  Approximate Bayesian Computation (ABC) is a widely applicable and popular
approach to estimating unknown parameters of mechanistic models. As ABC
analyses are computationally expensive, parallelization on high-performance
infrastructure is often necessary. However, the existing parallelization
strategies leave resources unused at times and thus do not optimally leverage
them yet. We present look-ahead scheduling, a wall-time minimizing
parallelization strategy for ABC Sequential Monte Carlo algorithms, which
utilizes all available resources at practically all times by proactive sampling
for prospective tasks. Our strategy can be integrated in e.g. adaptive distance
function and summary statistic selection schemes, which is essential in
practice. Evaluation of the strategy on different problems and numbers of
parallel cores reveals speed-ups of typically 10-20% and up to 50% compared to
the best established approach. Thus, the proposed strategy allows to
substantially improve the cost and run-time efficiency of ABC methods on
high-performance infrastructure.
