[Xcelerate]

I wonder how much of the programming language problem is due to churn of the user base. Looking over many comments in this thread, I see “Oh, back when I did HPC...” I used Titan for my own work back in 2012. But after my PhD, I never touched HPC again. So the people writing the code use what’s there but don’t stay long enough to help incentivize new or better languages. Now on the hardware side (e.g., design of interconnects), that more commonly seems to be a full career.

The other issue is that to really get the value out of these machines, you sort of have to tailor your code to the machine itself to some degree. The DOE likes to fund projects that really show off the unique capabilities of supercomputers, and if your project could in principle be done on the cloud or a university cluster, it’s likely to be rejected at the proposal stage. So it’s sort of “all or nothing” in the sense that many codebases for HPC are one-off or even have machine-specific adaptations (e.g., see LAMMPS). No new general purpose language would really make this easier.

[kinow]

Churn of the user base could be playing a role in this, but I think it may not be too significant. In Europe there are multiple universities with HPC masters, which provide new users/devs to HPC. I worked with HPCs in New Zealand, and now I am doing the same in Spain. We hire multiple people from other HPC centers in Germany/UK/Italy, and equally lose people to those sites.

I think the field is actually increasing with AI, digital twins, more industry projects (CFD, oil, models for fisheries, simulations for health diseases, etc.).