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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Data Partitioning Strategies for Graph Workloads on Heterogeneous Clusters

SESSION: Management of Graph Workloads


EVENT TAG(S): System Software, Clouds and Distributed Computing

TIME: 3:30PM - 4:00PM


AUTHOR(S):Michael LeBeane, Shuang Song, Reena Panda, Jee Ho Ryoo, Lizy K. John



Large scale graph analytics are an important class of problem in the modern data center. However, while data centers are trending towards a large number of heterogeneous processing nodes, graph analytics frameworks still operate under the assumption of uniform compute resources. In this paper, we develop heterogeneity-aware data ingress strategies for graph analytics workloads using the popular PowerGraph framework. We illustrate how simple estimates of relative node computational throughput can guide heterogeneity-aware data partitioning algorithms to provide balanced graph cutting decisions. Our work enhances five online data ingress strategies from a variety of sources to optimize application execution for throughput differences in heterogeneous data centers. The proposed partitioning algorithms improve the runtime of several popular machine learning and data mining applications by as much as a 65% and on average by 32% as compared to the default, balanced partitioning approaches.

Chair/Author Details:

Manoj Kumar (Chair) - IBM Corporation|

Michael LeBeane - The University of Texas at Austin

Shuang Song - The University of Texas at Austin

Reena Panda - The University of Texas at Austin

Jee Ho Ryoo - The University of Texas at Austin

Lizy K. John - The University of Texas at Austin

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Paper provided by the ACM Digital Library

Paper also available from IEEE Computer Society