Table of Contents generated with DocToc

• EasyCL
  • Example Usage
  • Examples
  • API
  • CLArray and CLWrapper objects
  • CLWrapper objects
  • CLArray objects
• Kernel store
• device dirty flag
• kernel run-time templates
• templated kernels (New!)
• passing structs (New!)
  • How to build
    • Building on linux
      • Pre-requisites
      • Optional requirements
      • Procedure
    • Building on Windows
      • Pre-requisites
      • Optional requirements
      • Procedure
  • How to run self-tests
  • How to check my OpenCL installation/configuration?
  • What if I've found a bug?
  • What if I want a new feature?
  • What if I just have a question?
• Recent changes
  • License

Easy to run kernels using OpenCL. (renamed from OpenCLHelper)

• makes it easy to pass input and output arguments
• handles much of the boilerplate
• uses clew to load opencl dynamically

Imagine we have a kernel with the following signature, in the file /tmp/foo.cl:

kernel void my_kernel( int N, global float *one, global float *two, local float *one_local, global float *result ) {
    // kernel code here...
}

... then we can call it like:

#include "EasyCL.h"

if( !EasyCL::isOpenCLAvailable() ) {
    cout << "opencl library not found" << endl;
    exit(-1);
}
EasyCL *cl = EasyCL::createForFirstGpu();
CLKernel *kernel = cl->buildKernel("somekernelfile.cl", "test_function");
int in[5];
int out[5];
for( int i = 0; i < 5; i++ ) {
    in[i] = i * 3;
}
kernel->in( 5, in );
kernel->out( 5, out );
kernel->run_1d( 5, 5 ); // global workgroup size = 5, local workgroup size = 5
delete kernel;
// use the results in 'out' array here

More generally, you can call on 2d and 3d workgroups by using the kernel->run method:

const size_t local_ws[1]; local_ws[0] = 512;
const size_t global_ws[1]; global_ws[0] = EasyCL::roundUp(local_ws[0], size);
kernel->run( 1, global_ws, local_ws ); // 1 is number of dimensions, could be 2, or 3

'Fluent' style is also possible, eg:

kernel->in(10)->in(5)->out( 5, outarray )->run_1d( 5, 5 );

If you use EasyCL::createForFirstGpu(), EasyCL will bind to the first OpenCL-enabled GPU (or accelerator), that it finds. If you want to use a different device, or an OpenCL-enabled CPU, you can use one of the following method:

EasyCL::createForIndexedGpu( int gpuindex ); // looks for opencl-enabled gpus, and binds to the (gpuindex+1)th one
EasyCL::createForFirstGpuOtherwiseCpu();
EasyCL::createForPlatformDeviceIndexes( int platformIndex, int deviceIndex );
EasyCL::createForPlatformDeviceIds( int platformId, int deviceId ); // you can get these ids by running `gpuinfo` first

You can run gpuinfo to get a list of platforms and devices on your system.

There are some examples in the test subdirectory.

There are some examples in the test subdirectory.

• create a couple of Wrapper objects, pass them into a kernel, look at the results, see method testfloatwrapper, main ) of testfloatwrapper.cpp
• (New!) to use with clBLAS, see testclblas.cpp

// constructor:
EasyCL::EasyCL();
// choose different gpu index
void EasyCL::gpu( int gpuindex );

// compile kernel
CLKernel *EasyCL::buildKernel( string kernelfilepath, string kernelname, string options = "" );

// Note that you pass `#define`s in through the `options` parameters, like `-D TANH`, or `-D TANH -D BIASED`

// passing arguments to kernel:

CLKernel::in( int integerinput );

CLKernel::in( int arraysize, const float *inputarray ); // size in number of floats
CLKernel::in( int arraysize, const int *inputarray ); // size in number of ints
CLKernel::out( int arraysize, float *outputarray ); // size in number of floats
CLKernel::out( int arraysize, int *outputarray ); // size in number of ints
CLKernel::inout( int arraysize, float *inoutarray ); // size in number of floats
CLKernel::inout( int arraysize, int *inoutarray ); // size in number of ints

// to allocate local arrays, as passed-in kernel parameters:
CLKernel::localFloats( int localarraysize ); // size in number of floats
CLKernel::localInts( int localarraysize ); // size in number of ints

// running kernel, getting result back, and cleaning up:
CLKernel::run_1d( int global_ws, int local_ws );
CLKernel::run( int number_dimensions, size_t *global_ws, size_t *local_ws );

// helper function:
EasyCL::roundUp( int quantizationSize, int desiredTotalSize );

To make it possible to reuse data between kernels, without moving back to PC main memory, and back onto the GPU, you can use CLWrapper objects.

These can be created on the GPU, or on the host, and moved backwards and forwards between each other, as required. They can be passed as an 'input' and 'output' to a CLKernel object. They can be reused between kernels.

There are two 'flavors':

• CLArray: more automated, but more memory copying, since creates a new array on the host
• CLWrapper: wraps an existing host array, you'll need to call copyToDevice() and copyToHost() yourself

CLArray objects are the first implementation. CLWrapper objects are the second implementation. You can use either, but note that CLWrapper objects are the ones that I use myself.

Compared to CLArray objects, CLWrapper objects need less memory copying, since they wrap an existing native array, but you will need to call copyToDevice() and copyToHost() yourself.

if( !EasyCL::isOpenCLAvailable() ) {
    cout << "opencl library not found" << endl;
    exit(-1);
}
cout << "found opencl library" << endl;

EasyCL cl;
CLKernel *kernel = cl.buildKernel("../test/testeasycl.cl", "test_int");
int in[5];
for( int i = 0; i < 5; i++ ) {
    in[i] = i * 3;
}
int out[5];
CLWrapper *inwrapper = cl.wrap(5, in);
CLWrapper *outwrapper = cl.wrap(5, out);
inwrapper->copyToDevice();
kernel->in( inwrapper );
kernel->out( outwrapper );
kernel->run_1d( 5, 5 );
outwrapper->copyToHost();
assertEquals( out[0] , 7 );
assertEquals( out[1] , 10 );
assertEquals( out[2] , 13 );
assertEquals( out[3] , 16 );
assertEquals( out[4] , 19 );
cout << "tests completed ok" << endl;

Can copy between buffers (New!):

wrapper1->copyTo( wrapper2 );

CLWrapper objects are currently available as CLIntWrapper and CLFloatWrapper.

Compared to CLWrapper objects, CLArray objects are more automated, but involve more memory copying.

EasyCL cl;

CLArrayFloat *one = cl.arrayFloat(10000); // create CLArray object for 10,000 floats
(*one)[0] = 5; // give some data...
(*one)[1] = 7; 

CLArrayFloat *two = cl.arrayFloat(10000);

// pass to kernel:
kernel->in(one)->out(two);

You can then take the 'two' CLArray object, and pass it as the 'input' to a different kernel, or you can use operator[] to read values from it.

Currently, CLArray is available as 'CLArrayFloat' and 'CLArrayInt'.

You can store kernels in the store, under a unique name each, to facilitate kernel caching

// store:
cl->storeKernel( "mykernelname", somekernel ); // name must be not used yet

// check exists:
cl->kernelExists( "mykernelname" );

// retrieve:
CLKernel *kernel = cl->getKernel( "mykernelname" );

New: you can transfer kernel ownership to EasyCL object, by passing third parameter deleteWithCl = true. Then, when the EasyCL object is deleted, so will be the kernel.

// store:
cl->storeKernel( "mykernelname", somekernel, true ); // this kernel will be deleted when
                                                     // `cl` object is deleted

For CLWrapper objects, if the wrapper is passed to a kernel via out or inout, and then that kernel is run, then isDeviceDirty() will return true, until ->copyToHost() is called. So, you can use this to determine whether you need to run ->copyToHost() prior to reading the host-side array.

The following methods will reset the flag to false:

• copyToDevice()
• copyToHost()

This is a new feature, as of May 15 2015, and might have some bugs prior to May 31 2015 (ie, about 2 weeks, long enough for me to find any bugs).

• If you activate build option KERNEL_TEMPLATING, then you can use templating with kernels, at runtime, using Lua
• Simple variable substitution by using eg {{some_variable_name}}
• Embed lua code, including loops, if statements etc, ... using eg '{% for i=0,5 do %}... code here ... {% end %}`
• The magic is done using the [templates/TemplatedKernel.h](templates/TemplatedKernel.h] class
• See examples in test/testTemplatedKernel.cpp
• Note that this templating method is based on John Nachtimwald's work at https://john.nachtimwald.com/2014/08/06/using-lua-as-a-templating-engine/ ( MIT License )

• Turns out this can be important, and is actually already available, just embedded in the cpp files, locked away
• Simply #include new "CLKernel_structs.h" header, in order to be able to pass structs
• See test/testStructs.cpp for an example

• OpenCL needs to be installed, which means things like:
  • in linux, you'll need a libOpenCL.so installed, and
  • an OpenCL implementation, ie some kind of .so file, and
  • an appropriate text file at /etc/OpenCL/vendors/somename.icd , containing the full path to the Open CL implementation .so file
• git (only needed to obtain the source-code)
• cmake
• g++

• if you want to use Kernel templating, then you need Lua5.1 library installed, otherwise choose cmake option KERNEL_TEMPLATING = OFF

git clone --recursive https://github.com/hughperkins/EasyCL.git
cd EasyCL
mkdir build
cd build
cmake ..
make

• Dont forget the --recursive, otherwise you will see odd errors about clew/src/clew.c missing!
  • If this happens, you can try git submodule init and then git submodule update.

• OpenCL-enabled GPU and driver
• git (only needed to obtain the source-code)
• cmake
• Visual Studio (tested with Visual Studio 2013 Community Edition)

• if you want to use Kernel templating, then you need Lua5.1 library present, otherwise choose cmake option KERNEL_TEMPLATING = OFF

• Open git bash, and run git clone --recursive https://github.com/hughperkins/EasyCL.git
• Open cmake:
  • set source directory to the git-cloned directory from previous step
  • Set build directory to a subdirectory build-win32, or build-win64, according to which platform you are building for
  • click configure, choose appropriate build platform, eg visual studio 2013, or visual studio 2013 win64
  • click generate
• Open visual studio
  • open any of the projects in the build-win32 or build-win64 build directory
  • change build type from Debug to Release
  • from build menu, choose build solution
• after building, you will need to copy the *.cl files from the test directory into the directory where you will run the tests from (if you can figure out a way to automate this, please send a pull request :-) )

To check clew library is working ok (ie finding and loading the opencl library, etc):

./gpuinfo

... should print some information about your graphics card

Unit-tests:

To run:

make easycl_unittests
./easycl_unittests

• unit-tests are created using googletest
• you dont need to download/install googletest though, since the necessary files are included in the thirdparty directory. Just build EasyCL, and run unittests!

• In Ubuntu, you can use clinfo (install via sudo apt-get install clinfo), to check the OpenCL installation itself is ok. If this says 'no installations found', then it's an OpenCL configuration issue.
  • note that clinfo is broken on CUDA, I think? But OpenCL will still work ok: try gpuinfo instead
• Run gpuinfo to list available platforms and devices
• If no gpu-capabable devices found, you probably want to check things like:
  • do you have an OpenCL-capable GPU installed?
  • are the drivers installed?
  • is the ICD setup?

• Ideally, create a simple test case, just 10-30 lines if possible, and either just paste it directly as an issue, or else fork the repository, and ideally add it into the test directory, as an additional gtest-compliant test.
• (and then, obviously post an issue to alert me)

• Post a request as an issue
• Or, fork the repository, add the feature, and send me a pull request

• Post as an issue

• 2015 June 27th:
  • merged bundle-lua to master
  • Added StatefulTimer.h (was in DeepCL )
• 2015 June 18-25th:
  • on branch bundle-lua:
    • builds on Windows again
    • started bundling lua sourcecode, so dont need lua libraries etc
    • Added new version of CLWrapper->copyTo, wihch has additional parameters , srcOffset, dstOffset, count
    • Added StatefulTimer.h (was in DeepCL
• 2015 June 17th:
  • Merged changes to master:
    • DevicesInfo::getNumDevices() now returns 0, if no platforms available, rather than throwing exception
    • templates are expanded recursively now, so eg you can include other templates inside your template, and those will be expanded correctly (if you dislike this, please raise an issue to make it an option; easy to add)
    • added EasyCL::createForIndexedDevice, which creates an instance for the indexed device, over all opencl-enabled devices, gpu or not
    • more diagnostic output if a kernel fails to build, including line-numbers :-)
    • added getRenderedKernel method to KernelTemplater class
• 2015 June:
  • added kernel templates, using Lua
  • added CLWrapper->copyTo() method
  • made it possible to pass arrays of 1 or more structs into CLKernels
  • added install targets to the build
  • added options to the build to turn unit-tests on/off
• 2015 May 11: just noticed there is an over-aggressive assert in gpuinfo, that exits if not exactly one platform => fixed
• 2015 May 10: Added CLWrapper.devicedirty flag, which is set whenever the wrapper is passed to a kernel via out or inout, and that kernel is run
• 2015 May 3: Added kernel store methods: storeKernel( string name, CLKernel *kernel ), getKernel( string name ), kernelExists( string name ), to facilitate per-connection kernel caching
• 2015 May 3: Added getCl() to CLWrapper types
• 2015 May 1:Renamed from OpenCLHelper to EasyCL (easier to type, and remember)
• Added getBuffer to CLWrapper, to give access to the underlying buffer, eg can use this for using with clBLAS
• Added CLWrapper instantiation for unsigned char

EasyCL is available under MPL v2 license, http://mozilla.org/MPL/2.0/