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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GraphReduce: Processing Large-Scale Graphs on Accelerator-Based Systems

SESSION: Graph Analytics on HPC systems

EVENT TYPE: Papers, Best Student Paper Finalists

EVENT TAG(S): Architectures, Clouds and Distributed Computing, Analytics

TIME: 4:30PM - 5:00PM

SESSION CHAIR(S): Dilma Da Silva

AUTHOR(S):Dipanjan Sengupta, Shuaiwen Leon Song, Kapil Agarwal, Karsten Schwan



Recent work on real-world graph analytics has sought to leverage the massive amount of parallelism offered by GPU devices, but challenges remain due to the inherent irregularity of graph algorithms and limitations in GPU-resident memory for storing large graphs. We present GraphReduce, a highly efficient and scalable GPU-based framework that operates on graphs that exceed the device’s internal memory capacity. GraphReduce adopts a combination of edge- and vertex-centric implementations of the Gather-Apply-Scatter programming model and operates on multiple asynchronous GPU streams to fully exploit the high degrees of parallelism in GPUs with efficient graph data movement between the host and device. GraphReduce-based programming is performed via device functions that include gatherMap, gatherReduce, apply, and scatter, implemented by programmers for the graph algorithms they wish to realize. Extensive experimental evaluations for a wide variety of graph inputs and algorithms demonstrate that GraphReduce significantly outperforms other competing out-of-memory approaches.

Chair/Author Details:

Dilma Da Silva (Chair) - Texas A&M University|

Dipanjan Sengupta - Georgia Institute of Technology

Shuaiwen Leon Song - Pacific Northwest National Laboratory

Kapil Agarwal - Georgia Institute of Technology

Karsten Schwan - Georgia Institute of Technology

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Paper provided by the ACM Digital Library

Paper also available from IEEE Computer Society