1. Publishing applications on the web via the Easa Portal and integrating the Sun Grid Engine Publishing applications on the web via the Easa Portal and integrating the Sun Grid Engine By Michael Griffiths & Deniz Savas CiCS Dept. Sheffield University M.Griffiths@sheffield.ac.uk D.Savas@sheffield.ac.uk http://www.sheffield.ac.uk/wrgrid Sept 2007

2. Sheffield is in South Yorkshire, England

3. Sheffield University- facts Established in 1828 70 academic departments in 7 faculties Number of Undergraduate Students: 25,500 Number of Post Graduate/Research Students: 5,600 Number of International Students : 3,100

4. ‘iceberg’ the HPC Cluster at the Computer Centre AMD Opteron based, supplied by Sun Microsystems. Processors: 320 ( 160 of these are designated to the Physics Dept. for the PP project ) Performance: 300GFLOPs Main Memory: 800GB User filestore: 9TB Temporary disk space: 10TB Physical size: 8 racks Power usage: 50KW

5. ‘iceberg’ cluster hardware components 160 general-purpose-use cpu’s; 80 of these are in dual-core configuration with 2 GBytes of memory each. ( V20 Model ) (i.e 40 boxes with 2 cpus + 4 GBytes ) 80 are in quad-core configurations with 4 GBytes memory each. ( V40 Model ) ( i.e 20 boxes with 4 cpus + 16 GBytes ) These are also connected via a Myrinet Switch at 2Gbps connection speed. IPMI Service Processors : Each box contains a service processor with separate network interface for remote monitoring and control. Inside a V20

6. Iceberg Cluster Configuration HEAD NODE Worker node 7 Service Proc 7 Worker node 1 Service Proc 1 Worker node 6 Service Proc 6 Worker node n Service Proc n Worker node 56 Service Proc n Worker node 2 Service Proc 2 Worker node n Service Proc n Worker node 57 Service Proc 57 Worker node 4 Service Proc 4 Worker node 9 Service Proc 9 Worker node n Service Proc n Worker node 59 Service Proc 59 Worker node 3 Service Proc 1 Worker node 8 Service Proc 8 Worker node n Service Proc n Worker node 58 Service Proc 58 Worker node 5 Service Proc 5 Worker node 10 Service Proc 10 Worker node n Service Proc n Worker node 60 Service Proc 60 Eth0 Eth1 nfs mounted onto Worker nodes Shared file store All remote access License server Myranet Connected Workers

7. Iceberg Cluster Configuration HEAD NODE Worker node 7 Service Proc n Worker node 1 Service Proc 1 Worker node 6 Service Proc n Worker node n Service Proc n Worker node 56 Service Proc n Worker node 2 Service Proc 2 Worker node n Service Proc n Worker node 57 Service Proc n Worker node 4 Service Proc 1 Worker node 9 Service Proc n Worker node n Service Proc n Worker node 59 Service Proc n Worker node 3 Service Proc 1 Worker node 8 Service Proc n Worker node n Service Proc n Worker node 58 Service Proc n Worker node 5 Service Proc 1 Worker node 10 Service Proc n Worker node n Service Proc n Worker node 60 Service Proc n Eth0 nfs mounted onto Worker nodes Shared file store Eth1

8. Iceberg Cluster Configuration Worker node 7 Service Proc n Worker node 1 Service Proc 1 Worker node 6 Service Proc n Worker node n Service Proc n Worker node 56 Service Proc n Worker node 2 Service Proc 2 Worker node n Service Proc n Worker node 57 Service Proc n Worker node 4 Service Proc 1 Worker node 9 Service Proc n Worker node n Service Proc n Worker node 59 Service Proc n Worker node 3 Service Proc 1 Worker node 8 Service Proc n Worker node n Service Proc n Worker node 58 Service Proc n Worker node 5 Service Proc 1 Worker node 10 Service Proc n Worker node n Service Proc n Worker node 60 Service Proc n HEAD NODE

9. White Rose Grid YHMAN Network

10. Grid & HPC applications development tools Development Fortran77,90, C, C++, Java compilers MPI / MPICH-gm OpenMP Nag Mk 20, 21 ACML Grid Sun Grid Engine Globus 2.4.3 (via gpt 3.0) SRB s-client tools

11. Using the White Rose Grid Application Portal

12. Features and Capabilities Web accessible management and execution of applications Provides a service for rapid authoring and publication of custom applications Easily integrate multiple heterogeneous resources

13. Potential benefits of an applications portal More efficient use of resources Ease of use Familiar GUI interface Capturing of expert knowledge Better presentation of legacy software

14. Potential Development Building Expert Systems Allowing novice expert to take advantage of parallel HPC resources Providing HPC services over the grid HPC centres collaborating with each other without having to provide individual usernames, file-storage etc to remote users.

15. WRG – Application Portal Based on EASA Three Usage Modes Users Run applications Have storage space Review old results Authors Build and publish applications Administrators

16. Using Accessing Managing Applications Workspace Results Help

17. Using:Accessing Start up a web browser & http://www.shef.ac.uk/wrgrid/eas a.html Login using provided user name and password

18. Using:Help Select Help and Support tab to register Apply to Admin for an account Apply to authors to register applications

19. Using:Managing Installing a client Setting password Setting Mode user/author

20. Using:Applications View and select available applications

21. Running An Application

22. User Interface

23. Using:Workspace Storage for uploaded files and old job files

24. Using:Results Check results View job progress Export to spreadsheet

25. Using: Results Viewing Results

26. Using:Help Documentation Contacts

27. Conclusions Disadvantages Thick Client, License costs Advantages Rapid publication Enable virtualization of HPC resources Make applications available to broader community, become application focused Effective on a network with low bandwidth Make applications available to collaboration partners over the internet and outside own organisation

28. Demonstration Applications Developed for EASA Demonstration of Metascheduling Across White Rose Grid Monitoring of usage across White Rose Grid Running Applications on the local cluster Fluent Ansys Generic Matlab and Scilab applications

29. Metascheduler Demonstration:Background Enable utilisation of resources across White Rose Grid Exploit use of task arrays Job submission is seamless Demonstration uses a generic scilab application that runs on any of the White Rose Grid Nodes Simplistic, but; effective, manageable and sustainable

30. Metascheduler Demonstration: Method Query and Compare job queues for WRG nodes qstat –g c Use slots available and total number of slots to generate weights for different queues Compare weights for all queues on different nodes and use to select node Use standard EASA job submission technique to submit job to selected node EASA does not know about clusters Special easaqsub submits job to sge, monitors job status will remove job if wait time exceeded, easaqsub job monitor has completed EASA knows that EASA compute task has completed

31. Metascheduler Demonstration: Running Scilab User provides scilab scriptfile Required resource file e.g. datafiles or files for scilab library routines Can provide zipped bundle of scilab resources Set job submission information and then submit job

32. Metascheduler Demonstration: Job Submission Provide jobname and job description Information used for metascheduling Jobtime (hours) Waittime (hours) Number of tasks (for job array) Submission method Use metascheduling Select a particular node

33. Metascheduler Demonstration : Further Developments Current method successful! Correctly selects clusters and improves turnaround for scilab compute tasks Current pattern can be extended to other EASA applications Provide distributed storage across White rose Grid Develop metascheduling strategy introduce greater dependency on user job requirements for node selection Exploit other metascheduling systems e.g. SGE transfer queues, CONDOR-G THE END