chundoong-lab-ta/SamsungDS22/submissions/HW6/sk0812.min/mat_mul.cu

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2022-09-29 18:01:45 +09:00
#include "mat_mul.h"
#include <cstdio>
#include <cuda_runtime.h>
#define CUDA_CALL(f) \
{ \
cudaError_t err = (f); \
if (err != cudaSuccess) { \
fprintf(stderr, "CUDA error at [%s:%d] %d %s\n", __FILE__, __LINE__, \
err, cudaGetErrorString(err)); \
exit(1); \
} \
}
#define MAX_NUM_GPU 4
#define TS 16
int num_devices = 0;
__global__ void sgemm(float *A, float *B, float *C, int M, int N, int K) {
__shared__ float Asub[TS][TS];
__shared__ float Bsub[TS][TS];
int bx = blockIdx.x, by = blockIdx.y,
tx = threadIdx.x, ty = threadIdx.y,
Row = by * TS + ty,
Col = bx * TS + tx;
float Pvalue = 0;
int numARows=M;
int numAColumns=K;
int numBRows=K;
int numBColumns=N;
int numCRows=M;
int numCColumns=N;
const int numTiles = (K+TS-1)/TS;
for (int m = 0; m < (numAColumns-1)/TS+1; ++m) {
if (Row < numARows && m*TS+tx < numAColumns)
Asub[ty][tx] = A[Row*numAColumns + m*TS+tx];
else
Asub[ty][tx] = 0;
if (Col < numBColumns && m*TS+ty < numBRows)
Bsub[ty][tx] = B[(m*TS+ty)*numBColumns+Col];
else
Bsub[ty][tx] = 0;
__syncthreads();
for (int k = 0; k < TS; ++k)
Pvalue += Asub[ty][k] * Bsub[k][tx];
__syncthreads();
}
if (Row < numCRows && Col < numCColumns)
C[Row*numCColumns+Col] = Pvalue;
}
// Array of device (GPU) pointers
static float *a_d[MAX_NUM_GPU];
static float *b_d[MAX_NUM_GPU];
static float *c_d[MAX_NUM_GPU];
static int M, N, K;
static int Mstart[MAX_NUM_GPU], Mend[MAX_NUM_GPU];
void mat_mul(float *_A, float *_B, float *_C, int _M, int _N, int _K) {
// Launch kernel on every GPU
for (int i = 0; i < num_devices; i++) {
dim3 blockDim(TS, TS, 1);
dim3 gridDim((_N-1)/TS+1, ( Mend[i] - Mstart[i]-1)/TS+1, 1);
CUDA_CALL( cudaSetDevice(i) );
sgemm<<<gridDim, blockDim>>>(a_d[i], b_d[i], c_d[i], Mend[i] - Mstart[i], N, K);
}
// DO NOT REMOVE; NEEDED FOR TIME MEASURE
for (int i = 0; i < num_devices; i++) {
CUDA_CALL( cudaDeviceSynchronize() );
}
}
void mat_mul_init(float *A, float *B, float *C, int _M, int _N, int _K) {
M = _M, N = _N, K = _K;
CUDA_CALL( cudaGetDeviceCount(&num_devices) );
printf("Using %d devices\n", num_devices);
for (int i = 0; i < num_devices; i++) {
cudaDeviceProp prop;
CUDA_CALL( cudaGetDeviceProperties(&prop, i) );
// Try printing more detailed information here
printf("[GPU %d] %s\n", i, prop.name);
}
if (num_devices <= 0) {
printf("No CUDA device found. Aborting\n");
exit(1);
}
// Setup problem size for each GPU
for (int i = 0; i < num_devices; i++) {
Mstart[i] = (M / num_devices) * i;
Mend[i] = (M / num_devices) * (i + 1);
}
Mend[num_devices - 1] = M;
// Allocate device memory for each GPU
for (int i = 0; i < num_devices; i++) {
CUDA_CALL( cudaSetDevice(i) );
CUDA_CALL( cudaMalloc(&a_d[i], (Mend[i] - Mstart[i]) * K * sizeof(float)) );
CUDA_CALL( cudaMalloc(&b_d[i], K * N * sizeof(float)) );
CUDA_CALL( cudaMalloc(&c_d[i], (Mend[i] - Mstart[i]) * N * sizeof(float)) );
//printf("numD=%d, start=%d, end=%d\n", num_devices, Mstart[i], Mend[i]);
}
// Upload A and B matrix to every GPU
for (int i = 0; i < num_devices; i++) {
CUDA_CALL( cudaMemcpy(a_d[i], A + Mstart[i] * K,
(Mend[i] - Mstart[i]) * K * sizeof(float),
cudaMemcpyHostToDevice) );
CUDA_CALL( cudaMemcpy(b_d[i], B, K * N * sizeof(float), cudaMemcpyHostToDevice) );
}
// DO NOT REMOVE; NEEDED FOR TIME MEASURE
for (int i = 0; i < num_devices; i++) {
CUDA_CALL( cudaDeviceSynchronize() );
}
}
void mat_mul_final(float *A, float *B, float *C, int M, int N, int K) {
// Do any post-matmul cleanup work here.
// Download C matrix from GPUs
for (int i = 0; i < num_devices; i++) {
CUDA_CALL( cudaMemcpy(C + Mstart[i] * N, c_d[i],
(Mend[i] - Mstart[i]) * N * sizeof(float),
cudaMemcpyDeviceToHost) );
}
// DO NOT REMOVE; NEEDED FOR TIME MEASURE
for (int i = 0; i < num_devices; i++) {
CUDA_CALL( cudaDeviceSynchronize() );
}
}