• Why is getting good performance important?

  • When should I not care about getting good performance?


  • Know the scientific value of GROMACS performance

  • Know the human value of getting “good enough” performance

Why do we need good performance from GROMACS

Molecular dynamics simulations iteratively solve equations of motion to sample a thermodynamic ensemble. Particle positions are used to compute forces, which update velocities, which in turn update the positions to new values. This procedure iterates often billions of times. Eventually we will sample all parts of the ensemble, ie. “solve” the equations of motion.

The time steps involved are normally femtoseconds, however we normally want to make conclusions about macroscopic observables that change much more slowly. Since we need to make very many observations to have a statistically relevant sampling from the ensemble, it’s important to make sure that each of those tiny time steps is computed as efficiently as possible. The more observations you make, the more likely your scientific conclusions are reliable. That is why so much effort has gone into engineering GROMACS to compute those as efficiently as possible. Your compute budget is probably limited and you want to get the best value from it.

If you just run gmx mdrun then it looks at your simulation and the available hardware and does an OK job at guessing what might be best. Sometimes it tries some alternative equivalent approaches and uses the most efficient one. Unfortunately the range of possible simulations and available hardware is huge, so the automation often doesn’t get the best result. Sometimes you need to get involved and guide the choice.

When do we not care about performance/efficiency?

There’s a trade-off between human time and computer time. Computers are cheaper than humans, and it can be a false economy to spend a lot of human time in order to work out how to use less computer time.

  • If it’s a very short-lived task, like the minimization and equilibration phases of an MD simulation, then generally you should just accept the default performance.

  • If it’s a something you will do only once, don’t worry much about optimizing it.

  • If you’re just beginning your work with GROMACS, focus on learning new things, not maximizing performance.

Rule of thumb: Unless you’re going to run the same kind of simulation on the same hardware for more than a week, reading another paper or designing another simulation will be a better use of your time than playing with anything in this tutorial. The computer can do your simulation in the background and you’ve done something profitable in the meantime.

Easy global wins

When performance matters, you should be running mdrun on a cluster where the sysadmins have typically done a good job installing GROMACS for you. But maybe your sysadmin is an over-worked postdoc, etc. mdrun reports in the log file how it was built, and it is worth knowing what to look for, so you can either suggest an improvement or do it yourself. This will improve things every time you run mdrun. You can also see this output from gmx -version or gmx_mpi -version.

Key points:

  • seek to use the latest version of compilers, GPU drivers, GPU libraries

  • check the log file for warnings that GROMACS could be configured better for your hardware

  • don’t use a version compiled with double precision unless you know a specific reason why you need to


Checking the build configuration of GROMACS will run fast.

See also

Check for the current list of suggestions from the GROMACS developers.


  • Consider optimizing long-running production simulations

  • Otherwise, go do something with scientific value!