GraphReduce: Processing Large-Scale Graphs on Accelerator-Based Systems
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.
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- Conference: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC'15), November 15-20, 2015, Austin, Texas, Paper No. 28
- ACM , New York, NY, United States(US).
- Research Org:
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
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- United States
- Architecture optimization, reuse, performance, energy, locality, cache