Replication Data for: Load-Balancing for Scalable Simulations with Large Particle Numbers (ICPSR doi:10.18419/darus-1851)

View:

Part 1: Document Description
Part 2: Study Description
Part 3: Data Files Description
Part 4: Variable Description
Part 5: Other Study-Related Materials
Entire Codebook

Document Description

Citation

Title:

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

Identification Number:

doi:10.18419/darus-1851

Distributor:

DaRUS

Date of Distribution:

2021-06-07

Version:

1

Bibliographic Citation:

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]

Study Description

Citation

Title:

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

Identification Number:

doi:10.18419/darus-1851

Authoring Entity:

Hirschmann, Steffen (Universität Stuttgart)

Grant Number:

SFB 716

Grant Number:

EXC 2075 - 390740016

Distributor:

DaRUS

Access Authority:

Hirschmann, Steffen

Depositor:

Hirschmann, Steffen

Date of Deposit:

2021-05-14

Study Scope

Keywords:

Computer and Information Science

Topic Classification:

Parallel algorithms in computer science (68W10), Distributed algorithms (68W15), Approximation algorithms (68W25), Parallel numerical computation (65Y05), Applications to the sciences (65Z05), Performance evaluation, queueing, and scheduling in the context of computer systems, Turbulent combustion; reactive turbulence (76F80), Combustion (80A25)

Abstract:

This dataset contains input data, scripts, etc. for replicating the numerical experiments in Steffen Hirschmann's dissertation.<br> The data is prefixed with folders indicating the specific experiment as is the processing metadata.<br> The experiments are: <ul> <li>00_periodicity_experiment: Experiment in Chapter 5.1. Agglomerate moves over boundary and we inspect how well different partitioning methods perform.</li> <li>01_homogeneous_scaling: Experiment in Chapter 8.1. Weak scaling of ESPResSo with and without our additions of a homogeneous fluid.</li> <li>02_droplet_formation: Experiment in Chapter 8.1. Comparison of the behavior of a non-homogeneous particle distribution with and without load-balancing.</li> <li>03_coupled_lbm_md: Experiment in Chapter 8.2. Weak scaling evaluation of our joint Lattice-Boltzmann (LBM) Molecular Dynamics (MD) partitioning.</li> <li>04_lb_adaptions: Experiments in Chapter 8.3. Simulation of a spinodal decomposition with different load-balancing methods and different variants.</li> <li>05_heterogeneity: Script for evaluating the heterogeneity measure defined in Chapter 7.1.</li> <li>06_soot_particle_agglomeration: Script to simulate the soot particle agglomeration scenario. Methodology described in Chapter 4, simulation described in Chapter 8.4.</li> </ul> The indications to chapters reference the publication described in "Related Publication".

Kind of Data:

program source code

Kind of Data:

input data

Methodology and Processing

Sources Statement

Data Access

Citation Requirement:

See "Related Publication".

Conditions:

The code and software included under "software/" (ESPResSo as well as our additions and custom patches) in this dataset are licensed under the GPLv3.

Notes:

CC BY Waiver

Other Study Description Materials

Related Publications

Citation

Bibliographic Citation:

Hirschmann, S.: Load-Balancing for Scalable Simulations with Large Particle Numbers. PhD thesis. University of Stuttgart, 2021, submitted.

File Description--f63027

File: scaling.tab

  • Number of cases: 10

  • No. of variables per record: 1

  • Type of File: text/tab-separated-values

Notes:

UNF:6:0J2k1mxPjtjCMq6q8Vbw6g==

File Description--f63047

File: simulation-result-data-extracted.tab

  • Number of cases: 12

  • No. of variables per record: 1

  • Type of File: text/tab-separated-values

Notes:

UNF:6:E2YeVaO+v2VpzS+UkjzWWg==

Variable Description

List of Variables:

Variables

nproc default p4est

f63027 Location:

Variable Format: character

Notes: UNF:6:0J2k1mxPjtjCMq6q8Vbw6g==

x graph p4est

f63047 Location:

Variable Format: character

Notes: UNF:6:E2YeVaO+v2VpzS+UkjzWWg==

Other Study-Related Materials

Label:

agglomerate-1954.txt

Text:

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

Notes:

text/plain

Other Study-Related Materials

Label:

calculate_heterogeneity.py

Notes:

text/x-python

Other Study-Related Materials

Label:

case6-100mio.py

Notes:

text/x-python

Other Study-Related Materials

Label:

espresso-4.1.2+generic_dd-1.zip

Text:

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

Notes:

application/zip

Other Study-Related Materials

Label:

espresso-old_custom.zip

Text:

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

Notes:

application/zip

Other Study-Related Materials

Label:

extract-all.awk

Notes:

application/x-awk

Other Study-Related Materials

Label:

flowfield_patch_for_ESPResSo-4.1.2+generic_dd.diff

Text:

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

Notes:

text/x-patch

Other Study-Related Materials

Label:

generate-grid-mpiio-data.cc

Notes:

text/x-c++src

Other Study-Related Materials

Label:

generate-uniform-random-mpiio-data.cc

Notes:

text/x-c++src

Other Study-Related Materials

Label:

lbm-md-coupled-sim.py

Notes:

text/x-python

Other Study-Related Materials

Label:

lj-default-1700ppp.dat

Text:

Average, minimum and maximum runtime as well as imbalance every 4000 time steps. Data generated using the standard Cartesian domain decomposition of ESPResSo.

Notes:

text/x-fixed-field

Other Study-Related Materials

Label:

lj-reba-1700ppp.dat

Text:

Average, minimum and maximum runtime as well as imbalance every 4000 time steps. Data generated using the SFC-based spatial domain decomposition optimized for the number of particles per subdomain.

Notes:

text/x-fixed-field

Other Study-Related Materials

Label:

measurement_patch_for_ESPResSo-4.1.2+generic_dd.diff

Text:

Patch to add measurements to ESPResSo-4.1.2+generic_dd

Notes:

text/x-patch

Other Study-Related Materials

Label:

measurement.tcl

Notes:

text/x-tcl

Other Study-Related Materials

Label:

parse-output-files.py

Notes:

text/x-python

Other Study-Related Materials

Label:

plot.py

Notes:

text/x-python

Other Study-Related Materials

Label:

plot-results.py

Notes:

text/x-python

Other Study-Related Materials

Label:

plot-scaling.py

Notes:

text/x-python

Other Study-Related Materials

Label:

plot-setup.py

Text:

Plots the setup. Input is a txt file of the particle positions (agglomerate-1954.txt).

Notes:

text/x-python

Other Study-Related Materials

Label:

run.sh

Notes:

application/x-shellscript

Other Study-Related Materials

Label:

simulate-agglomerate.py

Notes:

text/x-python

Other Study-Related Materials

Label:

simulation_script.tcl

Notes:

text/x-tcl

Other Study-Related Materials

Label:

spinodal_decomposition.py

Notes:

text/x-python

Other Study-Related Materials

Label:

spinodal-load-balancing.py

Notes:

text/x-python