A wall-time minimizing parallelization strategy for approximate Bayesian computation

PLoS One. 2024 Feb 22;19(2):e0294015. doi: 10.1371/journal.pone.0294015. eCollection 2024.


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.

MeSH terms

  • Algorithms*
  • Bayes Theorem
  • Monte Carlo Method
  • Virion*

Grants and funding

The authors acknowledge the Gauss Centre for Supercomputing e.V. (www.gauss-centre.eu) for funding this project by providing computing time on the GCS Supercomputer JUWELS at Jülich Supercomputing Centre (JSC). This work was supported by the German Federal Ministry of Education and Research (BMBF) (FitMultiCell/031L0159C and EMUNE/031L0293C) and the German Research Foundation (DFG) under Germany’s Excellence Strategy (EXC 2047 390873048 and EXC 2151 390685813 and the Schlegel Professorship for JH). YS acknowledges support by the Joachim Herz Foundation. FG was supported by the Chica and Heinz Schaller Foundation. There was no additional external funding received for this study.