NDLI: GTS: A Fast and Scalable Graph Processing Method based on Streaming Topology to GPUs

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GTS: A Fast and Scalable Graph Processing Method based on Streaming Topology to GPUs

Content Provider	ACM Digital Library
Author	Park, Himchan An, Kyuhyeon Kim, Min-Soo Seo, Hyunseok Kim, Jinwook
Abstract	A fast and scalable graph processing method becomes increasingly important as graphs become popular in a wide range of applications and their sizes are growing rapidly. Most of distributed graph processing methods require a lot of machines equipped with a total of thousands of CPU cores and a few terabyte main memory for handling billion-scale graphs. Meanwhile, GPUs could be a promising direction toward fast processing of large-scale graphs by exploiting thousands of GPU cores. All of the existing methods using GPUs, however, fail to process large-scale graphs that do not fit in main memory of a single machine. Here, we propose a fast and scalable graph processing method GTS that handles even RMAT32 (64 billion edges) very efficiently only by using a single machine. The proposed method stores graphs in PCI-E SSDs and executes a graph algorithm using thousands of GPU cores while streaming topology data of graphs to GPUs via PCI-E interface. GTS is fast due to no communication overhead and scalable due to no data duplication from graph partitioning among machines. Through extensive experiments, we show that GTS consistently and significantly outperforms the major distributed graph processing methods, GraphX, Giraph, and PowerGraph, and the state-of-the-art GPU-based method TOTEM.
Starting Page	447
Ending Page	461
Page Count	15
File Format	PDF
ISBN	9781450335317
DOI	10.1145/2882903.2915204
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-06-26
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Graph processing Gpus Ssds Stream
Content Type	Text
Resource Type	Article

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