How Well Do Graph-Processing Platforms Perform? An Empirical Performance Evaluation and Analysis

Graph-processing platforms are increasingly used in a variety of domains. Although both industry and academia are developing and tuning graph-processing algorithms and platforms, the performance of graph-processing platforms has never been explored or compared in-depth. Thus, users face the daunting...

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Bibliographic Details
Published in:	2014 IEEE 28th International Parallel and Distributed Processing Symposium pp. 395 - 404
Main Authors:	Yong Guo, Biczak, Marcin, Varbanescu, Ana Lucia, Iosup, Alexandru, Martella, Claudio, Willke, Theodore L.
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-05-2014
Subjects:	Algorithm design and analysis benchmark Benchmark testing graph processing Measurement performance evaluation Programming Scalability Terrestrial atmosphere Yarn
Online Access:	Get full text
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Summary:	Graph-processing platforms are increasingly used in a variety of domains. Although both industry and academia are developing and tuning graph-processing algorithms and platforms, the performance of graph-processing platforms has never been explored or compared in-depth. Thus, users face the daunting challenge of selecting an appropriate platform for their specific application. To alleviate this challenge, we propose an empirical method for benchmarking graph-processing platforms. We define a comprehensive process, and a selection of representative metrics, datasets, and algorithmic classes. We implement a benchmarking suite of five classes of algorithms and seven diverse graphs. Our suite reports on basic (user-lever) performance, resource utilization, scalability, and various overhead. We use our benchmarking suite to analyze and compare six platforms. We gain valuable insights for each platform and present the first comprehensive comparison of graph-processing platforms.
ISBN:	1479937991 9781479937998
ISSN:	1530-2075
DOI:	10.1109/IPDPS.2014.49