Performance Technology for Complex Parallel Systems REFERENCES.

Slides:



Advertisements
Similar presentations
1 ICS-FORTH Dimitris Plexousakis, Pisa, February 2001 WP1: User Requirements Collection and Analysis Dimitris Plexousakis Computer Science.
Advertisements

Barcelona Supercomputing Center. The BSC-CNS objectives: R&D in Computer Sciences, Life Sciences and Earth Sciences. Supercomputing support to external.
Performance Analysis Tools for High-Performance Computing Daniel Becker
Machine Learning-based Autotuning with TAU and Active Harmony Nicholas Chaimov University of Oregon Paradyn Week 2013 April 29, 2013.
Initial Design of a Test Suite for Automatic Performance Analysis Tools Bernd Mohr Forschungszentrum Jülich John von Neumann - Institut für Computing Germany.
Dynamic performance measurement control Dynamic event grouping Multiple configurable counters Selective instrumentation Application-Level Performance Access.
Productive Performance Tools for Heterogeneous Parallel Computing Allen D. Malony Department of Computer and Information Science University of Oregon Shigeo.
GridRPC Sources / Credits: IRISA/IFSIC IRISA/INRIA Thierry Priol et. al papers.
Cohere.open.ac.uk Discourse-Centric Learning Analytics LAK2011: 1st International Conference on Learning Analytics and Knowledge February 27-March 1, 2011.
Robert Bell, Allen D. Malony, Sameer Shende Department of Computer and Information Science Computational Science.
OmpP: A Profiling Tool for OpenMP Karl Fürlinger Michael Gerndt {fuerling, Technische Universität München.
Sameer Shende Department of Computer and Information Science Neuro Informatics Center University of Oregon Tool Interoperability.
Cyclic input of characters through a single digital button without visual feedback Yang Xiaoqing New Interaction Techniques Dept.
TAU Parallel Performance System DOD UGC 2004 Tutorial Allen D. Malony, Sameer Shende, Robert Bell Univesity of Oregon.
The TAU Performance Technology for Complex Parallel Systems (Performance Analysis Bring Your Own Code Workshop, NRL Washington D.C.) Sameer Shende, Allen.
On the Integration and Use of OpenMP Performance Tools in the SPEC OMP2001 Benchmarks Bernd Mohr 1, Allen D. Malony 2, Rudi Eigenmann 3 1 Forschungszentrum.
A Parallel Structured Ecological Model for High End Shared Memory Computers Dali Wang Department of Computer Science, University of Tennessee, Knoxville.
Case Study: PETSc ex19  Non-linear solver (snes)  2-D driven cavity code  uses velocity-velocity formulation  finite difference discretization on a.
Allen D. Malony, Sameer Shende Department of Computer and Information Science Computational Science Institute University.
The TAU Performance System: Advances in Performance Mapping Sameer Shende University of Oregon.
Parallel Simulation System Victor V. Okol’nishnikov Institute of Computational Mathematics and Mathematical Geophysics of SB RAS.
Allen D. Malony Department of Computer and Information Science Computational Science Institute University of Oregon TAU Performance.
“A Service-enabled Access Control Model for Distributed Data” Mark Turner, Philip Woodall Pennine Forum - 16 th September 2004.
Tile Reduction: the first step towards tile aware parallelization in OpenMP Ge Gan Department of Electrical and Computer Engineering Univ. of Delaware.
TAU: Performance Regression Testing Harness for FLASH Sameer Shende
On the Integration and Use of OpenMP Performance Tools in the SPEC OMP2001 Benchmarks Rudi Eigenmann Department of Electrical and Computer Engineering.
The TAU Performance System Sameer Shende, Allen D. Malony, Robert Bell University of Oregon.
Sameer Shende, Allen D. Malony Computer & Information Science Department Computational Science Institute University of Oregon.
Fine Grain MPI Earl J. Dodd Humaira Kamal, Alan University of British Columbia 1.
Allen D. Malony Performance Research Laboratory (PRL) Neuroinformatics Center (NIC) Department.
1 Score-P – A Joint Performance Measurement Run-Time Infrastructure for Periscope, Scalasca, TAU, and Vampir Markus Geimer 2), Bert Wesarg 1), Brian Wylie.
THE PEDAGOGICAL DESIGN OF TECHNOLOGY ENHANCED COLLABORATIVE LEARNING Minna Lakkala Centre for Research on Networked Learning and Knowledge Building, University.
revised CmpE 583 Fall 2006Discussion: OWL- 1 CmpE 583- Web Semantics: Theory and Practice DISCUSSION: OWL Atilla ELÇİ Computer Engineering.
Paradyn Week – April 14, 2004 – Madison, WI DPOMP: A DPCL Based Infrastructure for Performance Monitoring of OpenMP Applications Bernd Mohr Forschungszentrum.
Bologna, September 2003 Giorgia Lodi Department of Computer Science University of Bologna V.Ghini, F. Panzieri.
SC’13: Hands-on Practical Hybrid Parallel Application Performance Engineering Introduction to VI-HPS Brian Wylie Jülich Supercomputing Centre.
A Specification Language and Test Planner for Software Testing Aolat A. Adedeji 1 Mary Lou Soffa 1 1 DEPARTMENT OF COMPUTER SCIENCE, UNIVERSITY OF VIRGINIA.
Score-P – A Joint Performance Measurement Run-Time Infrastructure for Periscope, Scalasca, TAU, and Vampir Alexandru Calotoiu German Research School for.
Presented by KOJAK and SCALASCA Jack Dongarra, Shirley Moore, Farzona Pulatova, and Fengguang Song University of Tennessee and Oak Ridge National Laboratory.
Integrated Performance Views in Charm++: Projections meets TAU Scott Biersdorff Allen D. Malony Department Computer and Information Science University.
SEDA: An Architecture for Well-Conditioned, Scalable Internet Services Presented by Changdae Kim and Jaeung Han OFFENCE.
Architectural Blueprints The “4+1” View Model of Software Architecture
John Mellor-Crummey Robert Fowler Nathan Tallent Gabriel Marin Department of Computer Science, Rice University Los Alamos Computer Science Institute HPCToolkit.
Technology + Process SDCI HPC Improvement: High-Productivity Performance Engineering (Tools, Methods, Training) for NSF HPC Applications Rick Kufrin *,
Profile Analysis with ParaProf Sameer Shende Performance Reseaerch Lab, University of Oregon
Advanced Techniques for Scheduling, Reservation, and Access Management for Remote Laboratories Wolfgang Ziegler, Oliver Wäldrich Fraunhofer Institute SCAI.
Profiling Memory Subsystem Performance in an Advanced POWER Virtualization Environment The prominent role of the memory hierarchy as one of the major bottlenecks.
Abstract We present two Model Driven Engineering (MDE) tools, namely the Eclipse Modeling Framework (EMF) and Umple. We identify the structure and characteristic.
1CPSD Software Infrastructure for Application Development Laxmikant Kale David Padua Computer Science Department.
Dynamic performance measurement control Dynamic event grouping Multiple configurable counters Selective instrumentation Application-Level Performance Access.
Debugging parallel programs. Breakpoint debugging Probably the most widely familiar method of debugging programs is breakpoint debugging. In this method,
Experiment Management with Microsoft Project Gregor von Laszewski Leor E. Dilmanian Link to presentation on wiki 12:13:33Service Oriented Cyberinfrastructure.
WP2001 CPA4 Towards Dependable and Survivable Systems and Infrastructures Baton holder ANDREA SERVIDA European Commission DG Information Society C-4
System Support for High Performance Data Mining Ruoming Jin Leo Glimcher Xuan Zhang Gagan Agrawal Department of Computer and Information Sciences Ohio.
Integrated Performance Views in Charm++: Projections meets TAU Scott Biersdorff Allen D. Malony Department Computer and Information Science University.
Introduction to UNICORE Morris Riedel, Forschungszentrum Jülich (FZJ), Germany OMII – Europe Training, Edinburgh, UK 11th July 2007 – 12th July
Presented by Jack Dongarra University of Tennessee and Oak Ridge National Laboratory KOJAK and SCALASCA.
Centre for Aerospace Systems Design & Engineering (CASDE), IIT Bombay Presentation to the 3 rd Meeting of Joint Policy Committee June 10 th 2002.
معرفی مجموعه‌ای از الگوهای فرآيند مخصوص نرم‌افزارهای بی‌درنگ
Productive Performance Tools for Heterogeneous Parallel Computing
Performance Technology for Scalable Parallel Systems
TAU integration with Score-P
Allen D. Malony, Sameer Shende
TAU Parallel Performance System
TAU: A Framework for Parallel Performance Analysis
Allen D. Malony Computer & Information Science Department
Outline Introduction Motivation for performance mapping SEAA model
Parallel Program Analysis Framework for the DOE ACTS Toolkit
Department of Computer Science, University of Tennessee, Knoxville
TAU Performance DataBase Framework (PerfDBF)
Presentation transcript:

Performance Technology for Complex Parallel Systems REFERENCES

SC’01 Tutorial Nov. 7, 2001 Papers and Reports  B. Mohr, A. Malony, S. Shende, and F. Wolf, “Towards a Performance Tool Interface for OpenMP: An Approach Based on Directive Rewriting,” European Workshop on OpenMP (EWOMP 2001), Barcelona, September  F. Wolf and B. Mohr, “Automatic Performance Analysis of SMP Cluster Applications,” Technical Report IB , John von Neumann - Institut für Computing, Forschungszentrum Jülich, ZAM,  S. Shende and A. Malony, “Integration and Application of the TAU Performance System in Parallel Java Environments,” Proceedings of the Joint ACM Java Grande - ISCOPE 2001 Conference, June  S. Shende, A. D. Malony, and R. Ansell-Bell, “Instrumentation and Measurement Strategies for Flexible and Portable Empirical Performance Evaluation,” Proceedings Tools and Techniques for Performance Evaluation Workshop, PDPTA'01, C.S.R.E.A., June 2001.

SC’01 Tutorial Nov. 7, 2001 Papers and Reports  S. Shende, “The Role of Instrumentation and Mapping in Performance Measurement,” Ph.D. Dissertation, University of Oregon, August  T. Fahringer, M. Gerndt, B. Mohr, F. Wolf, G. Riley, and J. Träff, “Knowledge Specification for Automatic Performance Analysis – APART Technical Report, Revised Version,” Technical Report IB , John von Neumann - Institut für Computing, Forschungszentrum Jülich, ZAM,  B. Mohr, A. Malony, S. Shende, and F. Wolf, “Design and Prototype of a Performance Tool Interface for OpenMP,” 2nd Annual Los Alamos Computer Science Institute Symposium (LACSI 2001), October  F. Wolf and B. Mohr, “Automatic Performance Analysis of MPI Applications Based on Event Traces,” European Conference on Parallel Computing (Euro-Par 2000), München, August  S. Shende and A. Malony, “Performance Tools for Parallel Java Environments,” Proc. Second Workshop on Java for High Performance Computing, ICS 2000, May 2000.

SC’01 Tutorial Nov. 7, 2001 Papers and Reports  A. Malony and S. Shende, “Performance Technology for Complex Parallel and Distributed Systems,” Proc. Third Austrian-Hungarian Workshop on Distributed and Parallel Systems, DAPSYS 2000, "Distributed and Parallel Systems: From Concepts to Applications," (Eds. G. Kotsis and P. Kacsuk)Kluwer, Norwell, MA, August 2000, pp  K. Lindlan, J. Cuny, A. Malony, S. Shende, B. Mohr, R. Rivenburgh, and C. Rasmussen. “A Tool Framework for Static and Dynamic Analysis of Object-Oriented Software with Templates,” Proceedings of SC2000: High Performance Networking and Computing Conference, Dallas, November  A. Malony, “Tools for Parallel Computing: A Performance Evaluation Perspective,” in J. Blazewicz et. al. (Editors), Handbook on Parallel and Distributed Processing, Springer Verlag, pp ,  F. Wolf and B. Mohr, “EARL – Language Reference,” Technical Report IB , John von Neumann - Institut für Computing, Forschungszentrum Jülich, ZAM, 2000.

SC’01 Tutorial Nov. 7, 2001 Papers and Reports  F. Wolf and B. Mohr, “Specifying Performance Properties Using Compound Runtime Events – APART Technical Report,” Technical Report IB , John von Neumann - Institut für Computing, Forschungszentrum Jülich, ZAM,  S. Vajracharya, S. Karmesin, P. Beckman, J. Crotinger, A. Malony, S. Shende, R. Oldehoeft, and S. Smith, “SMARTS: Exploiting Temporal Locality and Parallelism through Vertical Execution,” Proceedings of ACM International Conference on Supercomputing (ICS '99), June  S. Shende, “Profiling and Tracing in Linux,” Proceedings of the Extreme Linux Workshop #2, USENIX, Monterey CA, June  S. Shende, A. D. Malony, J. Cuny, K. Lindlan, P. Beckman, and S. Karmesin, “Portable Profiling and Tracing for Parallel Scientific Applications using C++,” Proceedings of ACM SIGMETRICS Symposium on Parallel and Distributed Tools (SPDT '98), August 1998, pp

SC’01 Tutorial Nov. 7, 2001 Papers and Reports  B. Mohr, A. Malony, and J. Cuny, “TAU,” in G. Wilson (Ed.), Parallel Programming Using C++, MIT Press, 1996, p  K. Shanmugam, A. Malony, and B. Mohr, “Speedy: An Integrated Performance Extrapolation Tool for pC++ Programs,” Proceedings of the Joint Conference PERFORMANCE TOOLS '95 and MMB '95, Heidelberg, September  A. Malony, B. Mohr, P. Beckman, and D. Gannon, “Program Analysis and Tuning Tools for a Parallel Object Oriented Language: An Experiment with the TAU System,” Proceedings of the Workshop on Parallel Scientific Computing, Cape Cod, MA, October  A. Malony, B. Mohr, P. Beckman, D. Gannon, S. Yang, and F. Bodin, “Performance Analysis of pC++: A Portable Data-Parallel Programming System for Scalable Parallel Computers,” Proceedings of the 8th International Parallel Processing Symbosium (IPPS), Cancún, Mexico, April 1994, pp

SC’01 Tutorial Nov. 7, 2001 Web References  TAU:  PDT:  KOJAK (EXPERT, EARL, OPARI):  APART:

SC’01 Tutorial Nov. 7, 2001 Support Acknowledgement  TAU and PDT support  Department of Engergy (DOE)  DOE 2000 ACTS contract  DOE MICS contract  DOE ASCI Level 3 (LANL, LLNL)  DARPA  NSF National Young Investigator (NYI) award