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);
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382 | c.second = padd(pmul(a,b.second),c.second);
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383 | }
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384 |
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385 | EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, ResPacket& c, RhsPacket& /*tmp*/) const
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386 | {
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387 | c = cj.pmadd(a,b,c);
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388 | }
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389 |
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390 | EIGEN_STRONG_INLINE void acc(const Scalar& c, const Scalar& alpha, Scalar& r) const { r += alpha * c; }
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391 |
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392 | EIGEN_STRONG_INLINE void acc(const DoublePacket& c, const ResPacket& alpha, ResPacket& r) const
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393 | {
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394 | // assemble c
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395 | ResPacket tmp;
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396 | if((!ConjLhs)&&(!ConjRhs))
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397 | {
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398 | tmp = pcplxflip(pconj(ResPacket(c.second)));
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399 | tmp = padd(ResPacket(c.first),tmp);
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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
|
---|