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MEW22 22nd Machine Evaluation Workshop Microsoft


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The MEW Workshop is now established as a leading national event dedicated to distributed high performance scientific computing. The principle objective is to encourage close contact between the research communities from the Mathematics, Chemistry, Physics and Materials Programmes of EPSRC and the major vendors.

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MEW22 22nd Machine Evaluation Workshop Microsoft

  1. 1. On-premise Desktop Compute Cloud via Idle Win 7 Workstation Cores HPC HPC Head Node Edition Desktop User HPC Cluster Broker Node(s) Azure Compute Proxies Azure Compute Instances
  2. 2. With your Azuresubscription addadditional resourceswhen you need themPay only when youuse them
  3. 3. Application is accessed from • Embedded Azure Scheduler, not a Head Node • All runtimes: Parametric Sweep, MPI, Cluster SOA, Excel
  4. 4. National services / tightly coupled apps Departmental / mixed apps Ad hoc apps and queries Profitable Services Internet scale
  5. 5. Compute Compute Inactivity Period Average Average Usage Usage Time Time On and off workloads (e.g. batch Successful services needs to job) grow/scale Over provisioned capacity is wasted Keeping up w/growth is big IT Time to market can be cumbersome challenge Complex lead time for deploymentCompute Compute Average Average Usage Usage Time Time Unexpected/unplanned peak in demand Services with micro seasonality trends Sudden spike impacts performance Peaks due to periodic increased demand Can’t over provision for extreme cases IT complexity and wasted capacity
  6. 6. Compute Instance I/O Cost Per Instance CPU Memory Storage Performan Hour Size ceExtra Small 1.0 GHz 768 MB 20 GB Low $0.04 Small 1.6 GHz 1.75 GB 225 GB Moderate $0.12 Medium 2 x 1.6 GHz 3.5 GB 490 GB High $0.24 Large 4 x 1.6 GHz 7 GB 1,000 GB High $0.48Extra Large 8 x 1.6 GHz 14 GB 2,040 GB High $0.96
  7. 7. Windows Azure PlatformCompute Storage Management CDN “Operating system “Middleware “Relational database in the cloud” in the cloud” in the cloud”
  8. 8. 3) The « job » is divided in tasks. The tasks are put in a Queue 4) The worker get the tasks in the queue and process them 1) The user submit ajob trough the web UI 3 Queue 4 1 1 6 5 n Web Role Worker Role Blob 2 2) The job is added in the 5) Each worker post the results of his Table for future access computation in a Blob Table 6) The differents output are assembled to get the final result
  9. 9. HADOOP as a Service HADOOP on Premises Datawarehouse Low cost SQLServer Master Slave (s) Slave (s) Master Archive Storage Task Task Task Tracker Tracker Task Tracker Tracker MS BI PDW Job Job Tracker Tracker MapReduce MapReduce Name HDFS Name HDFS Node Node T SQL Server SQOOP T Data Data Data Data Node Node Node Analysis Node Service HIVE SQOOP SQOOPMulti-node Azure Cluster
  10. 10. © 2010 Microsoft Corporation. All rights reserved. Microsoft, Windows, Windows Vista and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries.The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, itshould not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation.MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.