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Graph analytics are at the heart of a broad range of applications such asdrug discovery, page ranking, and recommendation systems. When graph sizeexceeds memory size, out-of-core graph processing is needed. For the widelyused external memory graph processing systems, accessing storage becomes thebottleneck. We make the observation that nearly all graph algorithms have adynamically varying number of active vertices that must be processed in eachiteration. However, existing graph processing frameworks, such as GraphChi,load the entire graph in each iteration even if a small fraction of the graphis active. This limitation is due to the structure of the data storage used bythese systems. In this work, we propose to use a compressed sparse row (CSR)based graph storage that is more amenable for selectively loading only a fewactive vertices in each iteration. But CSR based systems suffers from randomupdate propagation to many target vertices. To solve this challenge, we proposeto use a multi-log update mechanism that logs updates separately, rather thandirectly update the active edges in a graph. Our proposed multi-log systemmaintains a separate log per each vertex interval. This separation enables usto efficiently process each vertex interval by just loading the correspondinglog. Further, while accessing SSD pages with fewer active vertex data, wereduce the read amplification due to the page granular accesses in SSD bylogging the active vertex data in the current iteration and efficiently readingthe log in the next iteration. Over the current state of the art out-of-coregraph processing framework, our PartitionedVC improves performance by up to$17.84\times$, $1.19\times$, $1.65\times$, $1.38\times$, $3.15\times$, and$6.00\times$ for the widely used bfs, pagerank, community detection, graphcoloring, maximal independent set, and random-walk applications, respectively.

PartitionedVC: Partitioned External Memory Graph Analytics Framework for SSDs