Evaluating the Flexibility of Dynamic Loop Scheduling on Heterogeneous Systems in the Presence of Fluctuating Load Using SimGrid

Abstract

Scientific applications running on heterogeneous computing systems, which often have unpredictable behavior, enhance their performance by employing loop scheduling techniques as methods to avoid load imbalance through an optimized assignment of their parallel loops. With current computing platforms facilitating petascale performance and promising exascale performance towards the end of the present decade, efficient and robust algorithms are required to guarantee optimal performance of parallel applications in the presence of unpredictable perturbations. A number of dynamic loop scheduling (DLS) methods based on probabilistic analyses have been developed to achieve the desired robust performance. In earlier work, two metrics (flexibility and resilience) have been formulated to quantify the robustness of various DLS methods in heterogeneous computing systems with uncertainties. In this work, to ensure robust performance of the scientific applications on current (petascale) and future (exascale) high performance computing systems, a simulation model was designed and integrated into the SimGrid simulation toolkit, thus enabling a comprehensive study of the robustness of the DLS methods which uses results of experimental cases with various combinations of number of processors, problem sizes, and scheduling methods. The DLS methods have been implemented into the simulation model and analyzed for the purpose of exploring their flexibility (robustness against unpredictable variations in the system load), when involved in a range of case scenarios comprised of various distributions characterizing loop iteration execution times and system availability. The simulation results reported are used to compare the robustness of the DLS methods under the various environments considered, using the flexibility metric.

An approach for realistically simulating the performance of scientific applications on high performance computing systems

“…The weak scalability of these DLS techniques was assessed in the presence of certain load imbalance sources (algorithmic and systemic). The flexibility, understood as the robustness against perturbations in the PE computing speed, of the same DLS techniques implemented using SG-MSG was also studied [32]. Moreover, the resilience, understood as the robustness against PE failure, of these DLS techniques on a heterogeneous computing system was studied using the SG-MSG interface [33].…”

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

“…Related work. Robustness denotes the maintenance of certain desired system characteristics despite fluctuations in the behavior of its components or its environment [1], whereas, flexibility [22] denotes the robustness of DLS to variations in the delivered computational speeds. The performance of sci-entific applications under perturbations in the delivered computational speed is studied with nonadaptive DLS techniques [13,25].…”

“…This observation raises the following question and motivates the present work: "Given an application, an HPC system, and both their characteristics and interplay, which DLS technique will achieve improved performance under unpredictable perturbations?" Earlier work studied the flexibility of DLS (robustness to reduced delivered computational speed) [22] and the selection of robust DLS using machine learning [23] with the SimGrid (SG) [8] simulation toolkit. The selection of DLS techniques for synthetic time-stepping scientific applications using reinforcement learning [4] was also studied using SG.…”

Portfolio-Based Selection of Robust Dynamic Loop Scheduling Algorithms Using Machine Learning

“…The resilience metric denotes the robustness of an application using a certain DLS method in the presence of processor failures that occur during the execution of the application. Furthermore, the flexibility metric and the developed robustness analysis have been successfully employed to evaluate the robustness of the DLS algorithms against system load fluctuations using discrete event simulations [7] [8]. Throughout the paper, the terms flexibility and robustness in presence of system availability variation are used interchangeably.…”