| // This file is part of Eigen, a lightweight C++ template library |
| // for linear algebra. |
| // |
| // Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr> |
| // |
| // This Source Code Form is subject to the terms of the Mozilla |
| // Public License v. 2.0. If a copy of the MPL was not distributed |
| // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. |
| |
| #ifndef EIGEN_GENERAL_BLOCK_PANEL_H |
| #define EIGEN_GENERAL_BLOCK_PANEL_H |
| |
| namespace Eigen { |
| |
| namespace internal { |
| |
| template<typename _LhsScalar, typename _RhsScalar, bool _ConjLhs=false, bool _ConjRhs=false> |
| class gebp_traits; |
| |
| |
| /** \internal \returns b if a<=0, and returns a otherwise. */ |
| inline std::ptrdiff_t manage_caching_sizes_helper(std::ptrdiff_t a, std::ptrdiff_t b) |
| { |
| return a<=0 ? b : a; |
| } |
| |
| /** \internal */ |
| inline void manage_caching_sizes(Action action, std::ptrdiff_t* l1=0, std::ptrdiff_t* l2=0) |
| { |
| static std::ptrdiff_t m_l1CacheSize = 0; |
| static std::ptrdiff_t m_l2CacheSize = 0; |
| if(m_l2CacheSize==0) |
| { |
| m_l1CacheSize = manage_caching_sizes_helper(queryL1CacheSize(),8 * 1024); |
| m_l2CacheSize = manage_caching_sizes_helper(queryTopLevelCacheSize(),1*1024*1024); |
| } |
| |
| if(action==SetAction) |
| { |
| // set the cpu cache size and cache all block sizes from a global cache size in byte |
| eigen_internal_assert(l1!=0 && l2!=0); |
| m_l1CacheSize = *l1; |
| m_l2CacheSize = *l2; |
| } |
| else if(action==GetAction) |
| { |
| eigen_internal_assert(l1!=0 && l2!=0); |
| *l1 = m_l1CacheSize; |
| *l2 = m_l2CacheSize; |
| } |
| else |
| { |
| eigen_internal_assert(false); |
| } |
| } |
| |
| /** \brief Computes the blocking parameters for a m x k times k x n matrix product |
| * |
| * \param[in,out] k Input: the third dimension of the product. Output: the blocking size along the same dimension. |
| * \param[in,out] m Input: the number of rows of the left hand side. Output: the blocking size along the same dimension. |
| * \param[in,out] n Input: the number of columns of the right hand side. Output: the blocking size along the same dimension. |
| * |
| * Given a m x k times k x n matrix product of scalar types \c LhsScalar and \c RhsScalar, |
| * this function computes the blocking size parameters along the respective dimensions |
| * for matrix products and related algorithms. The blocking sizes depends on various |
| * parameters: |
| * - the L1 and L2 cache sizes, |
| * - the register level blocking sizes defined by gebp_traits, |
| * - the number of scalars that fit into a packet (when vectorization is enabled). |
| * |
| * \sa setCpuCacheSizes */ |
| template<typename LhsScalar, typename RhsScalar, int KcFactor> |
| void computeProductBlockingSizes(std::ptrdiff_t& k, std::ptrdiff_t& m, std::ptrdiff_t& n) |
| { |
| EIGEN_UNUSED_VARIABLE(n); |
| // Explanations: |
| // Let's recall the product algorithms form kc x nc horizontal panels B' on the rhs and |
| // mc x kc blocks A' on the lhs. A' has to fit into L2 cache. Moreover, B' is processed |
| // per kc x nr vertical small panels where nr is the blocking size along the n dimension |
| // at the register level. For vectorization purpose, these small vertical panels are unpacked, |
| // e.g., each coefficient is replicated to fit a packet. This small vertical panel has to |
| // stay in L1 cache. |
| std::ptrdiff_t l1, l2; |
| |
| typedef gebp_traits<LhsScalar,RhsScalar> Traits; |
| enum { |
| kdiv = KcFactor * 2 * Traits::nr |
| * Traits::RhsProgress * sizeof(RhsScalar), |
| mr = gebp_traits<LhsScalar,RhsScalar>::mr, |
| mr_mask = (0xffffffff/mr)*mr |
| }; |
| |
| manage_caching_sizes(GetAction, &l1, &l2); |
| k = std::min<std::ptrdiff_t>(k, l1/kdiv); |
| std::ptrdiff_t _m = k>0 ? l2/(4 * sizeof(LhsScalar) * k) : 0; |
| if(_m<m) m = _m & mr_mask; |
| } |
| |
| template<typename LhsScalar, typename RhsScalar> |
| inline void computeProductBlockingSizes(std::ptrdiff_t& k, std::ptrdiff_t& m, std::ptrdiff_t& n) |
| { |
| computeProductBlockingSizes<LhsScalar,RhsScalar,1>(k, m, n); |
| } |
| |
| #ifdef EIGEN_HAS_FUSE_CJMADD |
| #define MADD(CJ,A,B,C,T) C = CJ.pmadd(A,B,C); |
| #else |
| |
| // FIXME (a bit overkill maybe ?) |
| |
| template<typename CJ, typename A, typename B, typename C, typename T> struct gebp_madd_selector { |
| EIGEN_ALWAYS_INLINE static void run(const CJ& cj, A& a, B& b, C& c, T& /*t*/) |
| { |
| c = cj.pmadd(a,b,c); |
| } |
| }; |
| |
| template<typename CJ, typename T> struct gebp_madd_selector<CJ,T,T,T,T> { |
| EIGEN_ALWAYS_INLINE static void run(const CJ& cj, T& a, T& b, T& c, T& t) |
| { |
| t = b; t = cj.pmul(a,t); c = padd(c,t); |
| } |
| }; |
| |
| template<typename CJ, typename A, typename B, typename C, typename T> |
| EIGEN_STRONG_INLINE void gebp_madd(const CJ& cj, A& a, B& b, C& c, T& t) |
| { |
| gebp_madd_selector<CJ,A,B,C,T>::run(cj,a,b,c,t); |
| } |
| |
| #define MADD(CJ,A,B,C,T) gebp_madd(CJ,A,B,C,T); |
| // #define MADD(CJ,A,B,C,T) T = B; T = CJ.pmul(A,T); C = padd(C,T); |
| #endif |
| |
| /* Vectorization logic |
| * real*real: unpack rhs to constant packets, ... |
| * |
| * 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), |
| * storing each res packet into two packets (2x2), |
| * at the end combine them: swap the second and addsub them |
| * cf*cf : same but with 2x4 blocks |
| * cplx*real : unpack rhs to constant packets, ... |
| * real*cplx : load lhs as (a0,a0,a1,a1), and mul as usual |
| */ |
| template<typename _LhsScalar, typename _RhsScalar, bool _ConjLhs, bool _ConjRhs> |
| class gebp_traits |
| { |
| public: |
| typedef _LhsScalar LhsScalar; |
| typedef _RhsScalar RhsScalar; |
| typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar; |
| |
| enum { |
| ConjLhs = _ConjLhs, |
| ConjRhs = _ConjRhs, |
| Vectorizable = packet_traits<LhsScalar>::Vectorizable && packet_traits<RhsScalar>::Vectorizable, |
| LhsPacketSize = Vectorizable ? packet_traits<LhsScalar>::size : 1, |
| RhsPacketSize = Vectorizable ? packet_traits<RhsScalar>::size : 1, |
| ResPacketSize = Vectorizable ? packet_traits<ResScalar>::size : 1, |
| |
| NumberOfRegisters = EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS, |
| |
| // register block size along the N direction (must be either 2 or 4) |
| nr = NumberOfRegisters/4, |
| |
| // register block size along the M direction (currently, this one cannot be modified) |
| mr = 2 * LhsPacketSize, |
| |
| WorkSpaceFactor = nr * RhsPacketSize, |
| |
| LhsProgress = LhsPacketSize, |
| RhsProgress = RhsPacketSize |
| }; |
| |
| typedef typename packet_traits<LhsScalar>::type _LhsPacket; |
| typedef typename packet_traits<RhsScalar>::type _RhsPacket; |
| typedef typename packet_traits<ResScalar>::type _ResPacket; |
| |
| typedef typename conditional<Vectorizable,_LhsPacket,LhsScalar>::type LhsPacket; |
| typedef typename conditional<Vectorizable,_RhsPacket,RhsScalar>::type RhsPacket; |
| typedef typename conditional<Vectorizable,_ResPacket,ResScalar>::type ResPacket; |
| |
| typedef ResPacket AccPacket; |
| |
| EIGEN_STRONG_INLINE void initAcc(AccPacket& p) |
| { |
| p = pset1<ResPacket>(ResScalar(0)); |
| } |
| |
| EIGEN_STRONG_INLINE void unpackRhs(DenseIndex n, const RhsScalar* rhs, RhsScalar* b) |
| { |
| for(DenseIndex k=0; k<n; k++) |
| pstore1<RhsPacket>(&b[k*RhsPacketSize], rhs[k]); |
| } |
| |
| EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacket& dest) const |
| { |
| dest = pload<RhsPacket>(b); |
| } |
| |
| EIGEN_STRONG_INLINE void loadLhs(const LhsScalar* a, LhsPacket& dest) const |
| { |
| dest = pload<LhsPacket>(a); |
| } |
| |
| EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, AccPacket& c, AccPacket& tmp) const |
| { |
| tmp = b; tmp = pmul(a,tmp); c = padd(c,tmp); |
| } |
| |
| EIGEN_STRONG_INLINE void acc(const AccPacket& c, const ResPacket& alpha, ResPacket& r) const |
| { |
| r = pmadd(c,alpha,r); |
| } |
| |
| protected: |
| // conj_helper<LhsScalar,RhsScalar,ConjLhs,ConjRhs> cj; |
| // conj_helper<LhsPacket,RhsPacket,ConjLhs,ConjRhs> pcj; |
| }; |
| |
| template<typename RealScalar, bool _ConjLhs> |
| class gebp_traits<std::complex<RealScalar>, RealScalar, _ConjLhs, false> |
| { |
| public: |
| typedef std::complex<RealScalar> LhsScalar; |
| typedef RealScalar RhsScalar; |
| typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar; |
| |
| enum { |
| ConjLhs = _ConjLhs, |
| ConjRhs = false, |
| Vectorizable = packet_traits<LhsScalar>::Vectorizable && packet_traits<RhsScalar>::Vectorizable, |
| LhsPacketSize = Vectorizable ? packet_traits<LhsScalar>::size : 1, |
| RhsPacketSize = Vectorizable ? packet_traits<RhsScalar>::size : 1, |
| ResPacketSize = Vectorizable ? packet_traits<ResScalar>::size : 1, |
| |
| NumberOfRegisters = EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS, |
| nr = NumberOfRegisters/4, |
| mr = 2 * LhsPacketSize, |
| WorkSpaceFactor = nr*RhsPacketSize, |
| |
| LhsProgress = LhsPacketSize, |
| RhsProgress = RhsPacketSize |
| }; |
| |
| typedef typename packet_traits<LhsScalar>::type _LhsPacket; |
| typedef typename packet_traits<RhsScalar>::type _RhsPacket; |
| typedef typename packet_traits<ResScalar>::type _ResPacket; |
| |
| typedef typename conditional<Vectorizable,_LhsPacket,LhsScalar>::type LhsPacket; |
| typedef typename conditional<Vectorizable,_RhsPacket,RhsScalar>::type RhsPacket; |
| typedef typename conditional<Vectorizable,_ResPacket,ResScalar>::type ResPacket; |
| |
| typedef ResPacket AccPacket; |
| |
| EIGEN_STRONG_INLINE void initAcc(AccPacket& p) |
| { |
| p = pset1<ResPacket>(ResScalar(0)); |
| } |
| |
| EIGEN_STRONG_INLINE void unpackRhs(DenseIndex n, const RhsScalar* rhs, RhsScalar* b) |
| { |
| for(DenseIndex k=0; k<n; k++) |
| pstore1<RhsPacket>(&b[k*RhsPacketSize], rhs[k]); |
| } |
| |
| EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacket& dest) const |
| { |
| dest = pload<RhsPacket>(b); |
| } |
| |
| EIGEN_STRONG_INLINE void loadLhs(const LhsScalar* a, LhsPacket& dest) const |
| { |
| dest = pload<LhsPacket>(a); |
| } |
| |
| EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, AccPacket& c, RhsPacket& tmp) const |
| { |
| madd_impl(a, b, c, tmp, typename conditional<Vectorizable,true_type,false_type>::type()); |
| } |
| |
| EIGEN_STRONG_INLINE void madd_impl(const LhsPacket& a, const RhsPacket& b, AccPacket& c, RhsPacket& tmp, const true_type&) const |
| { |
| tmp = b; tmp = pmul(a.v,tmp); c.v = padd(c.v,tmp); |
| } |
| |
| EIGEN_STRONG_INLINE void madd_impl(const LhsScalar& a, const RhsScalar& b, ResScalar& c, RhsScalar& /*tmp*/, const false_type&) const |
| { |
| c += a * b; |
| } |
| |
| EIGEN_STRONG_INLINE void acc(const AccPacket& c, const ResPacket& alpha, ResPacket& r) const |
| { |
| r = cj.pmadd(c,alpha,r); |
| } |
| |
| protected: |
| conj_helper<ResPacket,ResPacket,ConjLhs,false> cj; |
| }; |
| |
| template<typename RealScalar, bool _ConjLhs, bool _ConjRhs> |
| class gebp_traits<std::complex<RealScalar>, std::complex<RealScalar>, _ConjLhs, _ConjRhs > |
| { |
| public: |
| typedef std::complex<RealScalar> Scalar; |
| typedef std::complex<RealScalar> LhsScalar; |
| typedef std::complex<RealScalar> RhsScalar; |
| typedef std::complex<RealScalar> ResScalar; |
| |
| enum { |
| ConjLhs = _ConjLhs, |
| ConjRhs = _ConjRhs, |
| Vectorizable = packet_traits<RealScalar>::Vectorizable |
| && packet_traits<Scalar>::Vectorizable, |
| RealPacketSize = Vectorizable ? packet_traits<RealScalar>::size : 1, |
| ResPacketSize = Vectorizable ? packet_traits<ResScalar>::size : 1, |
| |
| nr = 2, |
| mr = 2 * ResPacketSize, |
| WorkSpaceFactor = Vectorizable ? 2*nr*RealPacketSize : nr, |
| |
| LhsProgress = ResPacketSize, |
| RhsProgress = Vectorizable ? 2*ResPacketSize : 1 |
| }; |
| |
| typedef typename packet_traits<RealScalar>::type RealPacket; |
| typedef typename packet_traits<Scalar>::type ScalarPacket; |
| struct DoublePacket |
| { |
| RealPacket first; |
| RealPacket second; |
| }; |
| |
| typedef typename conditional<Vectorizable,RealPacket, Scalar>::type LhsPacket; |
| typedef typename conditional<Vectorizable,DoublePacket,Scalar>::type RhsPacket; |
| typedef typename conditional<Vectorizable,ScalarPacket,Scalar>::type ResPacket; |
| typedef typename conditional<Vectorizable,DoublePacket,Scalar>::type AccPacket; |
| |
| EIGEN_STRONG_INLINE void initAcc(Scalar& p) { p = Scalar(0); } |
| |
| EIGEN_STRONG_INLINE void initAcc(DoublePacket& p) |
| { |
| p.first = pset1<RealPacket>(RealScalar(0)); |
| p.second = pset1<RealPacket>(RealScalar(0)); |
| } |
| |
| /* Unpack the rhs coeff such that each complex coefficient is spread into |
| * two packects containing respectively the real and imaginary coefficient |
| * duplicated as many time as needed: (x+iy) => [x, ..., x] [y, ..., y] |
| */ |
| EIGEN_STRONG_INLINE void unpackRhs(DenseIndex n, const Scalar* rhs, Scalar* b) |
| { |
| for(DenseIndex k=0; k<n; k++) |
| { |
| if(Vectorizable) |
| { |
| pstore1<RealPacket>((RealScalar*)&b[k*ResPacketSize*2+0], real(rhs[k])); |
| pstore1<RealPacket>((RealScalar*)&b[k*ResPacketSize*2+ResPacketSize], imag(rhs[k])); |
| } |
| else |
| b[k] = rhs[k]; |
| } |
| } |
| |
| EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, ResPacket& dest) const { dest = *b; } |
| |
| EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, DoublePacket& dest) const |
| { |
| dest.first = pload<RealPacket>((const RealScalar*)b); |
| dest.second = pload<RealPacket>((const RealScalar*)(b+ResPacketSize)); |
| } |
| |
| // nothing special here |
| EIGEN_STRONG_INLINE void loadLhs(const LhsScalar* a, LhsPacket& dest) const |
| { |
| dest = pload<LhsPacket>((const typename unpacket_traits<LhsPacket>::type*)(a)); |
| } |
| |
| EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, DoublePacket& c, RhsPacket& /*tmp*/) const |
| { |
| c.first = padd(pmul(a,b.first), c.first); |
| c.second = padd(pmul(a,b.second),c.second); |
| } |
| |
| EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, ResPacket& c, RhsPacket& /*tmp*/) const |
| { |
| c = cj.pmadd(a,b,c); |
| } |
| |
| EIGEN_STRONG_INLINE void acc(const Scalar& c, const Scalar& alpha, Scalar& r) const { r += alpha * c; } |
| |
| EIGEN_STRONG_INLINE void acc(const DoublePacket& c, const ResPacket& alpha, ResPacket& r) const |
| { |
| // assemble c |
| ResPacket tmp; |
| if((!ConjLhs)&&(!ConjRhs)) |
| { |
| tmp = pcplxflip(pconj(ResPacket(c.second))); |
| tmp = padd(ResPacket(c.first),tmp); |
| } |
| else if((!ConjLhs)&&(ConjRhs)) |
| { |
| tmp = pconj(pcplxflip(ResPacket(c.second))); |
| tmp = padd(ResPacket(c.first),tmp); |
| } |
| else if((ConjLhs)&&(!ConjRhs)) |
| { |
| tmp = pcplxflip(ResPacket(c.second)); |
| tmp = padd(pconj(ResPacket(c.first)),tmp); |
| } |
| else if((ConjLhs)&&(ConjRhs)) |
| { |
| tmp = pcplxflip(ResPacket(c.second)); |
| tmp = psub(pconj(ResPacket(c.first)),tmp); |
| } |
| |
| r = pmadd(tmp,alpha,r); |
| } |
| |
| protected: |
| conj_helper<LhsScalar,RhsScalar,ConjLhs,ConjRhs> cj; |
| }; |
| |
| template<typename RealScalar, bool _ConjRhs> |
| class gebp_traits<RealScalar, std::complex<RealScalar>, false, _ConjRhs > |
| { |
| public: |
| typedef std::complex<RealScalar> Scalar; |
| typedef RealScalar LhsScalar; |
| typedef Scalar RhsScalar; |
| typedef Scalar ResScalar; |
| |
| enum { |
| ConjLhs = false, |
| ConjRhs = _ConjRhs, |
| Vectorizable = packet_traits<RealScalar>::Vectorizable |
| && packet_traits<Scalar>::Vectorizable, |
| LhsPacketSize = Vectorizable ? packet_traits<LhsScalar>::size : 1, |
| RhsPacketSize = Vectorizable ? packet_traits<RhsScalar>::size : 1, |
| ResPacketSize = Vectorizable ? packet_traits<ResScalar>::size : 1, |
| |
| NumberOfRegisters = EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS, |
| nr = 4, |
| mr = 2*ResPacketSize, |
| WorkSpaceFactor = nr*RhsPacketSize, |
| |
| LhsProgress = ResPacketSize, |
| RhsProgress = ResPacketSize |
| }; |
| |
| typedef typename packet_traits<LhsScalar>::type _LhsPacket; |
| typedef typename packet_traits<RhsScalar>::type _RhsPacket; |
| typedef typename packet_traits<ResScalar>::type _ResPacket; |
| |
| typedef typename conditional<Vectorizable,_LhsPacket,LhsScalar>::type LhsPacket; |
| typedef typename conditional<Vectorizable,_RhsPacket,RhsScalar>::type RhsPacket; |
| typedef typename conditional<Vectorizable,_ResPacket,ResScalar>::type ResPacket; |
| |
| typedef ResPacket AccPacket; |
| |
| EIGEN_STRONG_INLINE void initAcc(AccPacket& p) |
| { |
| p = pset1<ResPacket>(ResScalar(0)); |
| } |
| |
| EIGEN_STRONG_INLINE void unpackRhs(DenseIndex n, const RhsScalar* rhs, RhsScalar* b) |
| { |
| for(DenseIndex k=0; k<n; k++) |
| pstore1<RhsPacket>(&b[k*RhsPacketSize], rhs[k]); |
| } |
| |
| EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacket& dest) const |
| { |
| dest = pload<RhsPacket>(b); |
| } |
| |
| EIGEN_STRONG_INLINE void loadLhs(const LhsScalar* a, LhsPacket& dest) const |
| { |
| dest = ploaddup<LhsPacket>(a); |
| } |
| |
| EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, AccPacket& c, RhsPacket& tmp) const |
| { |
| madd_impl(a, b, c, tmp, typename conditional<Vectorizable,true_type,false_type>::type()); |
| } |
| |
| EIGEN_STRONG_INLINE void madd_impl(const LhsPacket& a, const RhsPacket& b, AccPacket& c, RhsPacket& tmp, const true_type&) const |
| { |
| tmp = b; tmp.v = pmul(a,tmp.v); c = padd(c,tmp); |
| } |
| |
| EIGEN_STRONG_INLINE void madd_impl(const LhsScalar& a, const RhsScalar& b, ResScalar& c, RhsScalar& /*tmp*/, const false_type&) const |
| { |
| c += a * b; |
| } |
| |
| EIGEN_STRONG_INLINE void acc(const AccPacket& c, const ResPacket& alpha, ResPacket& r) const |
| { |
| r = cj.pmadd(alpha,c,r); |
| } |
| |
| protected: |
| conj_helper<ResPacket,ResPacket,false,ConjRhs> cj; |
| }; |
| |
| /* optimized GEneral packed Block * packed Panel product kernel |
| * |
| * Mixing type logic: C += A * B |
| * | A | B | comments |
| * |real |cplx | no vectorization yet, would require to pack A with duplication |
| * |cplx |real | easy vectorization |
| */ |
| template<typename LhsScalar, typename RhsScalar, typename Index, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs> |
| struct gebp_kernel |
| { |
| typedef gebp_traits<LhsScalar,RhsScalar,ConjugateLhs,ConjugateRhs> Traits; |
| typedef typename Traits::ResScalar ResScalar; |
| typedef typename Traits::LhsPacket LhsPacket; |
| typedef typename Traits::RhsPacket RhsPacket; |
| typedef typename Traits::ResPacket ResPacket; |
| typedef typename Traits::AccPacket AccPacket; |
| |
| enum { |
| Vectorizable = Traits::Vectorizable, |
| LhsProgress = Traits::LhsProgress, |
| RhsProgress = Traits::RhsProgress, |
| ResPacketSize = Traits::ResPacketSize |
| }; |
| |
| EIGEN_DONT_INLINE EIGEN_FLATTEN_ATTRIB |
| void operator()(ResScalar* res, Index resStride, const LhsScalar* blockA, const RhsScalar* blockB, Index rows, Index depth, Index cols, ResScalar alpha, |
| Index strideA=-1, Index strideB=-1, Index offsetA=0, Index offsetB=0, RhsScalar* unpackedB = 0) |
| { |
| Traits traits; |
| |
| if(strideA==-1) strideA = depth; |
| if(strideB==-1) strideB = depth; |
| conj_helper<LhsScalar,RhsScalar,ConjugateLhs,ConjugateRhs> cj; |
| // conj_helper<LhsPacket,RhsPacket,ConjugateLhs,ConjugateRhs> pcj; |
| Index packet_cols = (cols/nr) * nr; |
| const Index peeled_mc = (rows/mr)*mr; |
| // FIXME: |
| const Index peeled_mc2 = peeled_mc + (rows-peeled_mc >= LhsProgress ? LhsProgress : 0); |
| const Index peeled_kc = (depth/4)*4; |
| |
| if(unpackedB==0) |
| unpackedB = const_cast<RhsScalar*>(blockB - strideB * nr * RhsProgress); |
| |
| // loops on each micro vertical panel of rhs (depth x nr) |
| for(Index j2=0; j2<packet_cols; j2+=nr) |
| { |
| traits.unpackRhs(depth*nr,&blockB[j2*strideB+offsetB*nr],unpackedB); |
| |
| // loops on each largest micro horizontal panel of lhs (mr x depth) |
| // => we select a mr x nr micro block of res which is entirely |
| // stored into mr/packet_size x nr registers. |
| for(Index i=0; i<peeled_mc; i+=mr) |
| { |
| const LhsScalar* blA = &blockA[i*strideA+offsetA*mr]; |
| prefetch(&blA[0]); |
| |
| // gets res block as register |
| AccPacket C0, C1, C2, C3, C4, C5, C6, C7; |
| traits.initAcc(C0); |
| traits.initAcc(C1); |
| if(nr==4) traits.initAcc(C2); |
| if(nr==4) traits.initAcc(C3); |
| traits.initAcc(C4); |
| traits.initAcc(C5); |
| if(nr==4) traits.initAcc(C6); |
| if(nr==4) traits.initAcc(C7); |
| |
| ResScalar* r0 = &res[(j2+0)*resStride + i]; |
| ResScalar* r1 = r0 + resStride; |
| ResScalar* r2 = r1 + resStride; |
| ResScalar* r3 = r2 + resStride; |
| |
| prefetch(r0+16); |
| prefetch(r1+16); |
| prefetch(r2+16); |
| prefetch(r3+16); |
| |
| // performs "inner" product |
| // TODO let's check wether the folowing peeled loop could not be |
| // optimized via optimal prefetching from one loop to the other |
| const RhsScalar* blB = unpackedB; |
| for(Index k=0; k<peeled_kc; k+=4) |
| { |
| if(nr==2) |
| { |
| LhsPacket A0, A1; |
| RhsPacket B_0; |
| RhsPacket T0; |
| |
| EIGEN_ASM_COMMENT("mybegin2"); |
| traits.loadLhs(&blA[0*LhsProgress], A0); |
| traits.loadLhs(&blA[1*LhsProgress], A1); |
| traits.loadRhs(&blB[0*RhsProgress], B_0); |
| traits.madd(A0,B_0,C0,T0); |
| traits.madd(A1,B_0,C4,B_0); |
| traits.loadRhs(&blB[1*RhsProgress], B_0); |
| traits.madd(A0,B_0,C1,T0); |
| traits.madd(A1,B_0,C5,B_0); |
| |
| traits.loadLhs(&blA[2*LhsProgress], A0); |
| traits.loadLhs(&blA[3*LhsProgress], A1); |
| traits.loadRhs(&blB[2*RhsProgress], B_0); |
| traits.madd(A0,B_0,C0,T0); |
| traits.madd(A1,B_0,C4,B_0); |
| traits.loadRhs(&blB[3*RhsProgress], B_0); |
| traits.madd(A0,B_0,C1,T0); |
| traits.madd(A1,B_0,C5,B_0); |
| |
| traits.loadLhs(&blA[4*LhsProgress], A0); |
| traits.loadLhs(&blA[5*LhsProgress], A1); |
| traits.loadRhs(&blB[4*RhsProgress], B_0); |
| traits.madd(A0,B_0,C0,T0); |
| traits.madd(A1,B_0,C4,B_0); |
| traits.loadRhs(&blB[5*RhsProgress], B_0); |
| traits.madd(A0,B_0,C1,T0); |
| traits.madd(A1,B_0,C5,B_0); |
| |
| traits.loadLhs(&blA[6*LhsProgress], A0); |
| traits.loadLhs(&blA[7*LhsProgress], A1); |
| traits.loadRhs(&blB[6*RhsProgress], B_0); |
| traits.madd(A0,B_0,C0,T0); |
| traits.madd(A1,B_0,C4,B_0); |
| traits.loadRhs(&blB[7*RhsProgress], B_0); |
| traits.madd(A0,B_0,C1,T0); |
| traits.madd(A1,B_0,C5,B_0); |
| EIGEN_ASM_COMMENT("myend"); |
| } |
| else |
| { |
| EIGEN_ASM_COMMENT("mybegin4"); |
| LhsPacket A0, A1; |
| RhsPacket B_0, B1, B2, B3; |
| RhsPacket T0; |
| |
| traits.loadLhs(&blA[0*LhsProgress], A0); |
| traits.loadLhs(&blA[1*LhsProgress], A1); |
| traits.loadRhs(&blB[0*RhsProgress], B_0); |
| traits.loadRhs(&blB[1*RhsProgress], B1); |
| |
| traits.madd(A0,B_0,C0,T0); |
| traits.loadRhs(&blB[2*RhsProgress], B2); |
| traits.madd(A1,B_0,C4,B_0); |
| traits.loadRhs(&blB[3*RhsProgress], B3); |
| traits.loadRhs(&blB[4*RhsProgress], B_0); |
| traits.madd(A0,B1,C1,T0); |
| traits.madd(A1,B1,C5,B1); |
| traits.loadRhs(&blB[5*RhsProgress], B1); |
| traits.madd(A0,B2,C2,T0); |
| traits.madd(A1,B2,C6,B2); |
| traits.loadRhs(&blB[6*RhsProgress], B2); |
| traits.madd(A0,B3,C3,T0); |
| traits.loadLhs(&blA[2*LhsProgress], A0); |
| traits.madd(A1,B3,C7,B3); |
| traits.loadLhs(&blA[3*LhsProgress], A1); |
| traits.loadRhs(&blB[7*RhsProgress], B3); |
| traits.madd(A0,B_0,C0,T0); |
| traits.madd(A1,B_0,C4,B_0); |
| traits.loadRhs(&blB[8*RhsProgress], B_0); |
| traits.madd(A0,B1,C1,T0); |
| traits.madd(A1,B1,C5,B1); |
| traits.loadRhs(&blB[9*RhsProgress], B1); |
| traits.madd(A0,B2,C2,T0); |
| traits.madd(A1,B2,C6,B2); |
| traits.loadRhs(&blB[10*RhsProgress], B2); |
| traits.madd(A0,B3,C3,T0); |
| traits.loadLhs(&blA[4*LhsProgress], A0); |
| traits.madd(A1,B3,C7,B3); |
| traits.loadLhs(&blA[5*LhsProgress], A1); |
| traits.loadRhs(&blB[11*RhsProgress], B3); |
| |
| traits.madd(A0,B_0,C0,T0); |
| traits.madd(A1,B_0,C4,B_0); |
| traits.loadRhs(&blB[12*RhsProgress], B_0); |
| traits.madd(A0,B1,C1,T0); |
| traits.madd(A1,B1,C5,B1); |
| traits.loadRhs(&blB[13*RhsProgress], B1); |
| traits.madd(A0,B2,C2,T0); |
| traits.madd(A1,B2,C6,B2); |
| traits.loadRhs(&blB[14*RhsProgress], B2); |
| traits.madd(A0,B3,C3,T0); |
| traits.loadLhs(&blA[6*LhsProgress], A0); |
| traits.madd(A1,B3,C7,B3); |
| traits.loadLhs(&blA[7*LhsProgress], A1); |
| traits.loadRhs(&blB[15*RhsProgress], B3); |
| traits.madd(A0,B_0,C0,T0); |
| traits.madd(A1,B_0,C4,B_0); |
| traits.madd(A0,B1,C1,T0); |
| traits.madd(A1,B1,C5,B1); |
| traits.madd(A0,B2,C2,T0); |
| traits.madd(A1,B2,C6,B2); |
| traits.madd(A0,B3,C3,T0); |
| traits.madd(A1,B3,C7,B3); |
| } |
| |
| blB += 4*nr*RhsProgress; |
| blA += 4*mr; |
| } |
| // process remaining peeled loop |
| for(Index k=peeled_kc; k<depth; k++) |
| { |
| if(nr==2) |
| { |
| LhsPacket A0, A1; |
| RhsPacket B_0; |
| RhsPacket T0; |
| |
| traits.loadLhs(&blA[0*LhsProgress], A0); |
| traits.loadLhs(&blA[1*LhsProgress], A1); |
| traits.loadRhs(&blB[0*RhsProgress], B_0); |
| traits.madd(A0,B_0,C0,T0); |
| traits.madd(A1,B_0,C4,B_0); |
| traits.loadRhs(&blB[1*RhsProgress], B_0); |
| traits.madd(A0,B_0,C1,T0); |
| traits.madd(A1,B_0,C5,B_0); |
| } |
| else |
| { |
| LhsPacket A0, A1; |
| RhsPacket B_0, B1, B2, B3; |
| RhsPacket T0; |
| |
| traits.loadLhs(&blA[0*LhsProgress], A0); |
| traits.loadLhs(&blA[1*LhsProgress], A1); |
| traits.loadRhs(&blB[0*RhsProgress], B_0); |
| traits.loadRhs(&blB[1*RhsProgress], B1); |
| |
| traits.madd(A0,B_0,C0,T0); |
| traits.loadRhs(&blB[2*RhsProgress], B2); |
| traits.madd(A1,B_0,C4,B_0); |
| traits.loadRhs(&blB[3*RhsProgress], B3); |
| traits.madd(A0,B1,C1,T0); |
| traits.madd(A1,B1,C5,B1); |
| traits.madd(A0,B2,C2,T0); |
| traits.madd(A1,B2,C6,B2); |
| traits.madd(A0,B3,C3,T0); |
| traits.madd(A1,B3,C7,B3); |
| } |
| |
| blB += nr*RhsProgress; |
| blA += mr; |
| } |
| |
| if(nr==4) |
| { |
| ResPacket R0, R1, R2, R3, R4, R5, R6; |
| ResPacket alphav = pset1<ResPacket>(alpha); |
| |
| R0 = ploadu<ResPacket>(r0); |
| R1 = ploadu<ResPacket>(r1); |
| R2 = ploadu<ResPacket>(r2); |
| R3 = ploadu<ResPacket>(r3); |
| R4 = ploadu<ResPacket>(r0 + ResPacketSize); |
| R5 = ploadu<ResPacket>(r1 + ResPacketSize); |
| R6 = ploadu<ResPacket>(r2 + ResPacketSize); |
| traits.acc(C0, alphav, R0); |
| pstoreu(r0, R0); |
| R0 = ploadu<ResPacket>(r3 + ResPacketSize); |
| |
| traits.acc(C1, alphav, R1); |
| traits.acc(C2, alphav, R2); |
| traits.acc(C3, alphav, R3); |
| traits.acc(C4, alphav, R4); |
| traits.acc(C5, alphav, R5); |
| traits.acc(C6, alphav, R6); |
| traits.acc(C7, alphav, R0); |
| |
| pstoreu(r1, R1); |
| pstoreu(r2, R2); |
| pstoreu(r3, R3); |
| pstoreu(r0 + ResPacketSize, R4); |
| pstoreu(r1 + ResPacketSize, R5); |
| pstoreu(r2 + ResPacketSize, R6); |
| pstoreu(r3 + ResPacketSize, R0); |
| } |
| else |
| { |
| ResPacket R0, R1, R4; |
| ResPacket alphav = pset1<ResPacket>(alpha); |
| |
| R0 = ploadu<ResPacket>(r0); |
| R1 = ploadu<ResPacket>(r1); |
| R4 = ploadu<ResPacket>(r0 + ResPacketSize); |
| traits.acc(C0, alphav, R0); |
| pstoreu(r0, R0); |
| R0 = ploadu<ResPacket>(r1 + ResPacketSize); |
| traits.acc(C1, alphav, R1); |
| traits.acc(C4, alphav, R4); |
| traits.acc(C5, alphav, R0); |
| pstoreu(r1, R1); |
| pstoreu(r0 + ResPacketSize, R4); |
| pstoreu(r1 + ResPacketSize, R0); |
| } |
| |
| } |
| |
| if(rows-peeled_mc>=LhsProgress) |
| { |
| Index i = peeled_mc; |
| const LhsScalar* blA = &blockA[i*strideA+offsetA*LhsProgress]; |
| prefetch(&blA[0]); |
| |
| // gets res block as register |
| AccPacket C0, C1, C2, C3; |
| traits.initAcc(C0); |
| traits.initAcc(C1); |
| if(nr==4) traits.initAcc(C2); |
| if(nr==4) traits.initAcc(C3); |
| |
| // performs "inner" product |
| const RhsScalar* blB = unpackedB; |
| for(Index k=0; k<peeled_kc; k+=4) |
| { |
| if(nr==2) |
| { |
| LhsPacket A0; |
| RhsPacket B_0, B1; |
| |
| traits.loadLhs(&blA[0*LhsProgress], A0); |
| traits.loadRhs(&blB[0*RhsProgress], B_0); |
| traits.loadRhs(&blB[1*RhsProgress], B1); |
| traits.madd(A0,B_0,C0,B_0); |
| traits.loadRhs(&blB[2*RhsProgress], B_0); |
| traits.madd(A0,B1,C1,B1); |
| traits.loadLhs(&blA[1*LhsProgress], A0); |
| traits.loadRhs(&blB[3*RhsProgress], B1); |
| traits.madd(A0,B_0,C0,B_0); |
| traits.loadRhs(&blB[4*RhsProgress], B_0); |
| traits.madd(A0,B1,C1,B1); |
| traits.loadLhs(&blA[2*LhsProgress], A0); |
| traits.loadRhs(&blB[5*RhsProgress], B1); |
| traits.madd(A0,B_0,C0,B_0); |
| traits.loadRhs(&blB[6*RhsProgress], B_0); |
| traits.madd(A0,B1,C1,B1); |
| traits.loadLhs(&blA[3*LhsProgress], A0); |
| traits.loadRhs(&blB[7*RhsProgress], B1); |
| traits.madd(A0,B_0,C0,B_0); |
| traits.madd(A0,B1,C1,B1); |
| } |
| else |
| { |
| LhsPacket A0; |
| RhsPacket B_0, B1, B2, B3; |
| |
| traits.loadLhs(&blA[0*LhsProgress], A0); |
| traits.loadRhs(&blB[0*RhsProgress], B_0); |
| traits.loadRhs(&blB[1*RhsProgress], B1); |
| |
| traits.madd(A0,B_0,C0,B_0); |
| traits.loadRhs(&blB[2*RhsProgress], B2); |
| traits.loadRhs(&blB[3*RhsProgress], B3); |
| traits.loadRhs(&blB[4*RhsProgress], B_0); |
| traits.madd(A0,B1,C1,B1); |
| traits.loadRhs(&blB[5*RhsProgress], B1); |
| traits.madd(A0,B2,C2,B2); |
| traits.loadRhs(&blB[6*RhsProgress], B2); |
| traits.madd(A0,B3,C3,B3); |
| traits.loadLhs(&blA[1*LhsProgress], A0); |
| traits.loadRhs(&blB[7*RhsProgress], B3); |
| traits.madd(A0,B_0,C0,B_0); |
| traits.loadRhs(&blB[8*RhsProgress], B_0); |
| traits.madd(A0,B1,C1,B1); |
| traits.loadRhs(&blB[9*RhsProgress], B1); |
| traits.madd(A0,B2,C2,B2); |
| traits.loadRhs(&blB[10*RhsProgress], B2); |
| traits.madd(A0,B3,C3,B3); |
| traits.loadLhs(&blA[2*LhsProgress], A0); |
| traits.loadRhs(&blB[11*RhsProgress], B3); |
| |
| traits.madd(A0,B_0,C0,B_0); |
| traits.loadRhs(&blB[12*RhsProgress], B_0); |
| traits.madd(A0,B1,C1,B1); |
| traits.loadRhs(&blB[13*RhsProgress], B1); |
| traits.madd(A0,B2,C2,B2); |
| traits.loadRhs(&blB[14*RhsProgress], B2); |
| traits.madd(A0,B3,C3,B3); |
| |
| traits.loadLhs(&blA[3*LhsProgress], A0); |
| traits.loadRhs(&blB[15*RhsProgress], B3); |
| traits.madd(A0,B_0,C0,B_0); |
| traits.madd(A0,B1,C1,B1); |
| traits.madd(A0,B2,C2,B2); |
| traits.madd(A0,B3,C3,B3); |
| } |
| |
| blB += nr*4*RhsProgress; |
| blA += 4*LhsProgress; |
| } |
| // process remaining peeled loop |
| for(Index k=peeled_kc; k<depth; k++) |
| { |
| if(nr==2) |
| { |
| LhsPacket A0; |
| RhsPacket B_0, B1; |
| |
| traits.loadLhs(&blA[0*LhsProgress], A0); |
| traits.loadRhs(&blB[0*RhsProgress], B_0); |
| traits.loadRhs(&blB[1*RhsProgress], B1); |
| traits.madd(A0,B_0,C0,B_0); |
| traits.madd(A0,B1,C1,B1); |
| } |
| else |
| { |
| LhsPacket A0; |
| RhsPacket B_0, B1, B2, B3; |
| |
| traits.loadLhs(&blA[0*LhsProgress], A0); |
| traits.loadRhs(&blB[0*RhsProgress], B_0); |
| traits.loadRhs(&blB[1*RhsProgress], B1); |
| traits.loadRhs(&blB[2*RhsProgress], B2); |
| traits.loadRhs(&blB[3*RhsProgress], B3); |
| |
| traits.madd(A0,B_0,C0,B_0); |
| traits.madd(A0,B1,C1,B1); |
| traits.madd(A0,B2,C2,B2); |
| traits.madd(A0,B3,C3,B3); |
| } |
| |
| blB += nr*RhsProgress; |
| blA += LhsProgress; |
| } |
| |
| ResPacket R0, R1, R2, R3; |
| ResPacket alphav = pset1<ResPacket>(alpha); |
| |
| ResScalar* r0 = &res[(j2+0)*resStride + i]; |
| ResScalar* r1 = r0 + resStride; |
| ResScalar* r2 = r1 + resStride; |
| ResScalar* r3 = r2 + resStride; |
| |
| R0 = ploadu<ResPacket>(r0); |
| R1 = ploadu<ResPacket>(r1); |
| if(nr==4) R2 = ploadu<ResPacket>(r2); |
| if(nr==4) R3 = ploadu<ResPacket>(r3); |
| |
| traits.acc(C0, alphav, R0); |
| traits.acc(C1, alphav, R1); |
| if(nr==4) traits.acc(C2, alphav, R2); |
| if(nr==4) traits.acc(C3, alphav, R3); |
| |
| pstoreu(r0, R0); |
| pstoreu(r1, R1); |
| if(nr==4) pstoreu(r2, R2); |
| if(nr==4) pstoreu(r3, R3); |
| } |
| for(Index i=peeled_mc2; i<rows; i++) |
| { |
| const LhsScalar* blA = &blockA[i*strideA+offsetA]; |
| prefetch(&blA[0]); |
| |
| // gets a 1 x nr res block as registers |
| ResScalar C0(0), C1(0), C2(0), C3(0); |
| // TODO directly use blockB ??? |
| const RhsScalar* blB = &blockB[j2*strideB+offsetB*nr]; |
| for(Index k=0; k<depth; k++) |
| { |
| if(nr==2) |
| { |
| LhsScalar A0; |
| RhsScalar B_0, B1; |
| |
| A0 = blA[k]; |
| B_0 = blB[0]; |
| B1 = blB[1]; |
| MADD(cj,A0,B_0,C0,B_0); |
| MADD(cj,A0,B1,C1,B1); |
| } |
| else |
| { |
| LhsScalar A0; |
| RhsScalar B_0, B1, B2, B3; |
| |
| A0 = blA[k]; |
| B_0 = blB[0]; |
| B1 = blB[1]; |
| B2 = blB[2]; |
| B3 = blB[3]; |
| |
| MADD(cj,A0,B_0,C0,B_0); |
| MADD(cj,A0,B1,C1,B1); |
| MADD(cj,A0,B2,C2,B2); |
| MADD(cj,A0,B3,C3,B3); |
| } |
| |
| blB += nr; |
| } |
| res[(j2+0)*resStride + i] += alpha*C0; |
| res[(j2+1)*resStride + i] += alpha*C1; |
| if(nr==4) res[(j2+2)*resStride + i] += alpha*C2; |
| if(nr==4) res[(j2+3)*resStride + i] += alpha*C3; |
| } |
| } |
| // process remaining rhs/res columns one at a time |
| // => do the same but with nr==1 |
| for(Index j2=packet_cols; j2<cols; j2++) |
| { |
| // unpack B |
| traits.unpackRhs(depth, &blockB[j2*strideB+offsetB], unpackedB); |
| |
| for(Index i=0; i<peeled_mc; i+=mr) |
| { |
| const LhsScalar* blA = &blockA[i*strideA+offsetA*mr]; |
| prefetch(&blA[0]); |
| |
| // TODO move the res loads to the stores |
| |
| // get res block as registers |
| AccPacket C0, C4; |
| traits.initAcc(C0); |
| traits.initAcc(C4); |
| |
| const RhsScalar* blB = unpackedB; |
| for(Index k=0; k<depth; k++) |
| { |
| LhsPacket A0, A1; |
| RhsPacket B_0; |
| RhsPacket T0; |
| |
| traits.loadLhs(&blA[0*LhsProgress], A0); |
| traits.loadLhs(&blA[1*LhsProgress], A1); |
| traits.loadRhs(&blB[0*RhsProgress], B_0); |
| traits.madd(A0,B_0,C0,T0); |
| traits.madd(A1,B_0,C4,B_0); |
| |
| blB += RhsProgress; |
| blA += 2*LhsProgress; |
| } |
| ResPacket R0, R4; |
| ResPacket alphav = pset1<ResPacket>(alpha); |
| |
| ResScalar* r0 = &res[(j2+0)*resStride + i]; |
| |
| R0 = ploadu<ResPacket>(r0); |
| R4 = ploadu<ResPacket>(r0+ResPacketSize); |
| |
| traits.acc(C0, alphav, R0); |
| traits.acc(C4, alphav, R4); |
| |
| pstoreu(r0, R0); |
| pstoreu(r0+ResPacketSize, R4); |
| } |
| if(rows-peeled_mc>=LhsProgress) |
| { |
| Index i = peeled_mc; |
| const LhsScalar* blA = &blockA[i*strideA+offsetA*LhsProgress]; |
| prefetch(&blA[0]); |
| |
| AccPacket C0; |
| traits.initAcc(C0); |
| |
| const RhsScalar* blB = unpackedB; |
| for(Index k=0; k<depth; k++) |
| { |
| LhsPacket A0; |
| RhsPacket B_0; |
| traits.loadLhs(blA, A0); |
| traits.loadRhs(blB, B_0); |
| traits.madd(A0, B_0, C0, B_0); |
| blB += RhsProgress; |
| blA += LhsProgress; |
| } |
| |
| ResPacket alphav = pset1<ResPacket>(alpha); |
| ResPacket R0 = ploadu<ResPacket>(&res[(j2+0)*resStride + i]); |
| traits.acc(C0, alphav, R0); |
| pstoreu(&res[(j2+0)*resStride + i], R0); |
| } |
| for(Index i=peeled_mc2; i<rows; i++) |
| { |
| const LhsScalar* blA = &blockA[i*strideA+offsetA]; |
| prefetch(&blA[0]); |
| |
| // gets a 1 x 1 res block as registers |
| ResScalar C0(0); |
| // FIXME directly use blockB ?? |
| const RhsScalar* blB = &blockB[j2*strideB+offsetB]; |
| for(Index k=0; k<depth; k++) |
| { |
| LhsScalar A0 = blA[k]; |
| RhsScalar B_0 = blB[k]; |
| MADD(cj, A0, B_0, C0, B_0); |
| } |
| res[(j2+0)*resStride + i] += alpha*C0; |
| } |
| } |
| } |
| }; |
| |
| #undef CJMADD |
| |
| // pack a block of the lhs |
| // The traversal is as follow (mr==4): |
| // 0 4 8 12 ... |
| // 1 5 9 13 ... |
| // 2 6 10 14 ... |
| // 3 7 11 15 ... |
| // |
| // 16 20 24 28 ... |
| // 17 21 25 29 ... |
| // 18 22 26 30 ... |
| // 19 23 27 31 ... |
| // |
| // 32 33 34 35 ... |
| // 36 36 38 39 ... |
| template<typename Scalar, typename Index, int Pack1, int Pack2, int StorageOrder, bool Conjugate, bool PanelMode> |
| struct gemm_pack_lhs |
| { |
| EIGEN_DONT_INLINE void operator()(Scalar* blockA, const Scalar* EIGEN_RESTRICT _lhs, Index lhsStride, Index depth, Index rows, |
| Index stride=0, Index offset=0) |
| { |
| typedef typename packet_traits<Scalar>::type Packet; |
| enum { PacketSize = packet_traits<Scalar>::size }; |
| |
| EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK LHS"); |
| eigen_assert(((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride)); |
| eigen_assert( (StorageOrder==RowMajor) || ((Pack1%PacketSize)==0 && Pack1<=4*PacketSize) ); |
| conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj; |
| const_blas_data_mapper<Scalar, Index, StorageOrder> lhs(_lhs,lhsStride); |
| Index count = 0; |
| Index peeled_mc = (rows/Pack1)*Pack1; |
| for(Index i=0; i<peeled_mc; i+=Pack1) |
| { |
| if(PanelMode) count += Pack1 * offset; |
| |
| if(StorageOrder==ColMajor) |
| { |
| for(Index k=0; k<depth; k++) |
| { |
| Packet A, B, C, D; |
| if(Pack1>=1*PacketSize) A = ploadu<Packet>(&lhs(i+0*PacketSize, k)); |
| if(Pack1>=2*PacketSize) B = ploadu<Packet>(&lhs(i+1*PacketSize, k)); |
| if(Pack1>=3*PacketSize) C = ploadu<Packet>(&lhs(i+2*PacketSize, k)); |
| if(Pack1>=4*PacketSize) D = ploadu<Packet>(&lhs(i+3*PacketSize, k)); |
| if(Pack1>=1*PacketSize) { pstore(blockA+count, cj.pconj(A)); count+=PacketSize; } |
| if(Pack1>=2*PacketSize) { pstore(blockA+count, cj.pconj(B)); count+=PacketSize; } |
| if(Pack1>=3*PacketSize) { pstore(blockA+count, cj.pconj(C)); count+=PacketSize; } |
| if(Pack1>=4*PacketSize) { pstore(blockA+count, cj.pconj(D)); count+=PacketSize; } |
| } |
| } |
| else |
| { |
| for(Index k=0; k<depth; k++) |
| { |
| // TODO add a vectorized transpose here |
| Index w=0; |
| for(; w<Pack1-3; w+=4) |
| { |
| Scalar a(cj(lhs(i+w+0, k))), |
| b(cj(lhs(i+w+1, k))), |
| c(cj(lhs(i+w+2, k))), |
| d(cj(lhs(i+w+3, k))); |
| blockA[count++] = a; |
| blockA[count++] = b; |
| blockA[count++] = c; |
| blockA[count++] = d; |
| } |
| if(Pack1%4) |
| for(;w<Pack1;++w) |
| blockA[count++] = cj(lhs(i+w, k)); |
| } |
| } |
| if(PanelMode) count += Pack1 * (stride-offset-depth); |
| } |
| if(rows-peeled_mc>=Pack2) |
| { |
| if(PanelMode) count += Pack2*offset; |
| for(Index k=0; k<depth; k++) |
| for(Index w=0; w<Pack2; w++) |
| blockA[count++] = cj(lhs(peeled_mc+w, k)); |
| if(PanelMode) count += Pack2 * (stride-offset-depth); |
| peeled_mc += Pack2; |
| } |
| for(Index i=peeled_mc; i<rows; i++) |
| { |
| if(PanelMode) count += offset; |
| for(Index k=0; k<depth; k++) |
| blockA[count++] = cj(lhs(i, k)); |
| if(PanelMode) count += (stride-offset-depth); |
| } |
| } |
| }; |
| |
| // copy a complete panel of the rhs |
| // this version is optimized for column major matrices |
| // The traversal order is as follow: (nr==4): |
| // 0 1 2 3 12 13 14 15 24 27 |
| // 4 5 6 7 16 17 18 19 25 28 |
| // 8 9 10 11 20 21 22 23 26 29 |
| // . . . . . . . . . . |
| template<typename Scalar, typename Index, int nr, bool Conjugate, bool PanelMode> |
| struct gemm_pack_rhs<Scalar, Index, nr, ColMajor, Conjugate, PanelMode> |
| { |
| typedef typename packet_traits<Scalar>::type Packet; |
| enum { PacketSize = packet_traits<Scalar>::size }; |
| EIGEN_DONT_INLINE void operator()(Scalar* blockB, const Scalar* rhs, Index rhsStride, Index depth, Index cols, |
| Index stride=0, Index offset=0) |
| { |
| EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK RHS COLMAJOR"); |
| eigen_assert(((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride)); |
| conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj; |
| Index packet_cols = (cols/nr) * nr; |
| Index count = 0; |
| for(Index j2=0; j2<packet_cols; j2+=nr) |
| { |
| // skip what we have before |
| if(PanelMode) count += nr * offset; |
| const Scalar* b0 = &rhs[(j2+0)*rhsStride]; |
| const Scalar* b1 = &rhs[(j2+1)*rhsStride]; |
| const Scalar* b2 = &rhs[(j2+2)*rhsStride]; |
| const Scalar* b3 = &rhs[(j2+3)*rhsStride]; |
| for(Index k=0; k<depth; k++) |
| { |
| blockB[count+0] = cj(b0[k]); |
| blockB[count+1] = cj(b1[k]); |
| if(nr==4) blockB[count+2] = cj(b2[k]); |
| if(nr==4) blockB[count+3] = cj(b3[k]); |
| count += nr; |
| } |
| // skip what we have after |
| if(PanelMode) count += nr * (stride-offset-depth); |
| } |
| |
| // copy the remaining columns one at a time (nr==1) |
| for(Index j2=packet_cols; j2<cols; ++j2) |
| { |
| if(PanelMode) count += offset; |
| const Scalar* b0 = &rhs[(j2+0)*rhsStride]; |
| for(Index k=0; k<depth; k++) |
| { |
| blockB[count] = cj(b0[k]); |
| count += 1; |
| } |
| if(PanelMode) count += (stride-offset-depth); |
| } |
| } |
| }; |
| |
| // this version is optimized for row major matrices |
| template<typename Scalar, typename Index, int nr, bool Conjugate, bool PanelMode> |
| struct gemm_pack_rhs<Scalar, Index, nr, RowMajor, Conjugate, PanelMode> |
| { |
| enum { PacketSize = packet_traits<Scalar>::size }; |
| EIGEN_DONT_INLINE void operator()(Scalar* blockB, const Scalar* rhs, Index rhsStride, Index depth, Index cols, |
| Index stride=0, Index offset=0) |
| { |
| EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK RHS ROWMAJOR"); |
| eigen_assert(((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride)); |
| conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj; |
| Index packet_cols = (cols/nr) * nr; |
| Index count = 0; |
| for(Index j2=0; j2<packet_cols; j2+=nr) |
| { |
| // skip what we have before |
| if(PanelMode) count += nr * offset; |
| for(Index k=0; k<depth; k++) |
| { |
| const Scalar* b0 = &rhs[k*rhsStride + j2]; |
| blockB[count+0] = cj(b0[0]); |
| blockB[count+1] = cj(b0[1]); |
| if(nr==4) blockB[count+2] = cj(b0[2]); |
| if(nr==4) blockB[count+3] = cj(b0[3]); |
| count += nr; |
| } |
| // skip what we have after |
| if(PanelMode) count += nr * (stride-offset-depth); |
| } |
| // copy the remaining columns one at a time (nr==1) |
| for(Index j2=packet_cols; j2<cols; ++j2) |
| { |
| if(PanelMode) count += offset; |
| const Scalar* b0 = &rhs[j2]; |
| for(Index k=0; k<depth; k++) |
| { |
| blockB[count] = cj(b0[k*rhsStride]); |
| count += 1; |
| } |
| if(PanelMode) count += stride-offset-depth; |
| } |
| } |
| }; |
| |
| } // end namespace internal |
| |
| /** \returns the currently set level 1 cpu cache size (in bytes) used to estimate the ideal blocking size parameters. |
| * \sa setCpuCacheSize */ |
| inline std::ptrdiff_t l1CacheSize() |
| { |
| std::ptrdiff_t l1, l2; |
| internal::manage_caching_sizes(GetAction, &l1, &l2); |
| return l1; |
| } |
| |
| /** \returns the currently set level 2 cpu cache size (in bytes) used to estimate the ideal blocking size parameters. |
| * \sa setCpuCacheSize */ |
| inline std::ptrdiff_t l2CacheSize() |
| { |
| std::ptrdiff_t l1, l2; |
| internal::manage_caching_sizes(GetAction, &l1, &l2); |
| return l2; |
| } |
| |
| /** Set the cpu L1 and L2 cache sizes (in bytes). |
| * These values are use to adjust the size of the blocks |
| * for the algorithms working per blocks. |
| * |
| * \sa computeProductBlockingSizes */ |
| inline void setCpuCacheSizes(std::ptrdiff_t l1, std::ptrdiff_t l2) |
| { |
| internal::manage_caching_sizes(SetAction, &l1, &l2); |
| } |
| |
| } // end namespace Eigen |
| |
| #endif // EIGEN_GENERAL_BLOCK_PANEL_H |
