fb335f6a5f
CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
71 lines
2.8 KiB
C++
71 lines
2.8 KiB
C++
/***************************************************************************************************
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* Copyright (c) 2017-2019, 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 Unit tests for thread-level GEMM
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*/
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#pragma once
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#include "cutlass/array.h"
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namespace test {
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namespace nvrtc {
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namespace kernel {
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namespace thread {
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/////////////////////////////////////////////////////////////////////////////////////////////////
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/// Thread-level matrix multiply-accumulate
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template <typename Mma>
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__global__ void testbed_kernel(
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typename Mma::ElementC *D,
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typename Mma::ElementA const *A,
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typename Mma::ElementB const *B,
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typename Mma::ElementC const *C) {
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auto ptr_D = reinterpret_cast<cutlass::Array<typename Mma::ElementC, Mma::Shape::kMN> *>(D);
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auto ptr_A = reinterpret_cast<cutlass::Array<typename Mma::ElementA, Mma::Shape::kMK> const *>(A);
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auto ptr_B = reinterpret_cast<cutlass::Array<typename Mma::ElementB, Mma::Shape::kKN> const *>(B);
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auto ptr_C = reinterpret_cast<cutlass::Array<typename Mma::ElementC, Mma::Shape::kMN> const *>(C);
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Mma mma;
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auto a = *ptr_A;
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auto b = *ptr_B;
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auto c = *ptr_C;
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cutlass::Array<typename Mma::ElementC, Mma::Shape::kMN> d;
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mma(d, a, b, c);
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*ptr_D = d;
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}
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}
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}
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}
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}
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