Overall I think there is going to be a lot of "old" gpu compute hanging around, and now that writing kernels is a lot easier than it has been, we might as well try and see what algorithms we can get working there.

I originally picked up Mojo for the SIMD, not for the GPU kernels. The SIMD usability in Mojo is outstanding.

Paper on the tool I wrote: https://doi.org/10.1093/bioadv/vbaf292

Performance wise it's the first language in long time that isn't just an LLVM wrapper. LLVM is still involved, but they are using it differently than say, Rust or Zig.

Very excited for Mojo once it's open sourced later this year.

This is not really a fair statement. Literally all of software bears the weight of some early poor choice that then keeps moving forward via weight of momentum. FASTA and FASTQ formats are exceptionally dumb though.

I have not used Triton/Cute/Cutlass though, so I can't compare against anything other than Cuda really.

So, you can support either vendor with as-good-vendor-library performance. That’s not lock-in to me at least.

It’s not as good as the compiler being able to just magically produce optimized kernels for arbitrary hardware though, fully agree there. But it’s a big step forward from Cuda/HIP.

Modular also has its paid platform for serving models called Max. I’ve not used that but heard good things.

Why did you rule out Nextflow or Snakemake? I believe they both work with k8 clusters.

Argo doesn’t look great from my standpoint as a workflow author.

Technically snakemake can do it all. But in practice NF seems to scale up a bit better.

That said, if you don’t need the UI for scientists, I’d stick to snakemake.

Nextflow and Snakemake are the two most-used options in bioinformatics these days, with WDL trailing those two.

I really wish Nextflow was based on Scala and not Groovy, but so it goes.

There is a Draft up for dsl3 that adds static types to the channels that I’m very excited about. https://github.com/nf-core/fetchngs/pull/309

I don't mind when other programmers use AI, and use it myself. What I mind is the abdication of responsibility for the code or result. I don't think that we should be issuing a disclaimer when we use AI any more than when I used grep to do the log search. If we use it, we own the result of it as a tool and need to treat it as such. Extra important for generated code.

Preprint can be found here: https://www.biorxiv.org/content/10.1101/2025.06.04.657890v1

Code can be found here: https://github.com/BioRadOpenSource/ish

Comments URL: https://news.ycombinator.com/item?id=44247480

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