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. */
manage_caching_sizes_helper(std::ptrdiff_t a,std::ptrdiff_t b)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 */
72 template<typename LhsScalar, typename RhsScalar, int KcFactor, typename SizeType>
computeProductBlockingSizes(SizeType & k,SizeType & m,SizeType & n)73 void computeProductBlockingSizes(SizeType& k, SizeType& m, SizeType& n)
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);
94 k = std::min<SizeType>(k, l1/kdiv);
95 SizeType _m = k>0 ? l2/(4 * sizeof(LhsScalar) * k) : 0;
96 if(_m<m) m = _m & mr_mask;
97 }
98
99 template<typename LhsScalar, typename RhsScalar, typename SizeType>
computeProductBlockingSizes(SizeType & k,SizeType & m,SizeType & n)100 inline void computeProductBlockingSizes(SizeType& k, SizeType& m, SizeType& n)
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 {
rungebp_madd_selector112 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
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
1342