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+// SPDX-License-Identifier: Apache-2.0
+// 
+// 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 (ie. 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);
+      }
+    }
+  
+  
+  };
+
+
+
+//! @}