mirror of
https://github.com/NVIDIA/cuda-samples.git
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341 lines
9.9 KiB
C
341 lines
9.9 KiB
C
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/* Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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* * Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above copyright
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* notice, this list of conditions and the following disclaimer in the
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* documentation and/or other materials provided with the distribution.
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* * Neither the name of NVIDIA CORPORATION nor the names of its
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* contributors may be used to endorse or promote products derived
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* from this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
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* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
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* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
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* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
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* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*/
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#ifndef CUDA_INTERVAL_LIB_H
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#define CUDA_INTERVAL_LIB_H
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#include "cuda_interval_rounded_arith.h"
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// Interval template class and basic operations
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// Interface inspired from the Boost Interval library (www.boost.org)
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template <class T>
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class interval_gpu {
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public:
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__device__ __host__ interval_gpu();
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__device__ __host__ interval_gpu(T const &v);
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__device__ __host__ interval_gpu(T const &l, T const &u);
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__device__ __host__ T const &lower() const;
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__device__ __host__ T const &upper() const;
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static __device__ __host__ interval_gpu empty();
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private:
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T low;
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T up;
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};
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// Constructors
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template <class T>
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inline __device__ __host__ interval_gpu<T>::interval_gpu() {}
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template <class T>
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inline __device__ __host__ interval_gpu<T>::interval_gpu(T const &l, T const &u)
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: low(l), up(u) {}
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template <class T>
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inline __device__ __host__ interval_gpu<T>::interval_gpu(T const &v)
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: low(v), up(v) {}
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template <class T>
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inline __device__ __host__ T const &interval_gpu<T>::lower() const {
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return low;
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}
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template <class T>
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inline __device__ __host__ T const &interval_gpu<T>::upper() const {
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return up;
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}
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template <class T>
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inline __device__ __host__ interval_gpu<T> interval_gpu<T>::empty() {
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rounded_arith<T> rnd;
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return interval_gpu<T>(rnd.nan(), rnd.nan());
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}
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template <class T>
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inline __device__ __host__ bool empty(interval_gpu<T> x) {
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T hash = x.lower() + x.upper();
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return (hash != hash);
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}
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template <class T>
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inline __device__ T width(interval_gpu<T> x) {
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if (empty(x)) return 0;
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rounded_arith<T> rnd;
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return rnd.sub_up(x.upper(), x.lower());
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}
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// Arithmetic operations
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// Unary operators
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template <class T>
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inline __device__ interval_gpu<T> const &operator+(interval_gpu<T> const &x) {
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return x;
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}
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template <class T>
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inline __device__ interval_gpu<T> operator-(interval_gpu<T> const &x) {
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return interval_gpu<T>(-x.upper(), -x.lower());
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}
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// Binary operators
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template <class T>
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inline __device__ interval_gpu<T> operator+(interval_gpu<T> const &x,
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interval_gpu<T> const &y) {
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rounded_arith<T> rnd;
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return interval_gpu<T>(rnd.add_down(x.lower(), y.lower()),
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rnd.add_up(x.upper(), y.upper()));
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}
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template <class T>
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inline __device__ interval_gpu<T> operator-(interval_gpu<T> const &x,
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interval_gpu<T> const &y) {
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rounded_arith<T> rnd;
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return interval_gpu<T>(rnd.sub_down(x.lower(), y.upper()),
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rnd.sub_up(x.upper(), y.lower()));
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}
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inline __device__ float min4(float a, float b, float c, float d) {
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return fminf(fminf(a, b), fminf(c, d));
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}
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inline __device__ float max4(float a, float b, float c, float d) {
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return fmaxf(fmaxf(a, b), fmaxf(c, d));
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}
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inline __device__ double min4(double a, double b, double c, double d) {
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return fmin(fmin(a, b), fmin(c, d));
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}
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inline __device__ double max4(double a, double b, double c, double d) {
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return fmax(fmax(a, b), fmax(c, d));
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}
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template <class T>
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inline __device__ interval_gpu<T> operator*(interval_gpu<T> const &x,
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interval_gpu<T> const &y) {
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// Textbook implementation: 14 flops, but no branch.
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rounded_arith<T> rnd;
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return interval_gpu<T>(
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min4(rnd.mul_down(x.lower(), y.lower()),
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rnd.mul_down(x.lower(), y.upper()),
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rnd.mul_down(x.upper(), y.lower()),
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rnd.mul_down(x.upper(), y.upper())),
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max4(rnd.mul_up(x.lower(), y.lower()), rnd.mul_up(x.lower(), y.upper()),
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rnd.mul_up(x.upper(), y.lower()), rnd.mul_up(x.upper(), y.upper())));
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}
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// Center of an interval
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// Typically used for bisection
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template <class T>
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inline __device__ T median(interval_gpu<T> const &x) {
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rounded_arith<T> rnd;
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return rnd.median(x.lower(), x.upper());
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}
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// Intersection between two intervals (can be empty)
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template <class T>
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inline __device__ interval_gpu<T> intersect(interval_gpu<T> const &x,
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interval_gpu<T> const &y) {
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rounded_arith<T> rnd;
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T const &l = rnd.max(x.lower(), y.lower());
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T const &u = rnd.min(x.upper(), y.upper());
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if (l <= u)
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return interval_gpu<T>(l, u);
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else
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return interval_gpu<T>::empty();
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}
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// Division by an interval which does not contain 0.
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// GPU-optimized implementation assuming division is expensive
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template <class T>
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inline __device__ interval_gpu<T> div_non_zero(interval_gpu<T> const &x,
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interval_gpu<T> const &y) {
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rounded_arith<T> rnd;
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typedef interval_gpu<T> I;
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T xl, yl, xu, yu;
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if (y.upper() < 0) {
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xl = x.upper();
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xu = x.lower();
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} else {
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xl = x.lower();
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xu = x.upper();
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}
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if (x.upper() < 0) {
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yl = y.lower();
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yu = y.upper();
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} else if (x.lower() < 0) {
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if (y.upper() < 0) {
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yl = y.upper();
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yu = y.upper();
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} else {
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yl = y.lower();
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yu = y.lower();
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}
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} else {
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yl = y.upper();
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yu = y.lower();
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}
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return I(rnd.div_down(xl, yl), rnd.div_up(xu, yu));
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}
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template <class T>
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inline __device__ interval_gpu<T> div_positive(interval_gpu<T> const &x,
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T const &yu) {
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// assert(yu > 0);
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if (x.lower() == 0 && x.upper() == 0) return x;
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rounded_arith<T> rnd;
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typedef interval_gpu<T> I;
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const T &xl = x.lower();
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const T &xu = x.upper();
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if (xu < 0)
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return I(rnd.neg_inf(), rnd.div_up(xu, yu));
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else if (xl < 0)
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return I(rnd.neg_inf(), rnd.pos_inf());
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else
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return I(rnd.div_down(xl, yu), rnd.pos_inf());
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}
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template <class T>
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inline __device__ interval_gpu<T> div_negative(interval_gpu<T> const &x,
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T const &yl) {
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// assert(yu > 0);
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if (x.lower() == 0 && x.upper() == 0) return x;
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rounded_arith<T> rnd;
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typedef interval_gpu<T> I;
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const T &xl = x.lower();
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const T &xu = x.upper();
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if (xu < 0)
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return I(rnd.div_down(xu, yl), rnd.pos_inf());
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else if (xl < 0)
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return I(rnd.neg_inf(), rnd.pos_inf());
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else
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return I(rnd.neg_inf(), rnd.div_up(xl, yl));
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}
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template <class T>
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inline __device__ interval_gpu<T> div_zero_part1(interval_gpu<T> const &x,
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interval_gpu<T> const &y,
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bool &b) {
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if (x.lower() == 0 && x.upper() == 0) {
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b = false;
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return x;
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}
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rounded_arith<T> rnd;
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typedef interval_gpu<T> I;
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const T &xl = x.lower();
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const T &xu = x.upper();
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const T &yl = y.lower();
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const T &yu = y.upper();
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if (xu < 0) {
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b = true;
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return I(rnd.neg_inf(), rnd.div_up(xu, yu));
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} else if (xl < 0) {
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b = false;
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return I(rnd.neg_inf(), rnd.pos_inf());
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} else {
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b = true;
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return I(rnd.neg_inf(), rnd.div_up(xl, yl));
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}
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}
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template <class T>
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inline __device__ interval_gpu<T> div_zero_part2(interval_gpu<T> const &x,
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interval_gpu<T> const &y) {
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rounded_arith<T> rnd;
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typedef interval_gpu<T> I;
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const T &xl = x.lower();
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const T &xu = x.upper();
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const T &yl = y.lower();
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const T &yu = y.upper();
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if (xu < 0)
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return I(rnd.div_down(xu, yl), rnd.pos_inf());
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else
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return I(rnd.div_down(xl, yu), rnd.pos_inf());
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}
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template <class T>
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inline __device__ interval_gpu<T> division_part1(interval_gpu<T> const &x,
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interval_gpu<T> const &y,
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bool &b) {
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b = false;
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if (y.lower() <= 0 && y.upper() >= 0)
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if (y.lower() != 0)
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if (y.upper() != 0)
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return div_zero_part1(x, y, b);
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else
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return div_negative(x, y.lower());
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else if (y.upper() != 0)
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return div_positive(x, y.upper());
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else
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return interval_gpu<T>::empty();
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else
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return div_non_zero(x, y);
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}
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template <class T>
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inline __device__ interval_gpu<T> division_part2(interval_gpu<T> const &x,
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interval_gpu<T> const &y,
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bool b = true) {
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if (!b) return interval_gpu<T>::empty();
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return div_zero_part2(x, y);
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}
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template <class T>
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inline __device__ interval_gpu<T> square(interval_gpu<T> const &x) {
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typedef interval_gpu<T> I;
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rounded_arith<T> rnd;
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const T &xl = x.lower();
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const T &xu = x.upper();
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if (xl >= 0)
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return I(rnd.mul_down(xl, xl), rnd.mul_up(xu, xu));
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else if (xu <= 0)
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return I(rnd.mul_down(xu, xu), rnd.mul_up(xl, xl));
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else
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return I(static_cast<T>(0),
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rnd.max(rnd.mul_up(xl, xl), rnd.mul_up(xu, xu)));
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}
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#endif
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