  Cloud computing for high performance computing resources is an emerging
topic. This service is of interest to researchers who care about reproducible
computing, for software packages with complex installations, and for companies
or researchers who need the compute resources only occasionally or do not want
to run and maintain a supercomputer on their own. The connection between HPC
and containers is exemplified by the fact that Microsoft Azure's Eagle cloud
service machine is number three on the November 23 Top 500 list. For cloud
services, the HPC application and dependencies are installed in containers,
e.g. Docker, Singularity, or something else, and these containers are executed
on the physical hardware. Although containerization leverages the existing
Linux kernel and should not impose overheads on the computation, there is the
possibility that machine-specific optimizations might be lost, particularly
machine-specific installs of commonly used packages. In this paper, we will use
an astrophysics application using HPX-Kokkos and measure overheads on
homogeneous resources, e.g. Supercomputer Fugaku, using CPUs only and on
heterogenous resources, e.g. LSU's hybrid CPU and GPU system. We will report on
challenges in compiling, running, and using the containers as well as
performance performance differences.
