Title:
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
relation.isAuthorOfPublication fb5e00ae-9fb7-475d-8eac-50c48a46ea23
relation.isOrgUnitOfPublication c8892b3c-8db6-4b7b-a33a-1b67f7db2021
relation.isOrgUnitOfPublication 01ab2ef1-c6da-49c9-be98-fbd1d840d2b1
relation.isSeriesOfPublication 35c9e8fc-dd67-4201-b1d5-016381ef65b8
relation.isSeriesOfPublication 5a01f926-96af-453d-a75b-abc3e0f0abb3
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
GT-CSE-2013-02.pdf
Size:
186.2 KB
Format:
Adobe Portable Document Format
Description:
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
3.13 KB
Format:
Item-specific license agreed upon to submission
Description: