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Microsoft HPC User Group


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Presentation given at the Microsoft HPC User Group, Salt Lake City, 2012

Presentation given at the Microsoft HPC User Group, Salt Lake City, 2012

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  • [JC] Shouldn’t the title be “e-SC in Azure”?
  • Work Stealing vs Work scheduling
  • Important for drug discovery due to traceability requirements
  • Natural fit for storing provenance as it’s a graph to start with
  • C4S – Mutation Detection, NGS, e-SC available on Azure, data sets for bioinformatics
  • Large datasets need smart moving of data around – HDFS? otherwise hit the limit on storage account b/w
  • Single POFNot great for small files or 1M+ filesInstances can terminate resulting in loss of data
  • Transcript

    • 1. Fast Exploration of the QSARModel Space with e-ScienceCentral and Windows AzureSimon WoodmanJacek CalaHugo HidenPaul Watson
    • 2. VENUS-C Developing technology to ease scientific adoption of Cloud Computing• EU Funded Project – 11 Partners – 5 Technology/Infrastructure – 6 Scenario Partners – Open Call – 20 Pilot Studies – May 2010 to May 2012
    • 3. Architrave AEGEAN UPV.Bio UNEW COLB CoSBI CNR Scenario / Algorithm Users Programming .NE .NE C++ C++ Java C++ Java Language T T Type of Parameter Map / Batch HTC Workflow Data flow CEP workload sweep Reduce ExecutionVENUS- Environments EMIC Generic Worker BSC COMPS C Operating Windows Windows Linux BSC Super Computer System Windows (not in the cloud) Azure EMOTIV Infra- (not in the cloud) Cloud OpenNebula … On Premises Technology Estructure Cloud Paradigm PaaS IaaS Cloud Custome Provider MSFT ENG KTH BSC r
    • 4. The Problem What are the properties of this molecule?Toxicity Biological ActivitySolubility Perform experiments Time consuming Expensive Ethical constraints
    • 5. The alternative to Experiments Predict likely properties based on similar moleculesCHEMBL Database: data on 622,824 compounds, collected from 33,956 publicationsWOMBAT Database: data on 251,560 structures, for over 1,966 targetsWOMBAT-PK Database: data on 1230 compounds, for over 13,000 clinical measurements All these databases contain structure information and numerical activity data
    • 6. QSAR QSARQuantitative Structure Activity Relationship Activity ≈ f( )More accurately, Activity related to a quantifiable structural attributeActivity ≈ f( logP, number of atoms, shape....) Currently > 3,000 recognised attributes
    • 7. Method
    • 8. Branching WorkflowsPartition training & test Random split data 80:20 split Calculate descriptors Java CDK descriptors C++ CDL descriptors Correlation analysis Select descriptors Genetic algorithms Random selection Build model Linear regression Neural Network Partial Least Squares Classification Trees Add to database
    • 9. e-Science Central Platform for cloud based data analysisAzure JavaEC2 ROn Premise Octave Javascript
    • 10. Architecture <<web role>> Generic Worker e-SC control data Workflow engine web web browser browser rich client app <<web role>> <<web role>> Generic Worker QSAR workflow data <<Azure VM>> Workflow Explorer engine Web UI REST API e-Sciencee-SC blob Central main server JMS queue <<web role>> Azure Blob store Generic Worker store Workflow engine workflow invocations e-SC db backend <<Azure VM>>
    • 11. Workflow Architecture Worker Role• Single Message Install JRE Queue Install wf engine – Worker Failure Execute the engine Semantics Get Job from Queue – Elasticity Deploy Runtime?• Runtime Get Data Environments Execute Job –R – Octave Put Data – Java Put Next Jobs on Queue• Deployed only once
    • 12. Results• 250k models • QSAR Explorer – Linear Regression – Browse – PLS – Search – RPartitioning – Get Predictions – Neural Net• 460K workflow executions• 4.4M service calls
    • 13. Scalability: Large Scale QSAR 16:48480 datasets sequential time: 11 days GW 100 Nodes 200 Nodes 14:24 Azur e Response Time 3hr 19mins 1hr 50mins Speedup 94x 156x 12:00 Execution time [hh:mm] Efficiency 94% 78% 09:36 Cost $55.68 $51.84 250.0 07:12 200.0Relative processing speed-up 04:48 150.0 100.0 02:24 50.0 Azure ideal 00:00 GW 0 50 100 150 200 250 0.0 Number of processors 0 50 100 150 200 250 Number of processors
    • 14. Cloud Applicability• Bursty – ChEMBLdb updates (delta 10%) – New Modelling Methods (???)• Performance depends on how chatty the problem is – Deploy (incl download) dependencies once – Avoid storage bottlenecks
    • 15. Performance is great but …Drug Development requires us to capture the data and the process
    • 16. Provenance/Audit Requirements• How was a model generated? – What algorithm? – What descriptors• Are these results reproducible?• How have bugs manifested? – Which models affected – How do we regenerate affected models?• Performance Characteristics• How do we deal with new data?
    • 17. Storing Provenance• Neo4j – Open Source Graph Database – Nodes/Relationships + properties – Querying/traversing• Access – Java lib for OPM – e-SC library built on top of OPM lib – REST interface• Options for HA and Sharding for performance
    • 18. Provenance Model• Based on OPM – Processes, Artifacts, Age nts• Directed Graph• Multiple views of provenance – Dependent on security privileges
    • 19. Adding new model builders1. Add new block Enumerate2. Mine the provenance descriptors 1 n n3. Dynamically create Build and cross-validate Build and cross- validate a new RPart-m kind of model virtual workflows 1 14. One invocation per cross-validation data set 1 1 Test RPart-m Test ?-m• Work in progress…
    • 20. Future Work• Scalability and reliability – SQL Azure – Application server replication• Provenance visualization• Meta-QSAR – Provenance Mining• Cloud4Science – Applying lessons learned to new scenarios
    • 21. MOVEeCloud Project• Investigating the links between physical activity and common diseases – type 2 diabetes, cardiovascular disease,…• Wrist accelerometers worn over 1 week period• Measures movement at 100Hz in three axes• Processing ideal for Azure – Bursty data processing as new data gathered – Embarrassingly parallel – Large datasets
    • 22. MOVEeCloud Process Analysis and Classification R, Java, Octave Walkin Sedentar Sleep Sedentary Activity g y Methodology Clinician’s Patient Section for Report Interventions Papers
    • 23. Data Sizes100 samples / second 100 rows3600 seconds / hour 360,000 rows24 hours / day 8,640,000 rows7 days / study 60,480,000 rows / patient / visit Cohort size of 800 patients and multiple visits
    • 24. Working with larger data sets• As we add more workflow engines server load increases – One server can cope 200 engines if files are small• This is not the case with movement data – Only support 4 engines• Increase the bandwidth to the engines – Clustering appserver /database?
    • 25. HDFS• Implemented prior to Native HDFS on Azure• Easy to integrate with e-sc – Java system just requires libraries included in e-sc• Distributed store where bandwidth increases with number of machines – Bits of data spread around lots of machines• Concept of data location – Potential to route workflows to execute as close as possible to storage• Other applications also also built on top of HDFS – Open TSDB to store timeseries for movement data
    • 26. Drawbacks to HDFS• Needs a NAMENODE to co-ordinate everything – Single point of failure (in current HDFS)• Metadata stored in RAM – doesn’t scale beyond a million or so files – Bad for drug discovery)• Not particularly efficient for small files – There is an overhead to connecting to filesystem• If instances terminate can lose data if not backed up – Redundancy helps – Backing in Cloud and stage to HDFS for experiments – Might use it is a cache of “Hot” files and use Blobstore/S3 to back it all up
    • 27. e-SC WorkflowsChunking WorkflowChunk Processing Workflow
    • 28. System Setup• One machine for the e-sc server • 4 CPUs, 7GB RAM, 1TB Local storage• One machine for Namenode • 4 CPUs, 7GB RAM, 1TB local storage• Four workflow engines • 2 CPUs, 3.5 GB RAM, 500GB local storage• 2TB HDFS storage using workflow engines • Mounts up quickly• Increase priority of HDFS on engines • Competing for resources with workflow
    • 29. Initial Results For a single data set processing went from 60 to 16 minutes using 4 workflow engines running HDFS• 4 engines the limit for one e-sc server • Main server hit 100% CPU delivering data • No further improvements with more engines• Using HDFS CPU was consistently below 5% • More like our earlier scalability results• Once data had been chunked processing was the same for each chunk• The improvement lay entirely in staging and uploading results
    • 30. Questions?• Thank you to our generous funders – Microsoft Cloud 4 Science – EU FP7 - VENUS-C (RI-261565) – RCUK – SiDE (EP/G066019/1)• The Team – Jacek Cala – Paul Watson – Hugo Hiden