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132 lines
4.8 KiB
Plaintext
132 lines
4.8 KiB
Plaintext
/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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* * Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above copyright
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* notice, this list of conditions and the following disclaimer in the
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* documentation and/or other materials provided with the distribution.
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* * Neither the name of NVIDIA CORPORATION nor the names of its
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* contributors may be used to endorse or promote products derived
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* from this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
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* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
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* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
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* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
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* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*/
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/**
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* Matrix multiplication: C = A * B.
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* Host code.
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*
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* This sample implements matrix multiplication as described in Chapter 3
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* of the programming guide.
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* It has been written for clarity of exposition to illustrate various CUDA
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* programming principles, not with the goal of providing the most
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* performant generic kernel for matrix multiplication.
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*
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* See also:
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* V. Volkov and J. Demmel, "Benchmarking GPUs to tune dense linear algebra,"
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* in Proc. 2008 ACM/IEEE Conf. on Supercomputing (SC '08),
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* Piscataway, NJ: IEEE Press, 2008, pp. Art. 31:1-11.
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*/
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/**
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* Matrix multiplication (CUDA Kernel) on the device: C = A * B
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* wA is A's width and wB is B's width
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*/
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#include <cooperative_groups.h>
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template <int BLOCK_SIZE>
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__device__ void matrixMulCUDA(float *C, float *A, float *B, int wA, int wB) {
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// Handle to thread block group
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cooperative_groups::thread_block cta =
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cooperative_groups::this_thread_block();
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// Block index
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int bx = blockIdx.x;
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int by = blockIdx.y;
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// Thread index
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int tx = threadIdx.x;
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int ty = threadIdx.y;
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// Index of the first sub-matrix of A processed by the block
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int aBegin = wA * BLOCK_SIZE * by;
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// Index of the last sub-matrix of A processed by the block
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int aEnd = aBegin + wA - 1;
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// Step size used to iterate through the sub-matrices of A
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int aStep = BLOCK_SIZE;
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// Index of the first sub-matrix of B processed by the block
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int bBegin = BLOCK_SIZE * bx;
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// Step size used to iterate through the sub-matrices of B
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int bStep = BLOCK_SIZE * wB;
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// Csub is used to store the element of the block sub-matrix
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// that is computed by the thread
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float Csub = 0;
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// Loop over all the sub-matrices of A and B
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// required to compute the block sub-matrix
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for (int a = aBegin, b = bBegin; a <= aEnd; a += aStep, b += bStep) {
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// Declaration of the shared memory array As used to
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// store the sub-matrix of A
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__shared__ float As[BLOCK_SIZE][BLOCK_SIZE];
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// Declaration of the shared memory array Bs used to
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// store the sub-matrix of B
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__shared__ float Bs[BLOCK_SIZE][BLOCK_SIZE];
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// Load the matrices from device memory
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// to shared memory; each thread loads
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// one element of each matrix
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As[ty][tx] = A[a + wA * ty + tx];
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Bs[ty][tx] = B[b + wB * ty + tx];
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// Synchronize to make sure the matrices are loaded
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cooperative_groups::sync(cta);
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// Multiply the two matrices together;
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// each thread computes one element
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// of the block sub-matrix
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#pragma unroll
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for (int k = 0; k < BLOCK_SIZE; ++k) {
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Csub += As[ty][k] * Bs[k][tx];
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}
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// Synchronize to make sure that the preceding
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// computation is done before loading two new
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// sub-matrices of A and B in the next iteration
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cooperative_groups::sync(cta);
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}
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// Write the block sub-matrix to device memory;
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// each thread writes one element
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int c = wB * BLOCK_SIZE * by + BLOCK_SIZE * bx;
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C[c + wB * ty + tx] = Csub;
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}
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extern "C" __global__ void matrixMulCUDA_block16(float *C, float *A, float *B,
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int wA, int wB) {
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matrixMulCUDA<16>(C, A, B, wA, wB);
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}
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extern "C" __global__ void matrixMulCUDA_block32(float *C, float *A, float *B,
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int wA, int wB) {
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matrixMulCUDA<32>(C, A, B, wA, wB);
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}
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