Towards Scalable Performance Analysis of MPI Parallel Applications
2015 (English)Licentiate thesis, comprehensive summary (Other academic)
A considerably fraction of science discovery is nowadays relying on computer simulations. High Performance Computing (HPC) provides scientists with the means to simulate processes ranging from climate modeling to protein folding. However, achieving good application performance and making an optimal use of HPC resources is a heroic task due to the complexity of parallel software. Therefore, performance tools and runtime systems that help users to execute applications in the most optimal way are of utmost importance in the landscape of HPC. In this thesis, we explore different techniques to tackle the challenges of collecting, storing, and using fine-grained performance data. First, we investigate the automatic use of real-time performance data in order to run applications in an optimal way. To that end, we present a prototype of an adaptive task-based runtime system that uses real-time performance data for task scheduling. This runtime system has a performance monitoring component that provides real-time access to the performance behavior of anapplication while it runs. The implementation of this monitoring component is presented and evaluated within this thesis. Secondly, we explore lossless compression approaches for MPI monitoring. One of the main problems that performance tools face is the huge amount of fine-grained data that can be generated from an instrumented application. Collecting fine-grained data from a program is the best method to uncover the root causes of performance bottlenecks, however, it is unfeasible with extremely parallel applications or applications with long execution times. On the other hand, collecting coarse-grained data is scalable but sometimes not enough to discern the root cause of a performance problem. Thus, we propose a new method for performance monitoring of MPI programs using event flow graphs. Event flow graphs provide very low overhead in terms of execution time and storage size, and can be used to reconstruct fine-grained trace files of application events ordered in time.
Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2015. , viii, 39 p.
TRITA-CSC-A, ISSN 1653-5723 ; 2015:05
parallel computing, performance monitoring, performance tools, event flow graphs
Research subject Computer Science
IdentifiersURN: urn:nbn:se:kth:diva-165043ISBN: 978-91-7595-518-6OAI: oai:DiVA.org:kth-165043DiVA: diva2:806809
2015-05-20, The Visualization Studio, room 4451, Lindstedtsvägen 5, KTH, Stockholm, 10:00 (English)
Black-Schaffer, David, Associate professor
Laure, Erwin, Professor
QC 201505082015-05-082015-04-212015-05-08Bibliographically approved
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