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https://gitlab.com/libeigen/eigen.git
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9385793f71
are provided to handle not suported types seemlessly. Added a generic null-ary expression with null-ary functors. They replace Zero, Ones, Identity and Random.
109 lines
5.7 KiB
C++
109 lines
5.7 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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// 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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template <typename Scalar> inline Scalar ei_padd(const Scalar& a, const Scalar& b) { return a + b; }
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template <typename Scalar> inline Scalar ei_psub(const Scalar& a, const Scalar& b) { return a - b; }
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template <typename Scalar> inline Scalar ei_pmul(const Scalar& a, const Scalar& b) { return a * b; }
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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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template <typename Scalar> inline Scalar ei_pmax(const Scalar& a, const Scalar& b) { return std::max(a,b); }
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template <typename Scalar> inline Scalar ei_pload(const Scalar* from) { return *from; }
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template <typename Scalar> inline Scalar ei_pload1(const Scalar* from) { return *from; }
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template <typename Scalar> inline Scalar ei_pset1(const Scalar& from) { return from; }
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template <typename Scalar> inline void ei_pstore(Scalar* to, const Scalar& from) { (*to) = from; }
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template <typename Scalar> inline Scalar ei_pfirst(const Scalar& a) { return a; }
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#ifdef EIGEN_VECTORIZE_SSE
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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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inline __m128 ei_padd(const __m128& a, const __m128& b) { return _mm_add_ps(a,b); }
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inline __m128d ei_padd(const __m128d& a, const __m128d& b) { return _mm_add_pd(a,b); }
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inline __m128i ei_padd(const __m128i& a, const __m128i& b) { return _mm_add_epi32(a,b); }
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inline __m128 ei_psub(const __m128& a, const __m128& b) { return _mm_sub_ps(a,b); }
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inline __m128d ei_psub(const __m128d& a, const __m128d& b) { return _mm_sub_pd(a,b); }
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inline __m128i ei_psub(const __m128i& a, const __m128i& b) { return _mm_sub_epi32(a,b); }
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inline __m128 ei_pmul(const __m128& a, const __m128& b) { return _mm_mul_ps(a,b); }
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inline __m128d ei_pmul(const __m128d& a, const __m128d& b) { return _mm_mul_pd(a,b); }
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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_mul_epu32(a,b),
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_mm_slli_si128(
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_mm_mul_epu32(_mm_srli_si128(a,32),_mm_srli_si128(b,32)), 32));
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}
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inline __m128 ei_pmin(const __m128& a, const __m128& b) { return _mm_min_ps(a,b); }
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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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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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inline __m128 ei_pmax(const __m128& a, const __m128& b) { return _mm_max_ps(a,b); }
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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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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_pload1(const float* from) { return _mm_load1_ps(from); }
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inline __m128d ei_pload1(const double* from) { return _mm_load1_pd(from); }
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inline __m128i ei_pload1(const int* from) { return _mm_set1_epi32(*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 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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#endif // EIGEN_VECTORIZE_SSE
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#endif // EIGEN_PACKET_MATH_H
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