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GPAW Convergence Behavior

A talk given by Ansgar Schaefer studying convergence behaviour for rutiles is here (pdf).

General suggestions for helping GPAW convergence are here.

A discussion and suggestions for converging some simple systems can be found here.

Other convergence experience:

System

Who

Action

Graphene with vacancy

Felix/JensH

Increase Fermi Temp from 0.1 to 0.2, use cg

Graphene with vacancy

cpo

change nbands from -10 to -20, MixerDif(beta=0.03, nmaxold=5, weight=50.0)

Nitrogenase FeVCo for CO2 reduction

grabow

use Davidson solver (faster as well?), although later jvarley said MixerSum

Several surfaces

aap

Broyden mixer with Beta=0.5

GPAW Memory Estimation

The get a guess for the right number of nodes to run on for GPAW, run the
following line interactively:

gpaw-python <yourjob>.py --dry-run=<numberofnodes>
(e.g. gpaw-python graphene.py --dry-run=16)

Number of nodes should be a multiple of 8. This will run quickly
(because it doesn't do the calculation). Then check that the
following number is <3GB for the 8-core farm, <4GB for the 12-core farm:

Memory estimate
---------------
Calculator  574.32 MiB

Building a Private Version of GPAW

  • Use svn to check out the version of GPAW that you want to use
  • copy /afs/slac/g/suncat/bin/privgpaw.csh into whatever directory you like
  • edit the two variables GPAW_BASE ("base" GPAW release that you want to re-use for numpy, mpi etc.) and GPAW_HOME (directory where you checked out GPAW)
  • Use the commands: 
    privgpaw.csh build
privgpaw.csh test
privgpaw.csh bsub <arguments>

Some notes:

  • You can see a list of available installed versions here (to use for GPAW_BASE).
  • The syntax for the "bsub" option is identical to the gpaw-bsub command described here.
  • The above assumes that the base release is compatible with your checked out GPAW version. Talk to cpo if you have questions about this.
  • The above doesn't include support for a private version of ASE. When that becomes important we will add it.

Jacapo Parallel NEB Example

You can find a Jacapo parallel NEB example here. Some lines need to change for a restart. An example is here.

Some important notes:

  • the number of processors selected for a parallel NEB must be an integer multiple of the NumberOfImages-2 (the images at the end points are "fixed").
  • Parallel NEB settings (e.g. number of cores) can be debugged running in the suncat-test queue with 1 processor per non-fixed image.
  • this restart example turns on the "FIRE" and and "climb" parameters: this is for later in the calculation. The documentation here discusses the reasons for that (although perhaps doesn't spell out clearly what the criteria are for turning those on).
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