alpaka - Abstraction Library for Parallel Kernel Acceleration

The alpaka library is a header-only C++11 abstraction library for accelerator development.

Its aim is to provide performance portability across accelerators through the abstraction (not hiding!) of the underlying levels of parallelism.

It is platform independent and supports the concurrent and cooperative use of multiple devices such as the hosts CPU as well as attached accelerators as for instance CUDA GPUs and Xeon Phis (currently native execution only). A multitude of accelerator back-end variants using CUDA, OpenMP (2.0/4.0), Boost.Fiber, std::thread and also serial execution is provided and can be selected depending on the device. Only one implementation of the user kernel is required by representing them as function objects with a special interface. There is no need to write special CUDA, OpenMP or custom threading code. Accelerator back-ends can be mixed within a device stream. The decision which accelerator back-end executes which kernel can be made at runtime.

The alpaka API is currently unstable (alpha state).

The abstraction used is very similar to the CUDA grid-blocks-threads division strategy. Algorithms that should be parallelized have to be divided into a multi-dimensional grid consisting of small uniform work items. These functions are called kernels and are executed in parallel threads. The threads in the grid are organized in blocks. All threads in a block are executed in parallel and can interact via fast shared memory. Blocks are executed independently and can not interact in any way. The block execution order is unspecified and depends on the accelerator in use. By using this abstraction the execution can be optimally adapted to the available hardware.

Software License

alpaka is licensed under LGPLv3 or later.

Documentation

The source code documentation generated with doxygen is available here.

Accelerator Back-ends

Accelerator Back-end	Lib/API	Devices	Execution strategy grid-blocks	Execution strategy block-threads
Serial	n/a	Host CPU (single core)	sequential	sequential (1 thread/block)
OpenMP 2.0 blocks	OpenMP 2.0	Host CPU (multi core)	parallel (preemptive multitasking)	sequential (1 thread/block)
OpenMP 2.0 threads	OpenMP 2.0	Host CPU (multi core)	sequential	parallel (preemptive multitasking)
OpenMP 4.0	OpenMP 4.0	Host CPU (multi core)	parallel (undefined)	parallel (preemptive multitasking)
std::thread	std::thread	Host CPU (multi core)	sequential	parallel (preemptive multitasking)
Boost.Fiber	boost::fibers::fiber	Host CPU (single core)	sequential	parallel (cooperative multitasking)
CUDA 7.0	CUDA 7.0	NVIDIA GPUs SM 2.0+	parallel (undefined)	parallel (lock-step within warps)

Supported Compilers

This library uses C++11 (or newer when available).

Accelerator Back-end	gcc 4.9.2	gcc 5.2	clang 3.5/3.6	clang 3.7	MSVC 2015	icc 15.0+ (untested)
Serial	✅	✅	✅	✅	✅	✅
OpenMP 2.0 blocks	✅	✅	❌	✅	✅	✅
OpenMP 2.0 threads	✅	✅	❌	✅	✅	✅
OpenMP 4.0	✅	✅	❌	❌	❌	✅
std::thread	✅	✅	✅	✅	✅	✅
Boost.Fiber	✅	✅	✅	✅	❌	✅
CUDA 7.0	✅	❌	❌	❌	❌	✅

NOTE: ‼️ Currently the CUDA accelerator back-end can not be enabled together with the std::thread accelerator back-end or the Boost.Fiber accelerator back-end due to bugs in the NVIDIA nvcc compiler ‼️

Build status master branch:

Build status develop branch:

Dependencies

Boost 1.56+ is the only mandatory external dependency. The alpaka library itself just requires header-only libraries. However some of the accelerator back-end implementations require different boost libraries to be built.

When an accelerator back-end using Boost.Fiber is enabled, the develop branch of boost and the proposed boost library boost-fibers (develop branch) are required. boost-fibers, boost-context and all of its dependencies are required to be build in C++14 mode ./b2 cxxflags="-std=c++14".

When an accelerator back-end using CUDA is enabled, version 7.0 of the CUDA SDK is the minimum requirement.

When an accelerator back-end using OpenMP 2.0/4.0 is enabled, the compiler and the platform have to support the corresponding OpenMP version.

Usage

The library is header only so nothing has to be build. Only the include path (-I or export CPLUS_INCLUDE_PATH=) has to be set to <PATH-TO-ALPAKA-LIB>/include/. This allows to include the whole alpaka library with: #include <alpaka/alpaka.hpp>

Code not intended to be utilized by users is hidden in the detail namespace.

If you are building with the CUDA accelerator back-end enabled, your source files are required to have the ending .cu to comply with the nvcc (NVIDIA CUDA C++ compiler) rules for code files using CUDA. When the CUDA accelerator back-end is disabled, this is not required and a .cpp extension is enough. To allow both use-cases, it is desirable to have both, a .cpp file with the implementation and a .cu file containing only #include <PATH/TO/IMPL.cpp> to forward to the implementation. The build system then has to use the .cu files when the CUDA accelerator back-end is enabled and the .cpp files else. Examples how to do this with CMake can be found in the examples folder.

Authors

Maintainers and core developers

• Benjamin Worpitz

Participants, Former Members and Thanks

• Dr. Michael Bussmann
• Rene Widera
• Axel Huebl