By Liu Meiying, Risk Management Institute, NUS. http://www.youtube.com/watch?v=B0P6--tHNHY&p=83FA1CD871F4A4E5 The talk will cover the computation difficulties encountered in the RMI credit rating project and why researchers want or need to use the grid. The talk will highlight how the NUS Grid helps to solve the problems and how the Grid is being used regularly. The speaker will also suggest how the grid platform can be improved to further advance this research work.

1. Using NUS Grid in the RMI Credit Rating Initiative

2. Outline The computation difficulties encountered in the Risk Management Institute (RMI) credit rating project Why we finally choose to use the NUS Grid How the Grid helps to solve the problem and how the Grid is being used regularly How the NUS grid platform can be improved to further advance this research work

3. Computation Difficulties Encountered About the Credit Rating Initiative (CRI) This non-profit CRI is being undertaken by NUS RMI with the intent to spur research and development in the critical area of credit rating methodologies. The initiative includes building a proprietary database including data on over 40,000 companies across Asian, Asia-Pacific and North American economies. The 14 economies covered are: Australia, China, Hong Kong, Indonesia, India, Japan, Malaysia, Philippines, Singapore, South Korea, Taiwan, Thailand, Canada and United States. The credit rating model parameters are calibrated monthly and default probability forecasts are updated daily

4. Computation Difficulties Encountered Distance to Default (DTD)is the firm's volatility adjusted leverage measure , DTD is a very important input to the model. We estimate the parameters of DTD for each firm each month using the previous 20 years’ daily market capitalization and financial statements Difficulties in Computing DTD Large data set 20 years data of more than 40000 firms from 14 countries Complicated computation algorithm Each firm needs around 2-minute PC computation time On local PC, it may take few days to complete the DTD computation

5. Why Using The Grid We have tried several high-performance computation technique GPU machine. NUS Grid Why not using GPU The new nVidia Tesla C2050 GPU machine is bought for other applications in the project. Some limitations of GPU machine limited its application in DTD computation. Special knowledge in hardware and software of GPU machine is needed Slow development speed (more time on fine tuning the performance) The scalability is not so good as on grid In addition, GPU is a quite new technology and some functionality is not ready yet for this application. Therefore we finally choose to use the Grid for DTD computation.

6. Why Using The Grid The nature of the DTD computation algorithm is highly parallel and thus is fitted to the grid computing structure The NUS Grid significantly shorten the DTD computation time 2759 files, each contain 20 firms data, size around 5 MB It takes only a few hours on Grid to complete the whole thing Simple to use (no much programming difficulty) Compile the Matlab program Upload the compiled program as an application via website Submit the inputs via command line Helpful staffs, give us advice and suggestion on how to utilize the Grid.

7. Using the Grid – Home Page Home page at https://koala1.nus.edu.sg

8. Using the Grid – Upload Application Create Application for NUS GRID Compile MatlabProgram on Linux server by typing few command lines upload “meta-file” as an application onto NUS GRID via the website “Application Create/Update Wizard”

9. Using the Grid – Submit Input Files Zip the input files and submitted onto the Grid by typing one line of DOS command Specify the application name, input path, job description Note down the Job ID generated by the system.

10. Using the Grid – Check Job Status Check the job status at https://koala1.nus.edu.sg/workload/jobs/jobs.ud

11. Using the Grid – Retrieve Output Files Retrieve the output files by typing one line DOS command An output directory containing the output files, named “meta-username-jobID” will be created

12. Suggestions How the NUS Grid platform may be improved to further advance this project User interface Provide alternative to upload application using command line Control Inform the user when the job is done So that the user do not have to continually check the job status Timing estimation Estimated computation time Information on the available resources So that the user could make the decision when to submit the jobs