Hi Ray Kim, >> The literature mostly talks about feature acquisition through libclang. >> >> Than under this project, is the HPX distribution including tooling based >> on libclang?
No, the project's solution should be independent on any library. One requirement would be, that it works independent on the compiler. HPX itself provide performance counters [0]. These ones could be used for acquisition of data. Or just data you could measure during run time. >> And I guess part of a machine learning framework -such as xgboost, >> tinydnn, etc... >> >> will also need to be included in the distribution. No, I think this would be not needed. It really depends on your approach. If you collect data and try to generate a model and use the trained model without any machine learning during run time, I would recommend to use scikit-learn [1]. It is easy to use and you can easily play around with different models. For the case when you want to use machine learning during run time, I would recommend to use scikit-learn to find a suitable model. Once, you found this model, you should have a look into the model and implement these algorithms by yourself. I think that we do not need a very sophisticated model and it would be easy to implement this model. In addition, I recommend to read the previous mentioned paper to get some more details. Best, Patrick [0] http://stellar.cct.lsu.edu/files/hpx_0.9.5/html/hpx/manual/performance_counters/counters.html [1] http://scikit-learn.org/stable/ On 02/17/2018 12:14 AM, 김규래 wrote: > Hello Patrick, > > Thank you for your guidance. > > I have few more questions on the subject. > > > > The literature mostly talks about feature acquisition through libclang. > > Than under this project, is the HPX distribution including tooling based > on libclang? > > (And custom build system stuff for the additional compile time > computations?) > > > > And I guess part of a machine learning framework -such as xgboost, > tinydnn, etc... > > will also need to be included in the distribution. > > Am I understanding this project correctly? > > > > Great regards, > > Ray Kim > > > > -----Original Message----- > *From:* "Patrick Diehl"<[email protected]> > *To:* "김규래"<[email protected]>; <[email protected]>; > *Cc:* > *Sent:* 2018-02-16 (금) 07:17:52 > *Subject:* Re: [hpx-users] GSoC 2018, on "applying machine learning > technques ..." project > > > Hi Ray, > > welcome to the community. A good starting point for this project would > be to read this publication [0]: > > Zahra Khatami, Lukas Troska, Hartmut Kaiser, J. Ramanujan and Adrian > Serio, “HPX Smart Executors”, In Proceedings of ESPM2’17: Third > International Workshop on Extreme Scale Programming Models and > Middleware (ESPM2’17), 2017. doi: 10.1145/3152041.3152084, > >>> Then is the goal to analyze data? or also implement the algorithms? > > The goal is to provide a new execution policy [1], which utilizes > machine learning techniques to optimize the computational time. > > So the first step would be to analyze the data. > > In my opinion there are two different ways, one can integrate machine > learning. > > 1) You could collect the data and train a model to obtain parameters for > your function f(x,y,z) -> chunk size. In this case you could use any > existing machine learning library to get these parameters for your function. > > Here, you will implement this function in the execution policy to > estimate e.g. the "best" chunk size for given x,y,z. > > 2) You could train your model at run time to obtain e.g. the "best" > chunk size. In this case you have to implement your chosen machine > learning algorithm for the integration in hpx. > > Best, > > Patrick > > > [0] http://stellar.cct.lsu.edu/pubs/khatami_espm2_2017.pdf > [1] > https://stellar-group.github.io/hpx/docs/html/hpx/manual/parallel/executor_parameters.html > > On 15/02/18 01:53 PM, 김규래 wrote: >> HI, my name is Ray Kim. >> >> I'm a junior EE student in Sogang Univ. Korea. >> >> I have a little experience in C++ and HPC applications and machine > learning. >> >> Here is a link to my github profile for some of my personal projects. >> >> https://github.com/Red-Portal >> >> >> >> I'm interested in the project "Applying Machine Learning Techniques on >> HPX Parallel Algorithms", >> >> however I would like to have more details about it. >> >> The description talks about implementing the algorithms and analyzing >> the performance of these algorithms. >> >> Then is the goal to analyze data? or also implement the algorithms? >> >> >> >> Great admirations for everyone working on HPX. >> >> It would be an honor if I could work with you all. >> >> msca8h at naver dot com >> >> msca8h at sogang dot ac dot kr >> >> Ray Kim >> >> >> >> _______________________________________________ >> hpx-users mailing list >> [email protected] >> https://mail.cct.lsu.edu/mailman/listinfo/hpx-users >> > -- Patrick Diehl diehlpk.github.io
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