Parallel algorithm development and testing using Petri-object simulation. Issue 6 (2nd November 2021)

• Record Type: Journal Article
• Authors: Stetsenko, Inna V.; Pavlov, Alexander A.; Dyfuchyna, Oleksandra
• Is Part Of: International journal of parallel, emergent and distributed systems. Volume 36:Issue 6(2021)
• Page Start: 549
• Page End: 564
• Publication Date: 2021-11-02
• Subjects: Multithreaded programming -- parallel algorithm -- parallel simulation -- discrete event simulation -- stochastic Petri net -- Java; Parallel computers -- Periodicals; Electronic data processing -- Distributed processing -- Periodicals; Computer algorithms -- Periodicals; 004.35
• DOI: 10.1080/17445760.2021.1955113
• Languages: English
• ISSNs: 1744-5760

Abstract

Abstract : Parallel algorithms are problematic to develop because of the negative influence of synchronisation, complicated behaviour of threads' capturing computing resources. Experimental results show performance time's strong dependence on algorithm parameters, such as the number of subtasks and the complexity of each task. The optimal value of subtask complexity is revealed for the particular algorithm. It is the same for different complexity of the parallelised task (with the same computing resource). To guarantee algorithm speed-up it is important to have a method for investigating the efficiency of parallel algorithm before its implementation on specified computing resources. Stochastic Petri net potentially could be a high accuracy tool for investigating the efficiency of a parallel algorithm. However, a huge number of elements are needed to compose a model of non-trivial algorithm that limits the application of this tool in practice. Petri-object simulation method allows replication of Petri nets with specified parameters and model creation of a list of linked Petri-objects. Basic templates for the model creation of a multithreaded algorithm are developed. Applying these templates, the model of the parallel discrete event simulation algorithm is developed and investigated. By the model results, the algorithm parameters providing the least performance time can be determined.