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...

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Bibliographic Details
Published in2015 Data Compression Conference pp. 403 - 412
Main Authors Julian Shun, Dhulipala, Laxman, Blelloch, Guy E.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2015
Subjects
Arrays
Decoding
Encoding
Graph compression
Memory management
Parallel algorithms
Social network services
Sparse matrices
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Summary: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.
ISSN:1068-0314
2375-0359
DOI:10.1109/DCC.2015.8