Scheduling parallel identical machines to minimize makespan: A parallel approximation algorithm

Abstract Approximation algorithms for scheduling parallel machines have been studied for decades, leading to significant progress in terms of their approximation guarantees. The algorithms that provide near optimal performance are not feasible to use in practice due to their huge execution time requirements, thus underscoring the importance of developing efficient parallel approximation algorithms with near-optimal performance guarantees that are suitable for execution on current parallel systems, such as multi-core systems. We present the design and analysis of a parallel approximation algorithm for the problem of scheduling jobs on parallel identical machines to minimize makespan. The design of the parallel approximation algorithm is based on the best existing polynomial-time approximation scheme ( PTAS ) for the problem. To the best of our knowledge, this is the first practical parallel approximation algorithm for the minimum makespan scheduling problem that maintains the approximation guarantees of the sequential PTAS and it is specifically designed for execution on shared-memory parallel machines. We implement and run the algorithm on a large multi-core system and perform an extensive experimental analysis on data generated from realistic probability distributions. The results show that our proposed parallel approximation algorithm achieves significant speedup with respect to both the sequential PTAS and the CPLEX-based solver that solves the mixed integer program formulation of the problem.

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