Abstract:

For the last four decades, a byproduct of Moore's Law has been the

continuous and dramatic increase in the performance of sequential

applications.  Unfortunately, in the current and future generations of

processors, doubling the number of transistors is not leading to any

increase in sequential performance due to power and complexity issues. Thus,

all major processor vendors are moving towards multicore processors.  While

architects have known how to build parallel processors for over a half a

century, the main stumbling block for their wider acceptance has been the

difficulty in programming them. In the first part of the talk I will discuss

the path to multicores, address why parallel programming has been such a

difficult problem to solve and speculate on our ability to crack it this

time around.

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 the second part of the 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 a 16-core 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. 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.