Abstract-Computational GRIDs potentially offer low-cost, readily available, and large-scale high-performance platforms. For the parallel execution of programs, however, computational GRIDs pose serious challenges: they are heterogeneous and have hierarchical and often shared interconnects, with high and variable latencies between clusters. This paper investigates whether a programming language with high-level parallel coordination and a Distributed Shared Memory (DSM) model can deliver good and scalable performance on a range of computational GRID configurations. The high-level language Glasgow parallel Haskell (GpH) abstracts over the architectural complexities of the computational GRID, and we have developed GRID-GUM2, a sophisticated grid-specific implementation of GpH, to produce the first high-level DSM parallel language implementation for computational GRIDs. We report a systematic performance evaluation of GRID-GUM2 on combinations of high/low and homogeneous/heterogeneous computational GRIDs. We measure the performance of a small set of kernel parallel programs representing a variety of application areas, two parallel paradigms, and ranges of communication degree and parallel irregularity. We investigate GRI D-GUM2's performance scalability on medium-scale heterogeneous and high-latency computational GRIDs and analyze the performance with respect to the program characteristics of communication frequency and degree of irregular parallelism.
This paper demonstrates that it is possible to obtain good, scalable parallel performance by coordinating multiple instances of unaltered sequential computational algebra systems in order to deliver a single parallel system. The paper presents the first substantial parallel performance results for SymGrid-Par, a system that orchestrates computational algebra components into a high-performance parallel application. We show that SymGrid-Par is capable of exploiting different parallel/multicore architectures without any change to the computational algebra component. Ultimately, our intention is to extend our system so that it is capable of orchestrating heterogeneous computations across a high-performance computational grid.
This paper describes a very high-level approach that aims to orchestrate sequential components written using high-level domain-specific programming into high
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