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) |
Enzyme-inspired CO2 reduction |
grabow |
use Davidson solver (faster as well?) |
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 <3GiB:
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 use) and GPAW_HOME (directory where you checked out GPAW)
- Use the commands:
privgpaw.csh build privgpaw.csh test privgpaw.csh bsub <filename.py> <first bsub option> <second bsub option>