TUM in CrossGrid Role and Contribution

1. Fakultät für Informatik der Technischen Universität MünchenInformatik X: Rechnertechnik und Rechnerorganisation / Parallelrechnerarchitektur 24.05.2004 TUM in CrossGrid Role and Contribution http://wwwbode.cs.tum.edu/Par/tools/Fundings/CrossGrid.html

2. Fakultät für Informatik der Technischen Universität MünchenInformatik X: Rechnertechnik und Rechnerorganisation / Parallelrechnerarchitektur 24.05.2004 Role of TUM in Crossgrid • 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 1

3. Fakultät für Informatik der Technischen Universität MünchenInformatik X: Rechnertechnik und Rechnerorganisation / Parallelrechnerarchitektur 24.05.2004 What is G-PM? • Structure of 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 G-PM HLAC OCM-G (Task 3.3) PMC Benchmarks (Task 2.3) UIVC HLAC = High Level Analysis Component PMC = Performance Measurement Component UIVC = User Interface / Visualization Component OCM-G = Grid application monitoring system 2

4. Fakultät für Informatik der Technischen Universität MünchenInformatik X: Rechnertechnik und Rechnerorganisation / Parallelrechnerarchitektur 24.05.2004 What is the Purpose of HLAC? • HLAC adds a layer for high-level data analysis to G-PM, which provides two major functionalities to the user: • It enables to combine and/or correlate performance measurement data from different sources. E.g.: • measure the load imbalance bycomparing 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 • 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 3

5. Fakultät für Informatik der Technischen Universität MünchenInformatik X: Rechnertechnik und Rechnerorganisation / Parallelrechnerarchitektur 24.05.2004 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 • In G-PM user-defined metrics are supported by a Performance Metrics Specification Language (PMSL) 4

6. Fakultät für Informatik der Technischen Universität MünchenInformatik X: Rechnertechnik und Rechnerorganisation / Parallelrechnerarchitektur 24.05.2004 Main Achievements of TUM • 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) 5

7. Fakultät für Informatik der Technischen Universität MünchenInformatik X: Rechnertechnik und Rechnerorganisation / Parallelrechnerarchitektur 24.05.2004 Dissemination • Selected Presentations: • 2nd 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 2004. • M. Bubak, W. Funika, and R. Wismüller. A Performance Analysis Tool for Interactive Grid Applications. In Performance Analysis and Grid Computing, pp. 161-173. Kluwer Academic Publishers, 2003. 6