A novel approach toward parallel implementation of BFS algorithm using graphic processor unit

Farid, Fahmid Al; Uddin, Md. Sharif; Barman, Shohag; Ghods, Amirhossein; Das, Sowmitra; Hasan, Mehedi

doi:10.1109/iceeict.2015.7307536

Cited by 2 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A major concerns of the work in this paper is the desire to develop algorithms that scale well as the number of states of the FSM increases. To address this issue, we devised massively parallel algorithms for Graphics Processing Units (GPUs), motivated by the successful use of GPUs in a range of application domains [Busato and Bombieri 2015;Djidjev et al 2015;D ümmler and Egerland 2015;Farid et al 2015;Lai et al 2015;Liu and Huang 2015;Luo et al 2010;Merrill et al 2012;Mytkowicz et al 2014]. There has also been some recent work that used GPU computing on FSM-based testing problems [Hierons and T ürker 2016a,b;, although this was for deterministic FSMs.…”

Section: Introductionmentioning

confidence: 99%

Parallel Algorithms for Generating Distinguishing Sequences for Observable Non-deterministic FSMs

Hierons

Türker

2017

ACM Trans. Softw. Eng. Methodol.

View full text Add to dashboard Cite

A distinguishing sequence (DS) for a finite-state machine (FSM) is an input sequence that distinguishes every pair of states of the FSM. There are techniques that generate a test sequence with guaranteed fault detection power, and it has been found that shorter test sequences can be produced if DSs are used. Despite these benefits, however, until recently the only published DS generation algorithms have been for deterministic FSMs. This article develops a massively parallel algorithm, which can be used in Graphics Processing Units (GPUs) Computing, to generate DSs from partial observable non-deterministic FSMs. We also present the results of experiments using randomly generated FSMs and some benchmark FSMs. The results are promising and indicate that the proposed algorithm can derive DSs from partial observable non-deterministic FSMs with 32,000 states in an acceptable amount of time.

show abstract

Section: Introductionmentioning

confidence: 99%

Parallel Algorithms for Generating Distinguishing Sequences for Observable Non-deterministic FSMs

Hierons

Türker

2017

ACM Trans. Softw. Eng. Methodol.

View full text Add to dashboard Cite

show abstract

“…Despite the increasing interest in Graphics Processing Units (GPUs) [39], [40], [41], [42], [43], [44], no previous work has utilised GPU technology to generate CSs or HSIs. In this work, our primary motivation is to address the scalability problem raised when constructing CSs and HSIs and to do so through using GPU technology.…”

Section: Introductionmentioning

confidence: 99%

Parallel Algorithms for Generating Harmonised State Identifiers and Characterising Sets

Hierons

Türker

2016

IEEE Trans. Comput.

View full text Add to dashboard Cite

Abstract-Many automated finite state machine (FSM) based test generation algorithms require that a characterising set (CS) or a set of harmonised state identifiers (HSIs) is first produced. The only previously published algorithms for partial FSMs were brute-force algorithms with exponential worst case time complexity. This paper presents polynomial time algorithms and also massively parallel implementations of both the polynomial time algorithms and the brute-force algorithms. In the experiments the parallel algorithms scaled better than the sequential algorithms and took much less time. Interestingly, while the parallel version of the polynomial time algorithm was fastest for most sizes of FSMs, the parallel version of the brute-force algorithm scaled better due to lower memory requirements.

show abstract

A novel approach toward parallel implementation of BFS algorithm using graphic processor unit

Cited by 2 publications

References 8 publications

Parallel Algorithms for Generating Distinguishing Sequences for Observable Non-deterministic FSMs

Parallel Algorithms for Generating Distinguishing Sequences for Observable Non-deterministic FSMs

Parallel Algorithms for Generating Harmonised State Identifiers and Characterising Sets

Contact Info

Product

Resources

About