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SimAS: A Simulation-assisted Approach for the Scheduling Algorithm Selection under Perturbations by Mohammed, Ali; Ciorba, Florina M. is a scholarly article available to read on EtoBox.

What is SimAS: A Simulation-assisted Approach for the Scheduling Algorithm Selection under Perturbations about?

Many scientific applications consist of large and computationally-intensive loops. Dynamic loop self-scheduling (DLS) techniques are used to parallelize and to balance the load during the execution of such applications. Load imbalance arises from variations in the loop iteration (or tasks) execution times, caused by problem, algorithmic, or systemic characteristics. The variations in systemic characteristics are referred to as perturbations, and can be caused by other applications or processes that share the same resources, or a temporary system fault or malfunction. Therefore, the selection of the most efficient DLS technique is critical to achieve the best application performance. The following question motivates this work: Given an application, an HPC system, and their characteristics and interplay, which DLS technique will achieve improved performance under unpredictable perturbations? Existing studies focus on variations in the delivered computational speed only as the source of perturbations in the system. However, perturbations in available network bandwidth or latency are inevitable on production HPC systems. A Simulator-assisted scheduling (SimAS) is introduced as a new co

Author

Mohammed, Ali; Ciorba, Florina M.

Published

2019

Language

EN