This Message Is From an External Sender
This message came from outside your organization.
Please respond to the list.
The mpd parameter means "maximum projected dimension". You can think of the projected problem as the "sequential" part of the computation, that is not parallelized ("small" dense eigenproblem). When you run with MPI, everything will scale reasonably well except that part, so it is better to keep it small, specially when you request many eigenvalues. A value mpd=2000 might be too large, it may be better to reduce it to 500, say. The paper https://urldefense.us/v3/__https://doi.org/10.1016/j.cpc.2010.09.007__;!!G_uCfscf7eWS!aXJHpy74c0T8xbG9sb7hnBhdH3u-pXrWV3MdGl-kVmNSXkL7ZD9Ox7HGC6fbrAUCChmQwPE-_4lobvIUfmeLfLri$ includes a discussion on the ncv and mpd paramters, mostly in terms of memory usage.
Jose
> El 13 may 2024, a las 20:33, Sreeram R Venkat <srven...@utexas.edu> escribió:
>
> Thank you for the suggestions. I will try out LAPACK/ELPA and the n/2 method. For the latter, how should I choose the MPD? In the examples I could find online, it looked like they were using something like mpd ~ nev/10.
>
> Sreeram
>
