A parameter optimisation toolchain for Monte Carlo detector simulation

Abstract

Monte Carlo detector transport codes are one of the backbones in high-energy physics computing. They simulate the transport of a large variety of different particle types through complex detector geometries based on different physics models. Those simulations are usually configurable through a large set of parameters allowing for some tuning on the client side. Often, tuning the physics accuracy on the one hand and optimising the resource needs on the other hand are competing requirements. In this area, we are presenting a toolchain to tune Monte Carlo transport codes which is capable of automatically optimising large sets of parameters based on user-defined metrics. The toolchain consists of two central components. Firstly, the MCReplayEngine which is a quasi-Monte-Carlo transport engine able to fast replay pre-recorded MC steps. This engine for instance allows one to study the impact of parameter variations on quantities such as hits without the need to perform new full simulations. Secondly, it consists of an automatic and generic parameter optimisation framework called O2Tuner. The toolchain’s application in concrete use-cases will be presented. Its first application in ALICE led to a reduction of CPU time of Monte Carlo detector transport by 30%. In addition, further possible scenarios will be discussed.