This document compares the performance of six supercomputers with over 1,000 processors each on various synthetic benchmarks and applications. The supercomputers have different node sizes, processor counts, and interconnect technologies. Performance is analyzed using a model that breaks down run time into computation, communication, and I/O components. Results show that different systems perform best for different benchmarks and applications, depending on factors like the communication requirements and how well the application scales. The Blue Gene supercomputer shows strong scaling and I/O performance but has limitations in processor speed and memory size per node.

1. Supercomputer Performance Characterization Presented By: IQxplorer

3. Comparative performance results have been obtained on six computers at NCSA & SDSC, all with > 1,000 processors

6. Run-time components depend upon system parameters & code features Differences between point-to-point & collective communication are important too

8. Normalized memory access profiles for daxpy show better memory access, but more memory contention on Blue Gene compared DataStar

9. Each HPCC synthetic benchmark measures one or two system parameters in varying combinations

10. Relative speeds are shown for HPCC benchmarks on 6 computers at 1,024p; 4 different computers are fastest depending upon benchmark; 2 of these are also slowest, depending upon benchmark Data available soon at CIP Web site: www.ci-partnership.org

11. Absolute speeds are shown for HPCC & IOR benchmarks on SDSC computers; TG processors are fastest, BG & DS interconnects are fastest, & all three computers have similar I/O rates

12. Relative speeds are shown for 5 applications on 6 computers at various processor counts; Cobalt & DataStar are generally fastest

14. AWM 512^3 problem shows good strong scaling to 2,048p on Blue Gene & to 512p on DataStar, but not on TeraGrid cluster Data from Yifeng Cui

15. MILC medium problem shows superlinear speedup on Cobalt, Mercury, & DataStar at small processor counts; strong scaling ends for DataStar & Blue Gene above 2,048p

16. NAMD ApoA1 problem scales best on DataStar & Blue Gene; Cobalt is fastest below 512p, but the same speed as DataStar at 512p

17. WRF standard problem scales best on DataStar; Cobalt is fastest below 512p, but the same speed as DataStar at 512p

18. Communication fraction generally grows with processor count in strong scaling scans, such as for WRF standard problem on DataStar

21. Major applications ported and being run on BG at SDSC span various disciplines

22. Speed of BG relative to DataStar varies about clock speed ratio (0.47 = 0.7/1.5) for applications on ≥ 512p; CO & VN mode perform similarly (per MPI p)

23. DNS scaling on BG is generally better than on DataStar, but shows unusual variation; VN mode is somewhat slower than CO mode (per MPI p) Data from Dmitry Pekurovsky

24. If number of allocated processors is considered, then VN mode is faster than CO mode, and both modes show unusual variation Data from Dmitry Pekurovsky

25. IOR weak scaling scans using GPFS-WAN show BG in VN mode achieves 3.4 GB/s for writes (~DS) & 2.7 GB/s for reads (>DS)