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Publication Year: 2021

1 to 10 of 27 Results

Replication Data for: Load-Balancing for Scalable Simulations with Large Particle Numbers Jun 7, 2021 Hirschmann, Steffen, 2021, "Replication Data for: Load-Balancing for Scalable Simulations with Large Particle Numbers", https://doi.org/10.18419/darus-1851, DaRUS, V1, UNF:6:WXqt4IDAIDGnJB9tcwMhdg== [fileUNF] This dataset contains input data, scripts, etc. for replicating the numerical experiments in Steffen Hirschmann's dissertation. The data is prefixed with folders indicating the specific experiment as is the processing metadata. The experiments are: 00_periodicity_experiment: Expe...
agglomerate-1954.txt Jun 7, 2021 - Replication Data for: Load-Balancing for Scalable Simulations with Large Particle Numbers Plain Text - 144.2 KB - MD5: 174db7a3a2b1469ba7d814d403813009 DataInput Positions of the particles constituting the agglomerate. One particle position as X, Y, Z coordinates per row. Positions are measured in multiples of 20nm.
calculate_heterogeneity.py Jun 7, 2021 - Replication Data for: Load-Balancing for Scalable Simulations with Large Particle Numbers Python Source Code - 6.6 KB - MD5: b503a4a5a9ef40732092551cef929d42 CodePostprocessing
case6-100mio.py Jun 7, 2021 - Replication Data for: Load-Balancing for Scalable Simulations with Large Particle Numbers Python Source Code - 6.8 KB - MD5: 097a88bd4e844a2546906f6fe1e1a6ee Code
espresso-4.1.2+generic_dd-1.zip Jun 7, 2021 - Replication Data for: Load-Balancing for Scalable Simulations with Large Particle Numbers ZIP Archive - 17.1 MB - MD5: e4819b09c36c4e2c979e08675690d99e ESPResSo 4.1.2 with generic_dd module to use librepa as backend to ESPResSo.
espresso-old_custom.zip Jun 7, 2021 - Replication Data for: Load-Balancing for Scalable Simulations with Large Particle Numbers ZIP Archive - 25.4 MB - MD5: 2446b934682eae85e73df79ab1b2f634 Old version of ESPResSo with custom additions to perform coupled load-balancing.
extract-all.awk Jun 7, 2021 - Replication Data for: Load-Balancing for Scalable Simulations with Large Particle Numbers application/x-awk - 487 B - MD5: ebbfc9c5578973c2482d8bd1e434825d CodePostprocessing
flowfield_patch_for_ESPResSo-4.1.2+generic_dd.diff Jun 7, 2021 - Replication Data for: Load-Balancing for Scalable Simulations with Large Particle Numbers text/x-patch - 17.1 KB - MD5: 5985e01a97ae681e50e8d5781c05ec21 Patch to allow for the inclusion of an external time-dependent flow field like in our soot particle agglomeration simulation methodology.
generate-grid-mpiio-data.cc Jun 7, 2021 - Replication Data for: Load-Balancing for Scalable Simulations with Large Particle Numbers C++ Source - 4.0 KB - MD5: 3e3506b0fe6e40c7e1794a6cb44f7e8d CodeInput
generate-uniform-random-mpiio-data.cc Jun 7, 2021 - Replication Data for: Load-Balancing for Scalable Simulations with Large Particle Numbers C++ Source - 3.7 KB - MD5: 10bb1672ba2bdb60de8fa8d5b0409ddc CodeInput

Replication Data for: Load-Balancing for Scalable Simulations with Large Particle Numbers

Jun 7, 2021

Hirschmann, Steffen, 2021, "Replication Data for: Load-Balancing for Scalable Simulations with Large Particle Numbers", https://doi.org/10.18419/darus-1851, DaRUS, V1, UNF:6:WXqt4IDAIDGnJB9tcwMhdg== [fileUNF]

This dataset contains input data, scripts, etc. for replicating the numerical experiments in Steffen Hirschmann's dissertation. The data is prefixed with folders indicating the specific experiment as is the processing metadata. The experiments are: 00_periodicity_experiment: Expe...

agglomerate-1954.txt

Jun 7, 2021 - Replication Data for: Load-Balancing for Scalable Simulations with Large Particle Numbers

Plain Text - 144.2 KB -

DataInput

Positions of the particles constituting the agglomerate. One particle position as X, Y, Z coordinates per row. Positions are measured in multiples of 20nm.

calculate_heterogeneity.py

Jun 7, 2021 - Replication Data for: Load-Balancing for Scalable Simulations with Large Particle Numbers

Python Source Code - 6.6 KB -

CodePostprocessing

case6-100mio.py

Jun 7, 2021 - Replication Data for: Load-Balancing for Scalable Simulations with Large Particle Numbers

Python Source Code - 6.8 KB -

Code

espresso-4.1.2+generic_dd-1.zip

Jun 7, 2021 - Replication Data for: Load-Balancing for Scalable Simulations with Large Particle Numbers

ZIP Archive - 17.1 MB -

ESPResSo 4.1.2 with generic_dd module to use librepa as backend to ESPResSo.

espresso-old_custom.zip

Jun 7, 2021 - Replication Data for: Load-Balancing for Scalable Simulations with Large Particle Numbers

ZIP Archive - 25.4 MB -

Old version of ESPResSo with custom additions to perform coupled load-balancing.

extract-all.awk

Jun 7, 2021 - Replication Data for: Load-Balancing for Scalable Simulations with Large Particle Numbers

application/x-awk - 487 B -

CodePostprocessing

flowfield_patch_for_ESPResSo-4.1.2+generic_dd.diff

Jun 7, 2021 - Replication Data for: Load-Balancing for Scalable Simulations with Large Particle Numbers

text/x-patch - 17.1 KB -

Patch to allow for the inclusion of an external time-dependent flow field like in our soot particle agglomeration simulation methodology.

generate-grid-mpiio-data.cc

Jun 7, 2021 - Replication Data for: Load-Balancing for Scalable Simulations with Large Particle Numbers

C++ Source - 4.0 KB -

CodeInput

generate-uniform-random-mpiio-data.cc

Jun 7, 2021 - Replication Data for: Load-Balancing for Scalable Simulations with Large Particle Numbers

C++ Source - 3.7 KB -

CodeInput

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