Monte Carlo detector transport codes are one of the backbones in high-energy physics. They simulate the transport of a large variety of different particle types through complex detector geometries based on a multitude of physics models.
Those simulations are usually configured or tuned through large sets of parameters. 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 MCReplay engine which is a quasi-Monte-Carlo transport engine able to fast replay pre-recorded MC steps. This engine for instance allows to study the impact of cut 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 the reduction of CPU time of Monte Carlo detector transport by 30\%. In addition, further possible scenarios will be discussed.
|Consider for long presentation||No|