Halide is generally good for pipelines that do math on multidimensional arrays, so quite possibly. It has a property that I think would make it hard to use for some applications in scientific computing though - while you can mutate in-place, you can't mutate an array ("Func" in Halide) after it has been used by another pipeline stage. This means that the dependency graph between your pipeline stages has no cycles except for self-cycles, which lets us do lots of clever stuff like bounds inference to guarantee the program is correct regardless of the schedule. I can imagine things like flow simulation having a hard time with that constraint. In the deep learning context this means that CNNs are ideal, but RNNs can be awkward or impossible.
Andrew: have you considered adding something akin to Rust's lifetimes here? While you'd have restrictions on the schedule, it seems like it's still let you express who in the "graph" messes with the data as tightly as possible.
