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Efficient sparse computations using linear algebra aware compilers (2025)

58 points by matt_d 5 days ago | 7 comments

Curious how this would deal with things like Kahan Summation, which corrects floating point errors that theoretically wouldn't exist if you had infinite precision representations.

strujillo 3 hours ago

Sparse workloads are a really good fit for scientific discovery pipelines, especially when you're searching over candidate equation spaces.

In practice, even relatively small systems can surface meaningful structure. I’ve been using sparse regression (SINDy-style) on raw solar wind data and was able to recover things like the Sun’s rotation period (~25.1 days estimate) and non-trivial scaling laws.

What becomes limiting pretty quickly is compute efficiency when you scale candidate spaces, so compiler-level optimizations like this feel directly relevant to making these approaches practical at larger scales.

owlbite 7 hours ago

It will be interesting to see if this solves any issues that aren't already addressed by the likes of matlab / SciPy / Julia. Reading the paper it sounds a lot like "SciPy but with MLIR"?

geremiiah 7 hours ago

It's more like OpenXLA or the PyTorch compiler, that codegens Kokkos C++ kernels from MLIR defined input programs, which for example can be outputted from PyTorch. Kokkos is common in scientific computing workloads, so outputting readable kernels is a feature in itself. Beyond that there's a lot of engineering that can go into such a compiler to specifically optimize sparse workloads.

What I am missing is a comparison with JAX/OpenXLA and PyTorch with torch.compile().

Also instead of rebuilding a whole compiler framework they could have contributed to Torch Inductor or OpenXLA, unless they had some design decisions that were incompatible. But it's quite common for academic projects to try to reinvent the wheel. It's also not necessarily a bad thing. It's a pedagogical exercise.

convolvatron 4 hours ago

I think the exactly opposite, if someone was able to build a framework that doesn't overly constrain the problem, and doesn't require weeks of screwing around with the build, integration of half baked components and insane amounts of boilerplate, that would be a fantastic contribution in and of itself even it didn't advance the state of tensor compilation in any other way.

trevyn 6 hours ago

Isn't this where Mojo is going?

uoaei 5 hours ago

Speaking of, where is Mojo?