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Large scale-free graphs are famously difficult to process efficiently: theskewed vertex degree distribution makes it difficult to obtain balancedpartitioning. Our research instead aims to turn this into an advantage bypartitioning the workload to match the strength of the individual computingelements in a Hybrid, GPU-accelerated architecture. As a proof of concept wefocus on the direction-optimized breadth first search algorithm. We present thekey graph partitioning, workload allocation, and communication strategiesrequired for massive concurrency and good overall performance. We show thatexploiting specialization enables gains as high as 2.4x in terms oftime-to-solution and 2.0x in terms of energy efficiency by adding 2 GPUs to a 2CPU-only baseline, for synthetic graphs with up to 16 Billion undirected edgesas well as for large real-world graphs. We also show that, for a capped energyenvelope, it is more efficient to add a GPU than an additional CPU. Finally,our performance would place us at the top of today's [Green]Graph500 challengesfor Scale29 graphs.

Accelerating Direction-Optimized Breadth First Search on Hybrid Architectures