On the use of programmable hardware and reduced numerical precision in earth‐system modeling

• Published in: Journal of advances in modeling earth systems Vol. 7; no. 3; pp. 1393 - 1408
• Main Authors: Düben, Peter D., Russell, Francis P., Niu, Xinyu, Luk, Wayne, Palmer, T. N.
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.09.2015; John Wiley and Sons Inc
• Subjects: Accuracy; Atmospheric Processes; Computational Geophysics; Computational Models, Algorithms; Design; dynamical core; Earth; Energy consumption; Errors; Field programmable gate arrays; Fluid dynamics; FPGA; Hardware Solutions; High‐performance Computing; Hydrodynamics; Idealized Model; Informatics; Lorenz '95; Numerical Modeling; numerical precision; Numerical Solutions; Oceanography: General; Power consumption; programmable hardware; weather forecast; Europe; programmable hardware; numerical precision; FPGA; Lorenz '95; weather forecast; dynamical core
• ISSN: 1942-2466; 1942-2466
• DOI: 10.1002/2015MS000494

Programmable hardware, in particular Field Programmable Gate Arrays (FPGAs), promises a significant increase in computational performance for simulations in geophysical fluid dynamics compared with CPUs of similar power consumption. FPGAs allow adjusting the representation of floating‐point numbers to specific application needs. We analyze the performance‐precision trade‐off on FPGA hardware for the two‐scale Lorenz '95 model. We scale the size of this toy model to that of a high‐performance computing application in order to make meaningful performance tests. We identify the minimal level of precision at which changes in model results are not significant compared with a maximal precision version of the model and find that this level is very similar for cases where the model is integrated for very short or long intervals. It is therefore a useful approach to investigate model errors due to rounding errors for very short simulations (e.g., 50 time steps) to obtain a range for the level of precision that can be used in expensive long‐term simulations. We also show that an approach to reduce precision with increasing forecast time, when model errors are already accumulated, is very promising. We show that a speed‐up of 1.9 times is possible in comparison to FPGA simulations in single precision if precision is reduced with no strong change in model error. The single‐precision FPGA setup shows a speed‐up of 2.8 times in comparison to our model implementation on two 6‐core CPUs for large model setups. Key Points: Huge saving in computing cost via reduced numerical precision in earth‐system modeling Long and short‐term simulations have similar level of minimal numerical precision Numerical precision can be reduced with time in a weather forecast simulations