Yeah, this is numba. I find it really frustrating the extent to which people don't know about it.
Folks, numba is great. To first approximation: write your python function to "look like C"[1] and work over the numpy arrays you probably already have and understand, put a "@jit" decorator on it, and you're done. It JITs and runs your code natively at native performance.
With some extra effort, there are features for SIMD vectorization and thread parallelism, and even GPU backends (that, full disclosure, I've been pretty frustrated by -- but they do work).
[1] Which is to say, don't touch Python interpreter data structures. No dicts or objects or lists or strings, etc... If you're bound by the interpreter[2] you're bound by the interpreter and nothing is going to help you.
[2] A corrolary here is that the kind of C code numba is NOT good for is "following pointers between structs", which is something that can't be expressed in either python or numpy.
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Yeah, this is numba. I find it really frustrating the extent to which people don't know about it.
Folks, numba is great. To first approximation: write your python function to "look like C"[1] and work over the numpy arrays you probably already have and understand, put a "@jit" decorator on it, and you're done. It JITs and runs your code natively at native performance.
With some extra effort, there are features for SIMD vectorization and thread parallelism, and even GPU backends (that, full disclosure, I've been pretty frustrated by -- but they do work).
[1] Which is to say, don't touch Python interpreter data structures. No dicts or objects or lists or strings, etc... If you're bound by the interpreter[2] you're bound by the interpreter and nothing is going to help you.
[2] A corrolary here is that the kind of C code numba is NOT good for is "following pointers between structs", which is something that can't be expressed in either python or numpy.