GraphReduce: Large-Scale Graph Analytics on Accelerator-Based HPC Systems

Conference · Wed Sep 30 00:00:00 EDT 2015

DOI:https://doi.org/10.1109/IPDPSW.2015.16· OSTI ID:1236326

Sengupta, Dipanjan; Agarwal, Kapil; Song, Shuaiwen; Schwan, Karsten

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 both 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 the device.

• Research Organization: Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1236326
• Report Number(s): PNNL-SA-111320; KJ0402000
• Resource Relation: Conference: IEEE International Parallel and Distributed Processing Symposium Workshop (IPDPSW 2015), May 25-29, 2016, Hyderabad, India, 604-609
• Country of Publication: United States
• Language: English

Related Subjects

Architecture optimization
reuse
performance
energy
locality
cache