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46fa4c713f
* Introduce a new highly optimized matrix-matrix product for large matrices. The code is still highly experimental and it is activated only if you define EIGEN_WIP_PRODUCT at compile time. Currently the third dimension of the product must be a factor of the packet size (x4 for floats) and the right handed side matrix must be column major. Moreover, currently c = a*b; actually computes c += a*b !! Therefore, the code is provided for experimentation purpose only ! These limitations will be fixed soon or later to become the default product implementation.
246 lines
11 KiB
C++
246 lines
11 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra. Eigen itself is part of the KDE project.
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//
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// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
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//
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// Eigen is free software; you can redistribute it and/or
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// modify it under the terms of the GNU Lesser General Public
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// License as published by the Free Software Foundation; either
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// version 3 of the License, or (at your option) any later version.
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//
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// Alternatively, you can redistribute it and/or
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// modify it under the terms of the GNU General Public License as
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// published by the Free Software Foundation; either version 2 of
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// the License, or (at your option) any later version.
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//
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// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
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// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
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// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
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// GNU General Public License for more details.
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//
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// You should have received a copy of the GNU Lesser General Public
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// License and a copy of the GNU General Public License along with
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// Eigen. If not, see <http://www.gnu.org/licenses/>.
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#ifndef EIGEN_PACKET_MATH_H
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#define EIGEN_PACKET_MATH_H
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#ifndef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
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#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 16
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#endif
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// Default implementation for types not supported by the vectorization.
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// In practice these functions are provided to make easier the writting
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// of generic vectorized code. However, at runtime, they should never be
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// called, TODO so sould we raise an assertion or not ?
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/** \internal \returns a + b (coeff-wise) */
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template <typename Scalar> inline Scalar ei_padd(const Scalar& a, const Scalar& b) { return a + b; }
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/** \internal \returns a - b (coeff-wise) */
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template <typename Scalar> inline Scalar ei_psub(const Scalar& a, const Scalar& b) { return a - b; }
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/** \internal \returns a * b (coeff-wise) */
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template <typename Scalar> inline Scalar ei_pmul(const Scalar& a, const Scalar& b) { return a * b; }
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/** \internal \returns a * b - c (coeff-wise) */
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template <typename Scalar> inline Scalar ei_pmadd(const Scalar& a, const Scalar& b, const Scalar& c)
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{ return ei_padd(ei_pmul(a, b),c); }
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/** \internal \returns the min of \a a and \a b (coeff-wise) */
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template <typename Scalar> inline Scalar ei_pmin(const Scalar& a, const Scalar& b) { return std::min(a,b); }
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/** \internal \returns the max of \a a and \a b (coeff-wise) */
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template <typename Scalar> inline Scalar ei_pmax(const Scalar& a, const Scalar& b) { return std::max(a,b); }
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/** \internal \returns a packet version of \a *from, from must be 16 bytes aligned */
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template <typename Scalar> inline Scalar ei_pload(const Scalar* from) { return *from; }
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/** \internal \returns a packet with constant coefficients \a a, e.g.: (a,a,a,a) */
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template <typename Scalar> inline Scalar ei_pset1(const Scalar& a) { return a; }
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/** \internal copy the packet \a from to \a *to, \a to must be 16 bytes aligned */
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template <typename Scalar> inline void ei_pstore(Scalar* to, const Scalar& from) { (*to) = from; }
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/** \internal \returns the first element of a packet */
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template <typename Scalar> inline Scalar ei_pfirst(const Scalar& a) { return a; }
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/** \internal \returns a packet where the element i contains the sum of the packet of \a vec[i] */
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template <typename Scalar> inline Scalar ei_predux(const Scalar vecs[1]) { return vecs[0]; }
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/** \internal \returns the sum of the elements of \a a*/
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template <typename Scalar> inline Scalar ei_predux(const Scalar& a) { return a; }
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#ifdef EIGEN_VECTORIZE_SSE
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#ifdef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
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#undef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
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#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 16
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#endif
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template<> struct ei_packet_traits<float> { typedef __m128 type; enum {size=4}; };
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template<> struct ei_packet_traits<double> { typedef __m128d type; enum {size=2}; };
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template<> struct ei_packet_traits<int> { typedef __m128i type; enum {size=4}; };
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template<> inline __m128 ei_padd(const __m128& a, const __m128& b) { return _mm_add_ps(a,b); }
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template<> inline __m128d ei_padd(const __m128d& a, const __m128d& b) { return _mm_add_pd(a,b); }
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template<> inline __m128i ei_padd(const __m128i& a, const __m128i& b) { return _mm_add_epi32(a,b); }
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template<> inline __m128 ei_psub(const __m128& a, const __m128& b) { return _mm_sub_ps(a,b); }
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template<> inline __m128d ei_psub(const __m128d& a, const __m128d& b) { return _mm_sub_pd(a,b); }
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template<> inline __m128i ei_psub(const __m128i& a, const __m128i& b) { return _mm_sub_epi32(a,b); }
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template<> inline __m128 ei_pmul(const __m128& a, const __m128& b) { return _mm_mul_ps(a,b); }
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template<> inline __m128d ei_pmul(const __m128d& a, const __m128d& b) { return _mm_mul_pd(a,b); }
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template<> inline __m128i ei_pmul(const __m128i& a, const __m128i& b)
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{
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return _mm_or_si128(
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_mm_and_si128(
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_mm_mul_epu32(a,b),
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_mm_setr_epi32(0xffffffff,0,0xffffffff,0)),
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_mm_slli_si128(
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_mm_and_si128(
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_mm_mul_epu32(_mm_srli_si128(a,4),_mm_srli_si128(b,4)),
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_mm_setr_epi32(0xffffffff,0,0xffffffff,0)), 4));
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}
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// for some weird raisons, it has to be overloaded for packet integer
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template<> inline __m128i ei_pmadd(const __m128i& a, const __m128i& b, const __m128i& c) { return ei_padd(ei_pmul(a,b), c); }
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template<> inline __m128 ei_pmin(const __m128& a, const __m128& b) { return _mm_min_ps(a,b); }
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template<> inline __m128d ei_pmin(const __m128d& a, const __m128d& b) { return _mm_min_pd(a,b); }
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// FIXME this vectorized min operator is likely to be slower than the standard one
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template<> inline __m128i ei_pmin(const __m128i& a, const __m128i& b)
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{
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__m128i mask = _mm_cmplt_epi32(a,b);
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return _mm_or_si128(_mm_and_si128(mask,a),_mm_andnot_si128(mask,b));
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}
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template<> inline __m128 ei_pmax(const __m128& a, const __m128& b) { return _mm_max_ps(a,b); }
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template<> inline __m128d ei_pmax(const __m128d& a, const __m128d& b) { return _mm_max_pd(a,b); }
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// FIXME this vectorized max operator is likely to be slower than the standard one
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template<> inline __m128i ei_pmax(const __m128i& a, const __m128i& b)
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{
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__m128i mask = _mm_cmpgt_epi32(a,b);
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return _mm_or_si128(_mm_and_si128(mask,a),_mm_andnot_si128(mask,b));
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}
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inline __m128 ei_pload(const float* from) { return _mm_load_ps(from); }
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inline __m128d ei_pload(const double* from) { return _mm_load_pd(from); }
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inline __m128i ei_pload(const int* from) { return _mm_load_si128(reinterpret_cast<const __m128i*>(from)); }
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inline __m128 ei_ploadu(const float* from) { return _mm_loadu_ps(from); }
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inline __m128d ei_ploadu(const double* from) { return _mm_loadu_pd(from); }
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inline __m128i ei_ploadu(const int* from) { return _mm_loadu_si128(reinterpret_cast<const __m128i*>(from)); }
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inline __m128 ei_pset1(const float& from) { return _mm_set1_ps(from); }
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inline __m128d ei_pset1(const double& from) { return _mm_set1_pd(from); }
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inline __m128i ei_pset1(const int& from) { return _mm_set1_epi32(from); }
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inline void ei_pstore(float* to, const __m128& from) { _mm_store_ps(to, from); }
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inline void ei_pstore(double* to, const __m128d& from) { _mm_store_pd(to, from); }
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inline void ei_pstore(int* to, const __m128i& from) { _mm_store_si128(reinterpret_cast<__m128i*>(to), from); }
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inline void ei_pstoreu(float* to, const __m128& from) { _mm_storeu_ps(to, from); }
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inline void ei_pstoreu(double* to, const __m128d& from) { _mm_storeu_pd(to, from); }
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inline void ei_pstoreu(int* to, const __m128i& from) { _mm_store_si128(reinterpret_cast<__m128i*>(to), from); }
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inline float ei_pfirst(const __m128& a) { return _mm_cvtss_f32(a); }
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inline double ei_pfirst(const __m128d& a) { return _mm_cvtsd_f64(a); }
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inline int ei_pfirst(const __m128i& a) { return _mm_cvtsi128_si32(a); }
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#ifdef __SSE3__
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// TODO implement SSE2 versions as well as integer versions
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inline __m128 ei_predux(const __m128* vecs)
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{
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return _mm_hadd_ps(_mm_hadd_ps(vecs[0], vecs[1]),_mm_hadd_ps(vecs[2], vecs[3]));
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}
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inline __m128d ei_predux(const __m128d* vecs)
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{
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return _mm_hadd_pd(vecs[0], vecs[1]);
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}
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inline float ei_predux(const __m128& a)
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{
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__m128 tmp0 = _mm_hadd_ps(a,a);
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return ei_pfirst(_mm_hadd_ps(tmp0, tmp0));
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}
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inline double ei_predux(const __m128d& a) { return ei_pfirst(_mm_hadd_pd(a, a)); }
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#endif
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#elif defined(EIGEN_VECTORIZE_ALTIVEC)
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#ifdef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
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#undef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
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#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 4
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#endif
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static const vector int v0i = vec_splat_u32(0);
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static const vector int v16i_ = vec_splat_u32(-16);
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static const vector float v0f = (vector float) v0i;
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template<> struct ei_packet_traits<float> { typedef vector float type; enum {size=4}; };
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template<> struct ei_packet_traits<int> { typedef vector int type; enum {size=4}; };
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inline vector float ei_padd(const vector float a, const vector float b) { return vec_add(a,b); }
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inline vector int ei_padd(const vector int a, const vector int b) { return vec_add(a,b); }
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inline vector float ei_psub(const vector float a, const vector float b) { return vec_sub(a,b); }
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inline vector int ei_psub(const vector int a, const vector int b) { return vec_sub(a,b); }
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inline vector float ei_pmul(const vector float a, const vector float b) { return vec_madd(a,b, v0f); }
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inline vector int ei_pmul(const vector int a, const vector int b)
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{
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// Taken from http://
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//Set up constants
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vector int bswap, lowProduct, highProduct;
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//Do real work
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bswap = vec_rl( (vector unsigned int)b, (vector unsigned int)v16i_ );
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lowProduct = vec_mulo( (vector short)a,(vector short)b );
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highProduct = vec_msum((vector short)a,(vector short)bswap, v0i);
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highProduct = vec_sl( (vector unsigned int)highProduct, (vector unsigned int)v16i_ );
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return vec_add( lowProduct, highProduct );
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}
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inline vector float ei_pmadd(const vector float a, const vector float b, const vector float c) { return vec_madd(a, b, c); }
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inline vector float ei_pmin(const vector float a, const vector float b) { return vec_min(a,b); }
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inline vector int ei_pmin(const vector int a, const vector int b) { return vec_min(a,b); }
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inline vector float ei_pmax(const vector float a, const vector float b) { return vec_max(a,b); }
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inline vector int ei_pmax(const vector int a, const vector int b) { return vec_max(a,b); }
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inline vector float ei_pload(const float* from) { return vec_ld(0, from); }
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inline vector int ei_pload(const int* from) { return vec_ld(0, from); }
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inline vector float ei_pset1(const float& from)
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{
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static float __attribute__(aligned(16)) af[4];
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af[0] = from;
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vector float vc = vec_ld(0, af);
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vc = vec_splat(vc, 0);
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return vc;
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}
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inline vector int ei_pset1(const int& from)
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{
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static int __attribute__(aligned(16)) ai[4];
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ai[0] = from;
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vector int vc = vec_ld(0, ai);
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vc = vec_splat(vc, 0);
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return vc;
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}
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inline void ei_pstore(float* to, const vector float from) { vec_st(from, 0, to); }
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inline void ei_pstore(int* to, const vector int from) { vec_st(from, 0, to); }
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inline float ei_pfirst(const vector float a)
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{
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static float __attribute__(aligned(16)) af[4];
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vec_st(a, 0, af);
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return af[0];
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}
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inline int ei_pfirst(const vector int a)
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{
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static int __attribute__(aligned(16)) ai[4];
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vec_st(a, 0, ai);
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return ai[0];
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}
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#endif // EIGEN_VECTORIZE_ALTIVEC & SSE
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#endif // EIGEN_PACKET_MATH_H
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