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890 lines
37 KiB
C++
890 lines
37 KiB
C++
/* ************************************************************************
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* Copyright (C) 2021-2026 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 I, typename SIZE, typename Td, typename Sd, typename U>
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void syevd_heevd_checkBadArgs(const hipsolverHandle_t handle,
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const hipsolverDnParams_t params,
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const hipsolverEigMode_t evect,
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const hipsolverFillMode_t uplo,
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const I n,
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Td dA,
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const I lda,
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Sd dD,
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Td dWork,
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const SIZE lwork,
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Td hWork,
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const SIZE lworkHost,
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U dinfo,
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const int bc)
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{
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// handle
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EXPECT_ROCBLAS_STATUS(hipsolver_syevd_heevd(API,
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nullptr,
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params,
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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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dD,
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dWork,
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lwork,
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hWork,
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lworkHost,
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dinfo,
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bc),
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HIPSOLVER_STATUS_NOT_INITIALIZED);
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// values
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EXPECT_ROCBLAS_STATUS(hipsolver_syevd_heevd(API,
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handle,
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params,
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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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dD,
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dWork,
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lwork,
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hWork,
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lworkHost,
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dinfo,
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bc),
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HIPSOLVER_STATUS_INVALID_ENUM);
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EXPECT_ROCBLAS_STATUS(hipsolver_syevd_heevd(API,
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handle,
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params,
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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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dD,
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dWork,
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lwork,
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hWork,
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lworkHost,
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dinfo,
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bc),
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HIPSOLVER_STATUS_INVALID_ENUM);
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#if defined(__HIP_PLATFORM_HCC__) || defined(__HIP_PLATFORM_AMD__)
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// pointers
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EXPECT_ROCBLAS_STATUS(hipsolver_syevd_heevd(API,
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handle,
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params,
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evect,
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uplo,
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n,
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(Td) nullptr,
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lda,
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dD,
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dWork,
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lwork,
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hWork,
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lworkHost,
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dinfo,
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bc),
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HIPSOLVER_STATUS_INVALID_VALUE);
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EXPECT_ROCBLAS_STATUS(hipsolver_syevd_heevd(API,
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handle,
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params,
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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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(Sd) nullptr,
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dWork,
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lwork,
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hWork,
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lworkHost,
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dinfo,
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bc),
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HIPSOLVER_STATUS_INVALID_VALUE);
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EXPECT_ROCBLAS_STATUS(hipsolver_syevd_heevd(API,
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handle,
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params,
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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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dD,
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dWork,
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lwork,
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hWork,
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lworkHost,
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(U) nullptr,
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bc),
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HIPSOLVER_STATUS_INVALID_VALUE);
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#endif
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// params (only for DnX / int64_t path)
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if constexpr(!std::is_same_v<I, int32_t>)
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EXPECT_ROCBLAS_STATUS(hipsolver_syevd_heevd(API,
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handle,
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(hipsolverDnParams_t) 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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dD,
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dWork,
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lwork,
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hWork,
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lworkHost,
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dinfo,
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bc),
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HIPSOLVER_STATUS_INVALID_VALUE);
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}
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template <testAPI_t API,
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bool BATCHED,
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bool STRIDED,
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typename T,
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typename I = int,
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typename SIZE = int>
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void testing_syevd_heevd_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_params 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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I n = 1;
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I lda = 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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// SIZE size_dW, size_hW;
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// hipsolver_syevd_heevd_bufferSize(
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// API, handle, params, evect, uplo, n, dA.data(), lda, dD.data(), &size_dW, &size_hW);
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// host_strided_batch_vector<T> hWork(size_hW, 1, size_hW, 1);
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// device_strided_batch_vector<T> dWork(size_dW, 1, size_dW, 1);
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// if(size_dW)
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// CHECK_HIP_ERROR(dWork.memcheck());
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// // check bad arguments
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// syevd_heevd_checkBadArgs<API, I, SIZE>(handle,
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// params,
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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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// dD.data(),
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// dWork.data(),
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// size_dW,
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// hWork.data(),
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// size_hW,
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// dinfo.data(),
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// bc);
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}
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else
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{
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// memory allocations
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device_strided_batch_vector<T> dA(1, 1, 1, 1);
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device_strided_batch_vector<S> dD(1, 1, 1, 1);
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device_strided_batch_vector<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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SIZE size_dW, size_hW;
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hipsolver_syevd_heevd_bufferSize(
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API, handle, params, evect, uplo, n, dA.data(), lda, dD.data(), &size_dW, &size_hW);
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size_t bytes_dW;
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if constexpr(std::is_same_v<I, int32_t>)
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bytes_dW = std::getenv("HIPSOLVER_BUFFERSIZE_RETURN_BYTES") != nullptr
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? size_dW
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: sizeof(T) * size_dW;
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else
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bytes_dW = size_dW;
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host_strided_batch_vector<T> hWork(size_hW, 1, size_hW, 1);
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device_strided_batch_vector<T> dWork(bytes_dW, 1, bytes_dW, 1);
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if(size_dW)
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CHECK_HIP_ERROR(dWork.memcheck());
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// check bad arguments
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syevd_heevd_checkBadArgs<API, I, SIZE>(handle,
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params,
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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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dD.data(),
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dWork.data(),
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size_dW,
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hWork.data(),
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size_hW,
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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 I, typename Td, typename Th>
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void syevd_heevd_initData(const hipsolverHandle_t handle,
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const hipsolverEigMode_t evect,
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const I n,
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Td& dA,
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const I 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(I i = 0; i < n; i++)
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{
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for(I 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 && evect == HIPSOLVER_EIG_MODE_VECTOR)
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{
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for(I i = 0; i < n; i++)
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{
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for(I 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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typename T,
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typename I,
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typename SIZE,
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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 syevd_heevd_getError(const hipsolverHandle_t handle,
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const hipsolverDnParams_t params,
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const hipsolverEigMode_t evect,
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const hipsolverFillMode_t uplo,
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const I n,
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Td& dA,
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const I lda,
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Sd& dD,
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Td& dWork,
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const SIZE lwork,
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Th& hWork,
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const SIZE lworkHost,
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Id& dinfo,
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const int bc,
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Th& hA,
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Th& hAres,
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Sh& hD,
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Sh& hDres,
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Ih& hinfo,
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Ih& hinfoRes,
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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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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(int64_t(lda) * n * bc);
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// input data initialization
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syevd_heevd_initData<true, true, T, I>(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_syevd_heevd(API,
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handle,
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params,
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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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dD.data(),
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dWork.data(),
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lwork,
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hWork.data(),
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lworkHost,
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dinfo.data(),
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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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// 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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// (We expect the used input matrices to always converge. Testing
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// implicitly the equivalent non-converged matrix is very complicated and it boils
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// down to essentially run the algorithm again and until convergence is achieved).
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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)
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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(I 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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typename T,
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typename I,
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typename SIZE,
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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 syevd_heevd_getPerfData(const hipsolverHandle_t handle,
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const hipsolverDnParams_t params,
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const hipsolverEigMode_t evect,
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const hipsolverFillMode_t uplo,
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const I n,
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Td& dA,
|
|
const I lda,
|
|
Sd& dD,
|
|
Td& dWork,
|
|
const SIZE lwork,
|
|
Th& hWork,
|
|
const SIZE lworkHost,
|
|
Id& dinfo,
|
|
const int bc,
|
|
Th& hA,
|
|
Sh& hD,
|
|
Ih& hinfo,
|
|
double* gpu_time_used,
|
|
double* cpu_time_used,
|
|
const int hot_calls,
|
|
const bool perf)
|
|
{
|
|
constexpr bool COMPLEX = is_complex<T>;
|
|
using S = decltype(std::real(T{}));
|
|
|
|
int sizeE, ltwork;
|
|
if(!COMPLEX)
|
|
{
|
|
sizeE = (evect == HIPSOLVER_EIG_MODE_NOVECTOR ? 2 * n + 1 : 1 + 6 * n + 2 * n * n);
|
|
ltwork = 0;
|
|
}
|
|
else
|
|
{
|
|
sizeE = (evect == HIPSOLVER_EIG_MODE_NOVECTOR ? n : 1 + 5 * n + 2 * n * n);
|
|
ltwork = (evect == HIPSOLVER_EIG_MODE_NOVECTOR ? n + 1 : 2 * n + n * n);
|
|
}
|
|
int liwork = (evect == HIPSOLVER_EIG_MODE_NOVECTOR ? 1 : 3 + 5 * n);
|
|
|
|
std::vector<T> work(ltwork);
|
|
std::vector<S> hE(sizeE);
|
|
std::vector<int> iwork(liwork);
|
|
std::vector<T> A;
|
|
|
|
if(!perf)
|
|
{
|
|
syevd_heevd_initData<true, false, T, I>(handle, evect, n, dA, lda, bc, hA, A, 0);
|
|
|
|
// cpu-lapack performance (only if not in perf mode)
|
|
*cpu_time_used = get_time_us_no_sync();
|
|
for(int b = 0; b < bc; ++b)
|
|
cpu_syevd_heevd(evect,
|
|
uplo,
|
|
n,
|
|
hA[b],
|
|
lda,
|
|
hD[b],
|
|
work.data(),
|
|
ltwork,
|
|
hE.data(),
|
|
sizeE,
|
|
iwork.data(),
|
|
liwork,
|
|
hinfo[b]);
|
|
*cpu_time_used = get_time_us_no_sync() - *cpu_time_used;
|
|
}
|
|
|
|
syevd_heevd_initData<true, false, T, I>(handle, evect, n, dA, lda, bc, hA, A, 0);
|
|
|
|
// cold calls
|
|
for(int iter = 0; iter < 2; iter++)
|
|
{
|
|
syevd_heevd_initData<false, true, T, I>(handle, evect, n, dA, lda, bc, hA, A, 0);
|
|
|
|
CHECK_ROCBLAS_ERROR(hipsolver_syevd_heevd(API,
|
|
handle,
|
|
params,
|
|
evect,
|
|
uplo,
|
|
n,
|
|
dA.data(),
|
|
lda,
|
|
dD.data(),
|
|
dWork.data(),
|
|
lwork,
|
|
hWork.data(),
|
|
lworkHost,
|
|
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++)
|
|
{
|
|
syevd_heevd_initData<false, true, T, I>(handle, evect, n, dA, lda, bc, hA, A, 0);
|
|
|
|
start = get_time_us_sync(stream);
|
|
hipsolver_syevd_heevd(API,
|
|
handle,
|
|
params,
|
|
evect,
|
|
uplo,
|
|
n,
|
|
dA.data(),
|
|
lda,
|
|
dD.data(),
|
|
dWork.data(),
|
|
lwork,
|
|
hWork.data(),
|
|
lworkHost,
|
|
dinfo.data(),
|
|
bc);
|
|
*gpu_time_used += get_time_us_sync(stream) - start;
|
|
}
|
|
*gpu_time_used /= hot_calls;
|
|
}
|
|
|
|
template <testAPI_t API,
|
|
bool BATCHED,
|
|
bool STRIDED,
|
|
typename T,
|
|
typename I = int,
|
|
typename SIZE = int>
|
|
void testing_syevd_heevd(Arguments& argus)
|
|
{
|
|
using S = decltype(std::real(T{}));
|
|
|
|
// get arguments
|
|
hipsolver_local_handle handle;
|
|
hipsolver_local_params params;
|
|
char evectC = argus.get<char>("jobz");
|
|
char uploC = argus.get<char>("uplo");
|
|
I n = argus.get<int>("n");
|
|
I lda = argus.get<int>("lda", n);
|
|
|
|
hipsolverEigMode_t evect = char2hipsolver_evect(evectC);
|
|
hipsolverFillMode_t uplo = char2hipsolver_fill(uploC);
|
|
int bc = argus.batch_count;
|
|
int hot_calls = argus.iters;
|
|
|
|
// determine sizes
|
|
size_t size_A = size_t(lda) * n;
|
|
size_t size_D = n;
|
|
size_t size_Ares = (argus.unit_check || argus.norm_check) ? size_A : 0;
|
|
size_t size_Dres = (argus.unit_check || argus.norm_check) ? size_D : 0;
|
|
|
|
double max_error = 0, gpu_time_used = 0, cpu_time_used = 0;
|
|
|
|
// check invalid sizes
|
|
bool invalid_size = (n < 0 || lda < n || bc < 0);
|
|
if(invalid_size)
|
|
{
|
|
if(BATCHED)
|
|
{
|
|
// EXPECT_ROCBLAS_STATUS(hipsolver_syevd_heevd(API,
|
|
// handle,
|
|
// params,
|
|
// evect,
|
|
// uplo,
|
|
// n,
|
|
// (T* const*)nullptr,
|
|
// lda,
|
|
// (S*)nullptr,
|
|
// (T*)nullptr,
|
|
// (SIZE)0,
|
|
// (T*)nullptr,
|
|
// (SIZE)0,
|
|
// (int*)nullptr,
|
|
// bc),
|
|
// HIPSOLVER_STATUS_INVALID_VALUE);
|
|
}
|
|
else
|
|
{
|
|
EXPECT_ROCBLAS_STATUS(hipsolver_syevd_heevd(API,
|
|
handle,
|
|
params,
|
|
evect,
|
|
uplo,
|
|
n,
|
|
(T*)nullptr,
|
|
lda,
|
|
(S*)nullptr,
|
|
(T*)nullptr,
|
|
(SIZE)0,
|
|
(T*)nullptr,
|
|
(SIZE)0,
|
|
(int*)nullptr,
|
|
bc),
|
|
HIPSOLVER_STATUS_INVALID_VALUE);
|
|
}
|
|
|
|
if(argus.timing)
|
|
rocsolver_bench_inform(inform_invalid_size);
|
|
|
|
return;
|
|
}
|
|
|
|
// memory size query is necessary
|
|
SIZE size_dW, size_hW;
|
|
hipsolver_syevd_heevd_bufferSize(
|
|
API, handle, params, evect, uplo, n, (T*)nullptr, lda, (S*)nullptr, &size_dW, &size_hW);
|
|
|
|
size_t bytes_dW;
|
|
if constexpr(std::is_same_v<I, int32_t>)
|
|
bytes_dW = std::getenv("HIPSOLVER_BUFFERSIZE_RETURN_BYTES") != nullptr
|
|
? size_dW
|
|
: sizeof(T) * size_dW;
|
|
else
|
|
bytes_dW = size_dW;
|
|
|
|
if(argus.mem_query)
|
|
{
|
|
rocsolver_bench_inform(inform_mem_query, bytes_dW);
|
|
return;
|
|
}
|
|
|
|
// memory allocations (all cases)
|
|
// host
|
|
host_strided_batch_vector<S> hD(size_D, 1, size_D, bc);
|
|
host_strided_batch_vector<int> hinfo(1, 1, 1, bc);
|
|
host_strided_batch_vector<int> hinfoRes(1, 1, 1, bc);
|
|
host_strided_batch_vector<S> hDres(size_Dres, 1, size_D, bc);
|
|
host_strided_batch_vector<T> hWork(size_hW, 1, size_hW, 1);
|
|
// device
|
|
device_strided_batch_vector<S> dD(size_D, 1, size_D, bc);
|
|
device_strided_batch_vector<int> dinfo(1, 1, 1, bc);
|
|
device_strided_batch_vector<T> dWork(bytes_dW, 1, bytes_dW, 1); // bytes_dW accounts for bc
|
|
if(size_D)
|
|
CHECK_HIP_ERROR(dD.memcheck());
|
|
CHECK_HIP_ERROR(dinfo.memcheck());
|
|
if(size_dW)
|
|
CHECK_HIP_ERROR(dWork.memcheck());
|
|
|
|
if(BATCHED)
|
|
{
|
|
// // memory allocations
|
|
// host_batch_vector<T> hA(size_A, 1, bc);
|
|
// host_batch_vector<T> hAres(size_Ares, 1, bc);
|
|
// device_batch_vector<T> dA(size_A, 1, bc);
|
|
// if(size_A)
|
|
// CHECK_HIP_ERROR(dA.memcheck());
|
|
|
|
// // check computations
|
|
// if(argus.unit_check || argus.norm_check)
|
|
// {
|
|
// syevd_heevd_getError<API, T, I, SIZE>(handle,
|
|
// params,
|
|
// evect,
|
|
// uplo,
|
|
// n,
|
|
// dA,
|
|
// lda,
|
|
// dD,
|
|
// dWork,
|
|
// size_dW,
|
|
// hWork,
|
|
// size_hW,
|
|
// dinfo,
|
|
// bc,
|
|
// hA,
|
|
// hAres,
|
|
// hD,
|
|
// hDres,
|
|
// hinfo,
|
|
// hinfoRes,
|
|
// &max_error);
|
|
// }
|
|
|
|
// // collect performance data
|
|
// if(argus.timing)
|
|
// {
|
|
// syevd_heevd_getPerfData<API, T, I, SIZE>(handle,
|
|
// params,
|
|
// evect,
|
|
// uplo,
|
|
// n,
|
|
// dA,
|
|
// lda,
|
|
// dD,
|
|
// dWork,
|
|
// size_dW,
|
|
// hWork,
|
|
// size_hW,
|
|
// dinfo,
|
|
// bc,
|
|
// hA,
|
|
// hD,
|
|
// hinfo,
|
|
// &gpu_time_used,
|
|
// &cpu_time_used,
|
|
// hot_calls,
|
|
// argus.perf);
|
|
// }
|
|
}
|
|
|
|
else
|
|
{
|
|
// memory allocations
|
|
host_strided_batch_vector<T> hA(size_A, 1, size_A, bc);
|
|
host_strided_batch_vector<T> hAres(size_Ares, 1, size_A, bc);
|
|
device_strided_batch_vector<T> dA(size_A, 1, size_A, bc);
|
|
if(size_A)
|
|
CHECK_HIP_ERROR(dA.memcheck());
|
|
|
|
// check computations
|
|
if(argus.unit_check || argus.norm_check)
|
|
{
|
|
syevd_heevd_getError<API, T, I, SIZE>(handle,
|
|
params,
|
|
evect,
|
|
uplo,
|
|
n,
|
|
dA,
|
|
lda,
|
|
dD,
|
|
dWork,
|
|
size_dW,
|
|
hWork,
|
|
size_hW,
|
|
dinfo,
|
|
bc,
|
|
hA,
|
|
hAres,
|
|
hD,
|
|
hDres,
|
|
hinfo,
|
|
hinfoRes,
|
|
&max_error);
|
|
}
|
|
|
|
// collect performance data
|
|
if(argus.timing)
|
|
{
|
|
syevd_heevd_getPerfData<API, T, I, SIZE>(handle,
|
|
params,
|
|
evect,
|
|
uplo,
|
|
n,
|
|
dA,
|
|
lda,
|
|
dD,
|
|
dWork,
|
|
size_dW,
|
|
hWork,
|
|
size_hW,
|
|
dinfo,
|
|
bc,
|
|
hA,
|
|
hD,
|
|
hinfo,
|
|
&gpu_time_used,
|
|
&cpu_time_used,
|
|
hot_calls,
|
|
argus.perf);
|
|
}
|
|
}
|
|
|
|
// validate results for rocsolver-test
|
|
// using n * machine_precision as tolerance
|
|
if(argus.unit_check)
|
|
ROCSOLVER_TEST_CHECK(T, max_error, n);
|
|
|
|
// output results for rocsolver-bench
|
|
if(argus.timing)
|
|
{
|
|
if(!argus.perf)
|
|
{
|
|
std::cerr << "\n============================================\n";
|
|
std::cerr << "Arguments:\n";
|
|
std::cerr << "============================================\n";
|
|
if(BATCHED)
|
|
{
|
|
rocsolver_bench_output("jobz", "uplo", "n", "lda", "batch_c");
|
|
rocsolver_bench_output(evectC, uploC, n, lda, bc);
|
|
}
|
|
else
|
|
{
|
|
rocsolver_bench_output("jobz", "uplo", "n", "lda");
|
|
rocsolver_bench_output(evectC, uploC, n, lda);
|
|
}
|
|
std::cerr << "\n============================================\n";
|
|
std::cerr << "Results:\n";
|
|
std::cerr << "============================================\n";
|
|
if(argus.norm_check)
|
|
{
|
|
rocsolver_bench_output("cpu_time", "gpu_time", "error");
|
|
rocsolver_bench_output(cpu_time_used, gpu_time_used, max_error);
|
|
}
|
|
else
|
|
{
|
|
rocsolver_bench_output("cpu_time", "gpu_time");
|
|
rocsolver_bench_output(cpu_time_used, gpu_time_used);
|
|
}
|
|
std::cerr << std::endl;
|
|
}
|
|
else
|
|
{
|
|
if(argus.norm_check)
|
|
rocsolver_bench_output(gpu_time_used, max_error);
|
|
else
|
|
rocsolver_bench_output(gpu_time_used);
|
|
}
|
|
}
|
|
|
|
// ensure all arguments were consumed
|
|
argus.validate_consumed();
|
|
}
|