Hello, I am Ajai V George, I had previously expressed my interest in doing a GSoC project with HPX. For the past week I was going through the hpx documentation and tutorial. I have been testing out examples and trying to write my own applications. I have also looked through the relevant codebase needed to resolve issues #1141 and #1338. I also read through the two papers on ParalleX for understanding the philosophy behind HPX. I believe that I can definitely complete this project and continue contributing further.
Please have a look at my GSoC application and help me make it better. Here is a link <https://docs.google.com/document/d/1HVHUxlbknKjGroO4SWoLrJ8T-MljpBSohej8QKTBieY/edit?usp=sharing> to it. Regards, Ajai V George On Mon, Mar 13, 2017 at 2:21 AM, Ajai George <[email protected]> wrote: > Hello, > > I am Ajai V George from BITS Pilani, Goa Campus, India. My major is Electrical > and Electronics Engineering. I currently work with Professor Santonu > Sarkar from Centre for High Performance and Dependable Systems > (Department of Computer Science and Information Systems). My project is > Building 2D Algorithms with shared memory in Thrust which is a CUDA > library. I am implementing Berkeley Structured Grid Dwarf by extending > Thrust. > > Due to this project, I have significant experience with CUDA and with > implementing > STL like data structures in CUDA. I have created a block_2d data > structure for Thrust for 2d data storage. I created a Thrust and thus STL > compatible iterator for this. I also created an abstraction layer for > accessing windows (as described in structured grid dwarf) within this > data structure and an accompanying iterator which lazily generates these > windows. I have created highly optimized for_each, transform, reduce, and > transform_reduce functions for both the thrust 1D device vector and my > block_2d data structure. These algorithms use shared memory efficiently > with proper memory access coalescence and no shared memory bank > conflicts. The reduction algorithm also uses a highly optimized tree > based approach. I have also created this framework such that it can be > extended to be used with cuFFT and cuBLAS libraries easily. > > Due to the above background I believe, I can work on the Parallel > Algorithms for HPX project for GSoC 2017, specifically on extending > algorithms like scan, reduce, transform, for_each, fill, etc to work with > hpx::vector. I have already cloned the repo and have built hpx and am > currently looking through the source code to see what would be impacted by > the project and what changes would be required. > > I request help from the community in crafting my proposal, and in > understanding hpx codebase. > > Regards, > > Ajai V George >
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