Climate Variability Experiments on Cray XT4 and NERSC. John Dennis and Richard Loft National Center for Atmospheric Research Boulder, Colorado
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1 Optimizing High-Resolution Climate Variability Experiments on Cray XT4 and Cray XT5 Systems at NICS and NERSC John Dennis and Richard Loft National Center for Atmospheric Research Boulder, Colorado August 20, 2009 TOY09-NCAR 1
2 Science Motivation Compute systems used CCSM Coupled System Scaling S and Performance Benchmark results I/O variability Conclusions August 20, 2009 TOY09-NCAR 2
3 Ocean component of CCSM (Collins et al, 2006) August 20, 2009 Eddy-resolving POP (Maltrud & McClean,2005) TOY09-NCAR 3
4 0.5 atm ocn Heavy precipitation 0.5 atm + 1 ocn Strong SST gradient August 20, 2009 TOY09-NCAR 4
5 Interactive ensembles Multiple l instances of component models Explore the role of weather noise in climate Test hypothesis that noise is reddened and influences low-frequency components of climate system 35M CPU hours TeraGrid [2 nd largest] 6000 core job: 7 months non-stop August 20, 2009 TOY09-NCAR 5
6 PetaApps project members: J. Kinter, C. Stan (COLA) B. Kirtman (U of Miami) C. Bitz (U of Washington) W. Collins, K. Yelick (U of California) F. Bryan, J. Dennis, R. Loft, M. Vertenstein (NCAR) August 20, 2009 TOY09-NCAR 6
7 Department of Energy: CCPP Program Grants DE-FC03-97ER62402 [SciDAC] DE-PS02-07ER07-06 [SciDAC] National Science Foundation: OCI [PetaApps] OCE Cooperative Grant NSF01 August 20, 2009 TOY09-NCAR 7
8 Science Motivation Compute systems used CCSM Coupled System Scaling S and Performance Benchmark results I/O variability Conclusions August 20, 2009 TOY09-NCAR 8
9 Franklin Cray XT4 at NERSC Benjamin Franklin, one of America s first scientists, performed ground breaking work in energy efficiency, electricity, it materials, climate, ocean currents, transportation, health, medicine, acoustics and heat transfer. August 20, 2009 TOY09-NCAR Courtesy NERSC 9
10 August 20, 2009 TOY09-NCAR 10 Courtesy of Pat Kovatch, NICS
11 System Franklin Kraken Atlas processor AMD Opteron Quad core AMD Opteron Quad core AMD Opteron Dual core 2.3Ghz 2.3 Ghz 2.6 Ghz Memory/core 2 GB 2 GB/ 1GB 1.5 GB Sockets/node network Cray Seastar Cray Seastar IB total nodes 9,660 8,256 1,200 Total cores 38,640 66,048 9,600 August 20, 2009 TOY09-NCAR 11
12 Science Motivation Compute systems used CCSM Coupled System Scaling S and Performance Benchmark results I/O variability Conclusions August 20, 2009 TOY09-NCAR 12
13 Multiple component models on different grids Flux and state between components [CPL] Large code base: >1M lines Developed over 20+ years K lines are critically important t --> no comp kernels, need good compilers Demanding g on networks: need good message latency + bandwidth August 20, 2009 TOY09-NCAR 13
14 CCSM Coupling and Execution Flow CLM Land Model (land/atm fluxes and land albedos) CICE Sea Ice Model (ice/atm & ice/ocn fluxes and ice albedos) CPL - Coupler (ocean/atm fluxes and ocean albedos) CAM - Atmosphere (downward SW and bottom level states) August 20, 2009 POP - Ocean (sea surface temperature) TOY09-NCAR Courtesy Vertenstein, CGD/NCAR Coupling of CAM/CLM/CICE/AOF is every CAM timestep (30 min) Coupling of POP is every 24 hours (1º) and every 6 hours (.1º) CAM/CLM/CICE/AOF fluxes averaged over coupling interval 14
15 Science Motivation Compute systems used CCSM Coupled System Scaling S and Performance Benchmark results I/O variability Conclusions August 20, 2009 TOY09-NCAR 15
16 0.50 ATM [576 x 384 x 26] 0.50 LND [576 x 384 x 17] OCN [3600 x 2400 x 42 ] 0.1 ICE [3600 x 2400 x 20 ] 5 days/ no writing to disk 5 processor configurations: XS: 480 cores S: 1024 cores M: cores L: cores XL: cores August 20, 2009 TOY09-NCAR 16
17 1664 cores 3136 cores 49 sec SYPD Idle time/cores August 20, 2009 Increase core count for POP TOY09-NCAR 17
18 4028 cores 2.23 SYPD Reduced Idle time to 8% August 20, 2009 TOY09-NCAR 18
19 August 20, 2009 TOY09-NCAR 19
20 Science Motivation Compute systems used CCSM Coupled System Scaling S and Performance Benchmark results I/O variability Conclusions August 20, 2009 TOY09-NCAR 20
21 Lustre has a bad day! August 20, 2009 TOY09-NCAR 21
22 High = 92 MB/sec Low = 5 MB/sec Lustre August 20, variability 2009 doubled cost of disk TOY09-NCAR I/O from 11% to 23% of total 22
23 Eliminated page size [4 Kb] I/O ops for POP restart August 20, 2009 TOY09-NCAR 23
24 Preliminary ultra-high-resolution science runs Mesoscale processes: Atlantic storm track Control run in NICS (Teragrid) 84+ years complete Generating 2.5 TB of data per week! Future work: Improve disk I/O performance [10-25% of time] Improve memory footprint scalability OS jitter investigation August 20, 2009 TOY09-NCAR 24
25 NCAR: D. Bailey F. Bryan B. Eaton N. Hearn K. Lindsay N. Norton M. Vertenstein COLA: J. Kinter C. Stan U. Miami B. Kirtman UC U.C. Berkeley W. Collins K. Yelick (NERSC) U. Washington C. Bitz August 20, 2009 NICS: M. Fahey P. Kovatch ANL: R. Jacob R. Loy LANL: E. Hunke P. Jones M. Maltrud LLNL D. Bader D. Ivanova J. McClean (Scripps) A. Mirin ORNL: P. Worley and many more TOY09-NCAR Grant Support: DOE NSF DE-FC03-97ER62402 [SciDAC] DE-PS02-07ER07-06 [SciDAC] Cooperative Grant NSF01 OCI [PetaApps] CNS CNS CNS Computer Allocations: TeraGrid NICS DOE NERSC LLNL Grand Challenge Thanks for Assistance: Cray, NICS, and NERSC 25
26 John Dennis August 20, 2009 TOY09-NCAR 26
27 Current simulation is rather slow Why is simulation rate important? Heroic effort to generate necessary time series [ years] Example: 108 years -> 63 days of non-stop computing Simulated years per wall-clock day [SYPD] August 20, 2009 TOY09-NCAR 27
28 Typical climate rate: 5 SYPD Currently ~1 1.7 SYPD Single thread speed is not increasing Need N d more parallelism! li August 20, 2009 TOY09-NCAR 28
29 Increase core count Increase simulation rate August 20, 2009 TOY09-NCAR 29
30 Same SYPD Idle processors August 20, 2009 TOY09-NCAR 30
31 Motivation Increasing scalability POP CICE CAM CPL Conclusions August 20, 2009 TOY09-NCAR 31
32 August 20, 2009 TOY09-NCAR 32
33 Concerning trend in OS/HW evolution? August 20, 2009 TOY09-NCAR 33
34 Execution time of Barotropic section of POP on 7600 cores Kraken-XT5 [single sock.] 17.4s CE ardware H Kraken-XT5 [single sock.] XT5 37.7s Kraken-XT4 Franklin Athena XT4 13.6s 22.8s 19.7s Quad core XT3 RedStorm 12.2s Dual core Catamount CNL CE HD CE A2 27A OS version August 20, 2009 TOY09-NCAR 34
35 POP amplifies OS interference/jitter Past: Jitter kills reduction performance Current: t Contention ti at network interface Solutions: Fix OS [Cray] Modify partitioning algorithm [Dennis] Overlap comm/comp [Worley] OpenMP/MPI? August 20, 2009 TOY09-NCAR 35
36 Motivation Increasing simulation rate POP CICE CAM CPL Conclusions August 20, 2009 TOY09-NCAR 36
37 Developed at LANL Shares grid and infrastructure with POP Unique feature: computational load can disappear CICE 4.0 Sub-block data structures (POP2) Reuse techniques from POP2 [Dennis] Partitioning grid using weighted Space-filling curves: 40% reduction in execution time at 0.1 on 1800 cores August 20, 2009 TOY09-NCAR 37
38 Weight space-filling curve (wsfc) Estimate work based on Probability function Partition P for equal amounts of work Probability block contains Sea-ice Depends on climate scenario Control-run Paelo CO 2 doubling Estimate of probability Bad estimate -> Slower simulation rate August 20, 2009 TOY09-NCAR 38
39 Sea ice located at high latitude August 20, 2009 TOY09-NCAR 39
40 August 20, 2009 TOY09-NCAR 40
41 Large low latitudes Small high latitudes August 20, 2009 TOY09-NCAR 41
42 Partition for 5 processors August 20, 2009 TOY09-NCAR 42
43 Predict time for grid block i t p *(b i =c 1 * nocn i + c 2 * nice i + c 3 (bsx + bsy) + c 4 * fnice i * (bsx *bsy) where: nocn j : ocean points in block j nice i : sea ice points in block i where P i > fnice i = {1 if nice i > 0, 0 P i : probability sea ice present bsx,bsy: bsy: size of grid block Performance model: c = [c1 c2 c3 c4] August 20, 2009 TOY09-NCAR 43
44 Linear system: Ac = t m where t m is measured execution time Solve for performance model: c Prediction execution time Qt p for partition Q: QAc = Qt p August 20, 2009 TOY09-NCAR 44
45 August 20, 2009 TOY09-NCAR 45
46 Perfect a priori knowledge of sea-ice not possible What impact error in P i? Sea ice extent: Overestimate: erfc[45,55] Underestimate: d t erfc[70x60] Best to overestimate sea ice extent August 20, 2009 TOY09-NCAR 46
47 Single performance model? One per compute platform? One per resolution? For performance model: c Derived on BGL at low-resolution Test on ATLAS at high-resolution, high processor count August 20, 2009 TOY09-NCAR 47
48 August 20, 2009 TOY09-NCAR 48
49 August 20, 2009 TOY09-NCAR 49
50 Motivation Increasing simulation rate POP CICE CAM CPL Conclusions August 20, 2009 TOY09-NCAR 50
51 Not always the dominate cost Incremental Solution: Parallelizing tracers [Mirin,Worley] OpenMP/MPI Radical Solutions: New N scalable dynamical core/method August 20, 2009 TOY09-NCAR 51
52 Motivation Increasing simulation rate POP CICE CAM CPL Conclusions August 20, 2009 TOY09-NCAR 52
53 Total redesign versus previous generation 10-20x increase in core count Minor impact at 1800 cores Tested at ~12,000 cores on BGL August 20, 2009 TOY09-NCAR 53
54 Possible to increase scalability of high- resolution CCSM4 Load balance is critical POP imbalance causes contention for network CICE imbalance of computational/communication costs Improve parallel I/O performance Number of subtle issues August 20, 2009 TOY09-NCAR 54
55 NCAR: D. Bailey F. Bryan B. Eaton N. Hearn K. Lindsay N. Norton M. Vertenstein COLA: J. Kinter C. Stan U. Miami B. Kirtman UC U.C. Berkeley W. Collins K. Yelick (NERSC) U. Washington C. Bitz August 20, 2009 NICS: M. Fahey P. Kovatch ANL: R. Jacob R. Loy LANL: E. Hunke P. Jones M. Maltrud LLNL D. Bader D. Ivanova J. McClean (Scripps) A. Mirin ORNL: P. Worley and many more TOY09-NCAR Grant Support: DOE NSF DE-FC03-97ER62402 [SciDAC] DE-PS02-07ER07-06 [SciDAC] Cooperative Grant NSF01 OCI [PetaApps] CNS CNS CNS Computer Allocations: TeraGrid NICS DOE NERSC LLNL Grand Challenge Thanks for Assistance: Cray, NICS, and NERSC 55
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