high performance computing on graphics processing units: hgpu.org

Emerging hybrid accelerator architectures for high performance computing are often suited for the use of a data-parallel programming model. Unfortunately, programmers of these architectures face a steep learning curve that frequently requires learning a new language (e.g., OpenCL). Furthermore, the distributed (and frequently multi-level) nature of the memory organization of clusters of these machines provides an additional level of complexity. This paper presents preliminary work examining how programming with a local orientation can be employed to provide simpler access to accelerator architectures. A locally-oriented programming model is especially useful for the solution of algorithms requiring the application of a stencil or convolution kernel. In this programming model, a programmer codes the algorithm by modifying only a single array element (called the local element), but has read-only access to a small sub-array surrounding the local element. We demonstrate how a locally-oriented programming model can be adopted as a language extension using source-to-source program transformations.