How people imagine scalable parallelism works and how it actually works doesn’t have a lot of overlap. The code is often boringly single-threaded because that is optimal for performance.

The single biggest resource limit in most HPC code is memory bandwidth. If you are not addressing this then you are not addressing a real problem for most applications. For better or worse, C++ is really good at optimizing for memory bandwidth. Most of the suggested alternative languages are not.

It is that simple. The new languages address irrelevant problems. It is really difficult to design a language that is more friendly to memory bandwidth than C++. And that is the resource you desperately need to optimize for in most cases.

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.

1) It mentions in passing the hardware abstraction not being as universal as it seemed. This is more and more true, once we started doing fpgas, then asics, and as ARM and other platforms starting making headway, it fractured things a bit.

GPUs too: I'm still a bit upset about CUDA winning over OpenCL, but Vulkan compute gives me hope. I haven't messed with SYCL but it might be a future possibility too.

2) The real crux is the practical, production interfaces that HPC end users get. Normally I'm not exposing an entire cluster (or sub cluster) to a researcher. I give them predefined tools which handle the computation across nodes for them (SLURM is old but still huge in the HPC space for a reason! When I searched the article for "slurm" I got 0 hits!) When it comes to science, reproducibility is the name of the game, and having more idempotent and repeatable code structures are what help us gain real insights that others can verify or take and run with into new problem/solutions. Ad-hoc HPC programming doesn't do that well, like existing languages slurm and other orchestration layers handle.

Sidenote: One of the biggest advances recently is in RDMA improvements (remote direct memory access), because the RAM needs of these datasets are growing to crazy numbers, and often you have nodes being underutilized that are happy to help. I've only done RoCE myself though and not much with Infiniband, (sorry Yale, thats why I flubbed on the interview) but honestly, I still really like RoCE for cluster side and LACP for front facing ingress/egress.

The point is existing tooling can be massaged and often we don't need new languages. I did some work with Mellanox/Weka prior to them being bought by Nvidia on optimizing the kernel shims for NFSv4 for example. Old tech made fast again.

But, when I encountered OpenCL and CUDA about ten years ago, I was struck by just how much these were delivering the SPMD parallel programming model in finished products. Around 1995, these were often C dialects with some wonky compiler that each research group just barely kept together. By 2015, they were just bundled up inside a graphics driver or similarly commoditized runtime environment.

Also, the GPU of 2015 was delivering the throughput we dreamed of in supercomputers back then. A teraFLOP went from a strategic theme to something you could deploy to your desktop.

The article neglects that all of C, C++, and Fortran have evolved over the last 30 years.

Also, you'll find significant advances in the HPC library ecosystem over the trailing years. Consider, for example, Trilinos (https://trilinos.github.io/index.html) or Dakota (https://dakota.sandia.gov/about-dakota/) both of which push a ton of domain-agnostic capabilities into a C++ library instead of bolting them into a bespoke language. Communities of users tend to coalesce around shared libraries not creating new languages.

I've never worked in HPC but it seems it should be relatively simple to find a C/C++ dev that can pick up OpenMP, or one that already knows it, compared to hiring people who know Chapel.

The "scaling down" factor (how easy or interesting it is to use tool X for small use) seems a disadvantage of HPC-only languages, which creates a barrier to entry and a reduction in available workforce.

It may not be dead, but it seems much harder for languages to gain adoption.

I think there are several reasons; I also suspect AI contributes a bit to this.

People usually specialize in one or two language, so the more languages exist, the less variety we may see with regards to people ACTUALLY using the language. If I would be, say, 15 years old, I may pick up python and just stick with it rather than experiment and try out many languages. Or perhaps not even write software at all, if AI auto-writes most of it anyway.

(What HPC does need, IMNSHO, is to disband or disregard WG5/J3, get people who know what they're doing to fix the features they've botched or neglected for thirty years, and then have new procurements include RFCs that demand the fixed portable Fortran from system integrators rather than the ISO "standard".)

The Art of High Performance Computing (a comprehensive series of textbooks) - https://theartofhpc.com/

Previous discussion - https://news.ycombinator.com/item?id=38815334

That's not to say that new things don't happen there, it's just that I find a lot of old stuff that was shown to be bad decades ago still being in vogue in HPC. Probably because it's a relatively small field with a lot of people there being academics and not a lot of migration to/from other fields.

You've probably never heard of `module` (either Tcl or Lmod). This is a staple of HPC world. What this thing does is it sources or (tries to) remove some shell variables and functions into the shell used either interactively or by a batch job. This is a beyond atrocious idea to handle your working environment. The information leaks, becomes stale, you often end up loading the wrong thing into your environment. It's simply amazing how bad this thing is. And yet, it's just everywhere in HPC.

Another example: running anything in HPC, basically, means running Slurm batch jobs. There are alternatives, but those are even worse (eg. OpenPBS). When you dig into the configuration of these tools, you realize they've been written for pre-systemd Linux and are held together by a shoestring of shell scripting. They seldom if at all do the right thing when it comes to logging or general integration with the environment they run in. They can be simultaneously on the bleeding edge (eg. cgroup integration or accelerator driver integration) and be completely backwards when it comes to having a sensible service definition for systemd (eg. try to manage their service dependencies on their own instead of relying on systemd to do that for them).

In other words, imagine a steam-punk world, but now it's in software. That's sort of how HPC feels like after a decade or so in more popular programming fields.

Also, a lot of code written for HPC is written the way it is not because the writer chose the language or the environment. The typical setup is: university IT created a cluster with whatever tools they managed to put there eons ago, and you, the code writer, have to deal with... using CentOS6 by authenticating to university's AD... in your browser... through JupyterLab interface. And there's nothing you can do about it because the IT isn't there, is incompetent to the bone and as long as you can get your work done somehow, you'd prefer that over fighting to perfect your toolchain.

Bottom line, unless a language somehow becomes indispensable in this world, no matter its advantages, it's not going to be used because of the huge inertia and general unwillingness to do beyond the minimum.