Optimizing the parallelization

From VASP Wiki

To find the optimal parallelization setup of a VASP calculation, it is necessary to run tests for each system, algorithm and computer architecture. Below, we offer general advice on how to optimize the parallelization.

Optimizing the parallelization

For repetitive tasks, a few iterations estimate the performance of the full calculation very well. For example, run only a few electronic or ionic self-consistency steps (without reaching full convergence). Compare the time various parallelization setups need to perform these few iterations.

Try to get as close as possible to the actual system. Specifically, use the same or a very similar physical system (atoms, cell size, cutoff, ...); run on the target computational hardware (CPUs, interconnect, number of nodes, ...). If too many parameters are different, the parallel configuration may not be transferable to the production calculation.

In our experience, VASP yields the best performance by combining multiple parallelization options because the parallel efficiency of each level drops near its limit. By default, VASP distributes the number of bands (NBANDS) over the available MPI ranks. But it is often beneficial to add parallelization of the FFTs (NCORE), parallelization over k points (KPAR), and parallelization over separate calculations (IMAGES). Additionally, there are some parallelization options for specific algorithms in VASP, e.g., NOMEGAPAR for parallelization over imaginary frequency points in and RPA calculations. In summary, VASP parallelizes with

To optimize the parallelization, follow this recipe:

  1. Create a list of the relevant parallelization INCAR tags for the specific calculation. Read the documentation for each of the relevant tags to understand the limits and reasonable choices.
  2. For any calculation involving electronic minimization, it can be useful to first run VASP with ALGO=None set in the INCAR file. With ALGO=None the computational setup for the electronic minimization is done without actually performing the minimization. For instance, the FFTs are planned, and the irreducible k points of the first Brillouin zone are constructed. Therefore, some parameters, e.g., the default number of Kohn-Sham orbitals (NBANDS) and the total number of plane waves, are written to the OUTCAR file while using barely any computational time.
  3. Combine the information from the documentation and the dry run into a few possible candidates for a reasonable setup. Run test calculations on a subset of the production run e.g. by reducing the number of steps.
  4. Run the production calculation with the best performing setup.

For the common case of electronic minimization calculations, the following rules of thumb apply:

  • Aim to set the number of ranks to the default value of NBANDS divided by a small integer. Note that VASP will increase NBANDS to accommodate the number of ranks.
  • Choose NCORE as a factor of the cores per node to avoid communicating between nodes for the FFTs. Mind that NCORE cannot be set with OpenMP threading and/or the OpenACC GPU port.
  • The k-point parallelization is efficient but requires additional memory. Given sufficient memory, increase KPAR up to the number of irreducible k points. Keep in mind that KPAR should factorize the number of k points.
  • Finally, use the IMAGES tag to split several VASP runs into separate calculations. The limit is dictated by the number of desired calculations.

Related tags an articles

Parallelization, KPAR, NCORE, KPAR, IMAGES