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Vector primitives for CUDA
This commit is contained in:
Benoit Steiner
parent
152f3218ac
commit
95a430a2ca
@ -294,6 +294,11 @@ using std::ptrdiff_t;
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#include "src/Core/arch/NEON/Complex.h"
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#endif
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#if defined EIGEN_VECTORIZE_CUDA
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#include "src/Core/arch/CUDA/PacketMath.h"
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#include "src/Core/arch/CUDA/MathFunctions.h"
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#endif
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#include "src/Core/arch/Default/Settings.h"
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#include "src/Core/functors/BinaryFunctors.h"
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75
Eigen/src/Core/arch/CUDA/MathFunctions.h
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75
Eigen/src/Core/arch/CUDA/MathFunctions.h
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@ -0,0 +1,75 @@
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// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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#ifndef EIGEN_MATH_FUNCTIONS_CUDA_H
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#define EIGEN_MATH_FUNCTIONS_CUDA_H
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namespace Eigen {
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namespace internal {
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// Make sure this is only available when targeting a GPU: we don't want to
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// introduce conflicts between these packet_traits definitions and the ones
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// we'll use on the host side (SSE, AVX, ...)
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#if defined(__CUDACC__) && defined(EIGEN_USE_GPU)
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template<> EIGEN_STRONG_INLINE
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float4 plog<float4>(const float4& a)
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{
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return make_float4(logf(a.x), logf(a.y), logf(a.z), logf(a.w));
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}
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template<> EIGEN_STRONG_INLINE
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double2 plog<double2>(const double2& a)
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{
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return make_double2(log(a.x), log(a.y));
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}
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template<> EIGEN_STRONG_INLINE
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float4 pexp<float4>(const float4& a)
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{
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return make_float4(expf(a.x), expf(a.y), expf(a.z), expf(a.w));
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}
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template<> EIGEN_STRONG_INLINE
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double2 pexp<double2>(const double2& a)
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{
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return make_double2(exp(a.x), exp(a.y));
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}
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template<> EIGEN_STRONG_INLINE
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float4 psqrt<float4>(const float4& a)
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{
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return make_float4(sqrtf(a.x), sqrtf(a.y), sqrtf(a.z), sqrtf(a.w));
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}
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template<> EIGEN_STRONG_INLINE
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double2 psqrt<double2>(const double2& a)
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{
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return make_double2(sqrt(a.x), sqrt(a.y));
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}
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template<> EIGEN_STRONG_INLINE
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float4 prsqrt<float4>(const float4& a)
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{
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return make_float4(rsqrtf(a.x), rsqrtf(a.y), rsqrtf(a.z), rsqrtf(a.w));
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}
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template<> EIGEN_STRONG_INLINE
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double2 prsqrt<double2>(const double2& a)
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{
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return make_double2(rsqrt(a.x), rsqrt(a.y));
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}
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#endif
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} // end namespace internal
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} // end namespace Eigen
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#endif // EIGEN_MATH_FUNCTIONS_CUDA_H
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260
Eigen/src/Core/arch/CUDA/PacketMath.h
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260
Eigen/src/Core/arch/CUDA/PacketMath.h
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@ -0,0 +1,260 @@
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// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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#ifndef EIGEN_PACKET_MATH_CUDA_H
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#define EIGEN_PACKET_MATH_CUDA_H
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namespace Eigen {
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namespace internal {
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// Make sure this is only available when targeting a GPU: we don't want to
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// introduce conflicts between these packet_traits definitions and the ones
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// we'll use on the host side (SSE, AVX, ...)
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#if defined(__CUDACC__) && defined(EIGEN_USE_GPU)
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template<> struct is_arithmetic<float4> { enum { value = true }; };
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template<> struct is_arithmetic<double2> { enum { value = true }; };
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template<> struct packet_traits<float> : default_packet_traits
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{
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typedef float4 type;
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typedef float4 half;
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enum {
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Vectorizable = 1,
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AlignedOnScalar = 1,
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size=4,
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HasHalfPacket = 0,
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HasDiv = 1,
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HasSin = 0,
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HasCos = 0,
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HasLog = 1,
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HasExp = 1,
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HasSqrt = 1,
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HasRsqrt = 1,
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HasBlend = 0,
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};
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};
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template<> struct packet_traits<double> : default_packet_traits
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{
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typedef double2 type;
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typedef double2 half;
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enum {
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Vectorizable = 1,
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AlignedOnScalar = 1,
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size=2,
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HasHalfPacket = 0,
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HasDiv = 1,
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HasLog = 1,
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HasExp = 1,
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HasSqrt = 1,
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HasRsqrt = 1,
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HasBlend = 0,
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};
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};
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template<> struct unpacket_traits<float4> { typedef float type; enum {size=4}; typedef float4 half; };
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template<> struct unpacket_traits<double2> { typedef double type; enum {size=2}; typedef double2 half; };
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template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pset1<float4>(const float& from) {
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return make_float4(from, from, from, from);
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}
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template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pset1<double2>(const double& from) {
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return make_double2(from, from);
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}
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template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 plset<float>(const float& a) {
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return make_float4(a, a+1, a+2, a+3);
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}
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template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 plset<double>(const double& a) {
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return make_double2(a, a+1);
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}
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template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 padd<float4>(const float4& a, const float4& b) {
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return make_float4(a.x+b.x, a.y+b.y, a.z+b.z, a.w+b.w);
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}
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template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 padd<double2>(const double2& a, const double2& b) {
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return make_double2(a.x+b.x, a.y+b.y);
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}
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template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 psub<float4>(const float4& a, const float4& b) {
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return make_float4(a.x-b.x, a.y-b.y, a.z-b.z, a.w-b.w);
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}
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template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 psub<double2>(const double2& a, const double2& b) {
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return make_double2(a.x-b.x, a.y-b.y);
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}
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template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pnegate(const float4& a) {
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return make_float4(-a.x, -a.y, -a.z, -a.w);
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}
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template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pnegate(const double2& a) {
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return make_double2(-a.x, -a.y);
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}
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template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pconj(const float4& a) { return a; }
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template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pconj(const double2& a) { return a; }
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template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pmul<float4>(const float4& a, const float4& b) {
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return make_float4(a.x*b.x, a.y*b.y, a.z*b.z, a.w*b.w);
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}
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template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pmul<double2>(const double2& a, const double2& b) {
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return make_double2(a.x*b.x, a.y*b.y);
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}
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template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pdiv<float4>(const float4& a, const float4& b) {
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return make_float4(a.x/b.x, a.y/b.y, a.z/b.z, a.w/b.w);
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}
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template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pdiv<double2>(const double2& a, const double2& b) {
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return make_double2(a.x/b.x, a.y/b.y);
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}
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template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pmin<float4>(const float4& a, const float4& b) {
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return make_float4(fminf(a.x, b.x), fminf(a.y, b.y), fminf(a.z, b.z), fminf(a.w, b.w));
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}
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template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pmin<double2>(const double2& a, const double2& b) {
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return make_double2(fmin(a.x, b.x), fmin(a.y, b.y));
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}
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template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pmax<float4>(const float4& a, const float4& b) {
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return make_float4(fmaxf(a.x, b.x), fmaxf(a.y, b.y), fmaxf(a.z, b.z), fmaxf(a.w, b.w));
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}
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template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pmax<double2>(const double2& a, const double2& b) {
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return make_double2(fmax(a.x, b.x), fmax(a.y, b.y));
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}
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template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pload<float4>(const float* from) {
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return *reinterpret_cast<const float4*>(from);
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}
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template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pload<double2>(const double* from) {
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return *reinterpret_cast<const double2*>(from);
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}
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template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 ploadu<float4>(const float* from) {
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return make_float4(from[0], from[1], from[2], from[3]);
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}
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template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 ploadu<double2>(const double* from) {
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return make_double2(from[0], from[1]);
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}
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template<> EIGEN_STRONG_INLINE float4 ploaddup<float4>(const float* from) {
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return make_float4(from[0], from[0], from[1], from[1]);
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}
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template<> EIGEN_STRONG_INLINE double2 ploaddup<double2>(const double* from) {
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return make_double2(from[0], from[0]);
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}
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template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pstore<float>(float* to, const float4& from) {
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*reinterpret_cast<float4*>(to) = from;
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}
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template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pstore<double>(double* to, const double2& from) {
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*reinterpret_cast<double2*>(to) = from;
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}
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template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const float4& from) {
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to[0] = from.x;
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to[1] = from.y;
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to[2] = from.z;
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to[3] = from.w;
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}
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template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pstoreu<double>(double* to, const double2& from) {
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to[0] = from.x;
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to[1] = from.y;
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}
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#ifdef __CUDA_ARCH__
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template<>
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EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float4 ploadt_ro<float4, Aligned>(const float* from) {
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return __ldg((const float4*)from);
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}
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template<>
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EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double2 ploadt_ro<double2, Aligned>(const double* from) {
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return __ldg((const double2*)from);
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}
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template<>
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EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float4 ploadt_ro<float4, Unaligned>(const float* from) {
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return make_float4(__ldg(from+0), __ldg(from+1), __ldg(from+2), __ldg(from+3));
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}
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template<>
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EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double2 ploadt_ro<double2, Unaligned>(const double* from) {
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return make_double2(__ldg(from+0), __ldg(from+1));
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}
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#endif
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template<> EIGEN_DEVICE_FUNC inline float4 pgather<float, float4>(const float* from, int stride) {
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return make_float4(from[0*stride], from[1*stride], from[2*stride], from[3*stride]);
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}
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template<> EIGEN_DEVICE_FUNC inline double2 pgather<double, double2>(const double* from, int stride) {
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return make_double2(from[0*stride], from[1*stride]);
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}
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template<> EIGEN_DEVICE_FUNC inline void pscatter<float, float4>(float* to, const float4& from, int stride) {
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to[stride*0] = from.x;
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to[stride*1] = from.y;
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to[stride*2] = from.z;
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to[stride*3] = from.w;
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}
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template<> EIGEN_DEVICE_FUNC inline void pscatter<double, double2>(double* to, const double2& from, int stride) {
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to[stride*0] = from.x;
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to[stride*1] = from.y;
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}
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template<> EIGEN_DEVICE_FUNC inline void
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ptranspose(PacketBlock<float4,4>& kernel) {
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double tmp = kernel.packet[0].y;
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kernel.packet[0].y = kernel.packet[1].x;
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kernel.packet[1].x = tmp;
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tmp = kernel.packet[0].z;
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kernel.packet[0].z = kernel.packet[2].x;
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kernel.packet[2].x = tmp;
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tmp = kernel.packet[0].w;
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kernel.packet[0].w = kernel.packet[3].x;
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kernel.packet[3].x = tmp;
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tmp = kernel.packet[1].z;
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kernel.packet[1].z = kernel.packet[2].y;
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kernel.packet[2].y = tmp;
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tmp = kernel.packet[1].w;
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kernel.packet[1].w = kernel.packet[3].y;
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kernel.packet[3].y = tmp;
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tmp = kernel.packet[2].w;
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kernel.packet[2].w = kernel.packet[3].z;
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kernel.packet[3].z = tmp;
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}
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template<> EIGEN_DEVICE_FUNC inline void
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ptranspose(PacketBlock<double2,2>& kernel) {
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double tmp = kernel.packet[0].y;
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kernel.packet[0].y = kernel.packet[1].x;
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kernel.packet[1].x = tmp;
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}
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#endif
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} // end namespace internal
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} // end namespace Eigen
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#endif // EIGEN_PACKET_MATH_CUDA_H
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