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

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 intro-
duces 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 dis-
cuss our reasoning behind Cleo’s ordered and contiguous ar-
rays 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 arbi-
trary combinations and permutations of microphysical pro-
cesses and data output. These monoids make microphysics
and data output highly and easily configurable, which facil-
itates warm-cloud process understanding. Furthermore, they
reduce conditional code branching and enhance Cleo’s read-
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ability 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 in-
vite CLEO’s next stage of development, its optimisation for
large eddy simulations on multiple nodes of top-tier and ex-
ascale high-performance computers.