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 systems. The system has no restriction on the types of tasks that can be remotely executed, involves few application changes and no operating system change, supports a high degree of transparency for remote task execution, and incurs low overhead. The algorithms for managing resource load information and task placement take advantage of the clustering nature of large‐scale distributed systems; centralized algorithms are used within host clusters, and directed graph algorithms are used among the clusters to make Utopia scalable to thousands of hosts. Task placements in Utopia exploit the heterogeneous hosts and consider varying resource demands of the tasks. A range of mechanisms for remote execution is available in Utopia that provides varying degrees of transparency and efficiency. A number of applications have been developed for Utopia, ranging from a load sharing command interpreter, to parallel and distributed applications, to a distributed batch facility. For example, an enhanced Unix command interpreter allows arbitrary commands and user jobs to be executed remotely, and a parallel make facility achieves speed‐ups of 15 or more by processing a collection of tasks in parallel on a number of hosts.

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Abstract. 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 both parallelization schemes is first described, followed by the parallelization strategies themselves. Through experiments, we compare speedup and efficiency measures on four TSP problems varying from 318 to 657 cities. We then discuss factors that explain the difference in performance of the two approaches. Further experiments are presented to show the performance of the shared memory implementation when varying numbers of ants are distributed among the available processors. In this last set of experiments, the solution quality obtained is taken into account when analyzing speedup and efficiency measures.

The purpose of this paper is to propose an effective implementation of the Ant Colony Optimization metaheuristic on actual shared-memory parallel computers. We deal with the management of multiple colonies which use a global sharedmemory to exchange information. We report considerable speedups on a SMP node of multi-core processors while witnessing solution quality equal or greater than the original sequential implementation.