  Computing centres, including those used to process High-Energy Physics data
and simulations, are increasingly providing significant fractions of their
computing resources through hardware architectures other than x86 CPUs, with
GPUs being a common alternative. GPUs can provide excellent computational
performance at a good price point for tasks that can be suitably parallelized.
Charged particle (track) reconstruction is a computationally expensive
component of HEP data reconstruction, and thus needs to use available resources
in an efficient way. In this paper, an implementation of Kalman filter-based
track fitting using CUDA and running on GPUs is presented. This utilizes the
ACTS (A Common Tracking Software) toolkit; an open source and
experiment-independent toolkit for track reconstruction. The implementation
details and parallelization approach are described, along with the specific
challenges for such an implementation. Detailed performance benchmarking
results are discussed, which show encouraging performance gains over a
CPU-based implementation for representative configurations. Finally, a
perspective on the challenges and future directions for these studies is
outlined. These include more complex and realistic scenarios which can be
studied, and anticipated developments to software frameworks and standards
which may open up possibilities for greater flexibility and improved
performance.
