Hello Marcus/Ryan, I am Param Mirani and I want to contribute to MLpack by participating in GSOC 2020 in Profiling for Parallelism project. I have made some pull requests in trying to improve performance of the algorithms on multi-core processors using OpenMP. I had an idea and wanted to know your view on the same.
I wanted to work on following algorithms in the 12 week program based on my knowledge of machine learning algorithms. 1)KNN 2)Decision Trees 3)Perceptron 4)Back-propagation I expect these algorithms to have an excellent speed-up when they use OpenMP. However I feel we would have a difficulty in deciding which parts of the algorithm based on various programs I write for benchmarking of these algorithms. There are some more algorithms which could use OpenMP but speed-up may not be that significant. Along with these I would also like to understand 2-3 more algorithms and work with them in a similar way. I'd like to know if the timeline I've proposed is realistic, if all assumptions are correct, and if there are any more algorithms that you would like me to work on while working on this project. Thanks, Param Mirani.
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