mirror of
https://github.com/NVIDIA/cuda-samples.git
synced 2024-11-25 00:39:14 +08:00
132 lines
4.8 KiB
Plaintext
132 lines
4.8 KiB
Plaintext
/* Copyright (c) 2022, 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.
|
|
*/
|
|
|
|
/**
|
|
* Matrix multiplication: C = A * B.
|
|
* Host code.
|
|
*
|
|
* This sample implements matrix multiplication as described in Chapter 3
|
|
* of the programming guide.
|
|
* It has been written for clarity of exposition to illustrate various CUDA
|
|
* programming principles, not with the goal of providing the most
|
|
* performant generic kernel for matrix multiplication.
|
|
*
|
|
* See also:
|
|
* V. Volkov and J. Demmel, "Benchmarking GPUs to tune dense linear algebra,"
|
|
* in Proc. 2008 ACM/IEEE Conf. on Supercomputing (SC '08),
|
|
* Piscataway, NJ: IEEE Press, 2008, pp. Art. 31:1-11.
|
|
*/
|
|
|
|
/**
|
|
* Matrix multiplication (CUDA Kernel) on the device: C = A * B
|
|
* wA is A's width and wB is B's width
|
|
*/
|
|
|
|
#include <cooperative_groups.h>
|
|
|
|
template <int BLOCK_SIZE>
|
|
__device__ void matrixMulCUDA(float *C, float *A, float *B, int wA, int wB) {
|
|
// Handle to thread block group
|
|
cooperative_groups::thread_block cta =
|
|
cooperative_groups::this_thread_block();
|
|
// Block index
|
|
int bx = blockIdx.x;
|
|
int by = blockIdx.y;
|
|
|
|
// Thread index
|
|
int tx = threadIdx.x;
|
|
int ty = threadIdx.y;
|
|
|
|
// Index of the first sub-matrix of A processed by the block
|
|
int aBegin = wA * BLOCK_SIZE * by;
|
|
|
|
// Index of the last sub-matrix of A processed by the block
|
|
int aEnd = aBegin + wA - 1;
|
|
|
|
// Step size used to iterate through the sub-matrices of A
|
|
int aStep = BLOCK_SIZE;
|
|
|
|
// Index of the first sub-matrix of B processed by the block
|
|
int bBegin = BLOCK_SIZE * bx;
|
|
|
|
// Step size used to iterate through the sub-matrices of B
|
|
int bStep = BLOCK_SIZE * wB;
|
|
|
|
// Csub is used to store the element of the block sub-matrix
|
|
// that is computed by the thread
|
|
float Csub = 0;
|
|
|
|
// Loop over all the sub-matrices of A and B
|
|
// required to compute the block sub-matrix
|
|
for (int a = aBegin, b = bBegin; a <= aEnd; a += aStep, b += bStep) {
|
|
// Declaration of the shared memory array As used to
|
|
// store the sub-matrix of A
|
|
__shared__ float As[BLOCK_SIZE][BLOCK_SIZE];
|
|
|
|
// Declaration of the shared memory array Bs used to
|
|
// store the sub-matrix of B
|
|
__shared__ float Bs[BLOCK_SIZE][BLOCK_SIZE];
|
|
|
|
// Load the matrices from device memory
|
|
// to shared memory; each thread loads
|
|
// one element of each matrix
|
|
As[ty][tx] = A[a + wA * ty + tx];
|
|
Bs[ty][tx] = B[b + wB * ty + tx];
|
|
|
|
// Synchronize to make sure the matrices are loaded
|
|
cooperative_groups::sync(cta);
|
|
|
|
// Multiply the two matrices together;
|
|
// each thread computes one element
|
|
// of the block sub-matrix
|
|
#pragma unroll
|
|
for (int k = 0; k < BLOCK_SIZE; ++k) {
|
|
Csub += As[ty][k] * Bs[k][tx];
|
|
}
|
|
|
|
// Synchronize to make sure that the preceding
|
|
// computation is done before loading two new
|
|
// sub-matrices of A and B in the next iteration
|
|
cooperative_groups::sync(cta);
|
|
}
|
|
|
|
// Write the block sub-matrix to device memory;
|
|
// each thread writes one element
|
|
int c = wB * BLOCK_SIZE * by + BLOCK_SIZE * bx;
|
|
C[c + wB * ty + tx] = Csub;
|
|
}
|
|
|
|
extern "C" __global__ void matrixMulCUDA_block16(float *C, float *A, float *B,
|
|
int wA, int wB) {
|
|
matrixMulCUDA<16>(C, A, B, wA, wB);
|
|
}
|
|
|
|
extern "C" __global__ void matrixMulCUDA_block32(float *C, float *A, float *B,
|
|
int wA, int wB) {
|
|
matrixMulCUDA<32>(C, A, B, wA, wB);
|
|
}
|