Title:
Leveraging Memory Mapping for Fast and Scalable Graph Computation on a PC
Leveraging Memory Mapping for Fast and Scalable Graph Computation on a PC
dc.contributor.author | Lin, Zhiyuan | |
dc.contributor.author | Chau, Duen Horng | |
dc.contributor.corporatename | Georgia Institute of Technology. College of Computing | en_US |
dc.contributor.corporatename | Georgia Institute of Technology. School of Computational Science and Engineering | en_US |
dc.date.accessioned | 2013-08-19T15:39:24Z | |
dc.date.available | 2013-08-19T15:39:24Z | |
dc.date.issued | 2013-08 | |
dc.description | Research areas: Graph mining algorithms | en_US |
dc.description.abstract | Large graphs with billions of nodes and edges are increasingly common, calling for new kinds of scalable computation frameworks. Although popular, distributed approaches can be expensive to build, or require many resources to manage or tune. State-of-the-art approaches such as GraphChi and TurboGraph recently have demonstrated that a single machine can efficiently perform advanced computation on billion-node graphs. Although fast, they both use sophisticated data structures, memory management, and optimization techniques. We propose a minimalist approach that forgoes such complexities, by leveraging the memory mapping capability found on operating systems. Our experiments on large datasets, such as a 1.5 billion edge Twitter graph, show that our streamlined approach achieves up to 26 times faster than GraphChi, and comparable to TurboGraph. We con- tribute our crucial insight that by leveraging memory mapping, a fundamental operating system capability, we can outperform the latest graph computation techniques. | en_US |
dc.embargo.terms | null | en_US |
dc.identifier.uri | http://hdl.handle.net/1853/48715 | |
dc.language.iso | en_US | en_US |
dc.publisher | Georgia Institute of Technology | en_US |
dc.relation.ispartofseries | CSE Technical Reports ; GT-CSE-13-02 | en_US |
dc.subject | Graph mining | en_US |
dc.subject | Memory mapping | en_US |
dc.subject | Scalable algorithms | en_US |
dc.subject | Single machine | en_US |
dc.title | Leveraging Memory Mapping for Fast and Scalable Graph Computation on a PC | en_US |
dc.type | Text | |
dc.type.genre | Technical Report | |
dspace.entity.type | Publication | |
local.contributor.author | Chau, Duen Horng | |
local.contributor.corporatename | College of Computing | |
local.contributor.corporatename | School of Computational Science and Engineering | |
local.relation.ispartofseries | College of Computing Technical Report Series | |
local.relation.ispartofseries | School of Computational Science and Engineering Technical Report Series | |
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