  Simulation-based Inference (SBI) is a widely used set of algorithms to learn
the parameters of complex scientific simulation models. While primarily run on
CPUs in HPC clusters, these algorithms have been shown to scale in performance
when developed to be run on massively parallel architectures such as GPUs.
While parallelizing existing SBI algorithms provides us with performance gains,
this might not be the most efficient way to utilize the achieved parallelism.
This work proposes a new algorithm, that builds on an existing SBI method -
Approximate Bayesian Computation with Sequential Monte Carlo(ABC-SMC). This new
algorithm is designed to utilize the parallelism not only for performance gain,
but also toward qualitative benefits in the learnt parameters. The key idea is
to replace the notion of a single 'step-size' hyperparameter, which governs how
the state space of parameters is explored during learning, with step-sizes
sampled from a tuned Beta distribution. This allows this new ABC-SMC algorithm
to more efficiently explore the state-space of the parameters being learnt. We
test the effectiveness of the proposed algorithm to learn parameters for an
epidemiology model running on a Tesla T4 GPU. Compared to the parallelized
state-of-the-art SBI algorithm, we get similar quality results in $\sim 100$x
fewer simulations and observe ~80x lower run-to-run variance across 10
independent trials.
