232 lines
6.1 KiB
Plaintext
232 lines
6.1 KiB
Plaintext
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#include "mat_mul.h"
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#include <cstdio>
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#include <cuda_runtime.h>
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#define CUDA_CALL(f) \
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{ \
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cudaError_t err = (f); \
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if (err != cudaSuccess) { \
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fprintf(stderr, "CUDA error at [%s:%d] %d %s\n", __FILE__, __LINE__, \
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err, cudaGetErrorString(err)); \
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exit(1); \
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} \
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}
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#define MAX_NUM_GPU 4
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int num_devices = 0;
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//// 4800
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//#define TS 16
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//// 6900
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//#define TS 18
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// 7400
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#define TS 20
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//// 6400
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//#define TS 25
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__global__ void sgemm_no_wpt(float *A_d, float *B_d, float *C_d, int M_d, int N_d, int K_d) {
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int global_row = blockDim.x * blockIdx.x + threadIdx.x;
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int global_col = blockDim.y * blockIdx.y + threadIdx.y;
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int global_row_K_d = global_row * K_d;
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int numTiles = (K_d+TS-1)/TS;
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__shared__ float Asub[TS][TS];
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__shared__ float Bsub[TS][TS];
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int t;
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int k;
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float acc = 0;
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for(t = 0; t < TS*(numTiles-1); t += TS){
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int t_row = t + threadIdx.x;
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int t_col = t + threadIdx.y;
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Asub[threadIdx.x][threadIdx.y] = A_d[global_row_K_d + t_col];
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Bsub[threadIdx.x][threadIdx.y] = B_d[(t_row)*N_d + global_col];
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__syncthreads();
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for(k = 0; k < TS; k++){
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acc += Asub[threadIdx.x][k] * Bsub[k][threadIdx.y];
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}
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__syncthreads();
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}
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int t_row = TS * (numTiles-1) + threadIdx.x;
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int t_col = TS * (numTiles-1) + threadIdx.y;
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if(((global_row) >= M_d) || (t_col >= K_d)){
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Asub[threadIdx.x][threadIdx.y] = 0;
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}
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else{
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Asub[threadIdx.x][threadIdx.y] = A_d[(global_row)*K_d + t_col];
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}
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if(((t_row) >= K_d) || (global_col >= N_d)){
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Bsub[threadIdx.x][threadIdx.y] = 0;
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}
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else{
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Bsub[threadIdx.x][threadIdx.y] = B_d[(t_row)*N_d + global_col];
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}
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__syncthreads();
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for(k = 0; k < TS; k++){
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acc += Asub[threadIdx.x][k] * Bsub[k][threadIdx.y];
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}
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__syncthreads();
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if(((global_row) >= M_d) || (global_col >= N_d)) return;
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C_d[(global_row)*N_d + global_col] = acc;
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}
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// Array of device (GPU) pointers
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static float * a_d[MAX_NUM_GPU];
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static float * b_d[MAX_NUM_GPU];
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static float * c_d[MAX_NUM_GPU];
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static int M, N, K;
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static int Mbegin[MAX_NUM_GPU], Mend[MAX_NUM_GPU];
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void mat_mul(float *_A, float *_B, float *_C, int _M, int _N, int _K) {
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// Launch kernel on every GPU
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dim3 blockDim(TS, TS, 1);
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for (int i = 0; i < num_devices; i++) {
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dim3 gridDim(((Mend[i] - Mbegin[i])+TS-1)/(TS), (N+TS-1)/TS, 1);
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CUDA_CALL( cudaSetDevice(i) );
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sgemm_no_wpt<<<gridDim, blockDim>>>(a_d[i], b_d[i], c_d[i], Mend[i] - Mbegin[i], N, K);
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}
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// DO NOT REMOVE; NEEDED FOR TIME MEASURE
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for (int i = 0; i < num_devices; i++) {
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CUDA_CALL( cudaDeviceSynchronize() );
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}
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}
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void mat_mul_init(float *A, float *B, float *C, int _M, int _N, int _K) {
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M = _M, N = _N, K = _K;
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CUDA_CALL( cudaGetDeviceCount(&num_devices) );
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printf("Using %d devices\n", num_devices);
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for (int i = 0; i < num_devices; i++) {
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cudaDeviceProp prop;
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CUDA_CALL( cudaGetDeviceProperties(&prop, i) );
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// Try printing more detailed information here
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printf("[GPU %d] %s\n", i, prop.name);
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}
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if (num_devices <= 0) {
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printf("No CUDA device found. Aborting\n");
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exit(1);
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}
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// Setup problem size for each GPU
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for (int i = 0; i < num_devices; i++) {
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Mbegin[i] = (M / num_devices) * i;
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Mend[i] = (M / num_devices) * (i + 1);
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}
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Mend[num_devices - 1] = M;
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// Allocate device memory for each GPU
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for (int i = 0; i < num_devices; i++) {
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CUDA_CALL( cudaSetDevice(i) );
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CUDA_CALL( cudaMalloc(&a_d[i], (Mend[i] - Mbegin[i]) * K * sizeof(float)) );
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CUDA_CALL( cudaMalloc(&b_d[i], K * N * sizeof(float)) );
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CUDA_CALL( cudaMalloc(&c_d[i], (Mend[i] - Mbegin[i]) * N * sizeof(float)) );
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}
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// Upload A and B matrix to every GPU
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for (int i = 0; i < num_devices; i++) {
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CUDA_CALL( cudaMemcpy(a_d[i], A + Mbegin[i] * K,
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(Mend[i] - Mbegin[i]) * K * sizeof(float),
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cudaMemcpyHostToDevice) );
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CUDA_CALL( cudaMemcpy(b_d[i], B, K * N * sizeof(float), cudaMemcpyHostToDevice) );
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}
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// DO NOT REMOVE; NEEDED FOR TIME MEASURE
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for (int i = 0; i < num_devices; i++) {
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CUDA_CALL( cudaDeviceSynchronize() );
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}
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}
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void mat_mul_final(float *A, float *B, float *C, int M, int N, int K) {
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// Do any post-matmul cleanup work here.
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// Download C matrix from GPUs
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for (int i = 0; i < num_devices; i++) {
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CUDA_CALL( cudaMemcpy(C + Mbegin[i] * N, c_d[i],
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(Mend[i] - Mbegin[i]) * N * sizeof(float),
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cudaMemcpyDeviceToHost) );
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}
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// DO NOT REMOVE; NEEDED FOR TIME MEASURE
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for (int i = 0; i < num_devices; i++) {
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CUDA_CALL( cudaDeviceSynchronize() );
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}
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}
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//#define WPT 1
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//#define RTS (TS/WPT)
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//__global__ void sgemm(float *A_d, float *B_d, float *C_d, int M_d, int N_d, int K_d) {
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//
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// int global_row = blockDim.x * blockIdx.x + threadIdx.x;
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// int global_col = blockDim.y * blockIdx.y + threadIdx.y;
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//
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// int numTiles = (K_d+TS-1)/TS;
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//
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// __shared__ float Asub[TS][TS];
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// __shared__ float Bsub[TS][TS];
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//
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// int t;
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// int w;
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// int k;
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//
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// float acc[WPT];
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// for(w = 0; w < WPT; w++){
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// acc[w] = 0;
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// }
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//
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// for(t = 0; t < numTiles; t++){
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// int t_row = TS * t + threadIdx.x;
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// int t_col = TS * t + threadIdx.y;
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// for(w = 0; w < WPT; w++){
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// if(((global_row + w*RTS) >= M_d) || (t_col >= K_d)){
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// Asub[threadIdx.x + w*RTS][threadIdx.y] = 0;
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// }
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// else{
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// Asub[threadIdx.x + w*RTS][threadIdx.y] = A_d[(global_row + w*RTS)*K_d + t_col];
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// }
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//
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// if(((t_row + w*RTS) >= K_d) || (global_col >= N_d)){
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// Bsub[threadIdx.x + w*RTS][threadIdx.y] = 0;
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// }
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// else{
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// Bsub[threadIdx.x + w*RTS][threadIdx.y] = B_d[(t_row + w*RTS)*N_d + global_col];
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// }
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// }
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// __syncthreads();
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// for(k = 0; k < TS; k++){
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// for(w = 0; w < WPT; w++){
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// acc[w] += Asub[threadIdx.x + w*RTS][k] * Bsub[k][threadIdx.y];
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// }
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// }
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// __syncthreads();
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// }
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//
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// for(w = 0; w < WPT; w++){
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// if(((global_row + w*RTS) >= M_d) || (global_col >= N_d)) break;
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// C_d[(global_row + w*RTS)*N_d + global_col] = acc[w];
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// }
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//}
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