Hacker News Reader

Comment by dekhn | Hacker News Reader

that's old tech, these days it's usually some sort of PPPM (particle-particle particle-mesh) which parallelizes better.

But that's for classical simulations. Full configuration interaction is effecftively computing the schrodinger equation at unlimited precision, in principle if you could scale it up you could compute any molecular property desired, assuming QM is an accurate model for reality.

View on HN

p3m, well pme, is exactly what we used for our calculations ;)

i never did any qm work beyond basic parameterization

i'm guessing you are/were also computational physics guy :)

I was a computational biologist for many years, which included a bunch of biophysics. I did extensive work with PME about 20 years ago, on supercomputers. It's a pretty neat technique (https://en.wikipedia.org/wiki/Ewald_summation), once you wrap your head around it!

yup, we used PME for non-bonded calculations in our simulations and to calculate things like electric potentials. I finished a biophysics phd back in 2020 and focused mainly on fluid flow.

Pretty cool, what're you up to now?

helping genentech scientists move to the cloud. I stopped being a scientist a long time ago and now I just sort of help scientists with the stuff I'm already good at.

funny enough I'm doing the exact same thing in public sector education. I'm always curious where people in our field end-up.

i saw some of your other comments about being at google. did you touch jax-md at all?

https://github.com/google/jax-md

I talked to the team, but unfortunately, jax-md at the ttime didn't do bond angles or torsions, so it wasn't good for biomolecular simulations.

My work mostly predated tensorflow and was much more about massive-scale embarassingly parallel computing, and produced some interesting large-scale results from MD and protein folding.

https://www.nature.com/articles/nchem.1821 https://onlinelibrary.wiley.com/doi/full/10.1002/pro.2389

yup, i noticed that when i saw the first commits; in fact i thought it was someone's pet project. however, when i read that first odenet paper, it's clear keeping track of the gradients is extremely useful.

I'm very familiar with the first paper, the second author was on my committee.

so what does a cloud migration at a biotech company mean?

is it sort of a standard orchestrator + warehouse/lakehouse + distributed compute + cicd tools stack?

"is it sort of a standard orchestrator + warehouse/lakehouse + distributed compute + cicd tools stack?"

Ideally, yes, exactly. Except there are 100 orchestrators, 100 small local warehouses, and CI/CD is mostly jenkins.

Some things get forklifted over. I'm not trying to push people to adopt cloud native practices, just move them off physical onprem resources. Even that is a challenge because of data gravity.

Reply on news.ycombinator.com