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Professor Laxmikant
Kale is the director of the Parallel Programming Laboratory
and the Paul and Cynthia Saylor Professor of Computer Science
at the University of Illinois at Urbana-Champaign. Prof. Kale
has been working on various aspects of parallel computing,
with a focus on enhancing performance and productivity via
adaptive runtime systems, and with the belief that only
interdisciplinary research involving multiple CSE and other
applications can bring back well-honed abstractions into
Computer Science that will have a long-term impact on the
state-of-art. His collaborations include the widely used
Gordon-Bell award winning (SC 2002) biomolecular simulation
program NAMD, and other collaborations on computational
cosmology, quantum chemistry, rocket simulation, space-time
meshes, and other unstructured mesh applications. He takes
pride in his group's success in distributing and supporting
software embodying his research ideas, including Charm++,
Adaptive MPI and the BigSim framework. He and his team won the
HPC Challenge award at Supercomputing 2011, for their entry
based on Charm++.
L. V. Kale received the B.Tech degree in Electronics
Engineering from Benares Hindu University, Varanasi, India in
1977, and a M.E. degree in Computer Science from Indian
Institute of Science in Bangalore, India, in 1979. He received
a Ph.D. in computer science in from State University of New
York, Stony Brook, in 1985.
He worked as a scientist at the Tata Institute of Fundamental
Research from 1979 to 1981. He joined the faculty of the
University of Illinois at Urbana-Champaign as an Assistant
Professor in 1985, where he is currently employed as a
Professor. Prof. Kale is a fellow of the ACM and IEEE, and a
winner of the 2012 IEEE Sidney Fernbach award.
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Title: Extreme Scale
Computing for Computational Science and Engineering
Very large
supercomputers are being built and even larger ones,
representing the class of exascale machines with over 10^18
operations per second --- are planned to deployed as early as
2022. These computers will have a significant societal impact by
enabling accurate predictive science and design of engineering
artifacts, both via simulation capabilities. I will explain the
basic motivations behind this computational science and
computational engineering. I will survey the evolution of
supercomputers, including Petascale computers such as the Blue
Waters system at University of Illinois, current computers such
as US Department of Energy’s Summit system, and the planned
exascale machines in the US, and around the world. At the same
time, there is a revolution in using smaller “supercomputers”
(aka clusters) for increasing competitiveness in manufacturing
industries. Data Analytics and Machine Learning are new but huge
application areas for such computers. Programming such machines
is full of challenges that should be attractive to ambitious
young researchers. I will describe the programming methodologies
developed in my research group, based on adaptive runtime
systems in this context. I will end with some views on which
areas of supercomputing effort in India, both in education and
research, will be most fruitful.
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