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18 results for “load balancing”
Performance results of LeanMD on the Joliot-Curie supercomputer using different load balancing algorithms
<p>This dataset contains the raw output files generated from the execution of LeanMD on the Joliot-Curie supercomputer (20 SKL Irene nodes), the scripts used to generate them, and the scripts used to parse these results for statistical analysis and plotting.</p> <p><strong>Software information</strong>:</p> <ul> <li>OS: Red Hat Enterprise Linux 7.6</li> <li>OpenMPI: version 2.0.4</li> <li>Compilers: C/C++ Intel 17.0.6.256</li> <li>Charm++ version: v6.9.0-rc3, build mpi-linux-x86_64 --with-production</li> <li>LeanMD source: <a href="https://charm.cs.illinois.edu/gerrit/gitweb?p=benchmarks/leanmd.git">https://charm.cs.illinois.edu/gerrit/gitweb?p=benchmarks/leanmd.git</a></li> <li>Additional load balancers source: <a href="https://github.com/viniciusmctf/packing-schemes/tree/packs_2019-v1">https://github.com/viniciusmctf/packing-schemes/tree/packs_2019-v1</a></li> <li>Charm++, LeanMD, and the load balancers were compiled with -O3</li> </ul> <p><strong>File information</strong>:</p> <p>The raw result files are organized in four directories (oct18, oct23, oct24, and oct24_2).<br> Each directory contains the results of one batched execution in the supercomputer.<br> Each batch is composed of 10 repetitions of a set of experiments.<br> Each set of experiments includes different load balancing algorithms and different problem sizes.<br> Each set is randomly ordered to avoid interference coming from a specific order of execution.<br> Each raw file contains the appended output of the application and its load balancer for all 10 repetitions.<br> The name of the files indicate the load balancer and size of the problem.<br> For instance, `PackStealLB.240` means that the application was run with PackStealLB and the problem size parameter is 240.</p> <p><strong>Problem sizes</strong>:</p> <ul> <li>80: 80×11×5 cells of dimensions 15×15×30</li> <li>120: 120×11×5 cells of dimensions 15×15×30</li> <li>160: 160×11×5 cells of dimensions 15×15×30</li> <li>240: 240×11×5 cells of dimensions 15×15×30</li> <li>320: 320×11×5 cells of dimensions 15×15×30</li> </ul> <p>Each execution of LeanMD ran for 301 iterations with load balancing calls at iterations 40, 140, and 240.</p> <p><strong>Raw output files</strong>:</p> <p>Each raw output file starts with a Charm++ header providing information on the execution.</p> <p>For each step of the application, its execution time in ms is provided.</p> <p>Load balancing calls usually provide information about their start time, end time, and duration. Depending on the load balancer, more information is provided.</p> <p>Each raw file contains ten executions of the application with a given load balancer and input size.</p> <p><strong>Generating plots</strong>:</p> <p>The analysis of the results can be done by running the Jupyter notebook named "Analysis of load balancing results.ipynb"</p>
MPI load balancing simulation data sets (companion to IPDPS 2017)
<p>This package contains data sets and scripts (in an Org-mode file) related to our submission to IPDPS 2017, under the title "Using Simulation to Evaluate and Tune the Performance of Dynamic Load Balancing of an Over-decomposed Geophysics Application".</p> <p>The following contents are included:</p> <ul> <li><em>IPDPS2017.org :</em> Org mode (Emacs) file containing the shell (Bash) and R scripts used to: <ul> <li>run the load balancing simulation;</li> <li>process the traces of both real executions (Tau traces) and simulation (Pajé traces);</li> <li>generate the graphics.</li> </ul> </li> <li><em>lb_traces/:</em> this directory contains the raw traces from real executions and SMPI emulations of the Ondes3D application.</li> <li><em>processed_data/</em>: this directory contains the results of the processing of the traces in the form of CSV format data files which are be used to generate the graphics.</li> <li>i<em>mg</em>/: this directory contains the generate graphics, in PNG format.</li> </ul> <p> </p>
Data sets for the Simulated AMPI (SAMPI) load balancing simulation workflow and Ondes3D performance analysis (Companion to CCPE - Euro-Par 2017 special issue)
<p>This package contains data sets and scripts (in an Org-mode file) related to our submission to the special Euro-Par 2017 issue of the journal "Concurrency and Computation: Practice and Experience", under the title "Performance Modeling of a Geophysics Application to Accelerate Over-decomposition Parameter Tuning through Simulation".</p>
Data sets for the Simulated AMPI (SAMPI) load balancing simulation workflow and Ondes3D performance analysis (Companion to CCPE paper)
<p>This package contains data sets and scripts (in an Org-mode file) related to our submission to the journal "Concurrency and Computation: Practice and Experience", under the title <em>"Performance Modeling of a Geophysics Application to Accelerate the Tuning of Over-decomposition Parameters through Simulation"</em>.</p>
Total Load Profiles by Balancing Authority in the Western United States for GODEEEP
<p>This dataset contains time-series of total load profiles across Balancing Authorities (BAs) in the western United States (U.S.) electricity grid interconnection for the years 2025, 2030, 2035, 2040, 2045, and 2050. The data is provided for two different socioeconomic pathways and one climate scenario. The socioeconomic pathways -- Business-As-Usual (BAU_Climate) and Net-Zero without CCS (NetZeroNoCCS_Climate) -- are described by <a href="https://doi.org/10.5281/zenodo.7838871">https://doi.org/10.5281/zenodo.7838871</a>. The climate scenario -- Representative Concentration Pathway 8.5 hotter (rcp85hotter) -- is described by <a href="https://doi.org/10.57931/1885756">https://doi.org/10.57931/1885756</a>. Transportation loads in this dataset are derived from <a href="https://doi.org/10.5281/zenodo.8065137">https://doi.org/10.5281/zenodo.8065137</a>. Non-transportation loads are derived using the Total Electricity Loads (TELL) model which is available <a href="https://github.com/IMMM-SFA/tell">here</a>. The data was post-processed using a Jupyter notebook available <a href="https://github.com/GODEEEP/load_analysis/blob/main/notebooks/process_load_data.ipynb">here</a>.</p> <p>A brief summary of the files and directories in this data package is provided below. Each file in the "<em>total_loads</em>" subdirectory is a comma-separated-value (CSV) format with the following columns:</p> <ul> <li><strong>BA</strong> - Acronym of the balancing authority (BA) for this data point</li> <li><strong>Time_UTC</strong> - Timestamp of the hourly data in UTC format</li> <li><strong>Non-Transportation_Load_MWh</strong> - Total hourly load in the BA from non-transportation sources in megawatt hours</li> <li><strong>Transportation_Load_MWh</strong> - Total hourly load in the BA from transportation sources in megawatt hours</li> <li><strong>Total_Load_MWh</strong> - Sum of the load from non-transportation and transportation sources in megawatt hours</li> </ul> <p>Data in the "<em>gridview_ready_total_loads</em>" subdirectory contains the hourly total loads by BA in a format that is ready for ingestion into the GridView production cost model.</p> <p>This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).</p> <p>PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.</p>
Companion data for Communication-Aware Load Balancing of the LU Factorization over Heterogeneous Clusters
<p>This is the companion data repository for the paper entitled <strong>Communication-Aware Load Balancing of the LU Factorization over Heterogeneous Clusters</strong> by Lucas Leandro Nesi, Lucas Mello Schnorr, and Arnaud Legrand. The manuscript has been accepted in the <a href="https://icpads2020.comp.polyu.edu.hk/">ICPADS 2020</a>.</p>
Transportation Electrification Load Profiles by Balancing Authority and State-Level Electrification Rates in the Western United States for GODEEEP
<p>Time-series hourly electric charging load profiles for the transportation sector across Balancing Authorities (BAs) in the Western Electricity Coordinating Council (WECC) interconnect, annual fleet sizes by state and vehicle type, annual transportation sector energy usage by state and fuel, and annual transportation fuel usage by state. The data is provided for three different socioeconomic pathways and two different climate pathways, resulting in four total scenarios. The socioeconomic pathways--Net-Zero (<code>nz_climate</code>), Net-Zero allowing for Carbon Capture Sequestration (CCS) technology (<code>nz_ccs_climate</code>), and Net-Zero allowing for CCS with Inflation Reduction Act (IRA) policies (<code>nz_ira_ccs_climate</code>)--are described by <a href="https://doi.org/10.5281/zenodo.10642507">https://doi.org/10.5281/zenodo.10642507</a>. The climate pathways--Representative Concentration Pathway (RCP) 4.5 cooler (<code>rcp45cooler</code>) and RCP 8.5 hotter (<code>rcp85hotter</code>)--are described by <a href="https://doi.org/10.57931/1885756">https://doi.org/10.57931/1885756</a>. The climate influence is only considered for Light Duty Vehicles (LDVs).</p> <p>For additional details please consult the paper Acharya et al 2024, Impact of the Inflation Reduction Act and Carbon Capture on Transportation Electrification for a Net-Zero Western U.S. Grid, submitted, and the code repository <a href="https://github.com/GODEEEP/transportation_electrification">https://github.com/GODEEEP/transportation_electrification</a>.</p> <p>A brief summary of the files and directories in this data package is provided below. Text within chevrons implies a multiplicity of files, one for each actual value.</p> <ul> <li>nz_climate <ul> <li>rcp45cooler <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> <li>rcp85hotter <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> </ul> </li> <li>nz_ccs_climate <ul> <li>rcp45cooler <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> <li>rcp85hotter <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> </ul> </li> <li>nz_ira_ccs_climate <ul> <li>rcp45cooler <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> <li>rcp85hotter <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> </ul> </li> <li>WECC_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> <li>EV_electric_and_total_energy.csv</li> <li>LDV_fleet_size_all_fuel_types_state_wise.csv</li> <li>MDV_fleet_size_all_fuel_types_state_wise.csv</li> <li>HDV_fleet_size_all_fuel_types_state_wise.csv</li> </ul> <p> </p> <p><strong>Hourly transportation load:</strong></p> <ul> <li><code>time</code> - ISO 8601 timestamp representing the end of the hourly timestep; values are reported as the summation over the preceding hour</li> <li><code>balancing_authority</code> - Acronym of the balancing authority for this data point</li> <li><code>LDV_load_MWh</code> - Energy consumed by the charging of Light Duty Vehicles (LDVs) during the previous hour in Megawatt hours</li> <li><code>MDV_load_MWh</code> - Energy consumed by the charging of Medium Duty Vehicles (MDVs) during the previous hour in Megawatt hours</li> <li><code>HDV_load_MWh</code> - Energy consumed by the charging of Heavy Duty Vehicles (HDVs) during the previous hour in Megawatt hours</li> <li><code>passenger_rail_load_MWh</code> - Energy consumed by the charging of passenger rail vehicles during the previous hour in Megawatt hours</li> <li><code>freight_rail_load_MWh</code> - Energy consumed by the charging of freight rail vehicles during the previous hour in Megawatt hours</li> <li><code>aviation_load_MWh</code> - Energy consumed by the charging of aviation vehicles during the previous hour in Megawatt hours</li> <li><code>ship_load_MWh</code> - Energy consumed by the charging of ships during the previous hour in Megawatt hours</li> <li><code>transportation_load_MWh</code> - Total energy consumed by the charging of vehicles during the previous hour in Megawatt hours (summation of the other columns)</li> </ul> <p>The WECC files provide summations of all BAs for each scenario, with the same columns as above excepting <code>balancing_authority</code></p> <p><strong>State-wise fleet sizes by vehicle type:</strong></p> <ul> <li><code>scenario</code> - the socioeconomic pathway, one of <code>nz_climate</code>, <code>nz_ccs_climate</code>, or <code>nz_ira_ccs_climate</code></li> <li><code>state</code> - two letter abbreviation of the state within the Western U.S. Interconnection</li> <li><code>year</code> - 5 year increments from 2020 to 2050</li> <li><code>technology</code> - fuel type such as BEV (battery electric vehicle), FCEV (fuel cell electric vehicle), hybrid liquids and liquids (refined liquids)</li> <li><code>veh_type</code> - one of LDV, MDV, or HDV (Light, Medium, or Heavy Duty Vehicle)</li> <li><code>fleet_size</code> - the number of vehicles</li> </ul> <p>To calculate an electrification rate in terms of fleet size for a given scenario, state, year, and veh<em>type, we divide the fleet</em>size for BEV technology by the summation of fleet_size for all technologies.</p> <p><strong>State-wise electric and total energy for LDVs, MDVs, and HDVs:</strong></p> <ul> <li><code>state</code> - two letter abbreviation of the state within the Western U.S. Interconnection</li> <li><code>year</code> - 5 year increments from 2020 to 2050</li> <li><code>scenario</code> - the socioeconomic pathway, one of <code>nz_climate</code>, <code>nz_ccs_climate</code>, or <code>nz_ira_ccs_climate</code></li> <li><code>hdv_total</code> - energy in ExaJoules consumed by all HDVs irrespective of fuel type</li> <li><code>ldv_total</code> - energy in ExaJoules consumed by all LDVs irrespective of fuel type</li> <li><code>mdv_total</code> - energy in ExaJoules consumed by all MDVs irrespective of fuel type</li> <li><code>hdv_electric</code> - electric energy in ExaJoules consumed by HDVs</li> <li><code>ldv_electric</code> - electric energy in ExaJoules consumed by LDVs</li> <li><code>mdv_electric</code> - electric energy in ExaJoules consumed by MDVs</li> </ul> <p>To calculate the electrification rate in terms of EV energy for a given scenario, state, year, and veh_type, we divide electric energy by the total energy.</p> <p><strong>State-wise transportation fuel mix:</strong></p> <ul> <li><code>state</code> - two letter abbreviation of the state within the Western U.S. Interconnection</li> <li><code>year</code> - 5 year increments from 2020 to 2050</li> <li><code>scenario</code> - the socioeconomic pathway, one of <code>nz_climate</code>, <code>nz_ccs_climate</code>, or <code>nz_ira_ccs_climate</code></li> <li><code>hydrogen</code> - hydrogen energy in ExaJoules consumed by the transportation sector</li> <li><code>electricity</code> - electric energy in ExaJoules consumed by the transportation sector</li> <li><code>refined liquids</code> - refined liquid energy in ExaJoules consumed by the transportation sector</li> </ul> <p><br><br></p> <p><strong>Changelog:</strong></p> <ul> <li>v2.0.0 - new set of scenarios; fuel mix data added</li> </ul> <p> </p> <p>This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).</p> <p>PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.</p>
Dynamic Load Balancing for Predictions of Storm Surge and Coastal Flooding-Model setup and source code
<p>Source code and model setup/inputs for the paper titled "Dynamic Load Balancing for Predictions of Storm Surge and Coastal Flooding" article. Simulations were conducted using a modified version of ADCIRC+DLB (ADCIRC + Dynamic Load Balancing) on unstructured triangular meshes.</p> <p>Contains:</p> <ol> <li>Model input files. <ol> <li>ADCIRC model input files for the ideal channel setup and Hurricane Irene simulation (*.13, *.14, *.15)</li> </ol> </li> <li>Zipped archive of the ADCIRC code (adcirc-cg-DLB.zip) used to produce the simulations for the paper.</li> <li>Step-by-step compilation and usage instructions for ADCIRC+DLB. <ol> <li>Installation.html </li> <li>Usage.html</li> </ol> </li> </ol>
Data from: Oral microbiomes from hunter-gatherers and traditional farmers reveal shifts in commensal balance and pathogen load linked to diet
Maladaptation to modern diets has been implicated in several chronic disorders. Given the higher prevalence of disease such as dental caries and chronic gum diseases in industrialized societies, we sought to investigate the impact of different subsistence strategies on oral health and physiology, as documented by the oral microbiome. To control for confounding variables such as environment and host genetics, we sampled saliva from three pairs of populations of hunter-gatherers and traditional farmers living in close proximity in the Philippines. Deep shotgun sequencing of salivary DNA generated high-coverage microbiomes along with human genomes. Comparing these microbiomes with publicly available data from individuals living on a Western diet revealed that abundance ratios of core species were significantly correlated with subsistence strategy, with hunter-gatherers and Westerners occupying either end of a gradient of Neisseria against Haemophilus, and traditional farmers falling in between. Species found preferentially in hunter-gatherers included microbes often considered as oral pathogens, despite their hosts' apparent good oral health. Discriminant analysis of gene functions revealed vitamin B5 autotrophy and urease-mediated pH regulation as candidate adaptations of the microbiome to the hunter-gatherer and Western diets, respectively. These results suggest that major transitions in diet selected for different communities of commensals and likely played a role in the emergence of modern oral pathogens.
Automated Load Balancing in OpenMP
<p>Full spectrum of results of "Automated Load Balancing in OpenMP". Performance of various scientific applications with various OpenMP scheduling algorithms and chunk parameter values. Evaluation of the performance of the newly introduced automatic load balancing methods in AUTO4OMP on the performance of scientific applications. AUTO4OMP automatically selects a suitable scheduling algorithm and a chunk parameter value during execution for OpenMP loops. </p>
Effects of Water Inertia Load Training on Lower Limb Joint Moments, Gait, and Balance in Elderly Women
ClinicalTrials.gov study NCT06705946. IPD Sharing: YES. Countries: 1. Publications: 10.
Data from: Oral microbiomes from hunter-gatherers and traditional farmers reveal shifts in commensal balance and pathogen load linked to diet
Open the record for dataset details and reuse information.
Data for WarpX milestone ECP-ADSE06.FY21.2: Assessment of dynamic load-balancing strategies on available exascale systems.
<p>This dataset includes the inputs, outputs, job submission scripts, and data analysis scripts used to prepare the WarpX milestone report for "Assessment of dynamic load-balancing strategies on available exascale systems." The code versions of WarpX, AMReX, and PICSAR used are stored in the outputs file for each run. </p>
Reduction of dietary glycemic load modifies expression of several miRNAs associated with energy balance and cancer pathways in premenopausal women
GEO Series GSE27474. Homo sapiens. 28 samples. Type: Non-coding RNA profiling by array.
Cognitive Load Effects on Balance and Postural Stability in Young Adults
ClinicalTrials.gov study NCT06941714. IPD Sharing: NO. Countries: 1. Publications: 0.
Effects of Improved Calf Muscle Function on Gait, Balance and Joint Loading in Older Adults
ClinicalTrials.gov study NCT03921801. IPD Sharing: YES. Countries: 1. Publications: 0.
Low Load With Blood Flow Restriction for Improving Strength, Balance, and Cognition in Older Adults
ClinicalTrials.gov study NCT06962514. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Liver transcriptional critical balance induced by acetaminophen is prevented by chronic copper loading
GEO Series GSE93937. Cebus capucinus; Homo sapiens. 24 samples. Type: Expression profiling by array.
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