In many cases, a program can have many configuration options. The choice can have a large impact on the performance, but may not always be trivial for the layman. It is possible to recommend options that are efficient in most cases. However, if the individual use case leads to significant differences in the optimal choice, automation is preferable. We have analyzed how Bayesian statistics can be applied here. Such an algorithm uses a probabilistic model to generate a good configuration by observing some test runs. For the case of molecular dynamics simulations, we implemented this idea into the C++ library AutoPas. This achieved on average significantly better results than brute force and purely random methods.      «

In many cases, a program can have many configuration options. The choice can have a large impact on the performance, but may not always be trivial for the layman. It is possible to recommend options that are efficient in most cases. However, if the individual use case leads to significant differences in the optimal choice, automation is preferable. We have analyzed how Bayesian statistics can be applied here. Such an algorithm uses a probabilistic model to generate a good configuration by observ...     »