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1105 lines
48 KiB
C++
1105 lines
48 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, typename T, typename S, typename SS, typename U>
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void syevdx_heevdx_checkBadArgs(const hipsolverHandle_t handle,
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const hipsolverEigMode_t evect,
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const hipsolverEigRange_t erange,
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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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const SS vl,
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const SS vu,
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const int il,
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const int iu,
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U hNev,
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S dW,
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const int stW,
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T dWork,
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const int lwork,
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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_syevdx_heevdx(API,
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nullptr,
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evect,
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erange,
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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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vl,
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vu,
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il,
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iu,
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hNev,
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dW,
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stW,
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dWork,
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lwork,
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dinfo,
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bc),
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HIPSOLVER_STATUS_NOT_INITIALIZED);
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// values
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EXPECT_ROCBLAS_STATUS(hipsolver_syevdx_heevdx(API,
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handle,
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hipsolverEigMode_t(-1),
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erange,
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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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vl,
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vu,
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il,
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iu,
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hNev,
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dW,
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stW,
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dWork,
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lwork,
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dinfo,
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bc),
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HIPSOLVER_STATUS_INVALID_ENUM);
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EXPECT_ROCBLAS_STATUS(hipsolver_syevdx_heevdx(API,
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handle,
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evect,
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hipsolverEigRange_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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vl,
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vu,
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il,
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iu,
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hNev,
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dW,
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stW,
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dWork,
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lwork,
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dinfo,
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bc),
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HIPSOLVER_STATUS_INVALID_ENUM);
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EXPECT_ROCBLAS_STATUS(hipsolver_syevdx_heevdx(API,
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handle,
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evect,
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erange,
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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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vl,
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vu,
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il,
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iu,
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hNev,
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dW,
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stW,
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dWork,
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lwork,
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dinfo,
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bc),
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HIPSOLVER_STATUS_INVALID_ENUM);
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#if defined(__HIP_PLATFORM_HCC__) || defined(__HIP_PLATFORM_AMD__)
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// pointers
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EXPECT_ROCBLAS_STATUS(hipsolver_syevdx_heevdx(API,
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handle,
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evect,
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erange,
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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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vl,
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vu,
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il,
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iu,
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hNev,
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dW,
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stW,
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dWork,
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lwork,
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dinfo,
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bc),
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HIPSOLVER_STATUS_INVALID_VALUE);
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EXPECT_ROCBLAS_STATUS(hipsolver_syevdx_heevdx(API,
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handle,
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evect,
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erange,
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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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vl,
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vu,
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il,
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iu,
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(U) nullptr,
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dW,
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stW,
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dWork,
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lwork,
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dinfo,
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bc),
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HIPSOLVER_STATUS_INVALID_VALUE);
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EXPECT_ROCBLAS_STATUS(hipsolver_syevdx_heevdx(API,
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handle,
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evect,
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erange,
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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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vl,
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vu,
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il,
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iu,
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hNev,
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(S) nullptr,
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stW,
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dWork,
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lwork,
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dinfo,
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bc),
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HIPSOLVER_STATUS_INVALID_VALUE);
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EXPECT_ROCBLAS_STATUS(hipsolver_syevdx_heevdx(API,
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handle,
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evect,
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erange,
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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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vl,
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vu,
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il,
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iu,
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hNev,
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dW,
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stW,
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dWork,
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lwork,
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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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}
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template <testAPI_t API, bool BATCHED, bool STRIDED, typename T>
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void testing_syevdx_heevdx_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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hipsolverEigMode_t evect = HIPSOLVER_EIG_MODE_VECTOR;
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hipsolverEigRange_t erange = HIPSOLVER_EIG_RANGE_V;
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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 stW = 1;
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int bc = 1;
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S vl = 0.0;
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S vu = 1.0;
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int il = 0;
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int iu = 0;
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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> hNev(1, 1, 1, 1);
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// device_batch_vector<T> dA(1, 1, 1);
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// device_strided_batch_vector<S> dW(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(dW.memcheck());
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// CHECK_HIP_ERROR(dinfo.memcheck());
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// int size_W;
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// hipsolver_syevdx_heevdx_bufferSize(API,
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// handle,
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// evect,
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// erange,
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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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// vl,
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// vu,
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// il,
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// iu,
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// hNev.data(),
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// dW.data(),
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// &size_W);
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// size_t bytes_W = std::getenv("HIPSOLVER_BUFFERSIZE_RETURN_BYTES") != nullptr ? size_W : sizeof(T) * size_W;
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// device_strided_batch_vector<T> dWork(bytes_W, 1, bytes_W, 1);
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// if(size_W)
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// CHECK_HIP_ERROR(dWork.memcheck());
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// // check bad arguments
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// syevdx_heevdx_checkBadArgs<API>(handle,
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// evect,
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// erange,
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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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// vl,
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// vu,
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// il,
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// iu,
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// hNev.data(),
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// dW.data(),
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// stW,
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// dWork.data(),
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// size_W,
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// dinfo.data(),
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// bc);
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}
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else
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{
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// memory allocations
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host_strided_batch_vector<int> hNev(1, 1, 1, 1);
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device_strided_batch_vector<T> dA(1, 1, 1, 1);
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device_strided_batch_vector<S> dW(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(dW.memcheck());
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CHECK_HIP_ERROR(dinfo.memcheck());
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int size_W;
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hipsolver_syevdx_heevdx_bufferSize(API,
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handle,
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evect,
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erange,
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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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vl,
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vu,
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il,
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iu,
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hNev.data(),
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dW.data(),
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&size_W);
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size_t bytes_W = std::getenv("HIPSOLVER_BUFFERSIZE_RETURN_BYTES") != nullptr
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? size_W
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: sizeof(T) * size_W;
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device_strided_batch_vector<T> dWork(bytes_W, 1, bytes_W, 1);
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if(size_W)
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CHECK_HIP_ERROR(dWork.memcheck());
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// check bad arguments
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syevdx_heevdx_checkBadArgs<API>(handle,
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evect,
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erange,
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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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vl,
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vu,
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il,
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iu,
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hNev.data(),
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dW.data(),
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stW,
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dWork.data(),
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size_W,
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dinfo.data(),
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bc);
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}
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}
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template <bool CPU, bool GPU, typename T, typename Td, typename Th>
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void syevdx_heevdx_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 = i; j < n; j++)
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{
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if(i == j)
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hA[b][i + j * lda] = std::real(hA[b][i + j * lda]) + 10;
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else
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{
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if(j == i + 1)
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{
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hA[b][i + j * lda] = (hA[b][i + j * lda] - 5) / 10;
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hA[b][j + i * lda] = conj(hA[b][i + j * lda]);
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}
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else
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hA[b][j + i * lda] = hA[b][i + j * lda] = 0;
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}
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}
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if(i == n / 4 || i == n / 2 || i == n - 1 || i == n / 7 || i == n / 5 || i == n / 3)
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hA[b][i + i * lda] *= -1;
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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(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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typename T,
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typename S,
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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 syevdx_heevdx_getError(const hipsolverHandle_t handle,
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const hipsolverEigMode_t evect,
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const hipsolverEigRange_t erange,
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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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const S vl,
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const S vu,
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const int il,
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const int iu,
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Ih& hNevRes,
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Sd& dW,
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const int stW,
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Td& dWork,
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const int lwork,
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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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Ih& hNev,
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Sh& hW,
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Sh& hWRes,
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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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int size_work = !COMPLEX ? 35 * n : 33 * n;
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int size_rwork = !COMPLEX ? 0 : 7 * n;
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int size_iwork = 5 * n;
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std::vector<T> work(size_work);
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std::vector<S> rwork(size_rwork);
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std::vector<int> iwork(size_iwork);
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std::vector<T> A(lda * n * bc);
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std::vector<T> Z(lda * n);
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std::vector<int> ifail(n);
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S abstol = 2 * get_safemin<S>();
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// input data initialization
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syevdx_heevdx_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_syevdx_heevdx(API,
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handle,
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evect,
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erange,
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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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vl,
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vu,
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il,
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iu,
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hNevRes.data(),
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dW.data(),
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stW,
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dWork.data(),
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lwork,
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dinfo.data(),
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bc));
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CHECK_HIP_ERROR(hWRes.transfer_from(dW));
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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_syevx_heevx(evect,
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erange,
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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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vl,
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vu,
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il,
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iu,
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|
abstol,
|
|
hNev[b],
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hW[b],
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Z.data(),
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|
lda,
|
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work.data(),
|
|
size_work,
|
|
rwork.data(),
|
|
iwork.data(),
|
|
ifail.data(),
|
|
hinfo[b]);
|
|
|
|
// Check info for non-convergence
|
|
*max_err = 0;
|
|
for(int b = 0; b < bc; ++b)
|
|
{
|
|
EXPECT_EQ(hinfo[b][0], hinfoRes[b][0]) << "where b = " << b;
|
|
if(hinfo[b][0] != hinfoRes[b][0])
|
|
*max_err += 1;
|
|
}
|
|
|
|
// Check number of returned eigenvalues
|
|
*max_err = 0;
|
|
for(int b = 0; b < bc; ++b)
|
|
{
|
|
EXPECT_EQ(hNev[b][0], hNevRes[b][0]) << "where b = " << b;
|
|
if(hNev[b][0] != hNevRes[b][0])
|
|
*max_err += 1;
|
|
}
|
|
|
|
// (We expect the used input matrices to always converge. Testing
|
|
// implicitly the equivalent non-converged matrix is very complicated and it boils
|
|
// down to essentially run the algorithm again and until convergence is achieved).
|
|
|
|
double err = 0;
|
|
|
|
for(int b = 0; b < bc; ++b)
|
|
{
|
|
if(evect != HIPSOLVER_EIG_MODE_VECTOR)
|
|
{
|
|
// only eigenvalues needed; can compare with LAPACK
|
|
|
|
// error is ||hW - hWRes|| / ||hW||
|
|
// using frobenius norm
|
|
if(hinfo[b][0] == 0)
|
|
err = norm_error('F', 1, hNev[b][0], 1, hW[b], hWRes[b]);
|
|
*max_err = err > *max_err ? err : *max_err;
|
|
}
|
|
else
|
|
{
|
|
// both eigenvalues and eigenvectors needed; need to implicitly test
|
|
// eigenvectors due to non-uniqueness of eigenvectors under scaling
|
|
if(hinfo[b][0] == 0)
|
|
{
|
|
// multiply A with each of the m eigenvectors and divide by corresponding
|
|
// eigenvalues
|
|
T alpha;
|
|
T beta = 0;
|
|
for(int j = 0; j < hNev[b][0]; j++)
|
|
{
|
|
alpha = T(1) / hWRes[b][j];
|
|
cpu_symv_hemv(uplo,
|
|
n,
|
|
alpha,
|
|
A.data() + b * lda * n,
|
|
lda,
|
|
hARes[b] + j * lda,
|
|
1,
|
|
beta,
|
|
hA[b] + j * lda,
|
|
1);
|
|
}
|
|
|
|
// error is ||hA - hARes|| / ||hA||
|
|
// using frobenius norm
|
|
err = norm_error('F', n, hNev[b][0], lda, hA[b], hARes[b]);
|
|
*max_err = err > *max_err ? err : *max_err;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
template <testAPI_t API,
|
|
typename T,
|
|
typename S,
|
|
typename Sd,
|
|
typename Td,
|
|
typename Id,
|
|
typename Sh,
|
|
typename Th,
|
|
typename Ih>
|
|
void syevdx_heevdx_getPerfData(const hipsolverHandle_t handle,
|
|
const hipsolverEigMode_t evect,
|
|
const hipsolverEigRange_t erange,
|
|
const hipsolverFillMode_t uplo,
|
|
const int n,
|
|
Td& dA,
|
|
const int lda,
|
|
const int stA,
|
|
const S vl,
|
|
const S vu,
|
|
const int il,
|
|
const int iu,
|
|
Ih& hNevRes,
|
|
Sd& dW,
|
|
const int stW,
|
|
Td& dWork,
|
|
const int lwork,
|
|
Id& dinfo,
|
|
const int bc,
|
|
Th& hA,
|
|
Ih& hNev,
|
|
Sh& hW,
|
|
Ih& hinfo,
|
|
double* gpu_time_used,
|
|
double* cpu_time_used,
|
|
const int hot_calls,
|
|
const bool perf)
|
|
{
|
|
constexpr bool COMPLEX = is_complex<T>;
|
|
|
|
int size_work = !COMPLEX ? 35 * n : 33 * n;
|
|
int size_rwork = !COMPLEX ? 0 : 7 * n;
|
|
int size_iwork = 5 * n;
|
|
|
|
std::vector<T> work(size_work);
|
|
std::vector<S> rwork(size_rwork);
|
|
std::vector<int> iwork(size_iwork);
|
|
std::vector<T> A;
|
|
std::vector<T> Z(lda * n);
|
|
std::vector<int> ifail(n);
|
|
|
|
S abstol = 2 * get_safemin<S>();
|
|
|
|
if(!perf)
|
|
{
|
|
syevdx_heevdx_initData<true, false, T>(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_syevx_heevx(evect,
|
|
erange,
|
|
uplo,
|
|
n,
|
|
hA[b],
|
|
lda,
|
|
vl,
|
|
vu,
|
|
il,
|
|
iu,
|
|
abstol,
|
|
hNev[b],
|
|
hW[b],
|
|
Z.data(),
|
|
lda,
|
|
work.data(),
|
|
size_work,
|
|
rwork.data(),
|
|
iwork.data(),
|
|
ifail.data(),
|
|
hinfo[b]);
|
|
*cpu_time_used = get_time_us_no_sync() - *cpu_time_used;
|
|
}
|
|
|
|
syevdx_heevdx_initData<true, false, T>(handle, evect, n, dA, lda, bc, hA, A, 0);
|
|
|
|
// cold calls
|
|
for(int iter = 0; iter < 2; iter++)
|
|
{
|
|
syevdx_heevdx_initData<false, true, T>(handle, evect, n, dA, lda, bc, hA, A, 0);
|
|
|
|
CHECK_ROCBLAS_ERROR(hipsolver_syevdx_heevdx(API,
|
|
handle,
|
|
evect,
|
|
erange,
|
|
uplo,
|
|
n,
|
|
dA.data(),
|
|
lda,
|
|
stA,
|
|
vl,
|
|
vu,
|
|
il,
|
|
iu,
|
|
hNevRes.data(),
|
|
dW.data(),
|
|
stW,
|
|
dWork.data(),
|
|
lwork,
|
|
dinfo.data(),
|
|
bc));
|
|
}
|
|
|
|
// gpu-lapack performance
|
|
hipStream_t stream;
|
|
CHECK_ROCBLAS_ERROR(hipsolverGetStream(handle, &stream));
|
|
double start;
|
|
|
|
for(int iter = 0; iter < hot_calls; iter++)
|
|
{
|
|
syevdx_heevdx_initData<false, true, T>(handle, evect, n, dA, lda, bc, hA, A, 0);
|
|
|
|
start = get_time_us_sync(stream);
|
|
hipsolver_syevdx_heevdx(API,
|
|
handle,
|
|
evect,
|
|
erange,
|
|
uplo,
|
|
n,
|
|
dA.data(),
|
|
lda,
|
|
stA,
|
|
vl,
|
|
vu,
|
|
il,
|
|
iu,
|
|
hNevRes.data(),
|
|
dW.data(),
|
|
stW,
|
|
dWork.data(),
|
|
lwork,
|
|
dinfo.data(),
|
|
bc);
|
|
*gpu_time_used += get_time_us_sync(stream) - start;
|
|
}
|
|
*gpu_time_used /= hot_calls;
|
|
}
|
|
|
|
template <testAPI_t API, bool BATCHED, bool STRIDED, typename T>
|
|
void testing_syevdx_heevdx(Arguments& argus)
|
|
{
|
|
using S = decltype(std::real(T{}));
|
|
|
|
// get arguments
|
|
hipsolver_local_handle handle;
|
|
char evectC = argus.get<char>("jobz");
|
|
char erangeC = argus.get<char>("range");
|
|
char uploC = argus.get<char>("uplo");
|
|
int n = argus.get<int>("n");
|
|
int lda = argus.get<int>("lda", n);
|
|
int stA = argus.get<int>("strideA", lda * n);
|
|
int stW = argus.get<int>("strideW", n);
|
|
|
|
S vl = S(argus.get<double>("vl", 0));
|
|
S vu = S(argus.get<double>("vu", erangeC == 'V' ? 1 : 0));
|
|
int il = argus.get<int>("il", erangeC == 'I' ? 1 : 0);
|
|
int iu = argus.get<int>("iu", erangeC == 'I' ? 1 : 0);
|
|
|
|
hipsolverEigMode_t evect = char2hipsolver_evect(evectC);
|
|
hipsolverEigRange_t erange = char2hipsolver_erange(erangeC);
|
|
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_W = n;
|
|
size_t size_ARes = (argus.unit_check || argus.norm_check) ? size_A : 0;
|
|
size_t size_WRes = (argus.unit_check || argus.norm_check) ? size_W : 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 || (erange == HIPSOLVER_EIG_RANGE_V && vl >= vu)
|
|
|| (erange == HIPSOLVER_EIG_RANGE_I && (il < 1 || iu < 0))
|
|
|| (erange == HIPSOLVER_EIG_RANGE_I && (iu > n || (n > 0 && il > iu))));
|
|
if(invalid_size)
|
|
{
|
|
if(BATCHED)
|
|
{
|
|
// EXPECT_ROCBLAS_STATUS(hipsolver_syevdx_heevdx(API,
|
|
// handle,
|
|
// evect,
|
|
// erange,
|
|
// uplo,
|
|
// n,
|
|
// (T* const*)nullptr,
|
|
// lda,
|
|
// stA,
|
|
// vl,
|
|
// vu,
|
|
// il,
|
|
// iu,
|
|
// (int*)nullptr,
|
|
// (S*)nullptr,
|
|
// stW,
|
|
// (T*)nullptr,
|
|
// 0,
|
|
// (int*)nullptr,
|
|
// bc),
|
|
// HIPSOLVER_STATUS_INVALID_VALUE);
|
|
}
|
|
else
|
|
{
|
|
EXPECT_ROCBLAS_STATUS(hipsolver_syevdx_heevdx(API,
|
|
handle,
|
|
evect,
|
|
erange,
|
|
uplo,
|
|
n,
|
|
(T*)nullptr,
|
|
lda,
|
|
stA,
|
|
vl,
|
|
vu,
|
|
il,
|
|
iu,
|
|
(int*)nullptr,
|
|
(S*)nullptr,
|
|
stW,
|
|
(T*)nullptr,
|
|
0,
|
|
(int*)nullptr,
|
|
bc),
|
|
HIPSOLVER_STATUS_INVALID_VALUE);
|
|
}
|
|
|
|
if(argus.timing)
|
|
rocsolver_bench_inform(inform_invalid_size);
|
|
|
|
return;
|
|
}
|
|
|
|
// memory size query is necessary
|
|
int size_Work;
|
|
hipsolver_syevdx_heevdx_bufferSize(API,
|
|
handle,
|
|
evect,
|
|
erange,
|
|
uplo,
|
|
n,
|
|
(T*)nullptr,
|
|
lda,
|
|
vl,
|
|
vu,
|
|
il,
|
|
iu,
|
|
(int*)nullptr,
|
|
(S*)nullptr,
|
|
&size_Work);
|
|
size_t bytes_Work = std::getenv("HIPSOLVER_BUFFERSIZE_RETURN_BYTES") != nullptr
|
|
? size_Work
|
|
: sizeof(T) * size_Work;
|
|
|
|
if(argus.mem_query)
|
|
{
|
|
rocsolver_bench_inform(inform_mem_query, bytes_Work);
|
|
return;
|
|
}
|
|
|
|
// memory allocations (all cases)
|
|
// host
|
|
host_strided_batch_vector<int> hNev(1, 1, 1, bc);
|
|
host_strided_batch_vector<int> hNevRes(1, 1, 1, bc);
|
|
host_strided_batch_vector<S> hW(size_W, 1, stW, 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> hWres(size_WRes, 1, stW, bc);
|
|
// device
|
|
device_strided_batch_vector<S> dW(size_W, 1, stW, bc);
|
|
device_strided_batch_vector<int> dinfo(1, 1, 1, bc);
|
|
device_strided_batch_vector<T> dWork(
|
|
bytes_Work, 1, bytes_Work, 1); // bytes_Work accounts for bc
|
|
if(size_W)
|
|
CHECK_HIP_ERROR(dW.memcheck());
|
|
CHECK_HIP_ERROR(dinfo.memcheck());
|
|
if(size_Work)
|
|
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)
|
|
// {
|
|
// syevdx_heevdx_getError<API, T>(handle,
|
|
// evect,
|
|
// erange,
|
|
// uplo,
|
|
// n,
|
|
// dA,
|
|
// lda,
|
|
// stA,
|
|
// vl,
|
|
// vu,
|
|
// il,
|
|
// iu,
|
|
// hNevRes,
|
|
// dW,
|
|
// stW,
|
|
// dWork,
|
|
// size_Work,
|
|
// dinfo,
|
|
// bc,
|
|
// hA,
|
|
// hARes,
|
|
// hNev,
|
|
// hW,
|
|
// hWres,
|
|
// hinfo,
|
|
// hinfoRes,
|
|
// &max_error);
|
|
// }
|
|
|
|
// // collect performance data
|
|
// if(argus.timing)
|
|
// {
|
|
// syevdx_heevdx_getPerfData<API, T>(handle,
|
|
// evect,
|
|
// erange,
|
|
// uplo,
|
|
// n,
|
|
// dA,
|
|
// lda,
|
|
// stA,
|
|
// vl,
|
|
// vu,
|
|
// il,
|
|
// iu,
|
|
// hNevRes,
|
|
// dW,
|
|
// stW,
|
|
// dWork,
|
|
// size_Work,
|
|
// dinfo,
|
|
// bc,
|
|
// hA,
|
|
// hNev,
|
|
// hW,
|
|
// hinfo,
|
|
// &gpu_time_used,
|
|
// &cpu_time_used,
|
|
// hot_calls,
|
|
// argus.perf);
|
|
// }
|
|
}
|
|
|
|
else
|
|
{
|
|
// memory allocations
|
|
host_strided_batch_vector<T> hA(size_A, 1, stA, bc);
|
|
host_strided_batch_vector<T> hARes(size_ARes, 1, stA, bc);
|
|
device_strided_batch_vector<T> dA(size_A, 1, stA, bc);
|
|
if(size_A)
|
|
CHECK_HIP_ERROR(dA.memcheck());
|
|
|
|
// check computations
|
|
if(argus.unit_check || argus.norm_check)
|
|
{
|
|
syevdx_heevdx_getError<API, T>(handle,
|
|
evect,
|
|
erange,
|
|
uplo,
|
|
n,
|
|
dA,
|
|
lda,
|
|
stA,
|
|
vl,
|
|
vu,
|
|
il,
|
|
iu,
|
|
hNevRes,
|
|
dW,
|
|
stW,
|
|
dWork,
|
|
size_Work,
|
|
dinfo,
|
|
bc,
|
|
hA,
|
|
hARes,
|
|
hNev,
|
|
hW,
|
|
hWres,
|
|
hinfo,
|
|
hinfoRes,
|
|
&max_error);
|
|
}
|
|
|
|
// collect performance data
|
|
if(argus.timing)
|
|
{
|
|
syevdx_heevdx_getPerfData<API, T>(handle,
|
|
evect,
|
|
erange,
|
|
uplo,
|
|
n,
|
|
dA,
|
|
lda,
|
|
stA,
|
|
vl,
|
|
vu,
|
|
il,
|
|
iu,
|
|
hNevRes,
|
|
dW,
|
|
stW,
|
|
dWork,
|
|
size_Work,
|
|
dinfo,
|
|
bc,
|
|
hA,
|
|
hNev,
|
|
hW,
|
|
hinfo,
|
|
&gpu_time_used,
|
|
&cpu_time_used,
|
|
hot_calls,
|
|
argus.perf);
|
|
}
|
|
}
|
|
|
|
// validate results for rocsolver-test
|
|
// using 3 * n * machine_precision as tolerance
|
|
if(argus.unit_check)
|
|
ROCSOLVER_TEST_CHECK(T, max_error, 3 * 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",
|
|
"range",
|
|
"uplo",
|
|
"n",
|
|
"lda",
|
|
"vl",
|
|
"vu",
|
|
"il",
|
|
"iu",
|
|
"strideW",
|
|
"batch_c");
|
|
rocsolver_bench_output(evectC, erangeC, uploC, n, lda, vl, vu, il, iu, stW, bc);
|
|
}
|
|
else if(STRIDED)
|
|
{
|
|
rocsolver_bench_output("jobz",
|
|
"range",
|
|
"uplo",
|
|
"n",
|
|
"lda",
|
|
"strideA",
|
|
"vl",
|
|
"vu",
|
|
"il",
|
|
"iu",
|
|
"strideW",
|
|
"batch_c");
|
|
rocsolver_bench_output(
|
|
evectC, erangeC, uploC, n, lda, stA, vl, vu, il, iu, stW, bc);
|
|
}
|
|
else
|
|
{
|
|
rocsolver_bench_output("jobz", "range", "uplo", "n", "lda", "vl", "vu", "il", "iu");
|
|
rocsolver_bench_output(evectC, erangeC, uploC, n, lda, vl, vu, il, iu);
|
|
}
|
|
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();
|
|
}
|