A yoke of oxen and a thousand chickens for heavy lifting graph processing

Large, real-world graphs are famously difficult to process efficiently. Not only they have a large memory footprint but most graph processing algorithms entail memory access patterns with poor locality, data-dependent parallelism, and a low compute-to- memory access ratio. Additionally, most real-world graphs have a low diameter and a highly heterogeneous node degree distribution. Partitioning these graphs and simultaneously achieve access locality and load-balancing is difficult if not impossible.

This paper demonstrates the feasibility of graph processing on heterogeneous (i.e., including both CPUs and GPUs) platforms as a cost-effective approach towards addressing the graph processing challenges above. To this end, this work (i) presents and evaluates a performance model that estimates the achievable performance on heterogeneous platforms; (ii) introduces TOTEM -- a processing engine based on the Bulk Synchronous Parallel (BSP) model that offers a convenient environment to simplify the implementation of graph algorithms on heterogeneous platforms; and, (iii) demonstrates TOTEM'S efficiency by implementing and evaluating two graph algorithms (PageRank and breadth-first search). TOTEM achieves speedups close to the model's prediction, and applies a number of optimizations that enable linear speedups with respect to the share of the graph offloaded for processing to accelerators.