[gmx-users] results produced by auto-tuning of Coulomb cut-off/grid for PME can not be reproduced by manually setting the Coulomb cut-off and grid spacing
Jiaqi Lin
jqlin at mit.edu
Thu Jan 15 03:22:32 CET 2015
Dear GMX developers,
I've encounter a problem in GROMACS concerning the auto-tuning feature
of PME that bugged me for months. As stated in the title, the
auto-tuning feature of mdrun changed my coulomb cutoff from 1.4 nm to
~3.3 nm (stated in md.log) when I set -npme to be 28 (128 total CPU
cores), and this giving me interesting simulation results. When I use
-notunepme, I found Coulomb (SR) and recip. giving me same energy but
the actual simulation result is different. This i can understand:
scaling between coulombic cut-off/grid size theoretically give same
accuracy to electrostatics (according to GMX manual and PME papers), but
there actually some numerical error due to grid mapping and even if the
energy is the same that does not mean system configuration has to be the
same (NVE ensemble: constant energy, different configuration).
However the thing i don't understand is the following. I am interested
in the result under large coulomb cut-off, so I try to manually set
cut-off and grid space with -notunepme, using the value tuned by mdrun
previously. This give me complete different simulation result, and the
energy is also different. I've tried to set rlist, rlistlong, or both to
equal rcoulomb (~3.3) still does not give me the result produced by
auto-tuning PME. In addition, simulation speed dramatically reduces when
I set rcoulomb to be ~3.3 (using -tunepme the speed remains nearly the
same no matter how large the cutoff is tuned to). I've tested this in
both GMX 4.6.5 and 5.0.1, same thing happens, so clearly it's not
because of versions. Thus the question is: what exactly happened to PME
calcualtion using the auto-tuning feature in mdrun, why it does give
different results when I manually set the coulomb cutoff and grid space
to the value tuned by mdrun without the auto-tuning feature (using
-notunepme)? Thank you for help.
additional info: I use Group cutoff-scheme , rvdw is 1.2.
md.log file:
DD step 9 load imb.: force 29.4% pme mesh/force 3.627
step 30: timed with pme grid 280 280 384, coulomb cutoff 1.400: 1026.4
M-cycles
step 50: timed with pme grid 256 256 324, coulomb cutoff 1.464: 850.3
M-cycles
step 70: timed with pme grid 224 224 300, coulomb cutoff 1.626: 603.6
M-cycles
step 90: timed with pme grid 200 200 280, coulomb cutoff 1.822: 555.2
M-cycles
step 110: timed with pme grid 160 160 208, coulomb cutoff 2.280: 397.0
M-cycles
step 130: timed with pme grid 144 144 192, coulomb cutoff 2.530: 376.0
M-cycles
step 150: timed with pme grid 128 128 160, coulomb cutoff 2.964: 343.7
M-cycles
step 170: timed with pme grid 112 112 144, coulomb cutoff 3.294: 334.8
M-cycles
Grid: 12 x 14 x 14 cells
step 190: timed with pme grid 84 84 108, coulomb cutoff 4.392: 346.2
M-cycles
step 190: the PME grid restriction limits the PME load balancing to a
coulomb cut-off of 4.392
step 210: timed with pme grid 128 128 192, coulomb cutoff 2.846: 360.6
M-cycles
step 230: timed with pme grid 128 128 160, coulomb cutoff 2.964: 343.6
M-cycles
step 250: timed with pme grid 120 120 160, coulomb cutoff 3.036: 340.4
M-cycles
step 270: timed with pme grid 112 112 160, coulomb cutoff 3.253: 334.3
M-cycles
step 290: timed with pme grid 112 112 144, coulomb cutoff 3.294: 334.7
M-cycles
step 310: timed with pme grid 84 84 108, coulomb cutoff 4.392: 348.0
M-cycles
optimal pme grid 112 112 160, coulomb cutoff 3.253
DD step 999 load imb.: force 18.4% pme mesh/force 0.918
At step 1000 the performance loss due to force load imbalance is 6.3 %
NOTE: Turning on dynamic load balancing
Step Time Lambda
1000 20.00000 0.00000
Energies (kJ/mol)
Bond G96Angle LJ (SR) Coulomb (SR) Coul. recip.
1.98359e+05 1.79181e+06 -1.08927e+07 -7.04736e+06 -2.32682e+05
Position Rest. Potential Kinetic En. Total Energy Temperature
6.20627e+04 -1.61205e+07 4.34624e+06 -1.17743e+07 3.00659e+02
Pressure (bar) Constr. rmsd
2.13582e+00 1.74243e-04
Best
Jiaqi
--
Jiaqi Lin
postdoc fellow
The Langer Lab
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