math/test/test_ibeta_float.cu
Matt Borland adf8abd346
Apply GPU markers to ibeta_inv_ab
Remove NVRTC workaround

Apply GPU markers to ibeta_inverse

Apply GPU markers to t_dist_inv

Fix warning suppression

Add dispatch function and remove workaround

Move disabling block

Make binomial GPU enabled

Add SYCL testing of ibeta

Add SYCL testing of ibeta_inv

Add SYCL testing of ibeta_inv_ab

Add SYCL testing of full beta suite

Add makers to fwd decls

Add special forward decls for NVRTC

Add betac nvrtc testing

Add betac CUDA testing

Add ibeta CUDA testing

Add ibeta NVRTC testing

Add ibetac NVRTC testing

Add ibeta_derviative testing to nvrtc

Add ibeta_derivative CUDA testing

Add cbrt policy overload for NVRTC

Fix NVRTC definition of BOOST_MATH_IF_CONSTEXPR

Add ibeta_inv and ibetac_inv NVRTC testing

Fix make pair helper on device

Add CUDA testing of ibeta_inv* and ibetac_inv*

Move location so that it also works on NVRTC

Add NVRTC testing of ibeta_inv* and ibetac_inv*

Fixup test sets since they ignore the policy

Make the beta dist GPU compatible

Add beta dist SYCL testing

Add beta dist CUDA testing

Add beta dist NVRTC testing
2024-08-30 13:46:01 -04:00

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// Copyright John Maddock 2016.
// Copyright Matt Borland 2024.
// Use, modification and distribution are subject to the
// Boost Software License, Version 1.0. (See accompanying file
// LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
#define BOOST_MATH_OVERFLOW_ERROR_POLICY ignore_error
#define BOOST_MATH_PROMOTE_DOUBLE_POLICY false
// floating-point value does not fit in required floating-point type
#pragma nv_diag_suppress 221
#include <iostream>
#include <iomanip>
#include <vector>
#include <boost/math/special_functions/beta.hpp>
#include <boost/math/special_functions/relative_difference.hpp>
#include <boost/array.hpp>
#include "cuda_managed_ptr.hpp"
#include "stopwatch.hpp"
// For the CUDA runtime routines (prefixed with "cuda_")
#include <cuda_runtime.h>
typedef float float_type;
/**
* CUDA Kernel Device code
*
*/
__global__ void cuda_test(const float_type *in1, const float_type *in2, const float_type *in3, float_type *out, int numElements)
{
using std::cos;
int i = blockDim.x * blockIdx.x + threadIdx.x;
if (i < numElements)
{
out[i] = boost::math::ibeta(in1[i], in2[i], in3[i]);
}
}
template <class T> struct table_type { typedef T type; };
typedef float_type T;
#define SC_(x) static_cast<T>(x)
#include "ibeta_data.ipp"
#include "ibeta_small_data.ipp"
/**
* Host main routine
*/
int main(void)
{
try{
// Consolidate the test data:
std::vector<float_type> v1, v2, v3;
for(unsigned i = 0; i < ibeta_data.size(); ++i)
{
v1.push_back(ibeta_data[i][0]);
v2.push_back(ibeta_data[i][1]);
v3.push_back(ibeta_data[i][2]);
}
for(unsigned i = 0; i < ibeta_small_data.size(); ++i)
{
v1.push_back(ibeta_small_data[i][0]);
v2.push_back(ibeta_small_data[i][1]);
v3.push_back(ibeta_small_data[i][2]);
}
// Error code to check return values for CUDA calls
cudaError_t err = cudaSuccess;
// Print the vector length to be used, and compute its size
int numElements = 50000;
std::cout << "[Vector operation on " << numElements << " elements]" << std::endl;
// Allocate the managed input vector A
cuda_managed_ptr<float_type> input_vector1(numElements);
cuda_managed_ptr<float_type> input_vector2(numElements);
cuda_managed_ptr<float_type> input_vector3(numElements);
// Allocate the managed output vector C
cuda_managed_ptr<float_type> output_vector(numElements);
// Initialize the input vectors
for (int i = 0; i < numElements; ++i)
{
int table_id = i % v1.size();
input_vector1[i] = v1[table_id];
input_vector2[i] = v2[table_id];
input_vector3[i] = v3[table_id];
}
// Launch the Vector Add CUDA Kernel
int threadsPerBlock = 256;
int blocksPerGrid =(numElements + threadsPerBlock - 1) / threadsPerBlock;
std::cout << "CUDA kernel launch with " << blocksPerGrid << " blocks of " << threadsPerBlock << " threads" << std::endl;
watch w;
cuda_test<<<blocksPerGrid, threadsPerBlock>>>(input_vector1.get(), input_vector2.get(), input_vector3.get(), output_vector.get(), numElements);
cudaDeviceSynchronize();
std::cout << "CUDA kernal done in " << w.elapsed() << "s" << std::endl;
err = cudaGetLastError();
if (err != cudaSuccess)
{
std::cerr << "Failed to launch vectorAdd kernel (error code " << cudaGetErrorString(err) << ")!" << std::endl;
return EXIT_FAILURE;
}
// Verify that the result vector is correct
std::vector<float_type> results;
results.reserve(numElements);
w.reset();
for(int i = 0; i < numElements; ++i)
results.push_back(boost::math::ibeta(input_vector1[i], input_vector2[i], input_vector3[i]));
double t = w.elapsed();
bool failed = false;
// check the results
for(int i = 0; i < numElements; ++i)
{
if (boost::math::isfinite(output_vector[i]))
{
if (boost::math::epsilon_difference(output_vector[i], results[i]) > 300)
{
std::cerr << "Result verification failed at element " << i << "!" << std::endl;
std::cerr << "Error rate was: " << boost::math::epsilon_difference(output_vector[i], results[i]) << "eps" << std::endl;
failed = true;
}
}
}
if (failed)
{
return EXIT_FAILURE;
}
std::cout << "Test PASSED with calculation time: " << t << "s" << std::endl;
std::cout << "Done\n";
}
catch(const std::exception& e)
{
std::cerr << "Stopped with exception: " << e.what() << std::endl;
}
return 0;
}