SiL: An Approach for Adjusting Applications to Heterogeneous Systems Under Perturbations

TL;DR

This paper introduces SiL, a control-theoretic approach for dynamically selecting loop scheduling techniques to optimize scientific application performance on heterogeneous HPC systems under various perturbations.

Contribution

It proposes a novel SiL method that dynamically adapts DLS techniques considering multiple perturbations, improving performance over static approaches.

Findings

SiL outperforms static DLS techniques in most scenarios.

Perturbations in network bandwidth and latency significantly impact application performance.

No single DLS technique is optimal for all perturbation conditions.

Abstract

Scientific applications consist of large and computationally-intensive loops. Dynamic loop scheduling (DLS) techniques are used to load balance the execution of such applications. Load imbalance can be caused by variations in loop iteration execution times due to problem, algorithmic, or systemic characteristics (also, perturbations). The following question motivates this work: "Given an application, a high-performance computing (HPC) system, and both their characteristics and interplay, which DLS technique will achieve improved performance under unpredictable perturbations?" Existing work only considers perturbations caused by variations in the HPC system delivered computational speeds. However, perturbations in available network bandwidth or latency are inevitable on production HPC systems. Simulator in the loop (SiL) is introduced, herein, as a new control-theoretic inspired approach to dynamically select DLS techniques that improve the performance of applications on heterogeneous HPC systems under perturbations. The present work examines the performance of six applications on a heterogeneous system under all above system perturbations. The SiL proof of concept is evaluated using simulation. The performance results confirm the initial hypothesis that no single DLS technique can deliver best performance in all scenarios, while the SiL-based DLS selection delivered improved application performance in most experiments.