Not to be confused with OpenCT.
Open Computing Language (OpenCL) is a framework for writing programs that execute across heterogeneous computing platforms (CPUs, GPUs, DSPs, FPGAs, ASICs, etc.).
The point of this article is to deal with Gentoo implementation and quirks so the overview will be short. For a better overview of the theoretical aspects, see the OpenCL wikipedia article.
OpenCL presents itself as a library with a simple interface:
- Standardized API headers (eg.
#include CL/cl.h) for C and C++
- The OpenCL library (eg.
The standard is made to provide many OpenCL platforms on one system, and each platform can see various devices. Each device has a certain compute characteristics (number of compute units, optimal vector size, memory limits, ...).
The OpenCL standard allows to load OpenCL kernels which are pieces of C99-like code that is JIT-compiled by the OpenCL implementations (most of them rely on LLVM to work), and execute these kernels on the target hardware. Functions are provided to compile the kernels, load them, transfer data back and forth from the target devices, etc.
Hardware vendors can benefit from this standard by implementing the OpenCL primitives for their hardware.
Installing an OpenCL implementation means adding a library implementing the OpenCL API, and a reference to the library path in the ICD (Installable Client Driver) database, as a file in /etc/OpenCL/vendors.
There is an ICD loader which can be provided by a generic loader or one of the implementations. For most uses of OpenCL one of these must be selected using eselect opencl.
The OpenCL host and device API calls, the memory hierarchy are standard, but it doesn't mean that code which is portable to different implementations will be efficient on all of them. Often, code has to be tested on one implementation to eliminate issues. See the implementation validation and implementation tuning sections..
Implementations on Linux
Here are upstream projects that provide an implementation of OpenCL.
- pocl is an OpenSource, LLVM-based OpenCL implementation for the CPU which is not necessarily the fastest, but is educational.
- mesa has a WIP implementation which works on some GPU devices.
- AMD provides its AMD APP SDK  which contains an OpenCL library.
- nVidia provides its CUDA toolkit  which contains an OpenCL library.
- Intel, on Linux, has an implementation for CPU devices, and another for GPU devices ("Beignet").
The implementations will be developed in further subsections.
The dev-libs/ocl-icd package provides a layer of indirection to enable different OpenCL implementations, and also provides the OpenCL.pc pkg-config descriptor.
As of 2015-03-15:
- the dev-lang/pocl package is available in portage.
Intel - CPU
The Intel CPU SDK is provided by the dev-util/intel-ocl-sdk which is in portage.
As of 2014-09-15:
- Use version 2.* if the 4.* dies on you.
Intel - GPU
The GPU implementation from Intel is Beignet. Beignet supports Ivy Bridge and later processors.
The dev-libs/beignet package is available in portage.
Linux kernels 3.15 and 3.16 may require the following workaround:
#!/bin/sh echo 0 > /sys/module/i915/parameters/enable_cmd_parser # OpenCL
Starting in Q1’2018, Beignet has been deprecated in favor of NEO OpenCL driver, but it is not in portage.
The newest OpenCL implementation from AMD is ROCm, Radeon Open Compute, which supports GFX8 and newer GPU chips (Fiji, Polaris, Vega). The GFX7 chips are enabled, but not officially supported. For older chips, use either the Mesa clover (above), or amdgpu-pro-opencl (below) implementations. The ROCm source is available on github, at RadeonOpenCompute/ROCm. There are beta-quality ebuilds, along with supporting software, available in a rocm overlay, also on github.
The old driver is provided by the dev-util/amdapp package, which is maintained in the Science overlay.There also exists dev-libs/amdgpu-pro-opencl -package which provides closed source OpenCL libraries from Ubuntu AMDGPU-PRO driver package. These libraries are normally used with the closed source AMDGPU-PRO drivers, but this package helps users to try if they can use them with open source AMDGPU drivers.
Mixing closed source OpenCL libraries with open source drivers isn't offically supported in any way. However there have been success at some levels. Users who encounter problems are encouraged to ask help from the forums and not report it as a bug.
The driver is provided by the dev-util/nvidia-cuda-toolkit package which is in the main ebuild repository.
Displaying info about OpenCL
You can see information about the system's OpenCL capabilities using dev-util/clinfo.
An implementation (especially the experimental ones) can be checked by running test suites such as:
The main vendors will provide some form of advanced documentation to use their hardware to the maximum:
- nVidia: For developers targetting nVidia hardware, nVidia provides a Best Practices Guide.
- AMD: For developers targetting AMD hardware, AMD provides an Optimization Guide.
- Beignet: For developers targetting the opensource OpenCL implementation for Intel GPU hardware, the Beignet projects provide a brief optimization guide
Some characteristics are not straightforward to find, and benchmarks are one way to find out.
LLVM DLL hell
LLVM is starting to become a central library, and it is used by most of the implementations. If the user/implementer is not careful, linkage issues can occur (symbol collisions, constructors getting called multiple times and not handled, etc.). This is something that can be noticed with many implementations also.TODO example