Replication Data for: Load-Balancing for Scalable Simulations with Large Particle Numbersdoi:10.18419/darus-1851DaRUS2021-06-071Hirschmann, 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]Replication Data for: Load-Balancing for Scalable Simulations with Large Particle Numbersdoi:10.18419/darus-1851Hirschmann, SteffenSFB 716EXC 2075 - 390740016DaRUSHirschmann, SteffenHirschmann, Steffen2021-05-14Computer and Information ScienceParallel 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 (68M20)Turbulent combustion; reactive turbulence (76F80)Combustion (80A25)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".program source codeinput dataSee "Related Publication".Hirschmann, S.: Load-Balancing for Scalable Simulations with Large Particle Numbers. PhD thesis. University of Stuttgart, 2021, submitted.Hirschmann, S.: Load-Balancing for Scalable Simulations with Large Particle Numbers. PhD thesis. University of Stuttgart, 2021, submitted.simulation-result-data-extracted.tab121text/tab-separated-valuesUNF:6:E2YeVaO+v2VpzS+UkjzWWg==scaling.tab101text/tab-separated-valuesUNF:6:0J2k1mxPjtjCMq6q8Vbw6g==x graph p4estUNF:6:E2YeVaO+v2VpzS+UkjzWWg==nproc default p4estUNF:6:0J2k1mxPjtjCMq6q8Vbw6g==agglomerate-1954.txtPositions of the particles constituting the agglomerate. One particle position as X, Y, Z coordinates per row.
Positions are measured in multiples of 20nm.text/plainplot-results.pytext/x-pythonplot-setup.pyPlots the setup. Input is a txt file of the particle positions (agglomerate-1954.txt).text/x-pythonsimulate-agglomerate.pytext/x-pythongenerate-grid-mpiio-data.cctext/x-c++srcmeasurement.tcltext/x-tclplot-scaling.pytext/x-pythonlj-default-1700ppp.datAverage, minimum and maximum runtime as well as imbalance every 4000 time steps.
Data generated using the standard Cartesian domain decomposition of ESPResSo.text/x-fixed-fieldlj-reba-1700ppp.datAverage, 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.text/x-fixed-fieldparse-output-files.pytext/x-pythonplot.pytext/x-pythonsimulation_script.tcltext/x-tcllbm-md-coupled-sim.pytext/x-pythonrun.shapplication/x-shellscriptextract-all.awkapplication/x-awkspinodal-load-balancing.pytext/x-pythonspinodal_decomposition.pytext/x-pythoncalculate_heterogeneity.pytext/x-pythoncase6-100mio.pytext/x-pythongenerate-uniform-random-mpiio-data.cctext/x-c++srcespresso-4.1.2+generic_dd-1.zipESPResSo 4.1.2 with generic_dd module to use librepa as backend to ESPResSo.application/zipespresso-old_custom.zipOld version of ESPResSo with custom additions to perform coupled load-balancing.application/zipflowfield_patch_for_ESPResSo-4.1.2+generic_dd.diffPatch to allow for the inclusion of an external time-dependent flow field like in our soot particle agglomeration simulation methodology.text/x-patchmeasurement_patch_for_ESPResSo-4.1.2+generic_dd.diffPatch to add measurements to ESPResSo-4.1.2+generic_ddtext/x-patch