Stencil Calculations with Algorithmic Skeletons for Heterogeneous Computing Environments

Abstract

The development of parallel applications is a difficult and error-prone task, especially for inexperienced programmers. Stencil operations are exceptionally complex for parallelization as synchronization and communication between the individual processes and threads are necessary. It gets even more difficult to efficiently distribute the computations and efficiently implement communication when heterogeneous computing environments are used. For using multiple nodes, each having multiple cores and accelerators such as GPUs, skills in combining frameworks such as MPI, OpenMP, and CUDA are required. The complexity of parallelizing the stencil operation increases the need for abstracting from the platform-specific details and simplify parallel programming. One way to abstract from details of parallel programming is to use algorithmic skeletons. This work introduces an implementation of the MapStencil skeleton that is able to generate parallel code for distributed memory environments, using multiple nodes with multicore CPUs and GPUs. Examples of practical applications of the MapStencil skeleton are the Jacobi Solver or the Canny Edge Detector. The main contribution of this paper is a discussion of the difficulties when implementing a universal Skeleton for MapStencil for heterogeneous computing environments and an outline of the identified best practices for communication intense skeletons.