  ABCpy is a highly modular scientific library for Approximate Bayesian
Computation (ABC) written in Python. The main contribution of this paper is to
document a software engineering effort that enables domain scientists to easily
apply ABC to their research without being ABC experts; using ABCpy they can
easily run large parallel simulations without much knowledge about
parallelization. Further, ABCpy enables ABC experts to easily develop new
inference schemes and evaluate them in a standardized environment and to extend
the library with new algorithms. These benefits come mainly from the modularity
of ABCpy. We give an overview of the design of ABCpy and provide a performance
evaluation concentrating on parallelization. This points us towards the
inherent imbalance in some of the ABC algorithms. We develop a dynamic
scheduling MPI implementation to mitigate this issue and evaluate the various
ABC algorithms according to their adaptability towards high-performance
computing.
