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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) | ||
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#define BOOST_MATH_OVERFLOW_ERROR_POLICY ignore_error | ||
#define BOOST_MATH_PROMOTE_DOUBLE_POLICY false | ||
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// Must be included first | ||
#include <nvrtc.h> | ||
#include <cuda.h> | ||
#include <cuda_runtime.h> | ||
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#include <iostream> | ||
#include <iomanip> | ||
#include <vector> | ||
#include <random> | ||
#include <exception> | ||
#include <boost/math/distributions/uniform.hpp> | ||
#include <boost/math/special_functions/fpclassify.hpp> | ||
#include <boost/math/special_functions/relative_difference.hpp> | ||
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typedef double float_type; | ||
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const char* cuda_kernel = R"( | ||
typedef double float_type; | ||
#include <cuda/std/type_traits> | ||
#include <boost/math/distributions/uniform.hpp> | ||
extern "C" __global__ | ||
void test_uniform_kernel(const float_type *in1, const float_type*, float_type *out, int numElements) | ||
{ | ||
int i = blockDim.x * blockIdx.x + threadIdx.x; | ||
if (i < numElements) | ||
{ | ||
out[i] = cdf(boost::math::uniform_distribution<float_type>(), in1[i]); | ||
} | ||
} | ||
)"; | ||
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void checkCUDAError(cudaError_t result, const char* msg) | ||
{ | ||
if (result != cudaSuccess) | ||
{ | ||
std::cerr << msg << ": " << cudaGetErrorString(result) << std::endl; | ||
exit(EXIT_FAILURE); | ||
} | ||
} | ||
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void checkCUError(CUresult result, const char* msg) | ||
{ | ||
if (result != CUDA_SUCCESS) | ||
{ | ||
const char* errorStr; | ||
cuGetErrorString(result, &errorStr); | ||
std::cerr << msg << ": " << errorStr << std::endl; | ||
exit(EXIT_FAILURE); | ||
} | ||
} | ||
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void checkNVRTCError(nvrtcResult result, const char* msg) | ||
{ | ||
if (result != NVRTC_SUCCESS) | ||
{ | ||
std::cerr << msg << ": " << nvrtcGetErrorString(result) << std::endl; | ||
exit(EXIT_FAILURE); | ||
} | ||
} | ||
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int main() | ||
{ | ||
try | ||
{ | ||
// Initialize CUDA driver API | ||
checkCUError(cuInit(0), "Failed to initialize CUDA"); | ||
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// Create CUDA context | ||
CUcontext context; | ||
CUdevice device; | ||
checkCUError(cuDeviceGet(&device, 0), "Failed to get CUDA device"); | ||
checkCUError(cuCtxCreate(&context, 0, device), "Failed to create CUDA context"); | ||
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nvrtcProgram prog; | ||
nvrtcResult res; | ||
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res = nvrtcCreateProgram(&prog, cuda_kernel, "test_uniform_kernel.cu", 0, nullptr, nullptr); | ||
checkNVRTCError(res, "Failed to create NVRTC program"); | ||
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nvrtcAddNameExpression(prog, "test_uniform_kernel"); | ||
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#ifdef BOOST_MATH_NVRTC_CI_RUN | ||
const char* opts[] = {"--std=c++14", "--gpu-architecture=compute_75", "--include-path=/home/runner/work/cuda-math/boost-root/libs/cuda-math/include/", "-I/usr/local/cuda/include"}; | ||
#else | ||
const char* opts[] = {"--std=c++14", "--include-path=/home/mborland/Documents/boost/libs/cuda-math/include/", "-I/usr/local/cuda/include"}; | ||
#endif | ||
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// Compile the program | ||
res = nvrtcCompileProgram(prog, sizeof(opts) / sizeof(const char*), opts); | ||
if (res != NVRTC_SUCCESS) | ||
{ | ||
size_t log_size; | ||
nvrtcGetProgramLogSize(prog, &log_size); | ||
char* log = new char[log_size]; | ||
nvrtcGetProgramLog(prog, log); | ||
std::cerr << "Compilation failed:\n" << log << std::endl; | ||
delete[] log; | ||
exit(EXIT_FAILURE); | ||
} | ||
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// Get PTX from the program | ||
size_t ptx_size; | ||
nvrtcGetPTXSize(prog, &ptx_size); | ||
char* ptx = new char[ptx_size]; | ||
nvrtcGetPTX(prog, ptx); | ||
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// Load PTX into CUDA module | ||
CUmodule module; | ||
CUfunction kernel; | ||
checkCUError(cuModuleLoadDataEx(&module, ptx, 0, 0, 0), "Failed to load module"); | ||
checkCUError(cuModuleGetFunction(&kernel, module, "test_uniform_kernel"), "Failed to get kernel function"); | ||
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int numElements = 5000; | ||
float_type *h_in1, *h_in2, *h_out; | ||
float_type *d_in1, *d_in2, *d_out; | ||
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// Allocate memory on the host | ||
h_in1 = new float_type[numElements]; | ||
h_in2 = new float_type[numElements]; | ||
h_out = new float_type[numElements]; | ||
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// Initialize input arrays | ||
std::mt19937_64 rng(42); | ||
std::uniform_real_distribution<float_type> dist(0.0f, 1.0f); | ||
for (int i = 0; i < numElements; ++i) | ||
{ | ||
h_in1[i] = static_cast<float_type>(dist(rng)); | ||
h_in2[i] = static_cast<float_type>(dist(rng)); | ||
} | ||
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checkCUDAError(cudaMalloc(&d_in1, numElements * sizeof(float_type)), "Failed to allocate device memory for d_in1"); | ||
checkCUDAError(cudaMalloc(&d_in2, numElements * sizeof(float_type)), "Failed to allocate device memory for d_in2"); | ||
checkCUDAError(cudaMalloc(&d_out, numElements * sizeof(float_type)), "Failed to allocate device memory for d_out"); | ||
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checkCUDAError(cudaMemcpy(d_in1, h_in1, numElements * sizeof(float_type), cudaMemcpyHostToDevice), "Failed to copy data to device for d_in1"); | ||
checkCUDAError(cudaMemcpy(d_in2, h_in2, numElements * sizeof(float_type), cudaMemcpyHostToDevice), "Failed to copy data to device for d_in2"); | ||
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int blockSize = 256; | ||
int numBlocks = (numElements + blockSize - 1) / blockSize; | ||
void* args[] = { &d_in1, &d_in2, &d_out, &numElements }; | ||
checkCUError(cuLaunchKernel(kernel, numBlocks, 1, 1, blockSize, 1, 1, 0, 0, args, 0), "Kernel launch failed"); | ||
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checkCUDAError(cudaMemcpy(h_out, d_out, numElements * sizeof(float_type), cudaMemcpyDeviceToHost), "Failed to copy data back to host for h_out"); | ||
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// Verify Result | ||
for (int i = 0; i < numElements; ++i) | ||
{ | ||
auto res = cdf(boost::math::uniform_distribution<float_type>(), h_in1[i]); | ||
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if (boost::math::isfinite(res)) | ||
{ | ||
if (boost::math::epsilon_difference(res, h_out[i]) > 300) | ||
{ | ||
std::cout << "error at line: " << i | ||
<< "\nParallel: " << h_out[i] | ||
<< "\n Serial: " << res | ||
<< "\n Dist: " << boost::math::epsilon_difference(res, h_out[i]) << std::endl; | ||
} | ||
} | ||
} | ||
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cudaFree(d_in1); | ||
cudaFree(d_in2); | ||
cudaFree(d_out); | ||
delete[] h_in1; | ||
delete[] h_in2; | ||
delete[] h_out; | ||
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nvrtcDestroyProgram(&prog); | ||
delete[] ptx; | ||
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cuCtxDestroy(context); | ||
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std::cout << "Kernel executed successfully." << std::endl; | ||
return 0; | ||
} | ||
catch(const std::exception& e) | ||
{ | ||
std::cerr << "Stopped with exception: " << e.what() << std::endl; | ||
return EXIT_FAILURE; | ||
} | ||
} |
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Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,191 @@ | ||
// 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 | ||
|
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// Must be included first | ||
#include <nvrtc.h> | ||
#include <cuda.h> | ||
#include <cuda_runtime.h> | ||
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#include <iostream> | ||
#include <iomanip> | ||
#include <vector> | ||
#include <random> | ||
#include <exception> | ||
#include <boost/math/distributions/uniform.hpp> | ||
#include <boost/math/special_functions/fpclassify.hpp> | ||
#include <boost/math/special_functions/relative_difference.hpp> | ||
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typedef float float_type; | ||
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const char* cuda_kernel = R"( | ||
typedef float float_type; | ||
#include <cuda/std/type_traits> | ||
#include <boost/math/distributions/uniform.hpp> | ||
extern "C" __global__ | ||
void test_uniform_kernel(const float_type *in1, const float_type*, float_type *out, int numElements) | ||
{ | ||
int i = blockDim.x * blockIdx.x + threadIdx.x; | ||
if (i < numElements) | ||
{ | ||
out[i] = cdf(boost::math::uniform_distribution<float_type>(), in1[i]); | ||
} | ||
} | ||
)"; | ||
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void checkCUDAError(cudaError_t result, const char* msg) | ||
{ | ||
if (result != cudaSuccess) | ||
{ | ||
std::cerr << msg << ": " << cudaGetErrorString(result) << std::endl; | ||
exit(EXIT_FAILURE); | ||
} | ||
} | ||
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void checkCUError(CUresult result, const char* msg) | ||
{ | ||
if (result != CUDA_SUCCESS) | ||
{ | ||
const char* errorStr; | ||
cuGetErrorString(result, &errorStr); | ||
std::cerr << msg << ": " << errorStr << std::endl; | ||
exit(EXIT_FAILURE); | ||
} | ||
} | ||
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void checkNVRTCError(nvrtcResult result, const char* msg) | ||
{ | ||
if (result != NVRTC_SUCCESS) | ||
{ | ||
std::cerr << msg << ": " << nvrtcGetErrorString(result) << std::endl; | ||
exit(EXIT_FAILURE); | ||
} | ||
} | ||
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int main() | ||
{ | ||
try | ||
{ | ||
// Initialize CUDA driver API | ||
checkCUError(cuInit(0), "Failed to initialize CUDA"); | ||
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// Create CUDA context | ||
CUcontext context; | ||
CUdevice device; | ||
checkCUError(cuDeviceGet(&device, 0), "Failed to get CUDA device"); | ||
checkCUError(cuCtxCreate(&context, 0, device), "Failed to create CUDA context"); | ||
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nvrtcProgram prog; | ||
nvrtcResult res; | ||
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res = nvrtcCreateProgram(&prog, cuda_kernel, "test_uniform_kernel.cu", 0, nullptr, nullptr); | ||
checkNVRTCError(res, "Failed to create NVRTC program"); | ||
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nvrtcAddNameExpression(prog, "test_uniform_kernel"); | ||
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#ifdef BOOST_MATH_NVRTC_CI_RUN | ||
const char* opts[] = {"--std=c++14", "--gpu-architecture=compute_75", "--include-path=/home/runner/work/cuda-math/boost-root/libs/cuda-math/include/", "-I/usr/local/cuda/include"}; | ||
#else | ||
const char* opts[] = {"--std=c++14", "--include-path=/home/mborland/Documents/boost/libs/cuda-math/include/", "-I/usr/local/cuda/include"}; | ||
#endif | ||
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// Compile the program | ||
res = nvrtcCompileProgram(prog, sizeof(opts) / sizeof(const char*), opts); | ||
if (res != NVRTC_SUCCESS) | ||
{ | ||
size_t log_size; | ||
nvrtcGetProgramLogSize(prog, &log_size); | ||
char* log = new char[log_size]; | ||
nvrtcGetProgramLog(prog, log); | ||
std::cerr << "Compilation failed:\n" << log << std::endl; | ||
delete[] log; | ||
exit(EXIT_FAILURE); | ||
} | ||
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// Get PTX from the program | ||
size_t ptx_size; | ||
nvrtcGetPTXSize(prog, &ptx_size); | ||
char* ptx = new char[ptx_size]; | ||
nvrtcGetPTX(prog, ptx); | ||
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// Load PTX into CUDA module | ||
CUmodule module; | ||
CUfunction kernel; | ||
checkCUError(cuModuleLoadDataEx(&module, ptx, 0, 0, 0), "Failed to load module"); | ||
checkCUError(cuModuleGetFunction(&kernel, module, "test_uniform_kernel"), "Failed to get kernel function"); | ||
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int numElements = 5000; | ||
float_type *h_in1, *h_in2, *h_out; | ||
float_type *d_in1, *d_in2, *d_out; | ||
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// Allocate memory on the host | ||
h_in1 = new float_type[numElements]; | ||
h_in2 = new float_type[numElements]; | ||
h_out = new float_type[numElements]; | ||
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// Initialize input arrays | ||
std::mt19937_64 rng(42); | ||
std::uniform_real_distribution<float_type> dist(0.0f, 1.0f); | ||
for (int i = 0; i < numElements; ++i) | ||
{ | ||
h_in1[i] = static_cast<float_type>(dist(rng)); | ||
h_in2[i] = static_cast<float_type>(dist(rng)); | ||
} | ||
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checkCUDAError(cudaMalloc(&d_in1, numElements * sizeof(float_type)), "Failed to allocate device memory for d_in1"); | ||
checkCUDAError(cudaMalloc(&d_in2, numElements * sizeof(float_type)), "Failed to allocate device memory for d_in2"); | ||
checkCUDAError(cudaMalloc(&d_out, numElements * sizeof(float_type)), "Failed to allocate device memory for d_out"); | ||
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checkCUDAError(cudaMemcpy(d_in1, h_in1, numElements * sizeof(float_type), cudaMemcpyHostToDevice), "Failed to copy data to device for d_in1"); | ||
checkCUDAError(cudaMemcpy(d_in2, h_in2, numElements * sizeof(float_type), cudaMemcpyHostToDevice), "Failed to copy data to device for d_in2"); | ||
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int blockSize = 256; | ||
int numBlocks = (numElements + blockSize - 1) / blockSize; | ||
void* args[] = { &d_in1, &d_in2, &d_out, &numElements }; | ||
checkCUError(cuLaunchKernel(kernel, numBlocks, 1, 1, blockSize, 1, 1, 0, 0, args, 0), "Kernel launch failed"); | ||
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checkCUDAError(cudaMemcpy(h_out, d_out, numElements * sizeof(float_type), cudaMemcpyDeviceToHost), "Failed to copy data back to host for h_out"); | ||
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// Verify Result | ||
for (int i = 0; i < numElements; ++i) | ||
{ | ||
auto res = cdf(boost::math::uniform_distribution<float_type>(), h_in1[i]); | ||
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if (boost::math::isfinite(res)) | ||
{ | ||
if (boost::math::epsilon_difference(res, h_out[i]) > 300) | ||
{ | ||
std::cout << "error at line: " << i | ||
<< "\nParallel: " << h_out[i] | ||
<< "\n Serial: " << res | ||
<< "\n Dist: " << boost::math::epsilon_difference(res, h_out[i]) << std::endl; | ||
} | ||
} | ||
} | ||
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cudaFree(d_in1); | ||
cudaFree(d_in2); | ||
cudaFree(d_out); | ||
delete[] h_in1; | ||
delete[] h_in2; | ||
delete[] h_out; | ||
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nvrtcDestroyProgram(&prog); | ||
delete[] ptx; | ||
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cuCtxDestroy(context); | ||
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std::cout << "Kernel executed successfully." << std::endl; | ||
return 0; | ||
} | ||
catch(const std::exception& e) | ||
{ | ||
std::cerr << "Stopped with exception: " << e.what() << std::endl; | ||
return EXIT_FAILURE; | ||
} | ||
} |
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