MPI has been the de-facto programming model for scientific parallel applications. However, it is hard to extract the maximum performance for irregular data-driven applications using MPI. the partitionedglobaladdress...
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Rapid advances in the performance and programmability of graphics accelerators have made GPU computing a compelling solution for a wide variety of application domains. However, the increased complexity as a result of ...
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ISBN:
(纸本)9781450306980
Rapid advances in the performance and programmability of graphics accelerators have made GPU computing a compelling solution for a wide variety of application domains. However, the increased complexity as a result of architectural heterogeneity and imbalances in hardware resources poses significant programming challenges in harnessing the performance advantages of GPU accelerated parallel systems. Moreover, the speedup derived from GPU often gets offset by longer communication latencies and inefficient task scheduling. To achieve the best possible performance, a suitable parallel programming model is therefore essential. In this paper, we explore a new hybrid parallel programming model that incorporates GPU acceleration withthe partitionedglobaladdressspace (pgas) programming paradigm. As we demonstrate, by combining Unified Parallel C (UPC) and CUDA as a case study, this hybrid model offers programmers with both enhanced programmability and powerful heterogeneous execution. Two application benchmarks, namely NAS Parallel Benchmark (NPB) FT and MG, are used to show the effectiveness of our proposed hybrid approach. Experimental results indicate that both implementations achieve significantly better performance due to optimization opportunities offered by the hybrid model, such as the funneled execution mode and fine-grained overlapping of communication and computation.
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