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
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.
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.
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.
Terascale direct numerical simulations of turbulent combustion using S3D
J. H. Chen;A. Choudhary;B. De Supinski;M. Devries.
Computational Science & Discovery (2009)
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)
DataStager: scalable data staging services for petascale applications
Hasan Abbasi;Matthew Wolf;Greg Eisenhauer;Scott Klasky.
Cluster Computing (2010)
DataSpaces: an interaction and coordination framework for coupled simulation workflows
Ciprian Docan;Manish Parashar;Scott Klasky.
Cluster Computing (2012)
PreDatA – preparatory data analytics on peta-scale machines
Fang Zheng;Hasan Abbasi;Ciprian Docan;Jay Lofstead.
international parallel and distributed processing symposium (2010)
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)
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)
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)
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)
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)
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