Compiler Support for Structured Data
Prof. Saman Amarasinghe leads the Commit compiler research group in MIT’s Computer Science & Artificial Intelligence Laboratory (CSAIL), which focuses on programming languages and compilers that maximize application performance on modern computing platforms. He is a world leader in the field of high-performance domain-specific languages. Prof. Amarasinghe’s group developed the Halide, TACO, Simit, StreamIt, StreamJIT, PetaBricks, MILK, Cimple, and GraphIt domain-specific languages and compilers, all of which combine language design and sophisticated compilation techniques to deliver unprecedented performance for targeted application domains such as image processing, stream computations, and graph analytics. Dr. Amarasinghe also pioneered the application of machine learning for compiler optimizations, from Meta optimization in 2003 to OpenTuner extendable autotuner today. With professor Anant Agarwal, he co-led the Raw architecture project, which did pioneering work on scalable multicores. Prof. Amarasinghe’s entrepreneurship activities include founding Determina, Inc. (acquired by VMWare) based on computer security research pioneered in his research group at MIT and co-founding Lanka Internet Services, Ltd., the first Internet Service Provider in Sri Lanka. Prof. Amarasinghe is also the faculty director of MIT Global Startup Labs, whose summer programs in 17 countries have helped to create more than 20 thriving startups. Prof. Amarasinghe developed the popular Performance Engineering of Software Systems (6.172) class with Professor Charles Leiserson. He also created individualized software project classes such as the Open Source Software Project Lab, the Open Source Entrepreneurship Lab, and the Bring Your Own Software Project Lab.
Sun 18 JunDisplayed time zone: Eastern Time (US & Canada) change
11:20 - 12:30 | |||
11:20 15mTalk | Accelerating Sparse Matrix Computations with Code Specialization CTSTA Maryam Mehri Dehnavi University of Toronto | ||
11:35 15mTalk | A General Distributed Framework for Contraction of a Sparse Tensor with a Tensor Network CTSTA Raghavendra Kanakagiri University of Illinois Urbana-Champaign | ||
11:50 15mTalk | Automatic Differentiation for Sparse TensorsVirtual CTSTA Amir Shaikhha University of Edinburgh | ||
12:05 15mTalk | Compiler Support for Structured Data CTSTA Saman Amarasinghe Massachusetts Institute of Technology | ||
12:20 10mPanel | Discussion CTSTA |