/*
 * SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 * SPDX-License-Identifier: LicenseRef-NvidiaProprietary
 *
 * NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
 * property and proprietary rights in and to this material, related
 * documentation and any modifications thereto. Any use, reproduction,
 * disclosure or distribution of this material and related documentation
 * without an express license agreement from NVIDIA CORPORATION or
 * its affiliates is strictly prohibited.
 */

#pragma once

namespace __nvhpc_std {

namespace __bl = __blas_std;

/*
 * Going from row-major to column-major
 * - Matrices are effectively transposed
 * - The upper-triangular part of a matrix becomes the lower-triangular matrices,
 *   and vice versa
 * In C with row-major layout        : C   = alpha * (  A     *B^t +  B     *A^t ) + beta * C
 * In cuBLAS with column-major layout: C^t = alpha * ( (B^t)^t*A^t + (A^t)^t*B^t ) + beta * C^t
 * which is what cuBLAS Syr2k computes with input A^t, B^t, C^t, and OP = trans
 */

template< class _ElementType_A
        , class _SizeType_A
        , ::std::size_t _numRows_A
        , ::std::size_t _numCols_A
        , class _Layout_A
        , class _Accessor_A
        , class _ElementType_B
        , class _SizeType_B
        , ::std::size_t _numRows_B
        , ::std::size_t _numCols_B
        , class _Layout_B
        , class _Accessor_B
        , class _ElementType_C
        , class _SizeType_C
        , ::std::size_t _numRows_C
        , ::std::size_t _numCols_C
        , class _Layout_C
        , class _Accessor_C
        >
void __symmetric_matrix_rank_2k_update_impl( __nvhpc_exec<__blas_exec_space> /* __exec */
                                           , _ElementType_C __alpha
                                           , _ElementType_C __beta
                                           , std::mdspan<_ElementType_A, std::extents<_SizeType_A, _numRows_A, _numCols_A>, _Layout_A, _Accessor_A> __A
                                           , std::mdspan<_ElementType_B, std::extents<_SizeType_B, _numRows_B, _numCols_B>, _Layout_B, _Accessor_B> __B
                                           , std::mdspan<_ElementType_C, std::extents<_SizeType_C, _numRows_C, _numCols_C>, _Layout_C, _Accessor_C> __C
                                           , char __trans
                                           , char __uplo
                                           )
{
#ifdef STDBLAS_VERBOSE
    __STDBLAS_BACKEND_MESSAGE( matrix_rank_2k_update, BLAS );
#endif

    __bl::__blas_syr2k( &__uplo
                      , &__trans
                      , __A.extent(0)
                      , __A.extent(1)
                      , __alpha
                      , __A.data_handle()
                      , std::linalg::__get_leading_dim( __A )
                      , __B.data_handle()
                      , std::linalg::__get_leading_dim( __B )
                      , __beta
                      , __C.data_handle()
                      , std::linalg::__get_leading_dim( __C )
                      );
}

}
