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.
