Vitamins (Visual and In Situ Analytics for Molecular Interactive Simulation) is an open source framework for the analysis and visualisation of large molecular dynamics trajectories directly acquired in-situ while the simulation is running, or read from files. Vitamins currently supports Gromacs and MdAnalysis and runs on Linux and Mac OS X. Vitamins is meant to be a flexible and high performance framework that users can customize to support their preferred tools and own analytics scenarios. Vitamins has been used for large simulation running on several thousands cores.

Main Features

  • Trajectory viewer
  • In-situ data extraction and analysis from a Gromacs simulation running in parallel.
  • Visualisation environment with 3D molecular system rendering and graph plotting
  • Save to disk Gromacs data. Outperforms the native Gromacs approach (very significant for high frequency savings).


The amount of data generated by molecular dynamics simulations of large molecular assemblies and the sheer size and complexity of the systems studied call for new ways to analyse, steer and interact with such calculations. Traditionally, the analysis is performed off-line once the huge amount of simulation results have been saved to disks, thereby stressing the supercomputer I/O systems, and making it increasingly difficult to handle post-processing and analysis from the scientist's office. The Vitamins framework is an alternative approach developed to couple the simulation with analysis tools to process the data as close as possible to their source of creation, saving a reduced, more manageable and pre-processed data set to disk. Vitamins supports a large variety of analysis and steering scenarios. Our framework can be used for live sessions (simulations short enough to be fully followed by the user) as well as batch sessions (long time batch executions). During interactive sessions, at run time, the user can display plots from analysis, visualise the molecular system and steer the simulation with a haptic device.


  • Marc Baaden, LBT
  • Matthieu Dreher, INRIA
  • Nicolas Ferey, LIMSI
  • Sébastien Limet, LIFO
  • Jessica Prevoteau-Jonquet, LBT
  • Marc Piuzzi, INRIA
  • Millian Poquet, LIFO
  • Bruno Raffin, INRIA
  • Sophie Robert, LIFO
  • Mikaël Trellet, LIMSI/LBT


This work has been partly funded by the French Agency for Research, project FVNANO ANR-07-CIS7-003 and EXAVIS ANR-11-MONU-003, as well as the “Initiative d'Excellence” program from the French State (Grant “DYNAMO”, ANR-11-LABX-0011).