Cleo: the fundamental design of a new superdroplet model for high computational performance (v0.39.0)

Abstract. Cleo is a novel implementation of the Super-Droplet Method (SDM) aiming to be efficient on and portable across exascale high-performance computers. It is motivated by the need to develop a SDM that can model warm-cloud microphysics in domains large enough to resolve shallow mesoscale cloud organisation 𝒪(100 km). This paper introduces Cleo's fundamental structure. First, it explains the choices we made to exploit shared memory spaces on single nodes of high-performance computers. In particular, we discuss our reasoning behind Cleo's ordered and contiguous arrays of structures for superdroplets and grid-boxes, and how we can allocate resources economically and portably through Cleo's MPI domain decomposition, Kokkos library thread parallelism, and coupling to a host dynamical driver. Second, this paper explains how Cleo uses monoids to construct arbitrary combinations and permutations of microphysical processes and data output. These monoids make microphysics and data output highly and easily configurable, which facilitates warm-cloud process understanding. Furthermore, they reduce conditional code branching and enhance Cleo's readability and maintainability. Performance tests on single nodes of Levante HPC System show Cleo has optimal linear scaling with increasing number of superdroplets as well as sufficient memory- and strong-scaling on both CPUs and GPUs to invite CLEO's next stage of development, its optimisation for large eddy simulations on multiple nodes of top-tier and exascale high-performance computers.