Questions tagged [benchmark-timings]

Questions asking for benchmark CPU/GPU timings or timing comparisons.

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5
votes
1answer
45 views

Case study: KPOINT and band parallelization in Quantum ESPRESSO

I am testing Quantum ESPRESSO with different values for Npool and bands in order to calculate run time. ...
8
votes
0answers
45 views

Benchmarking Monte Carlo simulations of polymers

I have written a simulation engine in C++ to run a Monte Carlo simulation of polymers on a lattice. My code basically plants a polymer on a lattice, and performs certain Monte Carlo moves, including ...
9
votes
2answers
723 views

How to calculate the computational time required for a MD simulation

I have a ligand and receptor to simulate with MD. These are my specs: Receptor has 4674 atoms, Ligand has 273 atoms CHARMM force field MD software GROMACS Computing machine: Amazon g4dn.xlarge (1 GPU ...
11
votes
1answer
316 views

Choice of coordinate system for geometry optimization

For geometry optimization, most QM codes tend to use internal coordinates. Most codes also support Cartesian coordinates. I have always heard the usual "use Cartesian as a last resort, always use ...
16
votes
2answers
438 views

What's the best way to compare two DFT codes?

What's the most efficient way to compare the two DFT codes in terms of computation time, basis sets (for example, one plane wave vs one atomic orbitals), functional performance, etc.?
15
votes
0answers
167 views

CP2K vs BigDFT comparison [closed]

I typically run DFT calculations with 1000 to 5000 atoms using CP2K. This works fine but I'm interested in BigDFT also. Is there anyone here that has experience using BigDFT and is able to compare ...
9
votes
3answers
924 views

Which software is good with generally contracted basis sets?

Recently, I learnt that basis sets can be contracted as segmented (like def2 or 6-31G) or general (like cc-PVTZ, or ANO). The general contraction allows all the primitives to appear in all shells, ...
24
votes
1answer
167 views

Benchmark Timings of Machine Learning Potentials vs Molecular Mechanics Force Fields

Machine learning is an increasingly common tool for developing force fields for molecular dynamics simulations. It's not totally clear what should be considered a machine-learning potential, but let's ...