Abstract
Pakman: a modular, efficient and portable tool for approximate Bayesian inference
Highlights
The development of high-throughput techniques in the biological sciences has resulted in an abundance of experimental data
Mathematical models are becoming increasingly popular in the biological sciences
These parallel advances have the potential to greatly expand our understanding of biological processes through mathematical modelling and data-driven parameter inference
Summary
The development of high-throughput techniques in the biological sciences has resulted in an abundance of experimental data. Pakman is designed around a modular framework where problem-specific tasks are performed by user executables. Our solution is to implement a layer of “Managers” between the Master and Workers, resulting in a Master–Manager–Worker architecture These Managers are parallel MPI processes that communicate with the Master through MPI and fork Workers as user executables to perform the actual work. Further efficiency improvements are achieved by implementing the Master and Managers with event-based loops This means that the Master and Manager processes are asleep most of the time and only take action when an event requires it (e.g. a Worker has finished its simulation and wants to report its results), leaving the CPU to focus on running simulations. Details on the Travis CI configuration can be found in the code repository
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