Accelerating datapath merging by task parallelisation on multicore systems. Issue 5 (3rd September 2019)

• Record Type: Journal Article
• Authors: Fazlali, Mahmood; Fallah, Mohammad K.; Hosseinpour, Naemeh; Katanforoush, Ali
• Is Part Of: International journal of parallel, emergent and distributed systems. Volume 34:Issue 5(2019)
• Page Start: 615
• Page End: 628
• Publication Date: 2019-09-03
• Subjects: Datapath merging -- Branch and Bound algorithm -- task parallelisation -- shared memory paradigm; Parallel computers -- Periodicals; Electronic data processing -- Distributed processing -- Periodicals; Computer algorithms -- Periodicals; 004.35
• DOI: 10.1080/17445760.2018.1552957
• Languages: English
• ISSNs: 1744-5760

Abstract

ABSTRACT: Datapath merging is an efficient approach to reduce hardware resources and configuration time in the synthesis of digital systems. In order to solve datapath merging, we have to find the maximum weighted clique, which is an NP-hard problem. So, datapath merging is a time-consuming process. In this article, we use OpenMP library to perform divide and conquer task parallelism to find the maximum weighted clique. Therefore, considerable reduction in the synthesis time and almost linear speedup has been achieved. The experimental results obtained from running this algorithm on different benchmarks represent speedup ranging from 1.2 times to 6.5 times for an 8-core system.