2012
Sewell, Christopher; Meredith, Jeremy; Moreland, Kenneth; Peterka, Tom; DeMarle, David; Lo, Li-ta; Ahrens, James; Maynard, Robert; Geveci, Berk
The SDAV software frameworks for visualization and analysis on next-generation multi-core and many-core architectures Proceedings Article
In: High Performance Computing, Networking, Storage and Analysis (SCC), 2012 SC Companion:, pp. 206–214, IEEE 2012, (LA-UR-12-26928).
Abstract | Links | BibTeX | Tags: data-parallel, in-situ, many-core architectures, mult-core architectures, visualization, VTK-m
@inproceedings{sewell2012sdav,
title = {The SDAV software frameworks for visualization and analysis on next-generation multi-core and many-core architectures},
author = {Christopher Sewell and Jeremy Meredith and Kenneth Moreland and Tom Peterka and David DeMarle and Li-ta Lo and James Ahrens and Robert Maynard and Berk Geveci},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/TheSDAVSoftwareFrameworksForVisualizationAndAnalysisOnNext-GenerationMulti-CoreAndMany-CoreArchitectures.pdf},
year = {2012},
date = {2012-01-01},
booktitle = {High Performance Computing, Networking, Storage and Analysis (SCC), 2012 SC Companion:},
pages = {206--214},
organization = {IEEE},
abstract = {This paper surveys the four software frameworks being developed as part of the visualization pillar of the SDAV (Scalable Data Management, Analysis, and Visualization) Institute, one of the SciDAC (Scientific Discovery through Advanced Computing) Institutes established by the ASCR (Advanced Scientific Computing Research) Program of the U.S. Department of Energy. These frameworks include EAVL (Extreme-scale Analysis and Visualization Library), Dax (Data Analysis at Extreme), DIY (Do It Yourself), and PISTON. The objective of these frameworks is to facilitate the adaptation of visualization and analysis algorithms to take advantage of the available parallelism in emerging multi-core and manycore hardware architectures, in anticipation of the need for such algorithms to be run in-situ with LCF (leadership-class facilities) simulation codes on supercomputers.},
note = {LA-UR-12-26928},
keywords = {data-parallel, in-situ, many-core architectures, mult-core architectures, visualization, VTK-m},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper surveys the four software frameworks being developed as part of the visualization pillar of the SDAV (Scalable Data Management, Analysis, and Visualization) Institute, one of the SciDAC (Scientific Discovery through Advanced Computing) Institutes established by the ASCR (Advanced Scientific Computing Research) Program of the U.S. Department of Energy. These frameworks include EAVL (Extreme-scale Analysis and Visualization Library), Dax (Data Analysis at Extreme), DIY (Do It Yourself), and PISTON. The objective of these frameworks is to facilitate the adaptation of visualization and analysis algorithms to take advantage of the available parallelism in emerging multi-core and manycore hardware architectures, in anticipation of the need for such algorithms to be run in-situ with LCF (leadership-class facilities) simulation codes on supercomputers.
: . .
1.
Sewell, Christopher; Meredith, Jeremy; Moreland, Kenneth; Peterka, Tom; DeMarle, David; Lo, Li-ta; Ahrens, James; Maynard, Robert; Geveci, Berk
The SDAV software frameworks for visualization and analysis on next-generation multi-core and many-core architectures Proceedings Article
In: High Performance Computing, Networking, Storage and Analysis (SCC), 2012 SC Companion:, pp. 206–214, IEEE 2012, (LA-UR-12-26928).
@inproceedings{sewell2012sdav,
title = {The SDAV software frameworks for visualization and analysis on next-generation multi-core and many-core architectures},
author = {Christopher Sewell and Jeremy Meredith and Kenneth Moreland and Tom Peterka and David DeMarle and Li-ta Lo and James Ahrens and Robert Maynard and Berk Geveci},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/TheSDAVSoftwareFrameworksForVisualizationAndAnalysisOnNext-GenerationMulti-CoreAndMany-CoreArchitectures.pdf},
year = {2012},
date = {2012-01-01},
booktitle = {High Performance Computing, Networking, Storage and Analysis (SCC), 2012 SC Companion:},
pages = {206--214},
organization = {IEEE},
abstract = {This paper surveys the four software frameworks being developed as part of the visualization pillar of the SDAV (Scalable Data Management, Analysis, and Visualization) Institute, one of the SciDAC (Scientific Discovery through Advanced Computing) Institutes established by the ASCR (Advanced Scientific Computing Research) Program of the U.S. Department of Energy. These frameworks include EAVL (Extreme-scale Analysis and Visualization Library), Dax (Data Analysis at Extreme), DIY (Do It Yourself), and PISTON. The objective of these frameworks is to facilitate the adaptation of visualization and analysis algorithms to take advantage of the available parallelism in emerging multi-core and manycore hardware architectures, in anticipation of the need for such algorithms to be run in-situ with LCF (leadership-class facilities) simulation codes on supercomputers.},
note = {LA-UR-12-26928},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper surveys the four software frameworks being developed as part of the visualization pillar of the SDAV (Scalable Data Management, Analysis, and Visualization) Institute, one of the SciDAC (Scientific Discovery through Advanced Computing) Institutes established by the ASCR (Advanced Scientific Computing Research) Program of the U.S. Department of Energy. These frameworks include EAVL (Extreme-scale Analysis and Visualization Library), Dax (Data Analysis at Extreme), DIY (Do It Yourself), and PISTON. The objective of these frameworks is to facilitate the adaptation of visualization and analysis algorithms to take advantage of the available parallelism in emerging multi-core and manycore hardware architectures, in anticipation of the need for such algorithms to be run in-situ with LCF (leadership-class facilities) simulation codes on supercomputers.