Scheduling Matters: Area-Oriented Heuristic for Resource Management

Benson, Jeremy; Estrada, Trilce; Rosenberg, Arnold L.; Taufer, Michela

doi:10.1109/sbac-pad.2016.35

Cited by 1 publication

(1 citation statement)

References 32 publications

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“…Global rescheduling is a well-studied problem in High Performance Computing (HPC) [1,9,10,12,13,14,15]. Redistributing the workload among PEs of a parallel system is a way to mitigate load imbalance created by dynamic applications.…”

Section: Related Workmentioning

confidence: 99%

A Batch Task Migration Approach for Decentralized Global Rescheduling

Freitas

Santana

Castro

et al. 2018

2018 30th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)

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Effectively mapping tasks of High Performance Computing (HPC) applications on parallel systems is crucial to assure substantial performance gains. As platforms and applications grow, load imbalance becomes a priority issue. Even though centralized rescheduling has been a viable solution to mitigate this problem, its efficiency is not able to keep up with the increasing size of shared memory platforms. To efficiently solve load imbalance today, and in the years to come, we should prioritize decentralized strategies developed for large scale platforms. In this paper, we propose our Batch Task Migration approach to improve decentralized global rescheduling, ultimately reducing communication costs and preserving task locality. We implemented and evaluated our approach in two different parallel platforms, using both synthetic workloads and a molecular dynamics (MD) benchmark. Our solution was able to achieve speedups of up to 3.75 and 1.15 on rescheduling time, when compared to other centralized and distributed approaches, respectively. Moreover, it improved the execution time of MD by factors up to 1.34 and 1.22 when compared to a scenario without load balancing on two different platforms.

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