numba-dpex
awkward
numba-dpex | awkward | |
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1 | 4 | |
69 | 793 | |
- | 0.6% | |
9.8 | 9.6 | |
3 days ago | about 4 hours ago | |
Python | Python | |
Apache License 2.0 | BSD 3-clause "New" or "Revised" License |
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numba-dpex
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Intel Extension for Scikit-Learn
> Intel are focused on data-parallel C++ for delivering high performance, rightly or wrongly.
They also invest efforts in making it possible to write high performance kernels in Python using an extension to the numba Python compiler:
https://github.com/IntelPython/numba-dppy
awkward
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Efficient Jagged Arrays
there's a whole ecosystem in Python originally developed for high energy physics data processing: https://github.com/scikit-hep/awkward all because Numpy demands square N-dimensional array
Same technique used everywhere, here's a simple Julia pkg for the same thing: https://github.com/JuliaArrays/ArraysOfArrays.jl/blob/3a6f5b...
But Julia at least has the decency to just support ragged Vector{Vector} out of the box, and it's not that slow
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The hand-picked selection of the best Python libraries released in 2021
Awkward Array.
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Awkward: Nested, jagged, differentiable, mixed type, GPU-enabled, JIT'd NumPy
Numba's @vectorize decorator (https://numba.pydata.org/numba-doc/latest/user/vectorize.htm...) makes a ufunc, and Awkward Array knows how to implicitly map ufuncs. (It is necessary to specify the signature in the @vectorize argument; otherwise, it won't be a true ufunc and Awkward won't recognize it.)
When Numba's JIT encounters a ctypes function, it goes to the ABI source and inserts a function pointer in the LLVM IR that it's generating. Unfortunately, that means that there is function-pointer indirection on each call, and whether that matters depends on how long-running the function is. If you mean that your assembly function is 0.1 ns per call or something, then yes, that function-pointer indirection is going to be the bottleneck. If you mean that your assembly function is 1 μs per call and that's fast, given what it does, then I think it would be alright.
If you need to remove the function-pointer indirection and still run on Awkward Arrays, there are other things we can do, but they're more involved. Ping me in a GitHub Issue or Discussion on https://github.com/scikit-hep/awkward-1.0
What are some alternatives?
pycrown - PyCrown - Fast raster-based individual tree segmentation for LiDAR data
sqlmodel - SQL databases in Python, designed for simplicity, compatibility, and robustness.