I dunno, since research has to be funded, and typically HPC clusters also have to allocate compute resources, the clunkiness of things like MATLAB actually does translate to wasted and inefficient funding.

All other things being equal, suppose two research programs are proposing to study roughly the same thing (say, some novel optimization or basic stuff on something like simple neural networks; and let's pretend this is some years ago when people still actually reached for MATLAB neural net tools). If both request significant compute allocations, and I see one is planning on MATLAB, and the other on PyTorch Lightning, I know for sure I would want to give the MATLAB users far less funding, or even none at all, since they're really going to struggle to properly leverage the CPUs and GPUs available to them, whereas the Lightning people will largely just have this work immediately, and almost certainly be able to iterate faster and be more likely to find something meaningful.

It's a contrived and unfair example, and in practice the real problem is actually the annoying MATLAB licensing mostly, but also it really is a fact that MATLAB screws up even basic stuff in HPC environments (see e.g. https://docs.alliancecan.ca/wiki/MATLAB#Simultaneous_paralle...).

> If both request significant compute allocations, and I see one is planning on MATLAB, and the other on PyTorch Lightning, I know for sure I would want to give the MATLAB users far less funding, or even none at all

I want to say the least offensive way possible that I really do hope you do not ever get to review NSF/NIH grants because of bias like this.

The example that you gave is/was also a solved problem in the HPC systems I used. SLURM/PBS, when properly configured, can ensure that local clusters do not spill over and not use resources that are not supposed to be used.