A wall-time minimizing parallelization strategy for approximate Bayesian computation
Alamoudi E, Reck F, Bundgaard N, Graw F, Brusch L, Hasenauer J, Schälte Y (2024)
Publication Type: Journal article
Publication year: 2024
Journal
Book Volume: 19
Article Number: e0294015
Journal Issue: 2 February
DOI: 10.1371/journal.pone.0294015
Abstract
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.
Authors with CRIS profile
Involved external institutions
Rheinische Friedrich-Wilhelms-Universität Bonn
Germany (DE)
Ruprecht-Karls-Universität Heidelberg
Germany (DE)
Technische Universität Dresden
Germany (DE)
Germany (DE)
Ruprecht-Karls-Universität Heidelberg
Germany (DE)
Technische Universität Dresden
Germany (DE)
How to cite
APA:
Alamoudi, E., Reck, F., Bundgaard, N., Graw, F., Brusch, L., Hasenauer, J., & Schälte, Y. (2024). A wall-time minimizing parallelization strategy for approximate Bayesian computation. PLoS ONE, 19(2 February). https://dx.doi.org/10.1371/journal.pone.0294015
MLA:
Alamoudi, Emad, et al. "A wall-time minimizing parallelization strategy for approximate Bayesian computation." PLoS ONE 19.2 February (2024).
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