2015
Sewell, Christopher; Heitmann, Katrin; Finkel, Hal; Zagaris, George; Parete-Koon, Suzanne; Fasel, Patricia; Pope, Adrian; Frontiere, Nicholas; Lo, Li-Ta; Messer, Bronson; Habib, Salman; Ahrens, James
Large-Scale Compute-Intensive Analysis via a Combined In-situ and Co-scheduling Workflow Approach Proceedings Article
In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, IEEE Press, Austin, Texas, 2015, (LA-UR-15-22830).
Abstract | Links | BibTeX | Tags: analysis, co-scheduling, compute-intensive, in-situ, large-scale, workflow
@inproceedings{Sewell:2015b,
title = {Large-Scale Compute-Intensive Analysis via a Combined In-situ and Co-scheduling Workflow Approach},
author = {Christopher Sewell and Katrin Heitmann and Hal Finkel and George Zagaris and Suzanne Parete-Koon and Patricia Fasel and Adrian Pope and Nicholas Frontiere and Li-Ta Lo and Bronson Messer and Salman Habib and James Ahrens},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/Large-ScaleCompute-IntensiveAnalysisViaACombinedIn-situAndCo-schedulingWorkflowApproach.pdf},
year = {2015},
date = {2015-01-01},
booktitle = {Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis},
publisher = {IEEE Press},
address = {Austin, Texas},
series = {SC '15},
abstract = {Large-scale simulations can produce hundreds of terabytes to peta- bytes of data, complicating and limiting the efficiency of work- flows. Traditionally, outputs are stored on the file system and an- alyzed in post-processing. With the rapidly increasing size and complexity of simulations, this approach faces an uncertain future. Trending techniques consist of performing the analysis in-situ, uti- lizing the same resources as the simulation, and/or off-loading sub- sets of the data to a compute-intensive analysis system. We intro- duce an analysis framework developed for HACC, a cosmological N-body code, that uses both in-situ and co-scheduling approaches for handling petabyte-scale outputs. We compare different anal- ysis set-ups ranging from purely off-line, to purely in-situ to in- situ/co-scheduling. The analysis routines are implemented using the PISTON/VTK-m framework, allowing a single implementation of an algorithm that simultaneously targets a variety of GPU, multi- core, and many-core architectures.},
note = {LA-UR-15-22830},
keywords = {analysis, co-scheduling, compute-intensive, in-situ, large-scale, workflow},
pubstate = {published},
tppubtype = {inproceedings}
}
Large-scale simulations can produce hundreds of terabytes to peta- bytes of data, complicating and limiting the efficiency of work- flows. Traditionally, outputs are stored on the file system and an- alyzed in post-processing. With the rapidly increasing size and complexity of simulations, this approach faces an uncertain future. Trending techniques consist of performing the analysis in-situ, uti- lizing the same resources as the simulation, and/or off-loading sub- sets of the data to a compute-intensive analysis system. We intro- duce an analysis framework developed for HACC, a cosmological N-body code, that uses both in-situ and co-scheduling approaches for handling petabyte-scale outputs. We compare different anal- ysis set-ups ranging from purely off-line, to purely in-situ to in- situ/co-scheduling. The analysis routines are implemented using the PISTON/VTK-m framework, allowing a single implementation of an algorithm that simultaneously targets a variety of GPU, multi- core, and many-core architectures.
: . .
1.
Sewell, Christopher; Heitmann, Katrin; Finkel, Hal; Zagaris, George; Parete-Koon, Suzanne; Fasel, Patricia; Pope, Adrian; Frontiere, Nicholas; Lo, Li-Ta; Messer, Bronson; Habib, Salman; Ahrens, James
Large-Scale Compute-Intensive Analysis via a Combined In-situ and Co-scheduling Workflow Approach Proceedings Article
In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, IEEE Press, Austin, Texas, 2015, (LA-UR-15-22830).
@inproceedings{Sewell:2015b,
title = {Large-Scale Compute-Intensive Analysis via a Combined In-situ and Co-scheduling Workflow Approach},
author = {Christopher Sewell and Katrin Heitmann and Hal Finkel and George Zagaris and Suzanne Parete-Koon and Patricia Fasel and Adrian Pope and Nicholas Frontiere and Li-Ta Lo and Bronson Messer and Salman Habib and James Ahrens},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/Large-ScaleCompute-IntensiveAnalysisViaACombinedIn-situAndCo-schedulingWorkflowApproach.pdf},
year = {2015},
date = {2015-01-01},
booktitle = {Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis},
publisher = {IEEE Press},
address = {Austin, Texas},
series = {SC '15},
abstract = {Large-scale simulations can produce hundreds of terabytes to peta- bytes of data, complicating and limiting the efficiency of work- flows. Traditionally, outputs are stored on the file system and an- alyzed in post-processing. With the rapidly increasing size and complexity of simulations, this approach faces an uncertain future. Trending techniques consist of performing the analysis in-situ, uti- lizing the same resources as the simulation, and/or off-loading sub- sets of the data to a compute-intensive analysis system. We intro- duce an analysis framework developed for HACC, a cosmological N-body code, that uses both in-situ and co-scheduling approaches for handling petabyte-scale outputs. We compare different anal- ysis set-ups ranging from purely off-line, to purely in-situ to in- situ/co-scheduling. The analysis routines are implemented using the PISTON/VTK-m framework, allowing a single implementation of an algorithm that simultaneously targets a variety of GPU, multi- core, and many-core architectures.},
note = {LA-UR-15-22830},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Large-scale simulations can produce hundreds of terabytes to peta- bytes of data, complicating and limiting the efficiency of work- flows. Traditionally, outputs are stored on the file system and an- alyzed in post-processing. With the rapidly increasing size and complexity of simulations, this approach faces an uncertain future. Trending techniques consist of performing the analysis in-situ, uti- lizing the same resources as the simulation, and/or off-loading sub- sets of the data to a compute-intensive analysis system. We intro- duce an analysis framework developed for HACC, a cosmological N-body code, that uses both in-situ and co-scheduling approaches for handling petabyte-scale outputs. We compare different anal- ysis set-ups ranging from purely off-line, to purely in-situ to in- situ/co-scheduling. The analysis routines are implemented using the PISTON/VTK-m framework, allowing a single implementation of an algorithm that simultaneously targets a variety of GPU, multi- core, and many-core architectures.