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937 lines
40 KiB
C++
937 lines
40 KiB
C++
/* ************************************************************************
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* Copyright (C) 2021-2025 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, bool STRIDED, typename T, typename S, typename U>
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void syevj_heevj_checkBadArgs(const hipsolverHandle_t handle,
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const hipsolverEigMode_t evect,
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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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T dWork,
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const int lwork,
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U dinfo,
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const hipsolverSyevjInfo_t params,
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const int bc)
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{
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// handle
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EXPECT_ROCBLAS_STATUS(hipsolver_syevj_heevj(API,
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STRIDED,
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nullptr,
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evect,
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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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dWork,
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lwork,
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dinfo,
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params,
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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_syevj_heevj(API,
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STRIDED,
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handle,
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hipsolverEigMode_t(-1),
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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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dWork,
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lwork,
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dinfo,
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params,
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bc),
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HIPSOLVER_STATUS_INVALID_ENUM);
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EXPECT_ROCBLAS_STATUS(hipsolver_syevj_heevj(API,
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STRIDED,
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handle,
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evect,
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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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dWork,
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lwork,
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dinfo,
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params,
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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_syevj_heevj(API,
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STRIDED,
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handle,
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evect,
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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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dWork,
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lwork,
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dinfo,
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params,
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bc),
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HIPSOLVER_STATUS_INVALID_VALUE);
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EXPECT_ROCBLAS_STATUS(hipsolver_syevj_heevj(API,
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STRIDED,
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handle,
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evect,
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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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dWork,
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lwork,
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dinfo,
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params,
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bc),
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HIPSOLVER_STATUS_INVALID_VALUE);
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EXPECT_ROCBLAS_STATUS(hipsolver_syevj_heevj(API,
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STRIDED,
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handle,
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evect,
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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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dWork,
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lwork,
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(U) nullptr,
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params,
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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_syevj_heevj_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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hipsolver_local_syevj_info params;
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hipsolverEigMode_t evect = HIPSOLVER_EIG_MODE_NOVECTOR;
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hipsolverFillMode_t uplo = HIPSOLVER_FILL_MODE_LOWER;
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int n = 1;
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int lda = 1;
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int stA = 1;
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int stD = 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<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(dinfo.memcheck());
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// int size_W;
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// hipsolver_syevj_heevj_bufferSize(
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// API, handle, evect, uplo, n, dA.data(), lda, dD.data(), &size_W, params, bc);
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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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// syevj_heevj_checkBadArgs<API, STRIDED>(handle,
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// evect,
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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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// dWork.data(),
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// size_W,
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// dinfo.data(),
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// params,
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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<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(dinfo.memcheck());
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int size_W;
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hipsolver_syevj_heevj_bufferSize(
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API, STRIDED, handle, evect, uplo, n, dA.data(), lda, dD.data(), &size_W, params, bc);
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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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syevj_heevj_checkBadArgs<API, STRIDED>(handle,
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evect,
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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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dWork.data(),
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size_W,
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dinfo.data(),
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params,
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bc);
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}
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}
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template <bool CPU, bool GPU, typename T, typename Td, typename Th>
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void syevj_heevj_initData(const hipsolverHandle_t handle,
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const hipsolverEigMode_t evect,
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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 bc,
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Th& hA,
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std::vector<T>& A,
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bool test = true)
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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] += 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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// make copy of original data to test vectors if required
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if(test)
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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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A[b * lda * n + i + j * lda] = hA[b][i + j * lda];
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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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bool STRIDED,
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typename T,
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typename Sd,
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typename Td,
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typename Id,
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typename Sh,
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typename Th,
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typename Uh,
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typename Ih>
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void syevj_heevj_getError(const hipsolverHandle_t handle,
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const hipsolverEigMode_t evect,
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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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Td& dWork,
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const int lwork,
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Id& dinfo,
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hipsolverSyevjInfo_t params,
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const double abstol,
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const int max_sweeps,
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const int sort_eig,
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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& hDres,
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Ih& hinfo,
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Ih& hinfoRes,
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Uh& hResidualRes,
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Ih& hSweepsRes,
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double* max_err)
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{
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constexpr bool COMPLEX = is_complex<T>;
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using S = decltype(std::real(T{}));
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S atol = (abstol <= 0) ? get_epsilon<T>() : abstol;
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int sizeE, ltwork;
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if(!COMPLEX)
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{
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sizeE = (evect == HIPSOLVER_EIG_MODE_NOVECTOR ? 2 * n + 1 : 1 + 6 * n + 2 * n * n);
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ltwork = 0;
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}
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else
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{
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sizeE = (evect == HIPSOLVER_EIG_MODE_NOVECTOR ? n : 1 + 5 * n + 2 * n * n);
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ltwork = (evect == HIPSOLVER_EIG_MODE_NOVECTOR ? n + 1 : 2 * n + n * n);
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}
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int liwork = (evect == HIPSOLVER_EIG_MODE_NOVECTOR ? 1 : 3 + 5 * n);
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std::vector<T> work(ltwork);
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std::vector<S> hE(sizeE);
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std::vector<int> iwork(liwork);
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std::vector<T> A(lda * n * bc);
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// input data initialization
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syevj_heevj_initData<true, true, T>(handle, evect, n, dA, lda, bc, hA, A);
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// execute computations
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// GPU lapack
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CHECK_ROCBLAS_ERROR(hipsolver_syevj_heevj(API,
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STRIDED,
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handle,
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evect,
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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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dWork.data(),
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lwork,
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dinfo.data(),
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params,
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bc));
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CHECK_HIP_ERROR(hDres.transfer_from(dD));
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CHECK_HIP_ERROR(hinfoRes.transfer_from(dinfo));
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if(evect == HIPSOLVER_EIG_MODE_VECTOR)
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CHECK_HIP_ERROR(hAres.transfer_from(dA));
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hipsolverXsyevjGetResidual(handle, params, hResidualRes.data());
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hipsolverXsyevjGetSweeps(handle, params, hSweepsRes.data());
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// CPU lapack
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for(int b = 0; b < bc; ++b)
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cpu_syevd_heevd(evect,
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uplo,
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n,
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hA[b],
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lda,
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hD[b],
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work.data(),
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ltwork,
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hE.data(),
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sizeE,
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iwork.data(),
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liwork,
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hinfo[b]);
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// Check info for non-convergence
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*max_err = 0;
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for(int b = 0; b < bc; ++b)
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{
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EXPECT_EQ(hinfo[b][0], hinfoRes[b][0]) << "where b = " << b;
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if(hinfo[b][0] != hinfoRes[b][0])
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*max_err += 1;
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}
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if(!STRIDED)
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{
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// Also check validity of residual
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for(rocblas_int b = 0; b < bc; ++b)
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{
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EXPECT_GE(hResidualRes[b][0], 0) << "where b = " << b;
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if(hResidualRes[b][0] < 0)
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*max_err += 1;
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else
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{
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S threshold = snorm('F', n, n, A.data() + b * lda * n, lda) * atol;
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EXPECT_LE(hResidualRes[b][0], threshold) << "where b = " << b;
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if(hResidualRes[b][0] > threshold)
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*max_err += 1;
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}
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}
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// Also check validity of sweeps
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for(rocblas_int b = 0; b < bc; ++b)
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{
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EXPECT_GE(hSweepsRes[b][0], 0) << "where b = " << b;
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EXPECT_LE(hSweepsRes[b][0], max_sweeps) << "where b = " << b;
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if(hSweepsRes[b][0] < 0 || hSweepsRes[b][0] > max_sweeps)
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*max_err += 1;
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}
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}
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double err = 0;
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for(int b = 0; b < bc; ++b)
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{
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if(evect != HIPSOLVER_EIG_MODE_VECTOR)
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{
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// only eigenvalues needed; can compare with LAPACK
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// error is ||hD - hDRes|| / ||hD||
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// using frobenius norm
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if(hinfo[b][0] == 0 && sort_eig)
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err = norm_error('F', 1, n, 1, hD[b], hDres[b]);
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*max_err = err > *max_err ? err : *max_err;
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}
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else
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{
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// both eigenvalues and eigenvectors needed; need to implicitly test
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// eigenvectors due to non-uniqueness of eigenvectors under scaling
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if(hinfo[b][0] == 0)
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{
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// multiply A with each of the n eigenvectors and divide by corresponding
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// eigenvalues
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T alpha;
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T beta = 0;
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for(int j = 0; j < n; j++)
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{
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alpha = T(1) / hDres[b][j];
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cpu_symv_hemv(uplo,
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n,
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alpha,
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A.data() + b * lda * n,
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lda,
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hAres[b] + j * lda,
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1,
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beta,
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hA[b] + j * lda,
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1);
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}
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// error is ||hA - hARes|| / ||hA||
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// using frobenius norm
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err = norm_error('F', n, n, lda, hA[b], hAres[b]);
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*max_err = err > *max_err ? err : *max_err;
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}
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}
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}
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}
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template <testAPI_t API,
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bool STRIDED,
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typename T,
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typename Sd,
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typename Td,
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typename Id,
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typename Sh,
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typename Th,
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typename Ih>
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void syevj_heevj_getPerfData(const hipsolverHandle_t handle,
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const hipsolverEigMode_t evect,
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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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Td& dWork,
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const int lwork,
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Id& dinfo,
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hipsolverSyevjInfo_t params,
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const int bc,
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Th& hA,
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Sh& hD,
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Ih& hinfo,
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double* gpu_time_used,
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double* cpu_time_used,
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const int hot_calls,
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const bool perf)
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{
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constexpr bool COMPLEX = is_complex<T>;
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using S = decltype(std::real(T{}));
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int sizeE, ltwork;
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if(!COMPLEX)
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{
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sizeE = (evect == HIPSOLVER_EIG_MODE_NOVECTOR ? 2 * n + 1 : 1 + 6 * n + 2 * n * n);
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ltwork = 0;
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}
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else
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{
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sizeE = (evect == HIPSOLVER_EIG_MODE_NOVECTOR ? n : 1 + 5 * n + 2 * n * n);
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ltwork = (evect == HIPSOLVER_EIG_MODE_NOVECTOR ? n + 1 : 2 * n + n * n);
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}
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int liwork = (evect == HIPSOLVER_EIG_MODE_NOVECTOR ? 1 : 3 + 5 * n);
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std::vector<T> work(ltwork);
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std::vector<S> hE(sizeE);
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std::vector<int> iwork(liwork);
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std::vector<T> A;
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if(!perf)
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{
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syevj_heevj_initData<true, false, T>(handle, evect, n, dA, lda, bc, hA, A, 0);
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// cpu-lapack performance (only if not in perf mode)
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*cpu_time_used = get_time_us_no_sync();
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for(int b = 0; b < bc; ++b)
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cpu_syevd_heevd(evect,
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uplo,
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n,
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hA[b],
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lda,
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hD[b],
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work.data(),
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ltwork,
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hE.data(),
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sizeE,
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iwork.data(),
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liwork,
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hinfo[b]);
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*cpu_time_used = get_time_us_no_sync() - *cpu_time_used;
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}
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syevj_heevj_initData<true, false, T>(handle, evect, n, dA, lda, bc, hA, A, 0);
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// cold calls
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for(int iter = 0; iter < 2; iter++)
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{
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syevj_heevj_initData<false, true, T>(handle, evect, n, dA, lda, bc, hA, A, 0);
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CHECK_ROCBLAS_ERROR(hipsolver_syevj_heevj(API,
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STRIDED,
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handle,
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evect,
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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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dWork.data(),
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lwork,
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dinfo.data(),
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params,
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bc));
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}
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// gpu-lapack performance
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hipStream_t stream;
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CHECK_ROCBLAS_ERROR(hipsolverGetStream(handle, &stream));
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double start;
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for(int iter = 0; iter < hot_calls; iter++)
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{
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syevj_heevj_initData<false, true, T>(handle, evect, n, dA, lda, bc, hA, A, 0);
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start = get_time_us_sync(stream);
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hipsolver_syevj_heevj(API,
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STRIDED,
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handle,
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evect,
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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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dWork.data(),
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lwork,
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dinfo.data(),
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params,
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bc);
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*gpu_time_used += get_time_us_sync(stream) - start;
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}
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*gpu_time_used /= hot_calls;
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}
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template <testAPI_t API, bool BATCHED, bool STRIDED, typename T>
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void testing_syevj_heevj(Arguments& argus)
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{
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using S = decltype(std::real(T{}));
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// get arguments
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hipsolver_local_handle handle;
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hipsolver_local_syevj_info params;
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char evectC = argus.get<char>("jobz");
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char uploC = argus.get<char>("uplo");
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int n = argus.get<int>("n");
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int lda = argus.get<int>("lda", n);
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int stA = lda * n;
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int stD = n;
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double abstol = argus.get<double>("tolerance", 2 * get_epsilon<T>());
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rocblas_int max_sweeps = argus.get<int>("max_sweeps", 100);
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rocblas_int sort_eig = argus.get<int>("sort_eig", 1);
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hipsolverXsyevjSetTolerance(params, abstol);
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hipsolverXsyevjSetMaxSweeps(params, max_sweeps);
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hipsolverXsyevjSetSortEig(params, sort_eig);
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hipsolverEigMode_t evect = char2hipsolver_evect(evectC);
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hipsolverFillMode_t uplo = char2hipsolver_fill(uploC);
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int bc = argus.batch_count;
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int hot_calls = argus.iters;
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// determine sizes
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size_t size_A = size_t(lda) * n;
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size_t size_D = n;
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size_t size_Ares = (argus.unit_check || argus.norm_check) ? size_A : 0;
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size_t size_Dres = (argus.unit_check || argus.norm_check) ? size_D : 0;
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double max_error = 0, gpu_time_used = 0, cpu_time_used = 0;
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// check invalid sizes
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bool invalid_size = (n < 0 || lda < n || bc < 0);
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if(invalid_size)
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{
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if(BATCHED)
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{
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// EXPECT_ROCBLAS_STATUS(hipsolver_syevj_heevj(API,
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// STRIDED,
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// handle,
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// evect,
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// uplo,
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// n,
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// (T* const*)nullptr,
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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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// (T*)nullptr,
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// 0,
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// (int*)nullptr,
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// params,
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// bc),
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// HIPSOLVER_STATUS_INVALID_VALUE);
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}
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else
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{
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EXPECT_ROCBLAS_STATUS(hipsolver_syevj_heevj(API,
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STRIDED,
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handle,
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evect,
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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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(S*)nullptr,
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stD,
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(T*)nullptr,
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0,
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(int*)nullptr,
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params,
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bc),
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HIPSOLVER_STATUS_INVALID_VALUE);
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}
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if(argus.timing)
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rocsolver_bench_inform(inform_invalid_size);
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return;
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}
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// memory size query is necessary
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int size_W;
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hipsolver_syevj_heevj_bufferSize(
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API, STRIDED, handle, evect, uplo, n, (T*)nullptr, lda, (S*)nullptr, &size_W, params, bc);
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size_t bytes_W
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= std::getenv("HIPSOLVER_BUFFERSIZE_RETURN_BYTES") != nullptr ? size_W : sizeof(T) * size_W;
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if(argus.mem_query)
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{
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rocsolver_bench_inform(inform_mem_query, bytes_W);
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return;
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}
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// memory allocations (all cases)
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// host
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host_strided_batch_vector<double> hResidualRes(1, 1, 1, bc);
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host_strided_batch_vector<int> hSweepsRes(1, 1, 1, bc);
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host_strided_batch_vector<S> hD(size_D, 1, stD, bc);
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host_strided_batch_vector<int> hinfo(1, 1, 1, bc);
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host_strided_batch_vector<int> hinfoRes(1, 1, 1, bc);
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host_strided_batch_vector<S> hDres(size_Dres, 1, stD, bc);
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// device
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device_strided_batch_vector<S> dD(size_D, 1, stD, bc);
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device_strided_batch_vector<int> dinfo(1, 1, 1, bc);
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device_strided_batch_vector<T> dWork(bytes_W, 1, bytes_W, 1); // bytes_W accounts for bc
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if(size_D)
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CHECK_HIP_ERROR(dD.memcheck());
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CHECK_HIP_ERROR(dinfo.memcheck());
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if(size_W)
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CHECK_HIP_ERROR(dWork.memcheck());
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if(BATCHED)
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{
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// // memory allocations
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// host_batch_vector<T> hA(size_A, 1, bc);
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// host_batch_vector<T> hAres(size_Ares, 1, bc);
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// device_batch_vector<T> dA(size_A, 1, bc);
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// if(size_A)
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// CHECK_HIP_ERROR(dA.memcheck());
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// // check computations
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// if(argus.unit_check || argus.norm_check)
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// {
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// syevj_heevj_getError<API, STRIDED, T>(handle,
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// evect,
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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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// dWork,
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// size_W,
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// dinfo,
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// params,
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// abstol,
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// max_sweeps,
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// sort_eig,
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// bc,
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// hA,
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// hAres,
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// hD,
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// hDres,
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// hinfo,
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// hinfoRes,
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// hResidualRes,
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// hSweepsRes,
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// &max_error);
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// }
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// // collect performance data
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// if(argus.timing)
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// {
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// syevj_heevj_getPerfData<API, STRIDED, T>(handle,
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// evect,
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// uplo,
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// n,
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// dA,
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// lda,
|
|
// stA,
|
|
// dD,
|
|
// stD,
|
|
// dWork,
|
|
// size_W,
|
|
// dinfo,
|
|
// params,
|
|
// bc,
|
|
// hA,
|
|
// hD,
|
|
// hinfo,
|
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// &gpu_time_used,
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// &cpu_time_used,
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// hot_calls,
|
|
// argus.perf);
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// }
|
|
}
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|
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else
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{
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// memory allocations
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host_strided_batch_vector<T> hA(size_A, 1, stA, bc);
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host_strided_batch_vector<T> hAres(size_Ares, 1, stA, bc);
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device_strided_batch_vector<T> dA(size_A, 1, stA, bc);
|
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if(size_A)
|
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CHECK_HIP_ERROR(dA.memcheck());
|
|
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// check computations
|
|
if(argus.unit_check || argus.norm_check)
|
|
{
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syevj_heevj_getError<API, STRIDED, T>(handle,
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evect,
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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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dWork,
|
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size_W,
|
|
dinfo,
|
|
params,
|
|
abstol,
|
|
max_sweeps,
|
|
sort_eig,
|
|
bc,
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hA,
|
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hAres,
|
|
hD,
|
|
hDres,
|
|
hinfo,
|
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hinfoRes,
|
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hResidualRes,
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hSweepsRes,
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|
&max_error);
|
|
}
|
|
|
|
// collect performance data
|
|
if(argus.timing)
|
|
{
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syevj_heevj_getPerfData<API, STRIDED, T>(handle,
|
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evect,
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uplo,
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n,
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|
dA,
|
|
lda,
|
|
stA,
|
|
dD,
|
|
stD,
|
|
dWork,
|
|
size_W,
|
|
dinfo,
|
|
params,
|
|
bc,
|
|
hA,
|
|
hD,
|
|
hinfo,
|
|
&gpu_time_used,
|
|
&cpu_time_used,
|
|
hot_calls,
|
|
argus.perf);
|
|
}
|
|
}
|
|
|
|
// validate results for rocsolver-test
|
|
// using 4 * n * machine_precision as tolerance
|
|
if(argus.unit_check)
|
|
ROCSOLVER_TEST_CHECK(T, max_error, 4 * 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("jobz",
|
|
"uplo",
|
|
"n",
|
|
"lda",
|
|
"strideD",
|
|
"tolerance",
|
|
"max_sweeps",
|
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"sort_eig",
|
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"batch_c");
|
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rocsolver_bench_output(
|
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evectC, uploC, n, lda, stD, abstol, max_sweeps, sort_eig, bc);
|
|
}
|
|
else if(STRIDED)
|
|
{
|
|
rocsolver_bench_output("jobz",
|
|
"uplo",
|
|
"n",
|
|
"lda",
|
|
"strideA",
|
|
"strideD",
|
|
"tolerance",
|
|
"max_sweeps",
|
|
"sort_eig",
|
|
"batch_c");
|
|
rocsolver_bench_output(
|
|
evectC, uploC, n, lda, stA, stD, abstol, max_sweeps, sort_eig, bc);
|
|
}
|
|
else
|
|
{
|
|
rocsolver_bench_output(
|
|
"jobz", "uplo", "n", "lda", "tolerance", "max_sweeps", "sort_eig");
|
|
rocsolver_bench_output(evectC, uploC, n, lda, abstol, max_sweeps, sort_eig);
|
|
}
|
|
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();
|
|
}
|