/usr/include/eigen3/Eigen/src/Core/products/GeneralBlockPanelKernel.h is in libeigen3-dev 3.2.2-3.
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1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 | // This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_GENERAL_BLOCK_PANEL_H
#define EIGEN_GENERAL_BLOCK_PANEL_H
namespace Eigen {
namespace internal {
template<typename _LhsScalar, typename _RhsScalar, bool _ConjLhs=false, bool _ConjRhs=false>
class gebp_traits;
/** \internal \returns b if a<=0, and returns a otherwise. */
inline std::ptrdiff_t manage_caching_sizes_helper(std::ptrdiff_t a, std::ptrdiff_t b)
{
return a<=0 ? b : a;
}
/** \internal */
inline void manage_caching_sizes(Action action, std::ptrdiff_t* l1=0, std::ptrdiff_t* l2=0)
{
static std::ptrdiff_t m_l1CacheSize = 0;
static std::ptrdiff_t m_l2CacheSize = 0;
if(m_l2CacheSize==0)
{
m_l1CacheSize = manage_caching_sizes_helper(queryL1CacheSize(),8 * 1024);
m_l2CacheSize = manage_caching_sizes_helper(queryTopLevelCacheSize(),1*1024*1024);
}
if(action==SetAction)
{
// set the cpu cache size and cache all block sizes from a global cache size in byte
eigen_internal_assert(l1!=0 && l2!=0);
m_l1CacheSize = *l1;
m_l2CacheSize = *l2;
}
else if(action==GetAction)
{
eigen_internal_assert(l1!=0 && l2!=0);
*l1 = m_l1CacheSize;
*l2 = m_l2CacheSize;
}
else
{
eigen_internal_assert(false);
}
}
/** \brief Computes the blocking parameters for a m x k times k x n matrix product
*
* \param[in,out] k Input: the third dimension of the product. Output: the blocking size along the same dimension.
* \param[in,out] m Input: the number of rows of the left hand side. Output: the blocking size along the same dimension.
* \param[in,out] n Input: the number of columns of the right hand side. Output: the blocking size along the same dimension.
*
* Given a m x k times k x n matrix product of scalar types \c LhsScalar and \c RhsScalar,
* this function computes the blocking size parameters along the respective dimensions
* for matrix products and related algorithms. The blocking sizes depends on various
* parameters:
* - the L1 and L2 cache sizes,
* - the register level blocking sizes defined by gebp_traits,
* - the number of scalars that fit into a packet (when vectorization is enabled).
*
* \sa setCpuCacheSizes */
template<typename LhsScalar, typename RhsScalar, int KcFactor, typename SizeType>
void computeProductBlockingSizes(SizeType& k, SizeType& m, SizeType& n)
{
EIGEN_UNUSED_VARIABLE(n);
// Explanations:
// Let's recall the product algorithms form kc x nc horizontal panels B' on the rhs and
// mc x kc blocks A' on the lhs. A' has to fit into L2 cache. Moreover, B' is processed
// per kc x nr vertical small panels where nr is the blocking size along the n dimension
// at the register level. For vectorization purpose, these small vertical panels are unpacked,
// e.g., each coefficient is replicated to fit a packet. This small vertical panel has to
// stay in L1 cache.
std::ptrdiff_t l1, l2;
typedef gebp_traits<LhsScalar,RhsScalar> Traits;
enum {
kdiv = KcFactor * 2 * Traits::nr
* Traits::RhsProgress * sizeof(RhsScalar),
mr = gebp_traits<LhsScalar,RhsScalar>::mr,
mr_mask = (0xffffffff/mr)*mr
};
manage_caching_sizes(GetAction, &l1, &l2);
k = std::min<SizeType>(k, l1/kdiv);
SizeType _m = k>0 ? l2/(4 * sizeof(LhsScalar) * k) : 0;
if(_m<m) m = _m & mr_mask;
}
template<typename LhsScalar, typename RhsScalar, typename SizeType>
inline void computeProductBlockingSizes(SizeType& k, SizeType& m, SizeType& n)
{
computeProductBlockingSizes<LhsScalar,RhsScalar,1>(k, m, n);
}
#ifdef EIGEN_HAS_FUSE_CJMADD
#define MADD(CJ,A,B,C,T) C = CJ.pmadd(A,B,C);
#else
// FIXME (a bit overkill maybe ?)
template<typename CJ, typename A, typename B, typename C, typename T> struct gebp_madd_selector {
EIGEN_ALWAYS_INLINE static void run(const CJ& cj, A& a, B& b, C& c, T& /*t*/)
{
c = cj.pmadd(a,b,c);
}
};
template<typename CJ, typename T> struct gebp_madd_selector<CJ,T,T,T,T> {
EIGEN_ALWAYS_INLINE static void run(const CJ& cj, T& a, T& b, T& c, T& t)
{
t = b; t = cj.pmul(a,t); c = padd(c,t);
}
};
template<typename CJ, typename A, typename B, typename C, typename T>
EIGEN_STRONG_INLINE void gebp_madd(const CJ& cj, A& a, B& b, C& c, T& t)
{
gebp_madd_selector<CJ,A,B,C,T>::run(cj,a,b,c,t);
}
#define MADD(CJ,A,B,C,T) gebp_madd(CJ,A,B,C,T);
// #define MADD(CJ,A,B,C,T) T = B; T = CJ.pmul(A,T); C = padd(C,T);
#endif
/* Vectorization logic
* real*real: unpack rhs to constant packets, ...
*
* cd*cd : unpack rhs to (b_r,b_r), (b_i,b_i), mul to get (a_r b_r,a_i b_r) (a_r b_i,a_i b_i),
* storing each res packet into two packets (2x2),
* at the end combine them: swap the second and addsub them
* cf*cf : same but with 2x4 blocks
* cplx*real : unpack rhs to constant packets, ...
* real*cplx : load lhs as (a0,a0,a1,a1), and mul as usual
*/
template<typename _LhsScalar, typename _RhsScalar, bool _ConjLhs, bool _ConjRhs>
class gebp_traits
{
public:
typedef _LhsScalar LhsScalar;
typedef _RhsScalar RhsScalar;
typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar;
enum {
ConjLhs = _ConjLhs,
ConjRhs = _ConjRhs,
Vectorizable = packet_traits<LhsScalar>::Vectorizable && packet_traits<RhsScalar>::Vectorizable,
LhsPacketSize = Vectorizable ? packet_traits<LhsScalar>::size : 1,
RhsPacketSize = Vectorizable ? packet_traits<RhsScalar>::size : 1,
ResPacketSize = Vectorizable ? packet_traits<ResScalar>::size : 1,
NumberOfRegisters = EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS,
// register block size along the N direction (must be either 2 or 4)
nr = NumberOfRegisters/4,
// register block size along the M direction (currently, this one cannot be modified)
mr = 2 * LhsPacketSize,
WorkSpaceFactor = nr * RhsPacketSize,
LhsProgress = LhsPacketSize,
RhsProgress = RhsPacketSize
};
typedef typename packet_traits<LhsScalar>::type _LhsPacket;
typedef typename packet_traits<RhsScalar>::type _RhsPacket;
typedef typename packet_traits<ResScalar>::type _ResPacket;
typedef typename conditional<Vectorizable,_LhsPacket,LhsScalar>::type LhsPacket;
typedef typename conditional<Vectorizable,_RhsPacket,RhsScalar>::type RhsPacket;
typedef typename conditional<Vectorizable,_ResPacket,ResScalar>::type ResPacket;
typedef ResPacket AccPacket;
EIGEN_STRONG_INLINE void initAcc(AccPacket& p)
{
p = pset1<ResPacket>(ResScalar(0));
}
EIGEN_STRONG_INLINE void unpackRhs(DenseIndex n, const RhsScalar* rhs, RhsScalar* b)
{
for(DenseIndex k=0; k<n; k++)
pstore1<RhsPacket>(&b[k*RhsPacketSize], rhs[k]);
}
EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacket& dest) const
{
dest = pload<RhsPacket>(b);
}
EIGEN_STRONG_INLINE void loadLhs(const LhsScalar* a, LhsPacket& dest) const
{
dest = pload<LhsPacket>(a);
}
EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, AccPacket& c, AccPacket& tmp) const
{
tmp = b; tmp = pmul(a,tmp); c = padd(c,tmp);
}
EIGEN_STRONG_INLINE void acc(const AccPacket& c, const ResPacket& alpha, ResPacket& r) const
{
r = pmadd(c,alpha,r);
}
protected:
// conj_helper<LhsScalar,RhsScalar,ConjLhs,ConjRhs> cj;
// conj_helper<LhsPacket,RhsPacket,ConjLhs,ConjRhs> pcj;
};
template<typename RealScalar, bool _ConjLhs>
class gebp_traits<std::complex<RealScalar>, RealScalar, _ConjLhs, false>
{
public:
typedef std::complex<RealScalar> LhsScalar;
typedef RealScalar RhsScalar;
typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar;
enum {
ConjLhs = _ConjLhs,
ConjRhs = false,
Vectorizable = packet_traits<LhsScalar>::Vectorizable && packet_traits<RhsScalar>::Vectorizable,
LhsPacketSize = Vectorizable ? packet_traits<LhsScalar>::size : 1,
RhsPacketSize = Vectorizable ? packet_traits<RhsScalar>::size : 1,
ResPacketSize = Vectorizable ? packet_traits<ResScalar>::size : 1,
NumberOfRegisters = EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS,
nr = NumberOfRegisters/4,
mr = 2 * LhsPacketSize,
WorkSpaceFactor = nr*RhsPacketSize,
LhsProgress = LhsPacketSize,
RhsProgress = RhsPacketSize
};
typedef typename packet_traits<LhsScalar>::type _LhsPacket;
typedef typename packet_traits<RhsScalar>::type _RhsPacket;
typedef typename packet_traits<ResScalar>::type _ResPacket;
typedef typename conditional<Vectorizable,_LhsPacket,LhsScalar>::type LhsPacket;
typedef typename conditional<Vectorizable,_RhsPacket,RhsScalar>::type RhsPacket;
typedef typename conditional<Vectorizable,_ResPacket,ResScalar>::type ResPacket;
typedef ResPacket AccPacket;
EIGEN_STRONG_INLINE void initAcc(AccPacket& p)
{
p = pset1<ResPacket>(ResScalar(0));
}
EIGEN_STRONG_INLINE void unpackRhs(DenseIndex n, const RhsScalar* rhs, RhsScalar* b)
{
for(DenseIndex k=0; k<n; k++)
pstore1<RhsPacket>(&b[k*RhsPacketSize], rhs[k]);
}
EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacket& dest) const
{
dest = pload<RhsPacket>(b);
}
EIGEN_STRONG_INLINE void loadLhs(const LhsScalar* a, LhsPacket& dest) const
{
dest = pload<LhsPacket>(a);
}
EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, AccPacket& c, RhsPacket& tmp) const
{
madd_impl(a, b, c, tmp, typename conditional<Vectorizable,true_type,false_type>::type());
}
EIGEN_STRONG_INLINE void madd_impl(const LhsPacket& a, const RhsPacket& b, AccPacket& c, RhsPacket& tmp, const true_type&) const
{
tmp = b; tmp = pmul(a.v,tmp); c.v = padd(c.v,tmp);
}
EIGEN_STRONG_INLINE void madd_impl(const LhsScalar& a, const RhsScalar& b, ResScalar& c, RhsScalar& /*tmp*/, const false_type&) const
{
c += a * b;
}
EIGEN_STRONG_INLINE void acc(const AccPacket& c, const ResPacket& alpha, ResPacket& r) const
{
r = cj.pmadd(c,alpha,r);
}
protected:
conj_helper<ResPacket,ResPacket,ConjLhs,false> cj;
};
template<typename RealScalar, bool _ConjLhs, bool _ConjRhs>
class gebp_traits<std::complex<RealScalar>, std::complex<RealScalar>, _ConjLhs, _ConjRhs >
{
public:
typedef std::complex<RealScalar> Scalar;
typedef std::complex<RealScalar> LhsScalar;
typedef std::complex<RealScalar> RhsScalar;
typedef std::complex<RealScalar> ResScalar;
enum {
ConjLhs = _ConjLhs,
ConjRhs = _ConjRhs,
Vectorizable = packet_traits<RealScalar>::Vectorizable
&& packet_traits<Scalar>::Vectorizable,
RealPacketSize = Vectorizable ? packet_traits<RealScalar>::size : 1,
ResPacketSize = Vectorizable ? packet_traits<ResScalar>::size : 1,
nr = 2,
mr = 2 * ResPacketSize,
WorkSpaceFactor = Vectorizable ? 2*nr*RealPacketSize : nr,
LhsProgress = ResPacketSize,
RhsProgress = Vectorizable ? 2*ResPacketSize : 1
};
typedef typename packet_traits<RealScalar>::type RealPacket;
typedef typename packet_traits<Scalar>::type ScalarPacket;
struct DoublePacket
{
RealPacket first;
RealPacket second;
};
typedef typename conditional<Vectorizable,RealPacket, Scalar>::type LhsPacket;
typedef typename conditional<Vectorizable,DoublePacket,Scalar>::type RhsPacket;
typedef typename conditional<Vectorizable,ScalarPacket,Scalar>::type ResPacket;
typedef typename conditional<Vectorizable,DoublePacket,Scalar>::type AccPacket;
EIGEN_STRONG_INLINE void initAcc(Scalar& p) { p = Scalar(0); }
EIGEN_STRONG_INLINE void initAcc(DoublePacket& p)
{
p.first = pset1<RealPacket>(RealScalar(0));
p.second = pset1<RealPacket>(RealScalar(0));
}
/* Unpack the rhs coeff such that each complex coefficient is spread into
* two packects containing respectively the real and imaginary coefficient
* duplicated as many time as needed: (x+iy) => [x, ..., x] [y, ..., y]
*/
EIGEN_STRONG_INLINE void unpackRhs(DenseIndex n, const Scalar* rhs, Scalar* b)
{
for(DenseIndex k=0; k<n; k++)
{
if(Vectorizable)
{
pstore1<RealPacket>((RealScalar*)&b[k*ResPacketSize*2+0], real(rhs[k]));
pstore1<RealPacket>((RealScalar*)&b[k*ResPacketSize*2+ResPacketSize], imag(rhs[k]));
}
else
b[k] = rhs[k];
}
}
EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, ResPacket& dest) const { dest = *b; }
EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, DoublePacket& dest) const
{
dest.first = pload<RealPacket>((const RealScalar*)b);
dest.second = pload<RealPacket>((const RealScalar*)(b+ResPacketSize));
}
// nothing special here
EIGEN_STRONG_INLINE void loadLhs(const LhsScalar* a, LhsPacket& dest) const
{
dest = pload<LhsPacket>((const typename unpacket_traits<LhsPacket>::type*)(a));
}
EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, DoublePacket& c, RhsPacket& /*tmp*/) const
{
c.first = padd(pmul(a,b.first), c.first);
c.second = padd(pmul(a,b.second),c.second);
}
EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, ResPacket& c, RhsPacket& /*tmp*/) const
{
c = cj.pmadd(a,b,c);
}
EIGEN_STRONG_INLINE void acc(const Scalar& c, const Scalar& alpha, Scalar& r) const { r += alpha * c; }
EIGEN_STRONG_INLINE void acc(const DoublePacket& c, const ResPacket& alpha, ResPacket& r) const
{
// assemble c
ResPacket tmp;
if((!ConjLhs)&&(!ConjRhs))
{
tmp = pcplxflip(pconj(ResPacket(c.second)));
tmp = padd(ResPacket(c.first),tmp);
}
else if((!ConjLhs)&&(ConjRhs))
{
tmp = pconj(pcplxflip(ResPacket(c.second)));
tmp = padd(ResPacket(c.first),tmp);
}
else if((ConjLhs)&&(!ConjRhs))
{
tmp = pcplxflip(ResPacket(c.second));
tmp = padd(pconj(ResPacket(c.first)),tmp);
}
else if((ConjLhs)&&(ConjRhs))
{
tmp = pcplxflip(ResPacket(c.second));
tmp = psub(pconj(ResPacket(c.first)),tmp);
}
r = pmadd(tmp,alpha,r);
}
protected:
conj_helper<LhsScalar,RhsScalar,ConjLhs,ConjRhs> cj;
};
template<typename RealScalar, bool _ConjRhs>
class gebp_traits<RealScalar, std::complex<RealScalar>, false, _ConjRhs >
{
public:
typedef std::complex<RealScalar> Scalar;
typedef RealScalar LhsScalar;
typedef Scalar RhsScalar;
typedef Scalar ResScalar;
enum {
ConjLhs = false,
ConjRhs = _ConjRhs,
Vectorizable = packet_traits<RealScalar>::Vectorizable
&& packet_traits<Scalar>::Vectorizable,
LhsPacketSize = Vectorizable ? packet_traits<LhsScalar>::size : 1,
RhsPacketSize = Vectorizable ? packet_traits<RhsScalar>::size : 1,
ResPacketSize = Vectorizable ? packet_traits<ResScalar>::size : 1,
NumberOfRegisters = EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS,
nr = 4,
mr = 2*ResPacketSize,
WorkSpaceFactor = nr*RhsPacketSize,
LhsProgress = ResPacketSize,
RhsProgress = ResPacketSize
};
typedef typename packet_traits<LhsScalar>::type _LhsPacket;
typedef typename packet_traits<RhsScalar>::type _RhsPacket;
typedef typename packet_traits<ResScalar>::type _ResPacket;
typedef typename conditional<Vectorizable,_LhsPacket,LhsScalar>::type LhsPacket;
typedef typename conditional<Vectorizable,_RhsPacket,RhsScalar>::type RhsPacket;
typedef typename conditional<Vectorizable,_ResPacket,ResScalar>::type ResPacket;
typedef ResPacket AccPacket;
EIGEN_STRONG_INLINE void initAcc(AccPacket& p)
{
p = pset1<ResPacket>(ResScalar(0));
}
EIGEN_STRONG_INLINE void unpackRhs(DenseIndex n, const RhsScalar* rhs, RhsScalar* b)
{
for(DenseIndex k=0; k<n; k++)
pstore1<RhsPacket>(&b[k*RhsPacketSize], rhs[k]);
}
EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacket& dest) const
{
dest = pload<RhsPacket>(b);
}
EIGEN_STRONG_INLINE void loadLhs(const LhsScalar* a, LhsPacket& dest) const
{
dest = ploaddup<LhsPacket>(a);
}
EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, AccPacket& c, RhsPacket& tmp) const
{
madd_impl(a, b, c, tmp, typename conditional<Vectorizable,true_type,false_type>::type());
}
EIGEN_STRONG_INLINE void madd_impl(const LhsPacket& a, const RhsPacket& b, AccPacket& c, RhsPacket& tmp, const true_type&) const
{
tmp = b; tmp.v = pmul(a,tmp.v); c = padd(c,tmp);
}
EIGEN_STRONG_INLINE void madd_impl(const LhsScalar& a, const RhsScalar& b, ResScalar& c, RhsScalar& /*tmp*/, const false_type&) const
{
c += a * b;
}
EIGEN_STRONG_INLINE void acc(const AccPacket& c, const ResPacket& alpha, ResPacket& r) const
{
r = cj.pmadd(alpha,c,r);
}
protected:
conj_helper<ResPacket,ResPacket,false,ConjRhs> cj;
};
/* optimized GEneral packed Block * packed Panel product kernel
*
* Mixing type logic: C += A * B
* | A | B | comments
* |real |cplx | no vectorization yet, would require to pack A with duplication
* |cplx |real | easy vectorization
*/
template<typename LhsScalar, typename RhsScalar, typename Index, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
struct gebp_kernel
{
typedef gebp_traits<LhsScalar,RhsScalar,ConjugateLhs,ConjugateRhs> Traits;
typedef typename Traits::ResScalar ResScalar;
typedef typename Traits::LhsPacket LhsPacket;
typedef typename Traits::RhsPacket RhsPacket;
typedef typename Traits::ResPacket ResPacket;
typedef typename Traits::AccPacket AccPacket;
enum {
Vectorizable = Traits::Vectorizable,
LhsProgress = Traits::LhsProgress,
RhsProgress = Traits::RhsProgress,
ResPacketSize = Traits::ResPacketSize
};
EIGEN_DONT_INLINE
void operator()(ResScalar* res, Index resStride, const LhsScalar* blockA, const RhsScalar* blockB, Index rows, Index depth, Index cols, ResScalar alpha,
Index strideA=-1, Index strideB=-1, Index offsetA=0, Index offsetB=0, RhsScalar* unpackedB=0);
};
template<typename LhsScalar, typename RhsScalar, typename Index, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
EIGEN_DONT_INLINE
void gebp_kernel<LhsScalar,RhsScalar,Index,mr,nr,ConjugateLhs,ConjugateRhs>
::operator()(ResScalar* res, Index resStride, const LhsScalar* blockA, const RhsScalar* blockB, Index rows, Index depth, Index cols, ResScalar alpha,
Index strideA, Index strideB, Index offsetA, Index offsetB, RhsScalar* unpackedB)
{
Traits traits;
if(strideA==-1) strideA = depth;
if(strideB==-1) strideB = depth;
conj_helper<LhsScalar,RhsScalar,ConjugateLhs,ConjugateRhs> cj;
// conj_helper<LhsPacket,RhsPacket,ConjugateLhs,ConjugateRhs> pcj;
Index packet_cols = (cols/nr) * nr;
const Index peeled_mc = (rows/mr)*mr;
// FIXME:
const Index peeled_mc2 = peeled_mc + (rows-peeled_mc >= LhsProgress ? LhsProgress : 0);
const Index peeled_kc = (depth/4)*4;
if(unpackedB==0)
unpackedB = const_cast<RhsScalar*>(blockB - strideB * nr * RhsProgress);
// loops on each micro vertical panel of rhs (depth x nr)
for(Index j2=0; j2<packet_cols; j2+=nr)
{
traits.unpackRhs(depth*nr,&blockB[j2*strideB+offsetB*nr],unpackedB);
// loops on each largest micro horizontal panel of lhs (mr x depth)
// => we select a mr x nr micro block of res which is entirely
// stored into mr/packet_size x nr registers.
for(Index i=0; i<peeled_mc; i+=mr)
{
const LhsScalar* blA = &blockA[i*strideA+offsetA*mr];
prefetch(&blA[0]);
// gets res block as register
AccPacket C0, C1, C2, C3, C4, C5, C6, C7;
traits.initAcc(C0);
traits.initAcc(C1);
if(nr==4) traits.initAcc(C2);
if(nr==4) traits.initAcc(C3);
traits.initAcc(C4);
traits.initAcc(C5);
if(nr==4) traits.initAcc(C6);
if(nr==4) traits.initAcc(C7);
ResScalar* r0 = &res[(j2+0)*resStride + i];
ResScalar* r1 = r0 + resStride;
ResScalar* r2 = r1 + resStride;
ResScalar* r3 = r2 + resStride;
prefetch(r0+16);
prefetch(r1+16);
prefetch(r2+16);
prefetch(r3+16);
// performs "inner" product
// TODO let's check wether the folowing peeled loop could not be
// optimized via optimal prefetching from one loop to the other
const RhsScalar* blB = unpackedB;
for(Index k=0; k<peeled_kc; k+=4)
{
if(nr==2)
{
LhsPacket A0, A1;
RhsPacket B_0;
RhsPacket T0;
EIGEN_ASM_COMMENT("mybegin2");
traits.loadLhs(&blA[0*LhsProgress], A0);
traits.loadLhs(&blA[1*LhsProgress], A1);
traits.loadRhs(&blB[0*RhsProgress], B_0);
traits.madd(A0,B_0,C0,T0);
traits.madd(A1,B_0,C4,B_0);
traits.loadRhs(&blB[1*RhsProgress], B_0);
traits.madd(A0,B_0,C1,T0);
traits.madd(A1,B_0,C5,B_0);
traits.loadLhs(&blA[2*LhsProgress], A0);
traits.loadLhs(&blA[3*LhsProgress], A1);
traits.loadRhs(&blB[2*RhsProgress], B_0);
traits.madd(A0,B_0,C0,T0);
traits.madd(A1,B_0,C4,B_0);
traits.loadRhs(&blB[3*RhsProgress], B_0);
traits.madd(A0,B_0,C1,T0);
traits.madd(A1,B_0,C5,B_0);
traits.loadLhs(&blA[4*LhsProgress], A0);
traits.loadLhs(&blA[5*LhsProgress], A1);
traits.loadRhs(&blB[4*RhsProgress], B_0);
traits.madd(A0,B_0,C0,T0);
traits.madd(A1,B_0,C4,B_0);
traits.loadRhs(&blB[5*RhsProgress], B_0);
traits.madd(A0,B_0,C1,T0);
traits.madd(A1,B_0,C5,B_0);
traits.loadLhs(&blA[6*LhsProgress], A0);
traits.loadLhs(&blA[7*LhsProgress], A1);
traits.loadRhs(&blB[6*RhsProgress], B_0);
traits.madd(A0,B_0,C0,T0);
traits.madd(A1,B_0,C4,B_0);
traits.loadRhs(&blB[7*RhsProgress], B_0);
traits.madd(A0,B_0,C1,T0);
traits.madd(A1,B_0,C5,B_0);
EIGEN_ASM_COMMENT("myend");
}
else
{
EIGEN_ASM_COMMENT("mybegin4");
LhsPacket A0, A1;
RhsPacket B_0, B1, B2, B3;
RhsPacket T0;
traits.loadLhs(&blA[0*LhsProgress], A0);
traits.loadLhs(&blA[1*LhsProgress], A1);
traits.loadRhs(&blB[0*RhsProgress], B_0);
traits.loadRhs(&blB[1*RhsProgress], B1);
traits.madd(A0,B_0,C0,T0);
traits.loadRhs(&blB[2*RhsProgress], B2);
traits.madd(A1,B_0,C4,B_0);
traits.loadRhs(&blB[3*RhsProgress], B3);
traits.loadRhs(&blB[4*RhsProgress], B_0);
traits.madd(A0,B1,C1,T0);
traits.madd(A1,B1,C5,B1);
traits.loadRhs(&blB[5*RhsProgress], B1);
traits.madd(A0,B2,C2,T0);
traits.madd(A1,B2,C6,B2);
traits.loadRhs(&blB[6*RhsProgress], B2);
traits.madd(A0,B3,C3,T0);
traits.loadLhs(&blA[2*LhsProgress], A0);
traits.madd(A1,B3,C7,B3);
traits.loadLhs(&blA[3*LhsProgress], A1);
traits.loadRhs(&blB[7*RhsProgress], B3);
traits.madd(A0,B_0,C0,T0);
traits.madd(A1,B_0,C4,B_0);
traits.loadRhs(&blB[8*RhsProgress], B_0);
traits.madd(A0,B1,C1,T0);
traits.madd(A1,B1,C5,B1);
traits.loadRhs(&blB[9*RhsProgress], B1);
traits.madd(A0,B2,C2,T0);
traits.madd(A1,B2,C6,B2);
traits.loadRhs(&blB[10*RhsProgress], B2);
traits.madd(A0,B3,C3,T0);
traits.loadLhs(&blA[4*LhsProgress], A0);
traits.madd(A1,B3,C7,B3);
traits.loadLhs(&blA[5*LhsProgress], A1);
traits.loadRhs(&blB[11*RhsProgress], B3);
traits.madd(A0,B_0,C0,T0);
traits.madd(A1,B_0,C4,B_0);
traits.loadRhs(&blB[12*RhsProgress], B_0);
traits.madd(A0,B1,C1,T0);
traits.madd(A1,B1,C5,B1);
traits.loadRhs(&blB[13*RhsProgress], B1);
traits.madd(A0,B2,C2,T0);
traits.madd(A1,B2,C6,B2);
traits.loadRhs(&blB[14*RhsProgress], B2);
traits.madd(A0,B3,C3,T0);
traits.loadLhs(&blA[6*LhsProgress], A0);
traits.madd(A1,B3,C7,B3);
traits.loadLhs(&blA[7*LhsProgress], A1);
traits.loadRhs(&blB[15*RhsProgress], B3);
traits.madd(A0,B_0,C0,T0);
traits.madd(A1,B_0,C4,B_0);
traits.madd(A0,B1,C1,T0);
traits.madd(A1,B1,C5,B1);
traits.madd(A0,B2,C2,T0);
traits.madd(A1,B2,C6,B2);
traits.madd(A0,B3,C3,T0);
traits.madd(A1,B3,C7,B3);
}
blB += 4*nr*RhsProgress;
blA += 4*mr;
}
// process remaining peeled loop
for(Index k=peeled_kc; k<depth; k++)
{
if(nr==2)
{
LhsPacket A0, A1;
RhsPacket B_0;
RhsPacket T0;
traits.loadLhs(&blA[0*LhsProgress], A0);
traits.loadLhs(&blA[1*LhsProgress], A1);
traits.loadRhs(&blB[0*RhsProgress], B_0);
traits.madd(A0,B_0,C0,T0);
traits.madd(A1,B_0,C4,B_0);
traits.loadRhs(&blB[1*RhsProgress], B_0);
traits.madd(A0,B_0,C1,T0);
traits.madd(A1,B_0,C5,B_0);
}
else
{
LhsPacket A0, A1;
RhsPacket B_0, B1, B2, B3;
RhsPacket T0;
traits.loadLhs(&blA[0*LhsProgress], A0);
traits.loadLhs(&blA[1*LhsProgress], A1);
traits.loadRhs(&blB[0*RhsProgress], B_0);
traits.loadRhs(&blB[1*RhsProgress], B1);
traits.madd(A0,B_0,C0,T0);
traits.loadRhs(&blB[2*RhsProgress], B2);
traits.madd(A1,B_0,C4,B_0);
traits.loadRhs(&blB[3*RhsProgress], B3);
traits.madd(A0,B1,C1,T0);
traits.madd(A1,B1,C5,B1);
traits.madd(A0,B2,C2,T0);
traits.madd(A1,B2,C6,B2);
traits.madd(A0,B3,C3,T0);
traits.madd(A1,B3,C7,B3);
}
blB += nr*RhsProgress;
blA += mr;
}
if(nr==4)
{
ResPacket R0, R1, R2, R3, R4, R5, R6;
ResPacket alphav = pset1<ResPacket>(alpha);
R0 = ploadu<ResPacket>(r0);
R1 = ploadu<ResPacket>(r1);
R2 = ploadu<ResPacket>(r2);
R3 = ploadu<ResPacket>(r3);
R4 = ploadu<ResPacket>(r0 + ResPacketSize);
R5 = ploadu<ResPacket>(r1 + ResPacketSize);
R6 = ploadu<ResPacket>(r2 + ResPacketSize);
traits.acc(C0, alphav, R0);
pstoreu(r0, R0);
R0 = ploadu<ResPacket>(r3 + ResPacketSize);
traits.acc(C1, alphav, R1);
traits.acc(C2, alphav, R2);
traits.acc(C3, alphav, R3);
traits.acc(C4, alphav, R4);
traits.acc(C5, alphav, R5);
traits.acc(C6, alphav, R6);
traits.acc(C7, alphav, R0);
pstoreu(r1, R1);
pstoreu(r2, R2);
pstoreu(r3, R3);
pstoreu(r0 + ResPacketSize, R4);
pstoreu(r1 + ResPacketSize, R5);
pstoreu(r2 + ResPacketSize, R6);
pstoreu(r3 + ResPacketSize, R0);
}
else
{
ResPacket R0, R1, R4;
ResPacket alphav = pset1<ResPacket>(alpha);
R0 = ploadu<ResPacket>(r0);
R1 = ploadu<ResPacket>(r1);
R4 = ploadu<ResPacket>(r0 + ResPacketSize);
traits.acc(C0, alphav, R0);
pstoreu(r0, R0);
R0 = ploadu<ResPacket>(r1 + ResPacketSize);
traits.acc(C1, alphav, R1);
traits.acc(C4, alphav, R4);
traits.acc(C5, alphav, R0);
pstoreu(r1, R1);
pstoreu(r0 + ResPacketSize, R4);
pstoreu(r1 + ResPacketSize, R0);
}
}
if(rows-peeled_mc>=LhsProgress)
{
Index i = peeled_mc;
const LhsScalar* blA = &blockA[i*strideA+offsetA*LhsProgress];
prefetch(&blA[0]);
// gets res block as register
AccPacket C0, C1, C2, C3;
traits.initAcc(C0);
traits.initAcc(C1);
if(nr==4) traits.initAcc(C2);
if(nr==4) traits.initAcc(C3);
// performs "inner" product
const RhsScalar* blB = unpackedB;
for(Index k=0; k<peeled_kc; k+=4)
{
if(nr==2)
{
LhsPacket A0;
RhsPacket B_0, B1;
traits.loadLhs(&blA[0*LhsProgress], A0);
traits.loadRhs(&blB[0*RhsProgress], B_0);
traits.loadRhs(&blB[1*RhsProgress], B1);
traits.madd(A0,B_0,C0,B_0);
traits.loadRhs(&blB[2*RhsProgress], B_0);
traits.madd(A0,B1,C1,B1);
traits.loadLhs(&blA[1*LhsProgress], A0);
traits.loadRhs(&blB[3*RhsProgress], B1);
traits.madd(A0,B_0,C0,B_0);
traits.loadRhs(&blB[4*RhsProgress], B_0);
traits.madd(A0,B1,C1,B1);
traits.loadLhs(&blA[2*LhsProgress], A0);
traits.loadRhs(&blB[5*RhsProgress], B1);
traits.madd(A0,B_0,C0,B_0);
traits.loadRhs(&blB[6*RhsProgress], B_0);
traits.madd(A0,B1,C1,B1);
traits.loadLhs(&blA[3*LhsProgress], A0);
traits.loadRhs(&blB[7*RhsProgress], B1);
traits.madd(A0,B_0,C0,B_0);
traits.madd(A0,B1,C1,B1);
}
else
{
LhsPacket A0;
RhsPacket B_0, B1, B2, B3;
traits.loadLhs(&blA[0*LhsProgress], A0);
traits.loadRhs(&blB[0*RhsProgress], B_0);
traits.loadRhs(&blB[1*RhsProgress], B1);
traits.madd(A0,B_0,C0,B_0);
traits.loadRhs(&blB[2*RhsProgress], B2);
traits.loadRhs(&blB[3*RhsProgress], B3);
traits.loadRhs(&blB[4*RhsProgress], B_0);
traits.madd(A0,B1,C1,B1);
traits.loadRhs(&blB[5*RhsProgress], B1);
traits.madd(A0,B2,C2,B2);
traits.loadRhs(&blB[6*RhsProgress], B2);
traits.madd(A0,B3,C3,B3);
traits.loadLhs(&blA[1*LhsProgress], A0);
traits.loadRhs(&blB[7*RhsProgress], B3);
traits.madd(A0,B_0,C0,B_0);
traits.loadRhs(&blB[8*RhsProgress], B_0);
traits.madd(A0,B1,C1,B1);
traits.loadRhs(&blB[9*RhsProgress], B1);
traits.madd(A0,B2,C2,B2);
traits.loadRhs(&blB[10*RhsProgress], B2);
traits.madd(A0,B3,C3,B3);
traits.loadLhs(&blA[2*LhsProgress], A0);
traits.loadRhs(&blB[11*RhsProgress], B3);
traits.madd(A0,B_0,C0,B_0);
traits.loadRhs(&blB[12*RhsProgress], B_0);
traits.madd(A0,B1,C1,B1);
traits.loadRhs(&blB[13*RhsProgress], B1);
traits.madd(A0,B2,C2,B2);
traits.loadRhs(&blB[14*RhsProgress], B2);
traits.madd(A0,B3,C3,B3);
traits.loadLhs(&blA[3*LhsProgress], A0);
traits.loadRhs(&blB[15*RhsProgress], B3);
traits.madd(A0,B_0,C0,B_0);
traits.madd(A0,B1,C1,B1);
traits.madd(A0,B2,C2,B2);
traits.madd(A0,B3,C3,B3);
}
blB += nr*4*RhsProgress;
blA += 4*LhsProgress;
}
// process remaining peeled loop
for(Index k=peeled_kc; k<depth; k++)
{
if(nr==2)
{
LhsPacket A0;
RhsPacket B_0, B1;
traits.loadLhs(&blA[0*LhsProgress], A0);
traits.loadRhs(&blB[0*RhsProgress], B_0);
traits.loadRhs(&blB[1*RhsProgress], B1);
traits.madd(A0,B_0,C0,B_0);
traits.madd(A0,B1,C1,B1);
}
else
{
LhsPacket A0;
RhsPacket B_0, B1, B2, B3;
traits.loadLhs(&blA[0*LhsProgress], A0);
traits.loadRhs(&blB[0*RhsProgress], B_0);
traits.loadRhs(&blB[1*RhsProgress], B1);
traits.loadRhs(&blB[2*RhsProgress], B2);
traits.loadRhs(&blB[3*RhsProgress], B3);
traits.madd(A0,B_0,C0,B_0);
traits.madd(A0,B1,C1,B1);
traits.madd(A0,B2,C2,B2);
traits.madd(A0,B3,C3,B3);
}
blB += nr*RhsProgress;
blA += LhsProgress;
}
ResPacket R0, R1, R2, R3;
ResPacket alphav = pset1<ResPacket>(alpha);
ResScalar* r0 = &res[(j2+0)*resStride + i];
ResScalar* r1 = r0 + resStride;
ResScalar* r2 = r1 + resStride;
ResScalar* r3 = r2 + resStride;
R0 = ploadu<ResPacket>(r0);
R1 = ploadu<ResPacket>(r1);
if(nr==4) R2 = ploadu<ResPacket>(r2);
if(nr==4) R3 = ploadu<ResPacket>(r3);
traits.acc(C0, alphav, R0);
traits.acc(C1, alphav, R1);
if(nr==4) traits.acc(C2, alphav, R2);
if(nr==4) traits.acc(C3, alphav, R3);
pstoreu(r0, R0);
pstoreu(r1, R1);
if(nr==4) pstoreu(r2, R2);
if(nr==4) pstoreu(r3, R3);
}
for(Index i=peeled_mc2; i<rows; i++)
{
const LhsScalar* blA = &blockA[i*strideA+offsetA];
prefetch(&blA[0]);
// gets a 1 x nr res block as registers
ResScalar C0(0), C1(0), C2(0), C3(0);
// TODO directly use blockB ???
const RhsScalar* blB = &blockB[j2*strideB+offsetB*nr];
for(Index k=0; k<depth; k++)
{
if(nr==2)
{
LhsScalar A0;
RhsScalar B_0, B1;
A0 = blA[k];
B_0 = blB[0];
B1 = blB[1];
MADD(cj,A0,B_0,C0,B_0);
MADD(cj,A0,B1,C1,B1);
}
else
{
LhsScalar A0;
RhsScalar B_0, B1, B2, B3;
A0 = blA[k];
B_0 = blB[0];
B1 = blB[1];
B2 = blB[2];
B3 = blB[3];
MADD(cj,A0,B_0,C0,B_0);
MADD(cj,A0,B1,C1,B1);
MADD(cj,A0,B2,C2,B2);
MADD(cj,A0,B3,C3,B3);
}
blB += nr;
}
res[(j2+0)*resStride + i] += alpha*C0;
res[(j2+1)*resStride + i] += alpha*C1;
if(nr==4) res[(j2+2)*resStride + i] += alpha*C2;
if(nr==4) res[(j2+3)*resStride + i] += alpha*C3;
}
}
// process remaining rhs/res columns one at a time
// => do the same but with nr==1
for(Index j2=packet_cols; j2<cols; j2++)
{
// unpack B
traits.unpackRhs(depth, &blockB[j2*strideB+offsetB], unpackedB);
for(Index i=0; i<peeled_mc; i+=mr)
{
const LhsScalar* blA = &blockA[i*strideA+offsetA*mr];
prefetch(&blA[0]);
// TODO move the res loads to the stores
// get res block as registers
AccPacket C0, C4;
traits.initAcc(C0);
traits.initAcc(C4);
const RhsScalar* blB = unpackedB;
for(Index k=0; k<depth; k++)
{
LhsPacket A0, A1;
RhsPacket B_0;
RhsPacket T0;
traits.loadLhs(&blA[0*LhsProgress], A0);
traits.loadLhs(&blA[1*LhsProgress], A1);
traits.loadRhs(&blB[0*RhsProgress], B_0);
traits.madd(A0,B_0,C0,T0);
traits.madd(A1,B_0,C4,B_0);
blB += RhsProgress;
blA += 2*LhsProgress;
}
ResPacket R0, R4;
ResPacket alphav = pset1<ResPacket>(alpha);
ResScalar* r0 = &res[(j2+0)*resStride + i];
R0 = ploadu<ResPacket>(r0);
R4 = ploadu<ResPacket>(r0+ResPacketSize);
traits.acc(C0, alphav, R0);
traits.acc(C4, alphav, R4);
pstoreu(r0, R0);
pstoreu(r0+ResPacketSize, R4);
}
if(rows-peeled_mc>=LhsProgress)
{
Index i = peeled_mc;
const LhsScalar* blA = &blockA[i*strideA+offsetA*LhsProgress];
prefetch(&blA[0]);
AccPacket C0;
traits.initAcc(C0);
const RhsScalar* blB = unpackedB;
for(Index k=0; k<depth; k++)
{
LhsPacket A0;
RhsPacket B_0;
traits.loadLhs(blA, A0);
traits.loadRhs(blB, B_0);
traits.madd(A0, B_0, C0, B_0);
blB += RhsProgress;
blA += LhsProgress;
}
ResPacket alphav = pset1<ResPacket>(alpha);
ResPacket R0 = ploadu<ResPacket>(&res[(j2+0)*resStride + i]);
traits.acc(C0, alphav, R0);
pstoreu(&res[(j2+0)*resStride + i], R0);
}
for(Index i=peeled_mc2; i<rows; i++)
{
const LhsScalar* blA = &blockA[i*strideA+offsetA];
prefetch(&blA[0]);
// gets a 1 x 1 res block as registers
ResScalar C0(0);
// FIXME directly use blockB ??
const RhsScalar* blB = &blockB[j2*strideB+offsetB];
for(Index k=0; k<depth; k++)
{
LhsScalar A0 = blA[k];
RhsScalar B_0 = blB[k];
MADD(cj, A0, B_0, C0, B_0);
}
res[(j2+0)*resStride + i] += alpha*C0;
}
}
}
#undef CJMADD
// pack a block of the lhs
// The traversal is as follow (mr==4):
// 0 4 8 12 ...
// 1 5 9 13 ...
// 2 6 10 14 ...
// 3 7 11 15 ...
//
// 16 20 24 28 ...
// 17 21 25 29 ...
// 18 22 26 30 ...
// 19 23 27 31 ...
//
// 32 33 34 35 ...
// 36 36 38 39 ...
template<typename Scalar, typename Index, int Pack1, int Pack2, int StorageOrder, bool Conjugate, bool PanelMode>
struct gemm_pack_lhs
{
EIGEN_DONT_INLINE void operator()(Scalar* blockA, const Scalar* EIGEN_RESTRICT _lhs, Index lhsStride, Index depth, Index rows, Index stride=0, Index offset=0);
};
template<typename Scalar, typename Index, int Pack1, int Pack2, int StorageOrder, bool Conjugate, bool PanelMode>
EIGEN_DONT_INLINE void gemm_pack_lhs<Scalar, Index, Pack1, Pack2, StorageOrder, Conjugate, PanelMode>
::operator()(Scalar* blockA, const Scalar* EIGEN_RESTRICT _lhs, Index lhsStride, Index depth, Index rows, Index stride, Index offset)
{
typedef typename packet_traits<Scalar>::type Packet;
enum { PacketSize = packet_traits<Scalar>::size };
EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK LHS");
EIGEN_UNUSED_VARIABLE(stride)
EIGEN_UNUSED_VARIABLE(offset)
eigen_assert(((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride));
eigen_assert( (StorageOrder==RowMajor) || ((Pack1%PacketSize)==0 && Pack1<=4*PacketSize) );
conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj;
const_blas_data_mapper<Scalar, Index, StorageOrder> lhs(_lhs,lhsStride);
Index count = 0;
Index peeled_mc = (rows/Pack1)*Pack1;
for(Index i=0; i<peeled_mc; i+=Pack1)
{
if(PanelMode) count += Pack1 * offset;
if(StorageOrder==ColMajor)
{
for(Index k=0; k<depth; k++)
{
Packet A, B, C, D;
if(Pack1>=1*PacketSize) A = ploadu<Packet>(&lhs(i+0*PacketSize, k));
if(Pack1>=2*PacketSize) B = ploadu<Packet>(&lhs(i+1*PacketSize, k));
if(Pack1>=3*PacketSize) C = ploadu<Packet>(&lhs(i+2*PacketSize, k));
if(Pack1>=4*PacketSize) D = ploadu<Packet>(&lhs(i+3*PacketSize, k));
if(Pack1>=1*PacketSize) { pstore(blockA+count, cj.pconj(A)); count+=PacketSize; }
if(Pack1>=2*PacketSize) { pstore(blockA+count, cj.pconj(B)); count+=PacketSize; }
if(Pack1>=3*PacketSize) { pstore(blockA+count, cj.pconj(C)); count+=PacketSize; }
if(Pack1>=4*PacketSize) { pstore(blockA+count, cj.pconj(D)); count+=PacketSize; }
}
}
else
{
for(Index k=0; k<depth; k++)
{
// TODO add a vectorized transpose here
Index w=0;
for(; w<Pack1-3; w+=4)
{
Scalar a(cj(lhs(i+w+0, k))),
b(cj(lhs(i+w+1, k))),
c(cj(lhs(i+w+2, k))),
d(cj(lhs(i+w+3, k)));
blockA[count++] = a;
blockA[count++] = b;
blockA[count++] = c;
blockA[count++] = d;
}
if(Pack1%4)
for(;w<Pack1;++w)
blockA[count++] = cj(lhs(i+w, k));
}
}
if(PanelMode) count += Pack1 * (stride-offset-depth);
}
if(rows-peeled_mc>=Pack2)
{
if(PanelMode) count += Pack2*offset;
for(Index k=0; k<depth; k++)
for(Index w=0; w<Pack2; w++)
blockA[count++] = cj(lhs(peeled_mc+w, k));
if(PanelMode) count += Pack2 * (stride-offset-depth);
peeled_mc += Pack2;
}
for(Index i=peeled_mc; i<rows; i++)
{
if(PanelMode) count += offset;
for(Index k=0; k<depth; k++)
blockA[count++] = cj(lhs(i, k));
if(PanelMode) count += (stride-offset-depth);
}
}
// copy a complete panel of the rhs
// this version is optimized for column major matrices
// The traversal order is as follow: (nr==4):
// 0 1 2 3 12 13 14 15 24 27
// 4 5 6 7 16 17 18 19 25 28
// 8 9 10 11 20 21 22 23 26 29
// . . . . . . . . . .
template<typename Scalar, typename Index, int nr, bool Conjugate, bool PanelMode>
struct gemm_pack_rhs<Scalar, Index, nr, ColMajor, Conjugate, PanelMode>
{
typedef typename packet_traits<Scalar>::type Packet;
enum { PacketSize = packet_traits<Scalar>::size };
EIGEN_DONT_INLINE void operator()(Scalar* blockB, const Scalar* rhs, Index rhsStride, Index depth, Index cols, Index stride=0, Index offset=0);
};
template<typename Scalar, typename Index, int nr, bool Conjugate, bool PanelMode>
EIGEN_DONT_INLINE void gemm_pack_rhs<Scalar, Index, nr, ColMajor, Conjugate, PanelMode>
::operator()(Scalar* blockB, const Scalar* rhs, Index rhsStride, Index depth, Index cols, Index stride, Index offset)
{
EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK RHS COLMAJOR");
EIGEN_UNUSED_VARIABLE(stride)
EIGEN_UNUSED_VARIABLE(offset)
eigen_assert(((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride));
conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj;
Index packet_cols = (cols/nr) * nr;
Index count = 0;
for(Index j2=0; j2<packet_cols; j2+=nr)
{
// skip what we have before
if(PanelMode) count += nr * offset;
const Scalar* b0 = &rhs[(j2+0)*rhsStride];
const Scalar* b1 = &rhs[(j2+1)*rhsStride];
const Scalar* b2 = &rhs[(j2+2)*rhsStride];
const Scalar* b3 = &rhs[(j2+3)*rhsStride];
for(Index k=0; k<depth; k++)
{
blockB[count+0] = cj(b0[k]);
blockB[count+1] = cj(b1[k]);
if(nr==4) blockB[count+2] = cj(b2[k]);
if(nr==4) blockB[count+3] = cj(b3[k]);
count += nr;
}
// skip what we have after
if(PanelMode) count += nr * (stride-offset-depth);
}
// copy the remaining columns one at a time (nr==1)
for(Index j2=packet_cols; j2<cols; ++j2)
{
if(PanelMode) count += offset;
const Scalar* b0 = &rhs[(j2+0)*rhsStride];
for(Index k=0; k<depth; k++)
{
blockB[count] = cj(b0[k]);
count += 1;
}
if(PanelMode) count += (stride-offset-depth);
}
}
// this version is optimized for row major matrices
template<typename Scalar, typename Index, int nr, bool Conjugate, bool PanelMode>
struct gemm_pack_rhs<Scalar, Index, nr, RowMajor, Conjugate, PanelMode>
{
enum { PacketSize = packet_traits<Scalar>::size };
EIGEN_DONT_INLINE void operator()(Scalar* blockB, const Scalar* rhs, Index rhsStride, Index depth, Index cols, Index stride=0, Index offset=0);
};
template<typename Scalar, typename Index, int nr, bool Conjugate, bool PanelMode>
EIGEN_DONT_INLINE void gemm_pack_rhs<Scalar, Index, nr, RowMajor, Conjugate, PanelMode>
::operator()(Scalar* blockB, const Scalar* rhs, Index rhsStride, Index depth, Index cols, Index stride, Index offset)
{
EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK RHS ROWMAJOR");
EIGEN_UNUSED_VARIABLE(stride)
EIGEN_UNUSED_VARIABLE(offset)
eigen_assert(((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride));
conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj;
Index packet_cols = (cols/nr) * nr;
Index count = 0;
for(Index j2=0; j2<packet_cols; j2+=nr)
{
// skip what we have before
if(PanelMode) count += nr * offset;
for(Index k=0; k<depth; k++)
{
const Scalar* b0 = &rhs[k*rhsStride + j2];
blockB[count+0] = cj(b0[0]);
blockB[count+1] = cj(b0[1]);
if(nr==4) blockB[count+2] = cj(b0[2]);
if(nr==4) blockB[count+3] = cj(b0[3]);
count += nr;
}
// skip what we have after
if(PanelMode) count += nr * (stride-offset-depth);
}
// copy the remaining columns one at a time (nr==1)
for(Index j2=packet_cols; j2<cols; ++j2)
{
if(PanelMode) count += offset;
const Scalar* b0 = &rhs[j2];
for(Index k=0; k<depth; k++)
{
blockB[count] = cj(b0[k*rhsStride]);
count += 1;
}
if(PanelMode) count += stride-offset-depth;
}
}
} // end namespace internal
/** \returns the currently set level 1 cpu cache size (in bytes) used to estimate the ideal blocking size parameters.
* \sa setCpuCacheSize */
inline std::ptrdiff_t l1CacheSize()
{
std::ptrdiff_t l1, l2;
internal::manage_caching_sizes(GetAction, &l1, &l2);
return l1;
}
/** \returns the currently set level 2 cpu cache size (in bytes) used to estimate the ideal blocking size parameters.
* \sa setCpuCacheSize */
inline std::ptrdiff_t l2CacheSize()
{
std::ptrdiff_t l1, l2;
internal::manage_caching_sizes(GetAction, &l1, &l2);
return l2;
}
/** Set the cpu L1 and L2 cache sizes (in bytes).
* These values are use to adjust the size of the blocks
* for the algorithms working per blocks.
*
* \sa computeProductBlockingSizes */
inline void setCpuCacheSizes(std::ptrdiff_t l1, std::ptrdiff_t l2)
{
internal::manage_caching_sizes(SetAction, &l1, &l2);
}
} // end namespace Eigen
#endif // EIGEN_GENERAL_BLOCK_PANEL_H
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