pytorch  1.0rc1
About: PyTorch provides Tensor computation (like NumPy) with strong GPU acceleration and Deep Neural Networks (in Python) built on a tape-based autograd system. Release candidate.
  Fossies Dox: pytorch-1.0rc1.tar.gz  ("inofficial" and yet experimental doxygen-generated source code documentation)  

pytorch Documentation

Introduction

For many large applications C++ is the language of choice and so it seems reasonable to define C++ bindings for OpenCL.

The interface is contained with a single C++ header file cl.hpp and all definitions are contained within the namespace cl. There is no additional requirement to include cl.h and to use either the C++ or original C bindings it is enough to simply include cl.hpp.

The bindings themselves are lightweight and correspond closely to the underlying C API. Using the C++ bindings introduces no additional execution overhead.

For detail documentation on the bindings see:

The OpenCL C++ Wrapper API 1.2 (revision 09) http://www.khronos.org/registry/cl/specs/opencl-cplusplus-1.2.pdf

Example

The following example shows a general use case for the C++ bindings, including support for the optional exception feature and also the supplied vector and string classes, see following sections for decriptions of these features.

#define __CL_ENABLE_EXCEPTIONS
#if defined(__APPLE__) || defined(__MACOSX)
#include <OpenCL/cl.hpp>
#else
#include <CL/cl.hpp>
#endif
#include <cstdio>
#include <cstdlib>
#include <iostream>
const char * helloStr = "__kernel void "
"hello(void) "
"{ "
" "
"} ";
int
main(void)
{
cl_int err = CL_SUCCESS;
try {
std::vector<cl::Platform> platforms;
cl::Platform::get(&platforms);
if (platforms.size() == 0) {
std::cout << "Platform size 0\n";
return -1;
}
cl_context_properties properties[] =
{ CL_CONTEXT_PLATFORM, (cl_context_properties)(platforms[0])(), 0};
std::vector<cl::Device> devices = context.getInfo<CL_CONTEXT_DEVICES>();
std::make_pair(helloStr,strlen(helloStr)));
program_.build(devices);
cl::Kernel kernel(program_, "hello", &err);
cl::Event event;
cl::CommandQueue queue(context, devices[0], 0, &err);
queue.enqueueNDRangeKernel(
kernel,
NULL,
&event);
event.wait();
}
catch (cl::Error err) {
std::cerr
<< "ERROR: "
<< err.what()
<< "("
<< err.err()
<< ")"
<< std::endl;
}
return EXIT_SUCCESS;
}