D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 34 Citations 7,108 181 World Ranking 6178 National Ranking 2981

Overview

What is he best known for?

The fields of study he is best known for:

  • Operating system
  • Database
  • Artificial intelligence

His primary areas of investigation include Distributed computing, Visualization, Supercomputer, Workflow and Petascale computing. His Distributed computing study integrates concerns from other disciplines, such as Data modeling, Operating system, Data analysis and Memory management. His Visualization research is multidisciplinary, incorporating perspectives in Scalability, Dataspaces, End user and Software.

The concepts of his Supercomputer study are interwoven with issues in Computer security and Enhanced Data Rates for GSM Evolution. His studies deal with areas such as Automation, Multi-core processor, Executable and Data management as well as Workflow. His Petascale computing study also includes

  • Partition that intertwine with fields like Parallel computing, Data processing and Input/output,
  • NetCDF together with Database.

His most cited work include:

  • Terascale direct numerical simulations of turbulent combustion using S3D (420 citations)
  • Flexible IO and integration for scientific codes through the adaptable IO system (ADIOS) (235 citations)
  • Hello ADIOS: the challenges and lessons of developing leadership class I/O frameworks (146 citations)

What are the main themes of his work throughout his whole career to date?

His scientific interests lie mostly in Distributed computing, Workflow, Visualization, Scalability and Data science. His studies in Distributed computing integrate themes in fields like Data modeling, Supercomputer, Asynchronous communication, Analytics and Data analysis. In Workflow, Scott Klasky works on issues like Data management, which are connected to Automation.

As a part of the same scientific study, Scott Klasky usually deals with the Visualization, concentrating on Computational science and frequently concerns with Simulation and Data transmission. His Scalability research includes elements of Input/output, Software and Parallel computing. The Data science study combines topics in areas such as Metadata, Petascale computing and Middleware, Database.

He most often published in these fields:

  • Distributed computing (29.46%)
  • Workflow (24.48%)
  • Visualization (18.67%)

What were the highlights of his more recent work (between 2016-2021)?

  • Distributed computing (29.46%)
  • Supercomputer (9.13%)
  • Workflow (24.48%)

In recent papers he was focusing on the following fields of study:

The scientist’s investigation covers issues in Distributed computing, Supercomputer, Workflow, Visualization and Data science. The various areas that Scott Klasky examines in his Distributed computing study include Database-centric architecture, Pipeline and Task. He combines subjects such as Reduction, Input/output, Server, Software engineering and Computer data storage with his study of Supercomputer.

The study incorporates disciplines such as Scalability, Real-time computing, Data visualization, Software and Big data in addition to Workflow. Scott Klasky has included themes like Code coupling, World Wide Web and Computational complexity theory in his Visualization study. His Data science study incorporates themes from Data modeling, Data management, Canopus, Extreme scale and Software portability.

Between 2016 and 2021, his most popular works were:

  • Understanding and Modeling Lossy Compression Schemes on HPC Scientific Data (35 citations)
  • Predicting Output Performance of a Petascale Supercomputer (27 citations)
  • Personalized Search Inspired Fast Interactive Estimation of Distribution Algorithm and Its Application (25 citations)

In his most recent research, the most cited papers focused on:

  • Operating system
  • Artificial intelligence
  • Database

Scott Klasky focuses on Workflow, Data modeling, Scalability, Distributed computing and Big data. His Data modeling research is multidisciplinary, incorporating elements of Random access memory, Latency, Persistence, Stacker and Dataspaces. His Scalability research includes themes of Supercomputer, Server and Memory hierarchy.

His Distributed computing study combines topics from a wide range of disciplines, such as Database-centric architecture and Task. His biological study spans a wide range of topics, including Software, Data visualization and Data science.

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.

Best Publications

Terascale direct numerical simulations of turbulent combustion using S3D

J. H. Chen;A. Choudhary;B. De Supinski;M. Devries.
Computational Science & Discovery (2009)

537 Citations

Flexible IO and integration for scientific codes through the adaptable IO system (ADIOS)

Jay F. Lofstead;Scott Klasky;Karsten Schwan;Norbert Podhorszki.
challenges of large applications in distributed environments (2008)

389 Citations

DataStager: scalable data staging services for petascale applications

Hasan Abbasi;Matthew Wolf;Greg Eisenhauer;Scott Klasky.
Cluster Computing (2010)

316 Citations

DataSpaces: an interaction and coordination framework for coupled simulation workflows

Ciprian Docan;Manish Parashar;Scott Klasky.
Cluster Computing (2012)

288 Citations

PreDatA – preparatory data analytics on peta-scale machines

Fang Zheng;Hasan Abbasi;Ciprian Docan;Jay Lofstead.
international parallel and distributed processing symposium (2010)

247 Citations

Combining in-situ and in-transit processing to enable extreme-scale scientific analysis

Janine C. Bennett;Hasan Abbasi;Peer-Timo Bremer;Ray Grout.
ieee international conference on high performance computing data and analytics (2012)

246 Citations

Adaptable, metadata rich IO methods for portable high performance IO

Jay Lofstead;Fang Zheng;Scott Klasky;Karsten Schwan.
international parallel and distributed processing symposium (2009)

231 Citations

Hello ADIOS: the challenges and lessons of developing leadership class I/O frameworks

Qing Liu;Jeremy Logan;Yuan Tian;Hasan Abbasi.
Concurrency and Computation: Practice and Experience (2014)

220 Citations

Managing Variability in the IO Performance of Petascale Storage Systems

Jay Lofstead;Fang Zheng;Qing Liu;Scott Klasky.
ieee international conference on high performance computing data and analytics (2010)

219 Citations

Compressing the incompressible with ISABELA: in-situ reduction of spatio-temporal data

Sriram Lakshminarasimhan;Neil Shah;Stephane Ethier;Scott Klasky.
international conference on parallel processing (2011)

192 Citations

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