1. CENTRE FOR PARALLEL COMPUTING 8th IDGF Workshop Hannover, August 17 th 2011 International Desktop Grid Federation

2. CENTRE FOR PARALLEL COMPUTING Experiences with the University of Westminster Desktop Grid S C Winter, T Kiss, G Terstyanszky, D Farkas, T Delaitre

3. CENTRE FOR PARALLEL COMPUTING Contents Introduction to Westminster Local Desktop Grid (WLDG) – Architecture, deployment management – EDGeS Application Development Methodology (EADM) Application examples Conclusions

4. CENTRE FOR PARALLEL COMPUTING Introduction to Westminster Local Desktop Grid (WLDG) 1 2 3 4 5 6 New Cavendish St576 Marylebone Road559 Regent Street395 Wells Street 31 Little Titchfield St 66 Harrow Campus254

5. CENTRE FOR PARALLEL COMPUTING 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)

6. CENTRE FOR PARALLEL COMPUTING The DG Scenario

7. CENTRE FOR PARALLEL COMPUTING 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

8. CENTRE FOR PARALLEL COMPUTING 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

9. CENTRE FOR PARALLEL COMPUTING EADM – Defined Stages 1.Analysis of current application 2.Requirements analysis 3.Systems design 4.Detailed design 5.Implementation 6.Testing 7.Validation 8.Deployment 9.User support, maintenance & feedback

10. CENTRE FOR PARALLEL COMPUTING Application Examples Digital Alias-Free Signal Processing AutoDock Molecular Modelling

11. CENTRE FOR PARALLEL COMPUTING 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 (~10 10 ) compared. The analyses of the solutions are independent from each other  suitable for DG parallelisation

12. CENTRE FOR PARALLEL COMPUTING DASP - Parallelisation

13. CENTRE FOR PARALLEL COMPUTING DASP – Performance test results Period T (factor) Sequential DG worst DG median DG best # of work units Speedup (best case) # of nodes involved (median) 1813 min9 min7 min4 min503.359 20 2 hr 29 min 111 min 43 min20 min1007.597 22 26 hr 40 min 5h 1min 3 hr 24 min 2 hr 31 min 72311179 24~820 hrn/a 17 hr 54 min 98046372

14. CENTRE FOR PARALLEL COMPUTING 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

15. CENTRE FOR PARALLEL COMPUTING 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

16. CENTRE FOR PARALLEL COMPUTING 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

17. CENTRE FOR PARALLEL COMPUTING AutoDock component workflow gpf file pdb file (ligand) pdb file (receptor) prepare_ligand4.py prepare_receptor4.py pdbqt file AUTOGRIDAUTODOCK map files Pamela dpf file AUTODOCK dlg files SCRIPT1SCRIPT2 best dlg files pdb file

18. CENTRE FOR PARALLEL COMPUTING Computational workflow in P-GRADE receptor.pdb ligand.pdb Autogrid executables, Scripts (uploaded by the developer, 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

19. CENTRE FOR PARALLEL COMPUTING AutoDock – Performance test results

20. CENTRE FOR PARALLEL COMPUTING DG Drawbacks: The “Tail” Problem Jobs >> Nodes Jobs ≈ Nodes

21. CENTRE FOR PARALLEL COMPUTING 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

22. CENTRE FOR PARALLEL COMPUTING Cloudbursting: Indicative Results

23. CENTRE FOR PARALLEL COMPUTING 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

24. CENTRE FOR PARALLEL COMPUTING 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

25. CENTRE FOR PARALLEL COMPUTING 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

26. CENTRE FOR PARALLEL COMPUTING 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

27. CENTRE FOR PARALLEL COMPUTING 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

28. CENTRE FOR PARALLEL COMPUTING The End Any questions?