  Recent and future generation observatories will enable the study of variable
astronomical phenomena through their time-domain capabilities. High temporal
fidelity will allow for unprecedented investigations into the nature of
variable objects -- those objects that vary in brightness over time. A major
bottleneck in data processing pipelines is constructing light curve solutions
for catalogs of variable objects, as it is well-known that period finding
algorithms are computationally expensive. Furthermore, there are many period
finding algorithms that are often suited for specific science cases. In this
paper, we present the first GPU-accelerated Super Smoother algorithm. Super
Smoother is general purpose and uses cross-validation to fit line segments to a
time series, and as such, is more computationally expensive than other
algorithms, such as Lomb-Scargle. Because the algorithm requires making several
scans over the input time series for a tested frequency, we also propose a
novel generalized-validation variant of Super Smoother that only requires a
single scan over the data. We compare the performance of our algorithms to
analogous parallel multi-core CPU implementations on three catalogs of data,
and show that it is generally advantageous to use the GPU algorithm over the
CPU counterparts. Furthermore, we demonstrate that our single-pass variant of
Super Smoother is roughly equally as accurate at finding correct period
solutions as the original algorithm. Our software supports several features,
such as batching the computation to eliminate the possibility of exceeding
global memory on the GPU, processing a single object or batches of objects, and
we allow for scaling the algorithm across multiple GPUs.
