A library for uniform random number generation in OpenCL.
Streams of random numbers act as virtual random number generators. They can be created on the host computer in unlimited numbers, and then used either on the host or on computing devices by work items to generate random numbers. Each stream also has equally-spaced substreams, which are occasionally useful. The API is currently implemented for four different RNGs, namely the MRG31k3p, MRG32k3a, LFSR113 and Philox-4×32-10 generators.
- HTML Documentation
(generated with Doxygen) - Tutorial Document (please cite this document if you use clRNG)
clRNG: A Random Number API with Multiple Streams for OpenCL
Libraries related to clRNG, for probability distributions and quasi-Monte Carlo methods, are available:
The first public version of clRNG is v1.0.0 beta. Please go to releases for downloads.
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Install the runtime dependency:
- An OpenCL SDK, such as APP SDK.
-
Install the build dependencies:
- The CMake cross-platform build system. Visual Studio users can use CMake Tools for Visual Studio.
- A recent C compiler, such as GCC 4.9, or Visual Studio 2013.
-
Get the clRNG source code.
-
Configure the project using CMake (to generate standard makefiles) or CMake Tools for Visual Studio (to generate solution and project files).
-
Build the project.
-
Install the project (by default, the library will be installed in the
packagedirectory under the build directory). -
Point the environment variable
CLRNG_ROOTto the installation directory, i.e., the directory under whichinclude/clRNGcan be found. This step is optional if the library is installed under/usr, which is the default. -
In order to execute the example programs (under the
binsubdirectory of the installation directory) or to link clRNG into other software, the dynamic linker must be informed where to find the clRNG shared library. The name and location of the shared library generally depend on the platform. -
Optionally run the tests.
On a 64-bit Linux platform, steps 3 through 9 from above, executed in a Bash-compatible shell, could consist of:
git clone https://github.com/clMathLibraries/clRNG.git
mkdir clRNG.build; cd clRNG.build; cmake ../clRNG/src
make
make install
export CLRNG_ROOT=$PWD/package
export LD_LIBRARY_PATH=$CLRNG_ROOT/lib64:$LD_LIBRARY_PATH
$CLRNG_ROOT/bin/CTest
Examples can be found in src/client.
The compiled client program examples can be found under the bin subdirectory
of the installation package ($CLRNG_ROOT/bin under Linux).
Note that the examples expect an OpenCL GPU device to be available.
The simple example below shows how to use clRNG to generate random numbers by directly using device side headers (.clh) in your OpenCL kernel.
#include <stdlib.h>
#include <string.h>
#include "clRNG/clRNG.h"
#include "clRNG/mrg31k3p.h"
int main( void )
{
cl_int err;
cl_platform_id platform = 0;
cl_device_id device = 0;
cl_context_properties props[3] = { CL_CONTEXT_PLATFORM, 0, 0 };
cl_context ctx = 0;
cl_command_queue queue = 0;
cl_program program = 0;
cl_kernel kernel = 0;
cl_event event = 0;
cl_mem bufIn, bufOut;
float *out;
char *clrng_root;
char include_str[1024];
char build_log[4096];
size_t i = 0;
size_t numWorkItems = 64;
clrngMrg31k3pStream *streams = 0;
size_t streamBufferSize = 0;
size_t kernelLines = 0;
/* Sample kernel that calls clRNG device-side interfaces to generate random numbers */
const char *kernelSrc[] = {
" #define CLRNG_SINGLE_PRECISION \n",
" #include <clRNG/mrg31k3p.clh> \n",
" \n",
" __kernel void example(__global clrngMrg31k3pHostStream *streams, \n",
" __global float *out) \n",
" { \n",
" int gid = get_global_id(0); \n",
" \n",
" clrngMrg31k3pStream workItemStream; \n",
" clrngMrg31k3pCopyOverStreamsFromGlobal(1, &workItemStream, \n",
" &streams[gid]); \n",
" \n",
" out[gid] = clrngMrg31k3pRandomU01(&workItemStream); \n",
" } \n",
" \n",
};
/* Setup OpenCL environment. */
err = clGetPlatformIDs( 1, &platform, NULL );
err = clGetDeviceIDs( platform, CL_DEVICE_TYPE_GPU, 1, &device, NULL );
props[1] = (cl_context_properties)platform;
ctx = clCreateContext( props, 1, &device, NULL, NULL, &err );
queue = clCreateCommandQueue( ctx, device, 0, &err );
/* Make sure CLRNG_ROOT is specified to get library path */
clrng_root = getenv("CLRNG_ROOT");
if(clrng_root == NULL) printf("\nSpecify environment variable CLRNG_ROOT as described\n");
strcpy(include_str, "-I ");
strcat(include_str, clrng_root);
strcat(include_str, "/include");
/* Create sample kernel */
kernelLines = sizeof(kernelSrc) / sizeof(kernelSrc[0]);
program = clCreateProgramWithSource(ctx, kernelLines, kernelSrc, NULL, &err);
err = clBuildProgram(program, 1, &device, include_str, NULL, NULL);
if(err != CL_SUCCESS)
{
printf("\nclBuildProgram has failed\n");
clGetProgramBuildInfo(program, device, CL_PROGRAM_BUILD_LOG, 4096, build_log, NULL);
printf("%s", build_log);
}
kernel = clCreateKernel(program, "example", &err);
/* Create streams */
streams = clrngMrg31k3pCreateStreams(NULL, numWorkItems, &streamBufferSize, (clrngStatus *)&err);
/* Create buffers for the kernel */
bufIn = clCreateBuffer(ctx, CL_MEM_READ_ONLY | CL_MEM_COPY_HOST_PTR, streamBufferSize, streams, &err);
bufOut = clCreateBuffer(ctx, CL_MEM_WRITE_ONLY | CL_MEM_HOST_READ_ONLY, numWorkItems * sizeof(cl_float), NULL, &err);
/* Setup the kernel */
err = clSetKernelArg(kernel, 0, sizeof(bufIn), &bufIn);
err = clSetKernelArg(kernel, 1, sizeof(bufOut), &bufOut);
/* Execute the kernel and read back results */
err = clEnqueueNDRangeKernel(queue, kernel, 1, NULL, &numWorkItems, NULL, 0, NULL, &event);
err = clWaitForEvents(1, &event);
out = (float *)malloc(numWorkItems * sizeof(out[0]));
err = clEnqueueReadBuffer(queue, bufOut, CL_TRUE, 0, numWorkItems * sizeof(out[0]), out, 0, NULL, NULL);
/* Release allocated resources */
clReleaseEvent(event);
free(out);
clReleaseMemObject(bufIn);
clReleaseMemObject(bufOut);
clReleaseKernel(kernel);
clReleaseProgram(program);
clReleaseCommandQueue(queue);
clReleaseContext(ctx);
return 0;
}The documentation can be generated by running make from within the doc
directory. This requires Doxygen to be installed.
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