Yes, for over a decade now and it's already available in the cloud. It's a spinn-off of human brain project. Search for spinnaker neuromorphic computing.
I had a quick look around and it seems like this is very specifically designed and used for the Human Brain Project, which ended in 2023 and at least according to wiki the most recent version of this was from 2019. Is it still being worked on, and is it actively being developed for anything outside of brain simulation?
Don't worry, you're just getting old. It happens to all of us. At some point we simply stop being excited about new shiny things and start to prefer stuff we know and rely on. In especially bad cases, some start seeing new shiny things as threat to the stability of their lives.
I'm baffled by the fact that NUMA is still an issue in 2026. My impression is that this was all solved back in dotcom era already on those big SUNs. At least in HPC we solved this already in mid 2000s. Why is supposedly modern world still wasting time on this? Kernel these days exposes just about everything you would ever want to know about a system topology and every runtime should be making use of that information. If it does not, I cannot consider it ready for this century.
Because numa topology is an optimisation problem with a wide solution space, and that its configuration and setup depends on the amount of physical cpus and cores; how the RAM is connected to which lanes; and on and on it goes.
> If it does not, I cannot consider it ready for this century.
Sun was one of the few RISC design houses who stubbornly resisted NUMA in favour of SMP, which they have perfected in the hardware and in the software (Solaris). The «Solaris internals» book discusses the subject of SMP vs NUMA vs ccNUMA in the «Parallel systems architectures» section (3.2).
You might be thinking SGI who went in big on NUMA. IBM and HP have also built ccNUMA systems (e.g. HP Superdome and Superdome 2).
All these fancy HPC languages are all nice and dandy, but the hard reality I see on our cluster is that most of the work is done in Python, R and even Perl and awk. MPI barely reached us and people still prefer huge single machines to proper distributed computing. Yeah, bioinformatics is from another planet.
Bioinformatics is an outlier within HPC. It's less about numerical computing and more about processing string data with weird algorithms and data structures that are rarely used anywhere else.
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.
Bioinformatics is also seeing huge gains from rewriting the slow Python code into highly parallel Rust (way less fiddly than C++ for the typical academic dev).
This is not new either. Most of numpy and pandas and other stuff you use the Python C interface and pass arrays in and get data back. You can write small embeddable C libraries pretty easily for real crunching and you get the ease of writing python (basically comprehensible to researchers who understand The MATLAB )
To add on this, what I see gaining traction are "workflow managers", tools that let people specify flow of data through various tools. These can figure out how to parallelize things on their own so users are not burdened with this task.
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 haven't talked to anyone writing C++ code on a HPC cluster that I'm working on in a long, long time. And that's in industrial/chemical/automotive fields.
DB of known proteins is not where the money can be made, designing new proteins is. This is why AlphaFold3 (that can aid in this) is now wrapped in layers of legalese preventing you to actually use it in the way you want. At least that's what my lifescience users tell me. Big Pharma is now paying Big Money to DeepMind to make use of AF3 ...
