NDLI: WTF, GPU! computing twitter's who-to-follow on the GPU

Content Provider	ACM Digital Library
Author	Wang, Yangzihao Owens, John D. Geil, Afton
Abstract	In this paper, we investigate the potential of GPUs for performing link structure analysis of social graphs. Specifically, we implement Twitter's WTF ("Who to Follow") recommendation system on a single GPU. Our implementation shows promising results on moderate-sized social graphs. It can return the top-K relevant users for a single user in 172 ms when running on a subset of the 2009 Twitter follow graph with 16 million users and 85 million social relations. For our largest dataset, which contains 75% of the users (30 million) and 50% of the social relations (680 million) of the complete follow graph, this calculation takes 1.0 s. We also propose possible solutions to apply our system to follow graphs of larger sizes that do not fit into the on-board memory of a single GPU.
Starting Page	63
Ending Page	68
Page Count	6
File Format	PDF
ISBN	9781450331982
DOI	10.1145/2660460.2660481
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-10-01
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Graph processing Recommendation systems Gpu computing Twitter Online social networks
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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WTF, GPU! computing twitter's who-to-follow on the GPU