0] = min[0];
answers[1] = max[0];
answers[2] = avg[0];
answers[3] = sum[0];
}
}
int main(){
//--------------------------------------------------------------------------
// practice code (array)
// allocate variable on the GPU and CPU
int i;
float *inputAtoms, *d_inputAtoms, *answers, *d_answers;
size_t sizeArray;
sizeArray = sizeof(float) * BLOCK_SIZE;
inputAtoms = (float*) malloc ( sizeArray );
cudaMalloc( &d_inputAtoms, sizeArray );
sizeArray = sizeof(float) * 4;
answers = (float*) malloc ( sizeArray );
cudaMalloc( &d_answers, sizeArray );
// copy local variable to GPU
for ( i = 0; i < BLOCK_SIZE; ++i ) {
inputAtoms[i] = 1.0f + (float)i;
printf("%f ", inputAtoms[i]);
} printf("\n\n");
cudaMemcpy( d_inputAtoms, inputAtoms, sizeof(float) * BLOCK_SIZE, cudaMemcpyHostToDevice );
// run atomicAdd kernel
sumMaxMinKernel<<<NUM_BLOCKS, BLOCK_SIZE>>>(d_inputAtoms, d_answers);
cudaDeviceSynchronize();
// copy back result to local memory
cudaMemcpy( answers, d_answers, sizeof(float) * 4, cudaMemcpyDeviceToHost );
// report results and close
printf("min %f\n", answers[0]);
printf("max %f\n", answers[1]);
printf("average %f\n", answers[2]);
printf("sum %f\n", answers[3]);
free( inputAtoms );
cudaFree( d_inputAtoms );
free( answers );
cudaFree( d_answers );
return 0;
}
|
23,755 | #include "includes.h"
__global__ void computeMoment(int *readArr, int *writeArr, double *weightArr, int n){
int row = blockIdx.x*blockDim.x + threadIdx.x;
int col = blockIdx.y*blockDim.y + threadIdx.y;
// If coordinates are between boundaries
// update the write array accordingly
if(row < 517 && col < 517){
float influence = 0.0f;
for (int i=-2; i<3; i++)
{
for (int j=-2; j<3; j++)
{
//add extra n so that modulo behaves like mathematics modulo
//that is return only positive values
int y = (row+i+n)%n;
int x = (col+j+n)%n;
influence += weightArr[i*5 + j]*readArr[y*n + x];
}
}
writeArr[row*n + col] = readArr[row*n + col];
if (influence<-diff) writeArr[row*n + col] = -1;
else if (influence>diff) writeArr[row*n + col] = 1;
}
__syncthreads();
} |
23,756 | #include "includes.h"
__global__ void kernel_updateweights(int N, double *wt, double *x, double *q, double nu){
unsigned int tid = blockIdx.x*blockDim.x + threadIdx.x;
/* make sure to use only N threads */
if (tid<N) {
wt[tid]=((nu+1.0)/(nu+x[tid]*x[tid]));
q[tid]=wt[tid]-log(wt[tid]); /* so that its +ve */
}
} |
23,757 | #include <stdio.h>
#include <stdlib.h>
#include <time.h>
__global__ void convertToGray(int pixel, int cycle, unsigned char *red, unsigned char *green, unsigned char *blue, unsigned char *out)
{
for (int j = blockIdx.x; j < cycle; j+=gridDim.x)
{
for (int i = threadIdx.x; i < pixel; i+=blockDim.x)
{
out[i] = (red[i] + green[i] + blue[i]) / 3;
/*printf("%d,", out[i]);*/
}
}
}
int main()
{
int width, height, pixel, cycle, i;
unsigned char *red, *green, *blue, *out;
unsigned char *d_red, *d_green, *d_blue, *d_out;
/* input */
printf("width? >>> ");
scanf("%d", &width);
printf("height? >>> ");
scanf("%d", &height);
printf("cycle? >>> ");
scanf("%d", &cycle);
pixel = width * height;
/* Allocate host memory */
red = (unsigned char *)malloc(pixel * sizeof(unsigned char));
green = (unsigned char *)malloc(pixel * sizeof(unsigned char));
blue = (unsigned char *)malloc(pixel * sizeof(unsigned char));
out = (unsigned char *)malloc(pixel * sizeof(unsigned char));
/* Fail to assgin */
if ( red == NULL || pixel > 100000000 || cycle > 1000000 )
{
printf("too many pixels or cycles...");
return 0;
}
/* Make value of RGB */
for ( i = 0; i < pixel; i++) red[i] = rand() % 255;
for ( i = 0; i < pixel; i++) green[i] = rand() % 255;
for ( i = 0; i < pixel; i++) blue[i] = rand() % 255;
/* Start timer */
clock_t start = clock();
/* Allocate device memory */
cudaMalloc((void**)&d_red, pixel * sizeof(unsigned char));
cudaMalloc((void**)&d_green, pixel * sizeof(unsigned char));
cudaMalloc((void**)&d_blue, pixel * sizeof(unsigned char));
cudaMalloc((void**)&d_out, pixel * sizeof(unsigned char));
/* Transfer data from host to device memory */
cudaMemcpy(d_red, red, pixel * sizeof(unsigned char), cudaMemcpyHostToDevice);
cudaMemcpy(d_green, green, pixel * sizeof(unsigned char), cudaMemcpyHostToDevice);
cudaMemcpy(d_blue, blue, pixel * sizeof(unsigned char), cudaMemcpyHostToDevice);
cudaMemcpy(d_out, out, pixel * sizeof(unsigned char), cudaMemcpyHostToDevice);
/* Main function */
convertToGray<<< 512,1024>>>(pixel, cycle, d_red, d_green, d_blue, d_out);
/* Transfer data from device to host memory */
cudaMemcpy(out, d_out, pixel * sizeof(unsigned char), cudaMemcpyDeviceToHost);
/* Stop timer */
clock_t end = clock();
/* Show result */
printf("\n(width, height, pixel, cycle) = (%d, %d, %d, %d)\n", width, height, pixel, cycle);
printf( "clock:%f\n", (double)(end - start) / CLOCKS_PER_SEC);
/* Deallocate device memory */
cudaFree(d_red);
cudaFree(d_green);
cudaFree(d_blue);
cudaFree(d_out);
/* Deallocate host memory */
free(red);
free(green);
free(blue);
free(out);
return 0;
} |
23,758 | #include "includes.h"
__global__ void vecAdd_3(double *a, double *b, double *c, int n)
{
int id = threadIdx.x;
int id_1;
for(int i = 0; i < n; i++)
{
id_1 = id + i * n;
c[id_1] = a[id_1] + b[id_1];
}
} |
23,759 | #include <stdio.h>
#include <stdlib.h>
#include <math.h>
#define GRADIENT_SIZE 16
#define C_X_MIN -0.188
#define C_X_MAX -0.012
#define C_Y_MIN 0.554
#define C_Y_MAX 0.754
#define IMAGE_SIZE 4096
#define ARRAY_SIZE (3 * IMAGE_SIZE * IMAGE_SIZE * sizeof(unsigned char))
#define PIXEL_WIDTH ((C_X_MAX - C_X_MIN) / IMAGE_SIZE)
#define PIXEL_HEIGHT ((C_Y_MAX - C_Y_MIN) / IMAGE_SIZE)
#define ITERATION_MAX 200
int colors[51] = {
66, 30, 15,
25, 7, 26,
9, 1, 47,
4, 4, 73,
0, 7, 100,
12, 44, 138,
24, 82, 177,
57, 125, 209,
134, 181, 229,
211, 236, 248,
241, 233, 191,
248, 201, 95,
255, 170, 0,
204, 128, 0,
153, 87, 0,
106, 52, 3,
16, 16, 16,
};
int *d_colors;
int x_grid;
int y_grid;
int x_block;
int y_block;
unsigned char *image_buffer;
unsigned char *d_image_buffer;
cudaEvent_t allocation_start, allocation_end;
cudaEvent_t computing_start, computing_end;
cudaEvent_t memcpy_start, memcpy_end;
float allocation_time, computing_time, memcpy_time;
void init(int argc, char *argv[])
{
if (argc != 5) {
printf("usage: ./mandelbrot_cu x_grid y_grid x_blocks y_blocks");
exit(0);
}
sscanf(argv[1], "%d", &x_grid);
sscanf(argv[2], "%d", &y_grid);
sscanf(argv[3], "%d", &x_block);
sscanf(argv[4], "%d", &y_block);
cudaEventCreate(&allocation_start); cudaEventCreate(&allocation_end);
cudaEventCreate(&computing_start); cudaEventCreate(&computing_end);
cudaEventCreate(&memcpy_start); cudaEventCreate(&memcpy_end);
cudaEventRecord(allocation_start, 0);
cudaMallocHost((void **) &image_buffer, ARRAY_SIZE);
cudaMalloc((void **) &d_image_buffer, ARRAY_SIZE);
cudaMalloc((void **) &d_colors, 51 * sizeof(int));
cudaMemcpy(d_colors, colors, 51 * sizeof(int), cudaMemcpyHostToDevice);
cudaEventRecord(allocation_end, 0);
}
void write_to_file()
{
FILE * file;
const char *filename = "output.ppm";
const char *comment = "# ";
int max_color_component_value = 255;
file = fopen(filename,"wb");
fprintf(file, "P6\n %s\n %d\n %d\n %d\n", comment,
IMAGE_SIZE, IMAGE_SIZE, max_color_component_value);
for(int i = 0; i < IMAGE_SIZE * IMAGE_SIZE; i++){
fwrite(&image_buffer[3 * i], 1 , 3, file);
};
fclose(file);
}
__global__ void gpu_compute_mandelbrot(unsigned char *buffer, int *colors_d)
{
double z_x = 0.0;
double z_y = 0.0;
double z_x_squared = 0.0;
double z_y_squared = 0.0;
double escape_radius_squared = 4;
double c_x;
double c_y;
int i_y = blockIdx.y * blockDim.y + threadIdx.y;
int i_x = blockIdx.x * blockDim.x + threadIdx.x;
int color;
int iteration;
c_y = C_Y_MIN + i_y * PIXEL_HEIGHT;
if (fabs(c_y) < PIXEL_HEIGHT / 2)
c_y = 0.0;
c_x = C_X_MIN + i_x * PIXEL_WIDTH;
for (iteration = 0;
iteration < ITERATION_MAX && \
((z_x_squared + z_y_squared) < escape_radius_squared);
iteration++) {
z_y = 2 * z_x * z_y + c_y;
z_x = z_x_squared - z_y_squared + c_x;
z_x_squared = z_x * z_x;
z_y_squared = z_y * z_y;
}
color = (iteration == ITERATION_MAX) ? GRADIENT_SIZE : (iteration % GRADIENT_SIZE);
for (int i = 0; i < 3; i++) {
buffer[3 * ((IMAGE_SIZE * i_y) + i_x) + i] = colors_d[(3 * color) + i];
}
}
void compute_mandelbrot()
{
gpu_compute_mandelbrot<<<dim3(x_grid, y_grid), dim3(x_block, y_block)>>>(d_image_buffer, d_colors);
cudaDeviceSynchronize();
cudaEventRecord(memcpy_start, 0);
cudaMemcpy(image_buffer, d_image_buffer, ARRAY_SIZE, cudaMemcpyDeviceToHost);
cudaEventRecord(memcpy_end, 0);
}
int main(int argc, char *argv[])
{
init(argc, argv);
cudaEventRecord(computing_start, 0);
compute_mandelbrot();
cudaEventRecord(computing_end, 0);
write_to_file();
cudaFreeHost(image_buffer); cudaFree(d_image_buffer);
cudaFree(d_colors);
cudaEventElapsedTime(&allocation_time, allocation_start, allocation_end);
cudaEventElapsedTime(&computing_time, computing_start, computing_end);
cudaEventElapsedTime(&memcpy_time, memcpy_start, memcpy_end);
printf("%.6f\n", computing_time / 1000.0);
return 0;
} |
23,760 |
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#define NUM 1024
cudaError_t addWithCuda(double *c, const double *a, const double *b, long size);
__device__ double test(int i);
__global__ void addKernel(double *c, const double *a, const double *b)
{
int i = threadIdx.x;
for (int j = 0; j < NUM; j++) {
c[j] = test(i);
}
}
__device__ double test(int i) {
double tmp = (i - 9.9) * 3.1415926 * 3.1415926;
double ret = sin(tmp);
return ret;
}
int main()
{
const double a[NUM] = { 1, 2, 3, 4, 5 };
const double b[NUM] = { 10, 20, 30, 40, 50 };
double c[NUM] = { 0 };
// Add vectors in parallel.
cudaEvent_t start, stop;
cudaEventCreate(&start);
cudaEventCreate(&stop);
cudaEventRecord(start, 0);
cudaError_t cudaStatus = addWithCuda(c, a, b, NUM);
cudaEventRecord(stop, 0);
//confirm that all things have been done before "stop event"
cudaEventSynchronize(stop);
float elapseTime;
cudaEventElapsedTime(&elapseTime, start, stop);
printf("Time for I/O : %.5f ms\n", elapseTime);
printf("%.6f\n", c[2]);
if (cudaStatus != cudaSuccess) {
fprintf(stderr, "addWithCuda failed!");
return 1;
}
// cudaDeviceReset must be called before exiting in order for profiling and
// tracing tools such as Nsight and Visual Profiler to show complete traces.
cudaStatus = cudaDeviceReset();
if (cudaStatus != cudaSuccess) {
fprintf(stderr, "cudaDeviceReset failed!");
return 1;
}
system("pause");
return 0;
}
// Helper function for using CUDA to add vectors in parallel.
cudaError_t addWithCuda(double *c, const double *a, const double *b, long size)
{
double *dev_a = 0;
double *dev_b = 0;
double *dev_c = 0;
cudaError_t cudaStatus;
// Choose which GPU to run on, change this on a multi-GPU system.
cudaStatus = cudaSetDevice(0);
if (cudaStatus != cudaSuccess) {
fprintf(stderr, "cudaSetDevice failed! Do you have a CUDA-capable GPU installed?");
goto Error;
}
// Allocate GPU buffers for three vectors (two input, one output) .
cudaStatus = cudaMalloc((void**)&dev_c, size * sizeof(double));
if (cudaStatus != cudaSuccess) {
fprintf(stderr, "cudaMalloc failed!");
goto Error;
}
cudaStatus = cudaMalloc((void**)&dev_a, size * sizeof(double));
if (cudaStatus != cudaSuccess) {
fprintf(stderr, "cudaMalloc failed!");
goto Error;
}
cudaStatus = cudaMalloc((void**)&dev_b, size * sizeof(double));
if (cudaStatus != cudaSuccess) {
fprintf(stderr, "cudaMalloc failed!");
goto Error;
}
// Copy input vectors from host memory to GPU buffers.
cudaStatus = cudaMemcpy(dev_a, a, size * sizeof(double), cudaMemcpyHostToDevice);
if (cudaStatus != cudaSuccess) {
fprintf(stderr, "cudaMemcpy failed!");
goto Error;
}
cudaStatus = cudaMemcpy(dev_b, b, size * sizeof(double), cudaMemcpyHostToDevice);
if (cudaStatus != cudaSuccess) {
fprintf(stderr, "cudaMemcpy failed!");
goto Error;
}
// Launch a kernel on the GPU with one thread for each element.
addKernel<<<1, size>>>(dev_c, dev_a, dev_b);
// Check for any errors launching the kernel
cudaStatus = cudaGetLastError();
if (cudaStatus != cudaSuccess) {
fprintf(stderr, "addKernel launch failed: %s\n", cudaGetErrorString(cudaStatus));
goto Error;
}
// cudaDeviceSynchronize waits for the kernel to finish, and returns
// any errors encountered during the launch.
cudaStatus = cudaDeviceSynchronize();
if (cudaStatus != cudaSuccess) {
fprintf(stderr, "cudaDeviceSynchronize returned error code %d after launching addKernel!\n", cudaStatus);
goto Error;
}
// Copy output vector from GPU buffer to host memory.
cudaStatus = cudaMemcpy(c, dev_c, size * sizeof(double), cudaMemcpyDeviceToHost);
if (cudaStatus != cudaSuccess) {
fprintf(stderr, "cudaMemcpy failed!");
goto Error;
}
Error:
cudaFree(dev_c);
cudaFree(dev_a);
cudaFree(dev_b);
return cudaStatus;
}
|
23,761 | //
// Created by fiberhome on 2021/4/7.
//
// Device code
#include "cuda_runtime.h"
#include "iostream"
__global__ void MyKernel(int *d, int *a, int *b) {
int idx = threadIdx.x + blockIdx.x * blockDim.x;
d[idx] = a[idx] * b[idx];
}
// Host code
int main() {
int numBlocks; // Occupancy in terms of active blocks
int blockSize = 32;
// These variables are used to convert occupancy to warps
int device;
cudaDeviceProp prop;
int activeWarps;
int maxWarps;
cudaGetDevice(&device);
cudaGetDeviceProperties(&prop, device);
cudaOccupancyMaxActiveBlocksPerMultiprocessor(
&numBlocks,
MyKernel,
blockSize,
0);
activeWarps = numBlocks * blockSize / prop.warpSize;
maxWarps = prop.maxThreadsPerMultiProcessor / prop.warpSize;
std::cout << "Occupancy: " << (double) activeWarps / maxWarps * 100 << "%" << std::endl;
return 0;
}
|
23,762 | #include "GpuProjectionProcessing.cuh"
#include "GpuUtmWgsTransform.cuh"
#include "GpuTimer.cuh"
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <stdio.h>
using namespace winGpu;
__global__ void applyTransformUtmToWgsCoordsGpu(double xOrigin, double yOrigin, double xPixelSize, double yPixelSize,
int height, int width, int zone, bool southhemi, double* lon, double* lat)
{
int h = blockDim.x * blockIdx.x + threadIdx.x;
int w = blockDim.y * blockIdx.y + threadIdx.y;
if (height <= h || width <= w)
{
return;
}
double x = xOrigin + xPixelSize * w;
double y = yOrigin + yPixelSize * h;
double newLon = 0.0;
double newLat = 0.0;
UtmXYToLatLonGpu(x, y, zone, southhemi, newLon, newLat);
lon[h * width + w] = newLon;
lat[h * width + w] = newLat;
}
double winGpu::doTransformUtmToWgsCoordsGpu(double xOrigin, double yOrigin, double xPixelSize, double yPixelSize,
int height, int width, int zone, bool southhemi, double* lon, double* lat)
{
const size_t maxAvaliableCoords = 1500000;
int countRowsPerIter = maxAvaliableCoords / width;
int countIter = height / countRowsPerIter + 1;
const size_t size = width * countRowsPerIter;
dim3 threadsPerBlock(16, 16);
dim3 numBlocks(countRowsPerIter / threadsPerBlock.x + 1, width / threadsPerBlock.y + 1);
double* newLon = new double[size];
double* newLat = new double[size];
double* dev_lon = 0;
double* dev_lat = 0;
float time;
cudaSetDevice(0);
cudaMalloc((void**)&dev_lon, size * sizeof(double));
cudaMalloc((void**)&dev_lat, size * sizeof(double));
GPU_TIMER_START;
for (int i = 0; i < countIter; i++)
{
double newYOrigin = yOrigin + i * yPixelSize * countRowsPerIter;
applyTransformUtmToWgsCoordsGpu << <numBlocks, threadsPerBlock >> > (xOrigin, newYOrigin,
xPixelSize, yPixelSize, countRowsPerIter, width, zone, southhemi, dev_lon, dev_lat);
cudaDeviceSynchronize();
cudaMemcpy(newLon, dev_lon, size * sizeof(double), cudaMemcpyDeviceToHost);
cudaMemcpy(newLat, dev_lat, size * sizeof(double), cudaMemcpyDeviceToHost);
size_t countCoordsForCopy = i != countIter - 1 ? size :
width * height - countRowsPerIter * width * i;
for (int j = 0; j < countCoordsForCopy; j++)
{
lon[i * size + j] = newLon[j];
lat[i * size + j] = newLat[j];
}
}
GPU_TIMER_STOP(time);
cudaFree(dev_lon);
cudaFree(dev_lat);
delete[] newLon;
delete[] newLat;
return (double)time;
}
__global__ void applyTransformWgsToUtmCoordsGpu(double xOrigin, double yOrigin, double xPixelSize, double yPixelSize,
int height, int width, int zone, double* x, double* y)
{
int h = blockDim.x * blockIdx.x + threadIdx.x;
int w = blockDim.y * blockIdx.y + threadIdx.y;
if (height <= h || width <= w)
{
return;
}
double lon = xOrigin + xPixelSize * w;
double lat = yOrigin + yPixelSize * h;
double newX = 0.0;
double newY = 0.0;
LatLonToUtmXYGpu(lon, lat, zone, newX, newY);
x[h * width + w] = newX;
y[h * width + w] = newY;
}
double winGpu::doTransformWgsToUtmCoordsGpu(double xOrigin, double yOrigin, double xPixelSize, double yPixelSize,
int height, int width, int zone, double* x, double* y)
{
const size_t maxAvaliableCoords = 2000000;
int countRowsPerIter = maxAvaliableCoords / width;
int countIter = height / countRowsPerIter + 1;
const size_t size = width * countRowsPerIter;
dim3 threadsPerBlock(16, 16);
dim3 numBlocks(countRowsPerIter / threadsPerBlock.x + 1, width / threadsPerBlock.y + 1);
double* newX = new double[size];
double* newY = new double[size];
double* dev_x = 0;
double* dev_y = 0;
float time;
cudaSetDevice(0);
cudaMalloc((void**)&dev_x, size * sizeof(double));
cudaMalloc((void**)&dev_y, size * sizeof(double));
GPU_TIMER_START;
for (int i = 0; i < countIter; i++)
{
double newYOrigin = yOrigin + i * yPixelSize * countRowsPerIter;
applyTransformWgsToUtmCoordsGpu << <numBlocks, threadsPerBlock >> > (xOrigin, newYOrigin,
xPixelSize, yPixelSize, countRowsPerIter, width, zone, dev_x, dev_y);
cudaDeviceSynchronize();
cudaMemcpy(newX, dev_x, size * sizeof(double), cudaMemcpyDeviceToHost);
cudaMemcpy(newY, dev_y, size * sizeof(double), cudaMemcpyDeviceToHost);
size_t countCoordsForCopy = i != countIter - 1 ? size :
width * height - countRowsPerIter * width * i;
for (int j = 0; j < countCoordsForCopy; j++)
{
x[i * size + j] = newX[j];
y[i * size + j] = newY[j];
}
}
GPU_TIMER_STOP(time);
cudaFree(dev_x);
cudaFree(dev_y);
delete[] newX;
delete[] newY;
return (double)time;
}
|
23,763 | #include "includes.h"
__global__ void _setPrecisionKernel(float* data, size_t size, int* precision)
{
unsigned int idx = blockDim.x * blockIdx.x + threadIdx.x;
if (idx >= size) return;
int prec = precision[idx];
int mul = 1;
while(prec--) mul *= 10;
data[idx] = (float)(int)(data[idx]*mul);
data[idx] /= mul;
} |
23,764 | /* jacobi.c - Poisson problem in 3d
*
*/
#include <math.h>
#include <stdlib.h>
#include <stdio.h>
__device__
void jacobi_gpu(int N, double ***u, double ***v, double ***f, int iter_max) {
//int counter = 0;
int i = blockIdx.x * blockDim.x + threadIdx.x;
int j = blockIdx.y * blockDim.y + threadIdx.y;
int k = blockIdx.z * blockDim.z + threadIdx.z;
//v[i][j][k] = u[i][j][k];
//} while (counter <iter_max);
if(i > 0 && j > 0 && k > 0 && i<N-1 && j<N-1 && k<N-1){
//v[i][j][k] = u[i][j][k];
u[i][j][k] = 1./6.*(v[i-1][j][k]+v[i+1][j][k]+v[i][j-1][k]+v[i][j+1][k]+v[i][j][k-1]+v[i][j][k+1] + 1./((N)*(N)) * f[i][j][k]);
printf("i=%i j=%i k=%i | u=%f v=%f f=%f\n", i, j, k, u[i][j][k], v[i][j][k], f[i][j][k]);
}
}
// Kernel to be launched on a single thread
__global__
void jacobi_per_elem(int N, double ***u, double ***v, double ***f, int iter_max)
{
jacobi_gpu(N, u, v, f, iter_max);
} |
23,765 | #include "includes.h"
/**
* Computación Paralela (curso 1516)
*
* Alberto Gil
* Guillermo Cebrian
*/
// Includes generales
// Include para las utilidades de computación paralela
/**
* Estructura antena
*/
typedef struct {
int y;
int x;
} Antena;
/**
* Macro para acceder a las posiciones del mapa
*/
#define m(y,x) mapa[ (y * cols) + x ]
/**
* Definimos el tamaño de bloque
*/
#define TAMBLOCK 128
/* Funcion que inicializa la matriz al valor maximo */
/* Actualiza el mapa despues de colocar una antena*/
/**
* Función de ayuda para imprimir el mapa
*/
__global__ void gpu_init(int *mapad, int max, int size){
/*Identificaciones necesarios*/
int IDX_Thread = threadIdx.x; /*Identificacion del hilo en la dimension*/
int IDY_Thread = threadIdx.y; /*Identificacion del hilo en la dimension y*/
int IDX_block = blockIdx.x; /*Identificacion del bloque en la dimension x*/
int IDY_block = blockIdx.y; /*Identificacion del bloque en la dimension y */
int shapeGrid_X = gridDim.x; /*Numeros del bloques en la dimension */
int threads_per_block = blockDim.x * blockDim.y; /* Numero de hilos por bloque (1 dimension) */
/*Formula para calcular la posicion*/ //Posicion del vector dependiendo del hilo y del bloque
int position = threads_per_block * ((IDY_block * shapeGrid_X)+IDX_block)+((IDY_Thread*blockDim.x)+IDX_Thread);
//inicializamos
if(position<size) mapad[position] = max;
} |
23,766 | #define DATA_SIZE 10000000
#define BLOCKS_COUNT 1
#define THREADS_COUNT 64
__global__ void findMean(unsigned int dataForBlock, float *inputData, float *results)
{
int index = blockIdx.x * blockDim.x + threadIdx.x;
float result = 0;
for (int i = 0; i < dataForBlock; i++)
{
result += inputData[index * dataForBlock + i];
}
result /= dataForBlock;
results[index] = result;
}
void processWithGPU(float *blocks, float *results, unsigned int blockSize, unsigned int blocksCount)
{
unsigned int realDataCount = blockSize * blocksCount;
cudaSetDevice(0);
float *deviceInputData, *deviceResults;
cudaMalloc((void **)&deviceInputData, realDataCount * sizeof(float));
cudaMalloc((void **)&deviceResults, realDataCount * sizeof(float));
cudaMemcpy(deviceInputData, blocks, realDataCount * sizeof(float), cudaMemcpyHostToDevice);
findMean<<<1, blocksCount>>>(blockSize, deviceInputData, deviceResults);
cudaMemcpy((void *)results, deviceResults, blocksCount * sizeof(float), cudaMemcpyDeviceToHost);
cudaFree(deviceInputData);
cudaFree(deviceResults);
}
|
23,767 | #include "includes.h"
__global__ void concat(float* input1, float* input2, float* input3, float* input4, size_t num1, size_t num2, size_t num3, size_t num4, size_t maxNum, float* output, const int numPerBatch)
{
size_t i = blockDim.x * blockIdx.x + threadIdx.x;
for(;i < maxNum; i += size_t(blockDim.x * gridDim.x)){
size_t batchIdx = i / numPerBatch; // which batch this thread is working in
const int batchOffset = i - batchIdx * numPerBatch; // offset of current thread in current batch
if(batchOffset < num1){ // first input
output[i] = input1[batchOffset + batchIdx * num1];
}
else if(batchOffset < (num1 + num2)){ // second input
output[i] = input2[(batchOffset - num1) + batchIdx * num2];
}
else if(batchOffset < (num1 + num2 + num3)){ // third input
output[i] = input3[(batchOffset - (num1 + num2)) + batchIdx * num3];
}
else{ // fourth input
output[i] = input4[(batchOffset - (num1 + num2 + num3)) + batchIdx * num4];
}
}
} |
23,768 | #include <stdio.h>
#include <stdlib.h>
#define block_size 32
#define vector_size 100
__global__ void add( int *a, int *b, int *c ) {
int tid = (blockIdx.x*blockDim.x) + threadIdx.x; // this thread handles the data at its thread id
if (tid < vector_size){
c[tid] = a[tid] + b[tid]; // add vectors together
}
}
int main( void ) {
// Set device that we will use for our cuda code
// It will be either 0 or 1
cudaSetDevice(0);
// Time Variables
cudaEvent_t start, stop;
float time;
cudaEventCreate (&start);
cudaEventCreate (&stop);
// Input Arrays and variables
int *a = new int [vector_size];
int *b = new int [vector_size];
int *c_cpu = new int [vector_size];
int *c_gpu = new int [vector_size];
// Pointers in GPU memory
int *dev_a;
int *dev_b;
int *dev_c;
// fill the arrays 'a' and 'b' on the CPU
for (int i = 0; i < vector_size; i++) {
a[i] = rand()%10;
b[i] = rand()%10;
}
//
// CPU Calculation
//////////////////
printf("Running sequential job.\n");
cudaEventRecord(start,0);
// Calculate C in the CPU
for (int i = 0; i < vector_size; i++) {
c_cpu[i] = a[i] + b[i];
}
cudaEventRecord(stop,0);
cudaEventSynchronize(stop);
cudaEventElapsedTime(&time, start, stop);
printf("\tSequential Job Time: %.2f ms\n", time);
// allocate the memory on the GPU
// HERE
// copy the arrays 'a' and 'b' to the GPU
// HERE
//
// GPU Calculation
////////////////////////
printf("Running parallel job.\n");
cudaEventRecord(start,0);
// call the kernel
// HERE
cudaEventRecord(stop,0);
cudaEventSynchronize(stop);
cudaEventElapsedTime(&time, start, stop);
printf("\tParallel Job Time: %.2f ms\n", time);
// copy the array 'c' back from the GPU to the CPU
// HERE (there's one more at the end, don't miss it!)
// compare the results
int error = 0;
for (int i = 0; i < vector_size; i++) {
if (c_cpu[i] != c_gpu[i]){
error = 1;
printf( "Error starting element %d, %d != %d\n", i, c_gpu[i], c_cpu[i] );
}
if (error) break;
}
if (error == 0){
printf ("Correct result. No errors were found.\n");
}
// free the memory allocated on the GPU
// HERE
return 0;
}
|
23,769 | #include <iostream>
#include <stdio.h>
#include <math.h>
__global__ void cuPi(float *sum, int nbin, float step)
{
// Write your pi calculation kernel here
return;
}
int main(void)
{
int REAL_PI = 3.141592653589793238462643383;
int NBINS = 10; // modify this to achieve better performance
int STEP = 5; // modify this to achieve better performance
float* pi;
cudaMallocManaged(&pi, sizeof(float));
// Calculate Pi
cuPi<<<1, 1>>>(pi, NBINS, STEP);
// Wait for GPU to finish before accessing on host
cudaDeviceSynchronize();
printf("Pi calculated as %f\n", *pi);
float acc = REAL_PI - *pi;
printf("Accuracy of Pi calculation %f\n", acc);
// Free memory
cudaFree(pi);
return 0;
} |
23,770 |
#include <cuda.h>
#include "device_launch_parameters.h"
#include <stdio.h>
#include <stdlib.h>
#define N 512;
#define M 512;
void colorTogrey_BLUR(int *, int *,int,int);
// we have 3 channels corresponding to RGB
// The input image is encoded as unsigned characters [0, 255]
__global__
void blurKernel(int *Pin_d, int *Pout_d_B,
int width, int height, int BLUR_SIZE) {
int Col = threadIdx.x + blockIdx.x * blockDim.x;
int Row = threadIdx.y + blockIdx.y * blockDim.y;
unsigned int pixVal = 0;
unsigned int pixels = 0;
if (Col < (width) && Row < height) {
for(int blurRow = -BLUR_SIZE; blurRow < BLUR_SIZE+1; ++blurRow) {
for(int blurCol = -BLUR_SIZE; blurCol < BLUR_SIZE+1; ++blurCol) {
int curRow = Row + blurRow;
int curCol = Col + blurCol;
// Verify we have a valid image pixel
if(curRow > -1 && curRow < height && curCol > -1 && curCol < width) {
pixVal += Pin_d[curRow * width + curCol];
pixels++; // Keep track of number of pixels in the avg
}
}
}
// Write our new pixel value out
Pout_d_B[Row * width + Col] = int(pixVal / pixels);
}
}
int main()
{
int n=N;int m=M;
int *Pin_h = (int*) malloc( sizeof(int)*n*m);
int ind=0;
unsigned int tmp;
FILE *fp;
fp=fopen("testImage_Results_lena.txt","r");
while (!feof(fp)){
fscanf(fp,"%d",&tmp);
Pin_h[ind]=tmp;
ind=ind+1;
}
fclose(fp);
int *Pout_h_B = (int*) malloc( sizeof(int)*n*m); //for BLUR operation
colorTogrey_BLUR ( Pin_h, Pout_h_B, n, m);
FILE *fp4;
fp4=fopen("testImageResults_BLUR.txt","w");
for (int i=0; i < m; i++){
for (int j=0; j < n; j++){
fprintf(fp4,"%4d ",Pout_h_B[i*n+j]);}
fprintf(fp4,"\n");
}
fclose(fp4);
// free the memory we allocated on the CPU
free( Pin_h);
free( Pout_h_B );
return 0;
}
void colorTogrey_BLUR(int *Pin_h, int *Pout_h_B, int n, int m)
{
int size_in = (n *m*sizeof(int)); int size_out = (n*m*sizeof(int));
int *Pin_d; int *Pout_d_B;
int BLUR_SIZE = 7;
// Transfer Pin_h to device memory
cudaMalloc((void **) &Pin_d, size_in);
cudaMemcpy(Pin_d, Pin_h, size_in, cudaMemcpyHostToDevice);
// Allocate device memory for Pout_d
cudaMalloc((void **) &Pout_d_B, size_out);
dim3 dimGrid(ceil(n/16), ceil(m/16), 1);
dim3 dimBlock(16,16,1);
blurKernel<<<dimGrid,dimBlock>>>(Pin_d, Pout_d_B, n, m,BLUR_SIZE);
// Transfer Pout_d from device to host
cudaMemcpy(Pout_h_B, Pout_d_B, size_out, cudaMemcpyDeviceToHost);//copy Blurred Images to Host Memory
// Free device memory
cudaFree(Pin_d); cudaFree(Pout_d_B);
}
|
23,771 | #include <cuda_runtime.h>
#include <stdio.h>
unsigned char *d_red, *d_green, *d_blue;
float *d_filter;
uchar4 *d_inputImageRGBA__;
uchar4 *d_outputImageRGBA__;
float *h_filter__;
__global__ void gaussian_blur(const unsigned char* const inputChannel,
unsigned char* const outputChannel,
int numRows, int numCols, const float* const filter, const int filterWidth) {
int col = blockIdx.x * blockDim.x + threadIdx.x;
int row = blockIdx.y * blockDim.y + threadIdx.y;
if ( col >= numCols || row >= numRows )
return;
float result = 0.f;
//For every value in the filter around the pixel (c, r)
for (int filter_r = -filterWidth/2; filter_r <= filterWidth/2; ++filter_r) {
for (int filter_c = -filterWidth/2; filter_c <= filterWidth/2; ++filter_c) {
//Find the global image position for this filter position
//clamp to boundary of the image
int image_r = min(max(row + filter_r, 0), static_cast<int>(numRows - 1));
int image_c = min(max(col + filter_c, 0), static_cast<int>(numCols - 1));
float image_value = static_cast<float>(inputChannel[image_r * numCols + image_c]);
float filter_value = filter[(filter_r + filterWidth/2) * filterWidth + filter_c + filterWidth/2];
result += image_value * filter_value;
}
}
outputChannel[row * numCols + col] = result;
}
__global__ void separateChannels(const uchar4* const inputImageRGBA,
int numRows,
int numCols,
unsigned char* const redChannel,
unsigned char* const greenChannel,
unsigned char* const blueChannel) {
int absolute_image_position_x = blockDim.x * blockIdx.x + threadIdx.x;
int absolute_image_position_y = blockDim.y * blockIdx.y + threadIdx.y;
if ( absolute_image_position_x >= numCols || absolute_image_position_y >= numRows )
return;
int thread_1D_pos = absolute_image_position_y * numCols + absolute_image_position_x;
redChannel[thread_1D_pos] = inputImageRGBA[thread_1D_pos].x;
greenChannel[thread_1D_pos] = inputImageRGBA[thread_1D_pos].y;
blueChannel[thread_1D_pos] = inputImageRGBA[thread_1D_pos].z;
}
__global__ void recombineChannels(const unsigned char* const redChannel,
const unsigned char* const greenChannel,
const unsigned char* const blueChannel,
uchar4* const outputImageRGBA,
int numRows,
int numCols) {
const int2 thread_2D_pos = make_int2( blockIdx.x * blockDim.x + threadIdx.x,
blockIdx.y * blockDim.y + threadIdx.y);
const int thread_1D_pos = thread_2D_pos.y * numCols + thread_2D_pos.x;
if (thread_2D_pos.x >= numCols || thread_2D_pos.y >= numRows)
return;
unsigned char red = redChannel[thread_1D_pos];
unsigned char green = greenChannel[thread_1D_pos];
unsigned char blue = blueChannel[thread_1D_pos];
//Alpha should be 255 for no transparency
uchar4 outputPixel = make_uchar4(red, green, blue, 255);
outputImageRGBA[thread_1D_pos] = outputPixel;
}
void allocateMemoryAndCopyToGPU(const size_t numRowsImage, const size_t numColsImage, const float* const h_filter, const size_t filterWidth) {
//allocate memory for the three different channels
//original
cudaMalloc(&d_red, sizeof(unsigned char) * numRowsImage * numColsImage);
cudaMalloc(&d_green, sizeof(unsigned char) * numRowsImage * numColsImage);
cudaMalloc(&d_blue, sizeof(unsigned char) * numRowsImage * numColsImage);
//Allocate memory for the filter on the GPU
cudaMalloc(&d_filter, sizeof(float)*filterWidth*filterWidth);
cudaMemcpy(d_filter,h_filter,sizeof(float)*filterWidth*filterWidth,cudaMemcpyHostToDevice);
}
void cleanup() {
cudaFree(d_red);
cudaFree(d_green);
cudaFree(d_blue);
cudaFree(d_filter);
}
void setFilter(float **h_filter, int *filterWidth, int blurKernelWidth, float blurKernelSigma) {
//Normally blurKernelWidth = 9 and blurKernelSigma = 2.0
*h_filter = new float[blurKernelWidth * blurKernelWidth];
*filterWidth = blurKernelWidth;
float filterSum = 0.f; //for normalization
for (int r = -blurKernelWidth/2; r <= blurKernelWidth/2; ++r) {
for (int c = -blurKernelWidth/2; c <= blurKernelWidth/2; ++c) {
float filterValue = expf( -(float)(c * c + r * r) / (2.f * blurKernelSigma * blurKernelSigma));
(*h_filter)[(r + blurKernelWidth/2) * blurKernelWidth + c + blurKernelWidth/2] = filterValue;
filterSum += filterValue;
}
}
float normalizationFactor = 1.f / filterSum;
for (int r = -blurKernelWidth/2; r <= blurKernelWidth/2; ++r)
for (int c = -blurKernelWidth/2; c <= blurKernelWidth/2; ++c)
(*h_filter)[(r + blurKernelWidth/2) * blurKernelWidth + c + blurKernelWidth/2] *= normalizationFactor;
}
uchar4* blur_ops(uchar4* d_inputImageRGBA, size_t numRows, size_t numCols, int blurKernelWidth) {
float blurKernelSigma = blurKernelWidth/4.0f;
//Set filter array
float* h_filter;
int filterWidth;
setFilter(&h_filter, &filterWidth, blurKernelWidth, blurKernelSigma);
//Set reasonable block size (i.e., number of threads per block)
const dim3 blockSize(16,16,1);
//Calculate Grid SIze
int a=numCols/blockSize.x, b=numRows/blockSize.y;
const dim3 gridSize(a+1,b+1,1);
const size_t numPixels = numRows * numCols;
uchar4 *d_outputImageRGBA;
cudaMalloc((void **)&d_outputImageRGBA, sizeof(uchar4) * numPixels);
cudaMemset(d_outputImageRGBA, 0, numPixels * sizeof(uchar4)); //make sure no memory is left laying around
d_inputImageRGBA__ = d_inputImageRGBA;
d_outputImageRGBA__ = d_outputImageRGBA;
//blurred
unsigned char *d_redBlurred, *d_greenBlurred, *d_blueBlurred;
cudaMalloc(&d_redBlurred, sizeof(unsigned char) * numPixels);
cudaMalloc(&d_greenBlurred, sizeof(unsigned char) * numPixels);
cudaMalloc(&d_blueBlurred, sizeof(unsigned char) * numPixels);
cudaMemset(d_redBlurred, 0, sizeof(unsigned char) * numPixels);
cudaMemset(d_greenBlurred, 0, sizeof(unsigned char) * numPixels);
cudaMemset(d_blueBlurred, 0, sizeof(unsigned char) * numPixels);
allocateMemoryAndCopyToGPU(numRows, numCols, h_filter, filterWidth);
//Launch a kernel for separating the RGBA image into different color channels
separateChannels<<<gridSize, blockSize>>>(d_inputImageRGBA, numRows, numCols, d_red,d_green, d_blue);
cudaDeviceSynchronize();
//Call blur kernel here 3 times, once for each color channel.
gaussian_blur<<<gridSize, blockSize>>>(d_red, d_redBlurred, numRows, numCols, d_filter, filterWidth);
gaussian_blur<<<gridSize, blockSize>>>(d_green, d_greenBlurred, numRows, numCols, d_filter, filterWidth);
gaussian_blur<<<gridSize, blockSize>>>(d_blue, d_blueBlurred, numRows, numCols, d_filter, filterWidth);
cudaDeviceSynchronize();
//Now we recombine the results.
recombineChannels<<<gridSize, blockSize>>>(d_redBlurred,
d_greenBlurred,
d_blueBlurred,
d_outputImageRGBA,
numRows,
numCols);
cudaDeviceSynchronize();
//cleanup memory
cleanup();
cudaFree(d_redBlurred);
cudaFree(d_greenBlurred);
cudaFree(d_blueBlurred);
cudaDeviceSynchronize();
//Initialize memory on host for output uchar4*
uchar4* h_out;
h_out = (uchar4*)malloc(sizeof(uchar4) * numPixels);
//copy output from device to host
cudaMemcpy(h_out, d_outputImageRGBA, sizeof(uchar4) * numPixels, cudaMemcpyDeviceToHost);
cudaDeviceSynchronize();
//cleanup memory on device
cudaFree(d_inputImageRGBA__);
cudaFree(d_outputImageRGBA__);
delete[] h_filter__;
//return h_out
return h_out;
} |
23,772 | #include <stdio.h>
#include <math.h>
#include <stdlib.h>
const int TILE_SIZE = 8;
const int N_ROWS = 4;
#define N_TEST 1
__global__ void naive_transpose(const float * A, float * B, int size)
{
int index = threadIdx.x + blockIdx.x * blockDim.x;
int x = index % size;
int y = index / size;
B[y * size + x] = A[x * size + y];
}
__global__ void fast_transpose(const float * A, float * B, const int size)
{
__shared__ float tile[TILE_SIZE][TILE_SIZE + 1];
int col = blockIdx.x * blockDim.x + threadIdx.x;
int row = blockIdx.y * blockDim.y + threadIdx.y;
tile[threadIdx.x][threadIdx.y] = A[row * size + col];
__syncthreads();
B[col * size + row] = tile[threadIdx.x][threadIdx.y];
}
void fill_host(float* host_a, const int N)
{
int i,j;
for(j = 0; j < N; j++){
for(i = 0; i < N; i++)
host_a[j * N + i] = i;
}
}
void cpu_transpose(float * host_a, float * host_control, const int N)
{
int i, j;
for(j = 0; j < N; j++)
for(i = 0; i < N; i++)
host_control[j * N + i] = host_a[i * N + j];
}
int is_transpose(float * host_control, float * host_transpose, const int N)
{
int i,j;
for(j = 0; j < N; j++){
for(i = 0; i < N; i++){
if(host_control[j*N + i] != host_transpose[j*N + i]){
return 0;
}
}
}
return 1;
}
void print_matrix(const float * A, const int N)
{
int i, j;
for(i = 0; i < N; i++){
for(j = 0; j < N; j++)
printf("%.1f ", A[i*N + j]);
printf("\n");
}
printf("\n");
}
int main(int argc, char * argv[])
{
const int N = 8192;
const int size = N * N * sizeof(float);
dim3 dimGrid(N/TILE_SIZE, N/N_ROWS, 1);
dim3 dimBlock(TILE_SIZE, N_ROWS, 1);
float * host_a = (float*)malloc(size);
float * host_naive = (float*)malloc(size);
float * host_fast = (float*)malloc(size);
float * host_control = (float*)malloc(size);
float * device_a, * device_naive,* device_fast;
cudaMalloc((void**)&device_a, size);
cudaMalloc((void**)&device_naive, size);
cudaMalloc((void**)&device_fast, size);
fill_host(host_a, N);
cpu_transpose(host_a, host_control, N);
//print_matrix(host_a, N);
//print_matrix(host_control, N);
cudaMemcpy(device_a, host_a, size, cudaMemcpyHostToDevice);
cudaEvent_t begin, end;
cudaEventCreate(&begin);
cudaEventCreate(&end);
float ms;
printf("%25s\n", "naive_transpose");
cudaEventRecord(begin,0);
naive_transpose<<< dimGrid, dimBlock >>>(device_a, device_naive, N);
cudaEventRecord(end,0);
cudaEventSynchronize(end);
cudaEventElapsedTime(&ms, begin, end);
cudaMemcpy(host_naive, device_naive, size, cudaMemcpyDeviceToHost);
//print_matrix(host_naive, N);
printf("Correctness Test : %d\n", is_transpose(host_control, host_naive, N));
printf("Required time : %f\n", ms);
cudaEventRecord(begin, 0);
fast_transpose<<< dimGrid, dimBlock >>>(device_a, device_fast, N);
cudaEventRecord(end, 0);
cudaEventSynchronize(end);
cudaEventElapsedTime(&ms, begin, end);
cudaMemcpy(host_fast, device_fast, size, cudaMemcpyDeviceToHost);
//print_matrix(host_fast, N);
printf("Correctness Test : %d\n", is_transpose(host_control, host_fast, N));
printf("Required Time : %f\n",ms);
cudaEventDestroy(begin);
cudaEventDestroy(end);
cudaFree(device_a);
cudaFree(device_naive);
cudaFree(device_fast);
free(host_a);
free(host_control);
free(host_naive);
free(host_fast);
return 0;
}
|
23,773 | template<typename T>
__device__ void getColumnsValues(const T* matrix, const int* indices, T* result,
const int rows, const int cols) {
int bx = blockIdx.x;
int tx = threadIdx.x;
int row = bx * blockDim.x + tx;
if (row < rows) {
int col = indices[row];
result[row] = matrix[row * cols + col];
}
}
template<typename T>
__device__ void setColumnsValues(T* matrix, const int* indices, const T* values,
const int rows, const int cols) {
int bx = blockIdx.x;
int tx = threadIdx.x;
int row = bx * blockDim.x + tx;
if (row < rows) {
int col = indices[row];
matrix[row * cols + col] = values[row];
}
}
template<typename T>
__device__ void setColumnsValue(T* matrix, const int* indices, const T value,
const int rows, const int cols) {
int bx = blockIdx.x;
int tx = threadIdx.x;
int row = bx * blockDim.x + tx;
if (row < rows) {
int col = indices[row];
matrix[row * cols + col] = value;
}
} |
23,774 | // vAdd.cu
//
// driver and kernel call
#include <stdio.h>
#define BLOCK_SIZE 256
__global__ void getDensity (double *a_d, double *b_d, int numSlice)
{
// __shared__ double s[BLOCK_SIZE + 2];
int x = blockIdx.x * blockDim.x + threadIdx.x;
if (x != 0 && x < numSlice - 1) {
// if(threadIdx.x == 0) {
// s[0] = a_d[x-1];
// s[1] = a_d[x];
// }
// else if (threadIdx.x == BLOCK_SIZE - 1){
// s[BLOCK_SIZE +1] = a_d[x + 1];
// s[BLOCK_SIZE] = a_d[x];
// }
// else {
// s[threadIdx.x + 1] = a_d[x];
// }
b_d[x] = (a_d[x-1] + a_d[x+1])/2;
}
__syncthreads();
if (x == numSlice - 1){
b_d[numSlice - 1] = b_d[numSlice - 2];
}
// Copying result with the neighboring values, just to check the neighbor value
}
__global__ void initialize (double *a_d, double *b_d, int numSlice, int pointSource)
{
int x = blockIdx.x * blockDim.x + threadIdx.x;
if (x == 0){
a_d[x] = pointSource;
b_d[x] = pointSource;
}
else if (x < numSlice) {
a_d[x] = 0;
b_d[x] = 0;
}
}
extern "C" double compute(int numSlice, int time, double densityAt, int pointSource)
{
double *a_d, *b_d;
cudaMalloc ((void**) &a_d, sizeof(double) * numSlice);
cudaMalloc ((void**) &b_d, sizeof(double) * numSlice);
//cudaMalloc ((void**) &c_d, sizeof(double) * 5);
double density = 0;
initialize <<< ceil((float) numSlice/BLOCK_SIZE), BLOCK_SIZE>>> (a_d, b_d, numSlice, pointSource);
for (int n = 0; n < time; n++){
getDensity <<< ceil((float) numSlice/BLOCK_SIZE), BLOCK_SIZE>>> (a_d, b_d, numSlice);
a_d = b_d;
}
int index = (int)((numSlice - 1) * 0.7);
cudaMemcpy(&density, &a_d[index], sizeof(double), cudaMemcpyDeviceToHost);
cudaError_t err = cudaGetLastError();
if (err != cudaSuccess)
printf ("CUDA error: %s\n", cudaGetErrorString(err));
cudaFree (a_d);
cudaFree (b_d);
return density;
}
|
23,775 | #include "includes.h"
__global__ void reduction_interleaved_pairs_1(int * int_array, int * temp_array, int size)
{
int tid = threadIdx.x;
int gid = blockDim.x * blockIdx.x + threadIdx.x;
if (gid > size)
return;
for (int offset = blockDim.x / 2; offset > 0; offset = offset / 2)
{
if (tid < offset)
{
int_array[gid] += int_array[gid + offset];
}
__syncthreads();
}
if (tid == 0)
{
temp_array[blockIdx.x] = int_array[gid];
}
} |
23,776 | /**********************************************************
* This code is intended for for computing multiplication *
* for sub-product tree. *
* 2*poly_length <= T *
* According to this constraint subproduct tree level *
* where poly_length is not more than 129 is possible by *
* this code.
*ceil((poly_on_layer*0.5)/(floor(T/2*poly_length)) < 2^16 *
***********************************************************/
#include<iostream>
#include <ctime>
#include<cmath>
#define BASE_1 31
using namespace std;
const int Tmul = 512;
/***************************************************************
* one thread is responsible for computing one coefficient of c *
****************************************************************/
typedef int sfixn;
typedef unsigned int usfixn;
__device__ __host__ __inline__
sfixn add_mod(sfixn a, sfixn b, sfixn P) {
sfixn r = a + b;
r -= P;
r += (r >> BASE_1) & P;
return r;
}
__device__ __host__ __inline__
sfixn mul_mod(sfixn a, sfixn b, int P) {
double ninv = 1 / (double)P;
sfixn q = (sfixn) ((((double) a) * ((double) b)) * ninv);
sfixn res = a * b - q * P;
res += (res >> BASE_1) & P;
res -= P;
res += (res >> BASE_1) & P;
return res;
}
__global__ void listPlainMulGpu( sfixn *Mgpu, int start_offset, int length_poly, int poly_on_layer, int threadsForAmul, int mulInThreadBlock, int p)
{
__shared__ sfixn sM[2*Tmul];
int mulID= ((threadIdx.x/threadsForAmul) + blockIdx.x*mulInThreadBlock);
if( mulID < (poly_on_layer/2) && threadIdx.x < threadsForAmul*mulInThreadBlock)
{
int j = start_offset + ( mulID* length_poly*2);
int q = start_offset + ( poly_on_layer*length_poly) + ( mulID*(2*length_poly-1));
int t = (threadIdx.x/threadsForAmul);
int u = threadIdx.x % threadsForAmul;
int s = t*(4*length_poly-1);
int k = s + length_poly;
int l = k + length_poly;
int c = l+u;
int a, b, i;
sM[s+u] = Mgpu[j + u];
__syncthreads();
if(u != (2*length_poly-1) )
{
if(u < length_poly)
{
a = s;
b = k + u;
sM[c] = mul_mod(sM[a],sM[b],p);
++a; --b;
for(i = 0; i < u; ++i, ++a, --b)
sM[c] = add_mod(mul_mod(sM[a],sM[b],p),sM[c] ,p);
/*
Mgpu[j + u] = i;
*/
Mgpu[q+u] = sM[c];
}
else
{
b = l - 1;
a = (u - length_poly) + 1 + s;
sM[c] = mul_mod(sM[a],sM[b],p);
++a; --b;
int tempU = u;
u = (2*length_poly-2) - u;
for(i = 0; i < u; ++i, ++a, --b)
sM[c] = add_mod(mul_mod(sM[a],sM[b],p),sM[c] ,p);
/*
Mgpu[j + u] = i;
*/
Mgpu[q+tempU] = sM[c];
}
}/*
else
{
Mgpu[j + u] = -1;
}*/
}
}
sfixn add_modCPU(sfixn a, sfixn b, sfixn P) {
sfixn r = a + b;
r -= P;
r += (r >> BASE_1) & P;
return r;
}
sfixn mul_modCPU(sfixn a, sfixn b, int P) {
double ninv = 1 / (double)P;
sfixn q = (sfixn) ((((double) a) * ((double) b)) * ninv);
sfixn res = a * b - q * P;
res += (res >> BASE_1) & P;
res -= P;
res += (res >> BASE_1) & P;
return res;
}
void list_mul(sfixn *M , int n, int start_offset, int length_poly, int poly_on_layer, int result_offset, int p)
{
/********************************************************
* Number of threads responsible for one multiplication *
*********************************************************/
int threadsForAmul = 2*length_poly;
/************************************************
* Number of multiplications in one thread block *
*************************************************/
int mulInThreadBlock = (int)floor((double)Tmul/(double)threadsForAmul);
/****************************
* Number of blocks required *
****************************/
int blockNo = (int)ceil( ((double)poly_on_layer/(double) mulInThreadBlock)*0.5 );
sfixn *Mgpu;
cudaMalloc((void **)&Mgpu, sizeof(sfixn)*n);
cudaThreadSynchronize();
cudaMemcpy(Mgpu, M, sizeof(sfixn)*n, cudaMemcpyHostToDevice);
cudaThreadSynchronize();
cudaEvent_t start, stop;
cudaEventCreate(&start);
cudaEventCreate(&stop);
cudaEventRecord(start, 0);
listPlainMulGpu<<<blockNo, Tmul>>>(Mgpu, start_offset, length_poly, poly_on_layer, threadsForAmul, mulInThreadBlock, p);
cudaThreadSynchronize();
cudaEventRecord(stop, 0);
cudaEventSynchronize(stop);
float outerTime;
cudaEventElapsedTime(&outerTime, start, stop);
cout<<outerTime<<" miliseconds"<<endl;
cudaFree(Mgpu);
/*
cout<<"threadsForAmul: "<<threadsForAmul<<" mulInThreadBlock: "<<mulInThreadBlock<<" blockNo: " <<blockNo<<endl;
int i, j, k, l, q, a, b, c;
for(i = 0; i < poly_on_layer/2; ++i )
{
j = start_offset + 2*i*length_poly;
k = j + length_poly;
l = k + length_poly;
q = result_offset + i*(2*length_poly -1);
for(c = q; c < q+(2*length_poly -1); ++c)
M[c] = 0;
for(a = j; a < k; ++a)
{
c = q;
for(b = k; b < l; ++b, ++c)
{
M[c] = add_modCPU(mul_modCPU(M[a], M[b], p), M[c], p);
}
++q;
}
}
//
sfixn *temp = new sfixn[n];
cudaMemcpy(temp, Mgpu, sizeof(sfixn)*n, cudaMemcpyDeviceToHost);
//
cudaFree(Mgpu);
//
for(i = 0; i < n; ++i)
{
if(M[i] != temp[i])
{
cout<<i<<" "<<M[i]<<" "<<temp[i]<<endl;
break;
}
}
cout<<endl;
delete [] temp;
//
*/
}
int main(int argc, char *argv[])
{
int p= 7, start_offset = 0, length_poly = 4, poly_on_layer = 4, result_offset ;
if (argc > 1) start_offset = atoi(argv[1]);
if (argc > 2) length_poly = atoi(argv[2]);
if (argc > 3) poly_on_layer = atoi(argv[3]);
if (argc > 4) p = atoi(argv[4]);
result_offset = start_offset + length_poly*poly_on_layer;
/**************************************
* poly_on_layer MUST BE DIVISIBLE BY 2 *
***************************************/
int n, i;
n = result_offset + (2*length_poly-1)*(poly_on_layer/2);
sfixn *M = new int[n];
cout<<endl;
for(i = start_offset; i < result_offset; ++i)
M[i] = rand()% p;
cout<<endl;
cout<<p<<" "<<start_offset<<" "<<length_poly<<" "<<poly_on_layer<<" "<<result_offset<<" "<<n<<endl;
/*
for(i = 0; i < n; ++i)
cout<<M[i]<<" ";
cout<<endl;
*/
list_mul(M , n, start_offset, length_poly, poly_on_layer, result_offset, p );
delete [] M;
return 0;
}
|
23,777 | #include "includes.h"
/*******************************************************************************
*
*******************************************************************************/
/*************************************************************************
/*************************************************************************/
/*************************************************************************/
__global__ void drawColor(unsigned char* optr, const float* red, const float* green, const float* blue) {
// map from threadIdx/BlockIdx to pixel position
int x = threadIdx.x + blockIdx.x * blockDim.x;
int y = threadIdx.y + blockIdx.y * blockDim.y;
int offset = x + y * blockDim.x * gridDim.x;
float theRed = red[offset];
// theRed = (theRed / 50.0) + 0.5;
if (theRed < 0) theRed = 0;
if (theRed > 1) theRed = 1;
float theGreen = green[offset];
// theGreen = (theGreen / 50.0) + 0.5;
if (theGreen < 0) theGreen = 0;
if (theGreen > 1) theGreen = 1;
float theBlue = blue[offset];
// theBlue = (theBlue / 50.0) + 0.5;
if (theBlue < 0) theBlue = 0;
if (theBlue > 1) theBlue = 1;
optr[offset * 4 + 0] = 255 * theRed; // red
optr[offset * 4 + 1] = 255 * theGreen; // green
optr[offset * 4 + 2] = 255 * theBlue; // blue
optr[offset * 4 + 3] = 255; // alpha (opacity)
} |
23,778 | #include <stdio.h>
#include <math.h>
#include <float.h>
#include <thrust/extrema.h>
#include <thrust/device_ptr.h>
#define CSC(call) \
do { \
cudaError_t res = call; \
if (res != cudaSuccess) { \
fprintf(stderr, "ERROR in %s:%d. Message: %s\n", \
__FILE__, __LINE__, cudaGetErrorString(res)); \
exit(0); \
} \
} while(0)
__global__ void subtract_row(double *matrix, int n, int column) {
int idx = threadIdx.x + blockDim.x * blockIdx.x;
int idy = threadIdx.y + blockDim.y * blockIdx.y;
int offsetx = blockDim.x * gridDim.x;
int offsety = blockDim.y * gridDim.y;
int i, j;
double coeff;
double divisor = matrix[column * n + column];
for (i = 1 + column + idx; i < n; i += offsetx) {
coeff = matrix[column * n + i] / divisor;
for (j = 1 + column + idy; j < n + 1; j += offsety) {
matrix[j * n + i] -= coeff * matrix[j * n + column];
}
}
}
__global__ void reverse_subtract_row(double *matrix, int n, int column) {
int idx = threadIdx.x + blockDim.x * blockIdx.x;
int offsetx = blockDim.x * gridDim.x;
int i;
double coeff;
double divisor = matrix[column * n + column];
for (i = idx; i < column; i += offsetx) {
coeff = matrix[column * n + i] / divisor;
matrix[n * n + i] -= coeff * matrix[n * n + column];
}
}
__global__ void normalize(double *matrix, int n) {
int idx = threadIdx.x + blockDim.x * blockIdx.x;
int offsetx = blockDim.x * gridDim.x;
int i;
for (i = idx; i < n; i += offsetx) {
matrix[n * n + i] /= matrix[i * n + i];
}
}
__global__ void swap_rows(double *matrix, int n, int column, int max_row) {
int idx = threadIdx.x + blockDim.x * blockIdx.x;
int offsetx = blockDim.x * gridDim.x;
int i;
double tmp;
for (i = idx + column; i < n + 1; i+= offsetx) {
tmp = matrix[i * n + column];
matrix[i * n + column] = matrix[i * n + max_row];
matrix[i * n + max_row] = tmp;
}
}
struct compare {
__host__ __device__ bool operator ()(double lhs, double rhs) {
return fabs(lhs) < fabs(rhs);
}
};
void solve(double *matrix, int n) {
for (int column = 0; column < n; ++column) {
thrust::device_ptr<double> thrust_matrix =
thrust::device_pointer_cast(matrix) + n * column;
int max_row = thrust::max_element(thrust_matrix + column,
thrust_matrix + n, compare()) - thrust_matrix;
if (max_row >= n) {
continue;
}
swap_rows<<<32, 32>>>(matrix, n, column, max_row);
subtract_row<<<dim3(32, 32), dim3(32, 32)>>>(matrix, n, column);
}
for (int column = n - 1; column >= 0; --column) {
reverse_subtract_row<<<32, 32>>>(matrix, n, column);
}
normalize<<<32, 32>>>(matrix, n);
}
int main() {
int n;
scanf("%d", &n);
double *matrix = (double *) malloc(sizeof(double *) * (n + 1) * n);
for (int i = 0; i < n; ++i) {
for (int j = 0; j < n; ++j) {
scanf("%lf", matrix + j * n + i);
}
}
for (int i = 0; i < n; ++i) {
scanf("%lf", matrix + n * n + i);
}
double *device_matrix;
CSC(cudaMalloc(&device_matrix, sizeof(double) * (n + 1) * n));
CSC(cudaMemcpy(device_matrix, matrix, sizeof(double) * (n + 1) * n, cudaMemcpyHostToDevice));
solve(device_matrix, n);
CSC(cudaMemcpy(matrix + n * n, device_matrix + n * n, sizeof(double) * n, cudaMemcpyDeviceToHost));
for (int i = 0; i < n; ++i) {
printf("%.10e ", matrix[n * n + i]);
}
printf("\n");
CSC(cudaFree(device_matrix));
free(matrix);
return 0;
}
|
23,779 | #include <cuda_runtime.h>
#include <device_launch_parameters.h>
#include <stdio.h>
#include <time.h>
#define CHECK(call)\
{\
const cudaError_t error = call;\
if(error != cudaSuccess)\
{\
printf("Error: %s:%d,", __FILE__,__LINE__)\
printf("code:%d,reason:%s\n",error,cudaGetErrorString(error));\
exit(1);\
}\
}
void checkResult(float *hostRef, float *gpuRef,const int N){
double epsilon=1.0E-8;
bool match = 1;
for(int idx = 0; idx < N; idx++){
if(abs(hostRef[idx]-gpuRef[idx]) > epsilon){
match = 0;
printf("Arrays do not match!\n");
printf("host %5.2f gpu %5.2f at current %d\n",hostRef[idx],gpuRef[idx],idx);
break;
}
}
if(match)printf("Arrays match .\n\n");
}
//generate different seed for random number
void initiaData(float *ip,int size){
time_t t;
srand((unsigned)time(&t));
for(int i = 0; i < size; i++){
ip[i] = (float)(rand() & 0xFF)/10.0f;
}
}
void sumArrayOnHost(float *A,float *B, float *C, const int N){
for(int idx = 0; idx <N; idx++)
C[idx] = A[idx] + B[idx];
}
__global__ void sumArrayOnGPU(float *A,float *B, float *C){
int idx = threadIdx.x;
C[idx] = A[idx]+B[idx];
}
void printDeviceProp(const cudaDeviceProp &prop)
{
printf("Device Name : %s.\n", prop.name);
printf("totalGlobalMem : %d.\n", prop.totalGlobalMem);
printf("sharedMemPerBlock : %d.\n", prop.sharedMemPerBlock);
printf("regsPerBlock : %d.\n", prop.regsPerBlock);
printf("warpSize : %d.\n", prop.warpSize);
printf("memPitch : %d.\n", prop.memPitch);
printf("maxThreadsPerBlock : %d.\n", prop.maxThreadsPerBlock);
printf("maxThreadsDim[0 - 2] : %d %d %d.\n", prop.maxThreadsDim[0], prop.maxThreadsDim[1], prop.maxThreadsDim[2]);
printf("maxGridSize[0 - 2] : %d %d %d.\n", prop.maxGridSize[0], prop.maxGridSize[1], prop.maxGridSize[2]);
printf("totalConstMem : %d.\n", prop.totalConstMem);
printf("major.minor : %d.%d.\n", prop.major, prop.minor);
printf("clockRate : %d.\n", prop.clockRate);
printf("textureAlignment : %d.\n", prop.textureAlignment);
printf("deviceOverlap : %d.\n", prop.deviceOverlap);
printf("multiProcessorCount : %d.\n", prop.multiProcessorCount);
printf("\n\n\n");
}
int main(int argc, char **argv){
printf("%s Starting...\n",argv[0]);
//get the cuda device count
int count;
cudaGetDeviceCount(&count);
if(count == 0){
fprintf(stderr, "There is no device.\n");
exit(1);
}
//find the device >= 1.x
int idxDevice = 0;
for(int idxDevice = 0; idxDevice < count; ++idxDevice){
cudaDeviceProp prop;
if(cudaGetDeviceProperties(&prop,idxDevice) == cudaSuccess)
if(prop.major >= 1){
printDeviceProp(prop);
break;
}
}
if(idxDevice == count){
fprintf(stderr,"there is no device supporting CUDA 1.x. \n");
}
cudaSetDevice(idxDevice);
//set up data size of vectors
int nElm = 32;
printf("Vector size %d\n", nElm);
//malloc host memory
size_t nBytes = nElm * sizeof(float);
float *h_A,*h_B,*hostRef,*gpuRef;
h_A = (float *)malloc(nBytes);
h_B = (float *)malloc(nBytes);
hostRef = (float *)malloc(nBytes);
gpuRef = (float *)malloc(nBytes);
//initialize data at host side
initiaData(h_A, nElm);
initiaData(h_B, nElm);
memset(hostRef,0,nBytes);
memset(gpuRef,0,nBytes);
//malloc device global memory
float *d_A, *d_B, *d_C;
cudaMalloc((float **) &d_A,nBytes);
cudaMalloc((float **) &d_B,nBytes);
cudaMalloc((float **) &d_C,nBytes);
//transfer data from host to device
cudaMemcpy(d_A, h_A,nBytes,cudaMemcpyHostToDevice);
cudaMemcpy(d_B, h_B,nBytes,cudaMemcpyHostToDevice);
//invoke kenel at host side
dim3 block(nElm);
dim3 grid(nElm/block.x);
sumArrayOnGPU<<< grid, block >>>(d_A,d_B,d_C);
printf("Execution configuration <<<%d, %d>>>\n",grid.x,block.x);
//copy kenel result back to host side
cudaMemcpy(gpuRef, d_C,nBytes,cudaMemcpyDeviceToHost);
//add vector at host side for result checks
sumArrayOnHost(h_A,h_B, hostRef, nElm);
//check device results
checkResult(hostRef, gpuRef,nElm);
//free device global memory
cudaFree(d_A);
cudaFree(d_B);
cudaFree(d_C);
//free host memory
free(h_A);
free(h_B);
free(hostRef);
free(gpuRef);
return 0;
}
|
23,780 |
#include <iostream>
#include <fstream>
#include <cmath>
// lattice size
int const NX = 256;
int const NY = 256;
int const MaxIter = 200000; // number of Jacobi iterations
void checkError(cudaError_t e)
{
if (e != cudaSuccess)
{
std::cerr << "CUDA error: " << int(e) << " : " << cudaGetErrorString(e) << '\n';
abort();
}
}
// CUDA kernel to update the Tnew array based on the Told
// using Jacobi iterate of the Laplace operator
__global__
void Laplace(float* Told, float* Tnew)
{
// compute the x,y location of the lattice point
// handled by this thread
int x = blockIdx.x*blockDim.x + threadIdx.x ;
int y = blockIdx.y*blockDim.y + threadIdx.y ;
// get the natural index values of node (x,y) and its neighboring nodes
int P = y*NX + x; // node (x,y) N
int E = y*NX + (x+1); // node (x+1,y) |
int W = y*NX + (x-1); // node (x-1,y) W--P--E
int N = (y+1)*NX + x; // node (x,y+1) |
int S = (y-1)*NX + x; // node (x,y-1) S
// only update the interior points - fixed boundary conditions
if(x > 0 && x < (NX-1) && y > 0 && y < (NY-1))
{
Tnew[P] = 0.25*(Told[E] + Told[W] + Told[N] + Told[S]);
}
}
// Initial condition
void Initialize(float* T)
{
// set everything to zero
for(int x=0; x < NX; ++x)
{
for(int y=0; y < NY; ++y)
{
int index = y*NX + x;
T[index] = 0.0;
}
}
// set left wall to 1
for(int y=0; y < NY; ++y)
{
int index = y*NX;
T[index] = 1.0;
}
// set back wall to a ramp
for(int x=0; x < NX; ++x)
{
int index = (NY-1)*NX + x;
T[index] = 1.0 - float(x)/NX;
}
// set front wall to a sinusoid
for(int x=0; x < NX; ++x)
{
int index = x;
T[index] = std::cos(3.0 * M_PI_2 * float(x)/NX);
}
}
int main()
{
// The temperature matrix on the CPU
float* T = new float[NX*NY];
// Storage on the device (GPU)
float* T1_device;
float* T2_device;
// initialize array on the host
Initialize(T);
// allocate storage space on the GPU
checkError(cudaMalloc(&T1_device, NX*NY*sizeof(float)));
checkError(cudaMalloc(&T2_device, NX*NY*sizeof(float)));
// copy (initialized) host arrays to the GPU memory from CPU memory
checkError(cudaMemcpy(T1_device, T, NX*NY*sizeof(float), cudaMemcpyHostToDevice));
checkError(cudaMemcpy(T2_device, T, NX*NY*sizeof(float), cudaMemcpyHostToDevice));
// assign a 2D distribution of CUDA "threads" within each CUDA "block".
dim3 dimBlock(16, 16); // total of 256 threads per block
// calculate number of blocks along X and Y in a 2D CUDA "grid"
dim3 dimGrid((NX+dimBlock.x-1) / dimBlock.x, (NY+dimBlock.y-1) / dimBlock.y);
// begin Jacobi iteration
for (int k = 0; k < MaxIter; k += 2)
{
Laplace<<<dimGrid, dimBlock>>>(T1_device, T2_device); // update T1 using data stored in T2
Laplace<<<dimGrid, dimBlock>>>(T2_device, T1_device); // update T2 using data stored in T1
}
// copy final array to the CPU from the GPU
checkError(cudaMemcpy(T, T2_device, NX*NY*sizeof(float),cudaMemcpyDeviceToHost));
cudaDeviceSynchronize();
// print the results to a file
std::ofstream Out("temperature.dat");
for (int y = 0; y < NY; ++y)
{
for (int x = 0; x < NX; ++x)
{
int index = y*NX + x;
Out << x << ' ' << y << ' ' << T[index] << '\n';
}
Out << '\n';
}
Out.close();
// release memory
delete[] T;
cudaFree(T1_device);
cudaFree(T2_device);
}
|
23,781 | /* kernel routine starts with keyword __global__ */
#include <stdio.h>
#define N 4096
__global__ void matrixadd(float* A, float* B, float* C)
{
int i = blockIdx.x*blockDim.x+threadIdx.x;
int j = blockIdx.y*blockDim.y+threadIdx.y;
int index = i+j*N;
int k;
// if ((i<N) && (j<N)) {
for (k=0; k<N; k++)
C[index] += A[i+k*N] * B[k+j*N];
// C[index] = sqrt(sin(cos(A[index]))) + sqrt(sin(cos(B[index])));
}
int main(int argc, char * argv[])
{
float *host_A, *host_B, *host_C;
float *dev_A, *dev_B, *dev_C;
int i, j, n;
if (argc == 1) n = 16;
else n = atoi(argv[1]);
if (n > 32) {
printf("n must be less than 32\n");
exit(0);
}
/* 1. allocate host memory */
host_A = (float*)malloc( N*N*sizeof(float) );
host_B = (float*)malloc( N*N*sizeof(float) );
host_C = (float*)malloc( N*N*sizeof(float) );
/* 2. allocate GPU memory */
cudaMalloc( &dev_A, N*N*sizeof(float) );
cudaMalloc( &dev_B, N*N*sizeof(float) );
cudaMalloc( &dev_C, N*N*sizeof(float) );
/* initialize array A and B */
for( i = 0; i < N; ++i ) {
for( j = 0; j < N; ++j ) {
host_A[i+j*N] = (float) 1.0;
host_B[i+j*N] = (float) 1.0;
}
}
/* 3. Copydata (host_A and host_B) to GPU */
cudaMemcpy( dev_A, host_A, N*N*sizeof(float), cudaMemcpyHostToDevice );
cudaMemcpy( dev_B, host_B, N*N*sizeof(float), cudaMemcpyHostToDevice );
/* 4. call kernel routine to execute on GPU */
/* launch 1 thread per vector-element, 1024 threads per block */
dim3 dimBlock(n, n);
dim3 dimGrid(N/n, N/n);
matrixadd<<<dimGrid, dimBlock>>>( dev_A, dev_B, dev_C );
/* transfer results from GPU (dev_C) to CPU (host_C) */
cudaMemcpy( host_C, dev_C, N*N*sizeof(float), cudaMemcpyDeviceToHost );
#ifdef CHECK
{
FILE *fd;
fd = fopen("tmp333", "w");
for (i=0;i<N*N; i++) {
fprintf(fd, "%f\n", host_C[i]);
}
}
#endif
/* free host and GPU memory */
free(host_A);
free(host_B);
free(host_C);
cudaFree(dev_A);
cudaFree(dev_B);
cudaFree(dev_C);
return( 0 );
}
|
23,782 | /* It is essential for the programmer to choose the correct GPU device from a list of GPU devices
available to perform the calculations very efficiently,
For Example: To run the double precision applications devices with compute capacity of more than 1.3 are desired.
The following program utilizes the cudaDeviceProp structure to get the info on the same*/
// Note: There are lot of properties available with the cudaDeviceProp struct
#include <stdio.h>
#include <iostream>
using namespace std;
int main()
{
cudaDeviceProp prop;
int count;
cudaGetDeviceCount(&count); //Gives the number of devices
std::cout<<count<<std::endl;
for(int i=0;i<count;i++)
{
cudaGetDeviceProperties(&prop,i);
printf( "Name: %s\n", prop.name ); // Gives the name of the device
printf("Computation Capability: %d.%d\n", prop.major,prop.minor); // Givees the compute capability of the device
}
} |
23,783 | #include "includes.h"
//Udacity HW 4
//Radix Sorting
__global__ void scanBlks(unsigned int *in, unsigned int *out, unsigned int n, unsigned int *blkSums)
{
extern __shared__ int blkData[];
int i1 = blockIdx.x * 2 * blockDim.x + threadIdx.x;
int i2 = i1 + blockDim.x;
if (i1 < n)
blkData[threadIdx.x] = in[i1];
if (i2 < n)
blkData[threadIdx.x + blockDim.x] = in[i2];
__syncthreads();
for (int stride = 1; stride < 2 * blockDim.x; stride *= 2)
{
int blkDataIdx = (threadIdx.x + 1) * 2 * stride - 1;
if (blkDataIdx < 2 * blockDim.x)
blkData[blkDataIdx] += blkData[blkDataIdx - stride];
__syncthreads();
}
for (int stride = blockDim.x / 2; stride > 0; stride /= 2)
{
int blkDataIdx = (threadIdx.x + 1) * 2 * stride - 1 + stride;
if (blkDataIdx < 2 * blockDim.x)
blkData[blkDataIdx] += blkData[blkDataIdx - stride];
__syncthreads();
}
if (i1 < n)
out[i1] = blkData[threadIdx.x];
if (i2 < n)
out[i2] = blkData[threadIdx.x + blockDim.x];
if (blkSums != NULL && threadIdx.x == 0)
blkSums[blockIdx.x] = blkData[2 * blockDim.x - 1];
} |
23,784 | #include "includes.h"
#define CUDA_INPUT "input.txt"
#define CUDA_OUTPUT "cuda_output.txt"
int NUMPOINTS;
double ENDTIME;
double DT;
double ENDVALUES;
void InitialiseToZero(float* array);
__device__ void PrintPointsGPU(float* array, int size, double currentTime);
void PrintPointsCPU(float* array, double currentTime);
void ProcessOutput(float* array, int testCase, float time);
void CheckPoints(float* firstArray, float* secondArray);
__global__ void DiffuseHeat(float* currentPoints, float* nextPoints, const size_t size, double dx, double dt, const size_t endTime)
{
unsigned int threadIndex = (threadIdx.x + blockDim.x * blockIdx.x) + 1;
double currentTime = 0.0;
if (threadIndex > 0 && threadIndex < size-1)
{
while (currentTime < endTime)
{
nextPoints[threadIndex] = currentPoints[threadIndex] + 0.25*(currentPoints[threadIndex+1] - (2*currentPoints[threadIndex]) + currentPoints[threadIndex-1]);
__syncthreads();
currentPoints[threadIndex] = nextPoints[threadIndex];
currentTime += dt;
__syncthreads();
}
}
} |
23,785 | #include <bits/stdc++.h>
using namespace std;
void add(int N, float *X, float *Y)
{
for (int i=0; i<N; i++)
{
Y[i] = X[i] + Y[i];
}
}
int main(void)
{
int N = 1<<27;//1.34217728 *10^8 elements. 512 MB
//Allocate Memory (512*2=1GB).
float *X = new float[N];
float *Y = new float[N];
// initialize x and y arrays on the host
for(int i = 0; i < N; i++)
{
X[i] = 1.0f;
Y[i] = 2.0f;
}
double avg=0;
clock_t t;
// Runs add 10 times on CPU
for(int i=0;i<10;i++)
{
t=clock();//start time
add(N, X, Y);
t = clock() - t;//total time = end time - start time
printf ("CPU RUN-%d time = %f ms.\n",i,(((float)t)/CLOCKS_PER_SEC)*1000);
avg+=((((float)t)/CLOCKS_PER_SEC)*1000);//time is calculated in terms of clockcycle. Converted in millisecond
}
avg=avg/10;// average of 10 elements
printf ("CPU Avg time = %lf ms.\n",avg);
delete [] X;
delete [] Y;
return 0;
}
|
23,786 | //Perlin kernel (Rob Farber)
#include <stdio.h>
extern float gain, xStart, yStart, zOffset, octaves, lacunarity;
#define Z_PLANE 50.f
__constant__ unsigned char c_perm[256];
__shared__ unsigned char s_perm[256]; // shared memory copy of permuation array
unsigned char* d_perm=NULL; // global memory copy of permutation array
// host version of permutation array
const static unsigned char h_perm[] = {151,160,137,91,90,15,
131,13,201,95,96,53,194,233,7,225,140,36,103,30,69,142,8,99,37,240,21,10,23,
190, 6,148,247,120,234,75,0,26,197,62,94,252,219,203,117,35,11,32,57,177,33,
88,237,149,56,87,174,20,125,136,171,168, 68,175,74,165,71,134,139,48,27,166,
77,146,158,231,83,111,229,122,60,211,133,230,220,105,92,41,55,46,245,40,244,
102,143,54, 65,25,63,161, 1,216,80,73,209,76,132,187,208, 89,18,169,200,196,
135,130,116,188,159,86,164,100,109,198,173,186, 3,64,52,217,226,250,124,123,
5,202,38,147,118,126,255,82,85,212,207,206,59,227,47,16,58,17,182,189,28,42,
223,183,170,213,119,248,152,2,44,154,163, 70,221,153,101,155,167, 43,172,9,
129,22,39,253, 19,98,108,110,79,113,224,232,178,185, 112,104,218,246,97,228,
251,34,242,193,238,210,144,12,191,179,162,241, 81,51,145,235,249,14,239,107,
49,192,214, 31,181,199,106,157,184,84,204,176,115,121,50,45,127, 4,150,254,
138,236,205,93,222,114,67,29,24,72,243,141,128,195,78,66,215,61,156,180
};
__device__ inline int perm(int i) { return(s_perm[i&0xff]); }
__device__ inline float fade(float t) { return t * t * t * (t * (t * 6.f - 15.f) + 10.f); }
__device__ inline float lerpP(float t, float a, float b) { return a + t * (b - a); }
__device__ inline float grad(int hash, float x, float y, float z) {
int h = hash & 15; // CONVERT LO 4 BITS OF HASH CODE
float u = h<8 ? x : y, // INTO 12 GRADIENT DIRECTIONS.
v = h<4 ? y : h==12||h==14 ? x : z;
return ((h&1) == 0 ? u : -u) + ((h&2) == 0 ? v : -v);
}
__device__ float inoise(float x, float y, float z) {
int X = ((int)floorf(x)) & 255, // FIND UNIT CUBE THAT
Y = ((int)floorf(y)) & 255, // CONTAINS POINT.
Z = ((int)floorf(z)) & 255;
x -= floorf(x); // FIND RELATIVE X,Y,Z
y -= floorf(y); // OF POINT IN CUBE.
z -= floorf(z);
float u = fade(x), // COMPUTE FADE CURVES
v = fade(y), // FOR EACH OF X,Y,Z.
w = fade(z);
int A = perm(X)+Y, AA = perm(A)+Z, AB = perm(A+1)+Z, // HASH COORDINATES OF
B = perm(X+1)+Y, BA = perm(B)+Z, BB = perm(B+1)+Z; // THE 8 CUBE CORNERS,
return lerpP(w, lerpP(v, lerpP(u, grad(perm(AA), x , y , z ), // AND ADD
grad(perm(BA), x-1.f, y , z )), // BLENDED
lerpP(u, grad(perm(AB), x , y-1.f, z ), // RESULTS
grad(perm(BB), x-1.f, y-1.f, z ))), // FROM 8
lerpP(v, lerpP(u, grad(perm(AA+1), x , y , z-1.f ), // CORNERS
grad(perm(BA+1), x-1.f, y , z-1.f )), // OF CUBE
lerpP(u, grad(perm(AB+1), x , y-1.f, z-1.f ),
grad(perm(BB+1), x-1.f, y-1.f, z-1.f ))));
#ifdef ORIG
return(perm(X));
#endif
}
__device__ float fBm(float x, float y, int octaves,
float lacunarity = 2.0f, float gain = 0.5f)
{
float freq = 1.0f, amp = 0.5f;
float sum = 0.f;
for(int i=0; i<octaves; i++) {
sum += inoise(x*freq, y*freq, Z_PLANE)*amp;
freq *= lacunarity;
amp *= gain;
}
return sum;
}
__device__ inline uchar4 colorElevation(float texHeight)
{
uchar4 pos;
// color textel (r,g,b,a)
if (texHeight < -1.000f) pos = make_uchar4(000, 000, 128, 255); //deeps
else if (texHeight < -.2500f) pos = make_uchar4(000, 000, 255, 255); //shallow
else if (texHeight < 0.0000f) pos = make_uchar4(000, 128, 255, 255); //shore
else if (texHeight < 0.0125f) pos = make_uchar4(240, 240, 064, 255); //sand
else if (texHeight < 0.1250f) pos = make_uchar4(032, 160, 000, 255); //grass
else if (texHeight < 0.3750f) pos = make_uchar4(224, 224, 000, 255); //dirt
else if (texHeight < 0.7500f) pos = make_uchar4(128, 128, 128, 255); //rock
else pos = make_uchar4(255, 255, 255, 255); //snow
return(pos);
}
void checkCUDAError(const char *msg) {
cudaError_t err = cudaGetLastError();
if( cudaSuccess != err) {
fprintf(stderr, "Cuda error: %s: %s.\n", msg, cudaGetErrorString( err) );
exit(EXIT_FAILURE);
}
}
//Simple kernel fills an array with perlin noise
__global__ void k_perlin(float4* pos, uchar4 *colorPos,
unsigned int width, unsigned int height,
float2 start, float2 delta, float gain, float zOffset,
unsigned char* d_perm, float octaves, float lacunarity)
{
int idx = blockIdx.x * blockDim.x + threadIdx.x;
float xCur = start.x + ((float) (idx%width)) * delta.x;
float yCur = start.y + ((float) (idx/width)) * delta.y;
if(threadIdx.x < 256)
// Optimization: this causes bank conflicts
s_perm[threadIdx.x] = d_perm[threadIdx.x];
// this synchronization can be important if there are more that 256 threads
__syncthreads();
// Each thread creates one pixel location in the texture (textel)
if(idx < width*height) {
float w = fBm(xCur, yCur, octaves, lacunarity, gain) + zOffset;
colorPos[idx] = colorElevation(w);
float u = ((float) (idx%width))/(float) width;
float v = ((float) (idx/width))/(float) height;
u = u*2.f - 1.f;
v = v*2.f - 1.f;
w = (w>0.f)?w:0.f; // don't show region underwater
pos[idx] = make_float4( u, w, v, 1.0f);
}
}
uchar4 *eColor=NULL;
// Wrapper for the __global__ call that sets up the kernel call
extern "C" void launch_kernel(float4 *pos, uchar4* posColor,
unsigned int image_width, unsigned int image_height,
float time)
{
int nThreads=256; // must be equal or larger than 256! (see s_perm)
int totalThreads = image_height * image_width;
int nBlocks = totalThreads/nThreads;
nBlocks += ((totalThreads%nThreads)>0)?1:0;
float xExtent = 10.f;
float yExtent = 10.f;
float xDelta = xExtent/(float)image_width;
float yDelta = yExtent/(float)image_height;
if(!d_perm) { // for convenience allocate and copy d_perm here
cudaMalloc((void**) &d_perm,sizeof(h_perm));
cudaMemcpy(d_perm,h_perm,sizeof(h_perm),cudaMemcpyHostToDevice);
checkCUDAError("d_perm malloc or copy failed!");
}
k_perlin<<< nBlocks, nThreads>>>(pos, posColor, image_width, image_height,
make_float2(xStart, yStart),
make_float2(xDelta, yDelta),
gain, zOffset, d_perm,
octaves, lacunarity);
// make certain the kernel has completed
cudaThreadSynchronize();
checkCUDAError("kernel failed!");
}
|
23,787 | #include <cstdio>
#include <algorithm>
#include <cuda.h>
#include <sys/time.h>
static const int ThreadsPerBlock = 512;
static __global__ void collatz(const long upper, int* const maxlen)
{
const long i = threadIdx.x + blockIdx.x * (long)blockDim.x;
// compute sequence lengths
if (i < (upper + 1)/2) {
long val = 2*i + 1; // translate to i-th odd
int len = 1;
while (val != 1) {
len++;
if ((val % 2) == 0) {
val = val / 2; // even
} else {
val = 3 * val + 1; // odd
}
}
if(len > *maxlen) atomicMax(maxlen, len); // per instructions
}
}
static void CheckCuda()
{
cudaError_t e;
cudaDeviceSynchronize();
if (cudaSuccess != (e = cudaGetLastError())) {
fprintf(stderr, "CUDA error %d: %s\n", e, cudaGetErrorString(e));
exit(-1);
}
}
int main(int argc, char *argv[])
{
printf("Collatz v1.2\n");
// check command line
if (argc != 2) {fprintf(stderr, "USAGE: %s upper_bound\n", argv[0]); exit(-1);}
const long upper = atol(argv[1]);
if (upper < 5) {fprintf(stderr, "ERROR: upper_bound must be at least 5\n"); exit(-1);}
if ((upper % 2) != 1) {fprintf(stderr, "ERROR: upper_bound must be an odd number\n"); exit(-1);}
printf("upper bound: %ld\n", upper);
// allocate cpu vars
int* const maxlen = new int;
*maxlen = 0;
// allocate gpu vars
int* d_maxlen;
if (cudaSuccess != cudaMalloc((void **)&d_maxlen, sizeof(int))) {fprintf(stderr, "ERROR: could not allocate memory\n"); exit(-1);}
// initialize gpu vars
if (cudaSuccess != cudaMemcpy(d_maxlen, maxlen, sizeof(int), cudaMemcpyHostToDevice)) {fprintf(stderr, "ERROR: copying to device failed\n"); exit(-1);}
// start time
timeval start, end;
gettimeofday(&start, NULL);
// execute timed code
// because we're only testing odd values, there are (upper+1)/2 number of iterations
collatz<<<((upper+1)/2 + ThreadsPerBlock - 1) / ThreadsPerBlock, ThreadsPerBlock>>>(upper, d_maxlen);
cudaDeviceSynchronize();
// end time
gettimeofday(&end, NULL);
const double runtime = end.tv_sec - start.tv_sec + (end.tv_usec - start.tv_usec) / 1000000.0;
printf("compute time: %.4f s\n", runtime);
// get result from GPU
CheckCuda();
if (cudaSuccess != cudaMemcpy(maxlen, d_maxlen, sizeof(int), cudaMemcpyDeviceToHost)) {fprintf(stderr, "ERROR: copying to host failed\n"); exit(-1);}
// print result
printf("longest sequence: %d elements\n", *maxlen);
// clean up
free(maxlen);
cudaFree(d_maxlen);
return 0;
}
|
23,788 | /****************************************************************************
*
* cuda-hello1.cu - Hello world with CUDA (with dummy device code)
*
* Written in 2017 by Moreno Marzolla <moreno.marzolla(at)unibo.it>
*
* To the extent possible under law, the author(s) have dedicated all
* copyright and related and neighboring rights to this software to the
* public domain worldwide. This software is distributed without any warranty.
*
* You should have received a copy of the CC0 Public Domain Dedication
* along with this software. If not, see
* <http://creativecommons.org/publicdomain/zero/1.0/>.
*
* ---------------------------------------------------------------------------
*
* Based on the example shown in the CUDA toolkit documentation
* http://docs.nvidia.com/cuda/cuda-c-programming-guide/
*
* Compile with:
* nvcc cuda-hello1.cu -o cuda-hello1
*
* Run with:
* ./cuda-hello1
*
****************************************************************************/
#include <stdio.h>
__global__ void mykernel( void ) { }
int main( void )
{
mykernel<<<1,1>>>( );
printf("Hello, world!\n");
return 0;
}
|
23,789 | #include <stdio.h>
#define CUDA_CHECK_RETURN( value ) { \
cudaError_t _m_cudaStat = value; \
if ( _m_cudaStat != cudaSuccess ) { \
fprintf( stderr, "Error '%s' at line %d in file %s\n", \
cudaGetErrorString( _m_cudaStat ), __LINE__, __FILE__ ); \
exit( 1 ); \
} }
//
void bandwidthTest( int gpu1, int gpu2, size_t memSize )
{
printf( "Bandwidth test between " );
float measuredTime = 0.0f;
float bandwidthPeerAccessDisabled = 0.0f;
float bandwidthPeerAccessEnabled = 0.0f;
cudaEvent_t start, stop;
cudaDeviceProp deviceProp;
// alloc host memory
unsigned char* h_array = new unsigned char[ memSize ];
for( size_t i = 0; i < memSize; i++ )
h_array[ i ] = ( unsigned char )( i & 0xff );
// set first device
CUDA_CHECK_RETURN( cudaSetDevice( gpu1 ) );
// get gpu1 name
CUDA_CHECK_RETURN( cudaGetDeviceProperties( &deviceProp, gpu1 ) );
printf("GPU%d %s and", gpu1, deviceProp.name );
// create events
CUDA_CHECK_RETURN( cudaEventCreate( &start ) );
CUDA_CHECK_RETURN( cudaEventCreate( &stop ) );
// alloc mem on gpu1
unsigned char *d_gpu1Array;
CUDA_CHECK_RETURN( cudaMalloc( ( void ** )&d_gpu1Array, memSize ) );
CUDA_CHECK_RETURN( cudaMemcpy( d_gpu1Array, h_array, memSize, cudaMemcpyHostToDevice ) );
// set second gpu
CUDA_CHECK_RETURN( cudaSetDevice( gpu2 ) );
// get gpu2 name
CUDA_CHECK_RETURN( cudaGetDeviceProperties( &deviceProp, gpu1 ) );
printf(" GPU%d %s\n", gpu2, deviceProp.name );
// alloc mem on gpu2
unsigned char *d_gpu2Array;
CUDA_CHECK_RETURN( cudaMalloc( ( void ** )&d_gpu2Array, memSize ) );
// check accessible
int accessible = 0;
cudaDeviceCanAccessPeer( &accessible, gpu2, gpu1 );
printf( " GPU%d is capable of directly accessing memory from GPU%d: %s\n", gpu2, gpu1, accessible ? "yes" : "no" );
for( int i = 0; i < 1 + accessible; i++ )
{
CUDA_CHECK_RETURN( cudaSetDevice( gpu2 ) );
if( i == 1 )
CUDA_CHECK_RETURN( cudaDeviceEnablePeerAccess( gpu1, 0 ) );
// return back gpu1, cause create events on it
CUDA_CHECK_RETURN( cudaSetDevice( gpu1 ) );
CUDA_CHECK_RETURN( cudaEventRecord( start, 0 ) );
const size_t STEPS = 10;
for( size_t j = 0; j < STEPS; j++ )
CUDA_CHECK_RETURN( cudaMemcpyPeer( d_gpu2Array, gpu2, d_gpu1Array, gpu1, memSize ) );
CUDA_CHECK_RETURN( cudaEventRecord( stop, 0 ) );
// waiting
CUDA_CHECK_RETURN( cudaSetDevice( gpu2 ) );
CUDA_CHECK_RETURN( cudaDeviceSynchronize() );
CUDA_CHECK_RETURN( cudaSetDevice( gpu1 ) );
CUDA_CHECK_RETURN( cudaDeviceSynchronize() );
//
CUDA_CHECK_RETURN( cudaEventElapsedTime( &measuredTime, start, stop ) );
//
( i == 0 ? bandwidthPeerAccessDisabled : bandwidthPeerAccessEnabled ) = ( 1e3f * memSize * ( float )STEPS) / ( measuredTime * ( float )( 1 << 20 ) );
}
printf( " Bandwidth with enabled peer acces: %f mb/s\n", bandwidthPeerAccessEnabled );
printf( " Bandwidth with disabled peer acces: %f mb/s\n", bandwidthPeerAccessDisabled );
//
free( h_array );
CUDA_CHECK_RETURN( cudaEventDestroy( stop ) );
CUDA_CHECK_RETURN( cudaEventDestroy( start ) );
CUDA_CHECK_RETURN( cudaFree( d_gpu1Array ) );
CUDA_CHECK_RETURN( cudaSetDevice( gpu2 ) );
CUDA_CHECK_RETURN( cudaFree( d_gpu2Array) );
if( accessible )
CUDA_CHECK_RETURN( cudaDeviceDisablePeerAccess( gpu1 ) );
}
//
int main( int argc, char* argv[] )
{
size_t memSize = 1 << 29;
if( argc == 2 )
memSize = atoi( argv[ 1 ] );
int deviceCount;
CUDA_CHECK_RETURN( cudaGetDeviceCount( &deviceCount ) );
printf( "Device count: %d %u\n", deviceCount, memSize );
for( int i = 0; i < deviceCount; i++ )
for( int j = i + 1; j < deviceCount; j++ )
{
if( i == j )
continue;
printf( "\n" );
bandwidthTest( i, j, memSize );
}
return 0;
} |
23,790 | #include <stdexcept>
#include <iostream>
#include <stdio.h>
#include <assert.h>
#include <cmath>
#include <cuda.h>
#include <cuda_runtime.h>
__global__
void sin_kernel(int* __restrict__ out, const float* __restrict__ x) {
*out = std::pow(4,3);
}
//__global__
//void sinf_kernel(float* __restrict__ out, const float* __restrict__ x) {
// *out = sinf(*x);
//}
int main(int argc, char* argv[]) {
return 0;
}
|
23,791 | #include <fstream>
#include <time.h>
using namespace std;
class params{
private:
double r_min_ini, r_min_trg;
double r_max_ini, r_max_trg;
double i_min_ini, i_min_trg;
double i_max_ini, i_max_trg;
public:
int width, height;
double r_min, r_max, i_min, i_max;
int max;
params(){
r_min_ini = -2.86, r_min_trg = -0.1777;
r_max_ini = 1.86, r_max_trg = -0.1194;
i_min_ini = -1.33, i_min_trg = 1.0138;
i_max_ini = 1.33, i_max_trg = 1.0472;
width = 1920, height = 1080;
r_min = r_min_ini, r_max = r_max_ini, i_min = i_min_ini, i_max = i_max_ini;
max = 50;
}
void set_frame_number(int, int);
};
void params::set_frame_number(int n, int max){
if (n < max){
double t = 1 - pow(0.001, (double)n/(double)max);
r_min = r_min_ini + t*(r_min_trg - r_min_ini);
r_max = r_max_ini + t*(r_max_trg - r_max_ini);
i_min = i_min_ini + t*(i_min_trg - i_min_ini);
i_max = i_max_ini + t*(i_max_trg - i_max_ini);
}
else{
r_min = r_min_trg, r_max = r_max_trg, i_min = i_min_trg, i_max = i_max_trg;
}
}
void bmp_write(char * img_data, int width, int height, char * filename){
int size = height*width*3 + 54;
ofstream bmp(filename, ios::binary);
char bmp_header[54] = {'B', 'M',
(char)size, (char)(size >> 8), (char)(size >> 16), (char)(size >> 24),
0, 0, 0, 0, 54, 0, 0, 0, 40, 0, 0, 0, (char)width, (char)(width >> 8),
0, 0, (char)height,(char)(height >> 8), 0, 0, 1, 0, 24, 0, 0, 0, 0, 0,
(char)(size - 54), (char)((size - 54) >> 8),(char)((size - 54) >> 16),
(char)((size - 54) >> 24),0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0};
bmp.write(bmp_header, sizeof bmp_header);
bmp.write(img_data, size - 54);
bmp.close();
}
__host__ __device__ double iter(double r_c, double i_c, int max){
double r_t = 0.0, i_t = 0.0;
double r_t_2, i_t_2, mag;
for (int i = 0; i < max; i++){
r_t_2 = r_t * r_t - i_t * i_t;
i_t_2 = 2 * r_t * i_t;
r_t = r_t_2 + r_c;
i_t = i_t_2 + i_c;
mag = r_t*r_t + i_t*i_t;
if (mag > 4){
return (double)(i + 1 - log(log(mag))/log(2.0))/(double)max;
}
}
return 0;
}
__global__ void populate(char * d_img_data, double r_min, double r_max,
double i_min, double i_max, int width, int height, int max){
int x = blockIdx.x*blockDim.x + threadIdx.x;
int y = blockIdx.y*blockDim.y + threadIdx.y;
if (x < width && y < height){
double r = ((double)x/(double)width)*(r_max-r_min)+r_min;
double i = ((double)y/(double)height)*(i_max-i_min)+i_min;
double t = iter(r, i, max);
d_img_data[y*3*width + x*3 + 0] = (char)(255*sqrt(t));
d_img_data[y*3*width + x*3 + 1] = (char)(255*t);
d_img_data[y*3*width + x*3 + 2] = (char)(255*t*t);
}
}
void plot_frame_gpu(params plot, char * filename){
/*getting image data*/
char * h_img_data = new char[plot.width*plot.height*3*sizeof(char)];
char * d_img_data;
cudaMalloc((void **) &d_img_data, plot.width*plot.height*3*sizeof(char));
dim3 threads(32, 32, 1);
dim3 grid(ceil((double)plot.width/32.0),
ceil((double)plot.height/32.0), 1);
populate<<<grid, threads>>>(d_img_data, plot.r_min, plot.r_max, plot.i_min,
plot.i_max, plot.width, plot.height, plot.max);
cudaMemcpy(h_img_data, d_img_data, plot.width*plot.height*3*sizeof(char),
cudaMemcpyDeviceToHost);
cudaFree(d_img_data);
/*write to file*/
bmp_write(h_img_data, plot.width, plot.height, filename);
delete[] h_img_data;
}
void plot_frame_cpu(params plot, char * filename){
char * img_data = new char[plot.width*plot.height*3*sizeof(char)];
for (int y = 0; y < plot.height; y++){
for (int x = 0; x < plot.width; x++){
double r = ((double)x/(double)plot.width)*(plot.r_max-plot.r_min)+plot.r_min;
double i = ((double)y/(double)plot.height)*(plot.i_max-plot.i_min)+plot.i_min;
double t = iter(r, i, plot.max);
img_data[y*3*plot.width + x*3 + 0] = (char)(255*sqrt(t));
img_data[y*3*plot.width + x*3 + 1] = (char)(255*t);
img_data[y*3*plot.width + x*3 + 2] = (char)(255*t*t);
}
}
bmp_write(img_data, plot.width, plot.height, filename);
delete[] img_data;
}
int main(int argc, char ** argv){
int num_frames = 1000;
clock_t t1, t2;
char * filename = (char *)malloc(200*sizeof(char));
params plot;
/*TEST GPU*/
ofstream log_gpu("/home/keshav/Dropbox (MIT)/anim_gpu/log_gpu.txt");
for (int i = 0; i < num_frames; i++){
sprintf(filename, "/home/keshav/Dropbox (MIT)/anim_gpu/i%03d.bmp", i);
plot.set_frame_number(i, num_frames);
t1 = clock();
plot_frame_gpu(plot, filename);
t2 = clock();
log_gpu << 1000*(double)(t2-t1)/(CLOCKS_PER_SEC) << "\n";
if (!(i%(num_frames/100)) && (argc-1)) printf("Done %2.2f%% on GPU.\n",
100*(double)i/(double)num_frames);
}
log_gpu.close();
/*TEST CPU*/
ofstream log_cpu("/home/keshav/Dropbox (MIT)/anim_cpu/log_cpu.txt");
for (int i = 0; i < num_frames; i++){
sprintf(filename, "/home/keshav/Dropbox (MIT)/anim_cpu/i%03d.bmp", i);
plot.set_frame_number(i, num_frames);
t1 = clock();
plot_frame_cpu(plot, filename);
t2 = clock();
log_cpu << 1000*(double)(t2-t1)/(CLOCKS_PER_SEC) << "\n";
if (!(i%(num_frames/100)) && (argc-1)) printf("Done %2.2f%% on CPU.\n",
100*(double)i/(double)num_frames);
}
log_cpu.close();
delete[] filename;
return 0;
}
|
23,792 | #include <stdio.h>
#include <stdint.h>
typedef struct Node{
int a;
Node *hasNext;
} Node;
int main(){
Node *node = (Node *) malloc(sizeof(Node));
int address = reinterpret_cast<uintptr_t>(node);
Node *next = reinterpret_cast<Node *>(address);
printf("%p %x %p\n", node, address, next);
free(node);
return 0;
}
|
23,793 | #include <iostream>
#include <cuda.h>
#include <chrono>
#include <stdlib.h>
#include <ctime>
#include <cmath>
#include <limits>
__global__ void sum_vectors_if(double *a, int size){
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < size) {
if ((int(a[idx]) + idx) % 3 == 0) {
a[idx] = a[idx / 3] * (idx + 2);
} else {
if ((int(a[idx]) + idx) % 3 == 1) {
a[idx] = a[idx / 3] * (idx + 1);
} else {
a[idx] = a[idx / 3] * (idx - 2);
}
}
}
}
__global__ void sum_vectors(double *a, int size){
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < size) {
a[idx] += idx % 3;
}
}
int main(int argc, char **argv){
int n = (int)strtol(argv[1], NULL, 10);
size_t bytes = n * sizeof(double);
double *h_a;
h_a = (double *) malloc(bytes);
srand(3333);
for (int i = 0; i < n; i++){
int num = rand();
h_a[i] = num - num % 3;
}
double *d_a;
cudaMalloc(&d_a, bytes);
cudaMemcpy(d_a, h_a, bytes, cudaMemcpyHostToDevice);
int blockSize, gridSize;
blockSize = 1024;
gridSize = (n - 1) / 1024 + 1;
cudaEvent_t start, stop;
// sum_vectors_if
cudaEventCreate(&start);
cudaEventCreate(&stop);
cudaEventRecord(start);
sum_vectors_if<<<gridSize, blockSize>>>(d_a, n);
cudaDeviceSynchronize();
cudaEventRecord(stop);
cudaEventSynchronize(stop);
float sum_vectors_if = 0;
cudaEventElapsedTime(&sum_vectors_if, start, stop);
std::cout << "Gpu time: " << sum_vectors_if << " sum_vectors_if" << std::endl;
// sum_vectors
cudaEventCreate(&start);
cudaEventCreate(&stop);
cudaEventRecord(start);
sum_vectors<<<gridSize, blockSize>>>(d_a, n);
cudaDeviceSynchronize();
cudaEventRecord(stop);
cudaEventSynchronize(stop);
float sum_vectors = 0;
cudaEventElapsedTime(&sum_vectors, start, stop);
std::cout << "Gpu time: " << sum_vectors << " sum_vectors" << std::endl;
cudaFree(d_a);
free(h_a);
return 0;
}
|
23,794 | //
#include<cuda.h>
#include<stdio.h>
#include<math.h>
#include<ctime>
#include<time.h>
#define TILEWIDTH 32
#define TILE_WIDTH 32
__global__
void vecMulMatrixKernel(float* A, float* B, float* C, int n){
//each block loads the corresponding row of blocks of A matrix and column of blocks of B matrix, one block at a time and then clculates the product for that part then product of a the parts is added.
// each thread loads 2 elements one from A and one from B in each phase
// there are total gridDim.x phases
// the element loaded is the element at the same position as this thread but in a different block
//if run more thread than max then not run
int tx=threadIdx.x; int ty=threadIdx.y;
int bx=blockIdx.x; int by=blockIdx.y;
int row=by*blockDim.y+ty;
int col=bx*blockDim.x+tx;
__shared__ float Ads[TILEWIDTH][TILEWIDTH];
__shared__ float Bds[TILEWIDTH][TILEWIDTH];
if(row<n && col <n){
int i; float val=0.0;
for(i=0;i<gridDim.x-1;i++){
Ads[ty][tx] = A[ row*n + i*TILEWIDTH + tx];
Bds[ty][tx] = B[ (i*TILEWIDTH + ty)*n + col];
__syncthreads();
for(int k=0;k<TILEWIDTH;k++){
val+= Ads[ty][k]*Bds[tx][k];
}
__syncthreads();
}
if(i*TILEWIDTH + tx <n ) //if n was a multiple of blockDim then this was not required
Ads[ty][tx] = A[ row*n + i*TILEWIDTH + tx];
if(i*TILEWIDTH + ty <n )
Bds[ty][tx] = B[ (i*TILEWIDTH + ty)*n + col];
__syncthreads();
int m =n%TILEWIDTH;
if(m==0)
m=TILEWIDTH;
for(int k=0;k<m;k++){//printf("add");
val+= Ads[ty][k]*Bds[tx][k];
}
__syncthreads();
C[row*n + col]= val;
}
}
__global__ void MatrixMulKernel(float* Md, float* Nd, float* Pd, int Width)
{
__shared__ float Mds[TILE_WIDTH][TILE_WIDTH];
__shared__ float Nds[TILE_WIDTH][TILE_WIDTH];
int bx = blockIdx.x;
int by = blockIdx.y;
int tx = threadIdx.x;
int ty = threadIdx.y;
// Identify the row and column of the Pd element to work on
int Row = by * TILE_WIDTH + ty;
int Col = bx * TILE_WIDTH + tx;
float Pvalue = 0;
// Loop over the Md and Nd tiles required to compute the Pd element
for (int m = 0; m < Width/TILE_WIDTH; ++m) {
// Collaborative loading of Md and Nd tiles into shared memory
Mds[ty][tx] = Md[Row*Width + (m*TILE_WIDTH + tx)];
Nds[ty][tx] = Nd[Col + (m*TILE_WIDTH + ty)*Width];
__syncthreads();
for (int k = 0; k < TILE_WIDTH; ++k)
Pvalue += Mds[ty][k] * Nds[k][tx];
__syncthreads();
}
Pd[Row*Width+Col] = Pvalue;
}
int min2Power(int x){
int res=1;
while(res<x){
res*=2;
}
return res/2;
}
__host__
void vecMulMatrix(float* A,float* B,float* C, int n){
int size = n * n * sizeof(float);
float *d_A, *d_B, *d_C;
//Allocate device memory for A,B,C
cudaMalloc((void**)&d_A, size);
cudaMalloc((void**)&d_B, size);
cudaMalloc((void**)&d_C, size);
//copy A,B to device memory
cudaMemcpy(d_A, A, size, cudaMemcpyHostToDevice);
cudaMemcpy(d_B, B, size, cudaMemcpyHostToDevice);
//call kernal function that the calculates the product and stores it in C
dim3 dimBlock(TILEWIDTH,TILEWIDTH,1);
dim3 dimGrid(ceil(n/(float)TILEWIDTH),ceil(n/(float)TILEWIDTH),1);
// time_t t1=time(NULL);
// for(int i=0;i<270;i++)
// vecMulMatrixKernel<<<dimGrid,dimBlock >>>(d_A,d_B,d_C,n);
clock_t start,end;
start=clock();
MatrixMulKernel<<<dimGrid,dimBlock>>>(d_A,d_B,d_C,n);
cudaDeviceSynchronize();
end=clock();
printf("time:%lf\n",(double)(end-start)/CLOCKS_PER_SEC);
// time_t t2=time(NULL);
// printf("%d\n",t2-t1);
//copy C from devce memory
cudaMemcpy(C, d_C, size, cudaMemcpyDeviceToHost);
//free device memories
cudaFree(d_A);
cudaFree(d_B);
cudaFree(d_C);
}
//Kernal function that runs in each thread
int main(){
int maxThreads=640;
int n=1024*2;//640;
int i,j;
// float A[n][n],C[n][n],B[n][n];
// for(i=0;i<n;i++){
// for(j=0;j<n;j++){
// A[i][j]=(i+j)%1024;
// B[i][j]=(i*j)%1024;
// }
// }
float **A=(float**)malloc(1024*sizeof(float*));
float **B=(float**)malloc(1024*sizeof(float*));
float **C=(float**)malloc(1024*sizeof(float*));
for(int i=0;i<1024;i++){
A[i]=(float*)malloc(1024*sizeof(float));
B[i]=(float*)malloc(1024*sizeof(float));
C[i]=(float*)malloc(1024*sizeof(float));
}
vecMulMatrix(&A[0][0],&B[0][0],&C[0][0],n);
//for(i=0;i<n;i++){
// for(j=0;j<n;j++){
// printf("%.3f ",A[i][j]);
// }
// printf("\n");
// }
// printf("---\n");
// for(i=0;i<n;i++){
// for(j=0;j<n;j++){
// printf("%.3f ",B[i][j]);
// }
// printf("\n");
// }
// printf("---\n");
// for(i=0;i<n;i++){
// for(j=0;j<n;j++){
// printf("%.3f ",C[i][j]);
// }
// printf("\n");
// }
return 0;
}
|
23,795 | #include<iostream>
#include<cuda.h>
#include<stdio.h>
using namespace std;
#define TILE_WIDTH 4
__global__ void MatrixMulKernel( int* A, int* B, int* C,int m, int n, int k)
{
__shared__ int ds_A[TILE_WIDTH][TILE_WIDTH];
__shared__ int ds_B[TILE_WIDTH][TILE_WIDTH];
int bx = blockIdx.x; int by = blockIdx.y;
int tx = threadIdx.x; int ty = threadIdx.y;
int cx = blockIdx.x * blockDim.x; int cy = blockIdx.y * blockDim.y;
int Cx = cx + tx; int Cy = cy + ty;
int Row = by * blockDim.y + ty;
int Col = bx * blockDim.x + tx;
int Cvalue = 0;
int total_tiles = (n + TILE_WIDTH - 1) / TILE_WIDTH;
for (int t = 0; t < total_tiles; ++t)
{
int Ax = t * TILE_WIDTH + tx; int Ay = cy + ty;
int Bx = cx + tx; int By = t * TILE_WIDTH + ty;
if (Ax < n && Ay < m) {
ds_A[ty][tx] = A[Ay * n + Ax];
}
else {
ds_A[ty][tx] = 0;
}
if (Bx < n && By < k) {
ds_B[ty][tx] = B[By * n + Bx];
}
else {
ds_B[ty][tx] = 0;
}
__syncthreads();
for (int i = 0; i < TILE_WIDTH; i++) {
Cvalue+= ds_A[ty][i] * ds_B[i][tx];
}
__syncthreads();
}
if (Cx < n && Cy < m) {
C[Cy * n + Cx] = Cvalue;
}
}
int main()
{
int r1=30,r2=30,c1=30,c2 = 30;
int *a= new int[r1*c1];
int *b= new int[r2*c2];
int *c= new int[r1*c2];
for(int i=0;i<r1;i++)
for(int j=0;j<c1;j++)
{ a[i*c1+j] = rand()%100;}
for(int i=0;i<r2;i++)
{ for(int j=0;j<c2;j++)
{ b[i*c2+j]= rand()%100;}}
if(r2!=c1)
{
cout<<"not possible";
return 0;
}
else
{
int *d_a,*d_b,*d_c;
cudaMalloc((void**)&d_a,sizeof(int)*r1*c1);
cudaMalloc((void**)&d_b,sizeof(int)*r2*c2);
cudaMalloc((void**)&d_c,sizeof(int)*r1*c2);
cudaMemcpy(d_a,a,sizeof(int)*r1*c1,cudaMemcpyHostToDevice);
cudaMemcpy(d_b,b,sizeof(int)*r2*c2,cudaMemcpyHostToDevice);
const dim3 blocksize((c2/TILE_WIDTH) + 1, (r1/TILE_WIDTH) + 1, 1);//Number of Blocks required
const dim3 gridsize(TILE_WIDTH, TILE_WIDTH, 1);//Number of threads in each block
/*const dim3 blocksize(ceil(r1-1)/16 +1,ceil(c2-1)/16+1,1);
const dim3 gridsize(16,16,1);*/
//matrixmul<<<dim3(50,50),1>>>(d_a,d_b,d_c,r1,c2,r2);
MatrixMulKernel<<<blocksize,gridsize>>>(d_a,d_b,d_c,r1,c2,r2);
cudaMemcpy(c,d_c,sizeof(int)*r1*c2,cudaMemcpyDeviceToHost);
for(int i=0;i<r1;i++)
{ for(int j=0;j<c2;j++)
{ cout<<c[i*c2+j]<<" ";
}
cout<<endl;}
cout<<endl;
cudaFree(d_a);
cudaFree(d_b);
cudaFree(d_c);
}
return 0;
}
|
23,796 | #include <stdio.h>
#include <stdlib.h>
#include <inttypes.h>
// Define constants and data types
#define MAX_FILE_SIZE 1048576
#define STORAGE_SIZE 1085440
#define DATAFILE "./data.bin"
#define OUTPUTFILE "./snapshot.bin"
#define FILE_ENTRIES 1024
#define META_SIZE 31
#define DATA_SIZE 1024
#define LS_S_OFFSET 1024
#define LS_D_OFFSET 3072
#define META_OFFSET 5120
#define SIZE_OFFSET 21
#define CREATET_OFFSET SIZE_OFFSET+2
#define MODIFYT_OFFSET CREATET_OFFSET+4
#define DATA_OFFSET 36864
#define DNE 0xffffffff
typedef unsigned char uchar;
typedef uint32_t u32;
const int G_WRITE = 1;
const int G_READ = 2;
const int LS_S = 3;
const int LS_D = 4;
const int RM = 5;
const int RM_RF = 6;
// Declare variables
__device__ __managed__ u32 currentTime = 0;
__device__ __managed__ u32 fileCt = 0;
__device__ __managed__ uchar *volume;
// ==================================================================
// How I use volume:
// V--0 V--3072 V--36864
// ------------------------------------------------------------------
// | Temp | LS_S | LS_D | Metadata | Data |
// ------------------------------------------------------------------
// ^--1024 ^--5120
// Temp => For pending
// LS_S => Array for sorting by LS_S
// LS_D => Array for sorting by LS_D
// Metadata =>
// Entry structure:
// V--0 V--21 V--23 V--27
// -----------------------------------
// | Name | Size | CreateT | ModifyT |
// -----------------------------------
// Name := 21 bytes, for 20 char and '\0'
// Size := 2 bytes
// CreateT := 4 bytes
// ModifyT := 4 bytes
// Total size = 31 bytes
// ==================================================================
// Function
// ******************************************************************
// Initialize
void init_volume() {
for (int i = 0; i < STORAGE_SIZE; i++) {
volume[i] = 0;
}
}
// ******************************************************************
// ******************************************************************
// File I/O
int loadBinaryFile(const char *fileName, uchar *input, int fileSize) {
FILE *fptr = fopen(fileName, "rb");
// Get size
fseek(fptr, 0, SEEK_END);
int size = ftell(fptr);
rewind(fptr);
// Read data from input file
fread(input, sizeof(unsigned char), size, fptr);
if (fileSize < size) {
printf("ERROR: Input size is too small to store input data!\n");
}
fclose(fptr);
return size;
}
void writeBinaryFile(const char *fileName, uchar *input, int fileSize) {
FILE *fptr = fopen(fileName, "wb");
// Read data from input file
fwrite(input, sizeof(unsigned char), fileSize, fptr);
fclose(fptr);
}
// ******************************************************************
// ******************************************************************
// FS Helper function
__device__ inline int dstrlen(const char *str) {
int len = 0;
for (; str[len] != '\0'; len++);
return len;
}
__device__ bool isNameMatched(int fid, const char *name) {
bool isMatched = true;
int len = dstrlen(name);
for (int j = 0; j < len; j++) {
if (volume[META_OFFSET+fid*META_SIZE+j] != name[j]) {
isMatched = false;
break;
}
}
if (volume[META_OFFSET+fid*META_SIZE+len] != '\0')
isMatched = false;
return isMatched;
}
__device__ u32 createNewFile(const char *name) {
u32 fid = fileCt++;
int len = 0;
for (; name[len] != '\0'; len++) {
volume[META_OFFSET+fid*META_SIZE+len] = name[len];
}
volume[META_OFFSET+fid*META_SIZE+len] = '\0';
volume[META_OFFSET+fid*META_SIZE+SIZE_OFFSET] = 0;
volume[META_OFFSET+fid*META_SIZE+SIZE_OFFSET+1] = 0;
volume[META_OFFSET+fid*META_SIZE+CREATET_OFFSET] = (currentTime & 0xFF000000) >> 24;
volume[META_OFFSET+fid*META_SIZE+CREATET_OFFSET+1] = (currentTime & 0x00FF0000) >> 16;
volume[META_OFFSET+fid*META_SIZE+CREATET_OFFSET+2] = (currentTime & 0x0000FF00) >> 8;
volume[META_OFFSET+fid*META_SIZE+CREATET_OFFSET+3] = (currentTime & 0x000000FF);
volume[META_OFFSET+fid*META_SIZE+MODIFYT_OFFSET] = (currentTime & 0xFF000000) >> 24;
volume[META_OFFSET+fid*META_SIZE+MODIFYT_OFFSET+1] = (currentTime & 0x00FF0000) >> 16;
volume[META_OFFSET+fid*META_SIZE+MODIFYT_OFFSET+2] = (currentTime & 0x0000FF00) >> 8;
volume[META_OFFSET+fid*META_SIZE+MODIFYT_OFFSET+3] = (currentTime & 0x000000FF);
currentTime++;
return fid;
}
__device__ inline void updateFileSize(int fid, int len) {
volume[META_OFFSET+fid*META_SIZE+SIZE_OFFSET] = (len & 0x0000FF00) >> 8;
volume[META_OFFSET+fid*META_SIZE+SIZE_OFFSET+1] = (len & 0x000000FF);
}
__device__ inline void updateFileModifyTime(int fid) {
volume[META_OFFSET+fid*META_SIZE+MODIFYT_OFFSET] = (currentTime & 0xFF000000) >> 24;
volume[META_OFFSET+fid*META_SIZE+MODIFYT_OFFSET+1] = (currentTime & 0x00FF0000) >> 16;
volume[META_OFFSET+fid*META_SIZE+MODIFYT_OFFSET+2] = (currentTime & 0x0000FF00) >> 8;
volume[META_OFFSET+fid*META_SIZE+MODIFYT_OFFSET+3] = (currentTime & 0x000000FF);
currentTime++;
}
__device__ inline void updateData(int addr, uchar data) {
volume[addr] = data;
}
__device__ inline u32 getData(int addr) {
return volume[addr];
}
__device__ inline u32 getFileDataAddress(int fid) {
return DATA_OFFSET+fid*DATA_SIZE;
}
__device__ u32 getFid(const char *name) {
u32 fid = DNE;
for (u32 i = 0; i < fileCt; i++) {
if (isNameMatched(i, name)) {
fid = i;
break;
}
}
if (fid == DNE) {
fid = createNewFile(name);
}
return fid;
}
__device__ void sortD() {
for (int i = 0; i < fileCt; i++) {
volume[LS_D_OFFSET+i*2] = (i & 0x0000FF00) >> 8;
volume[LS_D_OFFSET+i*2+1] = i & 0x000000FF;
}
for (int i = 0; i < fileCt; i++) {
u32 swap = i;
u32 currentFid = (((u32)(volume[LS_D_OFFSET+i*2])) << 8)
| ((u32)(volume[LS_D_OFFSET+i*2+1]));
u32 max = ((u32)volume[META_OFFSET+currentFid*META_SIZE+MODIFYT_OFFSET] << 24)
| ((u32)volume[META_OFFSET+currentFid*META_SIZE+MODIFYT_OFFSET+1] << 16)
| ((u32)volume[META_OFFSET+currentFid*META_SIZE+MODIFYT_OFFSET+2] << 8)
| ((u32)volume[META_OFFSET+currentFid*META_SIZE+MODIFYT_OFFSET+3]);
for (int j = i+1; j < fileCt; j++) {
u32 tmpFid = (((u32)(volume[LS_D_OFFSET+j*2])) << 8)
| ((u32)(volume[LS_D_OFFSET+j*2+1]));
u32 tmp = ((u32)volume[META_OFFSET+tmpFid*META_SIZE+MODIFYT_OFFSET] << 24)
| ((u32)volume[META_OFFSET+tmpFid*META_SIZE+MODIFYT_OFFSET+1] << 16)
| ((u32)volume[META_OFFSET+tmpFid*META_SIZE+MODIFYT_OFFSET+2] << 8)
| ((u32)volume[META_OFFSET+tmpFid*META_SIZE+MODIFYT_OFFSET+3]);
if (tmp > max) {
currentFid = tmpFid;
max = tmp;
swap = j;
}
}
volume[LS_D_OFFSET+swap*2] = volume[LS_D_OFFSET+i*2];
volume[LS_D_OFFSET+swap*2+1] = volume[LS_D_OFFSET+i*2+1];
volume[LS_D_OFFSET+i*2] = (currentFid & 0x0000FF00) >> 8;
volume[LS_D_OFFSET+i*2+1] = currentFid & 0x000000FF;
}
}
__device__ void printD() {
printf("===sort by modified time===\n");
for (int i = 0; i < fileCt; i++) {
int fid = ((int)(volume[LS_D_OFFSET+i*2]) << 8) | ((int)(volume[LS_D_OFFSET+i*2+1]));
for (int j = 0; volume[META_OFFSET+fid*META_SIZE+j] != '\0'; j++)
printf("%c", volume[META_OFFSET+fid*META_SIZE+j]);
printf("\n");
}
}
__device__ void sortS() {
for (int i = 0; i < fileCt; i++) {
volume[LS_S_OFFSET+i*2] = (i & 0x0000FF00) >> 8;
volume[LS_S_OFFSET+i*2+1] = i & 0x000000FF;
}
for (int i = 0; i < fileCt; i++) {
u32 swap = i;
u32 currentFid = (((u32)(volume[LS_S_OFFSET+i*2])) << 8)
| ((u32)(volume[LS_S_OFFSET+i*2+1]));
u32 max = ((u32)volume[META_OFFSET+currentFid*META_SIZE+SIZE_OFFSET] << 8)
| ((u32)volume[META_OFFSET+currentFid*META_SIZE+SIZE_OFFSET+1]);
for (int j = i+1; j < fileCt; j++) {
u32 tmpFid = (((u32)(volume[LS_S_OFFSET+j*2])) << 8)
| ((u32)(volume[LS_S_OFFSET+j*2+1]));
u32 tmp = ((u32)volume[META_OFFSET+tmpFid*META_SIZE+SIZE_OFFSET] << 8)
| ((u32)volume[META_OFFSET+tmpFid*META_SIZE+SIZE_OFFSET+1]);
if (tmp > max) {
currentFid = tmpFid;
max = tmp;
swap = j;
} else if (tmp == max) {
u32 tct = ((u32)volume[META_OFFSET+tmpFid*META_SIZE+CREATET_OFFSET] << 24)
| ((u32)volume[META_OFFSET+tmpFid*META_SIZE+CREATET_OFFSET+1] << 16)
| ((u32)volume[META_OFFSET+tmpFid*META_SIZE+CREATET_OFFSET+2] << 8)
| ((u32)volume[META_OFFSET+tmpFid*META_SIZE+CREATET_OFFSET+3]);
u32 cct = ((u32)volume[META_OFFSET+currentFid*META_SIZE+CREATET_OFFSET] << 24)
| ((u32)volume[META_OFFSET+currentFid*META_SIZE+CREATET_OFFSET+1] << 16)
| ((u32)volume[META_OFFSET+currentFid*META_SIZE+CREATET_OFFSET+2] << 8)
| ((u32)volume[META_OFFSET+currentFid*META_SIZE+CREATET_OFFSET+3]);
if (cct > tct) {
currentFid = tmpFid;
max = tmp;
swap = j;
}
}
}
volume[LS_S_OFFSET+swap*2] = volume[LS_S_OFFSET+i*2];
volume[LS_S_OFFSET+swap*2+1] = volume[LS_S_OFFSET+i*2+1];
volume[LS_S_OFFSET+i*2] = (currentFid & 0x0000FF00) >> 8;
volume[LS_S_OFFSET+i*2+1] = currentFid & 0x000000FF;
}
}
__device__ void printS() {
printf("===sort by file size===\n");
for (int i = 0; i < fileCt; i++) {
int fid = ((int)(volume[LS_S_OFFSET+i*2]) << 8) | ((int)(volume[LS_S_OFFSET+i*2+1]));
u32 tmp = (((u32)(volume[META_OFFSET+fid*META_SIZE+SIZE_OFFSET])) << 8)
| ((u32)volume[META_OFFSET+fid*META_SIZE+SIZE_OFFSET+1]);
for (int j = 0; volume[META_OFFSET+fid*META_SIZE+j] != '\0'; j++)
printf("%c", volume[META_OFFSET+fid*META_SIZE+j]);
printf(" %d\n", tmp);
}
}
__device__ void deleteFileByName(const char *name) {
int target = DNE;
for (int i = 0; i < fileCt; i++) {
if(isNameMatched(i, name)) {
target = i;
break;
}
}
fileCt--;
if (target != fileCt) {
for (int i = 0; i < META_SIZE; i++) {
volume[META_OFFSET+target*META_SIZE+i] = volume[META_OFFSET+fileCt*META_SIZE+i];
}
for (int i = 0; i < volume[META_OFFSET+fileCt*META_OFFSET+SIZE_OFFSET]; i++) {
volume[DATA_OFFSET+target*DATA_SIZE+i] = volume[DATA_OFFSET+fileCt*DATA_SIZE+i];
}
}
}
// ******************************************************************
// ******************************************************************
// FS Operation
__device__ u32 open(const char *name, int type) {
u32 fid = getFid(name);
return fid;
}
__device__ void write(uchar *src, int len, u32 fid) {
updateFileSize(fid, len);
updateFileModifyTime(fid);
u32 entry = getFileDataAddress(fid);
for (u32 i = 0; i < len; i++) {
updateData(entry+i,src[i]);
}
}
__device__ void read(uchar *dst, int len, u32 fid) {
u32 entry = getFileDataAddress(fid);
for (u32 i = 0; i < len; i++) {
dst[i] = getData(entry+i);
}
}
__device__ void gsys(int type) {
switch (type) {
case LS_D:
sortD();
printD();
break;
case LS_S:
sortS();
printS();
break;
default:
sortS();
printS();
break;
}
}
__device__ void gsys(int type, const char *name) {
switch (type) {
case RM:
deleteFileByName(name);
break;
default:
deleteFileByName(name);
break;
}
}
// ******************************************************************
// ******************************************************************
// Kernel function
__global__ void mykernel(uchar *input, uchar *output) {
//####kernel start####
u32 fp = open("t.txt\0", G_WRITE);
write(input, 64, fp);
fp = open("b.txt\0", G_WRITE);
write(input+32, 32, fp);
fp = open("t.txt\0", G_WRITE);
write(input+32, 32, fp);
fp = open("t.txt\0", G_READ);
read(output, 32, fp);
gsys(LS_D);
gsys(LS_S);
fp = open("b.txt\0", G_WRITE);
write(input+64, 12, fp);
gsys(LS_S);
gsys(LS_D);
gsys(RM, "t.txt\0");
gsys(LS_S);
//####kernel end####
}
// ******************************************************************
int main() {
cudaMallocManaged(&volume, STORAGE_SIZE);
init_volume();
uchar *input, *output;
cudaMallocManaged(&input, MAX_FILE_SIZE);
cudaMallocManaged(&output, MAX_FILE_SIZE);
for (int i = 0; i < MAX_FILE_SIZE; i++) {
output[i] = 0;
}
loadBinaryFile(DATAFILE, input, MAX_FILE_SIZE);
cudaSetDevice(5);
mykernel<<<1, 1>>>(input, output);
cudaDeviceSynchronize();
writeBinaryFile(OUTPUTFILE, output, MAX_FILE_SIZE);
cudaDeviceReset();
return 0;
}
|
23,797 | #include <stdlib.h>
#include <fstream>
#include <thrust/sort.h>
#include <stdio.h>
#include <assert.h>
#include <math.h>
#include <cuda.h>
#define NUM_THREADS 256
int main( int argc, char **argv )
{
cudaThreadSynchronize();
}
|
23,798 | #include "includes.h"
/**
* Project TACO: Parallel ACO algorithm for TSP
* 15-418 Parallel Algorithms - Final Project
* Ivan Wang, Carl Lin
*/
#define MAX_THREADS 128
__device__ static inline int toIndex(int i, int j) {
return i * MAX_CITIES + j;
}
__global__ void seqPheroUpdate(float *phero, float *pheroReal, int *paths, float *tourLengths) {
memcpy(phero, pheroReal, sizeof(float) * MAX_CITIES * MAX_CITIES);
int from, to;
// evaporate
for (from = 0; from < MAX_CITIES; from++) {
for (to = 0; to < from; to++) {
phero[toIndex(from, to)] *= 1.0 - RHO;
if (phero[toIndex(from, to)] < 0.0) {
phero[toIndex(from, to)] = INIT_PHER;
}
phero[toIndex(to, from)] = phero[toIndex(from, to)];
}
}
//Add new pheromone to the trails
for (int ant = 0; ant < MAX_ANTS; ant++) {
for (int i = 0; i < MAX_CITIES; i++) {
from = paths[toIndex(ant, i)];
if (i < MAX_CITIES - 1) {
to = paths[toIndex(ant, i+1)];
} else {
to = paths[toIndex(ant, 0)];
}
phero[toIndex(from, to)] += (QVAL / tourLengths[ant]);
phero[toIndex(to, from)] = phero[toIndex(from, to)];
}
}
} |
23,799 | #include <err.h>
#include <cuda.h>
#include <stdio.h>
#include <unistd.h>
#include <stdint.h>
#define BUFLEN (4096)
int byte_sum(uint8_t* bytes, int size)
{
int i = 0;
int sum = 0;
for(; i< size; i++)
{
sum += bytes[i];
}
return sum;
}
void print_nonzeros(uint8_t* bytes, int size)
{
int i = 0;
for(; i< size; i++)
{
if (bytes[i] != 0)
printf("%x", bytes[i]);
}
return;
}
int
main(int argc, char *argv[])
{
unsigned char host_buf[BUFLEN];
unsigned char *gpu_buf;
uint64_t num_bytes =0;
while (cudaMalloc(&gpu_buf, BUFLEN) == cudaSuccess) {
num_bytes+=BUFLEN;
if (cudaMemcpy(host_buf, gpu_buf, BUFLEN, cudaMemcpyDeviceToHost))
err(1, "cudaMemcpy failed\n");
if (byte_sum(host_buf, BUFLEN) < 768)
{
continue;
}
write(fileno(stdout), host_buf, BUFLEN);
printf("\n");
}
printf("%lld bytes copied\n",num_bytes);
return 0;
}
|
23,800 | #include <stdio.h>
#include <cuda_runtime.h>
#include <time.h>
#include <sys/time.h>
#define CHECK(call) \
{ \
const cudaError_t error = call; \
if(error 1= cudaSuccess) \
{ \
printf("Error: %s : %d, ", __FILE__, __LINE__); \
printf("code: %d, reason: %s\n", error, cudaGetErrorString(error)); \
exit(1); \
} \
}
void printMatrix(int* data, const int nx, const int ny)
{
int* pdata = data;
printf("\nMatrix : (%d, %d)\n", nx, ny);
for(int i=0;i<ny;++i)
{
for(int j =0;j<nx;++j)
{
printf("%3d", pdata[j]);
}
pdata += nx;
printf("\n");
}
printf("\n");
}
__global__ void PrintThreadBlockIndex(int* data, const int nx, const int ny)
{
int ix = threadIdx.x + blockIdx.x * blockDim.x;
int iy = threadIdx.y + blockIdx.y * blockDim.y;
unsigned int idex = ix + iy * nx;
printf("thread index (%d, %d), block index (%d, %d) coordinate (%d, %d), global index %d and value %d\n",
threadIdx.x, threadIdx.y, blockIdx.x, blockIdx.y, ix, iy,idex, data[idex]);
}
void InitData(int* data, const int size)
{
time_t t;
srand((unsigned)time(&t));
for(int i =0; i < size; ++i)
{
data[i] = (int)(rand() & 0xFF) /10.0f;
}
}
int main(int argc, char const *argv[])
{
printf("%s Starting ... \n", argv[0]);
int dev = 0;
cudaDeviceProp deviceProp;
cudaGetDeviceProperties(&deviceProp,dev);
printf("Using Device %d, %s\n", dev, deviceProp.name);
cudaSetDevice(dev);
int nx = 8;
int ny = 6;
int nxy = nx * ny;
size_t nBytes = nxy * sizeof(int);
int* hA;
hA = (int*)malloc(nBytes);
InitData(hA, nxy);
printMatrix(hA, nx, ny);
int* dMat;
cudaMalloc((void**)&dMat, nBytes);
cudaMemcpy(dMat, hA, nBytes, cudaMemcpyHostToDevice);
dim3 block(4,2);
dim3 grid((nx + block.x -1) / block.x, (ny + block.y - 1) / block.y);
PrintThreadBlockIndex<<<grid, block>>>(dMat, nx, ny);
cudaDeviceSynchronize();
cudaFree(dMat);
free(hA);
cudaDeviceReset();
return 0;
} |
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