[136] | 1 | // This file is part of Eigen, a lightweight C++ template library
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| 2 | // for linear algebra.
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| 3 | //
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| 4 | // Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
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| 5 | //
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| 6 | // This Source Code Form is subject to the terms of the Mozilla
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| 7 | // Public License v. 2.0. If a copy of the MPL was not distributed
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| 8 | // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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| 9 |
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| 10 | #ifndef EIGEN_GENERAL_BLOCK_PANEL_H
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| 11 | #define EIGEN_GENERAL_BLOCK_PANEL_H
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| 12 |
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| 13 | namespace Eigen {
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| 14 |
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| 15 | namespace internal {
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| 16 |
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| 17 | template<typename _LhsScalar, typename _RhsScalar, bool _ConjLhs=false, bool _ConjRhs=false>
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| 18 | class gebp_traits;
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| 19 |
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| 20 |
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| 21 | /** \internal \returns b if a<=0, and returns a otherwise. */
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| 22 | inline std::ptrdiff_t manage_caching_sizes_helper(std::ptrdiff_t a, std::ptrdiff_t b)
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| 23 | {
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| 24 | return a<=0 ? b : a;
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| 25 | }
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| 26 |
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| 27 | /** \internal */
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| 28 | inline void manage_caching_sizes(Action action, std::ptrdiff_t* l1=0, std::ptrdiff_t* l2=0)
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| 29 | {
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| 30 | static std::ptrdiff_t m_l1CacheSize = 0;
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| 31 | static std::ptrdiff_t m_l2CacheSize = 0;
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| 32 | if(m_l2CacheSize==0)
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| 33 | {
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| 34 | m_l1CacheSize = manage_caching_sizes_helper(queryL1CacheSize(),8 * 1024);
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| 35 | m_l2CacheSize = manage_caching_sizes_helper(queryTopLevelCacheSize(),1*1024*1024);
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| 36 | }
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| 37 |
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| 38 | if(action==SetAction)
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| 39 | {
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| 40 | // set the cpu cache size and cache all block sizes from a global cache size in byte
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| 41 | eigen_internal_assert(l1!=0 && l2!=0);
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| 42 | m_l1CacheSize = *l1;
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| 43 | m_l2CacheSize = *l2;
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| 44 | }
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| 45 | else if(action==GetAction)
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| 46 | {
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| 47 | eigen_internal_assert(l1!=0 && l2!=0);
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| 48 | *l1 = m_l1CacheSize;
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| 49 | *l2 = m_l2CacheSize;
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| 50 | }
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| 51 | else
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| 52 | {
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| 53 | eigen_internal_assert(false);
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| 54 | }
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| 55 | }
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| 56 |
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| 57 | /** \brief Computes the blocking parameters for a m x k times k x n matrix product
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| 58 | *
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| 59 | * \param[in,out] k Input: the third dimension of the product. Output: the blocking size along the same dimension.
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| 60 | * \param[in,out] m Input: the number of rows of the left hand side. Output: the blocking size along the same dimension.
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| 61 | * \param[in,out] n Input: the number of columns of the right hand side. Output: the blocking size along the same dimension.
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| 62 | *
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| 63 | * Given a m x k times k x n matrix product of scalar types \c LhsScalar and \c RhsScalar,
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| 64 | * this function computes the blocking size parameters along the respective dimensions
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| 65 | * for matrix products and related algorithms. The blocking sizes depends on various
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| 66 | * parameters:
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| 67 | * - the L1 and L2 cache sizes,
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| 68 | * - the register level blocking sizes defined by gebp_traits,
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| 69 | * - the number of scalars that fit into a packet (when vectorization is enabled).
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| 70 | *
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| 71 | * \sa setCpuCacheSizes */
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| 72 | template<typename LhsScalar, typename RhsScalar, int KcFactor, typename SizeType>
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| 73 | void computeProductBlockingSizes(SizeType& k, SizeType& m, SizeType& n)
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| 74 | {
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| 75 | EIGEN_UNUSED_VARIABLE(n);
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| 76 | // Explanations:
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| 77 | // Let's recall the product algorithms form kc x nc horizontal panels B' on the rhs and
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| 78 | // mc x kc blocks A' on the lhs. A' has to fit into L2 cache. Moreover, B' is processed
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| 79 | // per kc x nr vertical small panels where nr is the blocking size along the n dimension
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| 80 | // at the register level. For vectorization purpose, these small vertical panels are unpacked,
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| 81 | // e.g., each coefficient is replicated to fit a packet. This small vertical panel has to
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| 82 | // stay in L1 cache.
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| 83 | std::ptrdiff_t l1, l2;
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| 84 |
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| 85 | typedef gebp_traits<LhsScalar,RhsScalar> Traits;
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| 86 | enum {
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| 87 | kdiv = KcFactor * 2 * Traits::nr
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| 88 | * Traits::RhsProgress * sizeof(RhsScalar),
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| 89 | mr = gebp_traits<LhsScalar,RhsScalar>::mr,
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| 90 | mr_mask = (0xffffffff/mr)*mr
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| 91 | };
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| 92 |
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| 93 | manage_caching_sizes(GetAction, &l1, &l2);
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| 94 | k = std::min<SizeType>(k, l1/kdiv);
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| 95 | SizeType _m = k>0 ? l2/(4 * sizeof(LhsScalar) * k) : 0;
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| 96 | if(_m<m) m = _m & mr_mask;
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| 97 | }
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| 98 |
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| 99 | template<typename LhsScalar, typename RhsScalar, typename SizeType>
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| 100 | inline void computeProductBlockingSizes(SizeType& k, SizeType& m, SizeType& n)
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| 101 | {
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| 102 | computeProductBlockingSizes<LhsScalar,RhsScalar,1>(k, m, n);
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| 103 | }
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| 104 |
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| 105 | #ifdef EIGEN_HAS_FUSE_CJMADD
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| 106 | #define MADD(CJ,A,B,C,T) C = CJ.pmadd(A,B,C);
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| 107 | #else
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| 108 |
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| 109 | // FIXME (a bit overkill maybe ?)
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| 110 |
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| 111 | template<typename CJ, typename A, typename B, typename C, typename T> struct gebp_madd_selector {
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| 112 | EIGEN_ALWAYS_INLINE static void run(const CJ& cj, A& a, B& b, C& c, T& /*t*/)
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| 113 | {
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| 114 | c = cj.pmadd(a,b,c);
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| 115 | }
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| 116 | };
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| 117 |
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| 118 | template<typename CJ, typename T> struct gebp_madd_selector<CJ,T,T,T,T> {
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| 119 | EIGEN_ALWAYS_INLINE static void run(const CJ& cj, T& a, T& b, T& c, T& t)
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| 120 | {
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| 121 | t = b; t = cj.pmul(a,t); c = padd(c,t);
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| 122 | }
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| 123 | };
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| 124 |
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| 125 | template<typename CJ, typename A, typename B, typename C, typename T>
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| 126 | EIGEN_STRONG_INLINE void gebp_madd(const CJ& cj, A& a, B& b, C& c, T& t)
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| 127 | {
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| 128 | gebp_madd_selector<CJ,A,B,C,T>::run(cj,a,b,c,t);
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| 129 | }
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| 130 |
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| 131 | #define MADD(CJ,A,B,C,T) gebp_madd(CJ,A,B,C,T);
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| 132 | // #define MADD(CJ,A,B,C,T) T = B; T = CJ.pmul(A,T); C = padd(C,T);
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| 133 | #endif
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| 134 |
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| 135 | /* Vectorization logic
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| 136 | * real*real: unpack rhs to constant packets, ...
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| 137 | *
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| 138 | * 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),
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| 139 | * storing each res packet into two packets (2x2),
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| 140 | * at the end combine them: swap the second and addsub them
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| 141 | * cf*cf : same but with 2x4 blocks
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| 142 | * cplx*real : unpack rhs to constant packets, ...
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| 143 | * real*cplx : load lhs as (a0,a0,a1,a1), and mul as usual
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| 144 | */
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| 145 | template<typename _LhsScalar, typename _RhsScalar, bool _ConjLhs, bool _ConjRhs>
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| 146 | class gebp_traits
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| 147 | {
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| 148 | public:
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| 149 | typedef _LhsScalar LhsScalar;
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| 150 | typedef _RhsScalar RhsScalar;
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| 151 | typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar;
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| 152 |
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| 153 | enum {
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| 154 | ConjLhs = _ConjLhs,
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| 155 | ConjRhs = _ConjRhs,
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| 156 | Vectorizable = packet_traits<LhsScalar>::Vectorizable && packet_traits<RhsScalar>::Vectorizable,
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| 157 | LhsPacketSize = Vectorizable ? packet_traits<LhsScalar>::size : 1,
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| 158 | RhsPacketSize = Vectorizable ? packet_traits<RhsScalar>::size : 1,
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| 159 | ResPacketSize = Vectorizable ? packet_traits<ResScalar>::size : 1,
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| 160 |
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| 161 | NumberOfRegisters = EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS,
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| 162 |
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| 163 | // register block size along the N direction (must be either 2 or 4)
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| 164 | nr = NumberOfRegisters/4,
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| 165 |
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| 166 | // register block size along the M direction (currently, this one cannot be modified)
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| 167 | mr = 2 * LhsPacketSize,
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| 168 |
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| 169 | WorkSpaceFactor = nr * RhsPacketSize,
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| 170 |
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| 171 | LhsProgress = LhsPacketSize,
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| 172 | RhsProgress = RhsPacketSize
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| 173 | };
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| 174 |
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| 175 | typedef typename packet_traits<LhsScalar>::type _LhsPacket;
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| 176 | typedef typename packet_traits<RhsScalar>::type _RhsPacket;
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| 177 | typedef typename packet_traits<ResScalar>::type _ResPacket;
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| 178 |
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| 179 | typedef typename conditional<Vectorizable,_LhsPacket,LhsScalar>::type LhsPacket;
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| 180 | typedef typename conditional<Vectorizable,_RhsPacket,RhsScalar>::type RhsPacket;
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| 181 | typedef typename conditional<Vectorizable,_ResPacket,ResScalar>::type ResPacket;
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| 182 |
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| 183 | typedef ResPacket AccPacket;
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| 184 |
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| 185 | EIGEN_STRONG_INLINE void initAcc(AccPacket& p)
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| 186 | {
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| 187 | p = pset1<ResPacket>(ResScalar(0));
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| 188 | }
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| 189 |
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| 190 | EIGEN_STRONG_INLINE void unpackRhs(DenseIndex n, const RhsScalar* rhs, RhsScalar* b)
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| 191 | {
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| 192 | for(DenseIndex k=0; k<n; k++)
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| 193 | pstore1<RhsPacket>(&b[k*RhsPacketSize], rhs[k]);
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| 194 | }
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| 195 |
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| 196 | EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacket& dest) const
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| 197 | {
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| 198 | dest = pload<RhsPacket>(b);
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| 199 | }
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| 200 |
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| 201 | EIGEN_STRONG_INLINE void loadLhs(const LhsScalar* a, LhsPacket& dest) const
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| 202 | {
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| 203 | dest = pload<LhsPacket>(a);
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| 204 | }
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| 205 |
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| 206 | EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, AccPacket& c, AccPacket& tmp) const
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| 207 | {
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| 208 | tmp = b; tmp = pmul(a,tmp); c = padd(c,tmp);
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| 209 | }
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| 210 |
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| 211 | EIGEN_STRONG_INLINE void acc(const AccPacket& c, const ResPacket& alpha, ResPacket& r) const
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| 212 | {
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| 213 | r = pmadd(c,alpha,r);
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| 214 | }
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| 215 |
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| 216 | protected:
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| 217 | // conj_helper<LhsScalar,RhsScalar,ConjLhs,ConjRhs> cj;
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| 218 | // conj_helper<LhsPacket,RhsPacket,ConjLhs,ConjRhs> pcj;
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| 219 | };
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| 220 |
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| 221 | template<typename RealScalar, bool _ConjLhs>
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| 222 | class gebp_traits<std::complex<RealScalar>, RealScalar, _ConjLhs, false>
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| 223 | {
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| 224 | public:
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| 225 | typedef std::complex<RealScalar> LhsScalar;
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| 226 | typedef RealScalar RhsScalar;
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| 227 | typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar;
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| 228 |
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| 229 | enum {
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| 230 | ConjLhs = _ConjLhs,
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| 231 | ConjRhs = false,
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| 232 | Vectorizable = packet_traits<LhsScalar>::Vectorizable && packet_traits<RhsScalar>::Vectorizable,
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| 233 | LhsPacketSize = Vectorizable ? packet_traits<LhsScalar>::size : 1,
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| 234 | RhsPacketSize = Vectorizable ? packet_traits<RhsScalar>::size : 1,
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| 235 | ResPacketSize = Vectorizable ? packet_traits<ResScalar>::size : 1,
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| 236 |
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| 237 | NumberOfRegisters = EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS,
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| 238 | nr = NumberOfRegisters/4,
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| 239 | mr = 2 * LhsPacketSize,
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| 240 | WorkSpaceFactor = nr*RhsPacketSize,
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| 241 |
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| 242 | LhsProgress = LhsPacketSize,
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| 243 | RhsProgress = RhsPacketSize
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| 244 | };
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| 245 |
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| 246 | typedef typename packet_traits<LhsScalar>::type _LhsPacket;
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| 247 | typedef typename packet_traits<RhsScalar>::type _RhsPacket;
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| 248 | typedef typename packet_traits<ResScalar>::type _ResPacket;
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| 249 |
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| 250 | typedef typename conditional<Vectorizable,_LhsPacket,LhsScalar>::type LhsPacket;
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| 251 | typedef typename conditional<Vectorizable,_RhsPacket,RhsScalar>::type RhsPacket;
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| 252 | typedef typename conditional<Vectorizable,_ResPacket,ResScalar>::type ResPacket;
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| 253 |
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| 254 | typedef ResPacket AccPacket;
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| 255 |
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| 256 | EIGEN_STRONG_INLINE void initAcc(AccPacket& p)
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| 257 | {
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| 258 | p = pset1<ResPacket>(ResScalar(0));
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| 259 | }
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| 260 |
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| 261 | EIGEN_STRONG_INLINE void unpackRhs(DenseIndex n, const RhsScalar* rhs, RhsScalar* b)
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| 262 | {
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| 263 | for(DenseIndex k=0; k<n; k++)
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| 264 | pstore1<RhsPacket>(&b[k*RhsPacketSize], rhs[k]);
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| 265 | }
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| 266 |
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| 267 | EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacket& dest) const
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| 268 | {
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| 269 | dest = pload<RhsPacket>(b);
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| 270 | }
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| 271 |
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| 272 | EIGEN_STRONG_INLINE void loadLhs(const LhsScalar* a, LhsPacket& dest) const
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| 273 | {
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| 274 | dest = pload<LhsPacket>(a);
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| 275 | }
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| 276 |
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| 277 | EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, AccPacket& c, RhsPacket& tmp) const
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| 278 | {
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| 279 | madd_impl(a, b, c, tmp, typename conditional<Vectorizable,true_type,false_type>::type());
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| 280 | }
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| 281 |
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| 282 | EIGEN_STRONG_INLINE void madd_impl(const LhsPacket& a, const RhsPacket& b, AccPacket& c, RhsPacket& tmp, const true_type&) const
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| 283 | {
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| 284 | tmp = b; tmp = pmul(a.v,tmp); c.v = padd(c.v,tmp);
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| 285 | }
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| 286 |
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| 287 | EIGEN_STRONG_INLINE void madd_impl(const LhsScalar& a, const RhsScalar& b, ResScalar& c, RhsScalar& /*tmp*/, const false_type&) const
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| 288 | {
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| 289 | c += a * b;
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| 290 | }
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| 291 |
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| 292 | EIGEN_STRONG_INLINE void acc(const AccPacket& c, const ResPacket& alpha, ResPacket& r) const
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| 293 | {
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| 294 | r = cj.pmadd(c,alpha,r);
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| 295 | }
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| 296 |
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| 297 | protected:
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| 298 | conj_helper<ResPacket,ResPacket,ConjLhs,false> cj;
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| 299 | };
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| 300 |
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| 301 | template<typename RealScalar, bool _ConjLhs, bool _ConjRhs>
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| 302 | class gebp_traits<std::complex<RealScalar>, std::complex<RealScalar>, _ConjLhs, _ConjRhs >
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| 303 | {
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| 304 | public:
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| 305 | typedef std::complex<RealScalar> Scalar;
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| 306 | typedef std::complex<RealScalar> LhsScalar;
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| 307 | typedef std::complex<RealScalar> RhsScalar;
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| 308 | typedef std::complex<RealScalar> ResScalar;
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| 309 |
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| 310 | enum {
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| 311 | ConjLhs = _ConjLhs,
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| 312 | ConjRhs = _ConjRhs,
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| 313 | Vectorizable = packet_traits<RealScalar>::Vectorizable
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| 314 | && packet_traits<Scalar>::Vectorizable,
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| 315 | RealPacketSize = Vectorizable ? packet_traits<RealScalar>::size : 1,
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| 316 | ResPacketSize = Vectorizable ? packet_traits<ResScalar>::size : 1,
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| 317 |
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| 318 | nr = 2,
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| 319 | mr = 2 * ResPacketSize,
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| 320 | WorkSpaceFactor = Vectorizable ? 2*nr*RealPacketSize : nr,
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| 321 |
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| 322 | LhsProgress = ResPacketSize,
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| 323 | RhsProgress = Vectorizable ? 2*ResPacketSize : 1
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| 324 | };
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| 325 |
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| 326 | typedef typename packet_traits<RealScalar>::type RealPacket;
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| 327 | typedef typename packet_traits<Scalar>::type ScalarPacket;
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| 328 | struct DoublePacket
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| 329 | {
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| 330 | RealPacket first;
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| 331 | RealPacket second;
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| 332 | };
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| 333 |
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| 334 | typedef typename conditional<Vectorizable,RealPacket, Scalar>::type LhsPacket;
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| 335 | typedef typename conditional<Vectorizable,DoublePacket,Scalar>::type RhsPacket;
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| 336 | typedef typename conditional<Vectorizable,ScalarPacket,Scalar>::type ResPacket;
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| 337 | typedef typename conditional<Vectorizable,DoublePacket,Scalar>::type AccPacket;
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| 338 |
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| 339 | EIGEN_STRONG_INLINE void initAcc(Scalar& p) { p = Scalar(0); }
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| 340 |
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| 341 | EIGEN_STRONG_INLINE void initAcc(DoublePacket& p)
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| 342 | {
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| 343 | p.first = pset1<RealPacket>(RealScalar(0));
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| 344 | p.second = pset1<RealPacket>(RealScalar(0));
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| 345 | }
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| 346 |
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| 347 | /* Unpack the rhs coeff such that each complex coefficient is spread into
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| 348 | * two packects containing respectively the real and imaginary coefficient
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| 349 | * duplicated as many time as needed: (x+iy) => [x, ..., x] [y, ..., y]
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| 350 | */
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| 351 | EIGEN_STRONG_INLINE void unpackRhs(DenseIndex n, const Scalar* rhs, Scalar* b)
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| 352 | {
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| 353 | for(DenseIndex k=0; k<n; k++)
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| 354 | {
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| 355 | if(Vectorizable)
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| 356 | {
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| 357 | pstore1<RealPacket>((RealScalar*)&b[k*ResPacketSize*2+0], real(rhs[k]));
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| 358 | pstore1<RealPacket>((RealScalar*)&b[k*ResPacketSize*2+ResPacketSize], imag(rhs[k]));
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| 359 | }
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| 360 | else
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| 361 | b[k] = rhs[k];
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| 362 | }
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| 363 | }
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| 364 |
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| 365 | EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, ResPacket& dest) const { dest = *b; }
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| 366 |
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| 367 | EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, DoublePacket& dest) const
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| 368 | {
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| 369 | dest.first = pload<RealPacket>((const RealScalar*)b);
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| 370 | dest.second = pload<RealPacket>((const RealScalar*)(b+ResPacketSize));
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| 371 | }
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| 372 |
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| 373 | // nothing special here
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| 374 | EIGEN_STRONG_INLINE void loadLhs(const LhsScalar* a, LhsPacket& dest) const
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| 375 | {
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| 376 | dest = pload<LhsPacket>((const typename unpacket_traits<LhsPacket>::type*)(a));
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| 377 | }
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| 378 |
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| 379 | EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, DoublePacket& c, RhsPacket& /*tmp*/) const
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| 380 | {
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| 381 | c.first = padd(pmul(a,b.first), c.first);
|
---|
| 382 | c.second = padd(pmul(a,b.second),c.second);
|
---|
| 383 | }
|
---|
| 384 |
|
---|
| 385 | EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, ResPacket& c, RhsPacket& /*tmp*/) const
|
---|
| 386 | {
|
---|
| 387 | c = cj.pmadd(a,b,c);
|
---|
| 388 | }
|
---|
| 389 |
|
---|
| 390 | EIGEN_STRONG_INLINE void acc(const Scalar& c, const Scalar& alpha, Scalar& r) const { r += alpha * c; }
|
---|
| 391 |
|
---|
| 392 | EIGEN_STRONG_INLINE void acc(const DoublePacket& c, const ResPacket& alpha, ResPacket& r) const
|
---|
| 393 | {
|
---|
| 394 | // assemble c
|
---|
| 395 | ResPacket tmp;
|
---|
| 396 | if((!ConjLhs)&&(!ConjRhs))
|
---|
| 397 | {
|
---|
| 398 | tmp = pcplxflip(pconj(ResPacket(c.second)));
|
---|
| 399 | tmp = padd(ResPacket(c.first),tmp);
|
---|
| 400 | }
|
---|
| 401 | else if((!ConjLhs)&&(ConjRhs))
|
---|
| 402 | {
|
---|
| 403 | tmp = pconj(pcplxflip(ResPacket(c.second)));
|
---|
| 404 | tmp = padd(ResPacket(c.first),tmp);
|
---|
| 405 | }
|
---|
| 406 | else if((ConjLhs)&&(!ConjRhs))
|
---|
| 407 | {
|
---|
| 408 | tmp = pcplxflip(ResPacket(c.second));
|
---|
| 409 | tmp = padd(pconj(ResPacket(c.first)),tmp);
|
---|
| 410 | }
|
---|
| 411 | else if((ConjLhs)&&(ConjRhs))
|
---|
| 412 | {
|
---|
| 413 | tmp = pcplxflip(ResPacket(c.second));
|
---|
| 414 | tmp = psub(pconj(ResPacket(c.first)),tmp);
|
---|
| 415 | }
|
---|
| 416 |
|
---|
| 417 | r = pmadd(tmp,alpha,r);
|
---|
| 418 | }
|
---|
| 419 |
|
---|
| 420 | protected:
|
---|
| 421 | conj_helper<LhsScalar,RhsScalar,ConjLhs,ConjRhs> cj;
|
---|
| 422 | };
|
---|
| 423 |
|
---|
| 424 | template<typename RealScalar, bool _ConjRhs>
|
---|
| 425 | class gebp_traits<RealScalar, std::complex<RealScalar>, false, _ConjRhs >
|
---|
| 426 | {
|
---|
| 427 | public:
|
---|
| 428 | typedef std::complex<RealScalar> Scalar;
|
---|
| 429 | typedef RealScalar LhsScalar;
|
---|
| 430 | typedef Scalar RhsScalar;
|
---|
| 431 | typedef Scalar ResScalar;
|
---|
| 432 |
|
---|
| 433 | enum {
|
---|
| 434 | ConjLhs = false,
|
---|
| 435 | ConjRhs = _ConjRhs,
|
---|
| 436 | Vectorizable = packet_traits<RealScalar>::Vectorizable
|
---|
| 437 | && packet_traits<Scalar>::Vectorizable,
|
---|
| 438 | LhsPacketSize = Vectorizable ? packet_traits<LhsScalar>::size : 1,
|
---|
| 439 | RhsPacketSize = Vectorizable ? packet_traits<RhsScalar>::size : 1,
|
---|
| 440 | ResPacketSize = Vectorizable ? packet_traits<ResScalar>::size : 1,
|
---|
| 441 |
|
---|
| 442 | NumberOfRegisters = EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS,
|
---|
| 443 | nr = 4,
|
---|
| 444 | mr = 2*ResPacketSize,
|
---|
| 445 | WorkSpaceFactor = nr*RhsPacketSize,
|
---|
| 446 |
|
---|
| 447 | LhsProgress = ResPacketSize,
|
---|
| 448 | RhsProgress = ResPacketSize
|
---|
| 449 | };
|
---|
| 450 |
|
---|
| 451 | typedef typename packet_traits<LhsScalar>::type _LhsPacket;
|
---|
| 452 | typedef typename packet_traits<RhsScalar>::type _RhsPacket;
|
---|
| 453 | typedef typename packet_traits<ResScalar>::type _ResPacket;
|
---|
| 454 |
|
---|
| 455 | typedef typename conditional<Vectorizable,_LhsPacket,LhsScalar>::type LhsPacket;
|
---|
| 456 | typedef typename conditional<Vectorizable,_RhsPacket,RhsScalar>::type RhsPacket;
|
---|
| 457 | typedef typename conditional<Vectorizable,_ResPacket,ResScalar>::type ResPacket;
|
---|
| 458 |
|
---|
| 459 | typedef ResPacket AccPacket;
|
---|
| 460 |
|
---|
| 461 | EIGEN_STRONG_INLINE void initAcc(AccPacket& p)
|
---|
| 462 | {
|
---|
| 463 | p = pset1<ResPacket>(ResScalar(0));
|
---|
| 464 | }
|
---|
| 465 |
|
---|
| 466 | EIGEN_STRONG_INLINE void unpackRhs(DenseIndex n, const RhsScalar* rhs, RhsScalar* b)
|
---|
| 467 | {
|
---|
| 468 | for(DenseIndex k=0; k<n; k++)
|
---|
| 469 | pstore1<RhsPacket>(&b[k*RhsPacketSize], rhs[k]);
|
---|
| 470 | }
|
---|
| 471 |
|
---|
| 472 | EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacket& dest) const
|
---|
| 473 | {
|
---|
| 474 | dest = pload<RhsPacket>(b);
|
---|
| 475 | }
|
---|
| 476 |
|
---|
| 477 | EIGEN_STRONG_INLINE void loadLhs(const LhsScalar* a, LhsPacket& dest) const
|
---|
| 478 | {
|
---|
| 479 | dest = ploaddup<LhsPacket>(a);
|
---|
| 480 | }
|
---|
| 481 |
|
---|
| 482 | EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, AccPacket& c, RhsPacket& tmp) const
|
---|
| 483 | {
|
---|
| 484 | madd_impl(a, b, c, tmp, typename conditional<Vectorizable,true_type,false_type>::type());
|
---|
| 485 | }
|
---|
| 486 |
|
---|
| 487 | EIGEN_STRONG_INLINE void madd_impl(const LhsPacket& a, const RhsPacket& b, AccPacket& c, RhsPacket& tmp, const true_type&) const
|
---|
| 488 | {
|
---|
| 489 | tmp = b; tmp.v = pmul(a,tmp.v); c = padd(c,tmp);
|
---|
| 490 | }
|
---|
| 491 |
|
---|
| 492 | EIGEN_STRONG_INLINE void madd_impl(const LhsScalar& a, const RhsScalar& b, ResScalar& c, RhsScalar& /*tmp*/, const false_type&) const
|
---|
| 493 | {
|
---|
| 494 | c += a * b;
|
---|
| 495 | }
|
---|
| 496 |
|
---|
| 497 | EIGEN_STRONG_INLINE void acc(const AccPacket& c, const ResPacket& alpha, ResPacket& r) const
|
---|
| 498 | {
|
---|
| 499 | r = cj.pmadd(alpha,c,r);
|
---|
| 500 | }
|
---|
| 501 |
|
---|
| 502 | protected:
|
---|
| 503 | conj_helper<ResPacket,ResPacket,false,ConjRhs> cj;
|
---|
| 504 | };
|
---|
| 505 |
|
---|
| 506 | /* optimized GEneral packed Block * packed Panel product kernel
|
---|
| 507 | *
|
---|
| 508 | * Mixing type logic: C += A * B
|
---|
| 509 | * | A | B | comments
|
---|
| 510 | * |real |cplx | no vectorization yet, would require to pack A with duplication
|
---|
| 511 | * |cplx |real | easy vectorization
|
---|
| 512 | */
|
---|
| 513 | template<typename LhsScalar, typename RhsScalar, typename Index, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
|
---|
| 514 | struct gebp_kernel
|
---|
| 515 | {
|
---|
| 516 | typedef gebp_traits<LhsScalar,RhsScalar,ConjugateLhs,ConjugateRhs> Traits;
|
---|
| 517 | typedef typename Traits::ResScalar ResScalar;
|
---|
| 518 | typedef typename Traits::LhsPacket LhsPacket;
|
---|
| 519 | typedef typename Traits::RhsPacket RhsPacket;
|
---|
| 520 | typedef typename Traits::ResPacket ResPacket;
|
---|
| 521 | typedef typename Traits::AccPacket AccPacket;
|
---|
| 522 |
|
---|
| 523 | enum {
|
---|
| 524 | Vectorizable = Traits::Vectorizable,
|
---|
| 525 | LhsProgress = Traits::LhsProgress,
|
---|
| 526 | RhsProgress = Traits::RhsProgress,
|
---|
| 527 | ResPacketSize = Traits::ResPacketSize
|
---|
| 528 | };
|
---|
| 529 |
|
---|
| 530 | EIGEN_DONT_INLINE
|
---|
| 531 | void operator()(ResScalar* res, Index resStride, const LhsScalar* blockA, const RhsScalar* blockB, Index rows, Index depth, Index cols, ResScalar alpha,
|
---|
| 532 | Index strideA=-1, Index strideB=-1, Index offsetA=0, Index offsetB=0, RhsScalar* unpackedB=0);
|
---|
| 533 | };
|
---|
| 534 |
|
---|
| 535 | template<typename LhsScalar, typename RhsScalar, typename Index, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
|
---|
| 536 | EIGEN_DONT_INLINE
|
---|
| 537 | void gebp_kernel<LhsScalar,RhsScalar,Index,mr,nr,ConjugateLhs,ConjugateRhs>
|
---|
| 538 | ::operator()(ResScalar* res, Index resStride, const LhsScalar* blockA, const RhsScalar* blockB, Index rows, Index depth, Index cols, ResScalar alpha,
|
---|
| 539 | Index strideA, Index strideB, Index offsetA, Index offsetB, RhsScalar* unpackedB)
|
---|
| 540 | {
|
---|
| 541 | Traits traits;
|
---|
| 542 |
|
---|
| 543 | if(strideA==-1) strideA = depth;
|
---|
| 544 | if(strideB==-1) strideB = depth;
|
---|
| 545 | conj_helper<LhsScalar,RhsScalar,ConjugateLhs,ConjugateRhs> cj;
|
---|
| 546 | // conj_helper<LhsPacket,RhsPacket,ConjugateLhs,ConjugateRhs> pcj;
|
---|
| 547 | Index packet_cols = (cols/nr) * nr;
|
---|
| 548 | const Index peeled_mc = (rows/mr)*mr;
|
---|
| 549 | // FIXME:
|
---|
| 550 | const Index peeled_mc2 = peeled_mc + (rows-peeled_mc >= LhsProgress ? LhsProgress : 0);
|
---|
| 551 | const Index peeled_kc = (depth/4)*4;
|
---|
| 552 |
|
---|
| 553 | if(unpackedB==0)
|
---|
| 554 | unpackedB = const_cast<RhsScalar*>(blockB - strideB * nr * RhsProgress);
|
---|
| 555 |
|
---|
| 556 | // loops on each micro vertical panel of rhs (depth x nr)
|
---|
| 557 | for(Index j2=0; j2<packet_cols; j2+=nr)
|
---|
| 558 | {
|
---|
| 559 | traits.unpackRhs(depth*nr,&blockB[j2*strideB+offsetB*nr],unpackedB);
|
---|
| 560 |
|
---|
| 561 | // loops on each largest micro horizontal panel of lhs (mr x depth)
|
---|
| 562 | // => we select a mr x nr micro block of res which is entirely
|
---|
| 563 | // stored into mr/packet_size x nr registers.
|
---|
| 564 | for(Index i=0; i<peeled_mc; i+=mr)
|
---|
| 565 | {
|
---|
| 566 | const LhsScalar* blA = &blockA[i*strideA+offsetA*mr];
|
---|
| 567 | prefetch(&blA[0]);
|
---|
| 568 |
|
---|
| 569 | // gets res block as register
|
---|
| 570 | AccPacket C0, C1, C2, C3, C4, C5, C6, C7;
|
---|
| 571 | traits.initAcc(C0);
|
---|
| 572 | traits.initAcc(C1);
|
---|
| 573 | if(nr==4) traits.initAcc(C2);
|
---|
| 574 | if(nr==4) traits.initAcc(C3);
|
---|
| 575 | traits.initAcc(C4);
|
---|
| 576 | traits.initAcc(C5);
|
---|
| 577 | if(nr==4) traits.initAcc(C6);
|
---|
| 578 | if(nr==4) traits.initAcc(C7);
|
---|
| 579 |
|
---|
| 580 | ResScalar* r0 = &res[(j2+0)*resStride + i];
|
---|
| 581 | ResScalar* r1 = r0 + resStride;
|
---|
| 582 | ResScalar* r2 = r1 + resStride;
|
---|
| 583 | ResScalar* r3 = r2 + resStride;
|
---|
| 584 |
|
---|
| 585 | prefetch(r0+16);
|
---|
| 586 | prefetch(r1+16);
|
---|
| 587 | prefetch(r2+16);
|
---|
| 588 | prefetch(r3+16);
|
---|
| 589 |
|
---|
| 590 | // performs "inner" product
|
---|
| 591 | // TODO let's check wether the folowing peeled loop could not be
|
---|
| 592 | // optimized via optimal prefetching from one loop to the other
|
---|
| 593 | const RhsScalar* blB = unpackedB;
|
---|
| 594 | for(Index k=0; k<peeled_kc; k+=4)
|
---|
| 595 | {
|
---|
| 596 | if(nr==2)
|
---|
| 597 | {
|
---|
| 598 | LhsPacket A0, A1;
|
---|
| 599 | RhsPacket B_0;
|
---|
| 600 | RhsPacket T0;
|
---|
| 601 |
|
---|
| 602 | EIGEN_ASM_COMMENT("mybegin2");
|
---|
| 603 | traits.loadLhs(&blA[0*LhsProgress], A0);
|
---|
| 604 | traits.loadLhs(&blA[1*LhsProgress], A1);
|
---|
| 605 | traits.loadRhs(&blB[0*RhsProgress], B_0);
|
---|
| 606 | traits.madd(A0,B_0,C0,T0);
|
---|
| 607 | traits.madd(A1,B_0,C4,B_0);
|
---|
| 608 | traits.loadRhs(&blB[1*RhsProgress], B_0);
|
---|
| 609 | traits.madd(A0,B_0,C1,T0);
|
---|
| 610 | traits.madd(A1,B_0,C5,B_0);
|
---|
| 611 |
|
---|
| 612 | traits.loadLhs(&blA[2*LhsProgress], A0);
|
---|
| 613 | traits.loadLhs(&blA[3*LhsProgress], A1);
|
---|
| 614 | traits.loadRhs(&blB[2*RhsProgress], B_0);
|
---|
| 615 | traits.madd(A0,B_0,C0,T0);
|
---|
| 616 | traits.madd(A1,B_0,C4,B_0);
|
---|
| 617 | traits.loadRhs(&blB[3*RhsProgress], B_0);
|
---|
| 618 | traits.madd(A0,B_0,C1,T0);
|
---|
| 619 | traits.madd(A1,B_0,C5,B_0);
|
---|
| 620 |
|
---|
| 621 | traits.loadLhs(&blA[4*LhsProgress], A0);
|
---|
| 622 | traits.loadLhs(&blA[5*LhsProgress], A1);
|
---|
| 623 | traits.loadRhs(&blB[4*RhsProgress], B_0);
|
---|
| 624 | traits.madd(A0,B_0,C0,T0);
|
---|
| 625 | traits.madd(A1,B_0,C4,B_0);
|
---|
| 626 | traits.loadRhs(&blB[5*RhsProgress], B_0);
|
---|
| 627 | traits.madd(A0,B_0,C1,T0);
|
---|
| 628 | traits.madd(A1,B_0,C5,B_0);
|
---|
| 629 |
|
---|
| 630 | traits.loadLhs(&blA[6*LhsProgress], A0);
|
---|
| 631 | traits.loadLhs(&blA[7*LhsProgress], A1);
|
---|
| 632 | traits.loadRhs(&blB[6*RhsProgress], B_0);
|
---|
| 633 | traits.madd(A0,B_0,C0,T0);
|
---|
| 634 | traits.madd(A1,B_0,C4,B_0);
|
---|
| 635 | traits.loadRhs(&blB[7*RhsProgress], B_0);
|
---|
| 636 | traits.madd(A0,B_0,C1,T0);
|
---|
| 637 | traits.madd(A1,B_0,C5,B_0);
|
---|
| 638 | EIGEN_ASM_COMMENT("myend");
|
---|
| 639 | }
|
---|
| 640 | else
|
---|
| 641 | {
|
---|
| 642 | EIGEN_ASM_COMMENT("mybegin4");
|
---|
| 643 | LhsPacket A0, A1;
|
---|
| 644 | RhsPacket B_0, B1, B2, B3;
|
---|
| 645 | RhsPacket T0;
|
---|
| 646 |
|
---|
| 647 | traits.loadLhs(&blA[0*LhsProgress], A0);
|
---|
| 648 | traits.loadLhs(&blA[1*LhsProgress], A1);
|
---|
| 649 | traits.loadRhs(&blB[0*RhsProgress], B_0);
|
---|
| 650 | traits.loadRhs(&blB[1*RhsProgress], B1);
|
---|
| 651 |
|
---|
| 652 | traits.madd(A0,B_0,C0,T0);
|
---|
| 653 | traits.loadRhs(&blB[2*RhsProgress], B2);
|
---|
| 654 | traits.madd(A1,B_0,C4,B_0);
|
---|
| 655 | traits.loadRhs(&blB[3*RhsProgress], B3);
|
---|
| 656 | traits.loadRhs(&blB[4*RhsProgress], B_0);
|
---|
| 657 | traits.madd(A0,B1,C1,T0);
|
---|
| 658 | traits.madd(A1,B1,C5,B1);
|
---|
| 659 | traits.loadRhs(&blB[5*RhsProgress], B1);
|
---|
| 660 | traits.madd(A0,B2,C2,T0);
|
---|
| 661 | traits.madd(A1,B2,C6,B2);
|
---|
| 662 | traits.loadRhs(&blB[6*RhsProgress], B2);
|
---|
| 663 | traits.madd(A0,B3,C3,T0);
|
---|
| 664 | traits.loadLhs(&blA[2*LhsProgress], A0);
|
---|
| 665 | traits.madd(A1,B3,C7,B3);
|
---|
| 666 | traits.loadLhs(&blA[3*LhsProgress], A1);
|
---|
| 667 | traits.loadRhs(&blB[7*RhsProgress], B3);
|
---|
| 668 | traits.madd(A0,B_0,C0,T0);
|
---|
| 669 | traits.madd(A1,B_0,C4,B_0);
|
---|
| 670 | traits.loadRhs(&blB[8*RhsProgress], B_0);
|
---|
| 671 | traits.madd(A0,B1,C1,T0);
|
---|
| 672 | traits.madd(A1,B1,C5,B1);
|
---|
| 673 | traits.loadRhs(&blB[9*RhsProgress], B1);
|
---|
| 674 | traits.madd(A0,B2,C2,T0);
|
---|
| 675 | traits.madd(A1,B2,C6,B2);
|
---|
| 676 | traits.loadRhs(&blB[10*RhsProgress], B2);
|
---|
| 677 | traits.madd(A0,B3,C3,T0);
|
---|
| 678 | traits.loadLhs(&blA[4*LhsProgress], A0);
|
---|
| 679 | traits.madd(A1,B3,C7,B3);
|
---|
| 680 | traits.loadLhs(&blA[5*LhsProgress], A1);
|
---|
| 681 | traits.loadRhs(&blB[11*RhsProgress], B3);
|
---|
| 682 |
|
---|
| 683 | traits.madd(A0,B_0,C0,T0);
|
---|
| 684 | traits.madd(A1,B_0,C4,B_0);
|
---|
| 685 | traits.loadRhs(&blB[12*RhsProgress], B_0);
|
---|
| 686 | traits.madd(A0,B1,C1,T0);
|
---|
| 687 | traits.madd(A1,B1,C5,B1);
|
---|
| 688 | traits.loadRhs(&blB[13*RhsProgress], B1);
|
---|
| 689 | traits.madd(A0,B2,C2,T0);
|
---|
| 690 | traits.madd(A1,B2,C6,B2);
|
---|
| 691 | traits.loadRhs(&blB[14*RhsProgress], B2);
|
---|
| 692 | traits.madd(A0,B3,C3,T0);
|
---|
| 693 | traits.loadLhs(&blA[6*LhsProgress], A0);
|
---|
| 694 | traits.madd(A1,B3,C7,B3);
|
---|
| 695 | traits.loadLhs(&blA[7*LhsProgress], A1);
|
---|
| 696 | traits.loadRhs(&blB[15*RhsProgress], B3);
|
---|
| 697 | traits.madd(A0,B_0,C0,T0);
|
---|
| 698 | traits.madd(A1,B_0,C4,B_0);
|
---|
| 699 | traits.madd(A0,B1,C1,T0);
|
---|
| 700 | traits.madd(A1,B1,C5,B1);
|
---|
| 701 | traits.madd(A0,B2,C2,T0);
|
---|
| 702 | traits.madd(A1,B2,C6,B2);
|
---|
| 703 | traits.madd(A0,B3,C3,T0);
|
---|
| 704 | traits.madd(A1,B3,C7,B3);
|
---|
| 705 | }
|
---|
| 706 |
|
---|
| 707 | blB += 4*nr*RhsProgress;
|
---|
| 708 | blA += 4*mr;
|
---|
| 709 | }
|
---|
| 710 | // process remaining peeled loop
|
---|
| 711 | for(Index k=peeled_kc; k<depth; k++)
|
---|
| 712 | {
|
---|
| 713 | if(nr==2)
|
---|
| 714 | {
|
---|
| 715 | LhsPacket A0, A1;
|
---|
| 716 | RhsPacket B_0;
|
---|
| 717 | RhsPacket T0;
|
---|
| 718 |
|
---|
| 719 | traits.loadLhs(&blA[0*LhsProgress], A0);
|
---|
| 720 | traits.loadLhs(&blA[1*LhsProgress], A1);
|
---|
| 721 | traits.loadRhs(&blB[0*RhsProgress], B_0);
|
---|
| 722 | traits.madd(A0,B_0,C0,T0);
|
---|
| 723 | traits.madd(A1,B_0,C4,B_0);
|
---|
| 724 | traits.loadRhs(&blB[1*RhsProgress], B_0);
|
---|
| 725 | traits.madd(A0,B_0,C1,T0);
|
---|
| 726 | traits.madd(A1,B_0,C5,B_0);
|
---|
| 727 | }
|
---|
| 728 | else
|
---|
| 729 | {
|
---|
| 730 | LhsPacket A0, A1;
|
---|
| 731 | RhsPacket B_0, B1, B2, B3;
|
---|
| 732 | RhsPacket T0;
|
---|
| 733 |
|
---|
| 734 | traits.loadLhs(&blA[0*LhsProgress], A0);
|
---|
| 735 | traits.loadLhs(&blA[1*LhsProgress], A1);
|
---|
| 736 | traits.loadRhs(&blB[0*RhsProgress], B_0);
|
---|
| 737 | traits.loadRhs(&blB[1*RhsProgress], B1);
|
---|
| 738 |
|
---|
| 739 | traits.madd(A0,B_0,C0,T0);
|
---|
| 740 | traits.loadRhs(&blB[2*RhsProgress], B2);
|
---|
| 741 | traits.madd(A1,B_0,C4,B_0);
|
---|
| 742 | traits.loadRhs(&blB[3*RhsProgress], B3);
|
---|
| 743 | traits.madd(A0,B1,C1,T0);
|
---|
| 744 | traits.madd(A1,B1,C5,B1);
|
---|
| 745 | traits.madd(A0,B2,C2,T0);
|
---|
| 746 | traits.madd(A1,B2,C6,B2);
|
---|
| 747 | traits.madd(A0,B3,C3,T0);
|
---|
| 748 | traits.madd(A1,B3,C7,B3);
|
---|
| 749 | }
|
---|
| 750 |
|
---|
| 751 | blB += nr*RhsProgress;
|
---|
| 752 | blA += mr;
|
---|
| 753 | }
|
---|
| 754 |
|
---|
| 755 | if(nr==4)
|
---|
| 756 | {
|
---|
| 757 | ResPacket R0, R1, R2, R3, R4, R5, R6;
|
---|
| 758 | ResPacket alphav = pset1<ResPacket>(alpha);
|
---|
| 759 |
|
---|
| 760 | R0 = ploadu<ResPacket>(r0);
|
---|
| 761 | R1 = ploadu<ResPacket>(r1);
|
---|
| 762 | R2 = ploadu<ResPacket>(r2);
|
---|
| 763 | R3 = ploadu<ResPacket>(r3);
|
---|
| 764 | R4 = ploadu<ResPacket>(r0 + ResPacketSize);
|
---|
| 765 | R5 = ploadu<ResPacket>(r1 + ResPacketSize);
|
---|
| 766 | R6 = ploadu<ResPacket>(r2 + ResPacketSize);
|
---|
| 767 | traits.acc(C0, alphav, R0);
|
---|
| 768 | pstoreu(r0, R0);
|
---|
| 769 | R0 = ploadu<ResPacket>(r3 + ResPacketSize);
|
---|
| 770 |
|
---|
| 771 | traits.acc(C1, alphav, R1);
|
---|
| 772 | traits.acc(C2, alphav, R2);
|
---|
| 773 | traits.acc(C3, alphav, R3);
|
---|
| 774 | traits.acc(C4, alphav, R4);
|
---|
| 775 | traits.acc(C5, alphav, R5);
|
---|
| 776 | traits.acc(C6, alphav, R6);
|
---|
| 777 | traits.acc(C7, alphav, R0);
|
---|
| 778 |
|
---|
| 779 | pstoreu(r1, R1);
|
---|
| 780 | pstoreu(r2, R2);
|
---|
| 781 | pstoreu(r3, R3);
|
---|
| 782 | pstoreu(r0 + ResPacketSize, R4);
|
---|
| 783 | pstoreu(r1 + ResPacketSize, R5);
|
---|
| 784 | pstoreu(r2 + ResPacketSize, R6);
|
---|
| 785 | pstoreu(r3 + ResPacketSize, R0);
|
---|
| 786 | }
|
---|
| 787 | else
|
---|
| 788 | {
|
---|
| 789 | ResPacket R0, R1, R4;
|
---|
| 790 | ResPacket alphav = pset1<ResPacket>(alpha);
|
---|
| 791 |
|
---|
| 792 | R0 = ploadu<ResPacket>(r0);
|
---|
| 793 | R1 = ploadu<ResPacket>(r1);
|
---|
| 794 | R4 = ploadu<ResPacket>(r0 + ResPacketSize);
|
---|
| 795 | traits.acc(C0, alphav, R0);
|
---|
| 796 | pstoreu(r0, R0);
|
---|
| 797 | R0 = ploadu<ResPacket>(r1 + ResPacketSize);
|
---|
| 798 | traits.acc(C1, alphav, R1);
|
---|
| 799 | traits.acc(C4, alphav, R4);
|
---|
| 800 | traits.acc(C5, alphav, R0);
|
---|
| 801 | pstoreu(r1, R1);
|
---|
| 802 | pstoreu(r0 + ResPacketSize, R4);
|
---|
| 803 | pstoreu(r1 + ResPacketSize, R0);
|
---|
| 804 | }
|
---|
| 805 |
|
---|
| 806 | }
|
---|
| 807 |
|
---|
| 808 | if(rows-peeled_mc>=LhsProgress)
|
---|
| 809 | {
|
---|
| 810 | Index i = peeled_mc;
|
---|
| 811 | const LhsScalar* blA = &blockA[i*strideA+offsetA*LhsProgress];
|
---|
| 812 | prefetch(&blA[0]);
|
---|
| 813 |
|
---|
| 814 | // gets res block as register
|
---|
| 815 | AccPacket C0, C1, C2, C3;
|
---|
| 816 | traits.initAcc(C0);
|
---|
| 817 | traits.initAcc(C1);
|
---|
| 818 | if(nr==4) traits.initAcc(C2);
|
---|
| 819 | if(nr==4) traits.initAcc(C3);
|
---|
| 820 |
|
---|
| 821 | // performs "inner" product
|
---|
| 822 | const RhsScalar* blB = unpackedB;
|
---|
| 823 | for(Index k=0; k<peeled_kc; k+=4)
|
---|
| 824 | {
|
---|
| 825 | if(nr==2)
|
---|
| 826 | {
|
---|
| 827 | LhsPacket A0;
|
---|
| 828 | RhsPacket B_0, B1;
|
---|
| 829 |
|
---|
| 830 | traits.loadLhs(&blA[0*LhsProgress], A0);
|
---|
| 831 | traits.loadRhs(&blB[0*RhsProgress], B_0);
|
---|
| 832 | traits.loadRhs(&blB[1*RhsProgress], B1);
|
---|
| 833 | traits.madd(A0,B_0,C0,B_0);
|
---|
| 834 | traits.loadRhs(&blB[2*RhsProgress], B_0);
|
---|
| 835 | traits.madd(A0,B1,C1,B1);
|
---|
| 836 | traits.loadLhs(&blA[1*LhsProgress], A0);
|
---|
| 837 | traits.loadRhs(&blB[3*RhsProgress], B1);
|
---|
| 838 | traits.madd(A0,B_0,C0,B_0);
|
---|
| 839 | traits.loadRhs(&blB[4*RhsProgress], B_0);
|
---|
| 840 | traits.madd(A0,B1,C1,B1);
|
---|
| 841 | traits.loadLhs(&blA[2*LhsProgress], A0);
|
---|
| 842 | traits.loadRhs(&blB[5*RhsProgress], B1);
|
---|
| 843 | traits.madd(A0,B_0,C0,B_0);
|
---|
| 844 | traits.loadRhs(&blB[6*RhsProgress], B_0);
|
---|
| 845 | traits.madd(A0,B1,C1,B1);
|
---|
| 846 | traits.loadLhs(&blA[3*LhsProgress], A0);
|
---|
| 847 | traits.loadRhs(&blB[7*RhsProgress], B1);
|
---|
| 848 | traits.madd(A0,B_0,C0,B_0);
|
---|
| 849 | traits.madd(A0,B1,C1,B1);
|
---|
| 850 | }
|
---|
| 851 | else
|
---|
| 852 | {
|
---|
| 853 | LhsPacket A0;
|
---|
| 854 | RhsPacket B_0, B1, B2, B3;
|
---|
| 855 |
|
---|
| 856 | traits.loadLhs(&blA[0*LhsProgress], A0);
|
---|
| 857 | traits.loadRhs(&blB[0*RhsProgress], B_0);
|
---|
| 858 | traits.loadRhs(&blB[1*RhsProgress], B1);
|
---|
| 859 |
|
---|
| 860 | traits.madd(A0,B_0,C0,B_0);
|
---|
| 861 | traits.loadRhs(&blB[2*RhsProgress], B2);
|
---|
| 862 | traits.loadRhs(&blB[3*RhsProgress], B3);
|
---|
| 863 | traits.loadRhs(&blB[4*RhsProgress], B_0);
|
---|
| 864 | traits.madd(A0,B1,C1,B1);
|
---|
| 865 | traits.loadRhs(&blB[5*RhsProgress], B1);
|
---|
| 866 | traits.madd(A0,B2,C2,B2);
|
---|
| 867 | traits.loadRhs(&blB[6*RhsProgress], B2);
|
---|
| 868 | traits.madd(A0,B3,C3,B3);
|
---|
| 869 | traits.loadLhs(&blA[1*LhsProgress], A0);
|
---|
| 870 | traits.loadRhs(&blB[7*RhsProgress], B3);
|
---|
| 871 | traits.madd(A0,B_0,C0,B_0);
|
---|
| 872 | traits.loadRhs(&blB[8*RhsProgress], B_0);
|
---|
| 873 | traits.madd(A0,B1,C1,B1);
|
---|
| 874 | traits.loadRhs(&blB[9*RhsProgress], B1);
|
---|
| 875 | traits.madd(A0,B2,C2,B2);
|
---|
| 876 | traits.loadRhs(&blB[10*RhsProgress], B2);
|
---|
| 877 | traits.madd(A0,B3,C3,B3);
|
---|
| 878 | traits.loadLhs(&blA[2*LhsProgress], A0);
|
---|
| 879 | traits.loadRhs(&blB[11*RhsProgress], B3);
|
---|
| 880 |
|
---|
| 881 | traits.madd(A0,B_0,C0,B_0);
|
---|
| 882 | traits.loadRhs(&blB[12*RhsProgress], B_0);
|
---|
| 883 | traits.madd(A0,B1,C1,B1);
|
---|
| 884 | traits.loadRhs(&blB[13*RhsProgress], B1);
|
---|
| 885 | traits.madd(A0,B2,C2,B2);
|
---|
| 886 | traits.loadRhs(&blB[14*RhsProgress], B2);
|
---|
| 887 | traits.madd(A0,B3,C3,B3);
|
---|
| 888 |
|
---|
| 889 | traits.loadLhs(&blA[3*LhsProgress], A0);
|
---|
| 890 | traits.loadRhs(&blB[15*RhsProgress], B3);
|
---|
| 891 | traits.madd(A0,B_0,C0,B_0);
|
---|
| 892 | traits.madd(A0,B1,C1,B1);
|
---|
| 893 | traits.madd(A0,B2,C2,B2);
|
---|
| 894 | traits.madd(A0,B3,C3,B3);
|
---|
| 895 | }
|
---|
| 896 |
|
---|
| 897 | blB += nr*4*RhsProgress;
|
---|
| 898 | blA += 4*LhsProgress;
|
---|
| 899 | }
|
---|
| 900 | // process remaining peeled loop
|
---|
| 901 | for(Index k=peeled_kc; k<depth; k++)
|
---|
| 902 | {
|
---|
| 903 | if(nr==2)
|
---|
| 904 | {
|
---|
| 905 | LhsPacket A0;
|
---|
| 906 | RhsPacket B_0, B1;
|
---|
| 907 |
|
---|
| 908 | traits.loadLhs(&blA[0*LhsProgress], A0);
|
---|
| 909 | traits.loadRhs(&blB[0*RhsProgress], B_0);
|
---|
| 910 | traits.loadRhs(&blB[1*RhsProgress], B1);
|
---|
| 911 | traits.madd(A0,B_0,C0,B_0);
|
---|
| 912 | traits.madd(A0,B1,C1,B1);
|
---|
| 913 | }
|
---|
| 914 | else
|
---|
| 915 | {
|
---|
| 916 | LhsPacket A0;
|
---|
| 917 | RhsPacket B_0, B1, B2, B3;
|
---|
| 918 |
|
---|
| 919 | traits.loadLhs(&blA[0*LhsProgress], A0);
|
---|
| 920 | traits.loadRhs(&blB[0*RhsProgress], B_0);
|
---|
| 921 | traits.loadRhs(&blB[1*RhsProgress], B1);
|
---|
| 922 | traits.loadRhs(&blB[2*RhsProgress], B2);
|
---|
| 923 | traits.loadRhs(&blB[3*RhsProgress], B3);
|
---|
| 924 |
|
---|
| 925 | traits.madd(A0,B_0,C0,B_0);
|
---|
| 926 | traits.madd(A0,B1,C1,B1);
|
---|
| 927 | traits.madd(A0,B2,C2,B2);
|
---|
| 928 | traits.madd(A0,B3,C3,B3);
|
---|
| 929 | }
|
---|
| 930 |
|
---|
| 931 | blB += nr*RhsProgress;
|
---|
| 932 | blA += LhsProgress;
|
---|
| 933 | }
|
---|
| 934 |
|
---|
| 935 | ResPacket R0, R1, R2, R3;
|
---|
| 936 | ResPacket alphav = pset1<ResPacket>(alpha);
|
---|
| 937 |
|
---|
| 938 | ResScalar* r0 = &res[(j2+0)*resStride + i];
|
---|
| 939 | ResScalar* r1 = r0 + resStride;
|
---|
| 940 | ResScalar* r2 = r1 + resStride;
|
---|
| 941 | ResScalar* r3 = r2 + resStride;
|
---|
| 942 |
|
---|
| 943 | R0 = ploadu<ResPacket>(r0);
|
---|
| 944 | R1 = ploadu<ResPacket>(r1);
|
---|
| 945 | if(nr==4) R2 = ploadu<ResPacket>(r2);
|
---|
| 946 | if(nr==4) R3 = ploadu<ResPacket>(r3);
|
---|
| 947 |
|
---|
| 948 | traits.acc(C0, alphav, R0);
|
---|
| 949 | traits.acc(C1, alphav, R1);
|
---|
| 950 | if(nr==4) traits.acc(C2, alphav, R2);
|
---|
| 951 | if(nr==4) traits.acc(C3, alphav, R3);
|
---|
| 952 |
|
---|
| 953 | pstoreu(r0, R0);
|
---|
| 954 | pstoreu(r1, R1);
|
---|
| 955 | if(nr==4) pstoreu(r2, R2);
|
---|
| 956 | if(nr==4) pstoreu(r3, R3);
|
---|
| 957 | }
|
---|
| 958 | for(Index i=peeled_mc2; i<rows; i++)
|
---|
| 959 | {
|
---|
| 960 | const LhsScalar* blA = &blockA[i*strideA+offsetA];
|
---|
| 961 | prefetch(&blA[0]);
|
---|
| 962 |
|
---|
| 963 | // gets a 1 x nr res block as registers
|
---|
| 964 | ResScalar C0(0), C1(0), C2(0), C3(0);
|
---|
| 965 | // TODO directly use blockB ???
|
---|
| 966 | const RhsScalar* blB = &blockB[j2*strideB+offsetB*nr];
|
---|
| 967 | for(Index k=0; k<depth; k++)
|
---|
| 968 | {
|
---|
| 969 | if(nr==2)
|
---|
| 970 | {
|
---|
| 971 | LhsScalar A0;
|
---|
| 972 | RhsScalar B_0, B1;
|
---|
| 973 |
|
---|
| 974 | A0 = blA[k];
|
---|
| 975 | B_0 = blB[0];
|
---|
| 976 | B1 = blB[1];
|
---|
| 977 | MADD(cj,A0,B_0,C0,B_0);
|
---|
| 978 | MADD(cj,A0,B1,C1,B1);
|
---|
| 979 | }
|
---|
| 980 | else
|
---|
| 981 | {
|
---|
| 982 | LhsScalar A0;
|
---|
| 983 | RhsScalar B_0, B1, B2, B3;
|
---|
| 984 |
|
---|
| 985 | A0 = blA[k];
|
---|
| 986 | B_0 = blB[0];
|
---|
| 987 | B1 = blB[1];
|
---|
| 988 | B2 = blB[2];
|
---|
| 989 | B3 = blB[3];
|
---|
| 990 |
|
---|
| 991 | MADD(cj,A0,B_0,C0,B_0);
|
---|
| 992 | MADD(cj,A0,B1,C1,B1);
|
---|
| 993 | MADD(cj,A0,B2,C2,B2);
|
---|
| 994 | MADD(cj,A0,B3,C3,B3);
|
---|
| 995 | }
|
---|
| 996 |
|
---|
| 997 | blB += nr;
|
---|
| 998 | }
|
---|
| 999 | res[(j2+0)*resStride + i] += alpha*C0;
|
---|
| 1000 | res[(j2+1)*resStride + i] += alpha*C1;
|
---|
| 1001 | if(nr==4) res[(j2+2)*resStride + i] += alpha*C2;
|
---|
| 1002 | if(nr==4) res[(j2+3)*resStride + i] += alpha*C3;
|
---|
| 1003 | }
|
---|
| 1004 | }
|
---|
| 1005 | // process remaining rhs/res columns one at a time
|
---|
| 1006 | // => do the same but with nr==1
|
---|
| 1007 | for(Index j2=packet_cols; j2<cols; j2++)
|
---|
| 1008 | {
|
---|
| 1009 | // unpack B
|
---|
| 1010 | traits.unpackRhs(depth, &blockB[j2*strideB+offsetB], unpackedB);
|
---|
| 1011 |
|
---|
| 1012 | for(Index i=0; i<peeled_mc; i+=mr)
|
---|
| 1013 | {
|
---|
| 1014 | const LhsScalar* blA = &blockA[i*strideA+offsetA*mr];
|
---|
| 1015 | prefetch(&blA[0]);
|
---|
| 1016 |
|
---|
| 1017 | // TODO move the res loads to the stores
|
---|
| 1018 |
|
---|
| 1019 | // get res block as registers
|
---|
| 1020 | AccPacket C0, C4;
|
---|
| 1021 | traits.initAcc(C0);
|
---|
| 1022 | traits.initAcc(C4);
|
---|
| 1023 |
|
---|
| 1024 | const RhsScalar* blB = unpackedB;
|
---|
| 1025 | for(Index k=0; k<depth; k++)
|
---|
| 1026 | {
|
---|
| 1027 | LhsPacket A0, A1;
|
---|
| 1028 | RhsPacket B_0;
|
---|
| 1029 | RhsPacket T0;
|
---|
| 1030 |
|
---|
| 1031 | traits.loadLhs(&blA[0*LhsProgress], A0);
|
---|
| 1032 | traits.loadLhs(&blA[1*LhsProgress], A1);
|
---|
| 1033 | traits.loadRhs(&blB[0*RhsProgress], B_0);
|
---|
| 1034 | traits.madd(A0,B_0,C0,T0);
|
---|
| 1035 | traits.madd(A1,B_0,C4,B_0);
|
---|
| 1036 |
|
---|
| 1037 | blB += RhsProgress;
|
---|
| 1038 | blA += 2*LhsProgress;
|
---|
| 1039 | }
|
---|
| 1040 | ResPacket R0, R4;
|
---|
| 1041 | ResPacket alphav = pset1<ResPacket>(alpha);
|
---|
| 1042 |
|
---|
| 1043 | ResScalar* r0 = &res[(j2+0)*resStride + i];
|
---|
| 1044 |
|
---|
| 1045 | R0 = ploadu<ResPacket>(r0);
|
---|
| 1046 | R4 = ploadu<ResPacket>(r0+ResPacketSize);
|
---|
| 1047 |
|
---|
| 1048 | traits.acc(C0, alphav, R0);
|
---|
| 1049 | traits.acc(C4, alphav, R4);
|
---|
| 1050 |
|
---|
| 1051 | pstoreu(r0, R0);
|
---|
| 1052 | pstoreu(r0+ResPacketSize, R4);
|
---|
| 1053 | }
|
---|
| 1054 | if(rows-peeled_mc>=LhsProgress)
|
---|
| 1055 | {
|
---|
| 1056 | Index i = peeled_mc;
|
---|
| 1057 | const LhsScalar* blA = &blockA[i*strideA+offsetA*LhsProgress];
|
---|
| 1058 | prefetch(&blA[0]);
|
---|
| 1059 |
|
---|
| 1060 | AccPacket C0;
|
---|
| 1061 | traits.initAcc(C0);
|
---|
| 1062 |
|
---|
| 1063 | const RhsScalar* blB = unpackedB;
|
---|
| 1064 | for(Index k=0; k<depth; k++)
|
---|
| 1065 | {
|
---|
| 1066 | LhsPacket A0;
|
---|
| 1067 | RhsPacket B_0;
|
---|
| 1068 | traits.loadLhs(blA, A0);
|
---|
| 1069 | traits.loadRhs(blB, B_0);
|
---|
| 1070 | traits.madd(A0, B_0, C0, B_0);
|
---|
| 1071 | blB += RhsProgress;
|
---|
| 1072 | blA += LhsProgress;
|
---|
| 1073 | }
|
---|
| 1074 |
|
---|
| 1075 | ResPacket alphav = pset1<ResPacket>(alpha);
|
---|
| 1076 | ResPacket R0 = ploadu<ResPacket>(&res[(j2+0)*resStride + i]);
|
---|
| 1077 | traits.acc(C0, alphav, R0);
|
---|
| 1078 | pstoreu(&res[(j2+0)*resStride + i], R0);
|
---|
| 1079 | }
|
---|
| 1080 | for(Index i=peeled_mc2; i<rows; i++)
|
---|
| 1081 | {
|
---|
| 1082 | const LhsScalar* blA = &blockA[i*strideA+offsetA];
|
---|
| 1083 | prefetch(&blA[0]);
|
---|
| 1084 |
|
---|
| 1085 | // gets a 1 x 1 res block as registers
|
---|
| 1086 | ResScalar C0(0);
|
---|
| 1087 | // FIXME directly use blockB ??
|
---|
| 1088 | const RhsScalar* blB = &blockB[j2*strideB+offsetB];
|
---|
| 1089 | for(Index k=0; k<depth; k++)
|
---|
| 1090 | {
|
---|
| 1091 | LhsScalar A0 = blA[k];
|
---|
| 1092 | RhsScalar B_0 = blB[k];
|
---|
| 1093 | MADD(cj, A0, B_0, C0, B_0);
|
---|
| 1094 | }
|
---|
| 1095 | res[(j2+0)*resStride + i] += alpha*C0;
|
---|
| 1096 | }
|
---|
| 1097 | }
|
---|
| 1098 | }
|
---|
| 1099 |
|
---|
| 1100 |
|
---|
| 1101 | #undef CJMADD
|
---|
| 1102 |
|
---|
| 1103 | // pack a block of the lhs
|
---|
| 1104 | // The traversal is as follow (mr==4):
|
---|
| 1105 | // 0 4 8 12 ...
|
---|
| 1106 | // 1 5 9 13 ...
|
---|
| 1107 | // 2 6 10 14 ...
|
---|
| 1108 | // 3 7 11 15 ...
|
---|
| 1109 | //
|
---|
| 1110 | // 16 20 24 28 ...
|
---|
| 1111 | // 17 21 25 29 ...
|
---|
| 1112 | // 18 22 26 30 ...
|
---|
| 1113 | // 19 23 27 31 ...
|
---|
| 1114 | //
|
---|
| 1115 | // 32 33 34 35 ...
|
---|
| 1116 | // 36 36 38 39 ...
|
---|
| 1117 | template<typename Scalar, typename Index, int Pack1, int Pack2, int StorageOrder, bool Conjugate, bool PanelMode>
|
---|
| 1118 | struct gemm_pack_lhs
|
---|
| 1119 | {
|
---|
| 1120 | EIGEN_DONT_INLINE void operator()(Scalar* blockA, const Scalar* EIGEN_RESTRICT _lhs, Index lhsStride, Index depth, Index rows, Index stride=0, Index offset=0);
|
---|
| 1121 | };
|
---|
| 1122 |
|
---|
| 1123 | template<typename Scalar, typename Index, int Pack1, int Pack2, int StorageOrder, bool Conjugate, bool PanelMode>
|
---|
| 1124 | EIGEN_DONT_INLINE void gemm_pack_lhs<Scalar, Index, Pack1, Pack2, StorageOrder, Conjugate, PanelMode>
|
---|
| 1125 | ::operator()(Scalar* blockA, const Scalar* EIGEN_RESTRICT _lhs, Index lhsStride, Index depth, Index rows, Index stride, Index offset)
|
---|
| 1126 | {
|
---|
| 1127 | typedef typename packet_traits<Scalar>::type Packet;
|
---|
| 1128 | enum { PacketSize = packet_traits<Scalar>::size };
|
---|
| 1129 |
|
---|
| 1130 | EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK LHS");
|
---|
| 1131 | EIGEN_UNUSED_VARIABLE(stride)
|
---|
| 1132 | EIGEN_UNUSED_VARIABLE(offset)
|
---|
| 1133 | eigen_assert(((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride));
|
---|
| 1134 | eigen_assert( (StorageOrder==RowMajor) || ((Pack1%PacketSize)==0 && Pack1<=4*PacketSize) );
|
---|
| 1135 | conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj;
|
---|
| 1136 | const_blas_data_mapper<Scalar, Index, StorageOrder> lhs(_lhs,lhsStride);
|
---|
| 1137 | Index count = 0;
|
---|
| 1138 | Index peeled_mc = (rows/Pack1)*Pack1;
|
---|
| 1139 | for(Index i=0; i<peeled_mc; i+=Pack1)
|
---|
| 1140 | {
|
---|
| 1141 | if(PanelMode) count += Pack1 * offset;
|
---|
| 1142 |
|
---|
| 1143 | if(StorageOrder==ColMajor)
|
---|
| 1144 | {
|
---|
| 1145 | for(Index k=0; k<depth; k++)
|
---|
| 1146 | {
|
---|
| 1147 | Packet A, B, C, D;
|
---|
| 1148 | if(Pack1>=1*PacketSize) A = ploadu<Packet>(&lhs(i+0*PacketSize, k));
|
---|
| 1149 | if(Pack1>=2*PacketSize) B = ploadu<Packet>(&lhs(i+1*PacketSize, k));
|
---|
| 1150 | if(Pack1>=3*PacketSize) C = ploadu<Packet>(&lhs(i+2*PacketSize, k));
|
---|
| 1151 | if(Pack1>=4*PacketSize) D = ploadu<Packet>(&lhs(i+3*PacketSize, k));
|
---|
| 1152 | if(Pack1>=1*PacketSize) { pstore(blockA+count, cj.pconj(A)); count+=PacketSize; }
|
---|
| 1153 | if(Pack1>=2*PacketSize) { pstore(blockA+count, cj.pconj(B)); count+=PacketSize; }
|
---|
| 1154 | if(Pack1>=3*PacketSize) { pstore(blockA+count, cj.pconj(C)); count+=PacketSize; }
|
---|
| 1155 | if(Pack1>=4*PacketSize) { pstore(blockA+count, cj.pconj(D)); count+=PacketSize; }
|
---|
| 1156 | }
|
---|
| 1157 | }
|
---|
| 1158 | else
|
---|
| 1159 | {
|
---|
| 1160 | for(Index k=0; k<depth; k++)
|
---|
| 1161 | {
|
---|
| 1162 | // TODO add a vectorized transpose here
|
---|
| 1163 | Index w=0;
|
---|
| 1164 | for(; w<Pack1-3; w+=4)
|
---|
| 1165 | {
|
---|
| 1166 | Scalar a(cj(lhs(i+w+0, k))),
|
---|
| 1167 | b(cj(lhs(i+w+1, k))),
|
---|
| 1168 | c(cj(lhs(i+w+2, k))),
|
---|
| 1169 | d(cj(lhs(i+w+3, k)));
|
---|
| 1170 | blockA[count++] = a;
|
---|
| 1171 | blockA[count++] = b;
|
---|
| 1172 | blockA[count++] = c;
|
---|
| 1173 | blockA[count++] = d;
|
---|
| 1174 | }
|
---|
| 1175 | if(Pack1%4)
|
---|
| 1176 | for(;w<Pack1;++w)
|
---|
| 1177 | blockA[count++] = cj(lhs(i+w, k));
|
---|
| 1178 | }
|
---|
| 1179 | }
|
---|
| 1180 | if(PanelMode) count += Pack1 * (stride-offset-depth);
|
---|
| 1181 | }
|
---|
| 1182 | if(rows-peeled_mc>=Pack2)
|
---|
| 1183 | {
|
---|
| 1184 | if(PanelMode) count += Pack2*offset;
|
---|
| 1185 | for(Index k=0; k<depth; k++)
|
---|
| 1186 | for(Index w=0; w<Pack2; w++)
|
---|
| 1187 | blockA[count++] = cj(lhs(peeled_mc+w, k));
|
---|
| 1188 | if(PanelMode) count += Pack2 * (stride-offset-depth);
|
---|
| 1189 | peeled_mc += Pack2;
|
---|
| 1190 | }
|
---|
| 1191 | for(Index i=peeled_mc; i<rows; i++)
|
---|
| 1192 | {
|
---|
| 1193 | if(PanelMode) count += offset;
|
---|
| 1194 | for(Index k=0; k<depth; k++)
|
---|
| 1195 | blockA[count++] = cj(lhs(i, k));
|
---|
| 1196 | if(PanelMode) count += (stride-offset-depth);
|
---|
| 1197 | }
|
---|
| 1198 | }
|
---|
| 1199 |
|
---|
| 1200 | // copy a complete panel of the rhs
|
---|
| 1201 | // this version is optimized for column major matrices
|
---|
| 1202 | // The traversal order is as follow: (nr==4):
|
---|
| 1203 | // 0 1 2 3 12 13 14 15 24 27
|
---|
| 1204 | // 4 5 6 7 16 17 18 19 25 28
|
---|
| 1205 | // 8 9 10 11 20 21 22 23 26 29
|
---|
| 1206 | // . . . . . . . . . .
|
---|
| 1207 | template<typename Scalar, typename Index, int nr, bool Conjugate, bool PanelMode>
|
---|
| 1208 | struct gemm_pack_rhs<Scalar, Index, nr, ColMajor, Conjugate, PanelMode>
|
---|
| 1209 | {
|
---|
| 1210 | typedef typename packet_traits<Scalar>::type Packet;
|
---|
| 1211 | enum { PacketSize = packet_traits<Scalar>::size };
|
---|
| 1212 | EIGEN_DONT_INLINE void operator()(Scalar* blockB, const Scalar* rhs, Index rhsStride, Index depth, Index cols, Index stride=0, Index offset=0);
|
---|
| 1213 | };
|
---|
| 1214 |
|
---|
| 1215 | template<typename Scalar, typename Index, int nr, bool Conjugate, bool PanelMode>
|
---|
| 1216 | EIGEN_DONT_INLINE void gemm_pack_rhs<Scalar, Index, nr, ColMajor, Conjugate, PanelMode>
|
---|
| 1217 | ::operator()(Scalar* blockB, const Scalar* rhs, Index rhsStride, Index depth, Index cols, Index stride, Index offset)
|
---|
| 1218 | {
|
---|
| 1219 | EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK RHS COLMAJOR");
|
---|
| 1220 | EIGEN_UNUSED_VARIABLE(stride)
|
---|
| 1221 | EIGEN_UNUSED_VARIABLE(offset)
|
---|
| 1222 | eigen_assert(((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride));
|
---|
| 1223 | conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj;
|
---|
| 1224 | Index packet_cols = (cols/nr) * nr;
|
---|
| 1225 | Index count = 0;
|
---|
| 1226 | for(Index j2=0; j2<packet_cols; j2+=nr)
|
---|
| 1227 | {
|
---|
| 1228 | // skip what we have before
|
---|
| 1229 | if(PanelMode) count += nr * offset;
|
---|
| 1230 | const Scalar* b0 = &rhs[(j2+0)*rhsStride];
|
---|
| 1231 | const Scalar* b1 = &rhs[(j2+1)*rhsStride];
|
---|
| 1232 | const Scalar* b2 = &rhs[(j2+2)*rhsStride];
|
---|
| 1233 | const Scalar* b3 = &rhs[(j2+3)*rhsStride];
|
---|
| 1234 | for(Index k=0; k<depth; k++)
|
---|
| 1235 | {
|
---|
| 1236 | blockB[count+0] = cj(b0[k]);
|
---|
| 1237 | blockB[count+1] = cj(b1[k]);
|
---|
| 1238 | if(nr==4) blockB[count+2] = cj(b2[k]);
|
---|
| 1239 | if(nr==4) blockB[count+3] = cj(b3[k]);
|
---|
| 1240 | count += nr;
|
---|
| 1241 | }
|
---|
| 1242 | // skip what we have after
|
---|
| 1243 | if(PanelMode) count += nr * (stride-offset-depth);
|
---|
| 1244 | }
|
---|
| 1245 |
|
---|
| 1246 | // copy the remaining columns one at a time (nr==1)
|
---|
| 1247 | for(Index j2=packet_cols; j2<cols; ++j2)
|
---|
| 1248 | {
|
---|
| 1249 | if(PanelMode) count += offset;
|
---|
| 1250 | const Scalar* b0 = &rhs[(j2+0)*rhsStride];
|
---|
| 1251 | for(Index k=0; k<depth; k++)
|
---|
| 1252 | {
|
---|
| 1253 | blockB[count] = cj(b0[k]);
|
---|
| 1254 | count += 1;
|
---|
| 1255 | }
|
---|
| 1256 | if(PanelMode) count += (stride-offset-depth);
|
---|
| 1257 | }
|
---|
| 1258 | }
|
---|
| 1259 |
|
---|
| 1260 | // this version is optimized for row major matrices
|
---|
| 1261 | template<typename Scalar, typename Index, int nr, bool Conjugate, bool PanelMode>
|
---|
| 1262 | struct gemm_pack_rhs<Scalar, Index, nr, RowMajor, Conjugate, PanelMode>
|
---|
| 1263 | {
|
---|
| 1264 | enum { PacketSize = packet_traits<Scalar>::size };
|
---|
| 1265 | EIGEN_DONT_INLINE void operator()(Scalar* blockB, const Scalar* rhs, Index rhsStride, Index depth, Index cols, Index stride=0, Index offset=0);
|
---|
| 1266 | };
|
---|
| 1267 |
|
---|
| 1268 | template<typename Scalar, typename Index, int nr, bool Conjugate, bool PanelMode>
|
---|
| 1269 | EIGEN_DONT_INLINE void gemm_pack_rhs<Scalar, Index, nr, RowMajor, Conjugate, PanelMode>
|
---|
| 1270 | ::operator()(Scalar* blockB, const Scalar* rhs, Index rhsStride, Index depth, Index cols, Index stride, Index offset)
|
---|
| 1271 | {
|
---|
| 1272 | EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK RHS ROWMAJOR");
|
---|
| 1273 | EIGEN_UNUSED_VARIABLE(stride)
|
---|
| 1274 | EIGEN_UNUSED_VARIABLE(offset)
|
---|
| 1275 | eigen_assert(((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride));
|
---|
| 1276 | conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj;
|
---|
| 1277 | Index packet_cols = (cols/nr) * nr;
|
---|
| 1278 | Index count = 0;
|
---|
| 1279 | for(Index j2=0; j2<packet_cols; j2+=nr)
|
---|
| 1280 | {
|
---|
| 1281 | // skip what we have before
|
---|
| 1282 | if(PanelMode) count += nr * offset;
|
---|
| 1283 | for(Index k=0; k<depth; k++)
|
---|
| 1284 | {
|
---|
| 1285 | const Scalar* b0 = &rhs[k*rhsStride + j2];
|
---|
| 1286 | blockB[count+0] = cj(b0[0]);
|
---|
| 1287 | blockB[count+1] = cj(b0[1]);
|
---|
| 1288 | if(nr==4) blockB[count+2] = cj(b0[2]);
|
---|
| 1289 | if(nr==4) blockB[count+3] = cj(b0[3]);
|
---|
| 1290 | count += nr;
|
---|
| 1291 | }
|
---|
| 1292 | // skip what we have after
|
---|
| 1293 | if(PanelMode) count += nr * (stride-offset-depth);
|
---|
| 1294 | }
|
---|
| 1295 | // copy the remaining columns one at a time (nr==1)
|
---|
| 1296 | for(Index j2=packet_cols; j2<cols; ++j2)
|
---|
| 1297 | {
|
---|
| 1298 | if(PanelMode) count += offset;
|
---|
| 1299 | const Scalar* b0 = &rhs[j2];
|
---|
| 1300 | for(Index k=0; k<depth; k++)
|
---|
| 1301 | {
|
---|
| 1302 | blockB[count] = cj(b0[k*rhsStride]);
|
---|
| 1303 | count += 1;
|
---|
| 1304 | }
|
---|
| 1305 | if(PanelMode) count += stride-offset-depth;
|
---|
| 1306 | }
|
---|
| 1307 | }
|
---|
| 1308 |
|
---|
| 1309 | } // end namespace internal
|
---|
| 1310 |
|
---|
| 1311 | /** \returns the currently set level 1 cpu cache size (in bytes) used to estimate the ideal blocking size parameters.
|
---|
| 1312 | * \sa setCpuCacheSize */
|
---|
| 1313 | inline std::ptrdiff_t l1CacheSize()
|
---|
| 1314 | {
|
---|
| 1315 | std::ptrdiff_t l1, l2;
|
---|
| 1316 | internal::manage_caching_sizes(GetAction, &l1, &l2);
|
---|
| 1317 | return l1;
|
---|
| 1318 | }
|
---|
| 1319 |
|
---|
| 1320 | /** \returns the currently set level 2 cpu cache size (in bytes) used to estimate the ideal blocking size parameters.
|
---|
| 1321 | * \sa setCpuCacheSize */
|
---|
| 1322 | inline std::ptrdiff_t l2CacheSize()
|
---|
| 1323 | {
|
---|
| 1324 | std::ptrdiff_t l1, l2;
|
---|
| 1325 | internal::manage_caching_sizes(GetAction, &l1, &l2);
|
---|
| 1326 | return l2;
|
---|
| 1327 | }
|
---|
| 1328 |
|
---|
| 1329 | /** Set the cpu L1 and L2 cache sizes (in bytes).
|
---|
| 1330 | * These values are use to adjust the size of the blocks
|
---|
| 1331 | * for the algorithms working per blocks.
|
---|
| 1332 | *
|
---|
| 1333 | * \sa computeProductBlockingSizes */
|
---|
| 1334 | inline void setCpuCacheSizes(std::ptrdiff_t l1, std::ptrdiff_t l2)
|
---|
| 1335 | {
|
---|
| 1336 | internal::manage_caching_sizes(SetAction, &l1, &l2);
|
---|
| 1337 | }
|
---|
| 1338 |
|
---|
| 1339 | } // end namespace Eigen
|
---|
| 1340 |
|
---|
| 1341 | #endif // EIGEN_GENERAL_BLOCK_PANEL_H
|
---|