Hybrid Parallel Programming for Blue Gene/P

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Mads Kristensen
Hans Happe
Brian Vinter


The concept of massively parallel processors has been taken to the extreme with the introduction of the BlueGene architectures from IBM. With hundreds of thousands of processors in one machine the parallelism is extreme, but so are the techniques that must be applied to obtain performance with that many processors. In this work we present optimizations of a Grid-based projector-augmented wave method software, GPAW, for the Blue Gene/P architecture. The improvements are achieved by exploring the advantage of shared and distributed memory programming also known as hybrid programming and blocked communication to improve latency hiding. The work focuses on optimizing a very time consuming operation in GPAW, the stencil operation, and different hybrid programming approaches are evaluated. The work succeeds in demonstrating a hybrid programming model, which is clearly beneficial compared to the original flat programming model. In total an improvement of 1.94 compared to the original implementation is obtained. The results we demonstrate here are reasonably general and may be applied to other stencil codes.

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