Abstract: One promising approach to parallel programming is the use of novel programming language techniques -- ones that reduce the burden on the programmers, while simultaneously increasing the compiler's ability to get good parallel performance. In this talk, I will introduce StreamIt: a language and compiler specifically designed to expose and exploit inherent parallelism in ``streaming applications'' such as audio, video, and network processing. StreamIt provides novel high-level representations to improve programmer productivity within the streaming domain. By exposing the communication patterns of the program, StreamIt allows the compiler to perform aggressive transformations and effectively utilize parallel resources. StreamIt is ideally suited for multicore architectures; recent experiments on the 16-core Raw machine demonstrate an 11x speedup over a single core.

Bio: Saman P. Amarasinghe is an Associate Professor 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). Currently he leads the Commit compiler group and was the co-leader of the MIT Raw project. Under Saman's guidance, the Commit group developed the StreamIt language and compiler for the streaming domain, Superword Level Parallelism for multimedia extensions, DynamoRIO dynamic instrumentation system, Program Shepherding to protect programs against external attacks, and Convergent Scheduling and Meta Optimization that uses machine learning techniques to simplify the design and improve the quality of compiler optimization. His research interests are in discovering novel approaches to improve the performance of modern computer systems and make them more secure without unduly increasing the complexity faced by either the end users, application developers, compiler writers, or computer architects. Saman was also the founder of Determina Corporation, which productized Program Shepherding. 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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