SiL: An Approach for Adjusting Applications to Heterogeneous Systems Under Perturbations
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 ch...
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Published in | Euro-Par 2018: Parallel Processing Workshops pp. 456 - 468 |
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Main Authors | , |
Format | Book Chapter |
Language | English |
Published |
Cham
Springer International Publishing
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Series | Lecture Notes in Computer Science |
Subjects | |
Online Access | Get full text |
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Summary: | 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 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, whereas the SiL-based DLS selection achieved improved application performance in most experiments. |
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ISBN: | 9783030105488 3030105482 |
ISSN: | 0302-9743 1611-3349 |
DOI: | 10.1007/978-3-030-10549-5_36 |