We present the first fully automated compiler-runtime system that successfully translates and executes OpenMP shared-address-space programs on laboratory-size clusters, for the complete set of regular, repetitive applications in the NAS Parallel Benchmarks. We introduce a hybrid compiler-runtime translation scheme. Compared to previous work, this scheme features a new runtime data flow analysis and new compiler techniques for improving data affinity and reducing communication costs. We present and discuss the performance of our translated programs, and compare them with the performance of the MPI, HPF and UPC versions of the benchmarks. The results show that our translated programs achieve 75% of the hand-coded MPI programs, on average.

Abstract. OpenMP is an explicit parallel programming model that offers reasonable productivity. Its memory model assumes a shared address space, and hence the direct translation -as done by common OpenMP compilers -requires an underlying shared-memory architecture. Many lab machines include 10s of processors, built from commodity components and thus include distributed address spaces. Despite many efforts to provide higher productivity for these platforms, the most common programming model uses message passing, which is substantially more tedious to program than shared-address-space models. This paper presents a compiler/runtime system that translates OpenMP programs into message passing variants and executes them on clusters up to 64 processors. We build on previous work that provided a proof of concept of such translation. The present paper describes compiler algorithms and runtime techniques that provide the automatic translation of a first class of OpenMP applications: those that exhibit regular write array subscripts and repetitive communication. We evaluate the translator on representative benchmarks of this class and compare their performance against hand-written MPI variants. In all but one case, our translated versions perform close to the hand-written variants.

Heterogeneous clusters with nodes containing one or more accelerators, such as GPUs, have become common. While MPI provides inter-address space communication, and OpenCL provides a process with access to heterogeneous computational resources, programmers are forced to write hybrid programs that manage the interaction of both of these systems. This paper describes an array programming interface that provides users with automatic and manual distributions of data and work. Using work distribution and kernel def and use information, communication among processes and devices in a process is performed automatically. By providing a unified programming model to the user, program development is simplified.

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