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.
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.
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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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.