Affiliation:
1. National Engineering Research Center for Big Data Technology and System, Service Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China
2. National University of Singapore, Singapore
Abstract
Many out-of-GPU-memory systems are recently designed to support iterative processing of large-scale graphs. However, these systems still suffer from long time to converge because of inefficient propagation of active vertices’ new states along graph paths. To efficiently support out-of-GPU-memory graph processing, this work designs a system
LargeGraph
. Different from existing out-of-GPU-memory systems, LargeGraph proposes a
dependency-aware data-driven execution approach
, which can significantly accelerate active vertices’ state propagations along graph paths with low data access cost and also high parallelism. Specifically, according to the dependencies between the vertices, it only loads and processes the graph data associated with dependency chains originated from active vertices for smaller access cost. Because most active vertices frequently use a small evolving set of paths for their new states’ propagation because of power-law property, this small set of paths are dynamically identified and maintained and efficiently handled on the GPU to accelerate most propagations for faster convergence, whereas the remaining graph data are handled over the CPU. For out-of-GPU-memory graph processing, LargeGraph outperforms four cutting-edge systems: Totem (5.19–11.62×), Graphie (3.02–9.41×), Garaph (2.75–8.36×), and Subway (2.45–4.15×).
Funder
National Natural Science Foundation of China
Zhejiang Lab
Fundamental Research Funds for the Central Universities
Publisher
Association for Computing Machinery (ACM)
Subject
Hardware and Architecture,Information Systems,Software
Reference55 articles.
1. Stanford. 2020. Stanford Large Network Dataset Collection.http://snap.stanford.edu/data/index.html. Stanford. 2020. Stanford Large Network Dataset Collection.http://snap.stanford.edu/data/index.html.
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