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Comments (7)

• snovv_crash
  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
  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.
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  owlbite
  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"?
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  trevyn
  Isn't this where Mojo is going?