Hi Gabriel, Thanks for your interest in this project. Logistic regression model was chosen since it was implemented in similar projects before. But this project should easily work with other learning models too. Binary regression model was chosen for selecting optimum policy, since the Target was to chose earthier sequential or parallel as a policy (0 or 1 -> binary) . For chunk sizes or prefetching distance, the optimum parameter was chosen between more than two candidates, that’s why we used multinomial regression model. About your last question, do you mean using one training data and one training model to choose chunk sizes, prefetching distances and policies together? I don’t think that’s a good idea, since each of them has different candidates and needs totally different training data.
Thanks, Zahra On Mon, Feb 19, 2018 at 3:44 PM Gabriel Laberge <[email protected]> wrote: > > Hi > I'm Gabriel Laberge and i'm interested in doing the ""Applying Machine > Learning Techniques on HPX Parallel Algorithms"" project. I'm quite > new to machine learning but I expect to learn a lot during the > project. I had a few questions to ask you about the HPX smart > executors from reading the article. > > > First of, Why were logistic regression chosen over other method that > you cited in the article (NN,SVM and decision tree). Would it be > possible to implement those methods in one compilation? > > Secondly, I was wondering why you used a binary regression to chose > between sequential and parrallel algorithms and you used Multinomial > regression to choose the chuck size and prefetching distance. Would > there be a possibility to use only one regression to choose all 3 > parameters? > > Thank you for your time. > Gabriel. > > -- Best Regards, Zahra Khatami | PhD Student Center for Computation & Technology (CCT) School of Electrical Engineering & Computer Science Louisiana State University 2027 Digital Media Center (DMC) Baton Rouge, LA 70803
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