derek.holloway
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903 lines
36 KiB
C++
903 lines
36 KiB
C++
/* ************************************************************************
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* Copyright (C) 2020-2024 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 U>
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void gels_checkBadArgs(const hipsolverHandle_t handle,
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const int m,
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const int n,
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const int nrhs,
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U dA,
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const int lda,
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const int stA,
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U dB,
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const int ldb,
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const int stB,
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U dX,
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const int ldx,
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const int stX,
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U dWork,
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const size_t lwork,
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int* niters,
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int* info,
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const int bc)
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{
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// handle
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EXPECT_ROCBLAS_STATUS(hipsolver_gels(API,
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false,
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nullptr,
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m,
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n,
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nrhs,
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dA,
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lda,
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stA,
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dB,
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ldb,
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stB,
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dX,
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ldx,
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stX,
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dWork,
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lwork,
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niters,
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info,
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bc),
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HIPSOLVER_STATUS_NOT_INITIALIZED);
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// values
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// N/A
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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_gels(API,
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false,
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handle,
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m,
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n,
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nrhs,
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(U) nullptr,
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lda,
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stA,
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dB,
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ldb,
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stB,
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dX,
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ldx,
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stX,
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dWork,
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lwork,
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niters,
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info,
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bc),
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HIPSOLVER_STATUS_INVALID_VALUE);
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EXPECT_ROCBLAS_STATUS(hipsolver_gels(API,
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false,
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handle,
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m,
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n,
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nrhs,
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dA,
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lda,
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stA,
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(U) nullptr,
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ldb,
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stB,
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dX,
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ldx,
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stX,
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dWork,
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lwork,
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niters,
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info,
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bc),
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HIPSOLVER_STATUS_INVALID_VALUE);
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EXPECT_ROCBLAS_STATUS(hipsolver_gels(API,
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false,
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handle,
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m,
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n,
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nrhs,
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dA,
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lda,
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stA,
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dB,
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ldb,
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stB,
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(U) nullptr,
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ldx,
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stX,
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dWork,
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lwork,
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niters,
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info,
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bc),
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HIPSOLVER_STATUS_INVALID_VALUE);
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EXPECT_ROCBLAS_STATUS(hipsolver_gels(API,
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false,
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handle,
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m,
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n,
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nrhs,
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dA,
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lda,
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stA,
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dB,
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ldb,
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stB,
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dX,
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ldx,
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stX,
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dWork,
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lwork,
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niters,
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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_gels_bad_arg()
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{
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// safe arguments
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hipsolver_local_handle handle;
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int m = 1;
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int n = 1;
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int nrhs = 1;
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int lda = 1;
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int ldb = 1;
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int ldx = 1;
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int stA = 1;
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int stB = 1;
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int stX = 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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// host_strided_batch_vector<int> hNIters(1, 1, 1, bc);
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// device_batch_vector<T> dA(1, 1, 1);
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// device_batch_vector<T> dB(1, 1, 1);
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// device_batch_vector<T> dX(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(dB.memcheck());
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// CHECK_HIP_ERROR(dX.memcheck());
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// CHECK_HIP_ERROR(dInfo.memcheck());
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// size_t size_W;
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// hipsolver_gels_bufferSize(
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// API, handle, m, n, nrhs, dA.data(), lda, dB.data(), ldb, dX.data(), ldx, &size_W);
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// device_strided_batch_vector<T> dWork(size_W, 1, size_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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// gels_checkBadArgs<API>(handle,
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// m,
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// n,
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// nrhs,
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// dA.data(),
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// lda,
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// stA,
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// dB.data(),
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// ldb,
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// stB,
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// dX.data(),
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// ldx,
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// stX,
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// dWork.data(),
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// size_W,
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// hNIters.data(),
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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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host_strided_batch_vector<int> hNIters(1, 1, 1, bc);
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device_strided_batch_vector<T> dA(1, 1, 1, 1);
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device_strided_batch_vector<T> dB(1, 1, 1, 1);
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device_strided_batch_vector<T> dX(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(dB.memcheck());
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CHECK_HIP_ERROR(dX.memcheck());
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CHECK_HIP_ERROR(dInfo.memcheck());
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size_t size_W;
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hipsolver_gels_bufferSize(
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API, handle, m, n, nrhs, dA.data(), lda, dB.data(), ldb, dX.data(), ldx, &size_W);
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device_strided_batch_vector<T> dWork(size_W, 1, size_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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gels_checkBadArgs<API>(handle,
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m,
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n,
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nrhs,
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dA.data(),
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lda,
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stA,
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dB.data(),
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ldb,
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stB,
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dX.data(),
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ldx,
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stX,
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dWork.data(),
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size_W,
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hNIters.data(),
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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, bool GPU, typename T, typename Td, typename Ud, typename Th, typename Uh>
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void gels_initData(const hipsolverHandle_t handle,
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const int m,
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const int n,
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const int nrhs,
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Td& dA,
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const int lda,
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const int stA,
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Td& dB,
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const int ldb,
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const int stB,
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Ud& dInfo,
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const int bc,
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Th& hA,
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Th& hB,
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Th& hX,
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Uh& hInfo)
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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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rocblas_init<T>(hB, true);
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const int max_index = std::max(0, std::min(m, n) - 1);
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std::uniform_int_distribution<int> sample_index(0, max_index);
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std::bernoulli_distribution coinflip(0.5);
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const int ldx = max(m, n);
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for(int b = 0; b < bc; ++b)
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{
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// scale A to avoid singularities
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for(int i = 0; i < m; 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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// populate hX with values from hB
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for(int i = 0; i < m; i++)
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for(int j = 0; j < nrhs; j++)
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hX[b][i + j * ldx] = hB[b][i + j * ldb];
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}
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}
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if(GPU)
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{
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// now copy pivoting indices and matrices to the GPU
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CHECK_HIP_ERROR(dA.transfer_from(hA));
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CHECK_HIP_ERROR(dB.transfer_from(hB));
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}
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}
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template <testAPI_t API,
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bool INPLACE,
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typename T,
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typename Td,
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typename Ud,
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typename Th,
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typename Uh>
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void gels_getError(const hipsolverHandle_t handle,
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const int m,
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const int n,
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const int nrhs,
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Td& dA,
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const int lda,
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const int stA,
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Td& dB,
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const int ldb,
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const int stB,
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Td& dX,
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const int ldx,
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const int stX,
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Td& dWork,
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const size_t lwork,
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Ud& dInfo,
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const int bc,
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Th& hA,
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Th& hB,
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Th& hBRes,
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Th& hX,
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Th& hXRes,
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Uh& hNIters,
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Uh& hInfo,
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Uh& hInfoRes,
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double* max_err)
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{
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int sizeW = max(1, min(m, n) + max(min(m, n), nrhs));
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std::vector<T> hW(sizeW);
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// input data initialization
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gels_initData<true, true, T>(
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handle, m, n, nrhs, dA, lda, stA, dB, ldb, stB, dInfo, bc, hA, hB, hX, hInfo);
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// execute computations
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// GPU lapack
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CHECK_ROCBLAS_ERROR(hipsolver_gels(API,
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INPLACE,
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handle,
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m,
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n,
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nrhs,
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dA.data(),
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lda,
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stA,
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dB.data(),
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ldb,
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stB,
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dX.data(),
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ldx,
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stX,
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dWork.data(),
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lwork,
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hNIters.data(),
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dInfo.data(),
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bc));
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CHECK_HIP_ERROR(hBRes.transfer_from(dB));
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CHECK_HIP_ERROR(hXRes.transfer_from(dX));
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CHECK_HIP_ERROR(hInfoRes.transfer_from(dInfo));
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// CPU lapack
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for(int b = 0; b < bc; ++b)
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{
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cpu_gels(
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HIPSOLVER_OP_N, m, n, nrhs, hA[b], lda, hX[b], max(m, n), hW.data(), sizeW, hInfo[b]);
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}
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// error is ||hX - hXRes|| / ||hX||
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// (THIS DOES NOT ACCOUNT FOR NUMERICAL REPRODUCIBILITY ISSUES.
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// IT MIGHT BE REVISITED IN THE FUTURE)
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// using vector-induced infinity norm
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double err;
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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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if(!INPLACE)
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{
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err = norm_error('F', m, nrhs, ldb, hB[b], hBRes[b]);
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*max_err = err > *max_err ? err : *max_err;
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if(hInfo[b][0] == 0)
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{
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err = norm_error('I', n, nrhs, max(m, n), hX[b], hXRes[b], ldx);
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*max_err = err > *max_err ? err : *max_err;
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}
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}
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else
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{
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if(hInfo[b][0] == 0)
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{
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err = norm_error('I', n, nrhs, max(m, n), hX[b], hBRes[b], ldb);
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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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// also check info for singularities
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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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err++;
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}
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*max_err += err;
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}
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template <testAPI_t API,
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bool INPLACE,
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typename T,
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typename Td,
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typename Ud,
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typename Th,
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typename Uh>
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void gels_getPerfData(const hipsolverHandle_t handle,
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const int m,
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const int n,
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const int nrhs,
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Td& dA,
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const int lda,
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const int stA,
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Td& dB,
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const int ldb,
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const int stB,
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Td& dX,
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const int ldx,
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const int stX,
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Td& dWork,
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const size_t lwork,
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Ud& dInfo,
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const int bc,
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Th& hA,
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Th& hB,
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Th& hX,
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Uh& hNIters,
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Uh& 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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int sizeW = max(1, min(m, n) + max(min(m, n), nrhs));
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std::vector<T> hW(sizeW);
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if(!perf)
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{
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gels_initData<true, false, T>(
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handle, m, n, nrhs, dA, lda, stA, dB, ldb, stB, dInfo, bc, hA, hB, hX, hInfo);
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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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{
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cpu_gels(HIPSOLVER_OP_N,
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m,
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n,
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nrhs,
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hA[b],
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lda,
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hX[b],
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max(m, n),
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hW.data(),
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sizeW,
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hInfo[b]);
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}
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*cpu_time_used = get_time_us_no_sync() - *cpu_time_used;
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}
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gels_initData<true, false, T>(
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handle, m, n, nrhs, dA, lda, stA, dB, ldb, stB, dInfo, bc, hA, hB, hX, hInfo);
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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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gels_initData<false, true, T>(
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handle, m, n, nrhs, dA, lda, stA, dB, ldb, stB, dInfo, bc, hA, hB, hX, hInfo);
|
|
|
|
CHECK_ROCBLAS_ERROR(hipsolver_gels(API,
|
|
INPLACE,
|
|
handle,
|
|
m,
|
|
n,
|
|
nrhs,
|
|
dA.data(),
|
|
lda,
|
|
stA,
|
|
dB.data(),
|
|
ldb,
|
|
stB,
|
|
dX.data(),
|
|
ldx,
|
|
stX,
|
|
dWork.data(),
|
|
lwork,
|
|
hNIters.data(),
|
|
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++)
|
|
{
|
|
gels_initData<false, true, T>(
|
|
handle, m, n, nrhs, dA, lda, stA, dB, ldb, stB, dInfo, bc, hA, hB, hX, hInfo);
|
|
|
|
start = get_time_us_sync(stream);
|
|
hipsolver_gels(API,
|
|
INPLACE,
|
|
handle,
|
|
m,
|
|
n,
|
|
nrhs,
|
|
dA.data(),
|
|
lda,
|
|
stA,
|
|
dB.data(),
|
|
ldb,
|
|
stB,
|
|
dX.data(),
|
|
ldx,
|
|
stX,
|
|
dWork.data(),
|
|
lwork,
|
|
hNIters.data(),
|
|
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,
|
|
bool INPLACE,
|
|
typename T,
|
|
bool COMPLEX = is_complex<T>>
|
|
void testing_gels(Arguments& argus)
|
|
{
|
|
// get arguments
|
|
hipsolver_local_handle handle;
|
|
int m = argus.get<int>("m");
|
|
int n = argus.get<int>("n", m);
|
|
int nrhs = argus.get<int>("nrhs", n);
|
|
int lda = argus.get<int>("lda", m);
|
|
int ldb = argus.get<int>("ldb", m);
|
|
int ldx = argus.get<int>("ldx", n);
|
|
int stA = argus.get<int>("strideA", lda * n);
|
|
int stB = argus.get<int>("strideB", ldb * nrhs);
|
|
int stX = argus.get<int>("strideX", ldx * nrhs);
|
|
|
|
int bc = argus.batch_count;
|
|
int hot_calls = argus.iters;
|
|
|
|
int stBRes = (argus.unit_check || argus.norm_check) ? stB : 0;
|
|
int stXRes = (argus.unit_check || argus.norm_check) ? stX : 0;
|
|
|
|
// check non-supported values
|
|
// N/A
|
|
|
|
// determine sizes
|
|
size_t size_A = size_t(lda) * n;
|
|
size_t size_B = size_t(ldb) * nrhs;
|
|
size_t size_X = size_t(ldx) * nrhs;
|
|
double max_error = 0, gpu_time_used = 0, cpu_time_used = 0;
|
|
|
|
size_t size_BRes = (argus.unit_check || argus.norm_check) ? size_B : 0;
|
|
size_t size_XRes = (argus.unit_check || argus.norm_check) ? size_X : 0;
|
|
|
|
// check invalid sizes
|
|
bool invalid_size = (m < 0 || n < 0 || nrhs < 0 || lda < m || ldb < m || ldx < n || bc < 0);
|
|
if(invalid_size)
|
|
{
|
|
if(BATCHED)
|
|
{
|
|
// EXPECT_ROCBLAS_STATUS(hipsolver_gels(API,
|
|
// INPLACE,
|
|
// handle,
|
|
// m,
|
|
// n,
|
|
// nrhs,
|
|
// (T* const*)nullptr,
|
|
// lda,
|
|
// stA,
|
|
// (T* const*)nullptr,
|
|
// ldb,
|
|
// stB,
|
|
// (T* const*)nullptr,
|
|
// ldx,
|
|
// stX,
|
|
// (T*)nullptr,
|
|
// 0,
|
|
// (int*)nullptr,
|
|
// (int*)nullptr,
|
|
// bc),
|
|
// HIPSOLVER_STATUS_INVALID_VALUE);
|
|
}
|
|
else
|
|
{
|
|
EXPECT_ROCBLAS_STATUS(hipsolver_gels(API,
|
|
INPLACE,
|
|
handle,
|
|
m,
|
|
n,
|
|
nrhs,
|
|
(T*)nullptr,
|
|
lda,
|
|
stA,
|
|
(T*)nullptr,
|
|
ldb,
|
|
stB,
|
|
(T*)nullptr,
|
|
ldx,
|
|
stX,
|
|
(T*)nullptr,
|
|
0,
|
|
(int*)nullptr,
|
|
(int*)nullptr,
|
|
bc),
|
|
HIPSOLVER_STATUS_INVALID_VALUE);
|
|
}
|
|
|
|
if(argus.timing)
|
|
rocsolver_bench_inform(inform_invalid_size);
|
|
|
|
return;
|
|
}
|
|
|
|
// memory size query is necessary
|
|
size_t size_W;
|
|
hipsolver_gels_bufferSize(
|
|
API, handle, m, n, nrhs, (T*)nullptr, lda, (T*)nullptr, ldb, (T*)nullptr, ldx, &size_W);
|
|
|
|
if(argus.mem_query)
|
|
{
|
|
rocsolver_bench_inform(inform_mem_query, size_W);
|
|
return;
|
|
}
|
|
|
|
if(BATCHED)
|
|
{
|
|
// // memory allocations
|
|
// host_batch_vector<T> hA(size_A, 1, bc);
|
|
// host_batch_vector<T> hB(size_B, 1, bc);
|
|
// host_batch_vector<T> hBRes(size_BRes, 1, bc);
|
|
// host_batch_vector<T> hX(max(m, n) * nrhs, 1, bc);
|
|
// host_batch_vector<T> hXRes(size_XRes, 1, bc);
|
|
// host_strided_batch_vector<int> hNIters(1, 1, 1, bc);
|
|
// host_strided_batch_vector<int> hInfo(1, 1, 1, bc);
|
|
// host_strided_batch_vector<int> hInfoRes(1, 1, 1, bc);
|
|
// device_batch_vector<T> dA(size_A, 1, bc);
|
|
// device_batch_vector<T> dB(size_B, 1, bc);
|
|
// device_batch_vector<T> dX(size_X, 1, bc);
|
|
// device_strided_batch_vector<int> dInfo(1, 1, 1, bc);
|
|
// device_strided_batch_vector<T> dWork(size_W, 1, size_W, 1); // size_W accounts for bc
|
|
// if(size_A)
|
|
// CHECK_HIP_ERROR(dA.memcheck());
|
|
// if(size_B)
|
|
// CHECK_HIP_ERROR(dB.memcheck());
|
|
// if(size_X)
|
|
// CHECK_HIP_ERROR(dX.memcheck());
|
|
// if(bc)
|
|
// CHECK_HIP_ERROR(dInfo.memcheck());
|
|
// if(size_W)
|
|
// CHECK_HIP_ERROR(dWork.memcheck());
|
|
|
|
// // check computations
|
|
// if(argus.unit_check || argus.norm_check)
|
|
// gels_getError<API, INPLACE, T>(handle,
|
|
// m,
|
|
// n,
|
|
// nrhs,
|
|
// dA,
|
|
// lda,
|
|
// stA,
|
|
// dB,
|
|
// ldb,
|
|
// stB,
|
|
// dX,
|
|
// ldx,
|
|
// stX,
|
|
// dWork,
|
|
// size_W,
|
|
// dInfo,
|
|
// bc,
|
|
// hA,
|
|
// hB,
|
|
// hBRes,
|
|
// hX,
|
|
// hXRes,
|
|
// hNIters,
|
|
// hInfo,
|
|
// hInfoRes,
|
|
// &max_error);
|
|
|
|
// // collect performance data
|
|
// if(argus.timing)
|
|
// gels_getPerfData<API, INPLACE, T>(handle,
|
|
// m,
|
|
// n,
|
|
// nrhs,
|
|
// dA,
|
|
// lda,
|
|
// stA,
|
|
// dB,
|
|
// ldb,
|
|
// stB,
|
|
// dX,
|
|
// ldx,
|
|
// stX,
|
|
// dWork,
|
|
// size_W,
|
|
// dInfo,
|
|
// bc,
|
|
// hA,
|
|
// hB,
|
|
// hX,
|
|
// hNIters,
|
|
// 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> hB(size_B, 1, stB, bc);
|
|
host_strided_batch_vector<T> hBRes(size_BRes, 1, stBRes, bc);
|
|
host_strided_batch_vector<T> hX(max(m, n) * nrhs, 1, max(m, n) * nrhs, bc);
|
|
host_strided_batch_vector<T> hXRes(size_XRes, 1, stXRes, bc);
|
|
host_strided_batch_vector<int> hNIters(1, 1, 1, bc);
|
|
host_strided_batch_vector<int> hInfo(1, 1, 1, bc);
|
|
host_strided_batch_vector<int> hInfoRes(1, 1, 1, bc);
|
|
device_strided_batch_vector<T> dA(size_A, 1, stA, bc);
|
|
device_strided_batch_vector<T> dB(size_B, 1, stB, bc);
|
|
device_strided_batch_vector<T> dX(size_X, 1, stX, bc);
|
|
device_strided_batch_vector<int> dInfo(1, 1, 1, bc);
|
|
device_strided_batch_vector<T> dWork(size_W, 1, size_W, 1); // size_W accounts for bc
|
|
if(size_A)
|
|
CHECK_HIP_ERROR(dA.memcheck());
|
|
if(size_B)
|
|
CHECK_HIP_ERROR(dB.memcheck());
|
|
if(size_X)
|
|
CHECK_HIP_ERROR(dX.memcheck());
|
|
if(bc)
|
|
CHECK_HIP_ERROR(dInfo.memcheck());
|
|
if(size_W)
|
|
CHECK_HIP_ERROR(dWork.memcheck());
|
|
|
|
// check computations
|
|
if(argus.unit_check || argus.norm_check)
|
|
gels_getError<API, INPLACE, T>(handle,
|
|
m,
|
|
n,
|
|
nrhs,
|
|
dA,
|
|
lda,
|
|
stA,
|
|
dB,
|
|
ldb,
|
|
stB,
|
|
dX,
|
|
ldx,
|
|
stX,
|
|
dWork,
|
|
size_W,
|
|
dInfo,
|
|
bc,
|
|
hA,
|
|
hB,
|
|
hBRes,
|
|
hX,
|
|
hXRes,
|
|
hNIters,
|
|
hInfo,
|
|
hInfoRes,
|
|
&max_error);
|
|
|
|
// collect performance data
|
|
if(argus.timing)
|
|
gels_getPerfData<API, INPLACE, T>(handle,
|
|
m,
|
|
n,
|
|
nrhs,
|
|
dA,
|
|
lda,
|
|
stA,
|
|
dB,
|
|
ldb,
|
|
stB,
|
|
dX,
|
|
ldx,
|
|
stX,
|
|
dWork,
|
|
size_W,
|
|
dInfo,
|
|
bc,
|
|
hA,
|
|
hB,
|
|
hX,
|
|
hNIters,
|
|
hInfo,
|
|
&gpu_time_used,
|
|
&cpu_time_used,
|
|
hot_calls,
|
|
argus.perf);
|
|
}
|
|
// validate results for rocsolver-test
|
|
// using max(m,n) * machine_precision as tolerance
|
|
if(argus.unit_check)
|
|
ROCSOLVER_TEST_CHECK(T, max_error, max(m, 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("m", "n", "nrhs", "lda", "ldb", "ldx", "batch_c");
|
|
rocsolver_bench_output(m, n, nrhs, lda, ldb, ldx, bc);
|
|
}
|
|
else if(STRIDED)
|
|
{
|
|
rocsolver_bench_output("m",
|
|
"n",
|
|
"nrhs",
|
|
"lda",
|
|
"ldb",
|
|
"ldx",
|
|
"strideA",
|
|
"strideB",
|
|
"strideX",
|
|
"batch_c");
|
|
rocsolver_bench_output(m, n, nrhs, lda, ldb, ldx, stA, stB, stX, bc);
|
|
}
|
|
else
|
|
{
|
|
rocsolver_bench_output("m", "n", "nrhs", "lda", "ldb", "ldx");
|
|
rocsolver_bench_output(m, n, nrhs, lda, ldb, ldx);
|
|
}
|
|
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();
|
|
}
|