Efficient Algorithms for Scheduling Moldable Tasks
Xiaohu Wu, Patrick Loiseau

TL;DR
This paper introduces a new model for scheduling moldable tasks that combines linear and monotonic speedup behaviors, and proposes approximation algorithms with improved bounds for makespan minimization and throughput maximization.
Contribution
It presents a novel hybrid speedup model for moldable tasks and develops approximation algorithms with better bounds than existing methods.
Findings
Achieves a pproximation for makespan minimization with improved bounds.
Provides a pproximation for throughput maximization under a common deadline.
Demonstrates the model's generality between classic monotonic and linear-speedup models.
Abstract
We study the problem of scheduling independent moldable tasks on processors that arises in large-scale parallel computations. When tasks are monotonic, the best known result is a -approximation algorithm for makespan minimization with a complexity linear in and polynomial in and where is arbitrarily small. We propose a new perspective of the existing speedup models: the speedup of a task is linear when the number of assigned processors is small (up to a threshold ) while it presents monotonicity when ranges in ; the bound indicates an unacceptable overhead when parallelizing on too many processors. The generality of this model is proved to be between the classic monotonic and linear-speedup models. For any given integer , let $u=\left\lceil…
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Taxonomy
TopicsScheduling and Optimization Algorithms · Optimization and Search Problems · Complexity and Algorithms in Graphs
