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SUMMARY Load sharing in large, heterogeneous distributed systems allows users to access vast amounts of computing resources scattered around the system and may provide substantial performance improvements to applications. We discuss the design and implementation issues in Utopia, a load sharing facility specifically built for large and heterogeneous(More)
The purpose of this paper is to propose effective parallelization strategies for the Ant Colony Optimization (ACO) metaheuristic on Graphics Processing Units (GPUs). The Max–Min Ant System (MMAS) algorithm augmented with 3-opt local search is used as a framework for the implementation of the parallel ants and multiple ant colonies general parallelization(More)
We present a shared memory approach to the parallelization of the Ant Colony Optimization (ACO) metaheuristic and a performance comparison with an existing message passing implementation. Our aim is to show that the shared memory approach is a competitive strategy for the parallelization of ACO algorithms. The sequential ACO algorithm on which are based(More)
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