Analysing the Performance of GPU Hash Tables for State Space Exploration

Nathan Cassee
(Eindhoven University of Technology, Eindhoven, The Netherlands)
Anton Wijs
(Eindhoven University of Technology, Eindhoven, The Netherlands)

In the past few years, General Purpose Graphics Processors (GPUs) have been used to significantly speed up numerous applications. One of the areas in which GPUs have recently led to a significant speed-up is model checking. In model checking, state spaces, i.e., large directed graphs, are explored to verify whether models satisfy desirable properties. GPUexplore is a GPU-based model checker that uses a hash table to efficiently keep track of already explored states. As a large number of states is discovered and stored during such an exploration, the hash table should be able to quickly handle many inserts and queries concurrently. In this paper, we experimentally compare two different hash tables optimised for the GPU, one being the GPUexplore hash table, and the other using Cuckoo hashing. We compare the performance of both hash tables using random and non-random data obtained from model checking experiments, to analyse the applicability of the two hash tables for state space exploration. We conclude that Cuckoo hashing is three times faster than GPUexplore hashing for random data, and that Cuckoo hashing is five to nine times faster for non-random data. This suggests great potential to further speed up GPUexplore in the near future.

In Timo Kehrer and Alice Miller: Proceedings Third Workshop on Graphs as Models (GaM 2017), Uppsala, Sweden, 23rd April 2017, Electronic Proceedings in Theoretical Computer Science 263, pp. 1–15.
Published: 22nd December 2017.

ArXived at: http://dx.doi.org/10.4204/EPTCS.263.1 bibtex PDF
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