Graphene: Fine-Grained IO Management for Graph Computing

Authors: 

Hang Liu and H. Howie Huang, The George Washington University

Abstract: 

As graphs continue to grow, external memory graph processing systems serve as a promising alternative to inmemory solutions for low cost and high scalability. Unfortunately, not only does this approach require considerable efforts in programming and IO management, but its performance also lags behind, in some cases by an order of magnitude. In this work, we strive to achieve an ambitious goal of achieving ease of programming and high IO performance (as in-memory processing) while maintaining graph data on disks (as external memory processing). To this end, we have designed and developed Graphene that consists of four new techniques: an IO request centric programming model, bitmap based asynchronous IO, direct hugepage support, and data and workload balancing. The evaluation shows that Graphene can not only run several times faster than several external-memory processing systems, but also performs comparably with in-memory processing on large graphs.

Open Access Media

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are posted after the event are also free and open to everyone. Support USENIX and our commitment to Open Access.

Presentation Audio

BibTeX
@inproceedings {202266,
author = {Hang Liu and H. Howie Huang},
title = {Graphene: Fine-Grained {IO} Management for Graph Computing},
booktitle = {15th {USENIX} Conference on File and Storage Technologies ({FAST} 17)},
year = {2017},
isbn = {978-1-931971-36-2},
address = {Santa Clara, CA},
pages = {285--300},
url = {https://www.usenix.org/conference/fast17/technical-sessions/presentation/liu},
publisher = {{USENIX} Association},
}