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Abstract

Harshvardhan, Brandon West, Adam Fidel, Nancy M. Amato, Lawrence Rauchwerger, "A Hybrid Approach To Processing Big Data Graphs on Memory-Restricted Systems," In Proc. Int. Par. and Dist. Proc. Symp. (IPDPS), pp. 799-808, Hyderabad, India, May 2015.
Proceedings(pdf, abstract)

With the advent of big-data, processing large graphs quickly has become increasingly important. Most existing approaches either utilize in-memory processing techniques that can only process graphs that fit completely in RAM, or disk-based techniques that sacrifice performance. In this work, we propose a novel RAM-Disk hybrid approach to graph processing that can scale well from a single shared-memory node to large distributed-memory systems. It works by partitioning the graph into subgraphs that fit in RAM and uses a paging-like technique to load subgraphs. We show that without modifying the algorithms, this approach can scale from small memory-constrained systems (such as tablets) to large-scale distributed machines with 16,000+ cores.