I am Nikhil Goel (github:nikhilgoel1997), a pre-final year student from
Birla Institute of Technology and Science, Pilani (BITS, Pilani). I've been
contributing to mlpack for the past month and have become familiar with the
codebase. In the past I've done projects on Sentiment analysis, Image
classification and Financial signal processing using machine learning.
I wanted to do a project which would help me improve my understanding of
multiple algorithms and Profiling for parallelization is ideal for that! In
that direction I've studied and grown familiar with the openMP library.
While I want to tackle every algorithm that is implemented in mlpack and
find a way to parallelize it or have a good explanation as to why it is not
parallelizable, doing it properly by 27th (Last day to submit the proposal)
might be a little difficult. Since the project description is vague, what
would be a good number of algorithms for which proper description on how to
parallelize is given in the proposal for a strong proposal. (I believe
there are 5 algorithms that have been parallelized in mlpack and till now,
I've found how to parallelize other algorithms like knn, logistic
regression, naive bayes, pca)
As for the API, I think having an additional option in the algorithm for
using multi-core can be given to the user. Is this a good idea?
I would love to hear suggestions from the mentors to understand if they
feel that I'm approaching this project the correct way.
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