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Ryan Senanayake is an Engineer on the R-Stream polyhedral compiler team. Some of the projects he is currently working on include improving R-Stream’s support for sparse computations, GPU code generation, and exascale programming models.
Ryan received his B.S. and M.E. degrees in Computer Science and Engineering from MIT in 2019 and 2020. While at MIT, Ryan performed research in the compilers group under Professor Saman Amarasinghe. As part of his thesis project, he designed and implemented a comprehensive optimization framework and GPU backend for the Sparse Tensor Algebra Compiler (TACO). This work allowed for the generation of sparse tensor algebra kernels that were competitive with hand-written kernels for both CPUs and GPUs. His thesis work was awarded the first place 2020 Charles and Jennifer Johnson Computer Science Thesis Award.
Earlier in Ryan’s career, at Singular Computing LLC, he was responsible for developing many of the initial parallel algorithms and applications for their approximate-arithmetic accelerator. Ryan has published Android apps with millions of downloads and has won awards in several programming competitions, both at the national and international level.