Title: The Tensor Algebra Compiler

Speaker: Saman P. Amarasinghe, MIT Professor

Time: 2018.9.14 16：30-17：30

Venue: Lecture Hall, FIT Building

Abstract：Tensor algebra is a powerful tool with applications in machine learning, data analytics, engineering and science. Increasingly often the tensors are sparse, which means most components are zeros. Programmers are left to write kernels for every operation, with different mixes of sparse and dense tensors in different formats. There are countless combinations, which makes it impossible to manually implement and optimize them all. The Tensor Algebra Compiler (TACO) is the first system to automatically generate kernels for any tensor algebra operation on tensors in any of the commonly used formats. Its performance is competitive with best-in-class hand-optimized kernels in popular libraries, while supporting far more tensor operations. For more information, see tensor-compiler.org.

Bio：Saman P. Amarasinghe is a Professor and the Associate Department Head in the Department of Electrical Engineering and Computer Science at Massachusetts Institute of Technology and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL) where he leads the Commit compiler group. Under Saman's guidance, the Commit group developed the StreamIt, PetaBricks, StreamJIT, Halide, Simit, and MILK programming languages and compilers, DynamoRIO dynamic instrumentation system, Superword level parallelism for SIMD vectorization, Program Shepherding to protect programs against external attacks, the OpenTuner extendable autotuner, and the Kendo deterministic execution system. He was the co-leader of the Raw architecture project. His research interests are in discovering novel approaches to improve the performance of modern computer systems without unduly increasing the complexity faced by the end users, application developers, compiler writers, or computer architects. Saman was the founder of Determina Corporation and a co-founder of Lanka Internet Services Ltd. Saman received his BS in Electrical Engineering and Computer Science from Cornell University in 1988, and his MSEE and Ph.D from Stanford University in 1990 and 1997, respectively.