What is he best known for?
The fields of study he is best known for:
- Operating system
- Programming language
- Parallel computing
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 most cited work include:
- Scalable molecular dynamics with NAMD (11850 citations)
- NAMD2: Greater Scalability for Parallel Molecular Dynamics (1962 citations)
- CHARM++: a portable concurrent object oriented system based on C++ (763 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Parallel computing (48.69%)
- Distributed computing (35.15%)
- Scalability (16.86%)
What were the highlights of his more recent work (between 2014-2021)?
- Distributed computing (35.15%)
- Parallel computing (48.69%)
- Scalability (16.86%)
In recent papers he was focusing on the following fields of study:
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.
Between 2014 and 2021, his most popular works were:
- Scalable molecular dynamics on CPU and GPU architectures with NAMD. (122 citations)
- Adaptive techniques for clustered N-body cosmological simulations (85 citations)
- Evaluating and Improving the Performance and Scheduling of HPC Applications in Cloud (54 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Programming language
- Central processing unit
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.
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