Multi-core model checking algorithms for LTL verification with fairness assumptions

The main challenge in model checking is the state space explosion. With developments in hardware today, most processors have many cores inside. To leverage on the advances in hardware, we can increase the performance of verifying large models by designing parallel algorithms to run efficiently on mu...

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Main Authors: HA, Xuan-Linh, QUAN, Thanh Tho, LIU, Yang, SUN, Jun
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Language:English
Published: Institutional Knowledge at Singapore Management University 2013
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Online Access:https://ink.library.smu.edu.sg/sis_research/5067
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spelling sg-smu-ink.sis_research-60702020-03-12T06:54:03Z Multi-core model checking algorithms for LTL verification with fairness assumptions HA, Xuan-Linh QUAN, Thanh Tho LIU, Yang SUN, Jun The main challenge in model checking is the state space explosion. With developments in hardware today, most processors have many cores inside. To leverage on the advances in hardware, we can increase the performance of verifying large models by designing parallel algorithms to run efficiently on multi-core architecture. This work focuses on this problem in the context of Linear Temporal Logic (LTL) model checking, which can be seen as finding accepting cycles in a graph. Recently, there are some parallel algorithms based on Nested Depth First Search (NDFS). In this work, we propose two new parallel algorithms based on strongly connected component (SCC) searching algorithm (i.e., Tarjan's algorithm). By finding all the SCCs in the graph, our approaches can not only check LTL properties, but also handle fairness assumptions all together. The experiments show that our new algorithms are comparable or faster than the state-of-the-art multi-core algorithms. 2013-05-12T07:00:00Z text https://ink.library.smu.edu.sg/sis_research/5067 info:doi/10.1109/APSEC.2013.79 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Software Engineering
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Software Engineering
spellingShingle Software Engineering
HA, Xuan-Linh
QUAN, Thanh Tho
LIU, Yang
SUN, Jun
Multi-core model checking algorithms for LTL verification with fairness assumptions
description The main challenge in model checking is the state space explosion. With developments in hardware today, most processors have many cores inside. To leverage on the advances in hardware, we can increase the performance of verifying large models by designing parallel algorithms to run efficiently on multi-core architecture. This work focuses on this problem in the context of Linear Temporal Logic (LTL) model checking, which can be seen as finding accepting cycles in a graph. Recently, there are some parallel algorithms based on Nested Depth First Search (NDFS). In this work, we propose two new parallel algorithms based on strongly connected component (SCC) searching algorithm (i.e., Tarjan's algorithm). By finding all the SCCs in the graph, our approaches can not only check LTL properties, but also handle fairness assumptions all together. The experiments show that our new algorithms are comparable or faster than the state-of-the-art multi-core algorithms.
format text
author HA, Xuan-Linh
QUAN, Thanh Tho
LIU, Yang
SUN, Jun
author_facet HA, Xuan-Linh
QUAN, Thanh Tho
LIU, Yang
SUN, Jun
author_sort HA, Xuan-Linh
title Multi-core model checking algorithms for LTL verification with fairness assumptions
title_short Multi-core model checking algorithms for LTL verification with fairness assumptions
title_full Multi-core model checking algorithms for LTL verification with fairness assumptions
title_fullStr Multi-core model checking algorithms for LTL verification with fairness assumptions
title_full_unstemmed Multi-core model checking algorithms for LTL verification with fairness assumptions
title_sort multi-core model checking algorithms for ltl verification with fairness assumptions
publisher Institutional Knowledge at Singapore Management University
publishDate 2013
url https://ink.library.smu.edu.sg/sis_research/5067
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