cuda-samples/Samples/simpleCUBLASXT/simpleCUBLASXT.cpp
2018-08-24 22:35:15 +05:30

302 lines
8.5 KiB
C++

/* Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of NVIDIA CORPORATION nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
/* This example demonstrates how to use the CUBLAS library
* by scaling an array of floating-point values on the device
* and comparing the result to the same operation performed
* on the host.
*/
/* Includes, system */
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
/* Includes, cuda */
#include <cublasXt.h>
#include <cuda_runtime.h>
#include <helper_cuda.h>
/* Matrix size */
//#define N (275)
#define N (1024)
// Restricting the max used GPUs as input matrix is not so large
#define MAX_NUM_OF_GPUS 2
/* Host implementation of a simple version of sgemm */
static void simple_sgemm(int n, float alpha, const float *A, const float *B,
float beta, float *C) {
int i;
int j;
int k;
for (i = 0; i < n; ++i) {
for (j = 0; j < n; ++j) {
float prod = 0;
for (k = 0; k < n; ++k) {
prod += A[k * n + i] * B[j * n + k];
}
C[j * n + i] = alpha * prod + beta * C[j * n + i];
}
}
}
void findMultipleBestGPUs(int &num_of_devices, int *device_ids) {
// Find the best CUDA capable GPU device
int current_device = 0;
int device_count;
checkCudaErrors(cudaGetDeviceCount(&device_count));
typedef struct gpu_perf_t {
uint64_t compute_perf;
int device_id;
} gpu_perf;
gpu_perf *gpu_stats = (gpu_perf *)malloc(sizeof(gpu_perf) * device_count);
cudaDeviceProp deviceProp;
int devices_prohibited = 0;
while (current_device < device_count) {
cudaGetDeviceProperties(&deviceProp, current_device);
// If this GPU is not running on Compute Mode prohibited,
// then we can add it to the list
int sm_per_multiproc;
if (deviceProp.computeMode != cudaComputeModeProhibited) {
if (deviceProp.major == 9999 && deviceProp.minor == 9999) {
sm_per_multiproc = 1;
} else {
sm_per_multiproc =
_ConvertSMVer2Cores(deviceProp.major, deviceProp.minor);
}
gpu_stats[current_device].compute_perf =
(uint64_t)deviceProp.multiProcessorCount * sm_per_multiproc *
deviceProp.clockRate;
gpu_stats[current_device].device_id = current_device;
} else {
devices_prohibited++;
}
++current_device;
}
if (devices_prohibited == device_count) {
fprintf(stderr,
"gpuGetMaxGflopsDeviceId() CUDA error:"
" all devices have compute mode prohibited.\n");
exit(EXIT_FAILURE);
} else {
gpu_perf temp_elem;
// Sort the GPUs by highest compute perf.
for (int i = 0; i < current_device - 1; i++) {
for (int j = 0; j < current_device - i - 1; j++) {
if (gpu_stats[j].compute_perf < gpu_stats[j + 1].compute_perf) {
temp_elem = gpu_stats[j];
gpu_stats[j] = gpu_stats[j + 1];
gpu_stats[j + 1] = temp_elem;
}
}
}
for (int i = 0; i < num_of_devices; i++) {
device_ids[i] = gpu_stats[i].device_id;
}
}
free(gpu_stats);
}
/* Main */
int main(int argc, char **argv) {
cublasStatus_t status;
float *h_A;
float *h_B;
float *h_C;
float *h_C_ref;
float *d_A = 0;
float *d_B = 0;
float *d_C = 0;
float alpha = 1.0f;
float beta = 0.0f;
int n2 = N * N;
int i;
float error_norm;
float ref_norm;
float diff;
cublasXtHandle_t handle;
int *devices = NULL;
int num_of_devices = 0;
checkCudaErrors(cudaGetDeviceCount(&num_of_devices));
if (num_of_devices > MAX_NUM_OF_GPUS) {
num_of_devices = MAX_NUM_OF_GPUS;
}
devices = (int *)malloc(sizeof(int) * num_of_devices);
findMultipleBestGPUs(num_of_devices, devices);
cudaDeviceProp deviceProp;
printf("Using %d GPUs\n", num_of_devices);
for (i = 0; i < num_of_devices; i++) {
checkCudaErrors(cudaGetDeviceProperties(&deviceProp, devices[i]));
printf("GPU ID = %d, Name = %s \n", devices[i], deviceProp.name);
}
/* Initialize CUBLAS */
printf("simpleCUBLASXT test running..\n");
status = cublasXtCreate(&handle);
if (status != CUBLAS_STATUS_SUCCESS) {
fprintf(stderr, "!!!! CUBLASXT initialization error\n");
return EXIT_FAILURE;
}
/* Select devices for use in CUBLASXT math functions */
status = cublasXtDeviceSelect(handle, num_of_devices, devices);
if (status != CUBLAS_STATUS_SUCCESS) {
fprintf(stderr, "!!!! CUBLASXT device selection error\n");
return EXIT_FAILURE;
}
/* Optional: Set a block size for CUBLASXT math functions */
status = cublasXtSetBlockDim(handle, 64);
if (status != CUBLAS_STATUS_SUCCESS) {
fprintf(stderr, "!!!! CUBLASXT set block dimension error\n");
return EXIT_FAILURE;
}
/* Allocate host memory for the matrices */
h_A = (float *)malloc(n2 * sizeof(h_A[0]));
if (h_A == 0) {
fprintf(stderr, "!!!! host memory allocation error (A)\n");
return EXIT_FAILURE;
}
h_B = (float *)malloc(n2 * sizeof(h_B[0]));
if (h_B == 0) {
fprintf(stderr, "!!!! host memory allocation error (B)\n");
return EXIT_FAILURE;
}
h_C_ref = (float *)malloc(n2 * sizeof(h_C[0]));
if (h_C_ref == 0) {
fprintf(stderr, "!!!! host memory allocation error (C_ref)\n");
return EXIT_FAILURE;
}
h_C = (float *)malloc(n2 * sizeof(h_C[0]));
if (h_C == 0) {
fprintf(stderr, "!!!! host memory allocation error (C)\n");
return EXIT_FAILURE;
}
/* Fill the matrices with test data */
for (i = 0; i < n2; i++) {
h_A[i] = rand() / (float)RAND_MAX;
h_B[i] = rand() / (float)RAND_MAX;
h_C[i] = rand() / (float)RAND_MAX;
h_C_ref[i] = h_C[i];
}
/* Performs operation using plain C code */
simple_sgemm(N, alpha, h_A, h_B, beta, h_C_ref);
/* Performs operation using cublas */
status = cublasXtSgemm(handle, CUBLAS_OP_N, CUBLAS_OP_N, N, N, N, &alpha, h_A,
N, h_B, N, &beta, h_C, N);
if (status != CUBLAS_STATUS_SUCCESS) {
fprintf(stderr, "!!!! kernel execution error.\n");
return EXIT_FAILURE;
}
/* Check result against reference */
error_norm = 0;
ref_norm = 0;
for (i = 0; i < n2; ++i) {
diff = h_C_ref[i] - h_C[i];
error_norm += diff * diff;
ref_norm += h_C_ref[i] * h_C_ref[i];
}
error_norm = (float)sqrt((double)error_norm);
ref_norm = (float)sqrt((double)ref_norm);
if (fabs(ref_norm) < 1e-7) {
fprintf(stderr, "!!!! reference norm is 0\n");
return EXIT_FAILURE;
}
/* Memory clean up */
free(h_A);
free(h_B);
free(h_C);
free(h_C_ref);
if (cudaFree(d_A) != cudaSuccess) {
fprintf(stderr, "!!!! memory free error (A)\n");
return EXIT_FAILURE;
}
if (cudaFree(d_B) != cudaSuccess) {
fprintf(stderr, "!!!! memory free error (B)\n");
return EXIT_FAILURE;
}
if (cudaFree(d_C) != cudaSuccess) {
fprintf(stderr, "!!!! memory free error (C)\n");
return EXIT_FAILURE;
}
/* Shutdown */
status = cublasXtDestroy(handle);
if (status != CUBLAS_STATUS_SUCCESS) {
fprintf(stderr, "!!!! shutdown error (A)\n");
return EXIT_FAILURE;
}
if (error_norm / ref_norm < 1e-6f) {
printf("simpleCUBLASXT test passed.\n");
exit(EXIT_SUCCESS);
} else {
printf("simpleCUBLASXT test failed.\n");
exit(EXIT_FAILURE);
}
}