Porting the microphysics model CASIM to GPU and KNL Cray machines

TL;DR

This paper explores porting the CASIM microphysics model to GPU and KNL architectures, demonstrating promising performance improvements and providing insights into optimization strategies for weather and climate models on novel hardware.

Contribution

The paper presents the first successful port of CASIM to GPU using OpenACC and to KNL using OpenMP, with detailed performance analysis and optimization insights.

Findings

CASIM benefits from GPU acceleration with OpenACC

Porting to KNL was easier but required careful tuning

Performance improvements over CPU implementations

Abstract

CASIM is a microphysics scheme which calculates the interaction between moisture droplets in the atmosphere and forms a critical part of weather and climate modelling codes. However the calculations involved are computationally intensive and so investigating whether CASIM can take advantage of novel hardware architectures and the likely increase in performance this might afford makes sense. In this paper we present work done in porting CASIM to GPUs via the directive driven OpenACC and also modifying CASIM to take advantage of the Knights Landing (KNL) processor using OpenMP. Due to the design, models extracting out specific computational kernels for offload to the GPU proved suboptimal and instead the entire scheme was ported over to the GPU. We consider the suitability and maturity of OpenACC for this approach as well as important optimisations that were identified. Enabling CASIM to take advantage of the KNL was significantly easier, but still required careful experimentation to understand the best design and configuration. The performance of both versions of CASIM, in comparison to the latest generation of CPUs is discussed, before identifying lessons learnt about the suitability of CASIM and other similar models for these architectures. The result of this work are versions of CASIM which show promising performance benefits when utilising both GPUs and KNLs and enable the communities to take advantage of these technologies, in addition to general techniques that can be applied to other similar weather and climate models.