2014
Widanagamaachchi, Wathsala; Bremer, Peer-Timo; Sewell, Christopher; Lo, Li-ta; Ahrens, James; Pascucci, Valerio
Data-Parallel Halo Finding with Variable Linking Lengths Proceedings Article
In: 2014, (LA-UR-14-23700).
Abstract | Links | BibTeX | Tags: clustering, cosomology, halo
@inproceedings{Widanagamaachchi2014,
title = {Data-Parallel Halo Finding with Variable Linking Lengths},
author = {Wathsala Widanagamaachchi and Peer-Timo Bremer and Christopher Sewell and Li-ta Lo and James Ahrens and Valerio Pascucci},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/Data-ParallelHaloFindingWithVariableLinkingLenghts.pdf},
year = {2014},
date = {2014-11-01},
abstract = {State-of-the-art cosmological simulations regularly contain billions of particles, providing scientists the opportunity to study the evolution of the Universe in great detail. However, the rate at which these simulations generate data severely taxes existing analysis techniques. Therefore, developing new scalable alternatives is essential for continued scientific progress. Here, we present a dataparallel, friends-of-friends halo finding algorithm that provides unprecedented flexibility in the analysis by extracting multiple linking lengths. Even for a single linking length, it is as fast as the existing techniques, and is portable to multi-threaded many-core systems as well as co-processing resources. Our system is implemented using PISTON and is coupled to an interactive analysis environment used to study halos at different linking lengths and track their evolution over time.},
note = {LA-UR-14-23700},
keywords = {clustering, cosomology, halo},
pubstate = {published},
tppubtype = {inproceedings}
}
State-of-the-art cosmological simulations regularly contain billions of particles, providing scientists the opportunity to study the evolution of the Universe in great detail. However, the rate at which these simulations generate data severely taxes existing analysis techniques. Therefore, developing new scalable alternatives is essential for continued scientific progress. Here, we present a dataparallel, friends-of-friends halo finding algorithm that provides unprecedented flexibility in the analysis by extracting multiple linking lengths. Even for a single linking length, it is as fast as the existing techniques, and is portable to multi-threaded many-core systems as well as co-processing resources. Our system is implemented using PISTON and is coupled to an interactive analysis environment used to study halos at different linking lengths and track their evolution over time.
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1.
Widanagamaachchi, Wathsala; Bremer, Peer-Timo; Sewell, Christopher; Lo, Li-ta; Ahrens, James; Pascucci, Valerio
Data-Parallel Halo Finding with Variable Linking Lengths Proceedings Article
In: 2014, (LA-UR-14-23700).
@inproceedings{Widanagamaachchi2014,
title = {Data-Parallel Halo Finding with Variable Linking Lengths},
author = {Wathsala Widanagamaachchi and Peer-Timo Bremer and Christopher Sewell and Li-ta Lo and James Ahrens and Valerio Pascucci},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/Data-ParallelHaloFindingWithVariableLinkingLenghts.pdf},
year = {2014},
date = {2014-11-01},
abstract = {State-of-the-art cosmological simulations regularly contain billions of particles, providing scientists the opportunity to study the evolution of the Universe in great detail. However, the rate at which these simulations generate data severely taxes existing analysis techniques. Therefore, developing new scalable alternatives is essential for continued scientific progress. Here, we present a dataparallel, friends-of-friends halo finding algorithm that provides unprecedented flexibility in the analysis by extracting multiple linking lengths. Even for a single linking length, it is as fast as the existing techniques, and is portable to multi-threaded many-core systems as well as co-processing resources. Our system is implemented using PISTON and is coupled to an interactive analysis environment used to study halos at different linking lengths and track their evolution over time.},
note = {LA-UR-14-23700},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
State-of-the-art cosmological simulations regularly contain billions of particles, providing scientists the opportunity to study the evolution of the Universe in great detail. However, the rate at which these simulations generate data severely taxes existing analysis techniques. Therefore, developing new scalable alternatives is essential for continued scientific progress. Here, we present a dataparallel, friends-of-friends halo finding algorithm that provides unprecedented flexibility in the analysis by extracting multiple linking lengths. Even for a single linking length, it is as fast as the existing techniques, and is portable to multi-threaded many-core systems as well as co-processing resources. Our system is implemented using PISTON and is coupled to an interactive analysis environment used to study halos at different linking lengths and track their evolution over time.