c53f3339bb
CUTLASS 2.3 adds GEMMs targeting Sparse Tensor Cores on the NVIDIA Ampere Architecture, fast SGEMM, and small matrix classes, bug fixes, and performance enhancements.
120 lines
3.9 KiB
C++
120 lines
3.9 KiB
C++
/***************************************************************************************************
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* Copyright (c) 2017-2020, NVIDIA CORPORATION. All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without modification, are permitted
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* provided that the following conditions are met:
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* * Redistributions of source code must retain the above copyright notice, this list of
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* conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above copyright notice, this list of
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* conditions and the following disclaimer in the documentation and/or other materials
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* provided with the distribution.
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* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
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* to endorse or promote products derived from this software without specific prior written
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* permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
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* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
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* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
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* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
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* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
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* STRICT LIABILITY, OR TOR (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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/*! \file
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\brief Templates exposing SIMD operators
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*/
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#pragma once
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#include "../array.h"
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#include "../numeric_types.h"
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namespace cutlass {
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namespace arch {
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/////////////////////////////////////////////////////////////////////////////////////////////////
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//
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// Element-wise operators
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//
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CUTLASS_HOST_DEVICE
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template <typename T, int N>
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Array<T, N> operator*(Array<T, N> const &a, Array<T, N> const &b) {
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Array<T, N> d;
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CUTLASS_PRAGMA_UNROLL
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for (int i = 0; i < N; ++i) {
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d[i] = a[i] * b[i];
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}
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return d;
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}
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CUTLASS_HOST_DEVICE
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template <typename T, int N>
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Array<T, N> operator+(Array<T, N> const &a, Array<T, N> const &b) {
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Array<T, N> d;
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CUTLASS_PRAGMA_UNROLL
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for (int i = 0; i < N; ++i) {
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d[i] = a[i] + b[i];
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}
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return d;
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}
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CUTLASS_HOST_DEVICE
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template <typename T, int N>
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Array<T, N> operator-(Array<T, N> const &a, Array<T, N> const &b) {
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Array<T, N> d;
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CUTLASS_PRAGMA_UNROLL
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for (int i = 0; i < N; ++i) {
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d[i] = a[i] - b[i];
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}
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return d;
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}
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/////////////////////////////////////////////////////////////////////////////////////////////////
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//
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// Multiply-accumulate operators
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//
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CUTLASS_HOST_DEVICE
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template <typename T, int N>
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Array<T, N> mac(Array<T, N> const &a, Array<T, N> const &b, Array<T, N> const &c) {
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Array<T, N> d;
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CUTLASS_PRAGMA_UNROLL
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for (int i = 0; i < N; ++i) {
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d[i] = a[i] * b[i] + c[i];
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}
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return d;
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}
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/////////////////////////////////////////////////////////////////////////////////////////////////
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//
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// Dot product operator
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//
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CUTLASS_HOST_DEVICE
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template <typename Element, typename Accumulator, int N>
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Accumulator dot(Array<T, N> const &a, Array<T, N> const &b, Accumulator accum) {
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CUTLASS_PRAGMA_UNROLL
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for (int i = 0; i < N; ++i) {
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accum += a[i] * b[i];
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}
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return accum;
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}
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/////////////////////////////////////////////////////////////////////////////////////////////////
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} // namespace arch
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} // namespace cutlass
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/////////////////////////////////////////////////////////////////////////////////////////////////
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#include "simd_sm60.h"
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#include "simd_sm61.h"
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/////////////////////////////////////////////////////////////////////////////////////////////////
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