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 > hpx-users@stellar.cct.lsu.edu > https://mail.cct.lsu.edu/mailman/listinfo/hpx-users > _______________________________________________ hpx-users mailing list hpx-users@stellar.cct.lsu.edu https://mail.cct.lsu.edu/mailman/listinfo/hpx-users
