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TUM in CrossGrid Role and Contribution Fakultät für Informatik der Technischen Universität München Informatik X: Rechnertechnik und Rechnerorganisation.

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Präsentation zum Thema: "TUM in CrossGrid Role and Contribution Fakultät für Informatik der Technischen Universität München Informatik X: Rechnertechnik und Rechnerorganisation."—  Präsentation transkript:

1 TUM in CrossGrid Role and Contribution Fakultät für Informatik der Technischen Universität München Informatik X: Rechnertechnik und Rechnerorganisation / Parallelrechnerarchitektur

2  Main roles:  Participation in Task 2.4 “Interactive and semiautomatic performance evaluation tools“  Implementation of the High Level Analysis Component within the Grid application performance analysis tool G-PM  Task leader of Task 2.1 “Tools requirements definition“ (finished)  Task leader of Task 2.5 “Integration, testing and refinement“  Additional roles:  Member of the Internal Review Board  Member of the Architecture Team  Member of the Integration Team  Deputy leader of WP 2 Role of TUM in Crossgrid 1 Fakultät für Informatik der Technischen Universität München Informatik X: Rechnertechnik und Rechnerorganisation / Parallelrechnerarchitektur

3 What is G-PM? G-PM is an on-line tool that allows application developers to measure, evaluate, and visualize the performance of Grid applications G-PM is a unique tool for computer scientists and Grid programmers It combines performance analysis of applications at multiple abstraction levels with the analysis of the Grid infrastructure HLAC = High Level Analysis Component PMC = Performance Measurement Component UIVC = User Interface / Visualization Component OCM-G = Grid application monitoring system Benchmarks (Task 2.3) PMC UIVC HLAC OCM-G (Task 3.3) G-PM 2 Fakultät für Informatik der Technischen Universität München Informatik X: Rechnertechnik und Rechnerorganisation / Parallelrechnerarchitektur  Structure of G-PM:

4 3  HLAC adds a layer for high-level data analysis to G-PM, which provides two major functionalities to the user: 1.It enables to combine and/or correlate performance measurement data from different sources. E.g.:  measure the load imbalance by comparing an application's CPU usage on each node  measure the portion of the maximum network bandwidth obtained by an application by comparing performance measurement data with benchmark data 2.It allows to measure application specific performance metrics. E.g:  the time used by one iteration of a solver  the response time of a specific request  convergence rate of an interative solver  These functionalities are offered via user-defined metrics What is the Purpose of HLAC? Fakultät für Informatik der Technischen Universität München Informatik X: Rechnertechnik und Rechnerorganisation / Parallelrechnerarchitektur

5 4 What are User-Defined Metrics?  User-defined metrics are performance metrics specified by the user at run-time according to his/her needs  often they are specific to the examined application  User-defined metrics can be based on existing metrics and optional information from the application:  occurance of important events (probes) in the application‘s execution  assosiation between related events (using a virtual time)  performance data computed by the application itself Fakultät für Informatik der Technischen Universität München Informatik X: Rechnertechnik und Rechnerorganisation / Parallelrechnerarchitektur  In G-PM user-defined metrics are supported by a Performance Metrics Specification Language (PMSL)

6 5 Main Achievements of TUM Fakultät für Informatik der Technischen Universität München Informatik X: Rechnertechnik und Rechnerorganisation / Parallelrechnerarchitektur  After the second project year, TUM has achieved:  definition of the PMSL language, based on requirements and examples of useful metrics for the CrossGrid applications  implementation of measurements of metrics defined via PMSL  parser for PMSL: translation into internal representation  simple optimizations  evaluation of measurements (centrally in G-PM, distributed evaluation is work in progress)  full integration of HLAC with G-PM and OCM-G  full integration of G-PM into the autobuild and deployment process  G-PM / HLAC has been used with most CrossGrid applications:  Blood flow simulation (Task 1.1)  Flooding simulation (Task 1.2)  High energy physics neural network training (Task 1.3)  (Air pollution is in progress)

7 6 Dissemination Fakultät für Informatik der Technischen Universität München Informatik X: Rechnertechnik und Rechnerorganisation / Parallelrechnerarchitektur  Selected Presentations:  2 nd AcrossGrids Conference, Nicosia, Cyprus, 2004  University of Siegen, Germany, 2003  APART Workshop at EuroPar 2003, Klagenfurt, Austria  Workshop on Clusters and Computational Grids for Scientific Computing 2002, Chateau de Faberges-de-la-Tour, France  Dagstuhl-Seminar “Performance Analysis and Distributed Computing“, Germany, 2002  Selected Publications:  R. Wismüller, M. Bubak, W. Funika, and B. Balis. A Performance Analysis Tool for Interactive Applications on the Grid. Intl. Journal of High Performance Computing Applications, 18(3), August  M. Bubak, W. Funika, and R. Wismüller. A Performance Analysis Tool for Interactive Grid Applications. In Performance Analysis and Grid Computing, pp Kluwer Academic Publishers, 2003.


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