22

One mistake you already described is using virtualization. Nothing is more reliable than running your code directly on your hardware, without intermediaries (even when the virtualization software and Windows are evolving each day). It will be better to have a dual-boot system if you really need Windows than virtualization. To avoid mistakes, you need to ...


19

"Are there critical mistakes to avoid when creating a matter modeling workstation (32-128 cores)?" 32-128 is quite a big range since at 128 you'd be limited to non-mainstream chips like the Ampere and as far as I know, the most cores in an Intel processor apart from the Xeon Phi line (which was discontinued in 2020 and wasn't a conventional ...


12

I would suggest to stay away from niche solutions and start with one mainstream machine. The most direct reason is that you get much more computing power for the same money if you buy mass-produced hardware. A more indirect reason is that everything you buy next year will be 20% cheaper or faster, your choice, cumulative for each year. If you want optimal ...


10

To invest in new hardware, just to have it crippled by Intel MKL afterwards. This was object of a question I asked here previously. In matter modeling, it's usual for people to compile software themselves, instead of relying in precompiled binaries, so it can be tuned to the particular processor model in their machine, for example, taking advantage of more ...


10

Buying an Intel CPU would be one, for sure. (unless you are buying an Ice Lake server) I think the biggest decision to make is to build a CPU box or a GPU box. For atom-centered Gaussian basis set quantum chemistry you probably going to want a CPU box, with lots of RAM and a very fast and a high endurance SSD.


9

I don't think this is something that you can model accurately in an ab initio manner, as there is no good method for predicting the thermal conductivity of amorphous materials yet, let alone materials that are both amorphous and have macroscopic inhomogeneities. IMHO the way to go is to collect experimental thermal conductivity data on soil samples with ...


7

Tensorflow and FireWorks are different kinds of software. The "workflow management" features of Tensorflow are primarily designed to manage running Tensorflow itself, while FireWorks and related tools are designed to manage running other software. Tensorflow is a library for machine learning. It can be used to develop, train and evaluate machine ...


7

One critical mistake is to invest heavily in GPUs before doing a cost/benefit analysis. A single GPU may cost $2000, which can already get you 2-3 good compute nodes. Many codes (e.g. https://gaussian.com/g16/gpus.pdf) advertise speedups that are underwhelming: the GPU speedup should be at least an order of magnitude, when the CPU code is already close to ...


6

Our Method We have created a machine learning model for estimating the thermal conductivity of any soil, which we have made publicly available at soilconductivity.com. Our model is significantly more accurate than any other existing method that we are aware of (MAE of around 0.08 W/mK), besides direct physical measurement of the soil, of course. (here is a ...


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