A wall-time minimizing parallelization strategy for approximate Bayesian computation

Research output: Contribution to journalResearch articleContributedpeer-review



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 computing 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 avoids idle times of computing units by preemptive sampling of subsequent generations. This allows to utilize all available resources. The strategy can be integrated with e.g. adaptive distance function and summary statistic selection schemes, which is essential in practice. Our key contribution is the theoretical assessment of the strategy of preemptive sampling and the proof of unbiasedness. Complementary, we provide an implementation and evaluate the strategy on different problems and numbers of parallel cores, showing speed-ups of typically 10-20% and up to 50% compared to the best established approach, with some variability. Thus, the proposed strategy allows to improve the cost and run-time efficiency of ABC methods on high-performance infrastructure.


Original languageEnglish
Article numbere0294015
JournalPloS one
Issue number2
Publication statusPublished - 22 Feb 2024

External IDs

PubMedCentral PMC10883530
ORCID /0000-0003-0137-5106/work/154741259
Scopus 85185794703


Research priority areas of TU Dresden


  • Bayes Theorem, Parameter estimation, Monte Carlo Method, Morpheus, Computational Modeling and Simulation