Open AccessOn the use of programmable hardware and reduced numerical precision in earth‐system modeling
Author(s) -
Düben Peter D.,
Russell Francis P.,
Niu Xinyu,
Luk Wayne,
Palmer T. N.
Publication year - 2015
Publication title -
journal of advances in modeling earth systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.03
H-Index - 58
ISSN - 1942-2466
DOI - 10.1002/2015ms000494
Subject(s) - field programmable gate array , computer science , rounding , single precision floating point format , floating point , double precision floating point format , scale (ratio) , range (aeronautics) , algorithm , parallel computing , computational science , computer hardware , physics , materials science , quantum mechanics , composite material , operating system
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.