TY - GEN
T1 - Performance optimization and load-balancing modeling for superparametrization by 3D les
AU - Van Den Oord, Gijs
AU - Chertova, Maria
AU - Jansson, Fredrik
AU - Pelupessy, Inti
AU - Siebesma, Pier
AU - Crommelin, Daan
PY - 2021
Y1 - 2021
N2 - In order to eliminate climate uncertainty w.r.t. cloud and convection parametrizations, superpramaterization (SP) [1] has emerged as one of the possible ways forward. We have implemented (regional) superparametrization of the ECMWF weather model OpenIFS [2] by cloud-resolving, three-dimensional large-eddy simulations. This setup, described in [3], contains a two-way coupling between a global meteorological model that resolves large-scale dynamics, with many local instances of the Dutch Atmospheric Large Eddy Simulation (DALES) [4], resolving cloud and boundary layer physics. The model is currently prohibitively expensive to run over climate or even seasonal time scales, and a global SP requires the allocation of millions of cores. In this paper, we study the performance and scaling behavior of the LES models and the coupling code and present our implemented optimizations. We mimic the observed load imbalance with a simple performance model and present strategies to improve hardware utilization in order to assess the feasibility of a world-covering superparametrization. We conclude that (quasi-)dynamical load-balancing can significantly reduce the runtime for such large-scale systems with wide variability in LES time-stepping speeds.
AB - In order to eliminate climate uncertainty w.r.t. cloud and convection parametrizations, superpramaterization (SP) [1] has emerged as one of the possible ways forward. We have implemented (regional) superparametrization of the ECMWF weather model OpenIFS [2] by cloud-resolving, three-dimensional large-eddy simulations. This setup, described in [3], contains a two-way coupling between a global meteorological model that resolves large-scale dynamics, with many local instances of the Dutch Atmospheric Large Eddy Simulation (DALES) [4], resolving cloud and boundary layer physics. The model is currently prohibitively expensive to run over climate or even seasonal time scales, and a global SP requires the allocation of millions of cores. In this paper, we study the performance and scaling behavior of the LES models and the coupling code and present our implemented optimizations. We mimic the observed load imbalance with a simple performance model and present strategies to improve hardware utilization in order to assess the feasibility of a world-covering superparametrization. We conclude that (quasi-)dynamical load-balancing can significantly reduce the runtime for such large-scale systems with wide variability in LES time-stepping speeds.
KW - Load-balancing
KW - Multiscale modeling
KW - Superparametrization
KW - Weather & climate simulation
UR - http://www.scopus.com/inward/record.url?scp=85114352864&partnerID=8YFLogxK
U2 - 10.1145/3468267.3470611
DO - 10.1145/3468267.3470611
M3 - Conference contribution
AN - SCOPUS:85114352864
T3 - Proceedings of the Platform for Advanced Scientific Computing Conference, PASC 2021
BT - Proceedings of the Platform for Advanced Scientific Computing Conference, PASC 2021
PB - Association for Computing Machinery (ACM)
T2 - 2021 Platform for Advanced Scientific Computing Conference, PASC 2021
Y2 - 5 July 2021 through 9 July 2021
ER -