From Piz Daint to Piz Kesch : the making of a GPU-based weather forecasting system. Oliver Fuhrer and Thomas C. Schulthess
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1 From Piz Daint to Piz Kesch : the making of a GPU-based weather forecasting system Oliver Fuhrer and Thomas C. Schulthess 1
2 Piz Daint Cray XC30 with 5272 hybrid, GPU accelerated compute nodes Compute node: > Host: Intel Xeon E (SandyBridge 8c) > Accelerator: One NVIDIA K20X GPU (GK110) 2
3 September 15, 2015 Today s Outlook: GPU-accelerated Weather Forecasting John Russell Piz Kesch Compute(Rack 1 AFCO(CRAYPD01 MotivAir 48P(Gi ge((ops) 48P(Gi ge((mgmt) Mel l anox(fdr(ib BLANK OSS MDS/MGS NetApp(E2760 ESMS MN MN PPN/Logi n PPN/Logi n BLANK BLANK 0 0 3
4 Today s (2015) production suite of Meteo Swiss COSMO-7 3x per day 72h forecast 6.6 km lateral grid, 60 layers ECMWF 2x per day 16 km lateral grid, 91 layers COSMO-2 8x per day 24h forecast 2.2 km lateral grid, 60 layers Some of the products generate from these simulations: Daily weather forecast on TV / radio Forecasting for air traffic control (Sky Guide) Safety management in event of nuclear incidents 4
5 Albis & Lema, CSCS production systems for Meteo Swiss Cray XE6 procured in spring 2012 based on 12-core AMD Opteron multi-core processors 5
6 Cloud resolving simulations Institute for Atmospheric and Climate Science Study at ETH Zürich (Prof. Schär) demonstrates cloud resolving models converge at 1-2km resolution (at least for convective clouds over the alpine region) Cloud ice Cloud liquid water Rain Accumulated surface precipitation 10 km 187 km 187 km COSMO model setup: Δx=550 m, Δt=4 sec Plots generated using INSIGHT Orographic convection simulation: local time, 11 July 2006 (Δt_plot=4 min) Source: Wolfgang Langhans and Christoph Schär, Institute for Atmospheric and Climate Science, ETH Zurich 6
7 Higher resolution is necessary for quantitative agreement with experiment (18 days for July 9-27, 2006) Altdorf (Reuss valley) Lodrino (Leventina) COSMO-2 COSMO-1 source: Oliver Fuhrer, MeteoSwiss 7
8 Improve resolution of Meteo Swiss model from 2 to 1 km 2x 2x Run on 4x the number of processors ~2-3x Sequential Doubling the resolution requires ~10x performance increase Time 8
9 Prognostic uncertainty The weather system is chaotic à rapid growth of small perturbations (butterfly effect) Start Prognostic timeframe Ensemble method: compute distribution over many simulations 9
10 Benefit of ensemble forecast (heavy thunderstorms on July 24, 2015) Adelboden reliable? source: Oliver Fuhrer, MeteoSwiss 10
11 Benefit of ensemble forecast (heavy thunderstorms on July 24, 2015) Adelboden source: Oliver Fuhrer, MeteoSwiss 11
12 Improving simulation quality requires higher performance what exactly and by how much? Resource determining factors for Meteo Swiss simulations Current model running through mid 2016 New model starting operation on in Jan COSMO-2: 24h forecast running in 30 min. 8x per day COSMO-1: 24h forecast running in 30 min. 8x per day (~10x COSMO-2) COSMO-2E: 21-member ensemble,120h forecast in 150 min., 2x per day (~26x COSMO-2) KENDA: 40-member ensemble,1h forecast in 15 min., 24x per day (~5x COSMO-2) New production system must deliver ~40x the simulations performance of Albis and Lema 12
13 State of the art implementation of new system for Meteo Swiss Albis & Lema: 3 cabinets Cray XE6 installed Q2/2012 New system needs to be installed Q2-3/2015 Assuming 2x improvement in per-socket performance: ~20x more X86 sockets would require 30 Cray XC cabinets New system for Meteo Swiss if we build it like the German Weather Service (DWD) did theirs, or UK Met Office, or ECMWF (30 racks XC) Current Cray XC30/XC40 platform (space for 40 racks XC) CSCS machine room Thinking inside the box is not a good option! 13
14 Co-Design our way out? Potential for co-design Time-to-solution driven (specs are clear) Exclusive usage Only one performance critical application Stable configuration (code & system) Current code can be improved Novel hardware has yet to be exploited Challenges for making it work Community code Large user base Performance portability Knowhow transfer Complex workflow High reliability required Rapidly evolving technology (hardware and software) 14
15 Leutwyler, D., O. Fuhrer, X. Lapillone, D. Lüthi, C. Schär, 2015: Continental-Scale Climate Simulation at Kilometer resolution. ETH Zurich Online Resource, DOI: online video: 15
16 Co-design approach Co-design software / workflow / hardware paying attention to Portability to other users and hardware architectures Achieve specified time-to-solution Optimise hardware footprint and energy Several collaboration pre-existed Software development since 2010: MeteoSwiss / C2SM@ETH Zurich / CSCS CSCS with Cray and NVIDIA for development of Piz Daint in 2013 Domain scientists and computer scientists Substantial software investments from HPCN Strategy: HP2C and PASC Extreme programming team Oliver Fuhrer (the perfect product owner) Tobias Gysi, Carlos Osuna, Xavier Lapillonne, Mauro Bianco, Andrea Arteaga (not all at the same time) CSCS experts: Ben Cumming, Gilles Fourestey, Guilherme Peretti-Pezzi NVIDIA experts: Peter Messmer, Christoph Angerer 16
17 COSMO: current and new (refactored) code main (current / Fortran) main (new / Fortran) dynamics (C++) physics (Fortran) dynamics (Fortran) physics (Fortran) with OpenMP / OpenACC stencil library X86 GPU Shared Infrastructure boundary conditions & halo exchg. Generic Comm. Library MPI MPI or whatever system system 17
18 A factor 40 improvement with the same footprint Current production system: Albis & Lema New system: Kesch & Escha 18
19 Piz Kesch / Piz Escha: appliance for meteorology Water cooled rack (48U) 12 compute nodes with 2 Intel Xeon E5-2690v GHz256 GB 2133 MHz DDR4 memory 8 NVIDIA Tesla K80 GPU 3 login nodes 5 post-processing nodes Mellanox FDR InfiniBand Cray CLFS Luster Storage Cray Programming Environment 19
20 Moore s Law Origin of factor 40 performance improvement Performance of COSMO running on new Piz Kesch compared to current production systems Current production system installed in 2012 New Piz Kesch/Escha installed in 2015 Processor performance 2.8x Moore s Law Improved system utilisation General software performance Port to GPU architecture Increase in number of processors Total performance improvement Bonus: simulation running on GPU is 3x more energy efficient compared to conventional state of the art CPU 2.8x 1.7x 2.3x 1.3x ~40x Software refactoring 20
21 Outlook Continue to invest in software domain specific libraries / embedded languages improve scientist s productivity through Python bindings refactor entire software toolchain Continued performance improvements for climate / meteorology simulations hardware-software co-design improved memory performance processor performance / explore new architectures Longterm investment in new model with even higher resolution 21
22 References and Collaborators Peter Messmer and his team at the NVIDIA co-design lab at ETH Zurich Teams at CSCS and Meteo Suisse, group of Christoph ETH Zurich O. Fuhrer, C. Osuna, X. Lapillonne, T. Gysi, B. Cumming, M. Bianco, A. Arteaga, T. C. Schulthess, Towards a performance portable, architecture agnostic implementation strategy for weather and climate models, Supercomputing Frontiers and Innovations, vol. 1, no. 1 (2014), see superfri.org G. Fourestey, B. Cumming, L. Gilly, and T. C. Schulthess, First experience with validating and using the Cray power management database tool, Proceedings of the Cray Users Group 2014 (CUG14) (see arxiv.org for reprint) B. Cumming, G. Fourestey, T. Gysi, O. Fuhrer, M. Fatica, and T. C. Schulthess, Application centric energy-efficiency study of distributed multi-core and hybrid CPU-GPU systems, Proceedings of the International Conference on High-Performance Computing, Networking, Storage and Analysis, SC 14, New York, NY, USA (2014). ACM T. Gysi, C. Osuna, O. Fuhrer, M. Bianco and T. C. Schulthess, STELLA: A domain-specific tool for structure grid methods in weather and climate models, to be published in Proceedings of the International Conference on High-Performance Computing, Networking, Storage and Analysis, SC 15, New York, NY, USA (2015). ACM paper at SC15: 1:30-2PM room 18AB 22
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