  The Fujitsu A64FX ARM-based processor is used in supercomputers such as
Fugaku in Japan and Isambard 2 in the UK and provides an interesting
combination of hardware features such as Scalable Vector Extension (SVE), and
native support for reduced-precision floating-point arithmetic. The goal of
this paper is to explore performance of the Julia programming language on the
A64FX processor, with a particular focus on reduced precision. Here, we present
a performance study on axpy to verify the compilation pipeline, demonstrating
that Julia can match the performance of tuned libraries. Additionally, we
investigate Message Passing Interface (MPI) scalability and throughput analysis
on Fugaku showing next to no significant overheads of Julia of its MPI
interface. To explore the usability of Julia to target various floating-point
precisions, we present results of ShallowWaters.jl, a shallow water model that
can be executed a various levels of precision. Even for such complex
applications, Julia's type-flexible programming paradigm offers both,
productivity and performance.
