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Online Performance Data Introspection with IPM
KTH, School of Computer Science and Communication (CSC), High Performance Computing and Visualization (HPCViz).ORCID iD: 0000-0001-9693-6265
KTH, School of Computer Science and Communication (CSC), High Performance Computing and Visualization (HPCViz).ORCID iD: 0000-0002-9901-9857
Ludwig-Maximilians-Universität München.
2014 (English)In: Proceedings of the 15th IEEE International Conference on High Performance Computing and Communications (HPCC 2013), IEEE Computer Society, 2014, 728-734 p.Conference paper, Published paper (Refereed)
Abstract [en]

Exascale systems will be heterogeneous architectures with multiple levels of concurrency and energy constraints. In such a complex scenario, performance monitoring and runtime systems play a major role to obtain good application performance and scalability. Furthermore, online access to performance data becomes a necessity to decide how to schedule resources and orchestrate computational elements: processes, threads, tasks, etc. We present the Performance Introspection API, an extension of the IPM tool that provides online runtime access to performance data from an application while it runs. We describe its design and implementation and show its overhead on several test benchmarks. We also present a real test case using the Performance Introspection API in conjunction with processor frequency scaling to reduce power consumption.

Place, publisher, year, edition, pages
IEEE Computer Society, 2014. 728-734 p.
National Category
Computer Systems
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-136212DOI: 10.1109/HPCC.and.EUC.2013.107Scopus ID: 2-s2.0-84903964607ISBN: 978-076955088-6 (print)OAI: oai:DiVA.org:kth-136212DiVA: diva2:675586
Conference
The 15th IEEE International Conference on High Performance Computing and Communications (HPCC 2013). Zhangjiajie , China , November 13-15, 2013.
Note

QC 20140602

Available from: 2013-12-04 Created: 2013-12-04 Last updated: 2015-05-08Bibliographically approved
In thesis
1. Towards Scalable Performance Analysis of MPI Parallel Applications
Open this publication in new window or tab >>Towards Scalable Performance Analysis of MPI Parallel Applications
2015 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

  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.
Series
TRITA-CSC-A, ISSN 1653-5723 ; 2015:05
Keyword
parallel computing, performance monitoring, performance tools, event flow graphs
National Category
Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-165043 (URN)978-91-7595-518-6 (ISBN)
Presentation
2015-05-20, The Visualization Studio, room 4451, Lindstedtsvägen 5, KTH, Stockholm, 10:00 (English)
Opponent
Supervisors
Note

QC 20150508

Available from: 2015-05-08 Created: 2015-04-21 Last updated: 2015-05-08Bibliographically approved

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Aguilar, XavierLaure, Erwin

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