TY - GEN
T1 - GPUexplore 2.0: unleashing GPU explicit-state model checking
AU - Wijs, A.
AU - Neele, T.
AU - Bosnacki, D.
PY - 2016
Y1 - 2016
N2 - In earlier work, we were the first to investigate the potential of using graphics processing units (GPUs) to speed up explicit-state model checking. Back then, the conclusion was clearly that this potential exists, having measured speed-ups of around 10 times, compared to state of- the-art single-core model checking. In this paper, we present a new version of our GPU model checker, GPUexplore. Since publication of our earlier work, we have identified and implemented several approaches to improve the performance of the model checker considerably. These include enhanced lock-less hashing of the states and improved thread synchronizations. We discuss experimental results that show the impact of both the progress in hardware in the last few years and our proposed optimisations. The new version of GPUexplore running on state-of the- art hardware can be more than 100 times faster than a sequential implementation for large models and is on average eight times faster than the previous version of the tool running on the same hardware.
AB - In earlier work, we were the first to investigate the potential of using graphics processing units (GPUs) to speed up explicit-state model checking. Back then, the conclusion was clearly that this potential exists, having measured speed-ups of around 10 times, compared to state of- the-art single-core model checking. In this paper, we present a new version of our GPU model checker, GPUexplore. Since publication of our earlier work, we have identified and implemented several approaches to improve the performance of the model checker considerably. These include enhanced lock-less hashing of the states and improved thread synchronizations. We discuss experimental results that show the impact of both the progress in hardware in the last few years and our proposed optimisations. The new version of GPUexplore running on state-of the- art hardware can be more than 100 times faster than a sequential implementation for large models and is on average eight times faster than the previous version of the tool running on the same hardware.
UR - http://www.scopus.com/inward/record.url?scp=84996563502&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-48989-6_42
DO - 10.1007/978-3-319-48989-6_42
M3 - Conference contribution
AN - SCOPUS:84996563502
SN - 978-3-319-48988-9
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 694
EP - 701
BT - FM 2016: Formal Methods - 21st International Symposium, Proceedings
PB - Springer
T2 - 21st International Symposium on Formal Methods, FM 2016
Y2 - 9 November 2016 through 11 November 2016
ER -