Parallel Program = Operator + Schedule + Parallel Data Structure
, The University of Texas at Austin - ECE
Date: Friday, May 09, 2014
Time: 10:00 AM to 11:30 AM Note: all times are in the Eastern Time Zone
Refreshments: 9:45 AM
Host: Professor Arvind, CSG - CSAIL - MIT
Contact: Sally O. Lee, 253-6837, firstname.lastname@example.org
Speaker URL: None
TALK: Parallel Program = Operator + Schedule + Parallel Data Structure
Multicore and manycore processors are now ubiquitous, but
parallel programming remains as difficult as it was 30-40
years ago. During this time, our community has explored
many promising approaches including declarative languages,
logic programming, and automatic parallelization, but these
approaches have not succeeded except in a few niche application
In this talk, I will argue that these problems arise largely
from the computation-centric abstractions that we currently
use to think about parallelism. In their
place, I will propose a novel data-centric foundation for
parallel programming called the operator formulation in which
algorithms are described in terms of actions on data structures.
This data-centric view of parallel algorithms shows that a
generalized form of data-parallelism called amorphous data-parallelism
is ubiquitous even in complex, irregular graph applications such as
mesh generation and partitioning algorithms, graph analytics,
and machine learning applications. Binding time considerations
provide a unification of parallelization techniques ranging
from static parallelization to speculative parallelization.
We have built a system called Galois, based on these ideas,
for exploiting amorphous data-parallelism on multicores and GPUs.
I will present experimental results from our group as well as from
other groups that are using this system.
Keshav Pingali is a Professor in the Department of Computer Science
at the University of Texas at Austin, and he holds the W.A."Tex"
Moncrief Chair of Computing in the Institute for Computational
Engineering and Sciences (ICES) at UT Austin. 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 and are in use in industry compilers.
His current research is focused on programming languages
and tools for multicore processors.
Pingali is a Fellow of the ACM, IEEE and AAAS, and a
Distinguished Alumnus of IIT Kanpur, India. He was the
co-Editor-in-chief of the ACM Transactions on Programming
Languages and Systems, and currently serves on the editorial boards
of the International Journal of Parallel Programming and Distributed
Computing. He also served on the NSF CISE Advisory Committee (2009-2012).
Created by Sally O. Lee at Monday, May 05, 2014 at 10:03 AM.