title: Towards Scalable Graph Computation on Mobile Devices.
abstract: Mobile devices have become increasingly central to our everyday activities, due to their portability, multi-touch capabilities, and ever-improving computational power. Such attractive features have spurred research interest in leveraging mobile devices for computation. We explore a novel approach that aims to use asinglemobile device to perform scalable graph computation on large graphs that do not fit in the device's limited main memory, opening up the possibility of performing on-device analysis of large datasets, without relying on the cloud. Based on the familiarmemory mappingcapability provided by today's mobile operating systems, our approach to scale up computation is powerful and intentionally kept simple to maximize its applicability across the iOS and Android platforms. Our experiments demonstrate that an iPad mini can perform fast computation on large real graphs with as many as272 millionedges (Google+ social graph), at a speed that is only a few times slower than a 13″ Macbook Pro. Through creating a real world iOS app with this technique, we demonstrate the strong potential application for scalable graph computation on a single mobile device using our approach.
authors: College of Computing, Georgia Tech, Atlanta, GA, USA.
authors: Yiqi
authors: Y
authors: Chen
authors: College of Computing, Georgia Tech, Atlanta, GA, USA.
authors: Zhiyuan
authors: Z
authors: Lin
authors: College of Computing, Georgia Tech, Atlanta, GA, USA.
authors: Robert
authors: R
authors: Pienta
authors: College of Computing, Georgia Tech, Atlanta, GA, USA.
authors: Minsuk
authors: M
authors: Kahng
authors: College of Computing, Georgia Tech, Atlanta, GA, USA.
authors: Duen Horng
authors: DH
authors: Chau
keywords: graph mining; memory mapping; mobile device; scalable algorithms