Towards a Science of Parallel Programming
Speaker: Keshav Pingali , University of Texas at Austin
Date: June 18 2009
Time: 3:45PM to 5:00PM
Location: 32-D463 (Star)
Host: Professor Arvind, CSAIL-MIT
Contact: Sally O. Lee, 3-6837, sally@csail.mit.edu
Relevant URL: When parallel programming started in the 70's and 80's, it was mostly art: languages such as functional and logic programming languages were designed and appreciated mainly for their elegance and beauty. More recently, parallel programming has become engineering: conventional languages like FORTRAN and C++ have been extended with constructs such as OpenMP, and we now spend our time benchmarking and tweaking large programs no one understands to obtain performance improvements of 5-10%. In spite of all this activity, we have few insights into how to write parallel programs to exploit the performance potential of multicore processors.
To break this impasse, we need a science of parallel programming. In this talk, I will introduce a concept called "amorphous data-parallelism" that provides a simple, unified picture of parallelism in a host of diverse applications ranging from mesh generation/refinement/partitioning to SAT solvers, maxflow algorithms, stencil computations and event-driven simulation. Then I will present a natural classification of these kinds of algorithms that provides insight into the structure of parallelism and locality in these algorithms, and into appropriate language and systems support for exploiting this parallelism.
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Biography:
Keshav Pingali is the W.A."Tex" Moncrief Chair
of Computing in the Computer Sciences department
at the University of Texas, Austin. He received
the Sc.D. degree from MIT in 1986. He was on the
faculty of the Department of Computer Science at
Cornell University from 1986 to 2006,
where he held the India Chair of Computer Science.
Pingali's research has focused on programming
languages and compiler technology for program
understanding, restructuring, and optimization.
His group is known for its contributions to
memory-hierarchy optimization; some of these
have been patented. Algorithms and tools
developed by his projects are used in many
commercial products such as Intel's IA-64 compiler,
SGI's MIPSPro compiler, and HP's PA-RISC compiler.
His current research is focused on programming languages
and tools for multicore processors.
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