Paper Info
Title
How Well Do Graph-Processing Platforms Perform? An Empirical Performance Evaluation and Analysis
Abstract
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.
Year
DOI
Venue
2014
10.1109/IPDPS.2014.49
IPDPS
Keywords
Field
DocType
resource utilization,empirical performance evaluation,data analysis,algorithmic classes,graph-processing platforms,representative metrics,datasets,graph processing, performance evaluation, benchmark,benchmarking suite,benchmark testing,performance evaluation,user-lever performance,graph processing,comprehensive process,benchmark,measurement,programming,scalability,algorithm design and analysis
Graph,Suite,Computer science,Benchmarking,Scalability,Distributed computing
Conference
ISSN
Citations 
PageRank 
1530-2075
46
1.23
References 
Authors
25
6
Name
Order
Citations
PageRank
Yong Guo1954.60
Marcin Biczak2461.23
Ana Lucia Varbanescu352044.83
Alexandru Iosup42042125.89
Claudio Martella51056.07
Theodore L. Willke645829.71