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+// 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
generated by cgit v1.2.3 (git 2.25.1) at 2024-06-13 09:20:44 +0000