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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 rust compiler actually has similar things, but they're not available in stable builds. I suppose there are some issues if principle why not to include them in stable. E.g: https://doc.rust-lang.org/std/intrinsics/fn.prefetch_read_da...
Maybe some time in the future good acceptable abstractions will be conceived for them.. Perhaps using just using nightly builds for HPC is not that far out, though.
Rust is typically slowest (often negligible <3%), C++ has better CUDA support, and C can be heavily optimized with inline assembly (very unforgiving to juniors.)
Also, heavily associated with coding style =3
https://en.wikipedia.org/wiki/The_Power_of_10:_Rules_for_Dev...
Even with HDL defined accelerators, that statement may not mean what people assume. =3
https://en.wikipedia.org/wiki/Latency_(engineering)
https://en.wikipedia.org/wiki/Clock_domain_crossing
https://en.wikipedia.org/wiki/Metastability_(electronics)
https://en.wikipedia.org/wiki/The_Power_of_10:_Rules_for_Dev...
https://www.youtube.com/watch?v=G2y8Sx4B2Sk
Memory bandwidth is often the problem, yes. Language abstractions for performance aim to, e.g., automatically manage caches (that must be handled manually in performant GPU code, for instance) with optimized memory tiling and other strategies. Kernel fusion is another nontrivial example that improves effective bandwidth.
Adding on the diversity of hardware that one needs to target (both within and among vendors), i.e., portability not just of function but of performance, makes the need for better tooling abundantly obvious. C++ isn't even an entrant in this space.
NVidia designs CUDA hardware specifically for the C++ memory model, they went through the trouble to refactor their original hardware across several years, so that all new cards would follow this model, even if PTX was designed as polyglot target.
Additionally, ISO C++ papers like senders/receivers are driven by NVidia employees working on CUDA.
In general, most modern CPU thread-safe code is still a bodge in most languages. If folks are unfortunate enough to encounter inseparable overlapping state sub-problems, than there is no magic pixie dust to escape the computational cost. On average, attempting to parallelize this type of code can end up >30% slower on identical hardware, and a GPU memory copy exchange can make it even worse.
Sometimes even compared to a large multi-core CPU, a pinned-core higher clock-speed chip will win out for those types of problems.
Thus, the mystery why most people revert to batching k copies of single-core-bound non-parallel version of a program was it reduces latency, stalls, cache thrashing, i/o saturation, and interprocess communication costs.
Exchange costs only balloon higher across networks, as however fast the cluster partition claims to be... the physics is still going to impose space-time constraints, as modern data-centers will spend >15% of energy cost just moving stuff around networks for lower efficiency code.
I like languages like Julia, as it implicitly abstracts the broadcast operator to handle which areas may be cleanly unrolled. However, much like Erlang/Elixir the multi-host parallelization is not cleanly implemented... yet...
The core problem with HPC software, has always been academics are best modeled like hermit-crabs with facilities. Once a lucky individual inherits a nice new shell, the pincers come out to all smaller entities who may approach with competing interests.
Best of luck, =3
"Crabs Trade Shells in the Strangest Way | BBC Earth"
https://www.youtube.com/watch?v=f1dnocPQXDQ
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.
(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".)
Distributed computing never really took off in bioinformatics, because most tasks are conveniently small. For example, a human genome is small enough that you can run most tasks involving a single genome on an average cost-effective server in a reasonable time. And that was already true 10–15 years ago. And if you have a lot of data, it usually means that you have many independent tasks.
Which is nice from the perspective of a tool developer. You don't have to deal with the bureaucracy of distributed computing, as it's the user's responsibility.
C++ is popular for developing bioinformatics tools. Some core tools are written in C, but actual C developers are rare. And Rust has become popular with new projects — to the extent that I haven't really seen C++20 or newer in the field.
So from what I see actual programming language doesn't matter as much as how the work is organized. Anything helping people simplify this task is of immediate benefit to the science.
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.
And even knowing OpenMP or MPI may not suffice if the site uses older versions or heterogeneous approaches with CUDA, FPGA, etc. Knowing the language and the shared/distributed mem libs help, but if your project needs a new senior dev than it may be a bit hard to find (although popularity of company/HPC, salary, and location also play a role).
So for e.g. when I did HPC simulation codes in magnetics, there was little point focusing on some of these areas because our codes were dominated by the long-range interaction cost which limited compute scaling. All of our effort was tuning those algorithms to the absolute max. We tried heterogenous CPU + GPU but had very mixed results, and at that time (2010s) the GPU memory wasn't large enough for the problems we cared about either.
I then moved to CFD in industry. The concerns there were totally different since everything is grid local. Partitioning over multi-GPU is simple since only the boundaries need to be exchanged on each iteration. The problems there were much more on the memory bandwidth and parallel file system performance side.
Basically, you have to learn to solve whatever challenges get thrown up by the specific domain problem.
> And even knowing OpenMP or MPI may not suffice if the site uses older versions
To be fair, you always have the option of compiling yourself, but most people I met in academia didn't have the background to do this. Spack and EasyBuild make this much much easier.
There are a couple of big things that are difficult to get your head around:
1) when and where to dispatch and split jobs (ie whats the setup cost of spinning up n binaries on n machines vs threading on y machines)
2) data exchange primitives, Shared file systems have quirks, and a they differ from system to system. But most of the time its better/easier/faster to dump shit to a file system than some fancy database/object store. Until its not. Distributed queues are great, unless you're using them wrong. Most of the time you need to use them wrong. (the share memory RPC is a whole another beast that fortunatly I've never had to work with directly. )
3) dealing with odd failures. As the number of parallel jobs increase the chance of getting a failure reaches 1. You need to bake in failure modes at the very start.
4) loading/saving data is often a bottle neck, lots of efficiecny comes from being clever in what you load, and _where_ you load it. (ie you have data affinity, which might be location based, or topology based, and you don't often have control over where your stuff is placed.)
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.
And Erlang has already run many telecom infrastructures for decades. Surprising given how fragile the multi-host implementation has proven.
Erlang/Elixir are neat languages, and right next to Julia for fun. =3
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.
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.
Your centos6 references made me chuckle :-)