2016
Moreland, Kenneth; Sewell, Christopher; Usher, William; Lo, Li-ta; Meredith, Jeremy; Pugmire, David; Kress, James; Schroots, Hendrik; Ma, Kwan-Liu; Childs, Hank; Larsen, Matthew; Chen, Chun-Ming; Maynard, Robert; Geveci, Berk
VTK-m: Accelerating the Visualization Toolkit for Massively Threaded Architectures Proceedings Article
In: pp. 48-58, IEEE Computer Graphics and Applications, 2016, ISSN: 0272-1716, (LA-UR-15-27306).
Abstract | Links | BibTeX | Tags: visualization, VTK-m
@inproceedings{Moreland:2016a,
title = {VTK-m: Accelerating the Visualization Toolkit for Massively Threaded Architectures},
author = {Kenneth Moreland and Christopher Sewell and William Usher and Li-ta Lo and Jeremy Meredith and David Pugmire and James Kress and Hendrik Schroots and Kwan-Liu Ma and Hank Childs and Matthew Larsen and Chun-Ming Chen and Robert Maynard and Berk Geveci},
url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7466740},
issn = {0272-1716},
year = {2016},
date = {2016-05-01},
pages = {48-58},
publisher = {IEEE Computer Graphics and Applications},
abstract = {One of the most critical challenges for high-performance computing (HPC) scientific visualization is execution on massively threaded processors. Of the many fundamental changes we are seeing in HPC systems, one of the most profound is a reliance on new processor types optimized for execution bandwidth over latency hiding. Our current production scientific visualization software is not designed for these new types of architectures. To address this issue, the VTK-m framework serves as a container for algorithms, provides flexible data representation, and simplifies the design of visualization algorithms on new and future computer architecture.},
note = {LA-UR-15-27306},
keywords = {visualization, VTK-m},
pubstate = {published},
tppubtype = {inproceedings}
}
One of the most critical challenges for high-performance computing (HPC) scientific visualization is execution on massively threaded processors. Of the many fundamental changes we are seeing in HPC systems, one of the most profound is a reliance on new processor types optimized for execution bandwidth over latency hiding. Our current production scientific visualization software is not designed for these new types of architectures. To address this issue, the VTK-m framework serves as a container for algorithms, provides flexible data representation, and simplifies the design of visualization algorithms on new and future computer architecture.
2015
Usher, William
Summer 2015 LANL Exit Talk Presentation
05.10.2015, (LA-UR-15-27730).
Abstract | Links | BibTeX | Tags: OpenMP, VTK-m
@misc{Usher2010,
title = {Summer 2015 LANL Exit Talk},
author = {William Usher},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/08/Summer_2015_LANL_Exit_Talk.pdf},
year = {2015},
date = {2015-10-05},
abstract = {This presentation summarizes summer work on writing the OpenMP backend and making general performance improvements and comparisions in VTK-m. In the area of performance measurements and improvements a benchmarking suite to VTK-m to compare backends and changes to backends was added. Additionally, the default storage type was migrated to use an aligned allocator to improve CPU and MIC performance. In the area of OpenMP backend Jeff Inman's hand-vectorized MIC scan was ported to a generic version in VTK-m, achieving somewhat comparable performance and he working on implementing a parallel quick sort for the backend as well, but still some work left to do.},
note = {LA-UR-15-27730},
keywords = {OpenMP, VTK-m},
pubstate = {published},
tppubtype = {presentation}
}
This presentation summarizes summer work on writing the OpenMP backend and making general performance improvements and comparisions in VTK-m. In the area of performance measurements and improvements a benchmarking suite to VTK-m to compare backends and changes to backends was added. Additionally, the default storage type was migrated to use an aligned allocator to improve CPU and MIC performance. In the area of OpenMP backend Jeff Inman's hand-vectorized MIC scan was ported to a generic version in VTK-m, achieving somewhat comparable performance and he working on implementing a parallel quick sort for the backend as well, but still some work left to do.
Lu, Kewei
Portable Data Parallel Visualization Algorithms with VTK-m Presentation
05.10.2015, (LA-UR-15-27724).
Abstract | Links | BibTeX | Tags: data parallel, VTK-m
@misc{Lu2015,
title = {Portable Data Parallel Visualization Algorithms with VTK-m},
author = {Kewei Lu
},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/08/Portable_Data_Parallel_Visualization_Algorithms_with_VTK-m.pdf},
year = {2015},
date = {2015-10-05},
abstract = {This presentation summarizes summer work to develop portable data parallel visualization algorithms in VTK-m. This included writing visualization filters for streamlines and stream surfaces as well as modifying the original isosurface implementation using the new data model and worklets.},
note = {LA-UR-15-27724},
keywords = {data parallel, VTK-m},
pubstate = {published},
tppubtype = {presentation}
}
This presentation summarizes summer work to develop portable data parallel visualization algorithms in VTK-m. This included writing visualization filters for streamlines and stream surfaces as well as modifying the original isosurface implementation using the new data model and worklets.
2014
Sewell, Christopher; Heitmann, Katrin; Lo, Li-Ta; Habib, Salman; Ahrens, James
Portable Parallel Halo and Center Finders for HACC Presentation
31.07.2014, (LA-UR-14-25437).
Abstract | Links | BibTeX | Tags: halo finding, PISTON, VTK-m
@misc{Sewell2014b,
title = {Portable Parallel Halo and Center Finders for HACC},
author = {Christopher Sewell and Katrin Heitmann and Li-Ta Lo and Salman Habib and James Ahrens},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/08/Portable_Parallel_Halo_and_Center_Finders_for_HACC.pdf},
year = {2014},
date = {2014-07-31},
abstract = {This presentation describes our work on finding halos and halo centers for the HACC cosmology code using our portable, data-parallel framework, which allows us to run on accelerators such as GPUs, providing significant speed-up. This work, which is part of the SDAV VTK-m project, enabled halo analysis to be performed on a very large data set (8192^3 particles across 16,384 nodes on Titan) for which analysis using the traditional CPU algorithms was not feasible.},
note = {LA-UR-14-25437},
keywords = {halo finding, PISTON, VTK-m},
pubstate = {published},
tppubtype = {presentation}
}
This presentation describes our work on finding halos and halo centers for the HACC cosmology code using our portable, data-parallel framework, which allows us to run on accelerators such as GPUs, providing significant speed-up. This work, which is part of the SDAV VTK-m project, enabled halo analysis to be performed on a very large data set (8192^3 particles across 16,384 nodes on Titan) for which analysis using the traditional CPU algorithms was not feasible.
2013
Sewell, Christopher; Lo, Li-ta; Ahrens, James
PISTON: An SDAV Framework for Portable High-Performance Data-Parallel Visualization and Analysis Operators Presentation
22.02.2013, (LA-UR-13-21083).
Abstract | Links | BibTeX | Tags: PISTON, VTK-m
@misc{Sewell2013b,
title = {PISTON: An SDAV Framework for Portable High-Performance Data-Parallel Visualization and Analysis Operators},
author = {Christopher Sewell and Li-ta Lo and James Ahrens},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/08/PISTON2.pdf},
year = {2013},
date = {2013-02-22},
abstract = {This presentation describes the overall goal of PISTON (to provide portability and performance for visualization and analysis operators on current and next-generation supercomputers), and summarizes the work on PISTON in relation to the SDAV (The SciDac Institute of Scalable Data Management, Analysis, and Visualization) Milestones. Specifically, it presents work related to general PISTON algorithm and infrastructure development; the halo finder operator; PISTON integration into VTK and ParaView; VPIC in-situ PISTON pipelines; and publications, presentations, and tutorials.},
note = {LA-UR-13-21083},
keywords = {PISTON, VTK-m},
pubstate = {published},
tppubtype = {presentation}
}
This presentation describes the overall goal of PISTON (to provide portability and performance for visualization and analysis operators on current and next-generation supercomputers), and summarizes the work on PISTON in relation to the SDAV (The SciDac Institute of Scalable Data Management, Analysis, and Visualization) Milestones. Specifically, it presents work related to general PISTON algorithm and infrastructure development; the halo finder operator; PISTON integration into VTK and ParaView; VPIC in-situ PISTON pipelines; and publications, presentations, and tutorials.
Ahrens, James; Sewell, Chris; Patchett, John
SDAV Visualization Area: VTK-m and In-Situ Highlights at Los Alamos Technical Report
2013, (LA-UR-13-27063).
Links | BibTeX | Tags: in situ, VTK-m
@techreport{info:lanl-repo/lareport/LA-UR-13-27063,
title = {SDAV Visualization Area: VTK-m and In-Situ Highlights at Los Alamos},
author = {James Ahrens and Chris Sewell and John Patchett},
url = {http://datascience.dsscale.org/wp-content/uploads/2017/09/LA-UR-13-27063.pdf},
year = {2013},
date = {2013-01-01},
note = {LA-UR-13-27063},
keywords = {in situ, VTK-m},
pubstate = {published},
tppubtype = {techreport}
}
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.
Moreland, Kenneth; Sewell, Christopher; Usher, William; Lo, Li-ta; Meredith, Jeremy; Pugmire, David; Kress, James; Schroots, Hendrik; Ma, Kwan-Liu; Childs, Hank; Larsen, Matthew; Chen, Chun-Ming; Maynard, Robert; Geveci, Berk
VTK-m: Accelerating the Visualization Toolkit for Massively Threaded Architectures Proceedings Article
In: pp. 48-58, IEEE Computer Graphics and Applications, 2016, ISSN: 0272-1716, (LA-UR-15-27306).
@inproceedings{Moreland:2016a,
title = {VTK-m: Accelerating the Visualization Toolkit for Massively Threaded Architectures},
author = {Kenneth Moreland and Christopher Sewell and William Usher and Li-ta Lo and Jeremy Meredith and David Pugmire and James Kress and Hendrik Schroots and Kwan-Liu Ma and Hank Childs and Matthew Larsen and Chun-Ming Chen and Robert Maynard and Berk Geveci},
url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7466740},
issn = {0272-1716},
year = {2016},
date = {2016-05-01},
pages = {48-58},
publisher = {IEEE Computer Graphics and Applications},
abstract = {One of the most critical challenges for high-performance computing (HPC) scientific visualization is execution on massively threaded processors. Of the many fundamental changes we are seeing in HPC systems, one of the most profound is a reliance on new processor types optimized for execution bandwidth over latency hiding. Our current production scientific visualization software is not designed for these new types of architectures. To address this issue, the VTK-m framework serves as a container for algorithms, provides flexible data representation, and simplifies the design of visualization algorithms on new and future computer architecture.},
note = {LA-UR-15-27306},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
One of the most critical challenges for high-performance computing (HPC) scientific visualization is execution on massively threaded processors. Of the many fundamental changes we are seeing in HPC systems, one of the most profound is a reliance on new processor types optimized for execution bandwidth over latency hiding. Our current production scientific visualization software is not designed for these new types of architectures. To address this issue, the VTK-m framework serves as a container for algorithms, provides flexible data representation, and simplifies the design of visualization algorithms on new and future computer architecture.
2.
Usher, William
Summer 2015 LANL Exit Talk Presentation
05.10.2015, (LA-UR-15-27730).
@misc{Usher2010,
title = {Summer 2015 LANL Exit Talk},
author = {William Usher},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/08/Summer_2015_LANL_Exit_Talk.pdf},
year = {2015},
date = {2015-10-05},
abstract = {This presentation summarizes summer work on writing the OpenMP backend and making general performance improvements and comparisions in VTK-m. In the area of performance measurements and improvements a benchmarking suite to VTK-m to compare backends and changes to backends was added. Additionally, the default storage type was migrated to use an aligned allocator to improve CPU and MIC performance. In the area of OpenMP backend Jeff Inman's hand-vectorized MIC scan was ported to a generic version in VTK-m, achieving somewhat comparable performance and he working on implementing a parallel quick sort for the backend as well, but still some work left to do.},
note = {LA-UR-15-27730},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
This presentation summarizes summer work on writing the OpenMP backend and making general performance improvements and comparisions in VTK-m. In the area of performance measurements and improvements a benchmarking suite to VTK-m to compare backends and changes to backends was added. Additionally, the default storage type was migrated to use an aligned allocator to improve CPU and MIC performance. In the area of OpenMP backend Jeff Inman's hand-vectorized MIC scan was ported to a generic version in VTK-m, achieving somewhat comparable performance and he working on implementing a parallel quick sort for the backend as well, but still some work left to do.
3.
Lu, Kewei
Portable Data Parallel Visualization Algorithms with VTK-m Presentation
05.10.2015, (LA-UR-15-27724).
@misc{Lu2015,
title = {Portable Data Parallel Visualization Algorithms with VTK-m},
author = {Kewei Lu
},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/08/Portable_Data_Parallel_Visualization_Algorithms_with_VTK-m.pdf},
year = {2015},
date = {2015-10-05},
abstract = {This presentation summarizes summer work to develop portable data parallel visualization algorithms in VTK-m. This included writing visualization filters for streamlines and stream surfaces as well as modifying the original isosurface implementation using the new data model and worklets.},
note = {LA-UR-15-27724},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
This presentation summarizes summer work to develop portable data parallel visualization algorithms in VTK-m. This included writing visualization filters for streamlines and stream surfaces as well as modifying the original isosurface implementation using the new data model and worklets.
4.
Sewell, Christopher; Heitmann, Katrin; Lo, Li-Ta; Habib, Salman; Ahrens, James
Portable Parallel Halo and Center Finders for HACC Presentation
31.07.2014, (LA-UR-14-25437).
@misc{Sewell2014b,
title = {Portable Parallel Halo and Center Finders for HACC},
author = {Christopher Sewell and Katrin Heitmann and Li-Ta Lo and Salman Habib and James Ahrens},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/08/Portable_Parallel_Halo_and_Center_Finders_for_HACC.pdf},
year = {2014},
date = {2014-07-31},
abstract = {This presentation describes our work on finding halos and halo centers for the HACC cosmology code using our portable, data-parallel framework, which allows us to run on accelerators such as GPUs, providing significant speed-up. This work, which is part of the SDAV VTK-m project, enabled halo analysis to be performed on a very large data set (8192^3 particles across 16,384 nodes on Titan) for which analysis using the traditional CPU algorithms was not feasible.},
note = {LA-UR-14-25437},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
This presentation describes our work on finding halos and halo centers for the HACC cosmology code using our portable, data-parallel framework, which allows us to run on accelerators such as GPUs, providing significant speed-up. This work, which is part of the SDAV VTK-m project, enabled halo analysis to be performed on a very large data set (8192^3 particles across 16,384 nodes on Titan) for which analysis using the traditional CPU algorithms was not feasible.
5.
Sewell, Christopher; Lo, Li-ta; Ahrens, James
PISTON: An SDAV Framework for Portable High-Performance Data-Parallel Visualization and Analysis Operators Presentation
22.02.2013, (LA-UR-13-21083).
@misc{Sewell2013b,
title = {PISTON: An SDAV Framework for Portable High-Performance Data-Parallel Visualization and Analysis Operators},
author = {Christopher Sewell and Li-ta Lo and James Ahrens},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/08/PISTON2.pdf},
year = {2013},
date = {2013-02-22},
abstract = {This presentation describes the overall goal of PISTON (to provide portability and performance for visualization and analysis operators on current and next-generation supercomputers), and summarizes the work on PISTON in relation to the SDAV (The SciDac Institute of Scalable Data Management, Analysis, and Visualization) Milestones. Specifically, it presents work related to general PISTON algorithm and infrastructure development; the halo finder operator; PISTON integration into VTK and ParaView; VPIC in-situ PISTON pipelines; and publications, presentations, and tutorials.},
note = {LA-UR-13-21083},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
This presentation describes the overall goal of PISTON (to provide portability and performance for visualization and analysis operators on current and next-generation supercomputers), and summarizes the work on PISTON in relation to the SDAV (The SciDac Institute of Scalable Data Management, Analysis, and Visualization) Milestones. Specifically, it presents work related to general PISTON algorithm and infrastructure development; the halo finder operator; PISTON integration into VTK and ParaView; VPIC in-situ PISTON pipelines; and publications, presentations, and tutorials.
6.
Ahrens, James; Sewell, Chris; Patchett, John
SDAV Visualization Area: VTK-m and In-Situ Highlights at Los Alamos Technical Report
2013, (LA-UR-13-27063).
@techreport{info:lanl-repo/lareport/LA-UR-13-27063,
title = {SDAV Visualization Area: VTK-m and In-Situ Highlights at Los Alamos},
author = {James Ahrens and Chris Sewell and John Patchett},
url = {http://datascience.dsscale.org/wp-content/uploads/2017/09/LA-UR-13-27063.pdf},
year = {2013},
date = {2013-01-01},
note = {LA-UR-13-27063},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
7.
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.