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// Copyright 2008-2016 Conrad Sanderson (http://conradsanderson.id.au)
// Copyright 2008-2016 National ICT Australia (NICTA)
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
// ------------------------------------------------------------------------
//! \addtogroup gemm_mixed
//! @{
//! \brief
//! Matrix multplication where the matrices have differing element types.
//! Uses caching for speedup.
//! Matrix 'C' is assumed to have been set to the correct size (i.e. taking into account transposes)
template<const bool do_trans_A=false, const bool do_trans_B=false, const bool use_alpha=false, const bool use_beta=false>
class gemm_mixed_large
{
public:
template<typename out_eT, typename in_eT1, typename in_eT2>
arma_hot
inline
static
void
apply
(
Mat<out_eT>& C,
const Mat<in_eT1>& A,
const Mat<in_eT2>& B,
const out_eT alpha = out_eT(1),
const out_eT beta = out_eT(0)
)
{
arma_extra_debug_sigprint();
const uword A_n_rows = A.n_rows;
const uword A_n_cols = A.n_cols;
const uword B_n_rows = B.n_rows;
const uword B_n_cols = B.n_cols;
if( (do_trans_A == false) && (do_trans_B == false) )
{
podarray<in_eT1> tmp(A_n_cols);
in_eT1* A_rowdata = tmp.memptr();
#if defined(ARMA_USE_OPENMP)
const bool use_mp = (B_n_cols >= 2) && (B.n_elem >= 8192) && (mp_thread_limit::in_parallel() == false);
#else
const bool use_mp = false;
#endif
if(use_mp)
{
#if defined(ARMA_USE_OPENMP)
{
const int n_threads = int( (std::min)( uword(mp_thread_limit::get()), uword(B_n_cols) ) );
for(uword row_A=0; row_A < A_n_rows; ++row_A)
{
tmp.copy_row(A, row_A);
#pragma omp parallel for schedule(static) num_threads(n_threads)
for(uword col_B=0; col_B < B_n_cols; ++col_B)
{
const in_eT2* B_coldata = B.colptr(col_B);
out_eT acc = out_eT(0);
for(uword i=0; i < B_n_rows; ++i)
{
acc += upgrade_val<in_eT1,in_eT2>::apply(A_rowdata[i]) * upgrade_val<in_eT1,in_eT2>::apply(B_coldata[i]);
}
if( (use_alpha == false) && (use_beta == false) ) { C.at(row_A,col_B) = acc; }
else if( (use_alpha == true ) && (use_beta == false) ) { C.at(row_A,col_B) = alpha*acc; }
else if( (use_alpha == false) && (use_beta == true ) ) { C.at(row_A,col_B) = acc + beta*C.at(row_A,col_B); }
else if( (use_alpha == true ) && (use_beta == true ) ) { C.at(row_A,col_B) = alpha*acc + beta*C.at(row_A,col_B); }
}
}
}
#endif
}
else
{
for(uword row_A=0; row_A < A_n_rows; ++row_A)
{
tmp.copy_row(A, row_A);
for(uword col_B=0; col_B < B_n_cols; ++col_B)
{
const in_eT2* B_coldata = B.colptr(col_B);
out_eT acc = out_eT(0);
for(uword i=0; i < B_n_rows; ++i)
{
acc += upgrade_val<in_eT1,in_eT2>::apply(A_rowdata[i]) * upgrade_val<in_eT1,in_eT2>::apply(B_coldata[i]);
}
if( (use_alpha == false) && (use_beta == false) ) { C.at(row_A,col_B) = acc; }
else if( (use_alpha == true ) && (use_beta == false) ) { C.at(row_A,col_B) = alpha*acc; }
else if( (use_alpha == false) && (use_beta == true ) ) { C.at(row_A,col_B) = acc + beta*C.at(row_A,col_B); }
else if( (use_alpha == true ) && (use_beta == true ) ) { C.at(row_A,col_B) = alpha*acc + beta*C.at(row_A,col_B); }
}
}
}
}
else
if( (do_trans_A == true) && (do_trans_B == false) )
{
#if defined(ARMA_USE_OPENMP)
const bool use_mp = (B_n_cols >= 2) && (B.n_elem >= 8192) && (mp_thread_limit::in_parallel() == false);
#else
const bool use_mp = false;
#endif
if(use_mp)
{
#if defined(ARMA_USE_OPENMP)
{
const int n_threads = int( (std::min)( uword(mp_thread_limit::get()), uword(B_n_cols) ) );
for(uword col_A=0; col_A < A_n_cols; ++col_A)
{
// col_A is interpreted as row_A when storing the results in matrix C
const in_eT1* A_coldata = A.colptr(col_A);
#pragma omp parallel for schedule(static) num_threads(n_threads)
for(uword col_B=0; col_B < B_n_cols; ++col_B)
{
const in_eT2* B_coldata = B.colptr(col_B);
out_eT acc = out_eT(0);
for(uword i=0; i < B_n_rows; ++i)
{
acc += upgrade_val<in_eT1,in_eT2>::apply(A_coldata[i]) * upgrade_val<in_eT1,in_eT2>::apply(B_coldata[i]);
}
if( (use_alpha == false) && (use_beta == false) ) { C.at(col_A,col_B) = acc; }
else if( (use_alpha == true ) && (use_beta == false) ) { C.at(col_A,col_B) = alpha*acc; }
else if( (use_alpha == false) && (use_beta == true ) ) { C.at(col_A,col_B) = acc + beta*C.at(col_A,col_B); }
else if( (use_alpha == true ) && (use_beta == true ) ) { C.at(col_A,col_B) = alpha*acc + beta*C.at(col_A,col_B); }
}
}
}
#endif
}
else
{
for(uword col_A=0; col_A < A_n_cols; ++col_A)
{
// col_A is interpreted as row_A when storing the results in matrix C
const in_eT1* A_coldata = A.colptr(col_A);
for(uword col_B=0; col_B < B_n_cols; ++col_B)
{
const in_eT2* B_coldata = B.colptr(col_B);
out_eT acc = out_eT(0);
for(uword i=0; i < B_n_rows; ++i)
{
acc += upgrade_val<in_eT1,in_eT2>::apply(A_coldata[i]) * upgrade_val<in_eT1,in_eT2>::apply(B_coldata[i]);
}
if( (use_alpha == false) && (use_beta == false) ) { C.at(col_A,col_B) = acc; }
else if( (use_alpha == true ) && (use_beta == false) ) { C.at(col_A,col_B) = alpha*acc; }
else if( (use_alpha == false) && (use_beta == true ) ) { C.at(col_A,col_B) = acc + beta*C.at(col_A,col_B); }
else if( (use_alpha == true ) && (use_beta == true ) ) { C.at(col_A,col_B) = alpha*acc + beta*C.at(col_A,col_B); }
}
}
}
}
else
if( (do_trans_A == false) && (do_trans_B == true) )
{
Mat<in_eT2> B_tmp;
op_strans::apply_mat_noalias(B_tmp, B);
gemm_mixed_large<false, false, use_alpha, use_beta>::apply(C, A, B_tmp, alpha, beta);
}
else
if( (do_trans_A == true) && (do_trans_B == true) )
{
// mat B_tmp = trans(B);
// dgemm_arma<true, false, use_alpha, use_beta>::apply(C, A, B_tmp, alpha, beta);
// By using the trans(A)*trans(B) = trans(B*A) equivalency,
// transpose operations are not needed
podarray<in_eT2> tmp(B_n_cols);
in_eT2* B_rowdata = tmp.memptr();
for(uword row_B=0; row_B < B_n_rows; ++row_B)
{
tmp.copy_row(B, row_B);
for(uword col_A=0; col_A < A_n_cols; ++col_A)
{
const in_eT1* A_coldata = A.colptr(col_A);
out_eT acc = out_eT(0);
for(uword i=0; i < A_n_rows; ++i)
{
acc += upgrade_val<in_eT1,in_eT2>::apply(B_rowdata[i]) * upgrade_val<in_eT1,in_eT2>::apply(A_coldata[i]);
}
if( (use_alpha == false) && (use_beta == false) ) { C.at(col_A,row_B) = acc; }
else if( (use_alpha == true ) && (use_beta == false) ) { C.at(col_A,row_B) = alpha*acc; }
else if( (use_alpha == false) && (use_beta == true ) ) { C.at(col_A,row_B) = acc + beta*C.at(col_A,row_B); }
else if( (use_alpha == true ) && (use_beta == true ) ) { C.at(col_A,row_B) = alpha*acc + beta*C.at(col_A,row_B); }
}
}
}
}
};
//! \brief
//! Matrix multplication where the matrices have differing element types.
template<const bool do_trans_A=false, const bool do_trans_B=false, const bool use_alpha=false, const bool use_beta=false>
class gemm_mixed
{
public:
//! immediate multiplication of matrices A and B, storing the result in C
template<typename out_eT, typename in_eT1, typename in_eT2>
inline
static
void
apply
(
Mat<out_eT>& C,
const Mat<in_eT1>& A,
const Mat<in_eT2>& B,
const out_eT alpha = out_eT(1),
const out_eT beta = out_eT(0)
)
{
arma_extra_debug_sigprint();
if((is_cx<in_eT1>::yes && do_trans_A) || (is_cx<in_eT2>::yes && do_trans_B))
{
// better-than-nothing handling of hermitian transpose
Mat<in_eT1> tmp_A;
Mat<in_eT2> tmp_B;
const bool predo_trans_A = ( (do_trans_A == true) && (is_cx<in_eT1>::yes) );
const bool predo_trans_B = ( (do_trans_B == true) && (is_cx<in_eT2>::yes) );
if(predo_trans_A) { op_htrans::apply_mat_noalias(tmp_A, A); }
if(predo_trans_B) { op_htrans::apply_mat_noalias(tmp_B, B); }
const Mat<in_eT1>& AA = (predo_trans_A == false) ? A : tmp_A;
const Mat<in_eT2>& BB = (predo_trans_B == false) ? B : tmp_B;
gemm_mixed_large<((predo_trans_A) ? false : do_trans_A), ((predo_trans_B) ? false : do_trans_B), use_alpha, use_beta>::apply(C, AA, BB, alpha, beta);
}
else
{
gemm_mixed_large<do_trans_A, do_trans_B, use_alpha, use_beta>::apply(C, A, B, alpha, beta);
}
}
};
//! @}
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