Through the fast-simulation project, we're working to develop the next-generation simulation software used for describing the passage of particles through matter. We aim to recast classical particle-transport simulation in a form that enhances both code- and data-locality and its vectorisation potential, using instruction-level parallelism to improve performance. Another important goal is to integrate generic fast-simulation techniques based on machine-learning approaches.
The main driving force behind this work is the LHC experiments’ vital need to increase the throughput of their simulated data samples for the HL-LHC era. Conservative projections suggest that simulation needs are likely to increase by a factor of ten compared to today. We expect that a speed-up of a factor of up to five can be achieved through code modernisation, with the additional speed-up being driven by fast-simulation approaches. Our research into new vectorisation techniques and the development of vectorised modules also has benefits in many other areas of computing for high-energy physics beyond simulation.
During 2017, we prototyped a new scheduling approach that splits the stepping procedure for particle tracks into stages, accumulating several particles before actually performing the actions involved at each stage. This approach makes it possible to vectorise additional components of the framework, such as magnetic-field propagation and the physics models. This new version has a much smaller memory footprint, is topology aware, and can be configured to benefit from memory locality. In addition, we carried out work to vectorise the modules for magnetic field propagation, as well as those for geometry navigation.
Development work on technologies to accelerate physics simulation workloads will continue; this work is being pursued as part of an international collaboration and is one of the main R&D activities of the ‘software development for experiments’ group at CERN’s Experimental Physics department. The project is now targeting possible applications in the existing LHC experiment frameworks, as well as extension to use cases in other fields of scientific research, such as image analysis or biological simulation.
- G. Amadio et al., GeantV alpha release, Proc. Advanced Computing and Analysis Techniques in Physics Research, Seattle, USA, 2017. cern.ch/go/67dM
- F. Carminati, A. Gheata and S. Vallecorsa, The GeantV prototype on KNL 2017 Intel Xeon Phi User's Group (IXPUG) (11 March), Presented at Annual Spring Conference, Cambridge, 2017. cern.ch/go/D7sG