It is well known that the availability of cost-effective and powerful parallel computers has enhanced the ability of the operations research community to solve laborious computational problems. But many researchers argue that the lack of portability of parallel algorithms is a major drawback to utilizing parallel computers. This paper studies the performance of a portable parallel unconstrained non-gradient optimization algorithm, when executed in various shared-memory multiprocessor systems, compared with its non-portable code. Analysis of covariance is used to analyse how the algorithm's performance is affected by several factors of interest. The results yield more insights into the parallel computing.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Statistics and Probability
- Statistics, Probability and Uncertainty
- Computational Theory and Mathematics