GeneralMatrixMatrixTriangular.h
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00001 // This file is part of Eigen, a lightweight C++ template library
00002 // for linear algebra.
00003 //
00004 // Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
00005 //
00006 // Eigen is free software; you can redistribute it and/or
00007 // modify it under the terms of the GNU Lesser General Public
00008 // License as published by the Free Software Foundation; either
00009 // version 3 of the License, or (at your option) any later version.
00010 //
00011 // Alternatively, you can redistribute it and/or
00012 // modify it under the terms of the GNU General Public License as
00013 // published by the Free Software Foundation; either version 2 of
00014 // the License, or (at your option) any later version.
00015 //
00016 // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
00017 // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
00018 // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
00019 // GNU General Public License for more details.
00020 //
00021 // You should have received a copy of the GNU Lesser General Public
00022 // License and a copy of the GNU General Public License along with
00023 // Eigen. If not, see <http://www.gnu.org/licenses/>.
00024 
00025 #ifndef EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_H
00026 #define EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_H
00027 
00028 namespace internal {
00029 
00030 /**********************************************************************
00031 * This file implements a general A * B product while
00032 * evaluating only one triangular part of the product.
00033 * This is more general version of self adjoint product (C += A A^T)
00034 * as the level 3 SYRK Blas routine.
00035 **********************************************************************/
00036 
00037 // forward declarations (defined at the end of this file)
00038 template<typename LhsScalar, typename RhsScalar, typename Index, int mr, int nr, bool ConjLhs, bool ConjRhs, int UpLo>
00039 struct tribb_kernel;
00040   
00041 /* Optimized matrix-matrix product evaluating only one triangular half */
00042 template <typename Index,
00043           typename LhsScalar, int LhsStorageOrder, bool ConjugateLhs,
00044           typename RhsScalar, int RhsStorageOrder, bool ConjugateRhs,
00045                               int ResStorageOrder, int  UpLo>
00046 struct general_matrix_matrix_triangular_product;
00047 
00048 // as usual if the result is row major => we transpose the product
00049 template <typename Index, typename LhsScalar, int LhsStorageOrder, bool ConjugateLhs,
00050                           typename RhsScalar, int RhsStorageOrder, bool ConjugateRhs, int  UpLo>
00051 struct general_matrix_matrix_triangular_product<Index,LhsScalar,LhsStorageOrder,ConjugateLhs,RhsScalar,RhsStorageOrder,ConjugateRhs,RowMajor,UpLo>
00052 {  
00053   typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar;
00054   static EIGEN_STRONG_INLINE void run(Index size, Index depth,const LhsScalar* lhs, Index lhsStride,
00055                                       const RhsScalar* rhs, Index rhsStride, ResScalar* res, Index resStride, ResScalar alpha)
00056   {
00057     general_matrix_matrix_triangular_product<Index,
00058         RhsScalar, RhsStorageOrder==RowMajor ? ColMajor : RowMajor, ConjugateRhs,
00059         LhsScalar, LhsStorageOrder==RowMajor ? ColMajor : RowMajor, ConjugateLhs,
00060         ColMajor, UpLo==Lower?Upper:Lower>
00061       ::run(size,depth,rhs,rhsStride,lhs,lhsStride,res,resStride,alpha);
00062   }
00063 };
00064 
00065 template <typename Index, typename LhsScalar, int LhsStorageOrder, bool ConjugateLhs,
00066                           typename RhsScalar, int RhsStorageOrder, bool ConjugateRhs, int  UpLo>
00067 struct general_matrix_matrix_triangular_product<Index,LhsScalar,LhsStorageOrder,ConjugateLhs,RhsScalar,RhsStorageOrder,ConjugateRhs,ColMajor,UpLo>
00068 {
00069   typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar;
00070   static EIGEN_STRONG_INLINE void run(Index size, Index depth,const LhsScalar* _lhs, Index lhsStride,
00071                                       const RhsScalar* _rhs, Index rhsStride, ResScalar* res, Index resStride, ResScalar alpha)
00072   {
00073     const_blas_data_mapper<LhsScalar, Index, LhsStorageOrder> lhs(_lhs,lhsStride);
00074     const_blas_data_mapper<RhsScalar, Index, RhsStorageOrder> rhs(_rhs,rhsStride);
00075 
00076     typedef gebp_traits<LhsScalar,RhsScalar> Traits;
00077 
00078     Index kc = depth; // cache block size along the K direction
00079     Index mc = size;  // cache block size along the M direction
00080     Index nc = size;  // cache block size along the N direction
00081     computeProductBlockingSizes<LhsScalar,RhsScalar>(kc, mc, nc);
00082     // !!! mc must be a multiple of nr:
00083     if(mc > Traits::nr)
00084       mc = (mc/Traits::nr)*Traits::nr;
00085 
00086     std::size_t sizeW = kc*Traits::WorkSpaceFactor;
00087     std::size_t sizeB = sizeW + kc*size;
00088     ei_declare_aligned_stack_constructed_variable(LhsScalar, blockA, kc*mc, 0);
00089     ei_declare_aligned_stack_constructed_variable(RhsScalar, allocatedBlockB, sizeB, 0);
00090     RhsScalar* blockB = allocatedBlockB + sizeW;
00091     
00092     gemm_pack_lhs<LhsScalar, Index, Traits::mr, Traits::LhsProgress, LhsStorageOrder> pack_lhs;
00093     gemm_pack_rhs<RhsScalar, Index, Traits::nr, RhsStorageOrder> pack_rhs;
00094     gebp_kernel <LhsScalar, RhsScalar, Index, Traits::mr, Traits::nr, ConjugateLhs, ConjugateRhs> gebp;
00095     tribb_kernel<LhsScalar, RhsScalar, Index, Traits::mr, Traits::nr, ConjugateLhs, ConjugateRhs, UpLo> sybb;
00096 
00097     for(Index k2=0; k2<depth; k2+=kc)
00098     {
00099       const Index actual_kc = (std::min)(k2+kc,depth)-k2;
00100 
00101       // note that the actual rhs is the transpose/adjoint of mat
00102       pack_rhs(blockB, &rhs(k2,0), rhsStride, actual_kc, size);
00103 
00104       for(Index i2=0; i2<size; i2+=mc)
00105       {
00106         const Index actual_mc = (std::min)(i2+mc,size)-i2;
00107 
00108         pack_lhs(blockA, &lhs(i2, k2), lhsStride, actual_kc, actual_mc);
00109 
00110         // the selected actual_mc * size panel of res is split into three different part:
00111         //  1 - before the diagonal => processed with gebp or skipped
00112         //  2 - the actual_mc x actual_mc symmetric block => processed with a special kernel
00113         //  3 - after the diagonal => processed with gebp or skipped
00114         if (UpLo==Lower)
00115           gebp(res+i2, resStride, blockA, blockB, actual_mc, actual_kc, (std::min)(size,i2), alpha,
00116                -1, -1, 0, 0, allocatedBlockB);
00117 
00118         sybb(res+resStride*i2 + i2, resStride, blockA, blockB + actual_kc*i2, actual_mc, actual_kc, alpha, allocatedBlockB);
00119 
00120         if (UpLo==Upper)
00121         {
00122           Index j2 = i2+actual_mc;
00123           gebp(res+resStride*j2+i2, resStride, blockA, blockB+actual_kc*j2, actual_mc, actual_kc, (std::max)(Index(0), size-j2), alpha,
00124                -1, -1, 0, 0, allocatedBlockB);
00125         }
00126       }
00127     }
00128   }
00129 };
00130 
00131 // Optimized packed Block * packed Block product kernel evaluating only one given triangular part
00132 // This kernel is built on top of the gebp kernel:
00133 // - the current destination block is processed per panel of actual_mc x BlockSize
00134 //   where BlockSize is set to the minimal value allowing gebp to be as fast as possible
00135 // - then, as usual, each panel is split into three parts along the diagonal,
00136 //   the sub blocks above and below the diagonal are processed as usual,
00137 //   while the triangular block overlapping the diagonal is evaluated into a
00138 //   small temporary buffer which is then accumulated into the result using a
00139 //   triangular traversal.
00140 template<typename LhsScalar, typename RhsScalar, typename Index, int mr, int nr, bool ConjLhs, bool ConjRhs, int UpLo>
00141 struct tribb_kernel
00142 {
00143   typedef gebp_traits<LhsScalar,RhsScalar,ConjLhs,ConjRhs> Traits;
00144   typedef typename Traits::ResScalar ResScalar;
00145   
00146   enum {
00147     BlockSize  = EIGEN_PLAIN_ENUM_MAX(mr,nr)
00148   };
00149   void operator()(ResScalar* res, Index resStride, const LhsScalar* blockA, const RhsScalar* blockB, Index size, Index depth, ResScalar alpha, RhsScalar* workspace)
00150   {
00151     gebp_kernel<LhsScalar, RhsScalar, Index, mr, nr, ConjLhs, ConjRhs> gebp_kernel;
00152     Matrix<ResScalar,BlockSize,BlockSize,ColMajor> buffer;
00153 
00154     // let's process the block per panel of actual_mc x BlockSize,
00155     // again, each is split into three parts, etc.
00156     for (Index j=0; j<size; j+=BlockSize)
00157     {
00158       Index actualBlockSize = std::min<Index>(BlockSize,size - j);
00159       const RhsScalar* actual_b = blockB+j*depth;
00160 
00161       if(UpLo==Upper)
00162         gebp_kernel(res+j*resStride, resStride, blockA, actual_b, j, depth, actualBlockSize, alpha,
00163                     -1, -1, 0, 0, workspace);
00164 
00165       // selfadjoint micro block
00166       {
00167         Index i = j;
00168         buffer.setZero();
00169         // 1 - apply the kernel on the temporary buffer
00170         gebp_kernel(buffer.data(), BlockSize, blockA+depth*i, actual_b, actualBlockSize, depth, actualBlockSize, alpha,
00171                     -1, -1, 0, 0, workspace);
00172         // 2 - triangular accumulation
00173         for(Index j1=0; j1<actualBlockSize; ++j1)
00174         {
00175           ResScalar* r = res + (j+j1)*resStride + i;
00176           for(Index i1=UpLo==Lower ? j1 : 0;
00177               UpLo==Lower ? i1<actualBlockSize : i1<=j1; ++i1)
00178             r[i1] += buffer(i1,j1);
00179         }
00180       }
00181 
00182       if(UpLo==Lower)
00183       {
00184         Index i = j+actualBlockSize;
00185         gebp_kernel(res+j*resStride+i, resStride, blockA+depth*i, actual_b, size-i, depth, actualBlockSize, alpha,
00186                     -1, -1, 0, 0, workspace);
00187       }
00188     }
00189   }
00190 };
00191 
00192 } // end namespace internal
00193 
00194 // high level API
00195 
00196 template<typename MatrixType, unsigned int UpLo>
00197 template<typename ProductDerived, typename _Lhs, typename _Rhs>
00198 TriangularView<MatrixType,UpLo>& TriangularView<MatrixType,UpLo>::assignProduct(const ProductBase<ProductDerived, _Lhs,_Rhs>& prod, const Scalar& alpha)
00199 {
00200   typedef typename internal::remove_all<typename ProductDerived::LhsNested>::type Lhs;
00201   typedef internal::blas_traits<Lhs> LhsBlasTraits;
00202   typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhs;
00203   typedef typename internal::remove_all<ActualLhs>::type _ActualLhs;
00204   const ActualLhs actualLhs = LhsBlasTraits::extract(prod.lhs());
00205   
00206   typedef typename internal::remove_all<typename ProductDerived::RhsNested>::type Rhs;
00207   typedef internal::blas_traits<Rhs> RhsBlasTraits;
00208   typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhs;
00209   typedef typename internal::remove_all<ActualRhs>::type _ActualRhs;
00210   const ActualRhs actualRhs = RhsBlasTraits::extract(prod.rhs());
00211 
00212   typename ProductDerived::Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(prod.lhs().derived()) * RhsBlasTraits::extractScalarFactor(prod.rhs().derived());
00213 
00214   internal::general_matrix_matrix_triangular_product<Index,
00215     typename Lhs::Scalar, _ActualLhs::Flags&RowMajorBit ? RowMajor : ColMajor, LhsBlasTraits::NeedToConjugate,
00216     typename Rhs::Scalar, _ActualRhs::Flags&RowMajorBit ? RowMajor : ColMajor, RhsBlasTraits::NeedToConjugate,
00217     MatrixType::Flags&RowMajorBit ? RowMajor : ColMajor, UpLo>
00218     ::run(m_matrix.cols(), actualLhs.cols(),
00219           &actualLhs.coeffRef(0,0), actualLhs.outerStride(), &actualRhs.coeffRef(0,0), actualRhs.outerStride(),
00220           const_cast<Scalar*>(m_matrix.data()), m_matrix.outerStride(), actualAlpha);
00221   
00222   return *this;
00223 }
00224 
00225 #endif // EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_H


libicr
Author(s): Robert Krug
autogenerated on Mon Jan 6 2014 11:32:44