Towards Scalable Graph Computation on Mobile Devices.

Yiqi Chen, Zhiyuan Lin, Robert Pienta, Minsuk Kahng, Duen Horng Chau
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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 a single mobile 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 familiar memory mapping capability 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 as 272 million edges (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.

Abstract Image

Abstract Image

Abstract Image

在移动设备上实现可扩展的图形计算
移动设备因其便携性、多点触控功能和不断提升的计算能力,已日益成为我们日常活动的核心。这些诱人的特性激发了人们对利用移动设备进行计算的研究兴趣。我们探索了一种新方法,旨在利用单个移动设备对无法容纳在设备有限主内存中的大型图进行可扩展图计算,从而开辟了在设备上分析大型数据集而无需依赖云的可能性。基于当今移动操作系统提供的熟悉的内存映射功能,我们的扩展计算方法功能强大,并有意保持简单,以最大限度地提高其在 iOS 和 Android 平台上的适用性。我们的实验证明,iPad mini 可以在多达 2.72 亿条边的大型真实图(Google+ 社交图)上执行快速计算,速度仅比 13 英寸 Macbook Pro 慢几倍。通过使用该技术创建一个真实世界的 iOS 应用程序,我们展示了使用我们的方法在单个移动设备上进行可扩展图计算的强大应用潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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