On Fri, 2013-11-22 at 10:19 -0800, yunshi11 . wrote:
> Dear all,
>
> I have a enzyme-substrate system that includes 80 QM+link atoms (with PM3,
> TIP3P water, PME implemented).
>
> As suggested in Amber12 manual, the scaling of such calculations are not
> good. In my test, both 24-core run and 12-core run gave me 20 ps per day,
> which means 50 days for 1 ns. Thus I wonder how to improve the performance.
Stop using QM. As long as you are using QM on a part of your system,
there is a severe limit to how fast simulations will run. In fact, as
you have seen the best you can get is 20 ps/day, regardless of how many
CPUs you use.
> Should I adjust the number of cores according to the number of QM/link
> atoms? It seems kind of inefficient that NO QM atoms have been assigned to
> 4 of the 24 cores, while the other 20 cores take 4 QM atoms each.
Semi-empirical QM is not particularly parallelizable, in stark contrast
to high-level ab-initio techniques. Unlike HF, DFT, and the more
expensive correlated methods, the bottleneck of semi-empirical
techniques is diagonalization of the Fock matrix rather than computing
the integrals. Diagonalization is not efficiently parallelized, which
means the slowest part of the QM calculation must be run by 1 CPU. You
can throw more CPUs at it, but they will all end up waiting for the
slowest CPU to finish diagonalizing the matrix.
> How about using GPUs?
No. First, Amber does not have GPU support for QM. Also, the matrix
diagonalization makes semi-empirical QM methods difficult to port to
GPUs efficiently, making it unlikely that Amber's QM will ever be ported
to GPUs.
HTH,
Jason
--
Jason M. Swails
BioMaPS,
Rutgers University
Postdoctoral Researcher
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Received on Fri Nov 22 2013 - 11:00:03 PST
