Narayan Kamath | c981c48 | 2012-11-02 10:59:05 +0000 | [diff] [blame] | 1 | // This file is part of Eigen, a lightweight C++ template library |
| 2 | // for linear algebra. |
| 3 | // |
| 4 | // Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr> |
| 5 | // |
| 6 | // This Source Code Form is subject to the terms of the Mozilla |
| 7 | // Public License v. 2.0. If a copy of the MPL was not distributed |
| 8 | // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. |
| 9 | |
| 10 | #ifndef EIGEN_GENERAL_BLOCK_PANEL_H |
| 11 | #define EIGEN_GENERAL_BLOCK_PANEL_H |
| 12 | |
| 13 | namespace Eigen { |
| 14 | |
| 15 | namespace internal { |
| 16 | |
| 17 | template<typename _LhsScalar, typename _RhsScalar, bool _ConjLhs=false, bool _ConjRhs=false> |
| 18 | class gebp_traits; |
| 19 | |
| 20 | |
| 21 | /** \internal \returns b if a<=0, and returns a otherwise. */ |
| 22 | inline std::ptrdiff_t manage_caching_sizes_helper(std::ptrdiff_t a, std::ptrdiff_t b) |
| 23 | { |
| 24 | return a<=0 ? b : a; |
| 25 | } |
| 26 | |
| 27 | /** \internal */ |
| 28 | inline void manage_caching_sizes(Action action, std::ptrdiff_t* l1=0, std::ptrdiff_t* l2=0) |
| 29 | { |
| 30 | static std::ptrdiff_t m_l1CacheSize = 0; |
| 31 | static std::ptrdiff_t m_l2CacheSize = 0; |
| 32 | if(m_l2CacheSize==0) |
| 33 | { |
| 34 | m_l1CacheSize = manage_caching_sizes_helper(queryL1CacheSize(),8 * 1024); |
| 35 | m_l2CacheSize = manage_caching_sizes_helper(queryTopLevelCacheSize(),1*1024*1024); |
| 36 | } |
| 37 | |
| 38 | if(action==SetAction) |
| 39 | { |
| 40 | // set the cpu cache size and cache all block sizes from a global cache size in byte |
| 41 | eigen_internal_assert(l1!=0 && l2!=0); |
| 42 | m_l1CacheSize = *l1; |
| 43 | m_l2CacheSize = *l2; |
| 44 | } |
| 45 | else if(action==GetAction) |
| 46 | { |
| 47 | eigen_internal_assert(l1!=0 && l2!=0); |
| 48 | *l1 = m_l1CacheSize; |
| 49 | *l2 = m_l2CacheSize; |
| 50 | } |
| 51 | else |
| 52 | { |
| 53 | eigen_internal_assert(false); |
| 54 | } |
| 55 | } |
| 56 | |
| 57 | /** \brief Computes the blocking parameters for a m x k times k x n matrix product |
| 58 | * |
| 59 | * \param[in,out] k Input: the third dimension of the product. Output: the blocking size along the same dimension. |
| 60 | * \param[in,out] m Input: the number of rows of the left hand side. Output: the blocking size along the same dimension. |
| 61 | * \param[in,out] n Input: the number of columns of the right hand side. Output: the blocking size along the same dimension. |
| 62 | * |
| 63 | * Given a m x k times k x n matrix product of scalar types \c LhsScalar and \c RhsScalar, |
| 64 | * this function computes the blocking size parameters along the respective dimensions |
| 65 | * for matrix products and related algorithms. The blocking sizes depends on various |
| 66 | * parameters: |
| 67 | * - the L1 and L2 cache sizes, |
| 68 | * - the register level blocking sizes defined by gebp_traits, |
| 69 | * - the number of scalars that fit into a packet (when vectorization is enabled). |
| 70 | * |
| 71 | * \sa setCpuCacheSizes */ |
Carlos Hernandez | 7faaa9f | 2014-08-05 17:53:32 -0700 | [diff] [blame] | 72 | template<typename LhsScalar, typename RhsScalar, int KcFactor, typename SizeType> |
| 73 | void computeProductBlockingSizes(SizeType& k, SizeType& m, SizeType& n) |
Narayan Kamath | c981c48 | 2012-11-02 10:59:05 +0000 | [diff] [blame] | 74 | { |
| 75 | EIGEN_UNUSED_VARIABLE(n); |
| 76 | // Explanations: |
| 77 | // Let's recall the product algorithms form kc x nc horizontal panels B' on the rhs and |
| 78 | // mc x kc blocks A' on the lhs. A' has to fit into L2 cache. Moreover, B' is processed |
| 79 | // per kc x nr vertical small panels where nr is the blocking size along the n dimension |
| 80 | // at the register level. For vectorization purpose, these small vertical panels are unpacked, |
| 81 | // e.g., each coefficient is replicated to fit a packet. This small vertical panel has to |
| 82 | // stay in L1 cache. |
| 83 | std::ptrdiff_t l1, l2; |
| 84 | |
| 85 | typedef gebp_traits<LhsScalar,RhsScalar> Traits; |
| 86 | enum { |
| 87 | kdiv = KcFactor * 2 * Traits::nr |
| 88 | * Traits::RhsProgress * sizeof(RhsScalar), |
| 89 | mr = gebp_traits<LhsScalar,RhsScalar>::mr, |
| 90 | mr_mask = (0xffffffff/mr)*mr |
| 91 | }; |
| 92 | |
| 93 | manage_caching_sizes(GetAction, &l1, &l2); |
Carlos Hernandez | 7faaa9f | 2014-08-05 17:53:32 -0700 | [diff] [blame] | 94 | k = std::min<SizeType>(k, l1/kdiv); |
| 95 | SizeType _m = k>0 ? l2/(4 * sizeof(LhsScalar) * k) : 0; |
Narayan Kamath | c981c48 | 2012-11-02 10:59:05 +0000 | [diff] [blame] | 96 | if(_m<m) m = _m & mr_mask; |
| 97 | } |
| 98 | |
Carlos Hernandez | 7faaa9f | 2014-08-05 17:53:32 -0700 | [diff] [blame] | 99 | template<typename LhsScalar, typename RhsScalar, typename SizeType> |
| 100 | inline void computeProductBlockingSizes(SizeType& k, SizeType& m, SizeType& n) |
Narayan Kamath | c981c48 | 2012-11-02 10:59:05 +0000 | [diff] [blame] | 101 | { |
| 102 | computeProductBlockingSizes<LhsScalar,RhsScalar,1>(k, m, n); |
| 103 | } |
| 104 | |
| 105 | #ifdef EIGEN_HAS_FUSE_CJMADD |
| 106 | #define MADD(CJ,A,B,C,T) C = CJ.pmadd(A,B,C); |
| 107 | #else |
| 108 | |
| 109 | // FIXME (a bit overkill maybe ?) |
| 110 | |
| 111 | template<typename CJ, typename A, typename B, typename C, typename T> struct gebp_madd_selector { |
| 112 | EIGEN_ALWAYS_INLINE static void run(const CJ& cj, A& a, B& b, C& c, T& /*t*/) |
| 113 | { |
| 114 | c = cj.pmadd(a,b,c); |
| 115 | } |
| 116 | }; |
| 117 | |
| 118 | template<typename CJ, typename T> struct gebp_madd_selector<CJ,T,T,T,T> { |
| 119 | EIGEN_ALWAYS_INLINE static void run(const CJ& cj, T& a, T& b, T& c, T& t) |
| 120 | { |
| 121 | t = b; t = cj.pmul(a,t); c = padd(c,t); |
| 122 | } |
| 123 | }; |
| 124 | |
| 125 | template<typename CJ, typename A, typename B, typename C, typename T> |
| 126 | EIGEN_STRONG_INLINE void gebp_madd(const CJ& cj, A& a, B& b, C& c, T& t) |
| 127 | { |
| 128 | gebp_madd_selector<CJ,A,B,C,T>::run(cj,a,b,c,t); |
| 129 | } |
| 130 | |
| 131 | #define MADD(CJ,A,B,C,T) gebp_madd(CJ,A,B,C,T); |
| 132 | // #define MADD(CJ,A,B,C,T) T = B; T = CJ.pmul(A,T); C = padd(C,T); |
| 133 | #endif |
| 134 | |
| 135 | /* Vectorization logic |
| 136 | * real*real: unpack rhs to constant packets, ... |
| 137 | * |
| 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), |
| 139 | * storing each res packet into two packets (2x2), |
| 140 | * at the end combine them: swap the second and addsub them |
| 141 | * cf*cf : same but with 2x4 blocks |
| 142 | * cplx*real : unpack rhs to constant packets, ... |
| 143 | * real*cplx : load lhs as (a0,a0,a1,a1), and mul as usual |
| 144 | */ |
| 145 | template<typename _LhsScalar, typename _RhsScalar, bool _ConjLhs, bool _ConjRhs> |
| 146 | class gebp_traits |
| 147 | { |
| 148 | public: |
| 149 | typedef _LhsScalar LhsScalar; |
| 150 | typedef _RhsScalar RhsScalar; |
| 151 | typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar; |
| 152 | |
| 153 | enum { |
| 154 | ConjLhs = _ConjLhs, |
| 155 | ConjRhs = _ConjRhs, |
| 156 | Vectorizable = packet_traits<LhsScalar>::Vectorizable && packet_traits<RhsScalar>::Vectorizable, |
| 157 | LhsPacketSize = Vectorizable ? packet_traits<LhsScalar>::size : 1, |
| 158 | RhsPacketSize = Vectorizable ? packet_traits<RhsScalar>::size : 1, |
| 159 | ResPacketSize = Vectorizable ? packet_traits<ResScalar>::size : 1, |
| 160 | |
| 161 | NumberOfRegisters = EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS, |
| 162 | |
| 163 | // register block size along the N direction (must be either 2 or 4) |
| 164 | nr = NumberOfRegisters/4, |
| 165 | |
| 166 | // register block size along the M direction (currently, this one cannot be modified) |
| 167 | mr = 2 * LhsPacketSize, |
| 168 | |
| 169 | WorkSpaceFactor = nr * RhsPacketSize, |
| 170 | |
| 171 | LhsProgress = LhsPacketSize, |
| 172 | RhsProgress = RhsPacketSize |
| 173 | }; |
| 174 | |
| 175 | typedef typename packet_traits<LhsScalar>::type _LhsPacket; |
| 176 | typedef typename packet_traits<RhsScalar>::type _RhsPacket; |
| 177 | typedef typename packet_traits<ResScalar>::type _ResPacket; |
| 178 | |
| 179 | typedef typename conditional<Vectorizable,_LhsPacket,LhsScalar>::type LhsPacket; |
| 180 | typedef typename conditional<Vectorizable,_RhsPacket,RhsScalar>::type RhsPacket; |
| 181 | typedef typename conditional<Vectorizable,_ResPacket,ResScalar>::type ResPacket; |
| 182 | |
| 183 | typedef ResPacket AccPacket; |
| 184 | |
| 185 | EIGEN_STRONG_INLINE void initAcc(AccPacket& p) |
| 186 | { |
| 187 | p = pset1<ResPacket>(ResScalar(0)); |
| 188 | } |
| 189 | |
| 190 | EIGEN_STRONG_INLINE void unpackRhs(DenseIndex n, const RhsScalar* rhs, RhsScalar* b) |
| 191 | { |
| 192 | for(DenseIndex k=0; k<n; k++) |
| 193 | pstore1<RhsPacket>(&b[k*RhsPacketSize], rhs[k]); |
| 194 | } |
| 195 | |
| 196 | EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacket& dest) const |
| 197 | { |
| 198 | dest = pload<RhsPacket>(b); |
| 199 | } |
| 200 | |
| 201 | EIGEN_STRONG_INLINE void loadLhs(const LhsScalar* a, LhsPacket& dest) const |
| 202 | { |
| 203 | dest = pload<LhsPacket>(a); |
| 204 | } |
| 205 | |
| 206 | EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, AccPacket& c, AccPacket& tmp) const |
| 207 | { |
| 208 | tmp = b; tmp = pmul(a,tmp); c = padd(c,tmp); |
| 209 | } |
| 210 | |
| 211 | EIGEN_STRONG_INLINE void acc(const AccPacket& c, const ResPacket& alpha, ResPacket& r) const |
| 212 | { |
| 213 | r = pmadd(c,alpha,r); |
| 214 | } |
| 215 | |
| 216 | protected: |
| 217 | // conj_helper<LhsScalar,RhsScalar,ConjLhs,ConjRhs> cj; |
| 218 | // conj_helper<LhsPacket,RhsPacket,ConjLhs,ConjRhs> pcj; |
| 219 | }; |
| 220 | |
| 221 | template<typename RealScalar, bool _ConjLhs> |
| 222 | class gebp_traits<std::complex<RealScalar>, RealScalar, _ConjLhs, false> |
| 223 | { |
| 224 | public: |
| 225 | typedef std::complex<RealScalar> LhsScalar; |
| 226 | typedef RealScalar RhsScalar; |
| 227 | typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar; |
| 228 | |
| 229 | enum { |
| 230 | ConjLhs = _ConjLhs, |
| 231 | ConjRhs = false, |
| 232 | Vectorizable = packet_traits<LhsScalar>::Vectorizable && packet_traits<RhsScalar>::Vectorizable, |
| 233 | LhsPacketSize = Vectorizable ? packet_traits<LhsScalar>::size : 1, |
| 234 | RhsPacketSize = Vectorizable ? packet_traits<RhsScalar>::size : 1, |
| 235 | ResPacketSize = Vectorizable ? packet_traits<ResScalar>::size : 1, |
| 236 | |
| 237 | NumberOfRegisters = EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS, |
| 238 | nr = NumberOfRegisters/4, |
| 239 | mr = 2 * LhsPacketSize, |
| 240 | WorkSpaceFactor = nr*RhsPacketSize, |
| 241 | |
| 242 | LhsProgress = LhsPacketSize, |
| 243 | RhsProgress = RhsPacketSize |
| 244 | }; |
| 245 | |
| 246 | typedef typename packet_traits<LhsScalar>::type _LhsPacket; |
| 247 | typedef typename packet_traits<RhsScalar>::type _RhsPacket; |
| 248 | typedef typename packet_traits<ResScalar>::type _ResPacket; |
| 249 | |
| 250 | typedef typename conditional<Vectorizable,_LhsPacket,LhsScalar>::type LhsPacket; |
| 251 | typedef typename conditional<Vectorizable,_RhsPacket,RhsScalar>::type RhsPacket; |
| 252 | typedef typename conditional<Vectorizable,_ResPacket,ResScalar>::type ResPacket; |
| 253 | |
| 254 | typedef ResPacket AccPacket; |
| 255 | |
| 256 | EIGEN_STRONG_INLINE void initAcc(AccPacket& p) |
| 257 | { |
| 258 | p = pset1<ResPacket>(ResScalar(0)); |
| 259 | } |
| 260 | |
| 261 | EIGEN_STRONG_INLINE void unpackRhs(DenseIndex n, const RhsScalar* rhs, RhsScalar* b) |
| 262 | { |
| 263 | for(DenseIndex k=0; k<n; k++) |
| 264 | pstore1<RhsPacket>(&b[k*RhsPacketSize], rhs[k]); |
| 265 | } |
| 266 | |
| 267 | EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacket& dest) const |
| 268 | { |
| 269 | dest = pload<RhsPacket>(b); |
| 270 | } |
| 271 | |
| 272 | EIGEN_STRONG_INLINE void loadLhs(const LhsScalar* a, LhsPacket& dest) const |
| 273 | { |
| 274 | dest = pload<LhsPacket>(a); |
| 275 | } |
| 276 | |
| 277 | EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, AccPacket& c, RhsPacket& tmp) const |
| 278 | { |
| 279 | madd_impl(a, b, c, tmp, typename conditional<Vectorizable,true_type,false_type>::type()); |
| 280 | } |
| 281 | |
| 282 | EIGEN_STRONG_INLINE void madd_impl(const LhsPacket& a, const RhsPacket& b, AccPacket& c, RhsPacket& tmp, const true_type&) const |
| 283 | { |
| 284 | tmp = b; tmp = pmul(a.v,tmp); c.v = padd(c.v,tmp); |
| 285 | } |
| 286 | |
| 287 | EIGEN_STRONG_INLINE void madd_impl(const LhsScalar& a, const RhsScalar& b, ResScalar& c, RhsScalar& /*tmp*/, const false_type&) const |
| 288 | { |
| 289 | c += a * b; |
| 290 | } |
| 291 | |
| 292 | EIGEN_STRONG_INLINE void acc(const AccPacket& c, const ResPacket& alpha, ResPacket& r) const |
| 293 | { |
| 294 | r = cj.pmadd(c,alpha,r); |
| 295 | } |
| 296 | |
| 297 | protected: |
| 298 | conj_helper<ResPacket,ResPacket,ConjLhs,false> cj; |
| 299 | }; |
| 300 | |
| 301 | template<typename RealScalar, bool _ConjLhs, bool _ConjRhs> |
| 302 | class gebp_traits<std::complex<RealScalar>, std::complex<RealScalar>, _ConjLhs, _ConjRhs > |
| 303 | { |
| 304 | public: |
| 305 | typedef std::complex<RealScalar> Scalar; |
| 306 | typedef std::complex<RealScalar> LhsScalar; |
| 307 | typedef std::complex<RealScalar> RhsScalar; |
| 308 | typedef std::complex<RealScalar> ResScalar; |
| 309 | |
| 310 | enum { |
| 311 | ConjLhs = _ConjLhs, |
| 312 | ConjRhs = _ConjRhs, |
| 313 | Vectorizable = packet_traits<RealScalar>::Vectorizable |
| 314 | && packet_traits<Scalar>::Vectorizable, |
| 315 | RealPacketSize = Vectorizable ? packet_traits<RealScalar>::size : 1, |
| 316 | ResPacketSize = Vectorizable ? packet_traits<ResScalar>::size : 1, |
| 317 | |
| 318 | nr = 2, |
| 319 | mr = 2 * ResPacketSize, |
| 320 | WorkSpaceFactor = Vectorizable ? 2*nr*RealPacketSize : nr, |
| 321 | |
| 322 | LhsProgress = ResPacketSize, |
| 323 | RhsProgress = Vectorizable ? 2*ResPacketSize : 1 |
| 324 | }; |
| 325 | |
| 326 | typedef typename packet_traits<RealScalar>::type RealPacket; |
| 327 | typedef typename packet_traits<Scalar>::type ScalarPacket; |
| 328 | struct DoublePacket |
| 329 | { |
| 330 | RealPacket first; |
| 331 | RealPacket second; |
| 332 | }; |
| 333 | |
| 334 | typedef typename conditional<Vectorizable,RealPacket, Scalar>::type LhsPacket; |
| 335 | typedef typename conditional<Vectorizable,DoublePacket,Scalar>::type RhsPacket; |
| 336 | typedef typename conditional<Vectorizable,ScalarPacket,Scalar>::type ResPacket; |
| 337 | typedef typename conditional<Vectorizable,DoublePacket,Scalar>::type AccPacket; |
| 338 | |
| 339 | EIGEN_STRONG_INLINE void initAcc(Scalar& p) { p = Scalar(0); } |
| 340 | |
| 341 | EIGEN_STRONG_INLINE void initAcc(DoublePacket& p) |
| 342 | { |
| 343 | p.first = pset1<RealPacket>(RealScalar(0)); |
| 344 | p.second = pset1<RealPacket>(RealScalar(0)); |
| 345 | } |
| 346 | |
| 347 | /* Unpack the rhs coeff such that each complex coefficient is spread into |
| 348 | * two packects containing respectively the real and imaginary coefficient |
| 349 | * duplicated as many time as needed: (x+iy) => [x, ..., x] [y, ..., y] |
| 350 | */ |
| 351 | EIGEN_STRONG_INLINE void unpackRhs(DenseIndex n, const Scalar* rhs, Scalar* b) |
| 352 | { |
| 353 | for(DenseIndex k=0; k<n; k++) |
| 354 | { |
| 355 | if(Vectorizable) |
| 356 | { |
| 357 | pstore1<RealPacket>((RealScalar*)&b[k*ResPacketSize*2+0], real(rhs[k])); |
| 358 | pstore1<RealPacket>((RealScalar*)&b[k*ResPacketSize*2+ResPacketSize], imag(rhs[k])); |
| 359 | } |
| 360 | else |
| 361 | b[k] = rhs[k]; |
| 362 | } |
| 363 | } |
| 364 | |
| 365 | EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, ResPacket& dest) const { dest = *b; } |
| 366 | |
| 367 | EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, DoublePacket& dest) const |
| 368 | { |
| 369 | dest.first = pload<RealPacket>((const RealScalar*)b); |
| 370 | dest.second = pload<RealPacket>((const RealScalar*)(b+ResPacketSize)); |
| 371 | } |
| 372 | |
| 373 | // nothing special here |
| 374 | EIGEN_STRONG_INLINE void loadLhs(const LhsScalar* a, LhsPacket& dest) const |
| 375 | { |
| 376 | dest = pload<LhsPacket>((const typename unpacket_traits<LhsPacket>::type*)(a)); |
| 377 | } |
| 378 | |
| 379 | EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, DoublePacket& c, RhsPacket& /*tmp*/) const |
| 380 | { |
| 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 | |
Carlos Hernandez | 7faaa9f | 2014-08-05 17:53:32 -0700 | [diff] [blame] | 530 | EIGEN_DONT_INLINE |
Narayan Kamath | c981c48 | 2012-11-02 10:59:05 +0000 | [diff] [blame] | 531 | void operator()(ResScalar* res, Index resStride, const LhsScalar* blockA, const RhsScalar* blockB, Index rows, Index depth, Index cols, ResScalar alpha, |
Carlos Hernandez | 7faaa9f | 2014-08-05 17:53:32 -0700 | [diff] [blame] | 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) |
Narayan Kamath | c981c48 | 2012-11-02 10:59:05 +0000 | [diff] [blame] | 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 | } |
Carlos Hernandez | 7faaa9f | 2014-08-05 17:53:32 -0700 | [diff] [blame] | 1099 | |
Narayan Kamath | c981c48 | 2012-11-02 10:59:05 +0000 | [diff] [blame] | 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 | { |
Carlos Hernandez | 7faaa9f | 2014-08-05 17:53:32 -0700 | [diff] [blame] | 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); |
Narayan Kamath | c981c48 | 2012-11-02 10:59:05 +0000 | [diff] [blame] | 1121 | }; |
| 1122 | |
Carlos Hernandez | 7faaa9f | 2014-08-05 17:53:32 -0700 | [diff] [blame] | 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 | |
Narayan Kamath | c981c48 | 2012-11-02 10:59:05 +0000 | [diff] [blame] | 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 }; |
Carlos Hernandez | 7faaa9f | 2014-08-05 17:53:32 -0700 | [diff] [blame] | 1212 | EIGEN_DONT_INLINE void operator()(Scalar* blockB, const Scalar* rhs, Index rhsStride, Index depth, Index cols, Index stride=0, Index offset=0); |
Narayan Kamath | c981c48 | 2012-11-02 10:59:05 +0000 | [diff] [blame] | 1213 | }; |
| 1214 | |
Carlos Hernandez | 7faaa9f | 2014-08-05 17:53:32 -0700 | [diff] [blame] | 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 | |
Narayan Kamath | c981c48 | 2012-11-02 10:59:05 +0000 | [diff] [blame] | 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 }; |
Carlos Hernandez | 7faaa9f | 2014-08-05 17:53:32 -0700 | [diff] [blame] | 1265 | EIGEN_DONT_INLINE void operator()(Scalar* blockB, const Scalar* rhs, Index rhsStride, Index depth, Index cols, Index stride=0, Index offset=0); |
Narayan Kamath | c981c48 | 2012-11-02 10:59:05 +0000 | [diff] [blame] | 1266 | }; |
| 1267 | |
Carlos Hernandez | 7faaa9f | 2014-08-05 17:53:32 -0700 | [diff] [blame] | 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 | |
Narayan Kamath | c981c48 | 2012-11-02 10:59:05 +0000 | [diff] [blame] | 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 |