Traversing Large Compressed Graphs on GPUs

GPUs can be used effectively for accelerating graph analytics, provided the datasets fit in GPU memory. This is often not the case for large real-world datasets such as social, web, or biological graphs. We propose a graph compression format for static unweighted graphs based on Elias-Fano encoding that is amenable to run-time decompression on massively parallel architectures such as GPUs. We show that we can compress a variety of large graphs by a factor of 1.55x over the commonly used compressed sparse row (CSR) representation. The scheme is particularly beneficial for cases where conventional CSR based approaches do not work at all due to memory capacity constraints, or incur a significant penalty for out-of-core processing. We implement GPU accelerated breadth first search for this graph representation and show that the runtime performance for in-memory compressed graphs is 3.8x-6.5x better than out-of-core implementations for CSR graphs. Further, our implementation is also 1.45x-2x faster than the current state of the art in GPU based compressed graph traversals while maintaining a competitive compression ratio. We also extend our work to other analytics applications such as single source shortest paths and PageRank. Finally, we explore the interplay between graph reordering, graph compression, and performance.