sponsored byACMIEEE The International Conference for High Performance 
Computing, Networking, Storage and Analysis
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SCHEDULE: NOV 15-20, 2015

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GraphBIG: Understanding Graph Computing in the Context of Industrial Solutions

SESSION: Graph Algorithms and Benchmarks


EVENT TAG(S): Performance, Graphs, Data-Intensive Computing, Algorithms

TIME: 2:30PM - 3:00PM

SESSION CHAIR(S): Umit Catalyurek

AUTHOR(S):Lifeng Nai, Yinglong Xia, Ilie G. Tanase, Hyesoon Kim, Ching-Yung Lin



With the emergence of data science, graph computing is becoming a crucial tool for processing big connected data. Although efficient implementations of specific graph applications exist, the behavior of full-spectrum graph computing remains unknown. To understand graph computing, we must consider multiple graph computation types, graph frameworks, data representations, and various data sources in a holistic way.

In this paper, we present GraphBIG, a benchmark suite inspired by IBM System G project. To cover major graph computation types and data sources, GraphBIG selects representative workloads and data sets from 21 real-world use cases of multiple application domains. Besides, by utilizing System G design, GraphBIG workloads incorporate modern graph frameworks and data representations. We characterized GraphBIG on real machines and observed extremely irregular memory patterns and significant diverse behaviors across different computation. GraphBIG helps users understand the architectural behavior of modern graph computing and enables future graph architecture and system research.

Chair/Author Details:

Umit Catalyurek (Chair) - Ohio State University|

Lifeng Nai - Georgia Institute of Technology

Yinglong Xia - IBM Corporation

Ilie G. Tanase - IBM Corporation

Hyesoon Kim - Georgia Institute of Technology

Ching-Yung Lin - IBM Corporation

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Paper provided by the ACM Digital Library

Paper also available from IEEE Computer Society