2017 - ACM Fellow For development of new parallel programming techniques and their deployment in high performance computing applications
2011 - IEEE Fellow For development of parallel programming techniques
Laxmikant V. Kale focuses on Parallel computing, Scalability, Load balancing, Distributed computing and Runtime system. He combines subjects such as Scheme, Object-oriented programming, Polygon mesh and Charm with his study of Parallel computing. His Scalability research includes themes of Computational complexity theory, Computational science, Workstation clusters, Interoperability and Massively parallel.
His work focuses on many connections between Computational science and other disciplines, such as Molecular graphics, that overlap with his field of interest in Tree, File format, Scripting language and Software design. His Load balancing study integrates concerns from other disciplines, such as Computation and Resource allocation. His studies deal with areas such as Supercomputer, Distributed memory and Dynamic priority scheduling as well as Distributed computing.
His primary areas of study are Parallel computing, Distributed computing, Scalability, Load balancing and Runtime system. His work is dedicated to discovering how Parallel computing, Programming paradigm are connected with Object and other disciplines. The study incorporates disciplines such as Network topology, Supercomputer and Resource allocation in addition to Distributed computing.
The Scalability study combines topics in areas such as IBM and Computational science. His work on Network Load Balancing Services as part of general Load balancing research is frequently linked to Load management and Dynamic load testing, bridging the gap between disciplines. His Runtime system study frequently draws connections to adjacent fields such as Component.
Laxmikant V. Kale spends much of his time researching Distributed computing, Parallel computing, Scalability, Load balancing and Runtime system. His Distributed computing study combines topics in areas such as Network topology, Supercomputer, Resource allocation and Programming paradigm. The concepts of his Parallel computing study are interwoven with issues in Extended memory, Overlay, Embedded system and Charm.
Laxmikant V. Kale has researched Scalability in several fields, including Computational science, IBM, Source code, Parallel programming model and Speedup. His Computational science research is multidisciplinary, incorporating perspectives in Software and Computation. His Runtime system research is multidisciplinary, relying on both Power management and Asynchronous communication.
Distributed computing, Parallel computing, Scalability, Load balancing and Network topology are his primary areas of study. His Distributed computing research integrates issues from Job scheduler, Scheduling, Resource allocation and Programming paradigm. His Parallel computing study incorporates themes from Embedded system, Charm and Source code.
He has included themes like Supercomputer, Vectorization, Set and Computational science in his Scalability study. Blue Waters is closely connected to Snapshot in his research, which is encompassed under the umbrella topic of Computational science. His research integrates issues of IBM, Fast Fourier transform and Cache in his study of Load balancing.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Scalable molecular dynamics with NAMD
James C. Phillips;Rosemary Braun;Wei Wang;James C. Gumbart.
Journal of Computational Chemistry (2005)
NAMD2: Greater Scalability for Parallel Molecular Dynamics
Laxmikant Kalé;Robert Skeel;Milind Bhandarkar;Robert Brunner.
Journal of Computational Physics (1999)
CHARM++: a portable concurrent object oriented system based on C++
Laxmikant V. Kale;Laxmikant V. Kale;Sanjeev Krishnan.
conference on object oriented programming systems languages and applications (1993)
Scalable Molecular Dynamics with NAMD.
James C. Phillips;Klaus Schulten;Abhinav Bhatele;Chao Mei.
Parallel Science and Engineering Applications (2013)
Scalable molecular dynamics on CPU and GPU architectures with NAMD.
James C. Phillips;David J. Hardy;Julio D.C. Maia;John E. Stone.
Journal of Chemical Physics (2020)
NAMD: a Parallel, Object-Oriented Molecular Dynamics Program
Mark T. Nelson;William Humphrey;Attila Gursoy;Andrew Dalke.
ieee international conference on high performance computing data and analytics (1996)
Scalable Molecular Dynamics with NAMD
J. C. Phillips;K. Schulten;A. Bhatele;C. Mei.
Parallel Science and Engineering Applications: The Charm++ Approach, Scalable Molecular Dynamics with NAMD, CRC Press, Boca Raton, FL 2013, pp. 61-77 (2012)
Toward Exascale Resilience
Franck Cappello;Al Geist;Bill Gropp;Laxmikant Kale.
ieee international conference on high performance computing data and analytics (2009)
NAMD: Biomolecular Simulation on Thousands of Processors
James C. Phillips;Gengbin Zheng;Sameer Kumar;Laxmikant V. Kalé.
conference on high performance computing (supercomputing) (2002)
BigSim: a parallel simulator for performance prediction of extremely large parallel machines
G. Zheng;Gunavardhan Kakulapati;L.V. Kale.
international parallel and distributed processing symposium (2004)
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