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I have to program the Floyd algorithm using OpenCL, it works fine but only with n<268. when n>=268 i have an "Access violation reading location" when calling clEnqueueWriteBuffer (the buffer_distances one, in the loop).
Here is my code:
graphe is an adjacency matrix, and distances is the distances matrix
int n;
printf("enter n value: ");
scanf("%d", &n);
printf("\n");
int n2 = n * n;
int matSize = n2 * sizeof(int*);
int* graphe = malloc(sizeof(int) * n2);
int* distances = malloc(sizeof(int) * n2);
//mat[i,j] => mat[i*n + j]
if (graphe == NULL)
printf("malloc failed\n");
init_graphe(graphe, n);
copy(graphe, distances, n);
initialization of opencl variables:
char* programSource = load_kernel("kernel.cl");
cl_int status;
// STEP 1: Discover and initialize the platforms
cl_uint numPlatforms = 0;
cl_platform_id* platforms = NULL;
status = clGetPlatformIDs(0, NULL, &numPlatforms);
printf("Number of platforms = %d\n", numPlatforms);
platforms = (cl_platform_id*)malloc(numPlatforms * sizeof(cl_platform_id));
status = clGetPlatformIDs(numPlatforms, platforms, NULL);
char Name[1000];
clGetPlatformInfo(platforms[0], CL_PLATFORM_NAME, sizeof(Name), Name, NULL);
printf("Name of platform : %s\n", Name);
fflush(stdout);
// STEP 2: Discover and initialize the devices
cl_uint numDevices = 0;
cl_device_id* devices = NULL;
status = clGetDeviceIDs(platforms[0], CL_DEVICE_TYPE_ALL, 0, NULL, &numDevices);
printf("Number of devices = %d\n", (int)numDevices);
devices = (cl_device_id*)malloc(numDevices * sizeof(cl_device_id));
status = clGetDeviceIDs(platforms[0], CL_DEVICE_TYPE_ALL, numDevices, devices, NULL);
for (int i = 0; i < numDevices; i++) {
clGetDeviceInfo(devices[i], CL_DEVICE_NAME, sizeof(Name), Name, NULL);
printf("Name of device %d: %s\n\n", i, Name);
}
// STEP 3: Create a context
fflush(stdout);
cl_context context = NULL;
context = clCreateContext(NULL, numDevices, devices, NULL, NULL, &status);
// STEP 4: Create a command queue
fflush(stdout);
cl_command_queue cmdQueue;
cmdQueue = clCreateCommandQueue(context, devices[0], 0, &status);
// STEP 5: Create device buffers
fflush(stdout);
cl_mem buffer_graphe;
cl_mem buffer_n;
cl_mem buffer_distances;
cl_mem buffer_k;
buffer_graphe = clCreateBuffer(context, CL_MEM_READ_WRITE, matSize, NULL, &status);
buffer_n = clCreateBuffer(context, CL_MEM_READ_WRITE, sizeof(int), NULL, &status);
buffer_distances = clCreateBuffer(context, CL_MEM_READ_WRITE, matSize, NULL, &status);
buffer_k = clCreateBuffer(context, CL_MEM_READ_WRITE, sizeof(int), NULL, &status);
fflush(stdout);
// STEP 6: Create and compile the program
cl_program program = clCreateProgramWithSource(context, 1, (const char**)&programSource, NULL, &status);
printf("Compilation\n");
fflush(stdout);
status = clBuildProgram(program, numDevices, devices, NULL, NULL, NULL);
// STEP 8: Create the kernel
cl_kernel kernel = NULL;
fflush(stdout);
kernel = clCreateKernel(program, "floyd", &status);
size_t globalWorkSize[2] = { n, n };
size_t localWorkSize[3] = { 20,20 };
Execution of the kernel:
clock_t start = clock();
int k;
for (k = 0; k < n; k++) {
status = clEnqueueWriteBuffer(cmdQueue, buffer_graphe, CL_TRUE, 0, matSize, graphe, 0, NULL, NULL);
status = clEnqueueWriteBuffer(cmdQueue, buffer_n, CL_TRUE, 0, sizeof(int), &n, 0, NULL, NULL);
status = clEnqueueWriteBuffer(cmdQueue, buffer_distances, CL_TRUE, 0, matSize, distances, 0, NULL, NULL);
status = clEnqueueWriteBuffer(cmdQueue, buffer_k, CL_TRUE, 0, sizeof(int), &k, 0, NULL, NULL);
status = clSetKernelArg(kernel, 0, sizeof(cl_mem), (void*)&buffer_graphe);
status = clSetKernelArg(kernel, 1, sizeof(cl_mem), (void*)&buffer_n);
status = clSetKernelArg(kernel, 2, sizeof(cl_mem), (void*)&buffer_distances);
status = clSetKernelArg(kernel, 3, sizeof(cl_mem), (void*)&buffer_k);
status = clEnqueueNDRangeKernel(cmdQueue, kernel, 2, NULL, globalWorkSize, NULL, 0, NULL, NULL);
clFinish(cmdQueue);
status = clEnqueueReadBuffer(cmdQueue, buffer_distances, CL_TRUE, 0, matSize, distances, 0, NULL, NULL);
clFinish(cmdQueue);
}
and the kernel:
void kernel floyd(global int* graphe, global int* n, global int* distances, global int* k)
{
int i = get_global_id(0);
int j = get_global_id(1);
int ij = i * (*n) + j;
int ik = i * (*n) + (*k);
int kj = (*k) * (*n) + j;
if (distances[ik] + distances[kj] < distances[ij]) {
distances[ij] = distances[ik] + distances[kj];
}
}
You have:
int matSize = n2 * sizeof(int*);
…
int* distances = malloc(sizeof(int) * n2);
…
status = clEnqueueWriteBuffer(cmdQueue, buffer_distances, CL_TRUE, 0, matSize, distances, 0, NULL, NULL);
Say n2 is 100.
matSize will be 800 on a 64-bit system. (sizeof(int*) = 8)
You allocate 400 bytes of memory for your distances array. (sizeof(int) = 4, typically)
You then copy 800 bytes (matSize) from distances into your OpenCL buffer. This overflows the end of the array. Whoops.
The bug is of course the use of sizeof(int*): you've got an array of ints, not an array of pointers, so this should be sizeof(int), which is what you're correctly doing in the malloc call. (I can't quite fathom why you're not using matSize there.) Although what you should probably be using is CLint, or one of the explicitly-sized types (int32_t in this case), because types in OpenCL kernels have very specific definitions which may or may not match those in host C code.
Additional Notes:
I'm not 100% convinced your data dependencies are safe here. No work-item should be reading an array entry that another is writing in the same kernel enqueueing batch. It seems to me that ij (written) for one of the work-items will be equal to ik (read) for the others in the row? Similar deal with ij and kj.
There's no need to read and re-write the distances buffer between iterations, if you're not modifying it on the host. Neither does graphe need re-writing every time if it's not changing.
You can pass scalar arguments such as k and n without a buffer. status = clSetKernelArg(kernel, 1, sizeof(n), &n); works fine if you change your kernel signature's argument to int n. (no dereference needed in the kernel then.)
A local work size of 20x20 is likely not optimal. If you're not using local memory or barriers, don't bother setting a local size at al.
You should be able to remove the clFinish calls, and you can change the buffer writes to be non-blocking once you've moved them outside the loop. This might give you an additional small speedup.
I think i found the solution, i replaced malloc by calloc, and now it works.
I made vector addition kernel and run it in the single gpu and multiple gpu.
However in Multi gpu case is much slower than single gpu in the same length of vector addition.
The structure of my code is one context one kernel and multi queues which has same number of devices..
How can I modify for faster in multi gpu case?
The code is below
#include <stdio.h>
#include <stdlib.h>
#include <sys/time.h>
#include <unistd.h>
#include <CL/cl.h>
#include <math.h>
//#define VECTOR_SIZE 640000
//#define LOCAL_SIZE 64
#define CHECK_ERROR(err) \
if (err != CL_SUCCESS) { \
printf("[%s:%d] OpenCL error %d\n", __FILE__, __LINE__, err); \
exit(EXIT_FAILURE); \
}
double get_time() {
struct timeval tv;
gettimeofday(&tv, NULL);
return (double)tv.tv_sec + (double)1e-6 * tv.tv_usec;
}
char *get_source_code(const char *file_name, size_t *len) {
char *source_code;
size_t length;
FILE *file = fopen(file_name, "r");
if (file == NULL) {
printf("[%s:%d] Failed to open %s\n", __FILE__, __LINE__, file_name);
exit(EXIT_FAILURE);
}
fseek(file, 0, SEEK_END);
length = (size_t)ftell(file);
rewind(file);
source_code = (char *)malloc(length + 1);
fread(source_code, length, 1, file);
source_code[length] = '\0';
fclose(file);
*len = length;
return source_code;
}
int main() {
// OpenCl variables
cl_platform_id platform;
//cl_device_id device;
cl_device_id *devices;
cl_device_id device_temp;
cl_context context;
//cl_command_queue queue;
cl_command_queue *queues;
cl_mem bufferA, bufferB, bufferC;
cl_program program;
char *kernel_source;
size_t kernel_source_size;
cl_kernel kernel;
//cl_kernel *kernels;
cl_int err;
//
size_t VECTOR_SIZE = 64000000 ;
int num_devices = 4;
size_t LOCAL_SIZE = 64;
// Time variables
double start;
double end;
// Get platform
err = clGetPlatformIDs(1, &platform, NULL);
CHECK_ERROR(err);
// Get GPU device
devices = (cl_device_id *) malloc(sizeof(cl_device_id)*num_devices);
err = clGetDeviceIDs(platform, CL_DEVICE_TYPE_GPU, num_devices, devices, NULL);
//err = clGetDeviceIDs(platform, CL_DEVICE_TYPE_GPU, 1, &device, NULL);
CHECK_ERROR(err);
// Create context
context = clCreateContext(NULL,num_devices, devices , NULL, NULL , &err);
//context = clCreateContext(NULL, 1, &device, NULL, NULL, &err);
CHECK_ERROR(err);
// Get kernel code
kernel_source = get_source_code("kernel.cl", &kernel_source_size);
// Create program
program = clCreateProgramWithSource(context, 1, (const char**)&kernel_source,
&kernel_source_size, &err);
CHECK_ERROR(err);
// Build program
err = clBuildProgram(program, num_devices, devices, "", NULL, NULL);
if(err == CL_BUILD_PROGRAM_FAILURE) {
size_t log_size;
char *log;
// Get program build
//err = clGetProgramBuildInfo(program, device, CL_PROGRAM_BUILD_LOG,
// 0, NULL, &log_size);
err = clGetProgramBuildInfo(program,devices[0],CL_PROGRAM_BUILD_LOG,0,NULL,&log_size);
CHECK_ERROR(err);
// Get build log
log = (char*)malloc(log_size + 1);
//err = clGetProgramBuildInfo(program, device, CL_PROGRAM_BUILD_LOG,
// log_size, log, NULL);
err = clGetProgramBuildInfo(program,devices[0],CL_PROGRAM_BUILD_LOG,log_size,log,NULL);
CHECK_ERROR(err);
log[log_size] = '\0';
printf("Compiler error : \n%s\n", log);
free(log);
exit(0);
}
CHECK_ERROR(err);
// Create Vector A, B, C
float *A = (float*)malloc(sizeof(float) * VECTOR_SIZE);
float *B = (float*)malloc(sizeof(float) * VECTOR_SIZE);
float *C = (float*)malloc(sizeof(float) * VECTOR_SIZE);
// Initial Vector A, B
//cl_ushort idx;
/*for(idx = 0; idx < VECTOR_SIZE; idx++) {
A[idx] = rand() % 100;
B[idx] = rand() % 100;
}*/
printf("start\n");
start = get_time();
for(int i = 0; i <VECTOR_SIZE; i++){
A[i] = sinf(i)*sinf(i);
B[i] = cosf(i)*cosf(i);
}
end = get_time();
printf("Initialization time : %f seconds elapsed\n", end-start);
// Create kernel
/*kernels = (cl_kernel *) malloc(sizeof(cl_kernel)*num_devices);
for(int i=0; i<num_devices; i++){
kernels[i] = clCreateKernel(program,"vec_add", &err);
CHECK_ERROR(err);
}*/
kernel = clCreateKernel(program, "vec_add", &err);
CHECK_ERROR(err);
// Create Buffer
bufferA = clCreateBuffer(context, CL_MEM_READ_ONLY, sizeof(float) * VECTOR_SIZE, NULL, &err);
CHECK_ERROR(err);
bufferB = clCreateBuffer(context, CL_MEM_READ_ONLY, sizeof(float) * VECTOR_SIZE, NULL, &err);
CHECK_ERROR(err);
bufferC = clCreateBuffer(context, CL_MEM_WRITE_ONLY, sizeof(float) * VECTOR_SIZE, NULL, &err);
CHECK_ERROR(err);
printf("error hi\n");
// Create command-queue
queues = (cl_command_queue *) malloc(sizeof(cl_command_queue)*num_devices);
for(int i=0; i<num_devices; i++){
if (i==0){
queues[i] = clCreateCommandQueue(context,devices[i],CL_QUEUE_OUT_OF_ORDER_EXEC_MODE_ENABLE,&err);
CHECK_ERROR(err);
}
else{
queues[i] = clCreateCommandQueue(context,devices[i], CL_QUEUE_OUT_OF_ORDER_EXEC_MODE_ENABLE, &err);
CHECK_ERROR(err);
}
}
printf("error bye\n");
//queue = clCreateCommandQueue(context, device, 0, &err);
//CHECK_ERROR(err);
// Write Buffer
for (int i = 0; i<num_devices; i++){
err = clEnqueueWriteBuffer(queues[i],bufferA,CL_FALSE,0,sizeof(float)*VECTOR_SIZE,A,0,NULL,NULL);
CHECK_ERROR(err);
err = clEnqueueWriteBuffer(queues[i],bufferB,CL_FALSE,0,sizeof(float)*VECTOR_SIZE,B,0,NULL,NULL);
CHECK_ERROR(err);
}
//err = clEnqueueWriteBuffer(queue, bufferA, CL_FALSE, 0, sizeof(float) * VECTOR_SIZE, A, 0, NULL, NULL);
//CHECK_ERROR(err);
//err = clEnqueueWriteBuffer(queue, bufferB, CL_FALSE, 0, sizeof(float) * VECTOR_SIZE, B, 0, NULL, NULL);
//CHECK_ERROR(err);
for(int i=0; i<num_devices; i++){
err=clFinish(queues[i]);
CHECK_ERROR(err);
}
// Set Kernel arguments
start = get_time();
/*for(int i=0; i<num_devices; i++){
err=clSetKernelArg(kernels[i], 0, sizeof(cl_mem), &bufferA);
CHECK_ERROR(err);
err=clSetKernelArg(kernels[i], 1, sizeof(cl_mem), &bufferB);
CHECK_ERROR(err);
err=clSetKernelArg(kernels[i], 2, sizeof(cl_mem), &bufferC);
CHECK_ERROR(err);
err=clSetKernelArg(kernels[i], 3, sizeof(unsigned int), &VECTOR_SIZE);
CHECK_ERROR(err);
}*/
err = clSetKernelArg(kernel, 0, sizeof(cl_mem), &bufferA);
CHECK_ERROR(err);
err = clSetKernelArg(kernel, 1, sizeof(cl_mem), &bufferB);
CHECK_ERROR(err);
err = clSetKernelArg(kernel, 2, sizeof(cl_mem), &bufferC);
CHECK_ERROR(err);
err = clSetKernelArg(kernel, 3, sizeof(unsigned int), &VECTOR_SIZE);
CHECK_ERROR(err);
end = get_time();
printf("Send Vector A, B to GPU : %f seconds elapsed\n", end - start);
for(int i=0; i<num_devices; i++){
err=clFinish(queues[i]);
CHECK_ERROR(err);
}
cl_event ooo_events[num_devices];
start = get_time();
// Execute Kernel
size_t global_size = VECTOR_SIZE;
size_t local_size = LOCAL_SIZE;
for(int i=0; i<num_devices; i++){
//start=get_time();
err= clEnqueueNDRangeKernel(queues[i],kernel,1,NULL,&global_size,&local_size,0,NULL,NULL);
CHECK_ERROR(err);
//err = clEnqueueNDRangeKernel(queues[i],kernels[i],1,NULL,&global_size, &local_size,0,NULL,NULL);
//CHECK_ERROR(err);
//end=get_time();
//printf("Calculate C : %f seconds elapsed\n", end-start);
}
//err = clEnqueueNDRangeKernel(queue, kernel, 1, NULL,&global_size, &local_size, 0, NULL, NULL);
//CHECK_ERROR(err);
for(int i=0; i<num_devices; i++){
err=clFinish(queues[i]);
CHECK_ERROR(err);
}
end = get_time();
printf("Calculate C : %f seconds elapsed\n", end - start);
// Read Buffer
start = get_time();
for(int i=0; i<num_devices; i++){
err = clEnqueueReadBuffer(queues[i],bufferC,CL_TRUE,0,sizeof(float)*VECTOR_SIZE,C,0,NULL,NULL);
CHECK_ERROR(err);
}
//err = clEnqueueReadBuffer(queue, bufferC, CL_TRUE, 0, sizeof(float) * VECTOR_SIZE, C, 0, NULL, NULL);
//CHECK_ERROR(err);
end = get_time();
printf("Receive C from GPU : %f seconds elapsed\n", end - start);
// Evaluate Vector C
start = get_time();
double sum = 0;
for(int i = 0; i < VECTOR_SIZE; i++) {
sum += C[i];
}
end = get_time();
printf("Verification time : %f seconds elapsed\n", end-start);
printf("%lf, %ld \n", sum,VECTOR_SIZE);
if (abs(VECTOR_SIZE - sum) < 1) {
printf("Verification success!\n");
}
printf("Sum : %f\n", sum);
// Release OpenCL object
clReleaseMemObject(bufferA);
clReleaseMemObject(bufferB);
clReleaseMemObject(bufferC);
free(A);
free(B);
free(C);
clReleaseKernel(kernel);
//clReleaseKernel(kernels[0]);
//clReleaseKernel(kernels[1]);
clReleaseProgram(program);
clReleaseCommandQueue(queues[0]);
clReleaseCommandQueue(queues[1]);
//clReleaseCommandQueue(queue);
clReleaseContext(context);
return 0;
}
Using multiple GPUs is only beneficial in terms of performance if the amount of computational work that each GPU performs takes more time then the communication, scheduling and synchronization overhead. This is true for a single GPU as well.
In your case, each GPU performs a simple vector addition. but that rarely takes more time then transferring the data to the GPU, waiting for the kernel to actually get scheduled for execution, etc.
Your code is not measuring the total kernel execution time but also the scheduling overhead.
I would advise you to use proper GPU profiling tools (depending on your GPU vendor) instead of manual CPU timings to properly examine what is going on. You can also try measuring kernel execution time via events.
This is my first time dealing with threads.
When I run the program without the GetCurrentThreadId() function it executes without any issue.
When I add that line of code it still executes but crashes once it reaches the end. Why is this?
#include <Windows.h>
#include <stdio.h>
#include <conio.h>
static int tix[500];
static int done = 0;
HANDLE ghSemaphore;
DWORD WINAPI ThreadFunction();
int main(void)
{
DWORD threadID1, threadID2, threadID3, threadID4;
HANDLE hThread1, hThread2, hThread3, hThread4;
for (int i = 0; i < 500; i++) //initialize array
{
tix[i] = 0;
}
ghSemaphore = CreateSemaphore(NULL, 1, 10, NULL);
hThread1 = CreateThread(NULL, 0, ThreadFunction, NULL, 0, &threadID1);
hThread2 = CreateThread(NULL, 0, ThreadFunction, NULL, 0, &threadID2);
hThread3 = CreateThread(NULL, 0, ThreadFunction, NULL, 0, &threadID3);
hThread4 = CreateThread(NULL, 0, ThreadFunction, NULL, 0, &threadID4);
//printf("The thread ID: %d.\n", threadID1);
//printf("The thread ID: %d.\n", threadID2);
//printf("The thread ID: %d.\n", threadID3);
//printf("The thread ID: %d.\n", threadID4);
if (done = 1)
{
CloseHandle(hThread1);
CloseHandle(hThread2);
CloseHandle(hThread3);
CloseHandle(hThread4);
}
for (int j = 0; j < 500; j++)
{
if (tix[j] = 0)
{
printf("not sold");
}
else if (tix[j] = 1)
{
printf("sold");
}
}
return 0;
}
DWORD WINAPI ThreadFunction()
{
WaitForSingleObject(ghSemaphore, 0);
printf("current thread running : %d\n", GetCurrentThreadId());
int i = 0;
if (done != 0) // if loop to test wether or not the array is full
{
while (tix[i] = 1) //traverse the array to find a open spot
{
i++;
}
tix[i] = 1;
}
if (i == 499) //if i is 499, set test variable to 1
{
done = 1;
return 0;
}
ReleaseSemaphore(ghSemaphore, 1, NULL);
}
Your thread function has incorrect signature. Thread takes one PVOID context argument:
DWORD WINAPI ThreadProc(
_In_ LPVOID lpParameter
);
Your threads could exit without releasing a semaphore. Also since you had initialized it with a value, that is greater than thread amount and never check WaitForSingleObject result, no synchronization is provided and multiple threads will modify a shared buffers in inconsistent manner. Even worse - nothing stops your program main thread exiting earlier, than ThreadFunction.
There is no return statement in the end of your thread function, so this is an undefined behavior. In fact it is a great wonder that your code even compiles. This entire approach to multithreading is incorrect and has to be remade from the scratch.
I have taken up OpenCL programming as part of a university project and I have a bit of a problem when I try to input data to a buffer object during the clCreateBuffer() routine.
The program is a simple two-dimensional matrix addition. The code is as follows:
#define _CRT_SECURE_NO_WARNINGS
#define PROGRAM_FILE "add_kernel.cl"
#define ADD_FUNC "add_matrix"
#define MATRIX_DIM 256
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <time.h>
#ifdef MAC
#include <OpenCL/cl.h>
#else
#include <CL/cl.h>
#endif
/* Find a GPU associated with the first available platform */
cl_device_id create_device() {
cl_platform_id platform;
cl_device_id dev;
int err;
/* Identify a platform */
err = clGetPlatformIDs(1, &platform, NULL);
if(err < 0) {
perror("Couldn't identify a platform");
exit(1);
}
/* Access a GPU */
err = clGetDeviceIDs(platform, CL_DEVICE_TYPE_GPU, 1, &dev, NULL);
if(err < 0) {
perror("Couldn't access any GPU type");
exit(1);
}
return dev;
}
cl_program build_program(cl_context ctx, cl_device_id dev, const char* filename) {
cl_program program;
FILE *program_handle;
char *program_buffer, *program_log;
size_t program_size, log_size;
int err;
/* Read program file and place content into buffer */
program_handle = fopen(filename, "r");
if(program_handle == NULL) {
perror("Couldn't find the program file");
exit(1);
}
fseek(program_handle, 0, SEEK_END);
program_size = ftell(program_handle);
rewind(program_handle);
program_buffer = (char*)malloc(program_size + 1);
program_buffer[program_size] = '\0';
fread(program_buffer, sizeof(char), program_size, program_handle);
fclose(program_handle);
/* Create program from file */
program = clCreateProgramWithSource(ctx, 1,
(const char**)&program_buffer, &program_size, &err);
if(err < 0) {
perror("Couldn't create the program");
exit(1);
}
free(program_buffer);
/* Build program */
err = clBuildProgram(program, 0, NULL, NULL, NULL, NULL);
if(err < 0) {
/* Find size of log and print to std output */
clGetProgramBuildInfo(program, dev, CL_PROGRAM_BUILD_LOG,
0, NULL, &log_size);
program_log = (char*) malloc(log_size + 1);
program_log[log_size] = '\0';
clGetProgramBuildInfo(program, dev, CL_PROGRAM_BUILD_LOG,
log_size + 1, program_log, NULL);
printf("%s\n", program_log);
free(program_log);
exit(1);
}
return program;
}
int main(){
/* Host/device data structures */
cl_device_id device;
cl_context context;
cl_command_queue queue;
cl_program program;
cl_kernel add_kernel;
size_t global_size;
cl_ulong mem_size;
cl_int i, j, err, check;
/* Data and buffers */
cl_uint matrix_dim;
float a_mat[MATRIX_DIM][MATRIX_DIM], b_mat[MATRIX_DIM][MATRIX_DIM],
c_mat[MATRIX_DIM][MATRIX_DIM], check_mat[MATRIX_DIM][MATRIX_DIM];
cl_mem a_buffer, b_buffer, c_buffer;
/* Initialize A, B, and check matrices */
srand((unsigned int)time(0));
for(i=0; i<MATRIX_DIM; i++) {
for(j=0; j<MATRIX_DIM; j++) {
a_mat[i][j] = (float)rand()/RAND_MAX;
}
}
srand((unsigned int)time(0));
for(i=0; i<MATRIX_DIM; i++) {
for(j=0; j<MATRIX_DIM; j++) {
b_mat[i][j] = (float)rand()/RAND_MAX;
check_mat[i][j] = 0.0f;
}
}
for(i=0; i<MATRIX_DIM; i++) {
for(j=0; j<MATRIX_DIM; j++) {
check_mat[i][j] += a_mat[i][j] + b_mat[i][j];
}
}
/* Create a device and context */
device = create_device();
context = clCreateContext(NULL, 1, &device, NULL, NULL, &err);
if(err < 0) {
perror("Couldn't create a context");
exit(1);
}
/* Build the program */
program = build_program(context, device, PROGRAM_FILE);
add_kernel = clCreateKernel(program, ADD_FUNC, &err);
if(err < 0) {
perror("Couldn't create a kernel");
exit(1);
};
/* Create buffers */
a_buffer = clCreateBuffer(context,
CL_MEM_READ_ONLY | CL_MEM_COPY_HOST_PTR,
sizeof(a_mat), a_mat, &err);
if(err < 0) {
perror("Couldn't create buffer A");
exit(1);
};
b_buffer = clCreateBuffer(context,
CL_MEM_READ_ONLY | CL_MEM_COPY_HOST_PTR,
sizeof(b_mat), b_mat, &err);
if(err < 0) {
perror("Couldn't create buffer B");
exit(1);
};
c_buffer = clCreateBuffer(context, CL_MEM_WRITE_ONLY,
sizeof(c_mat), NULL, &err);
if(err < 0) {
perror("Couldn't create buffer C");
exit(1);
};
/* Create a command queue */
queue = clCreateCommandQueue(context, device, 0, &err);
if(err < 0) {
perror("Couldn't create a command queue");
exit(1);
};
/* Create arguments for multiplication kernel */
err = clSetKernelArg(add_kernel, 0, sizeof(a_buffer), &a_buffer);
err |= clSetKernelArg(add_kernel, 1, sizeof(b_buffer), &b_buffer);
err |= clSetKernelArg(add_kernel, 2, sizeof(c_buffer), &c_buffer);
global_size = MATRIX_DIM * MATRIX_DIM;
//printf("%lu\n", global_size);
err = clEnqueueNDRangeKernel(queue, add_kernel, 1, NULL, &global_size,
NULL, 0, NULL, NULL);
if(err < 0) {
perror("Couldn't enqueue the addition kernel");
exit(1);
}
/* Read output buffer */
err = clEnqueueReadBuffer(queue, c_buffer, CL_TRUE, 0,
sizeof(c_mat), c_mat, 0, NULL, NULL);
if(err < 0) {
perror("Couldn't read the buffer");
exit(1);
}
/* Check result */
check = 1;
for(i=0; i<MATRIX_DIM; i++) {
for(j=0; j<MATRIX_DIM; j++) {
if(c_mat[i][j] != check_mat[i][j]){
check = 0;
break;
}
}
}
if(check)
printf("Addition check succeeded.\n");
else
printf("Addition check failed.\n");
/* Deallocate resources */
clReleaseMemObject(a_buffer);
clReleaseMemObject(b_buffer);
clReleaseMemObject(c_buffer);
clReleaseKernel(add_kernel);
clReleaseCommandQueue(queue);
clReleaseProgram(program);
clReleaseContext(context);
return 0;
}
The kernel code is the following:
__kernel void add_matrix(__global float* matrix_a,
__global float* matrix_b,
__global float* result) {
int i = get_global_id(0);
result[i] = matrix_a[i] + matrix_b[i];
}
Now, it works great for dimensions up to 358x358, but as soon as I put 359 in the MATRIX_DIM it crashes. It shows the usual "foo.exe has stopped working". I know it has to do something with the clCreateBuffer() command because if I remove the code from the first clCreateBuffer() and below, it runs and terminates fine, but as soon as I add even one it crashes.
The CL_DEVICE_MAX_MEM_ALLOC_SIZE option shows a number of 512MB of available memory and the data I am trying to pass is much less than that.
Is there anything I can do to increase the ammount of data I can process?
My GPU is a Radeon Sapphire HD5770
EDIT: After a suggestion in the comments I ran the debugger which yielded the following message:
Program received signal SIGSEGV, Segmentation fault.
In amdocl!_aclHsaLoader () (C:\WINDOWS\SysWOW64\amdocl.dll)
#15 0x00401355 in create_device () at C:\test\testcl.c:26
C:\test\testcl.c:26:503:beg:0x401355
I am really not sure what this means though. Any ideas?
The main problem is, that you allocate to much memory on the stack at these code lines, so that, you got a stack overflow:
float a_mat[MATRIX_DIM][MATRIX_DIM], b_mat[MATRIX_DIM][MATRIX_DIM],
c_mat[MATRIX_DIM][MATRIX_DIM], check_mat[MATRIX_DIM][MATRIX_DIM];
In my test here, the execution didn't even entered the main method. You have to allocate these matrices on the heap with:
float *a_mat = calloc(MATRIX_DIM*MATRIX_DIM, sizeof(*a_mat));
float *b_mat = calloc(MATRIX_DIM*MATRIX_DIM, sizeof(*b_mat));
float *c_mat = calloc(MATRIX_DIM*MATRIX_DIM, sizeof(*c_mat));
float *check_mat = calloc(MATRIX_DIM*MATRIX_DIM, sizeof(*check_mat));
But now, you have only a 1-dimensional (1D) data-buffer for each matrix, so that, you have to change every 2D index [i][j] into the corresponding 1D index [i*MATRIX_DIM][j], e.g.:
a_mat[i*MATRIX_DIM+j] = (float)rand()/RAND_MAX;
EDIT: You have to also update the calls to clCreateBuffer und clEnqueueReadBuffer. The matrix size cannot be determined with sizeof(matrix_name) anymore (where matrix_name is one of a_mat, b_mat, ...). You have to replace every such sizeof (there are 4 of some) with MATRIX_DIM*MATRIX_DIM*sizeof(*matrix_name). Don't forget the derefence before the matrix_name, e.g.:
a_buffer = clCreateBuffer(context, CL_MEM_READ_ONLY | CL_MEM_COPY_HOST_PTR,
MATRIX_DIM*MATRIX_DIM*sizeof(*a_mat), a_mat, &err);
(End of Edit).
Don't forget to release the data-buffers at the end:
free(a_mat);
free(b_mat);
free(c_mat);
free(check_mat);
To get even the kernel to run, I had even to fix the reading of the kernel program. The return value of ftell was always a little bit too large. The actual number of bytes is instead returned by fread. Thus, change these lines
program_buffer[program_size] = '\0';
fread(program_buffer, sizeof(char), program_size, program_handle);
to
program_size = fread(program_buffer, sizeof(char), program_size, program_handle); // changed
program_buffer[program_size] = '\0'; // moved here
I'm pretty a novice about opencl. I have tried about "get the summation of all cubes of every element in an array". Here's my kernel code:
kernel void cubeSum(global float *input,
local float *prods,
global float *output )
{
int gid = get_global_id( 0 );
int tnum = get_local_id( 0 ); // thread number
int wgNum = get_group_id( 0 ); // work-group number
int numItems = get_local_size( 0 );
prods[ tnum ] = input[ gid ] * input[ gid ] * input[gid]; // cube
for (int offset = 1; offset < numItems; offset *= 2) {
int mask = 2 * offset - 1;
barrier(CLK_LOCAL_MEM_FENCE);
if ( (tnum & mask) == 0 ) {
prods[tnum] += prods[tnum + offset];
}
}
barrier(CLK_LOCAL_MEM_FENCE);
if ( tnum == 0 )
output[wgNum] = prods[0];
}
I can't figure out why my result is not the same with sequential result. When the array is from 0 to 511, my result is sequential result minus 2048; when the array is from 0 to 1023, my result is sequential result plus 16384.
I will try to figure it out myself while I'm waiting for you answers.
Another question is I found it is hard to debug kernel code since the dataset is quite big and it runs concurrently. Any advice for debugging?
All the advices are appreciated =).
By the way, here's my host code:
#include <stdio.h>
#include <stdio.h>
#include <math.h>
#include <string.h>
#include <stdlib.h>
#include <OpenCL/opencl.h>
#define NUM_ELEMENTS (512)
#define LOCAL_SIZE (512)
#define MAX_SOURCE_SIZE (0x100000)
int main(int argc, const char * argv[])
{
float data[NUM_ELEMENTS]; //hA
float sum;
float sumTest;
size_t global;
size_t local;
size_t numWorkGroups;
size_t dataSize;
size_t resultsSize;
cl_device_id device;
cl_context context;
cl_command_queue cmdQueue;
cl_program program;
cl_kernel kernel;
cl_mem input;
cl_mem output;
FILE *fp;
//failed to use relative path here. permission problem?
char fileName[] = "/Users/sure/USC/590/cubeSum/cubeSum/cubeSum.cl";
char *source_str;
size_t source_size;
/* カーネルを含むソースコードをロード */
fp = fopen(fileName, "r");
if (!fp) {
fprintf(stderr, "Failed to load kernel.\n");
exit(1);
}
source_str = (char*)malloc(MAX_SOURCE_SIZE);
source_size = fread( source_str, 1, MAX_SOURCE_SIZE, fp);
fclose( fp );
//allocate the host memory buffers:
int i = 0;
unsigned int count = NUM_ELEMENTS;
for (i = 0; i < count; i++) {
data[i] = i;
}
//array size in bytes (will need this later):
dataSize = NUM_ELEMENTS * sizeof(float);
//opencl function status
cl_int status;
// Connect to a compute device
//
int gpu = 1;
status = clGetDeviceIDs(NULL, gpu ? CL_DEVICE_TYPE_GPU : CL_DEVICE_TYPE_CPU, 1, &device, NULL);
if (status != CL_SUCCESS)
{
printf("Error: Failed to create a device group!\n");
return EXIT_FAILURE;
}
//create an Opencl context
context = clCreateContext(NULL, 1, &device, NULL, NULL, &status);
//create a command queue
cmdQueue = clCreateCommandQueue( context, device, 0, &status );
//allocate memory buffers on the device
input = clCreateBuffer( context, CL_MEM_READ_ONLY, dataSize, NULL, &status ); //dA
//TODO: at this line, I don't have the value of local which is calculated by clGetKernelWorkGroupInfo
//need to figure out a way to avoid hardcode it.
output = clCreateBuffer( context, CL_MEM_WRITE_ONLY, sizeof(float) * NUM_ELEMENTS / LOCAL_SIZE, NULL, &status ); //dC
// enqueue the 2 commands to write data into the device buffers:
status = clEnqueueWriteBuffer( cmdQueue, input, CL_FALSE, 0, dataSize, data, 0, NULL, NULL );
// create the kernel program on the device:
program = clCreateProgramWithSource(context, 1, (const char **) & source_str, (const size_t *)&source_size, &status);
if (!program)
{
printf("Error: Failed to create compute program!\n");
return EXIT_FAILURE;
}
// Build the program executable
//
status = clBuildProgram(program, 0, NULL, NULL, NULL, NULL);
if (status != CL_SUCCESS)
{
size_t len;
char buffer[2048];
printf("Error: Failed to build program executable!\n");
clGetProgramBuildInfo(program, device, CL_PROGRAM_BUILD_LOG, sizeof(buffer), buffer, &len);
printf("%s\n", buffer);
exit(1);
}
//create compute kernel
kernel = clCreateKernel( program, "cubeSum", &status );
// Get the maximum work group size for executing the kernel on the device
//
status = clGetKernelWorkGroupInfo(kernel, device, CL_KERNEL_WORK_GROUP_SIZE, sizeof(local), &local, NULL);
if (status != CL_SUCCESS)
{
printf("Error: Failed to retrieve kernel work group info! %d\n", status);
exit(1);
}
global = count;
numWorkGroups = global / local;
float results[numWorkGroups]; //hC
resultsSize = numWorkGroups * sizeof(float);
//set kernel parameter
status = clSetKernelArg( kernel, 0, sizeof(cl_mem), &input );
status = clSetKernelArg( kernel, 1, sizeof(float), NULL );
status = clSetKernelArg( kernel, 2, sizeof(cl_mem), &output );
// Execute the kernel over the entire range of our 1d input data set
// using the maximum number of work group items for this device
//
status = clEnqueueNDRangeKernel(cmdQueue, kernel, 1, NULL, &global, &local, 0, NULL, NULL);
if (status)
{
printf("Error: Failed to execute kernel!\n");
return EXIT_FAILURE;
}
clFinish(cmdQueue);
status = clEnqueueReadBuffer( cmdQueue, output, CL_TRUE, 0, resultsSize, results, 0, NULL, NULL );
// Validate our results
//
sum = 0;
for (int i=0; i<numWorkGroups; i++) {
sum += results[i];
}
sumTest = 0;
for(i = 0; i < count; i++)
{
sumTest += data[i] * data[i] * data[i];
}
// Print a brief summary detailing the results
//
printf("Computed '%f/%f'!\n", sum, sumTest);
// Shutdown and cleanup
//
clReleaseMemObject(input);
clReleaseMemObject(output);
clReleaseProgram(program);
clReleaseKernel(kernel);
clReleaseCommandQueue(cmdQueue);
clReleaseContext(context);
return 0;
}
EDIT: Just found another thing. My code is correct if I just sum all element without cube/square. Thus, I'm gonna figure out how cube affect to my program.
You appear to only be allocating 4-bytes of local memory:
status = clSetKernelArg( kernel, 1, sizeof(float), NULL );
This should be the total amount of local memory required for that argument by the entire work-group. In the case of your kernel, this is (work-group-size * sizeof(float)).
So, you should instead have something like this:
status = clSetKernelArg( kernel, 1, local*sizeof(float), NULL );
The discrepancies you are seeing are likely coming from the limitations of floating point, since you are summing some very large numbers. If you initialise your inputs with smaller numbers (e.g. data[i] = i*0.01;), you should get results equal to your sequential implementation (I've verified this on my own system). This is why you don't see the errors when you remove the cube.