  Programming for a diverse set of compute accelerators in addition to the CPU
is a challenge. Maintaining separate source code for each architecture would
require lots of effort, and development of new algorithms would be daunting if
it had to be repeated many times. Fortunately there are several portability
technologies on the market such as Alpaka, Kokkos, and SYCL. These technologies
aim to improve the developer productivity by making it possible to use the same
source code for many different architectures. In this paper we use
heterogeneous pixel reconstruction code from the CMS experiment at the CERNL
LHC as a realistic use case of a GPU-targeting HEP reconstruction software, and
report experience from prototyping a portable version of it using Kokkos. The
development was done in a standalone program that attempts to model many of the
complexities of a HEP data processing framework such as CMSSW. We also compare
the achieved event processing throughput to the original CUDA code and a CPU
version of it.
