Accelerating datapath merging by task parallelisation on multicore systems

Mahmood Fazlali, Mohammad K. Fallah, Naemeh Hosseinpour, Ali Katanforoush

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)615-628
Number of pages14
JournalInternational Journal of Parallel, Emergent and Distributed Systems
Issue number5
Publication statusE-pub ahead of print - 3 Jan 2019


  • Branch and Bound algorithm
  • Datapath merging
  • shared memory paradigm
  • task parallelisation


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