Efficient Large-Scale Graph Processing on Hybrid CPU and GPU Systems

Understand

The increasing scale and wealth of inter-connected data, such as those accrued by social network applications, demand the design of new techniques and platforms to efficiently derive actionable knowledge from large-scale graphs.

• However, real-world graphs are famously difficult to process efficiently.
• Not only they have a large memory footprint, but also most graph algorithms entail memory access patterns with poor locality, data-dependent parallelism and a low compute-to-memory access ratio.
• Moreover, most real-world graphs have a highly heterogeneous node degree distribution, hence partitioning these graphs for parallel processing and simultaneously achieving access locality and load-balancing is difficult.