Thank you for your advice. As I told you yesterday, here is my Julia source code for tiling parallel matrix-matrix multiplication.
https://github.com/usefulhyun/mmm-julia/blob/master/src/mmm_tiling_parallel.jl It is quite rough version, because I made it hastily, but I checked it work correctly. I remember you are interested in mentoring students who want to work in linear algebra, is it including parallel algebra routine? Following your advice to pick one parallelism (using tiling, data parallelism), I want to work in tuning the performance of typical algorithm such as the singular value decomposition or linear solve and so on. Can you tell me what is the best choice I will be able to focus on? Specifically, What do you think which linear algebra algorithm would be suitable for cache-conscious data parallelism? Sincerely, Yonghyun. 2016년 3월 19일 토요일 오전 12시 32분 54초 UTC+9, Hyun 님의 말: > > Hello, my name is Yonghyun, I'm working towards on a master's degree in > Computer Science. > > I am interested in the idea "Native Julia implementations of massively > parallel dense linear algebra routines" and "Native Julia implementations > of massively parallel sparse linear algebra routines". > > My Proposal is like that I will optimize linear algebra operation by > several kind of parallelism such as instruction-level, data, pipelining, > task parallelism. In order to introducing all of these parallelizations and > maximizing the performance, I want to create new data type I named it > LazyEvalMat. Using lazy evaluation remove unnecessary computation and It > can improve the performance. > > However, I don't know it is appropriate topic. What I explained above is > my big picture that I want to achieve in the future. I think that 3 months > is not too long to finish them. > I tried creating my own matrix power function using Python before, I used > data parallelism(tiling), task parallelism(using topology) and pipelining > parallelism(using lowered dependency). > > May I kindly ask you what is a proper idea for me, whether the topic that > I am interested in is suitable for me and what should I do? > And How do I find my mentors? > > Best, > Yonghyun. >
