<span class="var-sub_title">Scale-Free Graph Processing on a NUMA Machine</span> SC18 Proceedings

The International Conference for High Performance Computing, Networking, Storage, and Analysis

IA^3 2018: 8th Workshop on Irregular Applications: Architectures and Algorithms

Scale-Free Graph Processing on a NUMA Machine

Authors: Tanuj K. Aasawat (University of British Columbia), Tahsin Reza (University of British Columbia), Matei Ripeanu (University of British Columbia)

Abstract: Modern shared-memory systems embrace the NUMA architecture which has proven to be more scalable than the SMP architecture. In many ways, a NUMA system resembles a shared-nothing distributed system: physically distinct processing units and memory regions. Memory accesses to remote NUMA domains are more expensive than local accesses. This poses the opportunity to transfer the know-how and design of distributed graph processing to develop shared-memory graph processing solutions optimized for NUMA systems. To this end, we explore if a distributed-memory like middleware that makes graph partitioning and communication between partitions explicit, can improve the performance on a NUMA system. We design and implement a NUMA aware graph processing framework that embraces design philosophies of distributed graph processing system: in particular explicit partitioning and inter-partition communication, and at the same time exploits optimization opportunities specific to single-node systems. We demonstrate up to 13.9x speedup over a state-of-the-art NUMA-aware framework, Polymer and up to 3.7x scalability on a four-socket NUMA machine using graphs with tens of billions of edges.

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