jewettaij
commented
2 years ago Hi Hassan There is currently no way to run moltemplate in parallel. I think it is likely that memory limitations are the main problem you are having. Moltemplate requires between 3GB up to 12 GB of RAM per 10^6 atoms in your simulation. This is very wasteful, but it's difficult to fix. (See historical details below.) The large memory used by moltemplate also contributes to the long time it takes moltemplate to run. (Computers run slow when running low on RAM and using swap.) To work around both of these issues, you have two options: 1) Here's a weird hack which can reduce memory usage and time: If possible, divide your system into smaller pieces. For example, divide your simulation into 8 identical pieces, each of which is half as large in the X,Y,Z directions. Use moltemplate.sh to create LAMMPS files describing one of these small pieces ("system.data" and "system.in.init" and "system.in.settings", and "system.in.charges" if present). Then use ltemplify.py to convert these files into a single LT file (eg "subsystem.lt". Then move your original files into another directory and create a new "system.lt" file which makes 8 copies of this "subsystem.lt". (I'm passing out now, but if I remember tomorrow, I'll edit this post to add some more details how this is done.) If you run out of memory again during this step, try using TopoTools to load the DATA file for the subsystem and duplicate it. (Topotools might be a lot faster than moltemplate as well.) 2) Alterntively, rent computer time from a machine with at least 128GB of RAM (preferably 256GB). Such computer can be rented from Amazon). (If I remember correctly the price was about a dollar per hour, and cheaper if you use "spot pricing") 3) You can reduce the time it takes to run moltemplate by running moltemplate.sh with the "-nocheck" argument. This will not reduce memory usage, but it should reduce the time it takes to run moltemplate by roughly a factor of 2. Unfortunately this also disables syntax-error messages which are very useful when you are designing your simulation. So only use this argument when you are certain there are no errors in your LT files. If you do this, I suggest you build a much smaller version of your system first and make sure there are no errors. Then when you are ready to build the full-size system, run moltemplate with the "-nocheck" argument.
History
Unfortunately, when I wrote moltemplate, I was not aware of the large memory overhead required by python. In python, every object you instantiate (including a tiny molecule in a simulation with a single atom) requires about a kilobyte of memory. (After some effort, I think I was able to bring this down to about 300 bytes using "slots"). I started out running simple, small coarse-grained simulations using moltemplate. I was not thinking of running huge simulations at the time. But moltemplate has grown more than I thought. I am honestly flattered that moltemplate has been so successful that these kinds of questions even come up. But I'm sorry it creates headaches for you.
hnadeem2
Hi andrew,
I have a martini based coursegrain simulation in which the size of the box is large. There are 3 species of atoms totaling around 12million atoms. Moltemplate always crashes through the run and it takes forever, is it possible it on multiple cores with mpi/mpiexec.
Hassan