CSS497 Undergraduate Research
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Transcript of CSS497 Undergraduate Research
CSS497 Undergraduate Research
Performance Comparison Among Agent Teamwork, Globus and
Condor
By Timothy Chuang
Advisor: Professor Munehiro Fukuda
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
Condor
User
Condor Pool X
Gateway
Gateway
GatewayClass ManagerClass Manager
Snapshot
Class Manager
Globus
LFS PBS GRAMs
DUROC/MPICH-G2
User
Agent Teamwork
FTPServer
UserA
UserB
UserB
snapshotsnapshot
snapshots snapshots
User program wrapper
SnapshotMethods
GridTCP
User program wrapper
SnapshotMethods
GridTCP
User program wrapper
SnapshotMethods
GridTCP
snapshot
User A’sProcess
User A’sProcess
User B’sProcess
TCPCommunication
Commander Agent
Commander Agent
Sentinel Agent
Sentinel Agent
Resource Agent
Sentinel Agent
Resource Agent
Bookkeeper Agent
BookkeeperAgent
ResultsResults
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
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
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
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
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!
Performance - MatrixMult
Performance – Wave2D
Performance – Mandelbrot
Performance – Distributed Grep
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
Useful Classes CSS301 – Technical Writing CSS343 – Data Structures and Algorithms CSS430 – Operating Systems CSS432 – Network Design CSS434 – Parallel and Distributed Computing
AcknowledgementsMy Faculty Advisor:
Professor Munehiro Fukuda
UWB Linux System Administrators:
Mr. David Grimmer
Mrs. Meryll Larkin
My Sponsor:
Mr. Joshua Phillips
Questions?