Hello Zahra and Kaiser, I have shared the final proposal with STE||AR Group through the GSoC website. I hope you look at it and provide me with feedback if you have time so that I can change it accordingly.
Thanks a lot for the support. Regards, Aditya On Sun, Apr 2, 2017 at 8:12 PM, Zahra Khatami <[email protected]> wrote: > Hi Aditya, > > Thank you for your interest in the machine learning project. As Dr. Kaiser > explained, a compiler gathers static information for ML, then ML will > select the parameters, such as chunk sizes, for HPX's techniques, such as > loop. > We have worked on this project since a couple of months ago, and so far we > have got interesting results from our implementation. > Our focus in the Summer is to implement our technique on a distributed > applications. > So if you have a background in ML and distributed computing, it would be > enough to work on this topic. > I am pretty sure that this phase will result in a conference paper as its > new and super interesting ;) > So if you are interested in this project, go ahead and write your proposal > before its deadline. > > > > 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 > > > On Sun, Apr 2, 2017 at 7:04 AM, Hartmut Kaiser <[email protected]> > wrote: > >> Hey Aditya, >> >> > It would be great if some of you could guide me through the project >> > selection phase so that I can make my proposal as soon as possible and >> get >> > it reviewed too. >> >> The machine learning project aims at using ML techniques to select >> runtime parameters based on information collected at compile time. For >> instance in order to decide whether to parallelize a particular loop the >> compiler looks at the loop body and extracts certain features, like the >> number of operations or the number of conditionals etc. It conveys this >> information to the runtime system through generated code. The runtime adds >> a couple of dynamic parameters like number of requested iterations and >> feeds this into a ML model to decide whether to run the loop in parallel or >> not. We would like to support this with a way for the user to be able to >> automatically train the ML model on his own code. >> >> I can't say anything about the Lustre backend, except that Lustre is a >> high-performance file system which we would like to be able to directly >> talk to from HPX. If you don't know what Lustre is this is not for you. >> >> All to All communications is a nice project, actually. In HPX we sorely >> need to implement a set of global communication patterns like broadcast, >> allgather, alltoall etc. All of this is well known (see MPI) except that we >> would like to adapt those to the asynchronous nature of HPX. >> >> HTH >> Regards Hartmut >> --------------- >> http://boost-spirit.com >> http://stellar.cct.lsu.edu >> >> >> > >> > Regards, >> > Aditya >> > >> > >> > >> > On Sun, Apr 2, 2017 at 5:21 AM, Aditya <[email protected]> >> wrote: >> > Hello again, >> > >> > It would be great if someone shed light on the below listed projects too >> > >> > 1. Applying Machine Learning Techniques on HPX Parallel Algorithms >> > 2. Adding Lustre backend to hpxio >> > 3. All to All Communications >> > >> > I believe I will be suitable for projects 2 and 3 (above). As part of my >> > undergrad thesis (mentioned in the earlier email) I worked with Lustre >> > briefly (we decided, lustre was an overkill for our scenario as we'd >> have >> > to re organize data among nodes even after the parallel read). I have >> > worked with MPI on several projects (my thesis and projects in the >> > parallel computing course) and have a basic understanding of all to all >> > communications work. >> > >> > If someone could explain what would be involved in project 1, it'd be >> > great. >> > >> > Also, please let me know what is expected of the student in projects 2 >> and >> > 3. >> > >> > Thanks again, >> > Aditya >> > >> > >> >> >> >
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