Extending OpenACC for Efficient Stencil Code Generation and Execution by Skeleton Frameworks

Abstract

The OpenACC programming model simplifies the programming for accelerator devices such as GPUs. Its abstract accelerator model defines a least common denominator for accelerator devices, thus it cannot represent architectural specifics of these devices without losing portability. Therefore, this general- purpose approach delivers good performance on average, but it misses optimization opportunities for code generation and execution of specific classes of applications. In this paper, we propose OpenACC extensions to enable efficient code generation and execution of stencil applications by parallel skeleton frameworks such as PSkel. Our results show that our stencil extensions may improve the performance of OpenACC in up to 28% and 45% on GPU and CPU, respectively. Moreover, we show that the work-partitioning mechanism offered by the skeleton framework, which splits the computation across CPU and GPU, may improve even further the performance of the applications in up to 18%.