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Currently, NNDescent runs slower than it is supposed to. Currently, the bottlenecks are functions generate_graph_updates
and sample_candidates
. In both implementations that I used to implement NNDescent namely Python implementation and C++ implementation these functions are implemented with technologies that enable parallel/concurrent computations (OMP and Numba). I wasn't able to implement this using vectorisation. For now, I don't have any other ideas to drastically improve NNDescent's runtime.
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