research-article

GraphBIG: understanding graph computing in the context of industrial solutions

Authors:

Ilie G. Tanase,

Ching-Yung LinAuthors Info & Claims

SC '15: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

Article No.: 69, Pages 1 - 12

https://doi.org/10.1145/2807591.2807626

Published: 15 November 2015 Publication History

Abstract

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 datastructures, workloads and data sets from 21 real-world use cases of multiple application domains. We characterized GraphBIG on real machines and observed extremely irregular memory patterns and significant diverse behavior across different computations. GraphBIG helps users understand the impact of modern graph computing on the hardware architecture and enables future architecture and system research.

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GraphBIG: understanding graph computing in the context of industrial solutions

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cover image ACM Conferences

SC '15: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

November 2015

985 pages

ISBN:9781450337236

DOI:10.1145/2807591

General Chair:
Jackie Kern
University of Illinois at Urbana-Champaign, Urbana, Illinois
,
Program Chair:
Jeffrey S. Vetter
Oak Ridge National Laboratory and Georgia Institute of Technology, Oak Ridge, Tennessee

Copyright © 2015 ACM.

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Published: 15 November 2015

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