GeneralBlockPanelKernel.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) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
00005 //
00006 // This Source Code Form is subject to the terms of the Mozilla
00007 // Public License v. 2.0. If a copy of the MPL was not distributed
00008 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
00009 
00010 #ifndef EIGEN_GENERAL_BLOCK_PANEL_H
00011 #define EIGEN_GENERAL_BLOCK_PANEL_H
00012 
00013 namespace Eigen { 
00014   
00015 namespace internal {
00016 
00017 template<typename _LhsScalar, typename _RhsScalar, bool _ConjLhs=false, bool _ConjRhs=false>
00018 class gebp_traits;
00019 
00020 
00022 inline std::ptrdiff_t manage_caching_sizes_helper(std::ptrdiff_t a, std::ptrdiff_t b)
00023 {
00024   return a<=0 ? b : a;
00025 }
00026 
00028 inline void manage_caching_sizes(Action action, std::ptrdiff_t* l1=0, std::ptrdiff_t* l2=0)
00029 {
00030   static std::ptrdiff_t m_l1CacheSize = 0;
00031   static std::ptrdiff_t m_l2CacheSize = 0;
00032   if(m_l2CacheSize==0)
00033   {
00034     m_l1CacheSize = manage_caching_sizes_helper(queryL1CacheSize(),8 * 1024);
00035     m_l2CacheSize = manage_caching_sizes_helper(queryTopLevelCacheSize(),1*1024*1024);
00036   }
00037   
00038   if(action==SetAction)
00039   {
00040     // set the cpu cache size and cache all block sizes from a global cache size in byte
00041     eigen_internal_assert(l1!=0 && l2!=0);
00042     m_l1CacheSize = *l1;
00043     m_l2CacheSize = *l2;
00044   }
00045   else if(action==GetAction)
00046   {
00047     eigen_internal_assert(l1!=0 && l2!=0);
00048     *l1 = m_l1CacheSize;
00049     *l2 = m_l2CacheSize;
00050   }
00051   else
00052   {
00053     eigen_internal_assert(false);
00054   }
00055 }
00056 
00072 template<typename LhsScalar, typename RhsScalar, int KcFactor>
00073 void computeProductBlockingSizes(std::ptrdiff_t& k, std::ptrdiff_t& m, std::ptrdiff_t& n)
00074 {
00075   EIGEN_UNUSED_VARIABLE(n);
00076   // Explanations:
00077   // Let's recall the product algorithms form kc x nc horizontal panels B' on the rhs and
00078   // mc x kc blocks A' on the lhs. A' has to fit into L2 cache. Moreover, B' is processed
00079   // per kc x nr vertical small panels where nr is the blocking size along the n dimension
00080   // at the register level. For vectorization purpose, these small vertical panels are unpacked,
00081   // e.g., each coefficient is replicated to fit a packet. This small vertical panel has to
00082   // stay in L1 cache.
00083   std::ptrdiff_t l1, l2;
00084 
00085   typedef gebp_traits<LhsScalar,RhsScalar> Traits;
00086   enum {
00087     kdiv = KcFactor * 2 * Traits::nr
00088          * Traits::RhsProgress * sizeof(RhsScalar),
00089     mr = gebp_traits<LhsScalar,RhsScalar>::mr,
00090     mr_mask = (0xffffffff/mr)*mr
00091   };
00092 
00093   manage_caching_sizes(GetAction, &l1, &l2);
00094   k = std::min<std::ptrdiff_t>(k, l1/kdiv);
00095   std::ptrdiff_t _m = k>0 ? l2/(4 * sizeof(LhsScalar) * k) : 0;
00096   if(_m<m) m = _m & mr_mask;
00097 }
00098 
00099 template<typename LhsScalar, typename RhsScalar>
00100 inline void computeProductBlockingSizes(std::ptrdiff_t& k, std::ptrdiff_t& m, std::ptrdiff_t& n)
00101 {
00102   computeProductBlockingSizes<LhsScalar,RhsScalar,1>(k, m, n);
00103 }
00104 
00105 #ifdef EIGEN_HAS_FUSE_CJMADD
00106   #define MADD(CJ,A,B,C,T)  C = CJ.pmadd(A,B,C);
00107 #else
00108 
00109   // FIXME (a bit overkill maybe ?)
00110 
00111   template<typename CJ, typename A, typename B, typename C, typename T> struct gebp_madd_selector {
00112     EIGEN_ALWAYS_INLINE static void run(const CJ& cj, A& a, B& b, C& c, T& /*t*/)
00113     {
00114       c = cj.pmadd(a,b,c);
00115     }
00116   };
00117 
00118   template<typename CJ, typename T> struct gebp_madd_selector<CJ,T,T,T,T> {
00119     EIGEN_ALWAYS_INLINE static void run(const CJ& cj, T& a, T& b, T& c, T& t)
00120     {
00121       t = b; t = cj.pmul(a,t); c = padd(c,t);
00122     }
00123   };
00124 
00125   template<typename CJ, typename A, typename B, typename C, typename T>
00126   EIGEN_STRONG_INLINE void gebp_madd(const CJ& cj, A& a, B& b, C& c, T& t)
00127   {
00128     gebp_madd_selector<CJ,A,B,C,T>::run(cj,a,b,c,t);
00129   }
00130 
00131   #define MADD(CJ,A,B,C,T)  gebp_madd(CJ,A,B,C,T);
00132 //   #define MADD(CJ,A,B,C,T)  T = B; T = CJ.pmul(A,T); C = padd(C,T);
00133 #endif
00134 
00135 /* Vectorization logic
00136  *  real*real: unpack rhs to constant packets, ...
00137  * 
00138  *  cd*cd : unpack rhs to (b_r,b_r), (b_i,b_i), mul to get (a_r b_r,a_i b_r) (a_r b_i,a_i b_i),
00139  *          storing each res packet into two packets (2x2),
00140  *          at the end combine them: swap the second and addsub them 
00141  *  cf*cf : same but with 2x4 blocks
00142  *  cplx*real : unpack rhs to constant packets, ...
00143  *  real*cplx : load lhs as (a0,a0,a1,a1), and mul as usual
00144  */
00145 template<typename _LhsScalar, typename _RhsScalar, bool _ConjLhs, bool _ConjRhs>
00146 class gebp_traits
00147 {
00148 public:
00149   typedef _LhsScalar LhsScalar;
00150   typedef _RhsScalar RhsScalar;
00151   typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar;
00152 
00153   enum {
00154     ConjLhs = _ConjLhs,
00155     ConjRhs = _ConjRhs,
00156     Vectorizable = packet_traits<LhsScalar>::Vectorizable && packet_traits<RhsScalar>::Vectorizable,
00157     LhsPacketSize = Vectorizable ? packet_traits<LhsScalar>::size : 1,
00158     RhsPacketSize = Vectorizable ? packet_traits<RhsScalar>::size : 1,
00159     ResPacketSize = Vectorizable ? packet_traits<ResScalar>::size : 1,
00160     
00161     NumberOfRegisters = EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS,
00162 
00163     // register block size along the N direction (must be either 2 or 4)
00164     nr = NumberOfRegisters/4,
00165 
00166     // register block size along the M direction (currently, this one cannot be modified)
00167     mr = 2 * LhsPacketSize,
00168     
00169     WorkSpaceFactor = nr * RhsPacketSize,
00170 
00171     LhsProgress = LhsPacketSize,
00172     RhsProgress = RhsPacketSize
00173   };
00174 
00175   typedef typename packet_traits<LhsScalar>::type  _LhsPacket;
00176   typedef typename packet_traits<RhsScalar>::type  _RhsPacket;
00177   typedef typename packet_traits<ResScalar>::type  _ResPacket;
00178 
00179   typedef typename conditional<Vectorizable,_LhsPacket,LhsScalar>::type LhsPacket;
00180   typedef typename conditional<Vectorizable,_RhsPacket,RhsScalar>::type RhsPacket;
00181   typedef typename conditional<Vectorizable,_ResPacket,ResScalar>::type ResPacket;
00182 
00183   typedef ResPacket AccPacket;
00184   
00185   EIGEN_STRONG_INLINE void initAcc(AccPacket& p)
00186   {
00187     p = pset1<ResPacket>(ResScalar(0));
00188   }
00189 
00190   EIGEN_STRONG_INLINE void unpackRhs(DenseIndex n, const RhsScalar* rhs, RhsScalar* b)
00191   {
00192     for(DenseIndex k=0; k<n; k++)
00193       pstore1<RhsPacket>(&b[k*RhsPacketSize], rhs[k]);
00194   }
00195 
00196   EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacket& dest) const
00197   {
00198     dest = pload<RhsPacket>(b);
00199   }
00200 
00201   EIGEN_STRONG_INLINE void loadLhs(const LhsScalar* a, LhsPacket& dest) const
00202   {
00203     dest = pload<LhsPacket>(a);
00204   }
00205 
00206   EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, AccPacket& c, AccPacket& tmp) const
00207   {
00208     tmp = b; tmp = pmul(a,tmp); c = padd(c,tmp);
00209   }
00210 
00211   EIGEN_STRONG_INLINE void acc(const AccPacket& c, const ResPacket& alpha, ResPacket& r) const
00212   {
00213     r = pmadd(c,alpha,r);
00214   }
00215 
00216 protected:
00217 //   conj_helper<LhsScalar,RhsScalar,ConjLhs,ConjRhs> cj;
00218 //   conj_helper<LhsPacket,RhsPacket,ConjLhs,ConjRhs> pcj;
00219 };
00220 
00221 template<typename RealScalar, bool _ConjLhs>
00222 class gebp_traits<std::complex<RealScalar>, RealScalar, _ConjLhs, false>
00223 {
00224 public:
00225   typedef std::complex<RealScalar> LhsScalar;
00226   typedef RealScalar RhsScalar;
00227   typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar;
00228 
00229   enum {
00230     ConjLhs = _ConjLhs,
00231     ConjRhs = false,
00232     Vectorizable = packet_traits<LhsScalar>::Vectorizable && packet_traits<RhsScalar>::Vectorizable,
00233     LhsPacketSize = Vectorizable ? packet_traits<LhsScalar>::size : 1,
00234     RhsPacketSize = Vectorizable ? packet_traits<RhsScalar>::size : 1,
00235     ResPacketSize = Vectorizable ? packet_traits<ResScalar>::size : 1,
00236     
00237     NumberOfRegisters = EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS,
00238     nr = NumberOfRegisters/4,
00239     mr = 2 * LhsPacketSize,
00240     WorkSpaceFactor = nr*RhsPacketSize,
00241 
00242     LhsProgress = LhsPacketSize,
00243     RhsProgress = RhsPacketSize
00244   };
00245 
00246   typedef typename packet_traits<LhsScalar>::type  _LhsPacket;
00247   typedef typename packet_traits<RhsScalar>::type  _RhsPacket;
00248   typedef typename packet_traits<ResScalar>::type  _ResPacket;
00249 
00250   typedef typename conditional<Vectorizable,_LhsPacket,LhsScalar>::type LhsPacket;
00251   typedef typename conditional<Vectorizable,_RhsPacket,RhsScalar>::type RhsPacket;
00252   typedef typename conditional<Vectorizable,_ResPacket,ResScalar>::type ResPacket;
00253 
00254   typedef ResPacket AccPacket;
00255 
00256   EIGEN_STRONG_INLINE void initAcc(AccPacket& p)
00257   {
00258     p = pset1<ResPacket>(ResScalar(0));
00259   }
00260 
00261   EIGEN_STRONG_INLINE void unpackRhs(DenseIndex n, const RhsScalar* rhs, RhsScalar* b)
00262   {
00263     for(DenseIndex k=0; k<n; k++)
00264       pstore1<RhsPacket>(&b[k*RhsPacketSize], rhs[k]);
00265   }
00266 
00267   EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacket& dest) const
00268   {
00269     dest = pload<RhsPacket>(b);
00270   }
00271 
00272   EIGEN_STRONG_INLINE void loadLhs(const LhsScalar* a, LhsPacket& dest) const
00273   {
00274     dest = pload<LhsPacket>(a);
00275   }
00276 
00277   EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, AccPacket& c, RhsPacket& tmp) const
00278   {
00279     madd_impl(a, b, c, tmp, typename conditional<Vectorizable,true_type,false_type>::type());
00280   }
00281 
00282   EIGEN_STRONG_INLINE void madd_impl(const LhsPacket& a, const RhsPacket& b, AccPacket& c, RhsPacket& tmp, const true_type&) const
00283   {
00284     tmp = b; tmp = pmul(a.v,tmp); c.v = padd(c.v,tmp);
00285   }
00286 
00287   EIGEN_STRONG_INLINE void madd_impl(const LhsScalar& a, const RhsScalar& b, ResScalar& c, RhsScalar& /*tmp*/, const false_type&) const
00288   {
00289     c += a * b;
00290   }
00291 
00292   EIGEN_STRONG_INLINE void acc(const AccPacket& c, const ResPacket& alpha, ResPacket& r) const
00293   {
00294     r = cj.pmadd(c,alpha,r);
00295   }
00296 
00297 protected:
00298   conj_helper<ResPacket,ResPacket,ConjLhs,false> cj;
00299 };
00300 
00301 template<typename RealScalar, bool _ConjLhs, bool _ConjRhs>
00302 class gebp_traits<std::complex<RealScalar>, std::complex<RealScalar>, _ConjLhs, _ConjRhs >
00303 {
00304 public:
00305   typedef std::complex<RealScalar>  Scalar;
00306   typedef std::complex<RealScalar>  LhsScalar;
00307   typedef std::complex<RealScalar>  RhsScalar;
00308   typedef std::complex<RealScalar>  ResScalar;
00309   
00310   enum {
00311     ConjLhs = _ConjLhs,
00312     ConjRhs = _ConjRhs,
00313     Vectorizable = packet_traits<RealScalar>::Vectorizable
00314                 && packet_traits<Scalar>::Vectorizable,
00315     RealPacketSize  = Vectorizable ? packet_traits<RealScalar>::size : 1,
00316     ResPacketSize   = Vectorizable ? packet_traits<ResScalar>::size : 1,
00317     
00318     nr = 2,
00319     mr = 2 * ResPacketSize,
00320     WorkSpaceFactor = Vectorizable ? 2*nr*RealPacketSize : nr,
00321 
00322     LhsProgress = ResPacketSize,
00323     RhsProgress = Vectorizable ? 2*ResPacketSize : 1
00324   };
00325   
00326   typedef typename packet_traits<RealScalar>::type RealPacket;
00327   typedef typename packet_traits<Scalar>::type     ScalarPacket;
00328   struct DoublePacket
00329   {
00330     RealPacket first;
00331     RealPacket second;
00332   };
00333 
00334   typedef typename conditional<Vectorizable,RealPacket,  Scalar>::type LhsPacket;
00335   typedef typename conditional<Vectorizable,DoublePacket,Scalar>::type RhsPacket;
00336   typedef typename conditional<Vectorizable,ScalarPacket,Scalar>::type ResPacket;
00337   typedef typename conditional<Vectorizable,DoublePacket,Scalar>::type AccPacket;
00338   
00339   EIGEN_STRONG_INLINE void initAcc(Scalar& p) { p = Scalar(0); }
00340 
00341   EIGEN_STRONG_INLINE void initAcc(DoublePacket& p)
00342   {
00343     p.first   = pset1<RealPacket>(RealScalar(0));
00344     p.second  = pset1<RealPacket>(RealScalar(0));
00345   }
00346 
00347   /* Unpack the rhs coeff such that each complex coefficient is spread into
00348    * two packects containing respectively the real and imaginary coefficient
00349    * duplicated as many time as needed: (x+iy) => [x, ..., x] [y, ..., y]
00350    */
00351   EIGEN_STRONG_INLINE void unpackRhs(DenseIndex n, const Scalar* rhs, Scalar* b)
00352   {
00353     for(DenseIndex k=0; k<n; k++)
00354     {
00355       if(Vectorizable)
00356       {
00357         pstore1<RealPacket>((RealScalar*)&b[k*ResPacketSize*2+0],             real(rhs[k]));
00358         pstore1<RealPacket>((RealScalar*)&b[k*ResPacketSize*2+ResPacketSize], imag(rhs[k]));
00359       }
00360       else
00361         b[k] = rhs[k];
00362     }
00363   }
00364 
00365   EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, ResPacket& dest) const { dest = *b; }
00366 
00367   EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, DoublePacket& dest) const
00368   {
00369     dest.first  = pload<RealPacket>((const RealScalar*)b);
00370     dest.second = pload<RealPacket>((const RealScalar*)(b+ResPacketSize));
00371   }
00372 
00373   // nothing special here
00374   EIGEN_STRONG_INLINE void loadLhs(const LhsScalar* a, LhsPacket& dest) const
00375   {
00376     dest = pload<LhsPacket>((const typename unpacket_traits<LhsPacket>::type*)(a));
00377   }
00378 
00379   EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, DoublePacket& c, RhsPacket& /*tmp*/) const
00380   {
00381     c.first   = padd(pmul(a,b.first), c.first);
00382     c.second  = padd(pmul(a,b.second),c.second);
00383   }
00384 
00385   EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, ResPacket& c, RhsPacket& /*tmp*/) const
00386   {
00387     c = cj.pmadd(a,b,c);
00388   }
00389   
00390   EIGEN_STRONG_INLINE void acc(const Scalar& c, const Scalar& alpha, Scalar& r) const { r += alpha * c; }
00391   
00392   EIGEN_STRONG_INLINE void acc(const DoublePacket& c, const ResPacket& alpha, ResPacket& r) const
00393   {
00394     // assemble c
00395     ResPacket tmp;
00396     if((!ConjLhs)&&(!ConjRhs))
00397     {
00398       tmp = pcplxflip(pconj(ResPacket(c.second)));
00399       tmp = padd(ResPacket(c.first),tmp);
00400     }
00401     else if((!ConjLhs)&&(ConjRhs))
00402     {
00403       tmp = pconj(pcplxflip(ResPacket(c.second)));
00404       tmp = padd(ResPacket(c.first),tmp);
00405     }
00406     else if((ConjLhs)&&(!ConjRhs))
00407     {
00408       tmp = pcplxflip(ResPacket(c.second));
00409       tmp = padd(pconj(ResPacket(c.first)),tmp);
00410     }
00411     else if((ConjLhs)&&(ConjRhs))
00412     {
00413       tmp = pcplxflip(ResPacket(c.second));
00414       tmp = psub(pconj(ResPacket(c.first)),tmp);
00415     }
00416     
00417     r = pmadd(tmp,alpha,r);
00418   }
00419 
00420 protected:
00421   conj_helper<LhsScalar,RhsScalar,ConjLhs,ConjRhs> cj;
00422 };
00423 
00424 template<typename RealScalar, bool _ConjRhs>
00425 class gebp_traits<RealScalar, std::complex<RealScalar>, false, _ConjRhs >
00426 {
00427 public:
00428   typedef std::complex<RealScalar>  Scalar;
00429   typedef RealScalar  LhsScalar;
00430   typedef Scalar      RhsScalar;
00431   typedef Scalar      ResScalar;
00432 
00433   enum {
00434     ConjLhs = false,
00435     ConjRhs = _ConjRhs,
00436     Vectorizable = packet_traits<RealScalar>::Vectorizable
00437                 && packet_traits<Scalar>::Vectorizable,
00438     LhsPacketSize = Vectorizable ? packet_traits<LhsScalar>::size : 1,
00439     RhsPacketSize = Vectorizable ? packet_traits<RhsScalar>::size : 1,
00440     ResPacketSize = Vectorizable ? packet_traits<ResScalar>::size : 1,
00441     
00442     NumberOfRegisters = EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS,
00443     nr = 4,
00444     mr = 2*ResPacketSize,
00445     WorkSpaceFactor = nr*RhsPacketSize,
00446 
00447     LhsProgress = ResPacketSize,
00448     RhsProgress = ResPacketSize
00449   };
00450 
00451   typedef typename packet_traits<LhsScalar>::type  _LhsPacket;
00452   typedef typename packet_traits<RhsScalar>::type  _RhsPacket;
00453   typedef typename packet_traits<ResScalar>::type  _ResPacket;
00454 
00455   typedef typename conditional<Vectorizable,_LhsPacket,LhsScalar>::type LhsPacket;
00456   typedef typename conditional<Vectorizable,_RhsPacket,RhsScalar>::type RhsPacket;
00457   typedef typename conditional<Vectorizable,_ResPacket,ResScalar>::type ResPacket;
00458 
00459   typedef ResPacket AccPacket;
00460 
00461   EIGEN_STRONG_INLINE void initAcc(AccPacket& p)
00462   {
00463     p = pset1<ResPacket>(ResScalar(0));
00464   }
00465 
00466   EIGEN_STRONG_INLINE void unpackRhs(DenseIndex n, const RhsScalar* rhs, RhsScalar* b)
00467   {
00468     for(DenseIndex k=0; k<n; k++)
00469       pstore1<RhsPacket>(&b[k*RhsPacketSize], rhs[k]);
00470   }
00471 
00472   EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacket& dest) const
00473   {
00474     dest = pload<RhsPacket>(b);
00475   }
00476 
00477   EIGEN_STRONG_INLINE void loadLhs(const LhsScalar* a, LhsPacket& dest) const
00478   {
00479     dest = ploaddup<LhsPacket>(a);
00480   }
00481 
00482   EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, AccPacket& c, RhsPacket& tmp) const
00483   {
00484     madd_impl(a, b, c, tmp, typename conditional<Vectorizable,true_type,false_type>::type());
00485   }
00486 
00487   EIGEN_STRONG_INLINE void madd_impl(const LhsPacket& a, const RhsPacket& b, AccPacket& c, RhsPacket& tmp, const true_type&) const
00488   {
00489     tmp = b; tmp.v = pmul(a,tmp.v); c = padd(c,tmp);
00490   }
00491 
00492   EIGEN_STRONG_INLINE void madd_impl(const LhsScalar& a, const RhsScalar& b, ResScalar& c, RhsScalar& /*tmp*/, const false_type&) const
00493   {
00494     c += a * b;
00495   }
00496 
00497   EIGEN_STRONG_INLINE void acc(const AccPacket& c, const ResPacket& alpha, ResPacket& r) const
00498   {
00499     r = cj.pmadd(alpha,c,r);
00500   }
00501 
00502 protected:
00503   conj_helper<ResPacket,ResPacket,false,ConjRhs> cj;
00504 };
00505 
00506 /* optimized GEneral packed Block * packed Panel product kernel
00507  *
00508  * Mixing type logic: C += A * B
00509  *  |  A  |  B  | comments
00510  *  |real |cplx | no vectorization yet, would require to pack A with duplication
00511  *  |cplx |real | easy vectorization
00512  */
00513 template<typename LhsScalar, typename RhsScalar, typename Index, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
00514 struct gebp_kernel
00515 {
00516   typedef gebp_traits<LhsScalar,RhsScalar,ConjugateLhs,ConjugateRhs> Traits;
00517   typedef typename Traits::ResScalar ResScalar;
00518   typedef typename Traits::LhsPacket LhsPacket;
00519   typedef typename Traits::RhsPacket RhsPacket;
00520   typedef typename Traits::ResPacket ResPacket;
00521   typedef typename Traits::AccPacket AccPacket;
00522 
00523   enum {
00524     Vectorizable  = Traits::Vectorizable,
00525     LhsProgress   = Traits::LhsProgress,
00526     RhsProgress   = Traits::RhsProgress,
00527     ResPacketSize = Traits::ResPacketSize
00528   };
00529 
00530   EIGEN_DONT_INLINE EIGEN_FLATTEN_ATTRIB
00531   void operator()(ResScalar* res, Index resStride, const LhsScalar* blockA, const RhsScalar* blockB, Index rows, Index depth, Index cols, ResScalar alpha,
00532                   Index strideA=-1, Index strideB=-1, Index offsetA=0, Index offsetB=0, RhsScalar* unpackedB = 0)
00533   {
00534     Traits traits;
00535     
00536     if(strideA==-1) strideA = depth;
00537     if(strideB==-1) strideB = depth;
00538     conj_helper<LhsScalar,RhsScalar,ConjugateLhs,ConjugateRhs> cj;
00539 //     conj_helper<LhsPacket,RhsPacket,ConjugateLhs,ConjugateRhs> pcj;
00540     Index packet_cols = (cols/nr) * nr;
00541     const Index peeled_mc = (rows/mr)*mr;
00542     // FIXME:
00543     const Index peeled_mc2 = peeled_mc + (rows-peeled_mc >= LhsProgress ? LhsProgress : 0);
00544     const Index peeled_kc = (depth/4)*4;
00545 
00546     if(unpackedB==0)
00547       unpackedB = const_cast<RhsScalar*>(blockB - strideB * nr * RhsProgress);
00548 
00549     // loops on each micro vertical panel of rhs (depth x nr)
00550     for(Index j2=0; j2<packet_cols; j2+=nr)
00551     {
00552       traits.unpackRhs(depth*nr,&blockB[j2*strideB+offsetB*nr],unpackedB); 
00553 
00554       // loops on each largest micro horizontal panel of lhs (mr x depth)
00555       // => we select a mr x nr micro block of res which is entirely
00556       //    stored into mr/packet_size x nr registers.
00557       for(Index i=0; i<peeled_mc; i+=mr)
00558       {
00559         const LhsScalar* blA = &blockA[i*strideA+offsetA*mr];
00560         prefetch(&blA[0]);
00561 
00562         // gets res block as register
00563         AccPacket C0, C1, C2, C3, C4, C5, C6, C7;
00564                   traits.initAcc(C0);
00565                   traits.initAcc(C1);
00566         if(nr==4) traits.initAcc(C2);
00567         if(nr==4) traits.initAcc(C3);
00568                   traits.initAcc(C4);
00569                   traits.initAcc(C5);
00570         if(nr==4) traits.initAcc(C6);
00571         if(nr==4) traits.initAcc(C7);
00572 
00573         ResScalar* r0 = &res[(j2+0)*resStride + i];
00574         ResScalar* r1 = r0 + resStride;
00575         ResScalar* r2 = r1 + resStride;
00576         ResScalar* r3 = r2 + resStride;
00577 
00578         prefetch(r0+16);
00579         prefetch(r1+16);
00580         prefetch(r2+16);
00581         prefetch(r3+16);
00582 
00583         // performs "inner" product
00584         // TODO let's check wether the folowing peeled loop could not be
00585         //      optimized via optimal prefetching from one loop to the other
00586         const RhsScalar* blB = unpackedB;
00587         for(Index k=0; k<peeled_kc; k+=4)
00588         {
00589           if(nr==2)
00590           {
00591             LhsPacket A0, A1;
00592             RhsPacket B_0;
00593             RhsPacket T0;
00594             
00595 EIGEN_ASM_COMMENT("mybegin2");
00596             traits.loadLhs(&blA[0*LhsProgress], A0);
00597             traits.loadLhs(&blA[1*LhsProgress], A1);
00598             traits.loadRhs(&blB[0*RhsProgress], B_0);
00599             traits.madd(A0,B_0,C0,T0);
00600             traits.madd(A1,B_0,C4,B_0);
00601             traits.loadRhs(&blB[1*RhsProgress], B_0);
00602             traits.madd(A0,B_0,C1,T0);
00603             traits.madd(A1,B_0,C5,B_0);
00604 
00605             traits.loadLhs(&blA[2*LhsProgress], A0);
00606             traits.loadLhs(&blA[3*LhsProgress], A1);
00607             traits.loadRhs(&blB[2*RhsProgress], B_0);
00608             traits.madd(A0,B_0,C0,T0);
00609             traits.madd(A1,B_0,C4,B_0);
00610             traits.loadRhs(&blB[3*RhsProgress], B_0);
00611             traits.madd(A0,B_0,C1,T0);
00612             traits.madd(A1,B_0,C5,B_0);
00613 
00614             traits.loadLhs(&blA[4*LhsProgress], A0);
00615             traits.loadLhs(&blA[5*LhsProgress], A1);
00616             traits.loadRhs(&blB[4*RhsProgress], B_0);
00617             traits.madd(A0,B_0,C0,T0);
00618             traits.madd(A1,B_0,C4,B_0);
00619             traits.loadRhs(&blB[5*RhsProgress], B_0);
00620             traits.madd(A0,B_0,C1,T0);
00621             traits.madd(A1,B_0,C5,B_0);
00622 
00623             traits.loadLhs(&blA[6*LhsProgress], A0);
00624             traits.loadLhs(&blA[7*LhsProgress], A1);
00625             traits.loadRhs(&blB[6*RhsProgress], B_0);
00626             traits.madd(A0,B_0,C0,T0);
00627             traits.madd(A1,B_0,C4,B_0);
00628             traits.loadRhs(&blB[7*RhsProgress], B_0);
00629             traits.madd(A0,B_0,C1,T0);
00630             traits.madd(A1,B_0,C5,B_0);
00631 EIGEN_ASM_COMMENT("myend");
00632           }
00633           else
00634           {
00635 EIGEN_ASM_COMMENT("mybegin4");
00636             LhsPacket A0, A1;
00637             RhsPacket B_0, B1, B2, B3;
00638             RhsPacket T0;
00639             
00640             traits.loadLhs(&blA[0*LhsProgress], A0);
00641             traits.loadLhs(&blA[1*LhsProgress], A1);
00642             traits.loadRhs(&blB[0*RhsProgress], B_0);
00643             traits.loadRhs(&blB[1*RhsProgress], B1);
00644 
00645             traits.madd(A0,B_0,C0,T0);
00646             traits.loadRhs(&blB[2*RhsProgress], B2);
00647             traits.madd(A1,B_0,C4,B_0);
00648             traits.loadRhs(&blB[3*RhsProgress], B3);
00649             traits.loadRhs(&blB[4*RhsProgress], B_0);
00650             traits.madd(A0,B1,C1,T0);
00651             traits.madd(A1,B1,C5,B1);
00652             traits.loadRhs(&blB[5*RhsProgress], B1);
00653             traits.madd(A0,B2,C2,T0);
00654             traits.madd(A1,B2,C6,B2);
00655             traits.loadRhs(&blB[6*RhsProgress], B2);
00656             traits.madd(A0,B3,C3,T0);
00657             traits.loadLhs(&blA[2*LhsProgress], A0);
00658             traits.madd(A1,B3,C7,B3);
00659             traits.loadLhs(&blA[3*LhsProgress], A1);
00660             traits.loadRhs(&blB[7*RhsProgress], B3);
00661             traits.madd(A0,B_0,C0,T0);
00662             traits.madd(A1,B_0,C4,B_0);
00663             traits.loadRhs(&blB[8*RhsProgress], B_0);
00664             traits.madd(A0,B1,C1,T0);
00665             traits.madd(A1,B1,C5,B1);
00666             traits.loadRhs(&blB[9*RhsProgress], B1);
00667             traits.madd(A0,B2,C2,T0);
00668             traits.madd(A1,B2,C6,B2);
00669             traits.loadRhs(&blB[10*RhsProgress], B2);
00670             traits.madd(A0,B3,C3,T0);
00671             traits.loadLhs(&blA[4*LhsProgress], A0);
00672             traits.madd(A1,B3,C7,B3);
00673             traits.loadLhs(&blA[5*LhsProgress], A1);
00674             traits.loadRhs(&blB[11*RhsProgress], B3);
00675 
00676             traits.madd(A0,B_0,C0,T0);
00677             traits.madd(A1,B_0,C4,B_0);
00678             traits.loadRhs(&blB[12*RhsProgress], B_0);
00679             traits.madd(A0,B1,C1,T0);
00680             traits.madd(A1,B1,C5,B1);
00681             traits.loadRhs(&blB[13*RhsProgress], B1);
00682             traits.madd(A0,B2,C2,T0);
00683             traits.madd(A1,B2,C6,B2);
00684             traits.loadRhs(&blB[14*RhsProgress], B2);
00685             traits.madd(A0,B3,C3,T0);
00686             traits.loadLhs(&blA[6*LhsProgress], A0);
00687             traits.madd(A1,B3,C7,B3);
00688             traits.loadLhs(&blA[7*LhsProgress], A1);
00689             traits.loadRhs(&blB[15*RhsProgress], B3);
00690             traits.madd(A0,B_0,C0,T0);
00691             traits.madd(A1,B_0,C4,B_0);
00692             traits.madd(A0,B1,C1,T0);
00693             traits.madd(A1,B1,C5,B1);
00694             traits.madd(A0,B2,C2,T0);
00695             traits.madd(A1,B2,C6,B2);
00696             traits.madd(A0,B3,C3,T0);
00697             traits.madd(A1,B3,C7,B3);
00698           }
00699 
00700           blB += 4*nr*RhsProgress;
00701           blA += 4*mr;
00702         }
00703         // process remaining peeled loop
00704         for(Index k=peeled_kc; k<depth; k++)
00705         {
00706           if(nr==2)
00707           {
00708             LhsPacket A0, A1;
00709             RhsPacket B_0;
00710             RhsPacket T0;
00711 
00712             traits.loadLhs(&blA[0*LhsProgress], A0);
00713             traits.loadLhs(&blA[1*LhsProgress], A1);
00714             traits.loadRhs(&blB[0*RhsProgress], B_0);
00715             traits.madd(A0,B_0,C0,T0);
00716             traits.madd(A1,B_0,C4,B_0);
00717             traits.loadRhs(&blB[1*RhsProgress], B_0);
00718             traits.madd(A0,B_0,C1,T0);
00719             traits.madd(A1,B_0,C5,B_0);
00720           }
00721           else
00722           {
00723             LhsPacket A0, A1;
00724             RhsPacket B_0, B1, B2, B3;
00725             RhsPacket T0;
00726 
00727             traits.loadLhs(&blA[0*LhsProgress], A0);
00728             traits.loadLhs(&blA[1*LhsProgress], A1);
00729             traits.loadRhs(&blB[0*RhsProgress], B_0);
00730             traits.loadRhs(&blB[1*RhsProgress], B1);
00731 
00732             traits.madd(A0,B_0,C0,T0);
00733             traits.loadRhs(&blB[2*RhsProgress], B2);
00734             traits.madd(A1,B_0,C4,B_0);
00735             traits.loadRhs(&blB[3*RhsProgress], B3);
00736             traits.madd(A0,B1,C1,T0);
00737             traits.madd(A1,B1,C5,B1);
00738             traits.madd(A0,B2,C2,T0);
00739             traits.madd(A1,B2,C6,B2);
00740             traits.madd(A0,B3,C3,T0);
00741             traits.madd(A1,B3,C7,B3);
00742           }
00743 
00744           blB += nr*RhsProgress;
00745           blA += mr;
00746         }
00747 
00748         if(nr==4)
00749         {
00750           ResPacket R0, R1, R2, R3, R4, R5, R6;
00751           ResPacket alphav = pset1<ResPacket>(alpha);
00752 
00753           R0 = ploadu<ResPacket>(r0);
00754           R1 = ploadu<ResPacket>(r1);
00755           R2 = ploadu<ResPacket>(r2);
00756           R3 = ploadu<ResPacket>(r3);
00757           R4 = ploadu<ResPacket>(r0 + ResPacketSize);
00758           R5 = ploadu<ResPacket>(r1 + ResPacketSize);
00759           R6 = ploadu<ResPacket>(r2 + ResPacketSize);
00760           traits.acc(C0, alphav, R0);
00761           pstoreu(r0, R0);
00762           R0 = ploadu<ResPacket>(r3 + ResPacketSize);
00763 
00764           traits.acc(C1, alphav, R1);
00765           traits.acc(C2, alphav, R2);
00766           traits.acc(C3, alphav, R3);
00767           traits.acc(C4, alphav, R4);
00768           traits.acc(C5, alphav, R5);
00769           traits.acc(C6, alphav, R6);
00770           traits.acc(C7, alphav, R0);
00771           
00772           pstoreu(r1, R1);
00773           pstoreu(r2, R2);
00774           pstoreu(r3, R3);
00775           pstoreu(r0 + ResPacketSize, R4);
00776           pstoreu(r1 + ResPacketSize, R5);
00777           pstoreu(r2 + ResPacketSize, R6);
00778           pstoreu(r3 + ResPacketSize, R0);
00779         }
00780         else
00781         {
00782           ResPacket R0, R1, R4;
00783           ResPacket alphav = pset1<ResPacket>(alpha);
00784 
00785           R0 = ploadu<ResPacket>(r0);
00786           R1 = ploadu<ResPacket>(r1);
00787           R4 = ploadu<ResPacket>(r0 + ResPacketSize);
00788           traits.acc(C0, alphav, R0);
00789           pstoreu(r0, R0);
00790           R0 = ploadu<ResPacket>(r1 + ResPacketSize);
00791           traits.acc(C1, alphav, R1);
00792           traits.acc(C4, alphav, R4);
00793           traits.acc(C5, alphav, R0);
00794           pstoreu(r1, R1);
00795           pstoreu(r0 + ResPacketSize, R4);
00796           pstoreu(r1 + ResPacketSize, R0);
00797         }
00798         
00799       }
00800       
00801       if(rows-peeled_mc>=LhsProgress)
00802       {
00803         Index i = peeled_mc;
00804         const LhsScalar* blA = &blockA[i*strideA+offsetA*LhsProgress];
00805         prefetch(&blA[0]);
00806 
00807         // gets res block as register
00808         AccPacket C0, C1, C2, C3;
00809                   traits.initAcc(C0);
00810                   traits.initAcc(C1);
00811         if(nr==4) traits.initAcc(C2);
00812         if(nr==4) traits.initAcc(C3);
00813 
00814         // performs "inner" product
00815         const RhsScalar* blB = unpackedB;
00816         for(Index k=0; k<peeled_kc; k+=4)
00817         {
00818           if(nr==2)
00819           {
00820             LhsPacket A0;
00821             RhsPacket B_0, B1;
00822 
00823             traits.loadLhs(&blA[0*LhsProgress], A0);
00824             traits.loadRhs(&blB[0*RhsProgress], B_0);
00825             traits.loadRhs(&blB[1*RhsProgress], B1);
00826             traits.madd(A0,B_0,C0,B_0);
00827             traits.loadRhs(&blB[2*RhsProgress], B_0);
00828             traits.madd(A0,B1,C1,B1);
00829             traits.loadLhs(&blA[1*LhsProgress], A0);
00830             traits.loadRhs(&blB[3*RhsProgress], B1);
00831             traits.madd(A0,B_0,C0,B_0);
00832             traits.loadRhs(&blB[4*RhsProgress], B_0);
00833             traits.madd(A0,B1,C1,B1);
00834             traits.loadLhs(&blA[2*LhsProgress], A0);
00835             traits.loadRhs(&blB[5*RhsProgress], B1);
00836             traits.madd(A0,B_0,C0,B_0);
00837             traits.loadRhs(&blB[6*RhsProgress], B_0);
00838             traits.madd(A0,B1,C1,B1);
00839             traits.loadLhs(&blA[3*LhsProgress], A0);
00840             traits.loadRhs(&blB[7*RhsProgress], B1);
00841             traits.madd(A0,B_0,C0,B_0);
00842             traits.madd(A0,B1,C1,B1);
00843           }
00844           else
00845           {
00846             LhsPacket A0;
00847             RhsPacket B_0, B1, B2, B3;
00848 
00849             traits.loadLhs(&blA[0*LhsProgress], A0);
00850             traits.loadRhs(&blB[0*RhsProgress], B_0);
00851             traits.loadRhs(&blB[1*RhsProgress], B1);
00852 
00853             traits.madd(A0,B_0,C0,B_0);
00854             traits.loadRhs(&blB[2*RhsProgress], B2);
00855             traits.loadRhs(&blB[3*RhsProgress], B3);
00856             traits.loadRhs(&blB[4*RhsProgress], B_0);
00857             traits.madd(A0,B1,C1,B1);
00858             traits.loadRhs(&blB[5*RhsProgress], B1);
00859             traits.madd(A0,B2,C2,B2);
00860             traits.loadRhs(&blB[6*RhsProgress], B2);
00861             traits.madd(A0,B3,C3,B3);
00862             traits.loadLhs(&blA[1*LhsProgress], A0);
00863             traits.loadRhs(&blB[7*RhsProgress], B3);
00864             traits.madd(A0,B_0,C0,B_0);
00865             traits.loadRhs(&blB[8*RhsProgress], B_0);
00866             traits.madd(A0,B1,C1,B1);
00867             traits.loadRhs(&blB[9*RhsProgress], B1);
00868             traits.madd(A0,B2,C2,B2);
00869             traits.loadRhs(&blB[10*RhsProgress], B2);
00870             traits.madd(A0,B3,C3,B3);
00871             traits.loadLhs(&blA[2*LhsProgress], A0);
00872             traits.loadRhs(&blB[11*RhsProgress], B3);
00873 
00874             traits.madd(A0,B_0,C0,B_0);
00875             traits.loadRhs(&blB[12*RhsProgress], B_0);
00876             traits.madd(A0,B1,C1,B1);
00877             traits.loadRhs(&blB[13*RhsProgress], B1);
00878             traits.madd(A0,B2,C2,B2);
00879             traits.loadRhs(&blB[14*RhsProgress], B2);
00880             traits.madd(A0,B3,C3,B3);
00881 
00882             traits.loadLhs(&blA[3*LhsProgress], A0);
00883             traits.loadRhs(&blB[15*RhsProgress], B3);
00884             traits.madd(A0,B_0,C0,B_0);
00885             traits.madd(A0,B1,C1,B1);
00886             traits.madd(A0,B2,C2,B2);
00887             traits.madd(A0,B3,C3,B3);
00888           }
00889 
00890           blB += nr*4*RhsProgress;
00891           blA += 4*LhsProgress;
00892         }
00893         // process remaining peeled loop
00894         for(Index k=peeled_kc; k<depth; k++)
00895         {
00896           if(nr==2)
00897           {
00898             LhsPacket A0;
00899             RhsPacket B_0, B1;
00900 
00901             traits.loadLhs(&blA[0*LhsProgress], A0);
00902             traits.loadRhs(&blB[0*RhsProgress], B_0);
00903             traits.loadRhs(&blB[1*RhsProgress], B1);
00904             traits.madd(A0,B_0,C0,B_0);
00905             traits.madd(A0,B1,C1,B1);
00906           }
00907           else
00908           {
00909             LhsPacket A0;
00910             RhsPacket B_0, B1, B2, B3;
00911 
00912             traits.loadLhs(&blA[0*LhsProgress], A0);
00913             traits.loadRhs(&blB[0*RhsProgress], B_0);
00914             traits.loadRhs(&blB[1*RhsProgress], B1);
00915             traits.loadRhs(&blB[2*RhsProgress], B2);
00916             traits.loadRhs(&blB[3*RhsProgress], B3);
00917 
00918             traits.madd(A0,B_0,C0,B_0);
00919             traits.madd(A0,B1,C1,B1);
00920             traits.madd(A0,B2,C2,B2);
00921             traits.madd(A0,B3,C3,B3);
00922           }
00923 
00924           blB += nr*RhsProgress;
00925           blA += LhsProgress;
00926         }
00927 
00928         ResPacket R0, R1, R2, R3;
00929         ResPacket alphav = pset1<ResPacket>(alpha);
00930 
00931         ResScalar* r0 = &res[(j2+0)*resStride + i];
00932         ResScalar* r1 = r0 + resStride;
00933         ResScalar* r2 = r1 + resStride;
00934         ResScalar* r3 = r2 + resStride;
00935 
00936                   R0 = ploadu<ResPacket>(r0);
00937                   R1 = ploadu<ResPacket>(r1);
00938         if(nr==4) R2 = ploadu<ResPacket>(r2);
00939         if(nr==4) R3 = ploadu<ResPacket>(r3);
00940 
00941                   traits.acc(C0, alphav, R0);
00942                   traits.acc(C1, alphav, R1);
00943         if(nr==4) traits.acc(C2, alphav, R2);
00944         if(nr==4) traits.acc(C3, alphav, R3);
00945 
00946                   pstoreu(r0, R0);
00947                   pstoreu(r1, R1);
00948         if(nr==4) pstoreu(r2, R2);
00949         if(nr==4) pstoreu(r3, R3);
00950       }
00951       for(Index i=peeled_mc2; i<rows; i++)
00952       {
00953         const LhsScalar* blA = &blockA[i*strideA+offsetA];
00954         prefetch(&blA[0]);
00955 
00956         // gets a 1 x nr res block as registers
00957         ResScalar C0(0), C1(0), C2(0), C3(0);
00958         // TODO directly use blockB ???
00959         const RhsScalar* blB = &blockB[j2*strideB+offsetB*nr];
00960         for(Index k=0; k<depth; k++)
00961         {
00962           if(nr==2)
00963           {
00964             LhsScalar A0;
00965             RhsScalar B_0, B1;
00966 
00967             A0 = blA[k];
00968             B_0 = blB[0];
00969             B1 = blB[1];
00970             MADD(cj,A0,B_0,C0,B_0);
00971             MADD(cj,A0,B1,C1,B1);
00972           }
00973           else
00974           {
00975             LhsScalar A0;
00976             RhsScalar B_0, B1, B2, B3;
00977 
00978             A0 = blA[k];
00979             B_0 = blB[0];
00980             B1 = blB[1];
00981             B2 = blB[2];
00982             B3 = blB[3];
00983 
00984             MADD(cj,A0,B_0,C0,B_0);
00985             MADD(cj,A0,B1,C1,B1);
00986             MADD(cj,A0,B2,C2,B2);
00987             MADD(cj,A0,B3,C3,B3);
00988           }
00989 
00990           blB += nr;
00991         }
00992                   res[(j2+0)*resStride + i] += alpha*C0;
00993                   res[(j2+1)*resStride + i] += alpha*C1;
00994         if(nr==4) res[(j2+2)*resStride + i] += alpha*C2;
00995         if(nr==4) res[(j2+3)*resStride + i] += alpha*C3;
00996       }
00997     }
00998     // process remaining rhs/res columns one at a time
00999     // => do the same but with nr==1
01000     for(Index j2=packet_cols; j2<cols; j2++)
01001     {
01002       // unpack B
01003       traits.unpackRhs(depth, &blockB[j2*strideB+offsetB], unpackedB);
01004 
01005       for(Index i=0; i<peeled_mc; i+=mr)
01006       {
01007         const LhsScalar* blA = &blockA[i*strideA+offsetA*mr];
01008         prefetch(&blA[0]);
01009 
01010         // TODO move the res loads to the stores
01011 
01012         // get res block as registers
01013         AccPacket C0, C4;
01014         traits.initAcc(C0);
01015         traits.initAcc(C4);
01016 
01017         const RhsScalar* blB = unpackedB;
01018         for(Index k=0; k<depth; k++)
01019         {
01020           LhsPacket A0, A1;
01021           RhsPacket B_0;
01022           RhsPacket T0;
01023 
01024           traits.loadLhs(&blA[0*LhsProgress], A0);
01025           traits.loadLhs(&blA[1*LhsProgress], A1);
01026           traits.loadRhs(&blB[0*RhsProgress], B_0);
01027           traits.madd(A0,B_0,C0,T0);
01028           traits.madd(A1,B_0,C4,B_0);
01029 
01030           blB += RhsProgress;
01031           blA += 2*LhsProgress;
01032         }
01033         ResPacket R0, R4;
01034         ResPacket alphav = pset1<ResPacket>(alpha);
01035 
01036         ResScalar* r0 = &res[(j2+0)*resStride + i];
01037 
01038         R0 = ploadu<ResPacket>(r0);
01039         R4 = ploadu<ResPacket>(r0+ResPacketSize);
01040 
01041         traits.acc(C0, alphav, R0);
01042         traits.acc(C4, alphav, R4);
01043 
01044         pstoreu(r0,               R0);
01045         pstoreu(r0+ResPacketSize, R4);
01046       }
01047       if(rows-peeled_mc>=LhsProgress)
01048       {
01049         Index i = peeled_mc;
01050         const LhsScalar* blA = &blockA[i*strideA+offsetA*LhsProgress];
01051         prefetch(&blA[0]);
01052 
01053         AccPacket C0;
01054         traits.initAcc(C0);
01055 
01056         const RhsScalar* blB = unpackedB;
01057         for(Index k=0; k<depth; k++)
01058         {
01059           LhsPacket A0;
01060           RhsPacket B_0;
01061           traits.loadLhs(blA, A0);
01062           traits.loadRhs(blB, B_0);
01063           traits.madd(A0, B_0, C0, B_0);
01064           blB += RhsProgress;
01065           blA += LhsProgress;
01066         }
01067 
01068         ResPacket alphav = pset1<ResPacket>(alpha);
01069         ResPacket R0 = ploadu<ResPacket>(&res[(j2+0)*resStride + i]);
01070         traits.acc(C0, alphav, R0);
01071         pstoreu(&res[(j2+0)*resStride + i], R0);
01072       }
01073       for(Index i=peeled_mc2; i<rows; i++)
01074       {
01075         const LhsScalar* blA = &blockA[i*strideA+offsetA];
01076         prefetch(&blA[0]);
01077 
01078         // gets a 1 x 1 res block as registers
01079         ResScalar C0(0);
01080         // FIXME directly use blockB ??
01081         const RhsScalar* blB = &blockB[j2*strideB+offsetB];
01082         for(Index k=0; k<depth; k++)
01083         {
01084           LhsScalar A0 = blA[k];
01085           RhsScalar B_0 = blB[k];
01086           MADD(cj, A0, B_0, C0, B_0);
01087         }
01088         res[(j2+0)*resStride + i] += alpha*C0;
01089       }
01090     }
01091   }
01092 };
01093 
01094 #undef CJMADD
01095 
01096 // pack a block of the lhs
01097 // The traversal is as follow (mr==4):
01098 //   0  4  8 12 ...
01099 //   1  5  9 13 ...
01100 //   2  6 10 14 ...
01101 //   3  7 11 15 ...
01102 //
01103 //  16 20 24 28 ...
01104 //  17 21 25 29 ...
01105 //  18 22 26 30 ...
01106 //  19 23 27 31 ...
01107 //
01108 //  32 33 34 35 ...
01109 //  36 36 38 39 ...
01110 template<typename Scalar, typename Index, int Pack1, int Pack2, int StorageOrder, bool Conjugate, bool PanelMode>
01111 struct gemm_pack_lhs
01112 {
01113   EIGEN_DONT_INLINE void operator()(Scalar* blockA, const Scalar* EIGEN_RESTRICT _lhs, Index lhsStride, Index depth, Index rows,
01114                   Index stride=0, Index offset=0)
01115   {
01116     typedef typename packet_traits<Scalar>::type Packet;
01117     enum { PacketSize = packet_traits<Scalar>::size };
01118 
01119     EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK LHS");
01120     eigen_assert(((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride));
01121     eigen_assert( (StorageOrder==RowMajor) || ((Pack1%PacketSize)==0 && Pack1<=4*PacketSize) );
01122     conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj;
01123     const_blas_data_mapper<Scalar, Index, StorageOrder> lhs(_lhs,lhsStride);
01124     Index count = 0;
01125     Index peeled_mc = (rows/Pack1)*Pack1;
01126     for(Index i=0; i<peeled_mc; i+=Pack1)
01127     {
01128       if(PanelMode) count += Pack1 * offset;
01129 
01130       if(StorageOrder==ColMajor)
01131       {
01132         for(Index k=0; k<depth; k++)
01133         {
01134           Packet A, B, C, D;
01135           if(Pack1>=1*PacketSize) A = ploadu<Packet>(&lhs(i+0*PacketSize, k));
01136           if(Pack1>=2*PacketSize) B = ploadu<Packet>(&lhs(i+1*PacketSize, k));
01137           if(Pack1>=3*PacketSize) C = ploadu<Packet>(&lhs(i+2*PacketSize, k));
01138           if(Pack1>=4*PacketSize) D = ploadu<Packet>(&lhs(i+3*PacketSize, k));
01139           if(Pack1>=1*PacketSize) { pstore(blockA+count, cj.pconj(A)); count+=PacketSize; }
01140           if(Pack1>=2*PacketSize) { pstore(blockA+count, cj.pconj(B)); count+=PacketSize; }
01141           if(Pack1>=3*PacketSize) { pstore(blockA+count, cj.pconj(C)); count+=PacketSize; }
01142           if(Pack1>=4*PacketSize) { pstore(blockA+count, cj.pconj(D)); count+=PacketSize; }
01143         }
01144       }
01145       else
01146       {
01147         for(Index k=0; k<depth; k++)
01148         {
01149           // TODO add a vectorized transpose here
01150           Index w=0;
01151           for(; w<Pack1-3; w+=4)
01152           {
01153             Scalar a(cj(lhs(i+w+0, k))),
01154                    b(cj(lhs(i+w+1, k))),
01155                    c(cj(lhs(i+w+2, k))),
01156                    d(cj(lhs(i+w+3, k)));
01157             blockA[count++] = a;
01158             blockA[count++] = b;
01159             blockA[count++] = c;
01160             blockA[count++] = d;
01161           }
01162           if(Pack1%4)
01163             for(;w<Pack1;++w)
01164               blockA[count++] = cj(lhs(i+w, k));
01165         }
01166       }
01167       if(PanelMode) count += Pack1 * (stride-offset-depth);
01168     }
01169     if(rows-peeled_mc>=Pack2)
01170     {
01171       if(PanelMode) count += Pack2*offset;
01172       for(Index k=0; k<depth; k++)
01173         for(Index w=0; w<Pack2; w++)
01174           blockA[count++] = cj(lhs(peeled_mc+w, k));
01175       if(PanelMode) count += Pack2 * (stride-offset-depth);
01176       peeled_mc += Pack2;
01177     }
01178     for(Index i=peeled_mc; i<rows; i++)
01179     {
01180       if(PanelMode) count += offset;
01181       for(Index k=0; k<depth; k++)
01182         blockA[count++] = cj(lhs(i, k));
01183       if(PanelMode) count += (stride-offset-depth);
01184     }
01185   }
01186 };
01187 
01188 // copy a complete panel of the rhs
01189 // this version is optimized for column major matrices
01190 // The traversal order is as follow: (nr==4):
01191 //  0  1  2  3   12 13 14 15   24 27
01192 //  4  5  6  7   16 17 18 19   25 28
01193 //  8  9 10 11   20 21 22 23   26 29
01194 //  .  .  .  .    .  .  .  .    .  .
01195 template<typename Scalar, typename Index, int nr, bool Conjugate, bool PanelMode>
01196 struct gemm_pack_rhs<Scalar, Index, nr, ColMajor, Conjugate, PanelMode>
01197 {
01198   typedef typename packet_traits<Scalar>::type Packet;
01199   enum { PacketSize = packet_traits<Scalar>::size };
01200   EIGEN_DONT_INLINE void operator()(Scalar* blockB, const Scalar* rhs, Index rhsStride, Index depth, Index cols,
01201                   Index stride=0, Index offset=0)
01202   {
01203     EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK RHS COLMAJOR");
01204     eigen_assert(((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride));
01205     conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj;
01206     Index packet_cols = (cols/nr) * nr;
01207     Index count = 0;
01208     for(Index j2=0; j2<packet_cols; j2+=nr)
01209     {
01210       // skip what we have before
01211       if(PanelMode) count += nr * offset;
01212       const Scalar* b0 = &rhs[(j2+0)*rhsStride];
01213       const Scalar* b1 = &rhs[(j2+1)*rhsStride];
01214       const Scalar* b2 = &rhs[(j2+2)*rhsStride];
01215       const Scalar* b3 = &rhs[(j2+3)*rhsStride];
01216       for(Index k=0; k<depth; k++)
01217       {
01218                   blockB[count+0] = cj(b0[k]);
01219                   blockB[count+1] = cj(b1[k]);
01220         if(nr==4) blockB[count+2] = cj(b2[k]);
01221         if(nr==4) blockB[count+3] = cj(b3[k]);
01222         count += nr;
01223       }
01224       // skip what we have after
01225       if(PanelMode) count += nr * (stride-offset-depth);
01226     }
01227 
01228     // copy the remaining columns one at a time (nr==1)
01229     for(Index j2=packet_cols; j2<cols; ++j2)
01230     {
01231       if(PanelMode) count += offset;
01232       const Scalar* b0 = &rhs[(j2+0)*rhsStride];
01233       for(Index k=0; k<depth; k++)
01234       {
01235         blockB[count] = cj(b0[k]);
01236         count += 1;
01237       }
01238       if(PanelMode) count += (stride-offset-depth);
01239     }
01240   }
01241 };
01242 
01243 // this version is optimized for row major matrices
01244 template<typename Scalar, typename Index, int nr, bool Conjugate, bool PanelMode>
01245 struct gemm_pack_rhs<Scalar, Index, nr, RowMajor, Conjugate, PanelMode>
01246 {
01247   enum { PacketSize = packet_traits<Scalar>::size };
01248   EIGEN_DONT_INLINE void operator()(Scalar* blockB, const Scalar* rhs, Index rhsStride, Index depth, Index cols,
01249                   Index stride=0, Index offset=0)
01250   {
01251     EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK RHS ROWMAJOR");
01252     eigen_assert(((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride));
01253     conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj;
01254     Index packet_cols = (cols/nr) * nr;
01255     Index count = 0;
01256     for(Index j2=0; j2<packet_cols; j2+=nr)
01257     {
01258       // skip what we have before
01259       if(PanelMode) count += nr * offset;
01260       for(Index k=0; k<depth; k++)
01261       {
01262         const Scalar* b0 = &rhs[k*rhsStride + j2];
01263                   blockB[count+0] = cj(b0[0]);
01264                   blockB[count+1] = cj(b0[1]);
01265         if(nr==4) blockB[count+2] = cj(b0[2]);
01266         if(nr==4) blockB[count+3] = cj(b0[3]);
01267         count += nr;
01268       }
01269       // skip what we have after
01270       if(PanelMode) count += nr * (stride-offset-depth);
01271     }
01272     // copy the remaining columns one at a time (nr==1)
01273     for(Index j2=packet_cols; j2<cols; ++j2)
01274     {
01275       if(PanelMode) count += offset;
01276       const Scalar* b0 = &rhs[j2];
01277       for(Index k=0; k<depth; k++)
01278       {
01279         blockB[count] = cj(b0[k*rhsStride]);
01280         count += 1;
01281       }
01282       if(PanelMode) count += stride-offset-depth;
01283     }
01284   }
01285 };
01286 
01287 } // end namespace internal
01288 
01291 inline std::ptrdiff_t l1CacheSize()
01292 {
01293   std::ptrdiff_t l1, l2;
01294   internal::manage_caching_sizes(GetAction, &l1, &l2);
01295   return l1;
01296 }
01297 
01300 inline std::ptrdiff_t l2CacheSize()
01301 {
01302   std::ptrdiff_t l1, l2;
01303   internal::manage_caching_sizes(GetAction, &l1, &l2);
01304   return l2;
01305 }
01306 
01312 inline void setCpuCacheSizes(std::ptrdiff_t l1, std::ptrdiff_t l2)
01313 {
01314   internal::manage_caching_sizes(SetAction, &l1, &l2);
01315 }
01316 
01317 } // end namespace Eigen
01318 
01319 #endif // EIGEN_GENERAL_BLOCK_PANEL_H


win_eigen
Author(s): Daniel Stonier
autogenerated on Mon Oct 6 2014 12:24:32