2012
Brownlee, Carson; Patchett, John; Lo, Li-Ta; DeMarle, David; Mitchell, Christopher; Ahrens, James; Hansen, Charles
A Study of Ray Tracing Large-Scale Scientific Data in Parallel Visualization Applications Proceedings Article
In: Eurographics Symposium on Parallel Graphics and Visualization, pp. 51–60, The Eurographics Association 2012, (LA-UR-pending).
Abstract | Links | BibTeX | Tags: Distributed/network graphics, Graphics Systems, parallel, ray tracing, visualization
@inproceedings{brownlee2012study,
title = {A Study of Ray Tracing Large-Scale Scientific Data in Parallel Visualization Applications},
author = {Carson Brownlee and John Patchett and Li-Ta Lo and David DeMarle and Christopher Mitchell and James Ahrens and Charles Hansen},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/RayTracing.pdf},
year = {2012},
date = {2012-01-01},
booktitle = {Eurographics Symposium on Parallel Graphics and Visualization},
pages = {51--60},
organization = {The Eurographics Association},
abstract = {Large-scale analysis and visualization is becoming increasingly important as supercomputers and their simula- tions produce larger and larger data. These large data sizes are pushing the limits of traditional rendering algo- rithms and tools thus motivating a study exploring these limits and their possible resolutions through alternative rendering algorithms . In order to better understand real-world performance with large data, this paper presents a detailed timing study on a large cluster with the widely used visualization tools ParaView and VisIt. The soft- ware ray tracer Manta was integrated into these programs in order to show that improved performance could be attained with software ray tracing on a distributed memory, GPU enabled, parallel visualization resource. Using the Texas Advanced Computing Center’s Longhorn cluster which has multi-core CPUs and GPUs with large-scale polygonal data, we find multi-core CPU ray tracing to be significantly faster than both software rasterization and hardware-accelerated rasterization in existing scientific visualization tools with large data.
},
note = {LA-UR-pending},
keywords = {Distributed/network graphics, Graphics Systems, parallel, ray tracing, visualization},
pubstate = {published},
tppubtype = {inproceedings}
}
Large-scale analysis and visualization is becoming increasingly important as supercomputers and their simula- tions produce larger and larger data. These large data sizes are pushing the limits of traditional rendering algo- rithms and tools thus motivating a study exploring these limits and their possible resolutions through alternative rendering algorithms . In order to better understand real-world performance with large data, this paper presents a detailed timing study on a large cluster with the widely used visualization tools ParaView and VisIt. The soft- ware ray tracer Manta was integrated into these programs in order to show that improved performance could be attained with software ray tracing on a distributed memory, GPU enabled, parallel visualization resource. Using the Texas Advanced Computing Center’s Longhorn cluster which has multi-core CPUs and GPUs with large-scale polygonal data, we find multi-core CPU ray tracing to be significantly faster than both software rasterization and hardware-accelerated rasterization in existing scientific visualization tools with large data.
2007
McCormick, Patrick; Inman, Jeff; Ahrens, James; Mohd-Yusof, Jamaludin; Roth, Greg; Cummins, Sharen
Scout: a data-parallel programming language for graphics processors Journal Article
In: Parallel Computing, vol. 33, no. 10, pp. 648–662, 2007, (LA-UR-07-2094).
Abstract | Links | BibTeX | Tags: data-parallel, Graphics Systems
@article{mccormick2007scout,
title = {Scout: a data-parallel programming language for graphics processors},
author = {Patrick McCormick and Jeff Inman and James Ahrens and Jamaludin Mohd-Yusof and Greg Roth and Sharen Cummins},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/ScoutADataParallelProgrammingLanguageForGraphicsProcessors.pdf},
year = {2007},
date = {2007-01-01},
journal = {Parallel Computing},
volume = {33},
number = {10},
pages = {648--662},
publisher = {Elsevier},
abstract = {Commodity graphics hardware has seen incredible growth in terms of performance, programmability, and arithmetic precision. Even though these trends have been primarily driven by the entertainment industry, the price-to-performance ratio of graphics processors (GPUs) has attracted the attention of many within the high-performance computing community. While the performance of the GPU is well suited for computational science, the programming interface, and several hardware limitations, have prevented their wide adoption. In this paper we present Scout, a data-parallel programming language for graphics processors that hides the nuances of both the underlying hardware and supporting graphics software layers. In addition to general-purpose programming constructs, the language provides extensions for scientific visualization operations that support the exploration of existing or computed data sets.},
note = {LA-UR-07-2094},
keywords = {data-parallel, Graphics Systems},
pubstate = {published},
tppubtype = {article}
}
Commodity graphics hardware has seen incredible growth in terms of performance, programmability, and arithmetic precision. Even though these trends have been primarily driven by the entertainment industry, the price-to-performance ratio of graphics processors (GPUs) has attracted the attention of many within the high-performance computing community. While the performance of the GPU is well suited for computational science, the programming interface, and several hardware limitations, have prevented their wide adoption. In this paper we present Scout, a data-parallel programming language for graphics processors that hides the nuances of both the underlying hardware and supporting graphics software layers. In addition to general-purpose programming constructs, the language provides extensions for scientific visualization operations that support the exploration of existing or computed data sets.
: . .
1.
Brownlee, Carson; Patchett, John; Lo, Li-Ta; DeMarle, David; Mitchell, Christopher; Ahrens, James; Hansen, Charles
A Study of Ray Tracing Large-Scale Scientific Data in Parallel Visualization Applications Proceedings Article
In: Eurographics Symposium on Parallel Graphics and Visualization, pp. 51–60, The Eurographics Association 2012, (LA-UR-pending).
@inproceedings{brownlee2012study,
title = {A Study of Ray Tracing Large-Scale Scientific Data in Parallel Visualization Applications},
author = {Carson Brownlee and John Patchett and Li-Ta Lo and David DeMarle and Christopher Mitchell and James Ahrens and Charles Hansen},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/RayTracing.pdf},
year = {2012},
date = {2012-01-01},
booktitle = {Eurographics Symposium on Parallel Graphics and Visualization},
pages = {51--60},
organization = {The Eurographics Association},
abstract = {Large-scale analysis and visualization is becoming increasingly important as supercomputers and their simula- tions produce larger and larger data. These large data sizes are pushing the limits of traditional rendering algo- rithms and tools thus motivating a study exploring these limits and their possible resolutions through alternative rendering algorithms . In order to better understand real-world performance with large data, this paper presents a detailed timing study on a large cluster with the widely used visualization tools ParaView and VisIt. The soft- ware ray tracer Manta was integrated into these programs in order to show that improved performance could be attained with software ray tracing on a distributed memory, GPU enabled, parallel visualization resource. Using the Texas Advanced Computing Center’s Longhorn cluster which has multi-core CPUs and GPUs with large-scale polygonal data, we find multi-core CPU ray tracing to be significantly faster than both software rasterization and hardware-accelerated rasterization in existing scientific visualization tools with large data.
},
note = {LA-UR-pending},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Large-scale analysis and visualization is becoming increasingly important as supercomputers and their simula- tions produce larger and larger data. These large data sizes are pushing the limits of traditional rendering algo- rithms and tools thus motivating a study exploring these limits and their possible resolutions through alternative rendering algorithms . In order to better understand real-world performance with large data, this paper presents a detailed timing study on a large cluster with the widely used visualization tools ParaView and VisIt. The soft- ware ray tracer Manta was integrated into these programs in order to show that improved performance could be attained with software ray tracing on a distributed memory, GPU enabled, parallel visualization resource. Using the Texas Advanced Computing Center’s Longhorn cluster which has multi-core CPUs and GPUs with large-scale polygonal data, we find multi-core CPU ray tracing to be significantly faster than both software rasterization and hardware-accelerated rasterization in existing scientific visualization tools with large data.
2.
McCormick, Patrick; Inman, Jeff; Ahrens, James; Mohd-Yusof, Jamaludin; Roth, Greg; Cummins, Sharen
Scout: a data-parallel programming language for graphics processors Journal Article
In: Parallel Computing, vol. 33, no. 10, pp. 648–662, 2007, (LA-UR-07-2094).
@article{mccormick2007scout,
title = {Scout: a data-parallel programming language for graphics processors},
author = {Patrick McCormick and Jeff Inman and James Ahrens and Jamaludin Mohd-Yusof and Greg Roth and Sharen Cummins},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/ScoutADataParallelProgrammingLanguageForGraphicsProcessors.pdf},
year = {2007},
date = {2007-01-01},
journal = {Parallel Computing},
volume = {33},
number = {10},
pages = {648--662},
publisher = {Elsevier},
abstract = {Commodity graphics hardware has seen incredible growth in terms of performance, programmability, and arithmetic precision. Even though these trends have been primarily driven by the entertainment industry, the price-to-performance ratio of graphics processors (GPUs) has attracted the attention of many within the high-performance computing community. While the performance of the GPU is well suited for computational science, the programming interface, and several hardware limitations, have prevented their wide adoption. In this paper we present Scout, a data-parallel programming language for graphics processors that hides the nuances of both the underlying hardware and supporting graphics software layers. In addition to general-purpose programming constructs, the language provides extensions for scientific visualization operations that support the exploration of existing or computed data sets.},
note = {LA-UR-07-2094},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Commodity graphics hardware has seen incredible growth in terms of performance, programmability, and arithmetic precision. Even though these trends have been primarily driven by the entertainment industry, the price-to-performance ratio of graphics processors (GPUs) has attracted the attention of many within the high-performance computing community. While the performance of the GPU is well suited for computational science, the programming interface, and several hardware limitations, have prevented their wide adoption. In this paper we present Scout, a data-parallel programming language for graphics processors that hides the nuances of both the underlying hardware and supporting graphics software layers. In addition to general-purpose programming constructs, the language provides extensions for scientific visualization operations that support the exploration of existing or computed data sets.