derek.holloway
7502bf64a4
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994 lines
41 KiB
C++
994 lines
41 KiB
C++
/* ************************************************************************
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* Copyright (C) 2020-2025 Advanced Micro Devices, Inc. All rights reserved.
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to deal
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* in the Software without restriction, including without limitation the rights
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* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell cop-
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* ies of the Software, and to permit persons to whom the Software is furnished
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* to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IM-
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* PLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
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* FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
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* COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
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* IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNE-
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* CTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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*
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*
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* ************************************************************************ */
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#pragma once
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#include "clientcommon.hpp"
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template <testAPI_t API, typename S, typename T, typename U, typename V>
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void sytrd_hetrd_checkBadArgs(const hipsolverHandle_t handle,
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const hipsolverFillMode_t uplo,
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const int n,
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T dA,
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const int lda,
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const int stA,
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S dD,
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const int stD,
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S dE,
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const int stE,
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U dTau,
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const int stP,
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U dWork,
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const int lwork,
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V dInfo,
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const int bc)
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{
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// handle
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EXPECT_ROCBLAS_STATUS(hipsolver_sytrd_hetrd(API,
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nullptr,
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uplo,
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n,
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dA,
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lda,
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stA,
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dD,
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stD,
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dE,
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stE,
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dTau,
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stP,
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dWork,
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lwork,
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dInfo,
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bc),
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HIPSOLVER_STATUS_NOT_INITIALIZED);
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// values
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EXPECT_ROCBLAS_STATUS(hipsolver_sytrd_hetrd(API,
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handle,
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hipsolverFillMode_t(-1),
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n,
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dA,
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lda,
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stA,
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dD,
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stD,
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dE,
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stE,
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dTau,
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stP,
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dWork,
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lwork,
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dInfo,
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bc),
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HIPSOLVER_STATUS_INVALID_ENUM);
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#if defined(__HIP_PLATFORM_HCC__) || defined(__HIP_PLATFORM_AMD__)
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// pointers
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EXPECT_ROCBLAS_STATUS(hipsolver_sytrd_hetrd(API,
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handle,
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uplo,
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n,
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(T) nullptr,
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lda,
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stA,
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dD,
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stD,
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dE,
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stE,
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dTau,
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stP,
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dWork,
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lwork,
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dInfo,
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bc),
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HIPSOLVER_STATUS_INVALID_VALUE);
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EXPECT_ROCBLAS_STATUS(hipsolver_sytrd_hetrd(API,
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handle,
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uplo,
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n,
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dA,
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lda,
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stA,
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(S) nullptr,
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stD,
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dE,
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stE,
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dTau,
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stP,
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dWork,
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lwork,
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dInfo,
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bc),
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HIPSOLVER_STATUS_INVALID_VALUE);
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EXPECT_ROCBLAS_STATUS(hipsolver_sytrd_hetrd(API,
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handle,
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uplo,
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n,
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dA,
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lda,
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stA,
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dD,
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stD,
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(S) nullptr,
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stE,
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dTau,
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stP,
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dWork,
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lwork,
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dInfo,
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bc),
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HIPSOLVER_STATUS_INVALID_VALUE);
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EXPECT_ROCBLAS_STATUS(hipsolver_sytrd_hetrd(API,
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handle,
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uplo,
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n,
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dA,
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lda,
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stA,
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dD,
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stD,
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dE,
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stE,
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(U) nullptr,
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stP,
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dWork,
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lwork,
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dInfo,
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bc),
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HIPSOLVER_STATUS_INVALID_VALUE);
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EXPECT_ROCBLAS_STATUS(hipsolver_sytrd_hetrd(API,
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handle,
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uplo,
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n,
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dA,
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lda,
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stA,
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dD,
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stD,
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dE,
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stE,
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dTau,
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stP,
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dWork,
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lwork,
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(V) nullptr,
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bc),
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HIPSOLVER_STATUS_INVALID_VALUE);
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#endif
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}
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template <testAPI_t API, bool BATCHED, bool STRIDED, typename T>
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void testing_sytrd_hetrd_bad_arg()
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{
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using S = decltype(std::real(T{}));
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// safe arguments
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hipsolver_local_handle handle;
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hipsolverFillMode_t uplo = HIPSOLVER_FILL_MODE_UPPER;
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int n = 2;
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int lda = 2;
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int stA = 1;
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int stD = 1;
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int stE = 1;
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int stP = 1;
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int bc = 1;
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if(BATCHED)
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{
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// // memory allocations
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// device_batch_vector<T> dA(1, 1, 1);
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// device_strided_batch_vector<S> dD(1, 1, 1, 1);
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// device_strided_batch_vector<S> dE(1, 1, 1, 1);
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// device_strided_batch_vector<T> dTau(1, 1, 1, 1);
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// device_strided_batch_vector<int> dInfo(1, 1, 1, 1);
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// CHECK_HIP_ERROR(dA.memcheck());
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// CHECK_HIP_ERROR(dD.memcheck());
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// CHECK_HIP_ERROR(dE.memcheck());
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// CHECK_HIP_ERROR(dTau.memcheck());
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// CHECK_HIP_ERROR(dInfo.memcheck());
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// int size_W;
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// hipsolver_sytrd_hetrd_bufferSize(
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// API, handle, uplo, n, dA.data(), lda, dD.data(), dE.data(), dTau.data(), &size_W);
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// size_t bytes_W = std::getenv("HIPSOLVER_BUFFERSIZE_RETURN_BYTES") != nullptr ? size_W : sizeof(T) * size_W;
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// device_strided_batch_vector<T> dWork(bytes_W, 1, bytes_W, 1);
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// if(size_W)
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// CHECK_HIP_ERROR(dWork.memcheck());
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// // check bad arguments
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// sytrd_hetrd_checkBadArgs<API>(handle,
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// uplo,
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// n,
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// dA.data(),
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// lda,
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// stA,
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// dD.data(),
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// stD,
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// dE.data(),
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// stE,
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// dTau.data(),
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// stP,
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// dWork.data(),
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// size_W,
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// dInfo.data(),
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// bc);
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}
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else
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{
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// memory allocations
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device_strided_batch_vector<T> dA(1, 1, 1, 1);
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device_strided_batch_vector<S> dD(1, 1, 1, 1);
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device_strided_batch_vector<S> dE(1, 1, 1, 1);
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device_strided_batch_vector<T> dTau(1, 1, 1, 1);
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device_strided_batch_vector<int> dInfo(1, 1, 1, 1);
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CHECK_HIP_ERROR(dA.memcheck());
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CHECK_HIP_ERROR(dD.memcheck());
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CHECK_HIP_ERROR(dE.memcheck());
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CHECK_HIP_ERROR(dTau.memcheck());
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CHECK_HIP_ERROR(dInfo.memcheck());
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int size_W;
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hipsolver_sytrd_hetrd_bufferSize(
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API, handle, uplo, n, dA.data(), lda, dD.data(), dE.data(), dTau.data(), &size_W);
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size_t bytes_W = std::getenv("HIPSOLVER_BUFFERSIZE_RETURN_BYTES") != nullptr
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? size_W
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: sizeof(T) * size_W;
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device_strided_batch_vector<T> dWork(bytes_W, 1, bytes_W, 1);
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if(size_W)
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CHECK_HIP_ERROR(dWork.memcheck());
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// check bad arguments
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sytrd_hetrd_checkBadArgs<API>(handle,
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uplo,
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n,
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dA.data(),
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lda,
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stA,
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dD.data(),
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stD,
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dE.data(),
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stE,
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dTau.data(),
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stP,
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dWork.data(),
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size_W,
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dInfo.data(),
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bc);
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}
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}
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template <bool CPU,
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bool GPU,
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typename T,
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typename Td,
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typename Th,
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std::enable_if_t<!is_complex<T>, int> = 0>
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void sytrd_hetrd_initData(
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const hipsolverHandle_t handle, const int n, Td& dA, const int lda, const int bc, Th& hA)
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{
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if(CPU)
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{
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rocblas_init<T>(hA, true);
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// scale A to avoid singularities
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for(int b = 0; b < bc; ++b)
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{
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for(int i = 0; i < n; i++)
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{
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for(int j = 0; j < n; j++)
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{
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if(i == j || i == j + 1 || i == j - 1)
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hA[b][i + j * lda] += 400;
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else
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hA[b][i + j * lda] -= 4;
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}
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}
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}
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}
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if(GPU)
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{
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// now copy to the GPU
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CHECK_HIP_ERROR(dA.transfer_from(hA));
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}
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}
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template <bool CPU,
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bool GPU,
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typename T,
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typename Td,
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typename Th,
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std::enable_if_t<is_complex<T>, int> = 0>
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void sytrd_hetrd_initData(
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const hipsolverHandle_t handle, const int n, Td& dA, const int lda, const int bc, Th& hA)
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{
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if(CPU)
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{
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rocblas_init<T>(hA, true);
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// scale A to avoid singularities
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for(int b = 0; b < bc; ++b)
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{
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for(int i = 0; i < n; i++)
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{
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for(int j = 0; j < n; j++)
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{
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if(i == j)
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hA[b][i + j * lda] = hA[b][i + j * lda].real() + 400;
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else if(i == j + 1 || i == j - 1)
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hA[b][i + j * lda] += 400;
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else
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hA[b][i + j * lda] -= 4;
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}
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}
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}
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}
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if(GPU)
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{
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// now copy to the GPU
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CHECK_HIP_ERROR(dA.transfer_from(hA));
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}
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}
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template <testAPI_t API,
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typename T,
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typename Sd,
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typename Td,
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typename Ud,
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typename Vd,
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typename Sh,
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typename Th,
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typename Uh,
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typename Vh>
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void sytrd_hetrd_getError(const hipsolverHandle_t handle,
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const hipsolverFillMode_t uplo,
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const int n,
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Td& dA,
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const int lda,
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const int stA,
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Sd& dD,
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const int stD,
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Sd& dE,
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const int stE,
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Ud& dTau,
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const int stP,
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Ud& dWork,
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const int lwork,
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Vd& dInfo,
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const int bc,
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Th& hA,
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Th& hARes,
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Sh& hD,
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Sh& hE,
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Uh& hTau,
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Vh& hInfo,
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Vh& hInfoRes,
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double* max_err)
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{
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using S = decltype(std::real(T{}));
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constexpr bool COMPLEX = is_complex<T>;
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std::vector<T> hW(32 * n);
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// input data initialization
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sytrd_hetrd_initData<true, true, T>(handle, n, dA, lda, bc, hA);
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// execute computations
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// GPU lapack
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CHECK_ROCBLAS_ERROR(hipsolver_sytrd_hetrd(API,
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handle,
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uplo,
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n,
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dA.data(),
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lda,
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stA,
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dD.data(),
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stD,
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dE.data(),
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stE,
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dTau.data(),
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stP,
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dWork.data(),
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lwork,
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dInfo.data(),
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bc));
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CHECK_HIP_ERROR(hARes.transfer_from(dA));
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CHECK_HIP_ERROR(hTau.transfer_from(dTau));
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CHECK_HIP_ERROR(hInfoRes.transfer_from(dInfo));
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// Reconstruct matrix A from the factorization for implicit testing
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// A = H(n-1)...H(2)H(1)*T*H(1)'H(2)'...H(n-1)' if upper
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// A = H(1)H(2)...H(n-1)*T*H(n-1)'...H(2)'H(1)' if lower
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std::vector<T> v(n);
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for(int b = 0; b < bc; ++b)
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{
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T* a = hARes[b];
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T* t = hTau[b];
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if(uplo == HIPSOLVER_FILL_MODE_LOWER)
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{
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for(int i = 0; i < n - 2; ++i)
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a[i + (n - 1) * lda] = 0;
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a[(n - 2) + (n - 1) * lda] = a[(n - 1) + (n - 2) * lda];
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// for each column
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for(int j = n - 2; j >= 0; --j)
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{
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// prepare T and v
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for(int i = 0; i < j - 1; ++i)
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a[i + j * lda] = 0;
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if(j > 0)
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a[(j - 1) + j * lda] = a[j + (j - 1) * lda];
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for(int i = j + 2; i < n; ++i)
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{
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v[i - j - 1] = a[i + j * lda];
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a[i + j * lda] = 0;
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}
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v[0] = 1;
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// apply householder reflector
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cpu_larf(HIPSOLVER_SIDE_LEFT,
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n - 1 - j,
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n - j,
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v.data(),
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1,
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t + j,
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a + (j + 1) + j * lda,
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lda,
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hW.data());
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if(COMPLEX)
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cpu_lacgv(1, t + j, 1);
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cpu_larf(HIPSOLVER_SIDE_RIGHT,
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n - j,
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n - 1 - j,
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v.data(),
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1,
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t + j,
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a + j + (j + 1) * lda,
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lda,
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hW.data());
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}
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}
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else
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{
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a[1] = a[lda];
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for(int i = 2; i < n; ++i)
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a[i] = 0;
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// for each column
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for(int j = 1; j <= n - 1; ++j)
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{
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// prepare T and v
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for(int i = 0; i < j - 1; ++i)
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{
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v[i] = a[i + j * lda];
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a[i + j * lda] = 0;
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}
|
|
v[j - 1] = 1;
|
|
if(j < n - 1)
|
|
a[(j + 1) + j * lda] = a[j + (j + 1) * lda];
|
|
for(int i = j + 2; i < n; ++i)
|
|
a[i + j * lda] = 0;
|
|
|
|
// apply householder reflector
|
|
cpu_larf(HIPSOLVER_SIDE_LEFT, j, j + 1, v.data(), 1, t + j - 1, a, lda, hW.data());
|
|
if(COMPLEX)
|
|
cpu_lacgv(1, t + j - 1, 1);
|
|
cpu_larf(HIPSOLVER_SIDE_RIGHT, j + 1, j, v.data(), 1, t + j - 1, a, lda, hW.data());
|
|
}
|
|
}
|
|
}
|
|
|
|
// error is ||hA - hARes|| / ||hA||
|
|
// using frobenius norm
|
|
double err;
|
|
*max_err = 0;
|
|
for(int b = 0; b < bc; ++b)
|
|
{
|
|
*max_err = (uplo == HIPSOLVER_FILL_MODE_LOWER)
|
|
? norm_error_lowerTr('F', n, n, lda, hA[b], hARes[b])
|
|
: norm_error_upperTr('F', n, n, lda, hA[b], hARes[b]);
|
|
}
|
|
|
|
// check info
|
|
err = 0;
|
|
for(int b = 0; b < bc; ++b)
|
|
{
|
|
EXPECT_EQ(hInfoRes[b][0], 0) << "where b = " << b;
|
|
if(hInfoRes[b][0] != 0)
|
|
err++;
|
|
}
|
|
*max_err += err;
|
|
}
|
|
|
|
template <testAPI_t API,
|
|
typename T,
|
|
typename Sd,
|
|
typename Td,
|
|
typename Ud,
|
|
typename Vd,
|
|
typename Sh,
|
|
typename Th,
|
|
typename Uh,
|
|
typename Vh>
|
|
void sytrd_hetrd_getPerfData(const hipsolverHandle_t handle,
|
|
const hipsolverFillMode_t uplo,
|
|
const int n,
|
|
Td& dA,
|
|
const int lda,
|
|
const int stA,
|
|
Sd& dD,
|
|
const int stD,
|
|
Sd& dE,
|
|
const int stE,
|
|
Ud& dTau,
|
|
const int stP,
|
|
Ud& dWork,
|
|
const int lwork,
|
|
Vd& dInfo,
|
|
const int bc,
|
|
Th& hA,
|
|
Sh& hD,
|
|
Sh& hE,
|
|
Uh& hTau,
|
|
Vh& hInfo,
|
|
double* gpu_time_used,
|
|
double* cpu_time_used,
|
|
const int hot_calls,
|
|
const bool perf)
|
|
{
|
|
using S = decltype(std::real(T{}));
|
|
|
|
std::vector<T> hW(32 * n);
|
|
|
|
if(!perf)
|
|
{
|
|
sytrd_hetrd_initData<true, false, T>(handle, n, dA, lda, bc, hA);
|
|
|
|
// cpu-lapack performance (only if not in perf mode)
|
|
*cpu_time_used = get_time_us_no_sync();
|
|
for(int b = 0; b < bc; ++b)
|
|
cpu_sytrd_hetrd(uplo, n, hA[b], lda, hD[b], hE[b], hTau[b], hW.data(), 32 * n);
|
|
*cpu_time_used = get_time_us_no_sync() - *cpu_time_used;
|
|
}
|
|
|
|
sytrd_hetrd_initData<true, false, T>(handle, n, dA, lda, bc, hA);
|
|
|
|
// cold calls
|
|
for(int iter = 0; iter < 2; iter++)
|
|
{
|
|
sytrd_hetrd_initData<false, true, T>(handle, n, dA, lda, bc, hA);
|
|
|
|
CHECK_ROCBLAS_ERROR(hipsolver_sytrd_hetrd(API,
|
|
handle,
|
|
uplo,
|
|
n,
|
|
dA.data(),
|
|
lda,
|
|
stA,
|
|
dD.data(),
|
|
stD,
|
|
dE.data(),
|
|
stE,
|
|
dTau.data(),
|
|
stP,
|
|
dWork.data(),
|
|
lwork,
|
|
dInfo.data(),
|
|
bc));
|
|
}
|
|
|
|
// gpu-lapack performance
|
|
hipStream_t stream;
|
|
CHECK_ROCBLAS_ERROR(hipsolverGetStream(handle, &stream));
|
|
double start;
|
|
|
|
for(int iter = 0; iter < hot_calls; iter++)
|
|
{
|
|
sytrd_hetrd_initData<false, true, T>(handle, n, dA, lda, bc, hA);
|
|
|
|
start = get_time_us_sync(stream);
|
|
hipsolver_sytrd_hetrd(API,
|
|
handle,
|
|
uplo,
|
|
n,
|
|
dA.data(),
|
|
lda,
|
|
stA,
|
|
dD.data(),
|
|
stD,
|
|
dE.data(),
|
|
stE,
|
|
dTau.data(),
|
|
stP,
|
|
dWork.data(),
|
|
lwork,
|
|
dInfo.data(),
|
|
bc);
|
|
*gpu_time_used += get_time_us_sync(stream) - start;
|
|
}
|
|
*gpu_time_used /= hot_calls;
|
|
}
|
|
|
|
template <testAPI_t API, bool BATCHED, bool STRIDED, typename T>
|
|
void testing_sytrd_hetrd(Arguments& argus)
|
|
{
|
|
using S = decltype(std::real(T{}));
|
|
|
|
// get arguments
|
|
hipsolver_local_handle handle;
|
|
char uploC = argus.get<char>("uplo");
|
|
int n = argus.get<int>("n");
|
|
int lda = argus.get<int>("lda", n);
|
|
int stA = argus.get<int>("strideA", lda * n);
|
|
int stD = argus.get<int>("strideD", n);
|
|
int stE = argus.get<int>("strideE", n - 1);
|
|
int stP = argus.get<int>("strideP", n - 1);
|
|
|
|
hipsolverFillMode_t uplo = char2hipsolver_fill(uploC);
|
|
int bc = argus.batch_count;
|
|
int hot_calls = argus.iters;
|
|
|
|
int stARes = (argus.unit_check || argus.norm_check) ? stA : 0;
|
|
|
|
// check non-supported values
|
|
if(uplo != HIPSOLVER_FILL_MODE_UPPER && uplo != HIPSOLVER_FILL_MODE_LOWER)
|
|
{
|
|
if(BATCHED)
|
|
{
|
|
// EXPECT_ROCBLAS_STATUS(hipsolver_sytrd_hetrd(API,
|
|
// handle,
|
|
// uplo,
|
|
// n,
|
|
// (T* const*)nullptr,
|
|
// lda,
|
|
// stA,
|
|
// (S*)nullptr,
|
|
// stD,
|
|
// (S*)nullptr,
|
|
// stE,
|
|
// (T*)nullptr,
|
|
// stP,
|
|
// (T*)nullptr,
|
|
// 0,
|
|
// (int*)nullptr,
|
|
// bc),
|
|
// HIPSOLVER_STATUS_INVALID_VALUE);
|
|
}
|
|
else
|
|
{
|
|
EXPECT_ROCBLAS_STATUS(hipsolver_sytrd_hetrd(API,
|
|
handle,
|
|
uplo,
|
|
n,
|
|
(T*)nullptr,
|
|
lda,
|
|
stA,
|
|
(S*)nullptr,
|
|
stD,
|
|
(S*)nullptr,
|
|
stE,
|
|
(T*)nullptr,
|
|
stP,
|
|
(T*)nullptr,
|
|
0,
|
|
(int*)nullptr,
|
|
bc),
|
|
HIPSOLVER_STATUS_INVALID_VALUE);
|
|
}
|
|
|
|
if(argus.timing)
|
|
rocsolver_bench_inform(inform_invalid_args);
|
|
|
|
return;
|
|
}
|
|
|
|
// determine sizes
|
|
size_t size_A = lda * n;
|
|
size_t size_D = n;
|
|
size_t size_E = n - 1;
|
|
size_t size_tau = n - 1;
|
|
double max_error = 0, gpu_time_used = 0, cpu_time_used = 0;
|
|
|
|
size_t size_ARes = (argus.unit_check || argus.norm_check) ? size_A : 0;
|
|
|
|
// check invalid sizes
|
|
bool invalid_size = (n < 0 || lda < n || bc < 0);
|
|
if(invalid_size)
|
|
{
|
|
if(BATCHED)
|
|
{
|
|
// EXPECT_ROCBLAS_STATUS(hipsolver_sytrd_hetrd(API,
|
|
// handle,
|
|
// uplo,
|
|
// n,
|
|
// (T* const*)nullptr,
|
|
// lda,
|
|
// stA,
|
|
// (S*)nullptr,
|
|
// stD,
|
|
// (S*)nullptr,
|
|
// stE,
|
|
// (T*)nullptr,
|
|
// stP,
|
|
// (T*)nullptr,
|
|
// 0,
|
|
// (int*)nullptr,
|
|
// bc),
|
|
// HIPSOLVER_STATUS_INVALID_VALUE);
|
|
}
|
|
else
|
|
{
|
|
EXPECT_ROCBLAS_STATUS(hipsolver_sytrd_hetrd(API,
|
|
handle,
|
|
uplo,
|
|
n,
|
|
(T*)nullptr,
|
|
lda,
|
|
stA,
|
|
(S*)nullptr,
|
|
stD,
|
|
(S*)nullptr,
|
|
stE,
|
|
(T*)nullptr,
|
|
stP,
|
|
(T*)nullptr,
|
|
0,
|
|
(int*)nullptr,
|
|
bc),
|
|
HIPSOLVER_STATUS_INVALID_VALUE);
|
|
}
|
|
|
|
if(argus.timing)
|
|
rocsolver_bench_inform(inform_invalid_size);
|
|
|
|
return;
|
|
}
|
|
|
|
// memory size query is necessary
|
|
int size_W;
|
|
hipsolver_sytrd_hetrd_bufferSize(
|
|
API, handle, uplo, n, (T*)nullptr, lda, (S*)nullptr, (S*)nullptr, (T*)nullptr, &size_W);
|
|
size_t bytes_W
|
|
= std::getenv("HIPSOLVER_BUFFERSIZE_RETURN_BYTES") != nullptr ? size_W : sizeof(T) * size_W;
|
|
|
|
if(argus.mem_query)
|
|
{
|
|
rocsolver_bench_inform(inform_mem_query, bytes_W);
|
|
return;
|
|
}
|
|
|
|
// memory allocations (all cases)
|
|
// host
|
|
host_strided_batch_vector<S> hD(size_D, 1, stD, bc);
|
|
host_strided_batch_vector<S> hE(size_E, 1, stE, bc);
|
|
host_strided_batch_vector<T> hTau(size_tau, 1, stP, bc);
|
|
host_strided_batch_vector<int> hInfo(1, 1, 1, bc);
|
|
host_strided_batch_vector<int> hInfoRes(1, 1, 1, bc);
|
|
// device
|
|
device_strided_batch_vector<S> dD(size_D, 1, stD, bc);
|
|
device_strided_batch_vector<S> dE(size_E, 1, stE, bc);
|
|
device_strided_batch_vector<T> dTau(size_tau, 1, stP, bc);
|
|
device_strided_batch_vector<int> dInfo(1, 1, 1, bc);
|
|
device_strided_batch_vector<T> dWork(bytes_W, 1, bytes_W, 1); // bytes_W accounts for bc
|
|
if(size_D)
|
|
CHECK_HIP_ERROR(dD.memcheck());
|
|
if(size_E)
|
|
CHECK_HIP_ERROR(dE.memcheck());
|
|
if(size_tau)
|
|
CHECK_HIP_ERROR(dTau.memcheck());
|
|
CHECK_HIP_ERROR(dInfo.memcheck());
|
|
if(size_W)
|
|
CHECK_HIP_ERROR(dWork.memcheck());
|
|
|
|
if(BATCHED)
|
|
{
|
|
// // memory allocations
|
|
// host_batch_vector<T> hA(size_A, 1, bc);
|
|
// host_batch_vector<T> hARes(size_ARes, 1, bc);
|
|
// device_batch_vector<T> dA(size_A, 1, bc);
|
|
// if(size_A)
|
|
// CHECK_HIP_ERROR(dA.memcheck());
|
|
|
|
// // check computations
|
|
// if(argus.unit_check || argus.norm_check)
|
|
// sytrd_hetrd_getError<API, T>(handle,
|
|
// uplo,
|
|
// n,
|
|
// dA,
|
|
// lda,
|
|
// stA,
|
|
// dD,
|
|
// stD,
|
|
// dE,
|
|
// stE,
|
|
// dTau,
|
|
// stP,
|
|
// dWork,
|
|
// size_W,
|
|
// dInfo,
|
|
// bc,
|
|
// hA,
|
|
// hARes,
|
|
// hD,
|
|
// hE,
|
|
// hTau,
|
|
// hInfo,
|
|
// hInfoRes,
|
|
// &max_error);
|
|
|
|
// // collect performance data
|
|
// if(argus.timing)
|
|
// sytrd_hetrd_getPerfData<API, T>(handle,
|
|
// uplo,
|
|
// n,
|
|
// dA,
|
|
// lda,
|
|
// stA,
|
|
// dD,
|
|
// stD,
|
|
// dE,
|
|
// stE,
|
|
// dTau,
|
|
// stP,
|
|
// dWork,
|
|
// size_W,
|
|
// dInfo,
|
|
// bc,
|
|
// hA,
|
|
// hD,
|
|
// hE,
|
|
// hTau,
|
|
// hInfo,
|
|
// &gpu_time_used,
|
|
// &cpu_time_used,
|
|
// hot_calls,
|
|
// argus.perf);
|
|
}
|
|
|
|
else
|
|
{
|
|
// memory allocations
|
|
host_strided_batch_vector<T> hA(size_A, 1, stA, bc);
|
|
host_strided_batch_vector<T> hARes(size_ARes, 1, stARes, bc);
|
|
device_strided_batch_vector<T> dA(size_A, 1, stA, bc);
|
|
if(size_A)
|
|
CHECK_HIP_ERROR(dA.memcheck());
|
|
|
|
// check computations
|
|
if(argus.unit_check || argus.norm_check)
|
|
sytrd_hetrd_getError<API, T>(handle,
|
|
uplo,
|
|
n,
|
|
dA,
|
|
lda,
|
|
stA,
|
|
dD,
|
|
stD,
|
|
dE,
|
|
stE,
|
|
dTau,
|
|
stP,
|
|
dWork,
|
|
size_W,
|
|
dInfo,
|
|
bc,
|
|
hA,
|
|
hARes,
|
|
hD,
|
|
hE,
|
|
hTau,
|
|
hInfo,
|
|
hInfoRes,
|
|
&max_error);
|
|
|
|
// collect performance data
|
|
if(argus.timing)
|
|
sytrd_hetrd_getPerfData<API, T>(handle,
|
|
uplo,
|
|
n,
|
|
dA,
|
|
lda,
|
|
stA,
|
|
dD,
|
|
stD,
|
|
dE,
|
|
stE,
|
|
dTau,
|
|
stP,
|
|
dWork,
|
|
size_W,
|
|
dInfo,
|
|
bc,
|
|
hA,
|
|
hD,
|
|
hE,
|
|
hTau,
|
|
hInfo,
|
|
&gpu_time_used,
|
|
&cpu_time_used,
|
|
hot_calls,
|
|
argus.perf);
|
|
}
|
|
|
|
// validate results for rocsolver-test
|
|
// using n * machine_precision as tolerance
|
|
if(argus.unit_check)
|
|
ROCSOLVER_TEST_CHECK(T, max_error, n);
|
|
|
|
// output results for rocsolver-bench
|
|
if(argus.timing)
|
|
{
|
|
if(!argus.perf)
|
|
{
|
|
std::cerr << "\n============================================\n";
|
|
std::cerr << "Arguments:\n";
|
|
std::cerr << "============================================\n";
|
|
if(BATCHED)
|
|
{
|
|
rocsolver_bench_output(
|
|
"uplo", "n", "lda", "strideD", "strideE", "strideP", "batch_c");
|
|
rocsolver_bench_output(uploC, n, lda, stD, stE, stP, bc);
|
|
}
|
|
else if(STRIDED)
|
|
{
|
|
rocsolver_bench_output(
|
|
"uplo", "n", "lda", "strideA", "strideD", "strideE", "strideP", "batch_c");
|
|
rocsolver_bench_output(uploC, n, lda, stA, stD, stE, stP, bc);
|
|
}
|
|
else
|
|
{
|
|
rocsolver_bench_output("uplo", "n", "lda");
|
|
rocsolver_bench_output(uploC, n, lda);
|
|
}
|
|
std::cerr << "\n============================================\n";
|
|
std::cerr << "Results:\n";
|
|
std::cerr << "============================================\n";
|
|
if(argus.norm_check)
|
|
{
|
|
rocsolver_bench_output("cpu_time", "gpu_time", "error");
|
|
rocsolver_bench_output(cpu_time_used, gpu_time_used, max_error);
|
|
}
|
|
else
|
|
{
|
|
rocsolver_bench_output("cpu_time", "gpu_time");
|
|
rocsolver_bench_output(cpu_time_used, gpu_time_used);
|
|
}
|
|
std::cerr << std::endl;
|
|
}
|
|
else
|
|
{
|
|
if(argus.norm_check)
|
|
rocsolver_bench_output(gpu_time_used, max_error);
|
|
else
|
|
rocsolver_bench_output(gpu_time_used);
|
|
}
|
|
}
|
|
|
|
// ensure all arguments were consumed
|
|
argus.validate_consumed();
|
|
}
|