Paralleliza(on and Performance of the NIM Weather Model on CPU, GPU and MIC Architectures
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1 Paralleliza(on and Performance of the NIM Weather Model on CPU, GPU and MIC Architectures Mark Gove? NOAA Earth System Research Laboratory
2 We Need Be?er Numerical Weather Predic(on Superstorm Sandy Hurricane Sandy Second most destruc(ve in U.S. History $75B in October damages 28, 2012 Over 200 deaths A European forecast that closely predicted Hurricane Sandy's onslaught days ahead of U.S. and other models is raising complaints in the meteorological community. "The U.S. does not lead the world; we are not No. 1 in weather forecascng, I'm very sorry to say that," says AccuWeather's Mike Smith Source: USA Today, October 30, 2012 Congressional Response: High Impact Weather Predic(on Program (HIWPP) Next Genera(on Weather Predic(on Program (NGGPS)
3 Three Years Later Hurricane Joaquin October 2, 2015 Some improvement NOAA s Hurricane Weather Research & Forecast Model intensity forecasts were accurate US research models had 20 precipita(on forecasts in South Carolina 36 hours in advance (verified) NY Times: Why U.S weather model has fallen behind WashingtonPost: Why the forecast cone of uncertainty is inadequate But European models predicted Joaquin would not make landfall (verified) All U..S models incorrectly predicted landfall The Na(onal Hurricane Center correctly never issued any hurricane watches or warnings for the mainland Forecasters relied on the European model for guidance
4 Weather Predic(on: Forecast Process Opera(onal weather predic(on models at NWS are required to run in about 1 percent of real-(me A one hour forecast produced in 8.5 minutes Data assimila(on, post processing are similarly constrained HPC Data NWP Assimila<on Forecaster PostProcessing Accelerators can speed up Assimila(on and Numerical Weather Predic(on (NWP) Stakeholders
5 Why Does NWP Need Accelerators? Increasing computer power has provided linear forecast improvement for decades CPU clock speeds have stalled Increased number of processing cores: MIC, GPU Lower energy requirements IBM IBM NCEP Operational Forecast Skill 36 and 72 Hour 500 MB over North America [100 * (1-S1/70) Method] IBM IBM CDC Hour Forecast 72 Hour Forecast IBM 360/ Years CYBER 205 CRAY Y-MP CRAY C90 IBM SP IBM P690 IBM P655+ IBM Power NCEP Central Operations January 2015
6 Resolu(on Ma?ers: Large Scale Ocean-Land-Atmosphere Interac(ons Global opera(onal weather models: 13KM
7 Resolu(on Ma?ers: Fine-Scale SimulaCon of a Tornado-Producing Super-Cell Storm Produces a Tornado 4-km More Intense UpdraGs 1-km Simula(ons with GFDL s variable-resolu(on FV 3, non-hydrosta(c (aka cloud-permijng) model. Courtesy of Lin and Harris (2015 manuscript)
8 Be?er Data Assimila(on = Be?er Forecasts Hurricane Joaquin 00Z October 1, N 00Z October 1, N Hurricane Joaquin Track Forecast 40 N US model w/old data assimila(on US model w/new data assimila(on Actual track (through 03Z 07 October) 35 N European model 30 N 25 N 80 W 70 W 60 W Source: Corey Guas(ni EMC s Model EvaluaCon Group
9 Formula to Radically Improve U.S. Weather Predic(on (and be #1) Increase resolu(on of global models to 3KM or finer Capture moisture, storm scale features Coupling atmosphere, ocean, chemistry, land surface Improve data assimila(on Use ensemble and (me-based varia(onal methods Massive increase in number of observa(ons handled Increase scalability to thousands of cores Increase in compu(ng (mes more than current models use
10 Non-hydrosta(c Icosahedral Model (NIM) Experimental global weather forecast model began in 2008 Uniform Icosahedral grid Designed for GPU, MIC Run on 10K GPUs, 600 MIC, 250K CPU cores Tested at 3KM resolu(on Single source code (Fortran) Serial, parallel execu(on on CPU, GPU, MIC Paralleliza(on direc(ves GPU OpenACC, F2C-ACC CPU OpenMP MIC OpenMP MPI SMS Useful for evalua(ng compilers, GPU & MIC hardware Fine-Grained Parallelism GPU Blocks in horizontal Threads in ver(cal CPU, MIC Threading in horizontal Vectoriza(on of ver(cal
11 Hardware Comparisons Performance comparisons in literature, presenta(ons can be misleading Ideally want: Same source code Op(mized for all architectures Standard, high volume chips Comparisons in terms of: Device Single node Mul(-node Cost benefit Programmability
12 Device Performance run(me (sec) NIM DYNAMICS 110 KM RESOLUTION 96 VERTICAL LEVELS Intel CPU NVIDIA GPU Intel MIC / Year Intel CPU (cores) NVIDIA GPU (cores) Intel MIC (cores) 2010/11 Westmere (12) Fermi (448) 2012 SandyBridge (16) Kepler K20x (2688) 2013 IvyBridge (20) Kepler K40 (2880) Knights Corner (61) 2014 Haswell (24) Kepler K80 (4992)
13 Single Node Performance Results from: NOAA / ESRL - August 2014 Run-<me (sec) Numeric values represent node run-(mes for each configura(on KM Resolu<on 40,968 Columns, 96 Ver<cal Levels 100 <me steps 58 Symmetric Mode Execu<on CPU run(me MIC run(me GPU run(me using F2C-ACC Node Type: 0 IB20 only IB24 only MIC only GPU only IB24 + MIC IB20 + GPU IB GPU IB20: Intel IvyBridge, 20 cores, 3.0GHz IB24: Intel IvyBridge 24 cores, 2.70 GHz GPU: Kepler K cores, 745 MHz MIC: KNC cores, 1.23GHZ
14 Single Node Performance - Strong Scaling - Intel IvyBridge with up to 4 NVIDIA K80s As the work per GPU decreases: inter-gpu communica(ons increases slightly efficiency decreases At least 10,000 columns per GPU is best 50 NIM Single Node Performance 40,962 Columns, 100 <mesteps Run<me (seconds) GPUs 0.95 Run(me 0.90 Communica(ons Parallel Efficiency Cols/GPU
15 Dynamics only CPU GPU Cost-Benefit Different CPUs and GPU configura(ons 40 Haswell CPUs, 20 K80 GPUs incorporate off-node MPI communica(ons All runs executed in the same (me Meets a ~1% opera(onal (me constraint for a 3KM resolu(on model 20K columns / GPU used which equates to 95% GPU strong scaling efficiency
16 Cost-Benefit NIM Dynamics 30KM resolu(on runs in same execu(on (me with: - 40 Intel Haswell CPU Nodes (list price: $6,500) - 20 NVIDIA K80 GPUs (list price: $5,000) Execu(on (me represents ~1.5% of real-(me for 3KM resolu(on ~2.75% of real-(me when model physics is included CPU versus GPU Cost-Benefit NIM 30 km resolu(on 230 Cost (thousands) CPU only CPU & GPU numcpus: K80s per CPU:
17 Lessons Learned: Code Design Avoid language constructs that are less well supported or difficult for compilers to op(mize Pointers, derived types Separate rou(nes for fine-grain (GPU, MIC) and coarse grain (MIC) Avoid single loop kernels High cost of kernel startup, synchroniza(on Avoid large kernels (GPU) Limited fast register, cache / shared memory Use scien(fic formula(ons that are highly parallel
18 Lessons Learned: Inter-Process Communica(ons Use of icosahedral grid gave flexibility in how columns could be distributed among MPI ranks MPI regions should be square to minimize points to be communicated Spiral ordering to eliminate MPI message packing and unpacking helped CPU, GPU, MIC GPUDirect gave 30% performance improvement CUDA Mul(-Process Service (MPS) sped up NIM by 35% on Titan Not reflected in the results shown
19 Lessons Learned: Fine-Grain Choice of innermost dimension important Vectoriza(on on CPU, MIC SIMD, Coalesced memory on GPU For NIM, ver(cal dimension used for dynamics Horizontal dimension for physics Innermost dimension should be mul(ple of 32 for GPU, bigger is be?er Mul(ple of 8 is sufficient for MIC Minimize branching Very few special cases in NIM
20 Improved OpenACC Compilers Performance of PGI nearly matches F2C-ACC Was 2.1X slower in 2014 Cray was 1.7X slower PGI does good job with analysis, data movement Use!$acc kernels to get the applica(on running 800 line MPAS kernel running on GPU in 10 minutes Use!$acc parallel to op(mize performance Use!$acc data to handle data movement Diagnos(c output to guide paralleliza(on, op(miza(on Cray, IBM comparisons planned
21 Summary Goal to radically improve U.S. Weather Predic(on (and be #1) Develop and run NGGPS model at 3KM MPAS or FV3 selec(on in May 2016 Lessons learned with NIM will guide paralleliza(on Significant improvement in data assimila(on Algorithms, techniques must be scalable to tens of thousands of compute cores Fine-grain compu(ng OpenACC, OpenMP compilers
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