1. Manchester Computing Supercomputing, Visualization & eScience Celia Russell, Stephen Pickles and Mike Jones Combining Data Workshop ESRC Research Methods Programme Manchester, December 18, 2002 SAMD Seamless Access to Multiple Datasets A ESRC/DTI e-Science demonstrator project http://www.sve.man.ac.uk/Research/AtoZ/SAMD

2. Supercomputing, Visualization & eScience2 SAMD Seamless Access to Multiple Datasets  A project to demonstrate the benefits of applying e- Science grid technologies to an ordinary social science query  We solve a genuine problem from the UK academic social science community - a multivariate analysis using a complex mathematical algorithm  Based on a major social science databank, the Office for National Statistics Time Series Data, hosted at MIMAS

3. Supercomputing, Visualization & eScience3 The problem  Published as Sensier, M., Osborn D.R. and Öcal N. (2002) ‘Asymmetric Interest Rate Effects for the UK Real Economy’, Oxford Bulletin of Economics and Statistics, Volume 64, September 2002, n°4  The research query looks at the effect interest rate changes had on Gross Domestic Product in the UK over the period 1960 – 2000

4. Supercomputing, Visualization & eScience4 Interest Rates in the UK

5. Supercomputing, Visualization & eScience5 UK GDP – quarterly changes

6. Supercomputing, Visualization & eScience6 The Model Where y is the quarterly change in GDP and z is the quarterly change in interest rates

7. Supercomputing, Visualization & eScience7 Before SAMD

8. Supercomputing, Visualization & eScience8 e-Science Grid

9. Supercomputing, Visualization & eScience9 SAMD Methodology We built a mini demonstrator grid for SAMD by:  Grid-enabling the NS Time Series Databank  Parallelising the code to represent the HPC facilities  Using Grid protocols for data transfer  Creating a graphical user interface that included a single sign-on  It all worked, and cut the data collection and analysis time down to around 8 minutes.

10. Supercomputing, Visualization & eScience10 Extending SAMD  The approach and methods of SAMD are applicable to more general social science applications involving data collection and analysis  More efficient handling of datasets – data is moved to where it's needed, not just to web browser  The single sign-on for all databanks means users can cross search datasets and perform cross analyses of multiple datasets from different providers  Grants access to high performance computing facilities on the grid without the user having to learn how to use them  Can automate routine enquiries  Cuts the time taken to run computing intensive problems by a factor of around 100

11. Supercomputing, Visualization & eScience11 Scaling up with the Grid E-Science Grids allow the social scientist to scale up their quantitative research by:  Including many more data points in their analysis  Developing more complex models incorporating more variables  Dropping assumptions  Visualising data  Creating new communities and collaborations  Exploring new types of analyses

12. Manchester Computing Supercomputing, Visualization & eScience SAMD Architecture

13. Supercomputing, Visualization & eScience13 Motivation Web-based access to socio-economic datasets such as Office of National Statistics Time series data has lead to greatly increased use, but:-  No standard authentication or authorisation –too many usernames and passwords to remember  To automate search and retrieval, can only emulate navigation through "screen scraping" –breaks whenever the interface is "improved" –discourages third party developments and periodic re-analysis  Data must be downloaded and saved to local disk –not necessarily the system on which subsequent analysis is to be performed –inefficient, especially for large datasets

14. Supercomputing, Visualization & eScience14 The SAMD solution  Use Grid Security Infrastructure for "single sign-on" authentication everywhere –Modified standard Apache web server to accept proxy credentials Permits re-use of existing CGI code  Use third party file transfers (grid-ftp) to move data directly to where it's needed  Use standard globus mechanisms to –Locate HPC facility for analysis –Stage analysis binary from local repository and run analysis job on HPC facility –Retrieve results

15. Supercomputing, Visualization & eScience15 Architecture

16. Supercomputing, Visualization & eScience16 What's new?  Web interfaces to datasets? –We show that there are more flexible ways of delivering access to data over the internet than through static web pages alone  Single sign-on? –We show that the domain of single sign-on can be much broader than provided by Athens  Graphical User Interfaces? –We show that it's possible for a third party to develop new tools independently of data providers –A short script can encapsulate all the essential functionality of the SAMD GUI  Integration, Interoperability!

17. Supercomputing, Visualization & eScience17 What's needed? Culture of Standards  If key datasets are Grid-enabled in a commonly understood, well-documented way, we create an environment in which third parties can develop tools and services that add real value by bringing together independent datasets  SAMD shows that such an environment is technically possible, but does not by itself establish any standard. –Look to Web services, Grid services, OGSA-DAI…

18. Manchester Computing Supercomputing, Visualization & eScience SAMD User Interfaces

19. Supercomputing, Visualization & eScience19 GUI: Single Sign-on Panel located at the top left  Uses X509 proxy certificates  grid-proxy-init –Creates your proxy credential  grid-proxy-destroy –Removes your proxy credential

20. Supercomputing, Visualization & eScience20 GUI: Data Acquisition The Interface to the SAMD-ONS web server, steps 1 to 8

21. Supercomputing, Visualization & eScience21 Data Search Search by Keyword 1 Request and Mutual Authentication using a proxy credential 2,3 Authorisation 4 Query Data Store

22. Supercomputing, Visualization & eScience22 Data Request Data moved to GridFTP server  1: send references to data  1,2,3: authentication & authorisation  4: ask datastore to move data (5)  6,7: datastore returns XML ticket

23. Supercomputing, Visualization & eScience23 Data Transfer Data moved to HPC engine  8: third party file transfer –from MIMAS to HPC engine, ready for analysis

24. Supercomputing, Visualization & eScience24 Finding an HPC Resource GIIS MDS Server  e.g. ginfo.grid-support.ac.uk Search for:  OS type eg: IRIX64  Minimum No. Processors  Jobmanager or manually enter your favourite Data Analysis panel

25. Supercomputing, Visualization & eScience25  Select an executable on the local machine  Stage job using Globus  Check status using Globus  Retrieve results using Globus  Clean-up using Globus  Even delete job using Globus Data Analysis panel Using the HPC Resource

26. Supercomputing, Visualization & eScience26 Command line automation Not everyone has the expertise or time to write a special- purpose GUI. Given a GSI-enabled web server and documented protocol to communicate with it, a few lines of shell script can do all the essential steps  Use grid-proxy-init to sign on  Use curl to talk https to the web server  Use GridFTP to move data to the HPC engine  Use globus-commands to –(stage and) run executable. –retrieve results –and clean-up

27. Supercomputing, Visualization & eScience27 Acknowledgments Funded by theand the Keith Cole Celia Russell Marianne Sensier Geoff Lane Tim Hateley Mark Riding Kevin Roy