Alpaka - An Abstraction Library for Parallel Kernel Acceleration

Alpaka - An Abstraction Library for Parallel Kernel Acceleration

Zenker, E.; Worpitz, B.; Widera, R.; Hübl, A.; Juckeland, G.; Knüpfer, A.; Nagel, W. E.; Bussmann, M.

Porting applications to new hardware or programming models is a tedious and error prone process. Every help that eases these burdens is saving developer time that can then be invested into the advancement of the application itself instead of preserving the status-quo on a new platform.

The Alpaka library defines and implements an abstract hierarchical redundant parallelism model. The model exploits parallelism and memory hierarchies on a node at all levels available in current hardware. By doing so, it allows to achieve platform and performance portability across various types of accelerators by ignoring specific unsupported
levels and utilizing only the ones supported on a specific
accelerator. All hardware types (multi- and many-core CPUs, GPUs and other accelerators) are supported for and can be programmed in the same way.
The Alpaka C++ template interface allows for straightforward extension of the library to support other accelerators and specialization of its internals for optimization.

Running Alpaka applications on a new (and supported) platform requires the change of only one source code line instead of a lot of #ifdefs.

Keywords: Heterogeneous computing; HPC; C++; CUDA; OpenMP; platform portability; performance portability

  • Contribution to proceedings
    The Sixth International Workshop on Accelerators and Hybrid Exascale Systems co-located with the 30th IEEE International Parallel & Distributed Processing Symposium, 23.-27.05.2016, Chicago Hyatt Regency Chicago, Illinois, USA
    Proceedings of the 30th IEEE International Parallel and Distributed Processing Symposium

Publ.-Id: 23564