That's fair, I think I should have just said "comparable to Zig". The type'd ness is what I was thinking of, but having actually written some zig in the last few days to play with their Io model / see what passing around an allocator is like, Zig is pretty fantastic.
I still prefer the structure in Mojo, but boy do I miss if/switch as expressions.
"requires" is a strong word, but I implemented an alignment kernel that can do alignments on the GPU.
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.
Me too! I've been using it for bioinformatics related work, and it is absolutely fantastic. I can't wait for it to hit fully open source status so it can be easily recommended.
Having written a lot of Mojo over the last two year, just for fun, it's a really cool language. Ownership model adjacent to Rust, comptime that is more powerful than Zig, Rich type system, first class SIMD support, etc.
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.
I've only tested this when writing my own parser where I could skip the record end checks, so idk if this improves perf on a existing parser. Excited to see what you find!
Removing the wrapping newline from the FASTA/FASTQ convention also dramatically improves parsing perf when you don't have to do as much lookahead to find record ends.
> a testament to the massive gap in perceived vs actual programming ability of the average bioinformatician.
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.
Because I was originally writing some very CPU intensive SIMD stuff, which Mojo is also fantastic for. Once I got that working and running nicely I decided to try getting the same algo running on GPU since, at the time, they had just open sourced the GPU parts of the stdlib. It was really easy to get going with.
I have not used Triton/Cute/Cutlass though, so I can't compare against anything other than Cuda really.
They allow you to write a kernel for Nvidia, or AMD, that can take full advantage of the Hardware of either one, then throw a compile time if-statement in there to switch which kernel to use based on the hardware available.
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.
I have used Mojo quite a bit. It’s fantastic and lives up to every claim it makes. When the compiler becomes open source I fully expect it to really start taking off for data science.
Modular also has its paid platform for serving models called Max. I’ve not used that but heard good things.