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Supercomputer Performance Characterization Presented By: IQxplorer
Here are some important computer performance questions ,[object Object],[object Object],[object Object],[object Object],[object Object]
Comparative performance results have been obtained on six computers at NCSA & SDSC, all with > 1,000 processors
These computers have shared-memory nodes of widely varying size connected by different switch types ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Performance can be better understood with a simple model ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Run-time components depend upon system parameters & code features Differences between point-to-point & collective communication are important too
Compute, communication, & I/O speeds have been measured for many synthetic & application benchmarks ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Normalized memory access profiles for daxpy show better memory access, but more memory contention on Blue Gene compared DataStar
Each HPCC synthetic benchmark measures one or two system parameters in varying combinations
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
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
Relative speeds are shown for 5 applications on 6 computers at various processor counts; Cobalt & DataStar are generally fastest
Good scaling is essential to take advantage of high processors counts ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
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
MILC medium problem shows superlinear speedup on Cobalt, Mercury, & DataStar at small processor counts; strong scaling ends for DataStar & Blue Gene above 2,048p
NAMD ApoA1 problem scales best on DataStar & Blue Gene; Cobalt is fastest below 512p, but the same speed as DataStar at 512p
WRF standard problem scales best on DataStar; Cobalt is fastest below 512p, but the same speed as DataStar at 512p
Communication fraction generally grows with processor count in strong scaling scans, such as for WRF standard problem on DataStar
A more careful look at Blue Gene shows many pluses ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
But there are also some minuses ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Major applications ported and being run on BG at SDSC span various disciplines
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)
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
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
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)
Blue Gene has more limited applicability than DataStar, but is a good choice if the application is right   ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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Super Computer

  • 2.
  • 3. Comparative performance results have been obtained on six computers at NCSA & SDSC, all with > 1,000 processors
  • 4.
  • 5.
  • 6. Run-time components depend upon system parameters & code features Differences between point-to-point & collective communication are important too
  • 7.
  • 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
  • 13.
  • 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
  • 19.
  • 20.
  • 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)
  • 26.