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Scale-free graph processing on a NUMA machine Aasawat, Tanuj Kr

Abstract

The importance of high-performance graph processing to solve big data problems targeting high-impact applications is greater than ever before. Graphs incur highly irregular memory accesses which leads to poor data locality, load imbalance, and data-dependent parallelism. Distributed graph processing frameworks, such as Google's Pregel, that employs memory-parallel, shared-nothing systems have experienced tremendous success in terms of scale and performance. Modern shared-memory systems embrace the so called Non-Uniform Memory Access (NUMA) architecture which has proven to be more scalable (in terms of numbers of cores and memory modules) than the Symmetric Multiprocessing (SMP) architecture. In many ways, a NUMA system resembles a shared-nothing distributed system: physically distinct processing cores and memory regions (although, cache-coherent in NUMA). 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 (which is surprisingly little-explored). In this dissertation, 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 treats the NUMA platform as a distributed system, and embraces its design principles; in particular explicit partitioning and inter-partition communication. We further explore design trade-offs to reduce communication overhead and propose a solution that embraces design philosophies of distributed graph processing system 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 machine using graphs with tens of billions of edges.

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Attribution-NonCommercial-NoDerivatives 4.0 International

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