1. CSS497 Undergraduate Research Performance Comparison Among Agent Teamwork, Globus and Condor By Timothy Chuang Advisor: Professor Munehiro Fukuda

2. Overview  Agent Teamwork – deployment of mobile agents Agents launch, monitor and resume jobs Fault-tolerant  Condor – opportunist job dispatcher Condor daemon searches for idle computing nodes on which to dispatch jobs Emphasize on job migration upon encountering an error  Globus – widely used grid computing middleware MPICH is required for parallel applications

3. Condor User Condor Pool X Gateway Class Manager Snapshot Class Manager

4. Globus LFSPBSGRAMs DUROC/MPICH-G2 User

5. Agent Teamwork FTP Server User A User B User B snapshot snapshots User program wrapper Snapshot Methods GridTCP User program wrapper Snapshot Methods GridTCP User program wrapper Snapshot Methods GridTCP snapshot User A’s Process User A’s Process User B’s Process TCP Communication Commander Agent Sentinel Agent Resource Agent Sentinel Agent Resource Agent Bookkeeper Agent Results

6. Project Objectives  Establish reference platform Condor Installation PVM installation  Implement parallel applications to run on PVM Matrix Multiplication Wave2D Simulation Mandelbrot Set Simulation Distributed Grep

7.  Modify parallel the same applications to utilize Agent Teamwork’s check pointing feature  Check previous Globus status Convert the same parallel applications to MPICH-G2  Conduct performance evaluation

8. Problems with Condor/PVM  Condor no longer fully Supports PVM PVM universe to dispatch jobs in is no longer functional  As a result, condor was dropped from the project

9. Evaluation of Agent Teamwork’s Fault-tolerance Performance  Applications used Matrix Multiplication Mandelbrot Set Renderer Wave2D Simulation Distributed Grep  Fault-tolerance Performance Evaluate the extra overhead of checkpointing and resumption

10. Challenges  Finding a large problem set that can scale well with the increasing number of computing nodes Certain problem sizes are limited to the master node’s memory – Matrix Multiplication  Debugging parallel applications Requires going through time consuming diagnosis  Finding the best check-pointing frequency for all applications Setting the frequency too low could take up to three hours to finish a job!

11. Performance - MatrixMult

12. Performance – Wave2D

13. Performance – Mandelbrot

14. Performance – Distributed Grep

15. Continued Work  Scale problem size to utilize all 64 computing nodes Conduct performance evaluation on multi-clusters  Conduct performance evaluation on Globus Compare Globus’ performance with Agent Teamwork

16. Useful Classes  CSS301 – Technical Writing  CSS343 – Data Structures and Algorithms  CSS430 – Operating Systems  CSS432 – Network Design  CSS434 – Parallel and Distributed Computing

17. Acknowledgements My Faculty Advisor: Professor Munehiro Fukuda UWB Linux System Administrators: Mr. David Grimmer Mrs. Meryll Larkin My Sponsor: Mr. Joshua Phillips

18. Questions?