numba acceleration #18
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Addition of
numbaJIT'ing to a number of different functions.JITKWis a constant passed to each@jitdecorator to easily enable/disable the compilation.Several functions also have a parallel
kwargto turn it on and off. I've tested a number of functions withparallel=True, and many of them will crash due to what I believe is accessing mutable objects in a shared memory space. I've added a comment these.I've also found that several functions run faster without parallelization. I've also added a comment to note these.
In order to add numba acceleration to the
kb2dfunction, I've had to remove all objects from the JIT'ed code. This includes thescipy.cKDTreeobject, which is instead evaluated prior to being passed into the compiled function. While this increases memory use, the computation time is decreased by a factor of >100, so the trade-off is worth it for most problems. Because of this significant change, I've created a new functionkb2d_jitthat is accelerated, and left thekb2dfunction as it was. Reimplementation of the k-d tree to allow numba compilation, would reduce the memory usage, but some language features used byscipy.KDTreearen't currently supported so it would require significant rework.There's also an immense amount of reformatting, and a lot of unnecessary white space deletion. This is done by an auto-formatting tool,
autopep8, to make the code conform to PEP 8 standards.