Project goal

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.

R&D topic
R&D Topic 2: Computing performance and software
Project coordinator(s)
Federico Carminati
Technical team members
Andrei Gheata, Andrea Luiselli (Intel), Sofia Vallecorsa, Andrea Zanetti (Intel)
Collaborator liaison(s)
Claudio Bellini, Laurent Duhem

Collaborators

Project background

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.

Recent progress

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.

We also began work to investigate novel machine-learning techniques for a generic approach to fast simulation. We achieved promising preliminary results by implementing an approach based on generative adversarial networks to simulate the calorimeter model proposed for the linear collider.

Next steps

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.

Publications

  • G Amadio et al., GeantV alpha release, Proc. Advanced Computing and Analysis Techniques in Physics Research, Seattle, USA, 2017. http://cern.ch/go/67dM
  • F. Carminati et al., Three dimensional Generative Adversarial Networks for fast simulation, ACAT 2017. http://cern.ch/go/BN6r
  • F. Carminati et al., Calorimetry with Deep Learning: Particle Classification, Energy Regression, and Simulation for High-Energy Physics, NIPS 2017. http://cern.ch/go/7vc8

Presentations

  • 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. http://cern.ch/go/D7sG
  • S. Vallecorsa, Machine Learning for Fast Simulation 2017 (June 24), Presented at ISC High Performance, Frankfurt, 2017. http://cern.ch/go/k6sV
  • E. Orlova, Deep learning for fast simulation: development for distributed computing systems (15 August), Presented at CERN openlab summer students’ lightning talks, Geneva, 2017. http://cern.ch/go/NW9k
  • A. Gheata, GeantV (Intel Code Modernisation) (21 September), Presented at CERN openlab Open Day, Geneva, 2017. http://cern.ch/go/gBS6
  • S. Vallecorsa, GANs for simulation (May 2017), Fermilab, Talk at DS@HEP workshop, 2017. http://cern.ch/go/m9Bl
  • S. Vallecorsa, GeantV – Adapting simulation to modern hardware (June 2017), Talk at PASC 2017 conference, Lugano, 2017. http://cern.ch/go/cPF8
  • S. Vallecorsa, Machine Learning-based fast simulation for GeantV (June2017), Talk at LPCC workshop, CERN, 2017. http://cern.ch/go/QqD7
  • S. Vallecorsa, Generative models for fast simulation (August 2017), Plenary talk at ACAT conference, Seattle, 2017. http://cern.ch/go/gl7l
  • S. Vallecorsa, Three dimensional Generative Adversarial Networks for fast simulation, ACAT 2017. http://cern.ch/go/jz6C
  • S. Vallecorsa et al., Tutorial on "3D convolutional GAN implementation in Neon'', Intel HPC Developers Conference 2017. http://cern.ch/go/ZtZ7
  • F. Carminati, S. Vallecorsa, G. Khattak, 3D convolutional GAN for fast simulation, IXPUG Spring Conference 2018. http://cern.ch/go/9TqS