Smaller and Faster: Parallel Processing of Compressed Graphs with Ligra+

We study compression techniques for parallel in-memory graph algorithms, and show that we can achieve reduced space usage while obtaining competitive or improved performance compared to running the algorithms on uncompressed graphs. We integrate the compression techniques into Ligra, a recent shared-memory graph processing system. This system, which we call Ligra+, is able to represent graphs using about half of the space for the uncompressed graphs on average. Furthermore, Ligra+ is slightly faster than Ligra on average on a 40-core machine with hyper-threading. Our experimental study shows that Ligra+ is able to process graphs using less memory, while performing as well as or faster than Ligra.