Parallel architectures with physically distributed memory providing computing cycles and large amounts of memory are becoming more and more common. To make such architectures truly usable, programming models and support tools are needed to ease the programming effort for these parallel systems. Automatic data distribution tools and techniques play an important role in achieving that goal. This paper discusses state-of-the-art approaches to fully automatic data and computation partitioning. A kernel application is used as a case study to illustrate the main differences of four representative approaches. The paper concludes with a discussion of promising future research directions for automatic data layout.

Peer Reviewed