Efficient approximation algorithms for scheduling moldable tasks

Xiaohu Wu and Patrick Loiseau

European Journal of Operational Research, 2023, vol. 310, issue 1, 71-83

Abstract: Moldable tasks allow schedulers to determine the number of processors assigned to each task, thus enabling efficient use of large-scale parallel processing systems. We consider the problem of scheduling independent moldable tasks on processors and propose a new perspective of the existing speedup models: as the number p of processors assigned to a task increases, the speedup is linear if p is small and becomes sublinear after p exceeds a threshold. Based on this, we propose an efficient approximation algorithm to minimize the makespan. As a by-product, we also propose an approximation algorithm to maximize the sum of values of tasks completed by a deadline; this scheduling objective is considered for moldable tasks for the first time while similar works have been done for other types of parallel tasks.

Keywords: Scheduling; Approximation algorithms; Moldable tasks (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:310:y:2023:i:1:p:71-83

DOI: 10.1016/j.ejor.2023.02.044

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