[jpecar]

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.

[jltsiren]

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.

[zozbot234]

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).

[calvinmorrison]

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 )

[uecker]

I would say the least thing the scientific community needs is the packaging mess of Python introduced also on the lower level via Rust.

[jpecar]

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.

[jkh1]

Most of the time in bio-related fields, we need high-throughput computing not high-performance computing.

[bluedino]

Python is huge in AI/ML etc as well.

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.