2003
Stompel, Aleksander; Ma, Kwan-Liu; Lum, Eric B; Ahrens, James; Patchett, John
SLIC: scheduled linear image compositing for parallel volume rendering Proceedings Article
In: Proceedings of the 2003 IEEE Symposium on Parallel and Large-Data Visualization and Graphics, pp. 6, IEEE Computer Society 2003, (LA-UR-03-5482).
Abstract | Links | BibTeX | Tags: high-performance computing, image com- positing, parallel rendering, PC clusters, visualization, vol- ume rendering
@inproceedings{stompel2003slic,
title = {SLIC: scheduled linear image compositing for parallel volume rendering},
author = {Aleksander Stompel and Kwan-Liu Ma and Eric B Lum and James Ahrens and John Patchett},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/SLICScheduledLinearImageCmpositingForParallelVolumeRendering.pdf},
year = {2003},
date = {2003-01-01},
booktitle = {Proceedings of the 2003 IEEE Symposium on Parallel and Large-Data Visualization and Graphics},
pages = {6},
organization = {IEEE Computer Society},
abstract = {Parallel volume rendering offers a feasible solution to the large data visualization problem by distributing both the data and rendering calculations among multiple computers connected by a network. In sort-last parallel volume rendering, each processor generates an image of its assigned subvolume, which is blended together with other images to derive the final image. Improving the efficiency of this compositing step, which requires interprocesssor communication, is the key to scalable, interactive rendering. The recent trend of using hardware-accelerated volume rendering demands further acceleration of the image compositing step. This paper presents a new optimized parallel image compositing algorithm and its performance on a PC cluster. Our test results show that this new algorithm offers significant savings over previous algorithms in both communication and compositing costs. On a 64-node PC cluster with a 100BaseT network interconnect, we can achieve interactive rendering rates for images at resolutions up to 1024 × 1024 pixels at several frames per second.},
note = {LA-UR-03-5482},
keywords = {high-performance computing, image com- positing, parallel rendering, PC clusters, visualization, vol- ume rendering},
pubstate = {published},
tppubtype = {inproceedings}
}
Parallel volume rendering offers a feasible solution to the large data visualization problem by distributing both the data and rendering calculations among multiple computers connected by a network. In sort-last parallel volume rendering, each processor generates an image of its assigned subvolume, which is blended together with other images to derive the final image. Improving the efficiency of this compositing step, which requires interprocesssor communication, is the key to scalable, interactive rendering. The recent trend of using hardware-accelerated volume rendering demands further acceleration of the image compositing step. This paper presents a new optimized parallel image compositing algorithm and its performance on a PC cluster. Our test results show that this new algorithm offers significant savings over previous algorithms in both communication and compositing costs. On a 64-node PC cluster with a 100BaseT network interconnect, we can achieve interactive rendering rates for images at resolutions up to 1024 × 1024 pixels at several frames per second.
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1.
Stompel, Aleksander; Ma, Kwan-Liu; Lum, Eric B; Ahrens, James; Patchett, John
SLIC: scheduled linear image compositing for parallel volume rendering Proceedings Article
In: Proceedings of the 2003 IEEE Symposium on Parallel and Large-Data Visualization and Graphics, pp. 6, IEEE Computer Society 2003, (LA-UR-03-5482).
@inproceedings{stompel2003slic,
title = {SLIC: scheduled linear image compositing for parallel volume rendering},
author = {Aleksander Stompel and Kwan-Liu Ma and Eric B Lum and James Ahrens and John Patchett},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/SLICScheduledLinearImageCmpositingForParallelVolumeRendering.pdf},
year = {2003},
date = {2003-01-01},
booktitle = {Proceedings of the 2003 IEEE Symposium on Parallel and Large-Data Visualization and Graphics},
pages = {6},
organization = {IEEE Computer Society},
abstract = {Parallel volume rendering offers a feasible solution to the large data visualization problem by distributing both the data and rendering calculations among multiple computers connected by a network. In sort-last parallel volume rendering, each processor generates an image of its assigned subvolume, which is blended together with other images to derive the final image. Improving the efficiency of this compositing step, which requires interprocesssor communication, is the key to scalable, interactive rendering. The recent trend of using hardware-accelerated volume rendering demands further acceleration of the image compositing step. This paper presents a new optimized parallel image compositing algorithm and its performance on a PC cluster. Our test results show that this new algorithm offers significant savings over previous algorithms in both communication and compositing costs. On a 64-node PC cluster with a 100BaseT network interconnect, we can achieve interactive rendering rates for images at resolutions up to 1024 × 1024 pixels at several frames per second.},
note = {LA-UR-03-5482},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Parallel volume rendering offers a feasible solution to the large data visualization problem by distributing both the data and rendering calculations among multiple computers connected by a network. In sort-last parallel volume rendering, each processor generates an image of its assigned subvolume, which is blended together with other images to derive the final image. Improving the efficiency of this compositing step, which requires interprocesssor communication, is the key to scalable, interactive rendering. The recent trend of using hardware-accelerated volume rendering demands further acceleration of the image compositing step. This paper presents a new optimized parallel image compositing algorithm and its performance on a PC cluster. Our test results show that this new algorithm offers significant savings over previous algorithms in both communication and compositing costs. On a 64-node PC cluster with a 100BaseT network interconnect, we can achieve interactive rendering rates for images at resolutions up to 1024 × 1024 pixels at several frames per second.