Implement efficient sefladjoint product (aka SYRK) : C += alpha * U U^T

It is currently available via SelfAdjointView::rankKupdate.
TODO: allows to write SelfAdjointView += u * u.adjoint()

4 files changed, 449 insertions, 2 deletions

diff --git a/Eigen/src/Core/SelfAdjointView.h b/Eigen/src/Core/SelfAdjointView.h
index 540f4fe93..b1b4f9e32 100644
--- a/Eigen/src/Core/SelfAdjointView.h
+++ b/Eigen/src/Core/SelfAdjointView.h
@@ -128,6 +128,16 @@ template<typename MatrixType, unsigned int UpLo> class SelfAdjointView
     template<typename DerivedU, typename DerivedV>
     void rank2update(const MatrixBase<DerivedU>& u, const MatrixBase<DerivedV>& v, Scalar alpha = Scalar(1));
 
+    /** Perform a symmetric rank K update of the selfadjoint matrix \c *this:
+      * \f$ this = this + \alpha ( u u^* ) \f$
+      * where \a u is a vector or matrix.
+      *
+      * Note that to perform \f$ this = this + \alpha ( u^* u ) \f$ you can simply
+      * call this function with u.adjoint().
+      */
+    template<typename DerivedU>
+    void rankKupdate(const MatrixBase<DerivedU>& u, Scalar alpha = Scalar(1));
+
 /////////// Cholesky module ///////////
 
     const LLT<PlainMatrixType, UpLo> llt() const;
diff --git a/Eigen/src/Core/products/GeneralMatrixMatrix.h b/Eigen/src/Core/products/GeneralMatrixMatrix.h
index b2f51ca5b..9166921fe 100644
--- a/Eigen/src/Core/products/GeneralMatrixMatrix.h
+++ b/Eigen/src/Core/products/GeneralMatrixMatrix.h
@@ -334,6 +334,19 @@ struct ei_gebp_kernel
 };
 
 // pack a block of the lhs
+// The travesal is as follow (mr==4):
+//   0  4  8 12 ...
+//   1  5  9 13 ...
+//   2  6 10 14 ...
+//   3  7 11 15 ...
+//
+//  16 20 24 28 ...
+//  17 21 25 29 ...
+//  18 22 26 30 ...
+//  19 23 27 31 ...
+//
+//  32 33 34 35 ...
+//  36 36 38 39 ...
 template<typename Scalar, int mr, int StorageOrder, bool Conjugate>
 struct ei_gemm_pack_lhs
 {
@@ -357,6 +370,11 @@ struct ei_gemm_pack_lhs
 
 // copy a complete panel of the rhs while expending each coefficient into a packet form
 // this version is optimized for column major matrices
+// The traversal order is as follow (nr==4):
+//  0  1  2  3   12 13 14 15   24 27
+//  4  5  6  7   16 17 18 19   25 28
+//  8  9 10 11   20 21 22 23   26 29
+//  .  .  .  .    .  .  .  .    .  .
 template<typename Scalar, int nr>
 struct ei_gemm_pack_rhs<Scalar, nr, ColMajor>
 {
diff --git a/Eigen/src/Core/products/SelfadjointProduct.h b/Eigen/src/Core/products/SelfadjointProduct.h
new file mode 100644
index 000000000..5d045a921
--- /dev/null
+++ b/Eigen/src/Core/products/SelfadjointProduct.h
@@ -0,0 +1,419 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <g.gael@free.fr>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#ifndef EIGEN_SELFADJOINT_PRODUCT_H
+#define EIGEN_SELFADJOINT_PRODUCT_H
+
+/**********************************************************************
+* This file implement a self adjoint product: C += A A^T updating only
+* an half of the selfadjoint matrix C.
+* It corresponds to the level 3 SYRK Blas routine.
+**********************************************************************/
+
+// forward declarations (defined at the end of this file)
+template<typename Scalar, int mr, int nr, typename Conj, int UpLo>
+struct ei_sybb_kernel;
+
+/* Optimized selfadjoint product (_SYRK) */
+template <typename Scalar,
+          int RhsStorageOrder,
+          int ResStorageOrder, bool AAT, int UpLo>
+struct ei_selfadjoint_product;
+
+// as usual if the result is row major => we transpose the product
+template <typename Scalar, int MatStorageOrder, bool AAT, int UpLo>
+struct ei_selfadjoint_product<Scalar,MatStorageOrder, RowMajor, AAT, UpLo>
+{
+  static EIGEN_STRONG_INLINE void run(int size, const Scalar* mat, int matStride, Scalar* res, int resStride, Scalar alpha)
+  {
+    ei_selfadjoint_product<Scalar, MatStorageOrder, ColMajor, !AAT, UpLo==LowerTriangular?UpperTriangular:LowerTriangular>
+      ::run(size, mat, matStride, res, resStride, alpha);
+  }
+};
+
+template <typename Scalar,
+          int MatStorageOrder, bool AAT, int UpLo>
+struct ei_selfadjoint_product<Scalar,MatStorageOrder, ColMajor, AAT, UpLo>
+{
+
+  static EIGEN_DONT_INLINE void run(
+    int size,
+    const Scalar* _mat, int matStride,
+    Scalar* res,        int resStride,
+    Scalar alpha)
+  {
+    ei_const_blas_data_mapper<Scalar, MatStorageOrder> mat(_mat,matStride);
+
+    if(AAT)
+      alpha = ei_conj(alpha);
+
+    typedef ei_product_blocking_traits<Scalar> Blocking;
+
+    int kc = std::min<int>(Blocking::Max_kc,size);  // cache block size along the K direction
+    int mc = std::min<int>(Blocking::Max_mc,size);  // cache block size along the M direction
+
+    Scalar* blockA = ei_aligned_stack_new(Scalar, kc*mc);
+    Scalar* blockB = ei_aligned_stack_new(Scalar, kc*size*Blocking::PacketSize);
+
+    // number of columns which can be processed by packet of nr columns
+    int packet_cols = (size/Blocking::nr)*Blocking::nr;
+
+    // note that the actual rhs is the transpose/adjoint of mat
+    typedef ei_conj_helper<NumTraits<Scalar>::IsComplex && !AAT, NumTraits<Scalar>::IsComplex && AAT> Conj;
+
+    ei_gebp_kernel<Scalar, Blocking::mr, Blocking::nr, Conj> gebp_kernel;
+
+    for(int k2=0; k2<size; k2+=kc)
+    {
+      const int actual_kc = std::min(k2+kc,size)-k2;
+
+      // note that the actual rhs is the transpose/adjoint of mat
+      ei_gemm_pack_rhs<Scalar,Blocking::nr,MatStorageOrder==RowMajor ? ColMajor : RowMajor>()
+        (blockB, &mat(0,k2), matStride, alpha, actual_kc, packet_cols, size);
+
+      for(int i2=0; i2<size; i2+=mc)
+      {
+        const int actual_mc = std::min(i2+mc,size)-i2;
+
+        ei_gemm_pack_lhs<Scalar,Blocking::mr,MatStorageOrder, false>()
+          (blockA, &mat(i2, k2), matStride, actual_kc, actual_mc);
+
+        // the selected actual_mc * size panel of res is split into three different part:
+        //  1 - before the diagonal => processed with gebp or skipped
+        //  2 - the actual_mc x actual_mc symmetric block => processed with a special kernel
+        //  3 - after the diagonal => processed with gebp or skipped
+        if (UpLo==LowerTriangular)
+          gebp_kernel(res, resStride, blockA, blockB, actual_mc, actual_kc, std::min(packet_cols,i2), i2, std::min(size,i2));
+
+        ei_sybb_kernel<Scalar, Blocking::mr, Blocking::nr, Conj, UpLo>()
+          (res+resStride*i2 + i2, resStride, blockA, blockB + actual_kc*Blocking::PacketSize*i2, actual_mc, actual_kc, std::min(actual_mc,std::max(packet_cols-i2,0)));
+
+        if (UpLo==UpperTriangular)
+        {
+          int j2 = i2+actual_mc;
+          gebp_kernel(res+resStride*j2, resStride, blockA, blockB+actual_kc*Blocking::PacketSize*j2, actual_mc, actual_kc,
+                      std::max(0,packet_cols-j2), i2, std::max(0,size-j2));
+        }
+      }
+    }
+    ei_aligned_stack_delete(Scalar, blockA, kc*mc);
+    ei_aligned_stack_delete(Scalar, blockB, kc*size*Blocking::PacketSize);
+  }
+};
+
+// high level API
+
+template<typename MatrixType, unsigned int UpLo>
+template<typename DerivedU>
+void SelfAdjointView<MatrixType,UpLo>
+::rankKupdate(const MatrixBase<DerivedU>& u, Scalar alpha)
+{
+  typedef ei_blas_traits<DerivedU> UBlasTraits;
+  typedef typename UBlasTraits::DirectLinearAccessType ActualUType;
+  typedef typename ei_cleantype<ActualUType>::type _ActualUType;
+  const ActualUType actualU = UBlasTraits::extract(u.derived());
+
+  Scalar actualAlpha = alpha * UBlasTraits::extractScalarFactor(u.derived());
+
+  enum { IsRowMajor = (ei_traits<MatrixType>::Flags&RowMajorBit)?1:0 };
+
+  ei_selfadjoint_product<Scalar,
+    _ActualUType::Flags&RowMajorBit ? RowMajor : ColMajor,
+    ei_traits<MatrixType>::Flags&RowMajorBit ? RowMajor : ColMajor,
+    !UBlasTraits::NeedToConjugate,
+    UpLo>::run(_expression().cols(), &actualU.coeff(0,0), actualU.stride(), const_cast<Scalar*>(_expression().data()), _expression().stride(), actualAlpha);
+}
+
+
+
+// optimized SYmmetric packed Block * packed Block product kernel
+// this kernel is very similar to the gebp kernel: the only differences are
+// the piece of code to avoid the writes off the diagonal
+//  => TODO find a way to factorize the two kernels in a single one
+template<typename Scalar, int mr, int nr, typename Conj, int UpLo>
+struct ei_sybb_kernel
+{
+  void operator()(Scalar* res, int resStride, const Scalar* blockA, const Scalar* blockB, int actual_mc, int actual_kc, int packet_cols)
+  {
+    typedef typename ei_packet_traits<Scalar>::type PacketType;
+    enum { PacketSize = ei_packet_traits<Scalar>::size };
+    Conj cj;
+    const int peeled_mc = (actual_mc/mr)*mr;
+    // loops on each cache friendly block of the result/rhs
+    for(int j2=0; j2<packet_cols; j2+=nr)
+    {
+      // here we selected a vertical mc x nr panel of the result that we'll
+      // process normally until the end of the diagonal (or from the start if upper)
+      //
+      int start_i = UpLo==LowerTriangular ? (j2/mr)*mr : 0;
+      int end_i   = UpLo==LowerTriangular ? actual_mc  : std::min(actual_mc,((j2+std::max(mr,nr))/mr)*mr);
+      for(int i=start_i; i<std::min(peeled_mc,end_i); i+=mr)
+      {
+        const Scalar* blA = &blockA[i*actual_kc];
+        #ifdef EIGEN_VECTORIZE_SSE
+        _mm_prefetch((const char*)(&blA[0]), _MM_HINT_T0);
+        #endif
+
+        // TODO move the res loads to the stores
+
+        // gets res block as register
+        PacketType C0, C1, C2, C3, C4, C5, C6, C7;
+                  C0 = ei_ploadu(&res[(j2+0)*resStride + i]);
+                  C1 = ei_ploadu(&res[(j2+1)*resStride + i]);
+        if(nr==4) C2 = ei_ploadu(&res[(j2+2)*resStride + i]);
+        if(nr==4) C3 = ei_ploadu(&res[(j2+3)*resStride + i]);
+                  C4 = ei_ploadu(&res[(j2+0)*resStride + i + PacketSize]);
+                  C5 = ei_ploadu(&res[(j2+1)*resStride + i + PacketSize]);
+        if(nr==4) C6 = ei_ploadu(&res[(j2+2)*resStride + i + PacketSize]);
+        if(nr==4) C7 = ei_ploadu(&res[(j2+3)*resStride + i + PacketSize]);
+
+        // performs "inner" product
+        // TODO let's check wether the flowing peeled loop could not be
+        //      optimized via optimal prefetching from one loop to the other
+        const Scalar* blB = &blockB[j2*actual_kc*PacketSize];
+        const int peeled_kc = (actual_kc/4)*4;
+        for(int k=0; k<peeled_kc; k+=4)
+        {
+          PacketType B0, B1, B2, B3, A0, A1;
+
+                    A0 = ei_pload(&blA[0*PacketSize]);
+                    A1 = ei_pload(&blA[1*PacketSize]);
+                    B0 = ei_pload(&blB[0*PacketSize]);
+                    B1 = ei_pload(&blB[1*PacketSize]);
+                    C0 = cj.pmadd(A0, B0, C0);
+          if(nr==4) B2 = ei_pload(&blB[2*PacketSize]);
+                    C4 = cj.pmadd(A1, B0, C4);
+          if(nr==4) B3 = ei_pload(&blB[3*PacketSize]);
+                    B0 = ei_pload(&blB[(nr==4 ? 4 : 2)*PacketSize]);
+                    C1 = cj.pmadd(A0, B1, C1);
+                    C5 = cj.pmadd(A1, B1, C5);
+                    B1 = ei_pload(&blB[(nr==4 ? 5 : 3)*PacketSize]);
+          if(nr==4) C2 = cj.pmadd(A0, B2, C2);
+          if(nr==4) C6 = cj.pmadd(A1, B2, C6);
+          if(nr==4) B2 = ei_pload(&blB[6*PacketSize]);
+          if(nr==4) C3 = cj.pmadd(A0, B3, C3);
+                    A0 = ei_pload(&blA[2*PacketSize]);
+          if(nr==4) C7 = cj.pmadd(A1, B3, C7);
+                    A1 = ei_pload(&blA[3*PacketSize]);
+          if(nr==4) B3 = ei_pload(&blB[7*PacketSize]);
+                    C0 = cj.pmadd(A0, B0, C0);
+                    C4 = cj.pmadd(A1, B0, C4);
+                    B0 = ei_pload(&blB[(nr==4 ? 8 : 4)*PacketSize]);
+                    C1 = cj.pmadd(A0, B1, C1);
+                    C5 = cj.pmadd(A1, B1, C5);
+                    B1 = ei_pload(&blB[(nr==4 ? 9 : 5)*PacketSize]);
+          if(nr==4) C2 = cj.pmadd(A0, B2, C2);
+          if(nr==4) C6 = cj.pmadd(A1, B2, C6);
+          if(nr==4) B2 = ei_pload(&blB[10*PacketSize]);
+          if(nr==4) C3 = cj.pmadd(A0, B3, C3);
+                    A0 = ei_pload(&blA[4*PacketSize]);
+          if(nr==4) C7 = cj.pmadd(A1, B3, C7);
+                    A1 = ei_pload(&blA[5*PacketSize]);
+          if(nr==4) B3 = ei_pload(&blB[11*PacketSize]);
+
+                    C0 = cj.pmadd(A0, B0, C0);
+                    C4 = cj.pmadd(A1, B0, C4);
+                    B0 = ei_pload(&blB[(nr==4 ? 12 : 6)*PacketSize]);
+                    C1 = cj.pmadd(A0, B1, C1);
+                    C5 = cj.pmadd(A1, B1, C5);
+                    B1 = ei_pload(&blB[(nr==4 ? 13 : 7)*PacketSize]);
+          if(nr==4) C2 = cj.pmadd(A0, B2, C2);
+          if(nr==4) C6 = cj.pmadd(A1, B2, C6);
+          if(nr==4) B2 = ei_pload(&blB[14*PacketSize]);
+          if(nr==4) C3 = cj.pmadd(A0, B3, C3);
+                    A0 = ei_pload(&blA[6*PacketSize]);
+          if(nr==4) C7 = cj.pmadd(A1, B3, C7);
+                    A1 = ei_pload(&blA[7*PacketSize]);
+          if(nr==4) B3 = ei_pload(&blB[15*PacketSize]);
+                    C0 = cj.pmadd(A0, B0, C0);
+                    C4 = cj.pmadd(A1, B0, C4);
+                    C1 = cj.pmadd(A0, B1, C1);
+                    C5 = cj.pmadd(A1, B1, C5);
+          if(nr==4) C2 = cj.pmadd(A0, B2, C2);
+          if(nr==4) C6 = cj.pmadd(A1, B2, C6);
+          if(nr==4) C3 = cj.pmadd(A0, B3, C3);
+          if(nr==4) C7 = cj.pmadd(A1, B3, C7);
+
+          blB += 4*nr*PacketSize;
+          blA += 4*mr;
+        }
+        // process remaining peeled loop
+        for(int k=peeled_kc; k<actual_kc; k++)
+        {
+          PacketType B0, B1, B2, B3, A0, A1;
+
+                    A0 = ei_pload(&blA[0*PacketSize]);
+                    A1 = ei_pload(&blA[1*PacketSize]);
+                    B0 = ei_pload(&blB[0*PacketSize]);
+                    B1 = ei_pload(&blB[1*PacketSize]);
+                    C0 = cj.pmadd(A0, B0, C0);
+          if(nr==4) B2 = ei_pload(&blB[2*PacketSize]);
+                    C4 = cj.pmadd(A1, B0, C4);
+          if(nr==4) B3 = ei_pload(&blB[3*PacketSize]);
+                    C1 = cj.pmadd(A0, B1, C1);
+                    C5 = cj.pmadd(A1, B1, C5);
+          if(nr==4) C2 = cj.pmadd(A0, B2, C2);
+          if(nr==4) C6 = cj.pmadd(A1, B2, C6);
+          if(nr==4) C3 = cj.pmadd(A0, B3, C3);
+          if(nr==4) C7 = cj.pmadd(A1, B3, C7);
+
+          blB += nr*PacketSize;
+          blA += mr;
+        }
+
+        // let's check whether the mr x nr block overlap the diagonal,
+        // is so then we have to carefully discard writes off the diagonal
+        if(UpLo==LowerTriangular ? i>=j2+nr : i+mr<=j2)
+        {
+                    ei_pstoreu(&res[(j2+0)*resStride + i], C0);
+                    ei_pstoreu(&res[(j2+1)*resStride + i], C1);
+          if(nr==4) ei_pstoreu(&res[(j2+2)*resStride + i], C2);
+          if(nr==4) ei_pstoreu(&res[(j2+3)*resStride + i], C3);
+                    ei_pstoreu(&res[(j2+0)*resStride + i + PacketSize], C4);
+                    ei_pstoreu(&res[(j2+1)*resStride + i + PacketSize], C5);
+          if(nr==4) ei_pstoreu(&res[(j2+2)*resStride + i + PacketSize], C6);
+          if(nr==4) ei_pstoreu(&res[(j2+3)*resStride + i + PacketSize], C7);
+        }
+        else
+        {
+          Scalar buf[mr*nr];
+          // overlap => copy to a temporary mr x nr buffer and then triangular copy
+                    ei_pstore(&buf[0*mr], C0);
+                    ei_pstore(&buf[1*mr], C1);
+          if(nr==4) ei_pstore(&buf[2*mr], C2);
+          if(nr==4) ei_pstore(&buf[3*mr], C3);
+                    ei_pstore(&buf[0*mr + PacketSize], C4);
+                    ei_pstore(&buf[1*mr + PacketSize], C5);
+          if(nr==4) ei_pstore(&buf[2*mr + PacketSize], C6);
+          if(nr==4) ei_pstore(&buf[3*mr + PacketSize], C7);
+
+          for(int j1=0; j1<nr; ++j1)
+            for(int i1=0; i1<mr; ++i1)
+            {
+              if(UpLo==LowerTriangular ? i+i1 >= j2+j1 : i+i1 <= j2+j1)
+                res[(j2+j1)*resStride + i+i1] = buf[i1 + j1 * mr];
+            }
+        }
+      }
+      for(int i=std::max(start_i,peeled_mc); i<std::min(end_i,actual_mc); i++)
+      {
+        const Scalar* blA = &blockA[i*actual_kc];
+        #ifdef EIGEN_VECTORIZE_SSE
+        _mm_prefetch((const char*)(&blA[0]), _MM_HINT_T0);
+        #endif
+
+        // gets a 1 x nr res block as registers
+        Scalar C0(0), C1(0), C2(0), C3(0);
+        const Scalar* blB = &blockB[j2*actual_kc*PacketSize];
+        for(int k=0; k<actual_kc; k++)
+        {
+          Scalar B0, B1, B2, B3, A0;
+
+                    A0 =  blA[k];
+                    B0 =  blB[0*PacketSize];
+                    B1 =  blB[1*PacketSize];
+                    C0 = cj.pmadd(A0, B0, C0);
+          if(nr==4) B2 =  blB[2*PacketSize];
+          if(nr==4) B3 =  blB[3*PacketSize];
+                    C1 = cj.pmadd(A0, B1, C1);
+          if(nr==4) C2 = cj.pmadd(A0, B2, C2);
+          if(nr==4) C3 = cj.pmadd(A0, B3, C3);
+
+          blB += nr*PacketSize;
+        }
+        if(UpLo==LowerTriangular ? i>=j2+nr : i+mr<=j2) {
+          res[(j2+0)*resStride + i] += C0;
+          res[(j2+1)*resStride + i] += C1;
+          if(nr==4) res[(j2+2)*resStride + i] += C2;
+          if(nr==4) res[(j2+3)*resStride + i] += C3;
+        }
+        else
+        {
+                    if(UpLo==LowerTriangular ? i>=j2+0 : i<=j2+0) res[(j2+0)*resStride + i] += C0;
+                    if(UpLo==LowerTriangular ? i>=j2+1 : i<=j2+1) res[(j2+1)*resStride + i] += C1;
+          if(nr==4) if(UpLo==LowerTriangular ? i>=j2+2 : i<=j2+2) res[(j2+2)*resStride + i] += C2;
+          if(nr==4) if(UpLo==LowerTriangular ? i>=j2+3 : i<=j2+3) res[(j2+3)*resStride + i] += C3;
+        }
+      }
+    }
+
+    // process remaining rhs/res columns one at a time
+    // => do the same but with nr==1
+    for(int j2=packet_cols; j2<actual_mc; j2++)
+    {
+      int start_i = UpLo==LowerTriangular ? (j2/mr)*mr : 0;
+      int end_i   = UpLo==LowerTriangular ? actual_mc  : std::min(actual_mc,j2+1);
+      for(int i=start_i; i<std::min(end_i,peeled_mc); i+=mr)
+      {
+        const Scalar* blA = &blockA[i*actual_kc];
+        #ifdef EIGEN_VECTORIZE_SSE
+        _mm_prefetch((const char*)(&blA[0]), _MM_HINT_T0);
+        #endif
+
+        // TODO move the res loads to the stores
+
+        // gets res block as register
+        PacketType C0, C4;
+        C0 = ei_ploadu(&res[(j2+0)*resStride + i]);
+        C4 = ei_ploadu(&res[(j2+0)*resStride + i + PacketSize]);
+
+        const Scalar* blB = &blockB[j2*actual_kc*PacketSize];
+        for(int k=0; k<actual_kc; k++)
+        {
+          PacketType B0, A0, A1;
+
+          A0 = ei_pload(&blA[0*PacketSize]);
+          A1 = ei_pload(&blA[1*PacketSize]);
+          B0 = ei_pload(&blB[0*PacketSize]);
+          C0 = cj.pmadd(A0, B0, C0);
+          C4 = cj.pmadd(A1, B0, C4);
+
+          blB += PacketSize;
+          blA += mr;
+        }
+
+        if(UpLo==LowerTriangular ? i>=j2 : i<=j2)                       ei_pstoreu(&res[(j2+0)*resStride + i], C0);
+        if(UpLo==LowerTriangular ? i+PacketSize>=j2 : i+PacketSize<=j2) ei_pstoreu(&res[(j2+0)*resStride + i + PacketSize], C4);
+      }
+      if(UpLo==LowerTriangular)
+        start_i = j2;
+      for(int i=std::max(start_i,peeled_mc); i<std::min(end_i,actual_mc); i++)
+      {
+        const Scalar* blA = &blockA[i*actual_kc];
+        #ifdef EIGEN_VECTORIZE_SSE
+        _mm_prefetch((const char*)(&blA[0]), _MM_HINT_T0);
+        #endif
+
+        // gets a 1 x 1 res block as registers
+        Scalar C0(0);
+        const Scalar* blB = &blockB[j2*actual_kc*PacketSize];
+        for(int k=0; k<actual_kc; k++)
+          C0 = cj.pmadd(blA[k], blB[k*PacketSize], C0);
+        res[(j2+0)*resStride + i] += C0;
+      }
+    }
+  }
+};
+
+#endif // EIGEN_SELFADJOINT_PRODUCT_H
diff --git a/Eigen/src/Core/util/BlasUtil.h b/Eigen/src/Core/util/BlasUtil.h
index 10cbfb069..c3a7289a8 100644
--- a/Eigen/src/Core/util/BlasUtil.h
+++ b/Eigen/src/Core/util/BlasUtil.h
@@ -140,10 +140,10 @@ struct ei_product_blocking_traits
     HalfRegisterCount = 8,
     #endif
 
-    // register block size along the N direction
+    // register block size along the N direction (must be either 2 or 4)
     nr = HalfRegisterCount/2,
 
-    // register block size along the M direction
+    // register block size along the M direction (this cannot be modified)
     mr = 2 * PacketSize,
 
     // max cache block size along the K direction