Abstract
Languages are being designed that simplify the tasks of creating, extending, and maintaining scientific application specifically for use on parallel computing architectures. Widespread adoption of any language by the high performance computing (HPC) community is strongly dependent upon achieved performance of applications. A common presumption is that performance is adversely affected as the level of abstraction increases. In this paper we report on our investigations into the potential of one such language, Chapel, to deliver performance while adhering to its code development and maintenance goals. In particular, we explore how the unconstrained memory model presented by Chapel may be exploited by the compiler and runtime system in order to efficiently execute computations common to numerous scientific application programs. Experiments, executed on a Cray X1E, AMD dual-core, and Intel quad- core processor based systems, reveal that with the appropriate architecture and runtime support, the Chapel model can achieve performance equal to the best Fortran/MPI, Co-Array Fortran, and OpenMP implementations, while substantially easing the burden on the application code developer.