GeneralBlockPanelKernel.h
Go to the documentation of this file.
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, typename SizeType>
00073 void computeProductBlockingSizes(SizeType& k, SizeType& m, SizeType& 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<SizeType>(k, l1/kdiv);
00095   SizeType _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, typename SizeType>
00100 inline void computeProductBlockingSizes(SizeType& k, SizeType& m, SizeType& 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
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 
00535 template<typename LhsScalar, typename RhsScalar, typename Index, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
00536 EIGEN_DONT_INLINE
00537 void gebp_kernel<LhsScalar,RhsScalar,Index,mr,nr,ConjugateLhs,ConjugateRhs>
00538   ::operator()(ResScalar* res, Index resStride, const LhsScalar* blockA, const RhsScalar* blockB, Index rows, Index depth, Index cols, ResScalar alpha,
00539                Index strideA, Index strideB, Index offsetA, Index offsetB, RhsScalar* unpackedB)
00540   {
00541     Traits traits;
00542     
00543     if(strideA==-1) strideA = depth;
00544     if(strideB==-1) strideB = depth;
00545     conj_helper<LhsScalar,RhsScalar,ConjugateLhs,ConjugateRhs> cj;
00546 //     conj_helper<LhsPacket,RhsPacket,ConjugateLhs,ConjugateRhs> pcj;
00547     Index packet_cols = (cols/nr) * nr;
00548     const Index peeled_mc = (rows/mr)*mr;
00549     // FIXME:
00550     const Index peeled_mc2 = peeled_mc + (rows-peeled_mc >= LhsProgress ? LhsProgress : 0);
00551     const Index peeled_kc = (depth/4)*4;
00552 
00553     if(unpackedB==0)
00554       unpackedB = const_cast<RhsScalar*>(blockB - strideB * nr * RhsProgress);
00555 
00556     // loops on each micro vertical panel of rhs (depth x nr)
00557     for(Index j2=0; j2<packet_cols; j2+=nr)
00558     {
00559       traits.unpackRhs(depth*nr,&blockB[j2*strideB+offsetB*nr],unpackedB); 
00560 
00561       // loops on each largest micro horizontal panel of lhs (mr x depth)
00562       // => we select a mr x nr micro block of res which is entirely
00563       //    stored into mr/packet_size x nr registers.
00564       for(Index i=0; i<peeled_mc; i+=mr)
00565       {
00566         const LhsScalar* blA = &blockA[i*strideA+offsetA*mr];
00567         prefetch(&blA[0]);
00568 
00569         // gets res block as register
00570         AccPacket C0, C1, C2, C3, C4, C5, C6, C7;
00571                   traits.initAcc(C0);
00572                   traits.initAcc(C1);
00573         if(nr==4) traits.initAcc(C2);
00574         if(nr==4) traits.initAcc(C3);
00575                   traits.initAcc(C4);
00576                   traits.initAcc(C5);
00577         if(nr==4) traits.initAcc(C6);
00578         if(nr==4) traits.initAcc(C7);
00579 
00580         ResScalar* r0 = &res[(j2+0)*resStride + i];
00581         ResScalar* r1 = r0 + resStride;
00582         ResScalar* r2 = r1 + resStride;
00583         ResScalar* r3 = r2 + resStride;
00584 
00585         prefetch(r0+16);
00586         prefetch(r1+16);
00587         prefetch(r2+16);
00588         prefetch(r3+16);
00589 
00590         // performs "inner" product
00591         // TODO let's check wether the folowing peeled loop could not be
00592         //      optimized via optimal prefetching from one loop to the other
00593         const RhsScalar* blB = unpackedB;
00594         for(Index k=0; k<peeled_kc; k+=4)
00595         {
00596           if(nr==2)
00597           {
00598             LhsPacket A0, A1;
00599             RhsPacket B_0;
00600             RhsPacket T0;
00601             
00602 EIGEN_ASM_COMMENT("mybegin2");
00603             traits.loadLhs(&blA[0*LhsProgress], A0);
00604             traits.loadLhs(&blA[1*LhsProgress], A1);
00605             traits.loadRhs(&blB[0*RhsProgress], B_0);
00606             traits.madd(A0,B_0,C0,T0);
00607             traits.madd(A1,B_0,C4,B_0);
00608             traits.loadRhs(&blB[1*RhsProgress], B_0);
00609             traits.madd(A0,B_0,C1,T0);
00610             traits.madd(A1,B_0,C5,B_0);
00611 
00612             traits.loadLhs(&blA[2*LhsProgress], A0);
00613             traits.loadLhs(&blA[3*LhsProgress], A1);
00614             traits.loadRhs(&blB[2*RhsProgress], B_0);
00615             traits.madd(A0,B_0,C0,T0);
00616             traits.madd(A1,B_0,C4,B_0);
00617             traits.loadRhs(&blB[3*RhsProgress], B_0);
00618             traits.madd(A0,B_0,C1,T0);
00619             traits.madd(A1,B_0,C5,B_0);
00620 
00621             traits.loadLhs(&blA[4*LhsProgress], A0);
00622             traits.loadLhs(&blA[5*LhsProgress], A1);
00623             traits.loadRhs(&blB[4*RhsProgress], B_0);
00624             traits.madd(A0,B_0,C0,T0);
00625             traits.madd(A1,B_0,C4,B_0);
00626             traits.loadRhs(&blB[5*RhsProgress], B_0);
00627             traits.madd(A0,B_0,C1,T0);
00628             traits.madd(A1,B_0,C5,B_0);
00629 
00630             traits.loadLhs(&blA[6*LhsProgress], A0);
00631             traits.loadLhs(&blA[7*LhsProgress], A1);
00632             traits.loadRhs(&blB[6*RhsProgress], B_0);
00633             traits.madd(A0,B_0,C0,T0);
00634             traits.madd(A1,B_0,C4,B_0);
00635             traits.loadRhs(&blB[7*RhsProgress], B_0);
00636             traits.madd(A0,B_0,C1,T0);
00637             traits.madd(A1,B_0,C5,B_0);
00638 EIGEN_ASM_COMMENT("myend");
00639           }
00640           else
00641           {
00642 EIGEN_ASM_COMMENT("mybegin4");
00643             LhsPacket A0, A1;
00644             RhsPacket B_0, B1, B2, B3;
00645             RhsPacket T0;
00646             
00647             traits.loadLhs(&blA[0*LhsProgress], A0);
00648             traits.loadLhs(&blA[1*LhsProgress], A1);
00649             traits.loadRhs(&blB[0*RhsProgress], B_0);
00650             traits.loadRhs(&blB[1*RhsProgress], B1);
00651 
00652             traits.madd(A0,B_0,C0,T0);
00653             traits.loadRhs(&blB[2*RhsProgress], B2);
00654             traits.madd(A1,B_0,C4,B_0);
00655             traits.loadRhs(&blB[3*RhsProgress], B3);
00656             traits.loadRhs(&blB[4*RhsProgress], B_0);
00657             traits.madd(A0,B1,C1,T0);
00658             traits.madd(A1,B1,C5,B1);
00659             traits.loadRhs(&blB[5*RhsProgress], B1);
00660             traits.madd(A0,B2,C2,T0);
00661             traits.madd(A1,B2,C6,B2);
00662             traits.loadRhs(&blB[6*RhsProgress], B2);
00663             traits.madd(A0,B3,C3,T0);
00664             traits.loadLhs(&blA[2*LhsProgress], A0);
00665             traits.madd(A1,B3,C7,B3);
00666             traits.loadLhs(&blA[3*LhsProgress], A1);
00667             traits.loadRhs(&blB[7*RhsProgress], B3);
00668             traits.madd(A0,B_0,C0,T0);
00669             traits.madd(A1,B_0,C4,B_0);
00670             traits.loadRhs(&blB[8*RhsProgress], B_0);
00671             traits.madd(A0,B1,C1,T0);
00672             traits.madd(A1,B1,C5,B1);
00673             traits.loadRhs(&blB[9*RhsProgress], B1);
00674             traits.madd(A0,B2,C2,T0);
00675             traits.madd(A1,B2,C6,B2);
00676             traits.loadRhs(&blB[10*RhsProgress], B2);
00677             traits.madd(A0,B3,C3,T0);
00678             traits.loadLhs(&blA[4*LhsProgress], A0);
00679             traits.madd(A1,B3,C7,B3);
00680             traits.loadLhs(&blA[5*LhsProgress], A1);
00681             traits.loadRhs(&blB[11*RhsProgress], B3);
00682 
00683             traits.madd(A0,B_0,C0,T0);
00684             traits.madd(A1,B_0,C4,B_0);
00685             traits.loadRhs(&blB[12*RhsProgress], B_0);
00686             traits.madd(A0,B1,C1,T0);
00687             traits.madd(A1,B1,C5,B1);
00688             traits.loadRhs(&blB[13*RhsProgress], B1);
00689             traits.madd(A0,B2,C2,T0);
00690             traits.madd(A1,B2,C6,B2);
00691             traits.loadRhs(&blB[14*RhsProgress], B2);
00692             traits.madd(A0,B3,C3,T0);
00693             traits.loadLhs(&blA[6*LhsProgress], A0);
00694             traits.madd(A1,B3,C7,B3);
00695             traits.loadLhs(&blA[7*LhsProgress], A1);
00696             traits.loadRhs(&blB[15*RhsProgress], B3);
00697             traits.madd(A0,B_0,C0,T0);
00698             traits.madd(A1,B_0,C4,B_0);
00699             traits.madd(A0,B1,C1,T0);
00700             traits.madd(A1,B1,C5,B1);
00701             traits.madd(A0,B2,C2,T0);
00702             traits.madd(A1,B2,C6,B2);
00703             traits.madd(A0,B3,C3,T0);
00704             traits.madd(A1,B3,C7,B3);
00705           }
00706 
00707           blB += 4*nr*RhsProgress;
00708           blA += 4*mr;
00709         }
00710         // process remaining peeled loop
00711         for(Index k=peeled_kc; k<depth; k++)
00712         {
00713           if(nr==2)
00714           {
00715             LhsPacket A0, A1;
00716             RhsPacket B_0;
00717             RhsPacket T0;
00718 
00719             traits.loadLhs(&blA[0*LhsProgress], A0);
00720             traits.loadLhs(&blA[1*LhsProgress], A1);
00721             traits.loadRhs(&blB[0*RhsProgress], B_0);
00722             traits.madd(A0,B_0,C0,T0);
00723             traits.madd(A1,B_0,C4,B_0);
00724             traits.loadRhs(&blB[1*RhsProgress], B_0);
00725             traits.madd(A0,B_0,C1,T0);
00726             traits.madd(A1,B_0,C5,B_0);
00727           }
00728           else
00729           {
00730             LhsPacket A0, A1;
00731             RhsPacket B_0, B1, B2, B3;
00732             RhsPacket T0;
00733 
00734             traits.loadLhs(&blA[0*LhsProgress], A0);
00735             traits.loadLhs(&blA[1*LhsProgress], A1);
00736             traits.loadRhs(&blB[0*RhsProgress], B_0);
00737             traits.loadRhs(&blB[1*RhsProgress], B1);
00738 
00739             traits.madd(A0,B_0,C0,T0);
00740             traits.loadRhs(&blB[2*RhsProgress], B2);
00741             traits.madd(A1,B_0,C4,B_0);
00742             traits.loadRhs(&blB[3*RhsProgress], B3);
00743             traits.madd(A0,B1,C1,T0);
00744             traits.madd(A1,B1,C5,B1);
00745             traits.madd(A0,B2,C2,T0);
00746             traits.madd(A1,B2,C6,B2);
00747             traits.madd(A0,B3,C3,T0);
00748             traits.madd(A1,B3,C7,B3);
00749           }
00750 
00751           blB += nr*RhsProgress;
00752           blA += mr;
00753         }
00754 
00755         if(nr==4)
00756         {
00757           ResPacket R0, R1, R2, R3, R4, R5, R6;
00758           ResPacket alphav = pset1<ResPacket>(alpha);
00759 
00760           R0 = ploadu<ResPacket>(r0);
00761           R1 = ploadu<ResPacket>(r1);
00762           R2 = ploadu<ResPacket>(r2);
00763           R3 = ploadu<ResPacket>(r3);
00764           R4 = ploadu<ResPacket>(r0 + ResPacketSize);
00765           R5 = ploadu<ResPacket>(r1 + ResPacketSize);
00766           R6 = ploadu<ResPacket>(r2 + ResPacketSize);
00767           traits.acc(C0, alphav, R0);
00768           pstoreu(r0, R0);
00769           R0 = ploadu<ResPacket>(r3 + ResPacketSize);
00770 
00771           traits.acc(C1, alphav, R1);
00772           traits.acc(C2, alphav, R2);
00773           traits.acc(C3, alphav, R3);
00774           traits.acc(C4, alphav, R4);
00775           traits.acc(C5, alphav, R5);
00776           traits.acc(C6, alphav, R6);
00777           traits.acc(C7, alphav, R0);
00778           
00779           pstoreu(r1, R1);
00780           pstoreu(r2, R2);
00781           pstoreu(r3, R3);
00782           pstoreu(r0 + ResPacketSize, R4);
00783           pstoreu(r1 + ResPacketSize, R5);
00784           pstoreu(r2 + ResPacketSize, R6);
00785           pstoreu(r3 + ResPacketSize, R0);
00786         }
00787         else
00788         {
00789           ResPacket R0, R1, R4;
00790           ResPacket alphav = pset1<ResPacket>(alpha);
00791 
00792           R0 = ploadu<ResPacket>(r0);
00793           R1 = ploadu<ResPacket>(r1);
00794           R4 = ploadu<ResPacket>(r0 + ResPacketSize);
00795           traits.acc(C0, alphav, R0);
00796           pstoreu(r0, R0);
00797           R0 = ploadu<ResPacket>(r1 + ResPacketSize);
00798           traits.acc(C1, alphav, R1);
00799           traits.acc(C4, alphav, R4);
00800           traits.acc(C5, alphav, R0);
00801           pstoreu(r1, R1);
00802           pstoreu(r0 + ResPacketSize, R4);
00803           pstoreu(r1 + ResPacketSize, R0);
00804         }
00805         
00806       }
00807       
00808       if(rows-peeled_mc>=LhsProgress)
00809       {
00810         Index i = peeled_mc;
00811         const LhsScalar* blA = &blockA[i*strideA+offsetA*LhsProgress];
00812         prefetch(&blA[0]);
00813 
00814         // gets res block as register
00815         AccPacket C0, C1, C2, C3;
00816                   traits.initAcc(C0);
00817                   traits.initAcc(C1);
00818         if(nr==4) traits.initAcc(C2);
00819         if(nr==4) traits.initAcc(C3);
00820 
00821         // performs "inner" product
00822         const RhsScalar* blB = unpackedB;
00823         for(Index k=0; k<peeled_kc; k+=4)
00824         {
00825           if(nr==2)
00826           {
00827             LhsPacket A0;
00828             RhsPacket B_0, B1;
00829 
00830             traits.loadLhs(&blA[0*LhsProgress], A0);
00831             traits.loadRhs(&blB[0*RhsProgress], B_0);
00832             traits.loadRhs(&blB[1*RhsProgress], B1);
00833             traits.madd(A0,B_0,C0,B_0);
00834             traits.loadRhs(&blB[2*RhsProgress], B_0);
00835             traits.madd(A0,B1,C1,B1);
00836             traits.loadLhs(&blA[1*LhsProgress], A0);
00837             traits.loadRhs(&blB[3*RhsProgress], B1);
00838             traits.madd(A0,B_0,C0,B_0);
00839             traits.loadRhs(&blB[4*RhsProgress], B_0);
00840             traits.madd(A0,B1,C1,B1);
00841             traits.loadLhs(&blA[2*LhsProgress], A0);
00842             traits.loadRhs(&blB[5*RhsProgress], B1);
00843             traits.madd(A0,B_0,C0,B_0);
00844             traits.loadRhs(&blB[6*RhsProgress], B_0);
00845             traits.madd(A0,B1,C1,B1);
00846             traits.loadLhs(&blA[3*LhsProgress], A0);
00847             traits.loadRhs(&blB[7*RhsProgress], B1);
00848             traits.madd(A0,B_0,C0,B_0);
00849             traits.madd(A0,B1,C1,B1);
00850           }
00851           else
00852           {
00853             LhsPacket A0;
00854             RhsPacket B_0, B1, B2, B3;
00855 
00856             traits.loadLhs(&blA[0*LhsProgress], A0);
00857             traits.loadRhs(&blB[0*RhsProgress], B_0);
00858             traits.loadRhs(&blB[1*RhsProgress], B1);
00859 
00860             traits.madd(A0,B_0,C0,B_0);
00861             traits.loadRhs(&blB[2*RhsProgress], B2);
00862             traits.loadRhs(&blB[3*RhsProgress], B3);
00863             traits.loadRhs(&blB[4*RhsProgress], B_0);
00864             traits.madd(A0,B1,C1,B1);
00865             traits.loadRhs(&blB[5*RhsProgress], B1);
00866             traits.madd(A0,B2,C2,B2);
00867             traits.loadRhs(&blB[6*RhsProgress], B2);
00868             traits.madd(A0,B3,C3,B3);
00869             traits.loadLhs(&blA[1*LhsProgress], A0);
00870             traits.loadRhs(&blB[7*RhsProgress], B3);
00871             traits.madd(A0,B_0,C0,B_0);
00872             traits.loadRhs(&blB[8*RhsProgress], B_0);
00873             traits.madd(A0,B1,C1,B1);
00874             traits.loadRhs(&blB[9*RhsProgress], B1);
00875             traits.madd(A0,B2,C2,B2);
00876             traits.loadRhs(&blB[10*RhsProgress], B2);
00877             traits.madd(A0,B3,C3,B3);
00878             traits.loadLhs(&blA[2*LhsProgress], A0);
00879             traits.loadRhs(&blB[11*RhsProgress], B3);
00880 
00881             traits.madd(A0,B_0,C0,B_0);
00882             traits.loadRhs(&blB[12*RhsProgress], B_0);
00883             traits.madd(A0,B1,C1,B1);
00884             traits.loadRhs(&blB[13*RhsProgress], B1);
00885             traits.madd(A0,B2,C2,B2);
00886             traits.loadRhs(&blB[14*RhsProgress], B2);
00887             traits.madd(A0,B3,C3,B3);
00888 
00889             traits.loadLhs(&blA[3*LhsProgress], A0);
00890             traits.loadRhs(&blB[15*RhsProgress], B3);
00891             traits.madd(A0,B_0,C0,B_0);
00892             traits.madd(A0,B1,C1,B1);
00893             traits.madd(A0,B2,C2,B2);
00894             traits.madd(A0,B3,C3,B3);
00895           }
00896 
00897           blB += nr*4*RhsProgress;
00898           blA += 4*LhsProgress;
00899         }
00900         // process remaining peeled loop
00901         for(Index k=peeled_kc; k<depth; k++)
00902         {
00903           if(nr==2)
00904           {
00905             LhsPacket A0;
00906             RhsPacket B_0, B1;
00907 
00908             traits.loadLhs(&blA[0*LhsProgress], A0);
00909             traits.loadRhs(&blB[0*RhsProgress], B_0);
00910             traits.loadRhs(&blB[1*RhsProgress], B1);
00911             traits.madd(A0,B_0,C0,B_0);
00912             traits.madd(A0,B1,C1,B1);
00913           }
00914           else
00915           {
00916             LhsPacket A0;
00917             RhsPacket B_0, B1, B2, B3;
00918 
00919             traits.loadLhs(&blA[0*LhsProgress], A0);
00920             traits.loadRhs(&blB[0*RhsProgress], B_0);
00921             traits.loadRhs(&blB[1*RhsProgress], B1);
00922             traits.loadRhs(&blB[2*RhsProgress], B2);
00923             traits.loadRhs(&blB[3*RhsProgress], B3);
00924 
00925             traits.madd(A0,B_0,C0,B_0);
00926             traits.madd(A0,B1,C1,B1);
00927             traits.madd(A0,B2,C2,B2);
00928             traits.madd(A0,B3,C3,B3);
00929           }
00930 
00931           blB += nr*RhsProgress;
00932           blA += LhsProgress;
00933         }
00934 
00935         ResPacket R0, R1, R2, R3;
00936         ResPacket alphav = pset1<ResPacket>(alpha);
00937 
00938         ResScalar* r0 = &res[(j2+0)*resStride + i];
00939         ResScalar* r1 = r0 + resStride;
00940         ResScalar* r2 = r1 + resStride;
00941         ResScalar* r3 = r2 + resStride;
00942 
00943                   R0 = ploadu<ResPacket>(r0);
00944                   R1 = ploadu<ResPacket>(r1);
00945         if(nr==4) R2 = ploadu<ResPacket>(r2);
00946         if(nr==4) R3 = ploadu<ResPacket>(r3);
00947 
00948                   traits.acc(C0, alphav, R0);
00949                   traits.acc(C1, alphav, R1);
00950         if(nr==4) traits.acc(C2, alphav, R2);
00951         if(nr==4) traits.acc(C3, alphav, R3);
00952 
00953                   pstoreu(r0, R0);
00954                   pstoreu(r1, R1);
00955         if(nr==4) pstoreu(r2, R2);
00956         if(nr==4) pstoreu(r3, R3);
00957       }
00958       for(Index i=peeled_mc2; i<rows; i++)
00959       {
00960         const LhsScalar* blA = &blockA[i*strideA+offsetA];
00961         prefetch(&blA[0]);
00962 
00963         // gets a 1 x nr res block as registers
00964         ResScalar C0(0), C1(0), C2(0), C3(0);
00965         // TODO directly use blockB ???
00966         const RhsScalar* blB = &blockB[j2*strideB+offsetB*nr];
00967         for(Index k=0; k<depth; k++)
00968         {
00969           if(nr==2)
00970           {
00971             LhsScalar A0;
00972             RhsScalar B_0, B1;
00973 
00974             A0 = blA[k];
00975             B_0 = blB[0];
00976             B1 = blB[1];
00977             MADD(cj,A0,B_0,C0,B_0);
00978             MADD(cj,A0,B1,C1,B1);
00979           }
00980           else
00981           {
00982             LhsScalar A0;
00983             RhsScalar B_0, B1, B2, B3;
00984 
00985             A0 = blA[k];
00986             B_0 = blB[0];
00987             B1 = blB[1];
00988             B2 = blB[2];
00989             B3 = blB[3];
00990 
00991             MADD(cj,A0,B_0,C0,B_0);
00992             MADD(cj,A0,B1,C1,B1);
00993             MADD(cj,A0,B2,C2,B2);
00994             MADD(cj,A0,B3,C3,B3);
00995           }
00996 
00997           blB += nr;
00998         }
00999                   res[(j2+0)*resStride + i] += alpha*C0;
01000                   res[(j2+1)*resStride + i] += alpha*C1;
01001         if(nr==4) res[(j2+2)*resStride + i] += alpha*C2;
01002         if(nr==4) res[(j2+3)*resStride + i] += alpha*C3;
01003       }
01004     }
01005     // process remaining rhs/res columns one at a time
01006     // => do the same but with nr==1
01007     for(Index j2=packet_cols; j2<cols; j2++)
01008     {
01009       // unpack B
01010       traits.unpackRhs(depth, &blockB[j2*strideB+offsetB], unpackedB);
01011 
01012       for(Index i=0; i<peeled_mc; i+=mr)
01013       {
01014         const LhsScalar* blA = &blockA[i*strideA+offsetA*mr];
01015         prefetch(&blA[0]);
01016 
01017         // TODO move the res loads to the stores
01018 
01019         // get res block as registers
01020         AccPacket C0, C4;
01021         traits.initAcc(C0);
01022         traits.initAcc(C4);
01023 
01024         const RhsScalar* blB = unpackedB;
01025         for(Index k=0; k<depth; k++)
01026         {
01027           LhsPacket A0, A1;
01028           RhsPacket B_0;
01029           RhsPacket T0;
01030 
01031           traits.loadLhs(&blA[0*LhsProgress], A0);
01032           traits.loadLhs(&blA[1*LhsProgress], A1);
01033           traits.loadRhs(&blB[0*RhsProgress], B_0);
01034           traits.madd(A0,B_0,C0,T0);
01035           traits.madd(A1,B_0,C4,B_0);
01036 
01037           blB += RhsProgress;
01038           blA += 2*LhsProgress;
01039         }
01040         ResPacket R0, R4;
01041         ResPacket alphav = pset1<ResPacket>(alpha);
01042 
01043         ResScalar* r0 = &res[(j2+0)*resStride + i];
01044 
01045         R0 = ploadu<ResPacket>(r0);
01046         R4 = ploadu<ResPacket>(r0+ResPacketSize);
01047 
01048         traits.acc(C0, alphav, R0);
01049         traits.acc(C4, alphav, R4);
01050 
01051         pstoreu(r0,               R0);
01052         pstoreu(r0+ResPacketSize, R4);
01053       }
01054       if(rows-peeled_mc>=LhsProgress)
01055       {
01056         Index i = peeled_mc;
01057         const LhsScalar* blA = &blockA[i*strideA+offsetA*LhsProgress];
01058         prefetch(&blA[0]);
01059 
01060         AccPacket C0;
01061         traits.initAcc(C0);
01062 
01063         const RhsScalar* blB = unpackedB;
01064         for(Index k=0; k<depth; k++)
01065         {
01066           LhsPacket A0;
01067           RhsPacket B_0;
01068           traits.loadLhs(blA, A0);
01069           traits.loadRhs(blB, B_0);
01070           traits.madd(A0, B_0, C0, B_0);
01071           blB += RhsProgress;
01072           blA += LhsProgress;
01073         }
01074 
01075         ResPacket alphav = pset1<ResPacket>(alpha);
01076         ResPacket R0 = ploadu<ResPacket>(&res[(j2+0)*resStride + i]);
01077         traits.acc(C0, alphav, R0);
01078         pstoreu(&res[(j2+0)*resStride + i], R0);
01079       }
01080       for(Index i=peeled_mc2; i<rows; i++)
01081       {
01082         const LhsScalar* blA = &blockA[i*strideA+offsetA];
01083         prefetch(&blA[0]);
01084 
01085         // gets a 1 x 1 res block as registers
01086         ResScalar C0(0);
01087         // FIXME directly use blockB ??
01088         const RhsScalar* blB = &blockB[j2*strideB+offsetB];
01089         for(Index k=0; k<depth; k++)
01090         {
01091           LhsScalar A0 = blA[k];
01092           RhsScalar B_0 = blB[k];
01093           MADD(cj, A0, B_0, C0, B_0);
01094         }
01095         res[(j2+0)*resStride + i] += alpha*C0;
01096       }
01097     }
01098   }
01099 
01100 
01101 #undef CJMADD
01102 
01103 // pack a block of the lhs
01104 // The traversal is as follow (mr==4):
01105 //   0  4  8 12 ...
01106 //   1  5  9 13 ...
01107 //   2  6 10 14 ...
01108 //   3  7 11 15 ...
01109 //
01110 //  16 20 24 28 ...
01111 //  17 21 25 29 ...
01112 //  18 22 26 30 ...
01113 //  19 23 27 31 ...
01114 //
01115 //  32 33 34 35 ...
01116 //  36 36 38 39 ...
01117 template<typename Scalar, typename Index, int Pack1, int Pack2, int StorageOrder, bool Conjugate, bool PanelMode>
01118 struct gemm_pack_lhs
01119 {
01120   EIGEN_DONT_INLINE void operator()(Scalar* blockA, const Scalar* EIGEN_RESTRICT _lhs, Index lhsStride, Index depth, Index rows, Index stride=0, Index offset=0);
01121 };
01122 
01123 template<typename Scalar, typename Index, int Pack1, int Pack2, int StorageOrder, bool Conjugate, bool PanelMode>
01124 EIGEN_DONT_INLINE void gemm_pack_lhs<Scalar, Index, Pack1, Pack2, StorageOrder, Conjugate, PanelMode>
01125   ::operator()(Scalar* blockA, const Scalar* EIGEN_RESTRICT _lhs, Index lhsStride, Index depth, Index rows, Index stride, Index offset)
01126 {
01127   typedef typename packet_traits<Scalar>::type Packet;
01128   enum { PacketSize = packet_traits<Scalar>::size };
01129 
01130   EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK LHS");
01131   eigen_assert(((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride));
01132   eigen_assert( (StorageOrder==RowMajor) || ((Pack1%PacketSize)==0 && Pack1<=4*PacketSize) );
01133   conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj;
01134   const_blas_data_mapper<Scalar, Index, StorageOrder> lhs(_lhs,lhsStride);
01135   Index count = 0;
01136   Index peeled_mc = (rows/Pack1)*Pack1;
01137   for(Index i=0; i<peeled_mc; i+=Pack1)
01138   {
01139     if(PanelMode) count += Pack1 * offset;
01140 
01141     if(StorageOrder==ColMajor)
01142     {
01143       for(Index k=0; k<depth; k++)
01144       {
01145         Packet A, B, C, D;
01146         if(Pack1>=1*PacketSize) A = ploadu<Packet>(&lhs(i+0*PacketSize, k));
01147         if(Pack1>=2*PacketSize) B = ploadu<Packet>(&lhs(i+1*PacketSize, k));
01148         if(Pack1>=3*PacketSize) C = ploadu<Packet>(&lhs(i+2*PacketSize, k));
01149         if(Pack1>=4*PacketSize) D = ploadu<Packet>(&lhs(i+3*PacketSize, k));
01150         if(Pack1>=1*PacketSize) { pstore(blockA+count, cj.pconj(A)); count+=PacketSize; }
01151         if(Pack1>=2*PacketSize) { pstore(blockA+count, cj.pconj(B)); count+=PacketSize; }
01152         if(Pack1>=3*PacketSize) { pstore(blockA+count, cj.pconj(C)); count+=PacketSize; }
01153         if(Pack1>=4*PacketSize) { pstore(blockA+count, cj.pconj(D)); count+=PacketSize; }
01154       }
01155     }
01156     else
01157     {
01158       for(Index k=0; k<depth; k++)
01159       {
01160         // TODO add a vectorized transpose here
01161         Index w=0;
01162         for(; w<Pack1-3; w+=4)
01163         {
01164           Scalar a(cj(lhs(i+w+0, k))),
01165                   b(cj(lhs(i+w+1, k))),
01166                   c(cj(lhs(i+w+2, k))),
01167                   d(cj(lhs(i+w+3, k)));
01168           blockA[count++] = a;
01169           blockA[count++] = b;
01170           blockA[count++] = c;
01171           blockA[count++] = d;
01172         }
01173         if(Pack1%4)
01174           for(;w<Pack1;++w)
01175             blockA[count++] = cj(lhs(i+w, k));
01176       }
01177     }
01178     if(PanelMode) count += Pack1 * (stride-offset-depth);
01179   }
01180   if(rows-peeled_mc>=Pack2)
01181   {
01182     if(PanelMode) count += Pack2*offset;
01183     for(Index k=0; k<depth; k++)
01184       for(Index w=0; w<Pack2; w++)
01185         blockA[count++] = cj(lhs(peeled_mc+w, k));
01186     if(PanelMode) count += Pack2 * (stride-offset-depth);
01187     peeled_mc += Pack2;
01188   }
01189   for(Index i=peeled_mc; i<rows; i++)
01190   {
01191     if(PanelMode) count += offset;
01192     for(Index k=0; k<depth; k++)
01193       blockA[count++] = cj(lhs(i, k));
01194     if(PanelMode) count += (stride-offset-depth);
01195   }
01196 }
01197 
01198 // copy a complete panel of the rhs
01199 // this version is optimized for column major matrices
01200 // The traversal order is as follow: (nr==4):
01201 //  0  1  2  3   12 13 14 15   24 27
01202 //  4  5  6  7   16 17 18 19   25 28
01203 //  8  9 10 11   20 21 22 23   26 29
01204 //  .  .  .  .    .  .  .  .    .  .
01205 template<typename Scalar, typename Index, int nr, bool Conjugate, bool PanelMode>
01206 struct gemm_pack_rhs<Scalar, Index, nr, ColMajor, Conjugate, PanelMode>
01207 {
01208   typedef typename packet_traits<Scalar>::type Packet;
01209   enum { PacketSize = packet_traits<Scalar>::size };
01210   EIGEN_DONT_INLINE void operator()(Scalar* blockB, const Scalar* rhs, Index rhsStride, Index depth, Index cols, Index stride=0, Index offset=0);
01211 };
01212 
01213 template<typename Scalar, typename Index, int nr, bool Conjugate, bool PanelMode>
01214 EIGEN_DONT_INLINE void gemm_pack_rhs<Scalar, Index, nr, ColMajor, Conjugate, PanelMode>
01215   ::operator()(Scalar* blockB, const Scalar* rhs, Index rhsStride, Index depth, Index cols, Index stride, Index offset)
01216 {
01217   EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK RHS COLMAJOR");
01218   eigen_assert(((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride));
01219   conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj;
01220   Index packet_cols = (cols/nr) * nr;
01221   Index count = 0;
01222   for(Index j2=0; j2<packet_cols; j2+=nr)
01223   {
01224     // skip what we have before
01225     if(PanelMode) count += nr * offset;
01226     const Scalar* b0 = &rhs[(j2+0)*rhsStride];
01227     const Scalar* b1 = &rhs[(j2+1)*rhsStride];
01228     const Scalar* b2 = &rhs[(j2+2)*rhsStride];
01229     const Scalar* b3 = &rhs[(j2+3)*rhsStride];
01230     for(Index k=0; k<depth; k++)
01231     {
01232                 blockB[count+0] = cj(b0[k]);
01233                 blockB[count+1] = cj(b1[k]);
01234       if(nr==4) blockB[count+2] = cj(b2[k]);
01235       if(nr==4) blockB[count+3] = cj(b3[k]);
01236       count += nr;
01237     }
01238     // skip what we have after
01239     if(PanelMode) count += nr * (stride-offset-depth);
01240   }
01241 
01242   // copy the remaining columns one at a time (nr==1)
01243   for(Index j2=packet_cols; j2<cols; ++j2)
01244   {
01245     if(PanelMode) count += offset;
01246     const Scalar* b0 = &rhs[(j2+0)*rhsStride];
01247     for(Index k=0; k<depth; k++)
01248     {
01249       blockB[count] = cj(b0[k]);
01250       count += 1;
01251     }
01252     if(PanelMode) count += (stride-offset-depth);
01253   }
01254 }
01255 
01256 // this version is optimized for row major matrices
01257 template<typename Scalar, typename Index, int nr, bool Conjugate, bool PanelMode>
01258 struct gemm_pack_rhs<Scalar, Index, nr, RowMajor, Conjugate, PanelMode>
01259 {
01260   enum { PacketSize = packet_traits<Scalar>::size };
01261   EIGEN_DONT_INLINE void operator()(Scalar* blockB, const Scalar* rhs, Index rhsStride, Index depth, Index cols, Index stride=0, Index offset=0);
01262 };
01263 
01264 template<typename Scalar, typename Index, int nr, bool Conjugate, bool PanelMode>
01265 EIGEN_DONT_INLINE void gemm_pack_rhs<Scalar, Index, nr, RowMajor, Conjugate, PanelMode>
01266   ::operator()(Scalar* blockB, const Scalar* rhs, Index rhsStride, Index depth, Index cols, Index stride, Index offset)
01267 {
01268   EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK RHS ROWMAJOR");
01269   eigen_assert(((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride));
01270   conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj;
01271   Index packet_cols = (cols/nr) * nr;
01272   Index count = 0;
01273   for(Index j2=0; j2<packet_cols; j2+=nr)
01274   {
01275     // skip what we have before
01276     if(PanelMode) count += nr * offset;
01277     for(Index k=0; k<depth; k++)
01278     {
01279       const Scalar* b0 = &rhs[k*rhsStride + j2];
01280                 blockB[count+0] = cj(b0[0]);
01281                 blockB[count+1] = cj(b0[1]);
01282       if(nr==4) blockB[count+2] = cj(b0[2]);
01283       if(nr==4) blockB[count+3] = cj(b0[3]);
01284       count += nr;
01285     }
01286     // skip what we have after
01287     if(PanelMode) count += nr * (stride-offset-depth);
01288   }
01289   // copy the remaining columns one at a time (nr==1)
01290   for(Index j2=packet_cols; j2<cols; ++j2)
01291   {
01292     if(PanelMode) count += offset;
01293     const Scalar* b0 = &rhs[j2];
01294     for(Index k=0; k<depth; k++)
01295     {
01296       blockB[count] = cj(b0[k*rhsStride]);
01297       count += 1;
01298     }
01299     if(PanelMode) count += stride-offset-depth;
01300   }
01301 }
01302 
01303 } // end namespace internal
01304 
01307 inline std::ptrdiff_t l1CacheSize()
01308 {
01309   std::ptrdiff_t l1, l2;
01310   internal::manage_caching_sizes(GetAction, &l1, &l2);
01311   return l1;
01312 }
01313 
01316 inline std::ptrdiff_t l2CacheSize()
01317 {
01318   std::ptrdiff_t l1, l2;
01319   internal::manage_caching_sizes(GetAction, &l1, &l2);
01320   return l2;
01321 }
01322 
01328 inline void setCpuCacheSizes(std::ptrdiff_t l1, std::ptrdiff_t l2)
01329 {
01330   internal::manage_caching_sizes(SetAction, &l1, &l2);
01331 }
01332 
01333 } // end namespace Eigen
01334 
01335 #endif // EIGEN_GENERAL_BLOCK_PANEL_H


acado
Author(s): Milan Vukov, Rien Quirynen
autogenerated on Sat Jun 8 2019 19:37:10