Architectures and Precision Analysis for Modelling Atmospheric Variables with Chaotic Behaviour

The computationally intensive nature of atmospheric modelling is an ideal target for hardware acceleration. Performance of hardware designs can be improved through the use of reduced precision arithmetic, but maintaining appropriate accuracy is essential. We explore reduced precision optimisation for simulating chaotic systems, targeting atmospheric modelling in which even minor changes in arithmetic behaviour can have a significant impact on system behaviour. Hence, standard techniques for comparing numerical accuracy are inappropriate. We use the Hellinger distance to compare statistical behaviour between reduced-precision CPU implementations to guide FPGA designs of a chaotic system, and analyse accuracy, performance and power efficiency of the resulting implementations. Our results show that with only a limited loss in accuracy corresponding to less than 10% uncertainly in input parameters, a single Xilinx Virtex 6 SXT475 FPGA can be 13 times faster and 23 times more power efficient than a 6-core Intel Xeon X5650 processor.