Energy Idle Aware Stochastic Lexicographic Local Searches for Precedence-Constraint Task List Scheduling on Heterogeneous Systems

Alejandro Santiago,José Antonio Castan Rocha,Mirna Ponce-Flores,Mayra Guadalupe Treviño Berrones,J David Terán-Villanueva,Salvador Ibarra Martínez,Julio Laria Menchaca,Fausto Balderas

doi:10.3390/en14123473

Abstract

The use of parallel applications in High-Performance Computing (HPC) demands high computing times and energy resources. Inadequate scheduling produces longer computing times which, in turn, increases energy consumption and monetary cost. Task scheduling is an NP-Hard problem; thus, several heuristics methods appear in the literature. The main approaches can be grouped into the following categories: fast heuristics, metaheuristics, and local search. Fast heuristics and metaheuristics are used when pre-scheduling times are short and long, respectively. The third is commonly used when pre-scheduling time is limited by CPU seconds or by objective function evaluations. This paper focuses on optimizing the scheduling of parallel applications, considering the energy consumption during the idle time while no tasks are executing. Additionally, we detail a comparative literature study of the performance of lexicographic variants with local searches adapted to be stochastic and aware of idle energy consumption.

Highlights

According to the website www.top500.org, accessed on 10 June 2021, in November 2018, the top rank of High-Performance Computing (HPC) system Summit from the Oak Ridge National Laboratory is composed of 2,397,824 CPU
We use the local search approach, which fits for the above statement as in [3,4,5], where the stopping criterion is set to a small amount of fixed objective function evaluations or a small amount of time
We found at every presented comparison in this work that there is a statistical difference after analyzing all the computed p-values by the Friedman statistical test, which satisfies the condition p-value ≤ 0.05, giving a statistical significance of 95% [63]

Summary

Introduction

According to the website www.top500.org, accessed on 10 June 2021, in November 2018, the top rank of High-Performance Computing (HPC) system Summit from the Oak Ridge National Laboratory is composed of 2,397,824 CPU. Several scheduling methods have been developed in the literature; in our particular case, we review the approaches when HPC administrators have a restricted time to optimize the final scheduling. To this end, we use the local search approach, which fits for the above statement as in [3,4,5], where the stopping criterion is set to a small amount of fixed objective function evaluations or a small amount of time. Unlike constructive heuristics, which construct the solution, adding one decision variable at a time, or metaheuristics, which require thousands of objective function evaluations (a long-run) [6]

Methods

Results

Conclusion