Thanos: High-Performance CPU-GPU Based Balanced Graph Partitioning Using Cross-Decomposition

As graphs become larger and more complex, it is becoming nearly impossible to process them without graph partitioning. Graph partitioning creates many subgraphs which can be processed in parallel thus delivering high-speed computation results. However, graph partitioning is a difficult task. In this...

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Bibliographic Details
Published in:Proceedings of the ASP-DAC ... Asia and South Pacific Design Automation Conference pp. 91 - 96
Main Authors: Kim, Dae Hee, Nagi, Rakesh, Chen, Deming
Format: Conference Proceeding
Language:English
Published: IEEE 01.01.2020
Subjects:
Graphics processing units
Optimization
Parallel processing
Partitioning algorithms
Programming
Task analysis
ISSN:2153-697X
Online Access:Get full text
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Summary:As graphs become larger and more complex, it is becoming nearly impossible to process them without graph partitioning. Graph partitioning creates many subgraphs which can be processed in parallel thus delivering high-speed computation results. However, graph partitioning is a difficult task. In this work, we introduce Thanos, a fast graph partitioning tool which uses the cross-decomposition algorithm that iteratively partitions a graph. It also produces balanced loads of partitions. The algorithm is well suited for parallel GPU programming which leads to fast and high-quality graph partitioning solutions. Experimental results show that we have achieved 30× speedup and 35% better edge cut reduction compared to the CPU version of the popular graph partitioner, METIS, on average.
ISSN:2153-697X
DOI:10.1109/ASP-DAC47756.2020.9045588