1. GPU Performance Prediction GreenLight Education & Outreach Summer Workshop UCSD. La Jolla, California. July 1 – 2, 2009. Javier Delgado Gabriel Gazolla Constantinos Menelaou Lixi Wang Mark Joselli

2. Outline Motivation Role in Energy Efficiency Performance Modeling GPU programming for Weather Modeling GPU Programming for BLAST Model Testing Conclusion GreenLight Education & Outreach Summer Workshop UCSD. La Jolla, California. July 1 – 2, 2009.

3. Benefits GreenLight Education & Outreach Summer Workshop UCSD. La Jolla, California. July 1 – 2, 2009.

4. GPU Performance Improvement Over Time GreenLight Education & Outreach Summer Workshop UCSD. La Jolla, California. July 1 – 2, 2009. Source: nVidia.com

5. Sample Speedups GreenLight Education & Outreach Summer Workshop UCSD. La Jolla, California. July 1 – 2, 2009. Source: nVidia.com

6. Outline Motivation Role in Energy Efficiency Performance Modeling GPU programming for Weather Modeling GPU Programming for BLAST Model Testing Conclusion GreenLight Education & Outreach Summer Workshop UCSD. La Jolla, California. July 1 – 2, 2009.

7. Role in Energy Efficiency Idle GPU = wasted energy Maximally-loaded GPU = a lot of power consumption For example  Nvidia 8800 GTX consumes 137W @ max load  Intel Xeon LS5400 consumes 50W @ max load GreenLight Education & Outreach Summer Workshop UCSD. La Jolla, California. July 1 – 2, 2009. Source: http://mark.zoomcities.com/images/gfx/GFXpowerchartby3d.png (which is derived from data from http://www.xbitlabs.com) http://mark.zoomcities.com/images/gfx/GFXpowerchartby3d.png

8. Power Consumption GreenLight Education & Outreach Summer Workshop UCSD. La Jolla, California. July 1 – 2, 2009. http://www.xbitlabs.com/articles/video/display/gf8800gts320MB-roundup_8.html#sect0 http://www.xbitlabs.com/articles/video/display/xfx-gf-gtx285-gtx295_16.html

9. GPU Role in Energy Efficiency But... GreenLight Education & Outreach Summer Workshop UCSD. La Jolla, California. July 1 – 2, 2009. Source: John Michalakes and Manish Vachharajani

10. And... GreenLight Education & Outreach Summer Workshop UCSD. La Jolla, California. July 1 – 2, 2009.

11. Outline Motivation Role in Energy Efficiency Hurricane Mitigation Overview Performance Modeling GPU Programming for BLAST Model Testing Conclusion GreenLight Education & Outreach Summer Workshop UCSD. La Jolla, California. July 1 – 2, 2009.

12. Motivation Hurricanes cost coastal regions financial and personal damage Damage can be mitigated, but  Impact area prediction is inaccurate  Simulation using commodity computers is not precise Alarming Statistics  40% of (small-medium sized) companies shut down within 36 months, if forced closed for 3 or more days after a hurricane  Local communities lose jobs and hundreds of millions of dollars to their economy If 5% of businesses in South Florida recover one week earlier, then we can prevent $219,300,000 in non- property economic losses Hurricane Andrew, Florida 1992 Katrina, New Orleans 2005 Ike, Cuba 2008

13. Outline Motivation Role in Energy Efficiency Hurricane Mitigation Overview Performance Modeling GPU Programming for BLAST Model Testing Conclusion GreenLight Education & Outreach Summer Workshop UCSD. La Jolla, California. July 1 – 2, 2009.

14. Motivation for application profiling and performance prediction Optimal usage of grid resources through “smarter” meta-scheduling Many users overestimate job requirements Reduced idle time for compute resources Save utility and energy costs Optimal resource selection for most expedient job return time GreenLight Education & Outreach Summer Workshop UCSD. La Jolla, California. July 1 – 2, 2009.

15. Predicting Execution Time of Weather Research and Forecasting (WRF) Software Paradox of submitting computationally intensive jobs Underestimated run time = killed job Overestimating run time = long queue times When performing hurricane simulations, results are usually needed very quickly GreenLight Education & Outreach Summer Workshop UCSD. La Jolla, California. July 1 – 2, 2009.

16. Process GreenLight Education & Outreach Summer Workshop UCSD. La Jolla, California. July 1 – 2, 2009.

17. Typical Results on Large Clusters Input: Marenostrum – 8, 16, and 32 nodes – 1 process per node Output: Marenostrum – 8, 16, 32, 64, 96, and 128 nodes

18. Future Modeling Plans Model execution time with different GPU configurations Current GPU project objective: learn how to model GPU performance by porting WRF kernels to CUDA Test with different cards Test with different processor configurations Test with different number of nodes GreenLight Education & Outreach Summer Workshop UCSD. La Jolla, California. July 1 – 2, 2009.

19. Overview of GPU Benchmarking Project GreenLight Education & Outreach Summer Workshop UCSD. La Jolla, California. July 1 – 2, 2009. Understand Source code of existing CUDA-ported code Understand old source code (Fortran) Learn CUDA Port another module Benchmark Learn WRF Learn CUDA Learn Fortran

20. Status Code has been compiled and executed Regions of similarity are being identified – Fortran Program: 1729 lines – CUDA (C) Program: 1329 lines (incl init) Currently figuring out necessary code logic of existing ported kernel Preliminary documentation/report of findings GreenLight Education & Outreach Summer Workshop UCSD. La Jolla, California. July 1 – 2, 2009.

21. Outline Motivation Role in Energy Efficiency Hurricane Mitigation Overview Performance Modeling GPU Programming for BLAST Model Testing Conclusion GreenLight Education & Outreach Summer Workshop UCSD. La Jolla, California. July 1 – 2, 2009.

22. Purpose BLAST used extensively for sequence analysis Provides a different kind of application for testing GPU performance improvements Further improve our GPU programming and performance modeling knowledge GreenLight Education & Outreach Summer Workshop UCSD. La Jolla, California. July 1 – 2, 2009.

23. Status Literature review concerning other sequence analysis work with GPU Learning how BLAST works GreenLight Education & Outreach Summer Workshop UCSD. La Jolla, California. July 1 – 2, 2009.

24. Long-running, Fault-tolerant Weather Prediction Slight inaccuracies in initial conditions of domain can cause significant inaccuracies later Third component of this project: account for this using perturbation analysis The effects of perturbation on runtime must also be modeled GreenLight Education & Outreach Summer Workshop UCSD. La Jolla, California. July 1 – 2, 2009.

25. Conclusion GPU’s promise much faster job execution for different applications In order to maximize resource utilization, application execution time should be predictable Especially for time-critical applications that take long to execute GreenLight Education & Outreach Summer Workshop UCSD. La Jolla, California. July 1 – 2, 2009.

26. Thank You Questions? GreenLight Education & Outreach Summer Workshop UCSD. La Jolla, California. July 1 – 2, 2009.