Laxmikant V. Kale
· Research Professor & Paul and Cynthia Saylor Professor EmeritusVerifiedUniversity of Illinois Urbana-Champaign · Computer Science
Active 1984–2024
Research topics
- Computer Science
- Chemistry
- Parallel computing
- Operating system
- Computational science
- Physics
- Computational chemistry
Selected publications
Scalable molecular dynamics on CPU and GPU architectures with NAMD
The Journal of Chemical Physics · 2020 · 3184 citations
- Computer Science
- Computer Science
- Parallel computing
NAMDis a molecular dynamics program designed for high-performance simulations of very large biological objects on CPU- and GPU-based architectures. NAMD offers scalable performance on petascale parallel supercomputers consisting of hundreds of thousands of cores, as well as on inexpensive commodity clusters commonly found in academic environments. It is written in C++ and leans on Charm++ parallel objects for optimal performance on low-latency architectures. NAMD is a versatile, multipurpose code that gathers state-of-the-art algorithms to carry out simulations in apt thermodynamic ensembles, using the widely popular CHARMM, AMBER, OPLS, and GROMOS biomolecular force fields. Here, we review the main features of NAMD that allow both equilibrium and enhanced-sampling molecular dynamics simulations with numerical efficiency. We describe the underlying concepts utilized by NAMD and their implementation, most notably for handling long-range electrostatics; controlling the temperature, pressure, and pH; applying external potentials on tailored grids; leveraging massively parallel resources in multiple-copy simulations; and hybrid quantum-mechanical/molecular-mechanical descriptions. We detail the variety of options offered by NAMD for enhanced-sampling simulations aimed at determining free-energy differences of either alchemical or geometrical transformations and outline their applicability to specific problems. Last, we discuss the roadmap for the development of NAMD and our current efforts toward achieving optimal performance on GPU-based architectures, for pushing back the limitations that have prevented biologically realistic billion-atom objects to be fruitfully simulated, and for making large-scale simulations less expensive and easier to set up, run, and analyze. NAMD is distributed free of charge with its source code at www.ks.uiuc.edu.
Recent grants
Collaborative Research: CDS&E: Evolution of the High Redshift Galaxy and AGN Populations
NSF · $90k · 2013–2016
CSR---SMA: BigSim: Performance Prediction for Petascale Machines and Applications
NSF · $516k · 2007–2011
NSF · $1.0M · 2002–2008
NSF · $400k · 2019–2024
Simplifying Parallel Programming for CSE Applications using a Multi-Paradigm Approach
NSF · $828k · 2008–2013
Frequent coauthors
- 1279 shared
Guy L. Steele
Oracle (United States)
- 397 shared
Josep Torrellas
University of Illinois Urbana-Champaign
- 396 shared
Ryan Newton
Menlo School
- 379 shared
Eric J. Bohm
University of Illinois Urbana-Champaign
- 359 shared
Robert A. Geijn
- 328 shared
Pritish Jetley
University of Illinois Urbana-Champaign
- 319 shared
Glenn Martyna
IBM (United States)
- 318 shared
Pen-Chung Yew
Education
- 1985
Ph.D., Computer Science
Stony Brook University
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