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CENTRE FOR PARALLEL COMPUTING
8th IDGF Workshop Hannover, August 17th 2011International DesktopGrid Federation
CENTRE FOR PARALLEL COMPUTING
Experiences with theUniversity of WestminsterDesktop Grid
S C Winter, T Kiss, G Terstyanszky, D Farkas, T Delaitre
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Contents
• Introduction to Westminster Local Desktop Grid (WLDG)– Architecture, deployment management– EDGeS Application Development
Methodology (EADM)• Application examples• Conclusions
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Introduction to Westminster Local Desktop Grid (WLDG)
1
2
34
5
6
New Cavendish St576
Marylebone Road559
Regent Street 395Wells Street 31Little Titchfield St
66Harrow Campus 254
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WLDG Environment• DG Server on private University network • Over 1500 client nodes on 6 different
campuses• Most machines are dual core, all running
Windows• Running SZTAKI Local Desktop Grid package• Based on student laboratory PC’s
– If not used by student switch to DG mode– If no more work from DG server shutdown
(Green policy)
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The DG Scenario
BOINC Server
BOINC workers
UoW Local Desktop Grid
Create graph and concrete workflow, and submit to DG
End-user
gUSE WS P-GRADE portal WS P-GRADE
DG Submitter submits jobs and retrieve results via 3G Bridge
Workers: Download executable and input filesUpload: result
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WLDG: ZENworks deployment
• BOINC clients installed automatically and maintained by specifically developed Novell ZENworks objects– MSI file has been created to generate a ZENworks object
that installs the client software.– BOINC Client Install Shield Executable converted into an MSI
package (/a switch on the BOINC Client executable)– BOINC client is part of the generic image installed on all lab
PC’s throughout the University– Guaranteed that any newly purchased and installed PC
automatically becomes part of the WLDG• All clients registered under same user account
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EDGeS Application Development Methodology (EADM)• Generic methodology for DG application porting• Motivation: Special focus required when
porting/developing an application to a SG/DG platform
• Defines how the recommended software tools, eg. developed by EDGeS, can aid this process
• Supports iterative methods:– well-defined stages suggest a logical order– but (since in most cases process is non-linear) allows
revisiting and revising results of previous stages, at any point
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EADM – Defined Stages1. Analysis of current
application
1. Analysis of current application
2. Requirements analysis
2. Requirements analysis
3. Systems design
4. Detailed design
5. Implementation
6. Testing
7. Validation
8. Deployment
9. User support, maintenance & feedback
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Application Examples
• Digital Alias-Free Signal Processing• AutoDock Molecular Modelling
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Digital Alias-Free Signal Processing (DASP)• Users: Centre for Systems Analysis – University of Westminster
• Traditional DSP based on Uniform sampling– Suffers from aliasing
• Aim: Digital Alias-free Signal Processing (DASP)
– One solution is Periodic Non-uniform Sampling (PNS)
• The DASP application designs PNS sequences
• Selection of optimal sampling sequence is computationally expensive process– A linear equation has to be solved and a large number of solutions
(~1010 ) compared.
• The analyses of the solutions are independent from each other suitable for DG parallelisation
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DASP - Parallelisation
Solveprqrq rr )12(12
Find bestPermutation for solution 1, 1+m,
1+2m…
Find bestPermutation for solution 2, 2+m,
2+2m …
Find bestPermutation for solution m, 2m,
3m, …
…qr, qr+1, …, q2r-1 qr, qr+1, …, q2r-1 qr, qr+1, …, q2r-1
Find globally best solution
Locally bestsolution
Locally bestsolution
Locally bestsolution
Computer 1 Computer 2 Computer m
Solveprqrq rr )12(12
Find bestPermutation for solution 1, 1+m,
1+2m…
Find bestPermutation for solution 2, 2+m,
2+2m …
Find bestPermutation for solution m, 2m,
3m, …
…qr, qr+1, …, q2r-1 qr, qr+1, …, q2r-1 qr, qr+1, …, q2r-1
Find globally best solution
Locally bestsolution
Locally bestsolution
Locally bestsolution
Computer 1 Computer 2 Computer m
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DASP – Performance test results
Period T (factor) Sequential DG
worstDG
median DG best# of
work units
Speedup (best case)
# of nodes
involved (median)
18 13 min 9 min 7 min 4 min 50 3.3 59
20 2 hr29 min
111 min 43 min 20 min 100 7.5 97
22 26 hr40 min
5h 1min
3 hr24 min
2 hr 31 min 723 11 179
24 ~820 hr n/a n/a 17 hr54 min 980 46 372
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DASP – Addressing the performance issues• Inefficient load balancing
– solutions of the equation should be grouped based on the execution time required to analyse individual solutions
• Inefficient work unit generation – some of the solutions should be divided into subtasks (more
work units)– Limits to the possible speed-up
• User-community/application developers to consider redesigning the algorithm
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AutoDock Molecular Modelling
• Users:• Dept of Molecular & Applied Biosciences, UoW
• AutoDock: • a suite of automated docking tools• designed to predict how small molecules, such as
substrates or drug candidates, bind to a receptor of known 3D structure
• application components:– AutoDock performs the docking of the ligand to a set of grids
describing the target protein – AutoGrid pre-calculates these grids
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Need for Parallelisation• One run of AutoDock finishes in a reasonable time
on a single PC
• However, thousands of scenarios have to be simulated and analysed to get stable and meaningful results.– AutoDock has to be run multiple times with the same
input files but with random factors– Simulations runs are independent from each other –
suitable for DG
• AutoGrid does not require Grid resources
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AutoDock component workflow
gpf file
pdb file (ligand)
pdb file (receptor)
prepare_ligand4.py
prepare_receptor4.py
pdbqt file
pdbqt file
AUTOGRID AUTODOCKmap files
Pamela
dpf file
AUTODOCKAUTODOCKAUTODOCKAUTODOCK
dlg files
SCRIPT1SCRIPT2best dlg files pdb file
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Computational workflow inP-GRADE
receptor.pdb
ligand.pdb
Autogrid executables, Scripts (uploaded by thedeveloper , don’t change it)
gpf descriptor file
dpf descriptor file
output pdb file
Number of work units
1. The Generator job creates specified numbered of AutoDock jobs.
2. The AutoGrid job creates pdbqt files from the pdb files, runs the autogrid application and generates the map files. Zips them into an archive file. This archive will be the input of all AutoDock jobs.
3. The AutoDock jobs are running on the Desktop Grid. As output they provide dlg files.
4. The Collector job collects the dlg files. Takes the best results and concatenates them into a pdb file.
dlg files
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AutoDock – Performance test results
Speedup
0
20
40
60
80
100
120
140
160
180
200
10 100 1000 3000
# of work units
Sp
eed
up
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Tackling the Tail Problem
• Augment the DG infrastructure with more reliable nodes, eg. service grid or cloud resources
• Redesign scheduler to detect tail and resubmit tardy tasks to SG or cloud
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AutoDock - Conclusions
• CygWin on Windows implementation inhibited performance– can be improved using (eg.)
• DG to EGEE bridge• Cloudbursting
• AutoDock is black-box legacy application– source code not available – code-based improvement not possible
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Further Applications• Ultrasound Computer Tomography - Forschungszentrum Karlsruhe • EMMIL – E-marketplace optimization - SZTAKI• Anti-Cancer Drug Research (CancerGrid) - SZTAKI• Integrator of Stochastic Differential Equations in Plasmas - BIFI • Distributed Audio Retrieval - Cardiff University• Cellular Automata based Laser Dynamics - University of Sevilla• Radio Network Design – University of Extramadura • An X-ray diffraction spectrum analysis - University of Extramadura• DNA Sequence Comparison and Pattern Discovery - Erasmus Medical
Center• PLINK - Analysis of genotype/phenotype data - Atos Origin• 3D video rendering - University of Westminster
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Conclusions – Performance Issues
• Performance enhancements – accrue from cyclical enterprise level hardware
and software upgrades• Are countered by performance degradation
– arising from shared nature of resources• Need to focus on robust performance
measures– in face of random unpredictable run-time
behaviours
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Conclusions – Load Balancing Strategies
• Heterogranular workflows– Tasks can differ widely in size and run times– Performance prediction, based eg. on previous runs, can inform
mapping (up to a point) ..– .. but after this, may need to re-engineer code (white box only)– .. or consider offloading bottleneck tasks to reliable resources
• Homogranular workflows– Classic example: parameter sweep problem– Fine grain problems (#Tasks >> #Nodes) help smooth out the overall
performance, but ..– .. tail problem can be significant (especially if #Tasks ≈ #Nodes)– Smart detection of delayed tasks coupled with speculative duplication
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Conclusions – Deployment Issues• Integration within enterprise desktop management
environment has many advantages, eg.– PC’s and applications are continually upgraded– Hosts and licenses are “free” on the DG
• … but, also some drawbacks:– No direct control
• Typical environments can be slack and dirty• Corporate objectives can override DG service objectives• Examples: current UoW Win7 deployment, green agenda
– Service relationship, based on trust• DG bugs can easily damage trust relationship, if not caught quickly• Example: recent GenWrapper bug
– Non-dedicated resource• Must give way to priority users, eg. students