Performance Analysis, Profiling and Optimization of Weather Research and Forecasting (WRF) model Negin Sobhani 1,2, Davide Del Vento2,David Gill2, Sam Elliot3,2,and Srinath Vadlamani4 1 University of Iowa 2National Center for atmospheric Research(NCAR) 3University of Colorado at Boulder 4Paratools Inc

Outline Introduction WRF MPI Scalability Hybrid Parallelization Profiling WRF Intel Vtune Amplifier XE Taul tools Identifying hotspots and suggested areas for improvement

The Weather Research & Forecasting(WRF) Model Numerical weather prediction system Designed for both operational forecasting and atmospheric research Community model with large user base: More than 30,000 users in 150 countries Figure from WRF-ARW Technical Note

Previous Scaling Studies WRF has benchmarked on different systems. Figures from cisl.ucar.edu

TACC Stampede Supercomuter Aggregate Peak Performance : ~10 PFLOPS(PF) 6400+ Dell PowerEdge (C8220z) server nodes 2 Intel Xeon E5 (Sandy Bridge) processors and an Intel Xeon Phi Coprocessor (MIC Architecture) per each compute Node Each computer node has 32 GB of “host” memory with an additional 8GB of memory on the Xeon Phi coprocessor card 2.2 PF from Xeon E5 processors and 7.4 PF from Xeon Phi coprocessors Figures from tacc.utexas.edu

Hurricane Sandy Benchmark Coarser resolution 40-km (50x50) Time-step: 180 sec Finer resolution 4-km (500x 500) Time-step: 20 sec Time Period for both Simulation: 54 hour forecast Between 2012 Oct 27 12:00 UTC through 2012 Oct 29 18:00 UTC 60 vertical layers

Scalability Assessment (MPI Only) 500X500 horizontal grids MPI Bound Compute Bound Simulation Speed is duration of simulation per wall clock time

Scalability Assessment (MPI Only) Allinea Perfomance Reports Separate netcdf output file (io_form_history=102 in WRF namelist) 79% of total time spent on MPI I/O is calculated into MPI I/O - allenia PR calculating Wrf_opt=102 87% of total time spent on computation

Domain Decomposition (MPI only) Per Grid :1/4 Computation and 1/2 MPI

AVX compiler flag AVX (Intel® Advanced Vector Extensions) is a 256 bit instruction set extension More aggressive optimization Not working on intel 15 This issue has been reported to Intel Intel 15 is a little bit better than this!

Hybrid Parallelization Hybrid : distributed and shared memory parallelism(dmpar+smpar) As the number of threads increase the performance decreases The cores have never been oversubscribed Binding increases the performance significantly I_MPI_PROCESSOR_LIST= p1,p2 tacc_affinity script

Intel Vtune Amplifier XE Intel profiling and performance analysis tool Profiling includes stack sampling, thread profiling and hardware event sampling Collect performance statistics of different part of the code

What does make WRF expensive? Radiation Longwave Radiation Scheme RRTMG Scheme (ra_lw_physics =4) Shortwave Radiation Scheme CAM Scheme(ra_sw_physics = 3) Microphysics Scheme Thompson et al. 2008 (mp_physics =8) . But is this case representative of the significant effect of the dynamics on performance? Time(%)

Microphysics options summary Scheme mp_physics Simulation Speed # of Variables #timesteps/s Kessler 1 2493.6 3 13.8 Purdue Lin et al. 2 2043.8 6 11.3 WSM-3 2263.8 12.5 WSM-5 4 2012.3 5 11.2 Ferrier(current NAM) 2451.2 13.6 WSM-6 1859.5 10.3 Goddard 6 class 7 1929.9 10.6 Thompson et al. 8 1739.8 9.7 Milbrandt- Yau 2-moment 9 1189.3  13 6.6 Morison 2-moment 10 1475.5 8.2 WDM-5 14 1478.6  8 WDM-16 16 1358.8  9 7.5 Thompson microphysics is among the most expensive microphysics and it is widely used.

TAU tools TAU (Tuning and Analysis Utilities) is a program and performance analysis tool framework for high-performance parallel and distributed computing TAU can automatically instrument source code using a package called PDT for routines, loops, I/O, memory, phases, etc. Tau uses wallclock time and PAPI metrics to read hardware counters for profiling and tracing

Using Tau/PAPI for Advection Module 1- PDT instrumentation for module_advect_em 2- Manually instrumented code for higher granularity of desired loops TAU/PAPI variables analyzed: Time L1 and L2 Data Cache Misses (DCM) Conditional branch instructions mispredicted Floating point instruction and operations Single and double precision vector/SIMD instructions

Identified Hotspots 1- Positive Definite Advection Loop (32 lines) High Time High cache misses (both L1 and L2 Cache misses) High branch miss-prediction 2- x, y, z flux 5 advection equation loops High Cache misses Repeated through the code for different advection schemes

Moisture transport in ARW Until recently, many weather models did not conserve moisture because of the numerical challenges in advection schemes.  high bias in precipitation WRF-ARW scheme is conservative but not all of the advection schemes are. This introduces new masses to the system. Figure from Skamarock and Dudhia 2012 Advection schemes can introduce both positive and negative errors particularly at sharp gradients

Advection options in WRF moist_adv_opt =0 moist_adv_opt =1 moist_adv_opt =2 Explicit IFs to remove oscillations Explicit IFs to remove negative values and over shoots High number of explicit IFs are causing high branch mispredictions Figure from Skamarock and Dudhia 2012

The effect of optimization of advection module 1- Optimizing the positive definitive advection module Test1 : WRF only case Test 2: WRF-Chem case Case Advected Variables Maximum performance increase WRF Moisture 13% WRF-Chem Moisture- Tracers- Species-Scalars- Chemical concentration- Particles 21% * * The performance increase will be even significantly higher for dust and particle only WRF-Chem cases. This hotspot has a potential for being optimized and provides significant improvement in performance.

Identified Hotspots 1- Positive Definite Advection Loop High Time High cache misses (both L1 and L2 Cache misses) High branch miss-prediction 2- x, y, z flux 5 advection equation loops High Cache misses Repeated through the code for different advection schemes

The effect of optimization of advection equations 2- Flux 5 advection equations High Time and High L1 and L2 Data Cache Misses This loop is repeated throughout the code for x, y and z directions Very similar loop repeated for all the advection schemes Test1 : WRF 4 km benchmark with TAU instrumentation 58% time spent in advection is in these flux equations loops Many L1 data cache misses per iteration Many L2 data cache misses per iteration This hotspot has a potential for being optimized and provides significant improvement in performance.

Conclusion WRF shows good MPI scalability depending on the workload Thread Binding should be used for improving the performance of the WRF hybrid runs Intel Vtune Amplifier and Tau tools used for performance analysis of WRF code. Dynamics is identified as the most expensive part of ARW We identified the hotspots of the advection module and estimated the amount of performance increase from modifying these parts of the WRF code

Ongoing and Future Work Performance Improvement of advection module Analysis of hardware counters to fix branch mispredictions and cache misses Advection module vectorization for Intel Xeon Phi Coprocessors Reducing memory footprint by decreasing the number of temporary variables Exploring performance optimization with different compiler flags Loop transformation for enabling better vectorization

Acknowledgements Davide Del Vento Rich Loft Srinath Vadlamani Dave Gill Greg Carmichael All SIParCS admins and staff why

Microphysics Schemes Provides atmospheric heat and moisture tendencies Includes water vapor, cloud and precipitation processes Microphysical rates Surface rainfall Mielikainen et al. 2014

WRF Model Integration Procedure Begin time step Runge-Kutta loop (steps 1, 2, and 3) (i) advection, p-grad, buoyancy using (ii) physics if step 1, save for steps 2 and 3 (iii) mixing, other non-RK dynamics, save… (iv) assemble dynamics tendencies Acoustic step loop (i) advance U,V, then W, (ii) time-average U,V,W End acoustic loop Advance scalars using time-averaged U,V,W End Runge-Kutta loop Other physics (currently microphysics) End time step