gemm_emul_cache< do_trans_A, do_trans_B, use_alpha, use_beta > Class Template Reference [Gemm]

//! Partial emulation of ATLAS/BLAS gemm(), using caching for speedup. //! Matrix 'C' is assumed to have been set to the correct size (i.e. taking into account transposes) More...

#include <gemm.hpp>

List of all members.

Static Public Member Functions
template<typename eT >
static arma_hot void	apply (Mat< eT > &C, const Mat< eT > &A, const Mat< eT > &B, const eT alpha=eT(1), const eT beta=eT(0))

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Detailed Description

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_emul_cache< do_trans_A, do_trans_B, use_alpha, use_beta >

//! Partial emulation of ATLAS/BLAS gemm(), using caching for speedup. //! Matrix 'C' is assumed to have been set to the correct size (i.e. taking into account transposes)

Definition at line 27 of file gemm.hpp.

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Member Function Documentation

template<const bool do_trans_A = false, const bool do_trans_B = false, const bool use_alpha = false, const bool use_beta = false>

template<typename eT >

static arma_hot void gemm_emul_cache< do_trans_A, do_trans_B, use_alpha, use_beta >::apply	(	Mat< eT > &	C,
		const Mat< eT > &	A,
		const Mat< eT > &	B,
		const eT	alpha = eT(1),
		const eT	beta = eT(0)
	)			[inline, static]

Definition at line 37 of file gemm.hpp.

References Mat< eT >::at(), Mat< eT >::colptr(), Mat< eT >::n_cols, Mat< eT >::n_rows, and trans().

    {
    arma_extra_debug_sigprint();

    const u32 A_n_rows = A.n_rows;
    const u32 A_n_cols = A.n_cols;
    
    const u32 B_n_rows = B.n_rows;
    const u32 B_n_cols = B.n_cols;
    
    if( (do_trans_A == false) && (do_trans_B == false) )
      {
      arma_aligned podarray<eT> tmp(A_n_cols);
      eT* A_rowdata = tmp.memptr();
      
      for(u32 row_A=0; row_A < A_n_rows; ++row_A)
        {
        
        for(u32 col_A=0; col_A < A_n_cols; ++col_A)
          {
          A_rowdata[col_A] = A.at(row_A,col_A);
          }
        
        for(u32 col_B=0; col_B < B_n_cols; ++col_B)
          {
          const eT* B_coldata = B.colptr(col_B);
          
          eT acc = eT(0);
          for(u32 i=0; i < B_n_rows; ++i)
            {
            acc += A_rowdata[i] * 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) )
      {
      for(u32 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 eT* A_coldata = A.colptr(col_A);
        
        for(u32 col_B=0; col_B < B_n_cols; ++col_B)
          {
          const eT* B_coldata = B.colptr(col_B);
          
          eT acc = eT(0);
          for(u32 i=0; i < B_n_rows; ++i)
            {
            acc += A_coldata[i] * 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<eT> B_tmp = trans(B);
      gemm_emul_cache<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
      
      arma_aligned podarray<eT> tmp(B.n_cols);
      eT* B_rowdata = tmp.memptr();
      
      for(u32 row_B=0; row_B < B_n_rows; ++row_B)
        {
        
        for(u32 col_B=0; col_B < B_n_cols; ++col_B)
          {
          B_rowdata[col_B] = B.at(row_B,col_B);
          }
        
        for(u32 col_A=0; col_A < A_n_cols; ++col_A)
          {
          const eT* A_coldata = A.colptr(col_A);
          
          eT acc = eT(0);
          for(u32 i=0; i < A_n_rows; ++i)
            {
            acc += B_rowdata[i] * 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);
            }
          
          }
        }
      
      }
    }