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Optimizing Graph Processing on GPUs
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DOI：10.1109/TPDS.2016.2611659.png)
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Distributed vertex-centric model has been recently proposed for large-scale graph processing. Due to the simple but efficient programming abstraction, similar graph computing frameworks based on GPUs are gaining more and more attention. However, prior works of GPU-based graph processing suffer from load imbalance and irregular memory access because of the inherent characteristics of graph applications. In this paper, we propose a generalized graph computing framework for GPUs to simplify existing models but with higher performance. In particular, two novel algorithmic optimizations, lightweight approximate sorting and data layout transformation, are proposed to tackle the performance issues of current systems. With extensive experimental evaluation under a wide range of real world and synthetic workloads, we show that our systemcan achieve 1.6x to 4.5x speedups over the state-of-the-art.
Keyword:
GPGPU
graph computing
pregel
bulk synchronous model
load imbalance
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