简介: |
摘要：
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. ?
简历：
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.
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