Sciweavers

Share
IPPS
1999
IEEE

A Parallel Genetic Algorithm for Task Mapping on Parallel Machines

10 years 1 months ago
A Parallel Genetic Algorithm for Task Mapping on Parallel Machines
In parallel processing systems, a fundamental consideration is the maximization of system performance through task mapping. A good allocation strategy may improve resource utilization and increase signi cantly the throughput of the system. We demonstrate how to map the tasks among the processors to meet performance criteria, such as minimizing execution time or communication delays. We review the Local Neighborhhod Search LNS strategy for the mapping problem.We base our approach on LNS since it was shown that this method outperforms a large number of heuristic-based algorithms. We call our mapping algorithm, that is based on LNS, Genetic Local Neighborhood Search GLNS, and its parallel version, Parallel Genetic Local Neighborhood Search P-GLNS. We implemented and compared all three of these mapping strategies. The experimental results demonstrate that 1 GLNS algorithm has better performance than LNS and, 2 The P-GLNS algorithm achieves near linear speedup.
S. Mounir Alaoui, Ophir Frieder, Tarek A. El-Ghaza
Added 03 Aug 2010
Updated 03 Aug 2010
Type Conference
Year 1999
Where IPPS
Authors S. Mounir Alaoui, Ophir Frieder, Tarek A. El-Ghazawi
Comments (0)
books