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75 results for “MPI”
MPI-Leipzig_Mind-Brain-Body
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MAR-MPI-ESM1-2-HR SSP585 European Alps (2015-2100)
<p>This deposit contains MAR simulations over the European Alps domain (7 kilometers resolution) forced by the MPI-ESM1-2-HR GCM (CMIP6 version) for the SSP585 projection (2015 to 2100). The version of the MAR model used is v.3.10, model set-up is described in detail in Beaumet et al., 2021 (https://doi.org/10.1007/s10113-021-01830-x) Contact person : Julien Beaumet (beaumetjulien@gmail.com), Martin Menegoz (martin.menegoz@univ-grenoble-alpes.fr)<br> The realization used is r1i1p1f1.<br> Data are available at the daily frequency, with one variable per file (10 years of data per file).<br> The available variables in this deposit are :</p> <p><strong>CC:</strong> Cloud cover, [0-1]<br> <strong>MB:</strong> Total snow water equivalent, [mm.We]<br> <strong>MBrr:</strong> Daily rainfall, [mm.We] (5)<br> <strong>MBsf:</strong> Daily snowfall, [mm.We] (5)<br> <strong>QQz: </strong> Near-surface specific humidity at constant height, [g/kg] (2)</p> <p><strong>TTmax:</strong> Near-surface maximum air temperature for the first model level above the surface (constant sigma), [C]<br> <strong>TTmin:</strong> Near-surface minimum air temperature for the first model level above the surface (constant sigma),[C]</p> <p><strong>TTz: </strong>Near-surface mean air temperature at constant-height, [C]<br> <strong>UUz:</strong> Near-surface zonal component of wind speed at constant height, [m/s]<br> <strong>VVz:</strong> Near surface Meridional component of wind speed at constant height, [m/s]<br> <strong>ZN3:</strong> Total snow height, [m]</p> <p>Other variables are available upon request (see email above).<br> AL : Surface albedo, [0-1]<br> CD: Low level Cloud cover, [0-1]<br> CM: Middle level Cloud cover, [0-1]<br> CU: High level Cloud cover, [0-1]<br> SP: Surface pressure, [hPa]<br> ST: Surface temperature, [C]<br> TT: Near-surface mean air temperature for the first three model level above the surface (constant sigma), [C] (1)<br> TTp: Constant pressure-level mean air temperature, [C] (4)<br> ZZ: Surface geopotential for the first three model level above the surface (constant sigma), [m]</p> <p>LWD: Surface downward longwave radiation, [W/m2]<br> LWU: Surface upward longwave radiation, [W/m2]</p> <p>SWD: Surface downward shortwave radiation, [W/m2]<br> SWU: Surface upward shortwave radiation, [W/m2]</p> <p>SHF: Surface sensible heat flux, [W/m2] LHF: Surface latent heat flux, [W/m2]</p> <p>* Latitude(LAT),longitude(LON)and surface elevation (SH) of each grid point can be read in MARgrid_EUy.nc file</p> <p>(1) For variable TT, ZZ model constant sigma level of 0.9997479<br> (2) For variables TTz, QQz constant height level is : 2m<br> (3) For variables UUz, VVz constant height level are : 10m<br> (4) Variables TTp, UUp, VVp available at pressure level : 925, 850, 800, 700, 600, 500, 200 hPa<br> (5) Snow height and snow water equivalent are available for three sectors which corresponds to three different vegetation type : The three vegetation type used can be readen in the file MARgrid_EUy.nc, with the variable VEG and their respectibe fraction for each grid point is given by the variable FRV. The third vegetation type (sector=3) mostly corresponds to bare soil or low crops by default, but sometimes its fraction=0, which gives unrealistic low values of snow height. In this case, using the max. value on the axis sector often gives the best results.<br> Legend of the vegetation type for the VEG variables : 0:NO_VEGETATION 1:CROPS_LOW 2:CROPS_MEDIUM 3:CROPS_HIGH 4:GRASS_LOW 5:GRASS_MEDIUM 6:GRASS_HIGH 7:BROADLEAF_LOW 8:BROADLEAF MEDIUM 9:BROADLEAF_HIGH 10:NEEDLELEAF_LOW 11:NEEDLELEAF MEDIUM 12:NEEDLELEAF_HIGH 13:City<br> ~ </p>
Regional climate simulations of surface precipitation and temperature for West Africa using COSMO-CLM based on MPI-LR (ECHAM6) and RCP4.5
<p>Regional climate model COSMO-CLM (CCLM) simulations with a horizontal resolution of 0.11° (approx. 12 km) for sub-Saharan West Africa under current and future climate conditions. The CCLM is driven by initial and lateral boundary conditions from the MPI-LR (ECHAM6), based on the emission scenario RCP4.5. The downscaled MPI-LR (ECHAM6) data for surface precipitation (P) and surface temperature (Tmin, Tmax) are provided for the baseline period (1981-2010) and two future time slices, i.e. the 2021–2050 and the 2071–2100 period. </p> <p> </p>
Scalasca trace analysis of HemeLB application execution with 13824 MPI processes on SuperMUC-NG
<p>The CompBioMed HPC CoE flagship application HemeLB was run with a 6.4 micron resolution "circle of Willis" geometry dataset on LRZ's SuperMUC-NG supercomputer, and its execution performance with 13824 MPI processes on 288 dual 24-core compute nodes measured by Score-P (using SIONlib) and analysed by Scalasca trace analyzer.</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>
Dataset: Exploring Techniques for the Analysis of Spontaneous Asynchronicity in MPI-Parallel Applications
<p>Dataset used in the paper "Exploring Techniques for the Analysis of Spontaneous Asynchronicity in MPI-Parallel Applications"</p>
Model results based on COSMOS climate model (old version MPI-ESM1)
<p>Nino 3.4 SST of individual models (COSMOS-Nordemg and COSMOS-Tiedtke) and supermodel</p>
Dataset: An Overhead Analysis of MPI Profiling and Tracing Tools
<p>Runtime measurements of ECP Proxy Applications with the following MPI performance analysis tools:</p> <ul> <li>Score-P</li> <li>mpiP</li> <li>IPM</li> <li>HPCToolkit</li> <li>Extrae</li> <li>Pilgrim</li> </ul>
Data and scripts for: Bayesian Phylogenetic Analysis on multi-core Compute Architectures: Implementation and evaluation of BEAGLE in RevBayes with MPI
<p>Phylogenies are central to many research areas in biology and commonly estimated using likelihood-based methods. Unfortunately, any likelihood-based method, including Bayesian inference, can be restrictively slow for large datasets–with many taxa and/or many sites in the sequence alignment–or complex substitution models. The primary limiting factor when using large datasets and/or complex models in probabilistic phylogenetic analyses is the likelihood calculation, which dominates the total computation time. To address this bottleneck, we incorporated the high-performance phylogenetic library BEAGLE into RevBayes, which enables multi-threading on multi-core CPUs and GPUs, as well as hardware-specific vectorized instructions for faster likelihood calculations. Our new implementation of RevBayes+BEAGLE retains the flexibility and dynamic nature that users expect from vanilla RevBayes. Additionally, we implemented a native parallelization within RevBayes without an external library using the message passing interface (MPI); RevBayes+MPI. We evaluated our new implementation of RevBayes+BEAGLE using multi-threading on CPUs and a powerful NVidia Titan V GPU against our native implementation of RevBayes+MPI. We found good improvements in speedup when multiple cores were used with up to 20-fold speedup when using multiple CPUs and over 90-fold speedup when using multiple GPU cores. The improvement depended on the data type used, DNA or amino acids, and the size of the alignment, but less on the size of the tree. We additionally investigated the cost of rescaling partial likelihoods to avoid numerical underflow and showed that unnecessarily frequent rescaling can increase runtimes 2.5 to 3-fold. Finally, we presented and compared a new approach to store partial likelihoods on branches instead of nodes which can speed up computations but comes at twice the memory requirements.</p> <p>Availability: The software described in the paper is available at https://github.com/revbayes/revbayes with documentation and tutorials found at https://revbayes.github.io.</p>
3D ToF MPI multi-frequency mesh dataset
<p>The accuracy of indirect 3D Time-of-Flight (3D ToF) measurements is often limited by multi-path interferences (MPI) caused by multi-layer ToF conditions. Taking multiple measurements of the same scene at different modulation frequencies allows separating the interfering signal components of the individual paths according to several optimization methods described in literature.</p> <p>Orthogonal matching pursuit (OMP) optimization has been reported to achieve good path separation performance and superior results compared to particle swarm optimization (PSO). This work presents improved PSO performance for MPI separation based on new experimental data and refined PSO strategy.</p> <p>The current PSO approach achieves good distance separation in the setup used with low RMS distance errors in the order of 20 cm in situations where the OMP approach shows RMS errors higher than 100 cm. The previously reported minimum distance difference limitation between two separate objects of 2.7 m for the OMP algorithm could be reduced to roughly 0.75 m for the PSO algorithm. The trade-off between image accuracy and computing effort is explored and presented with respect to PSO parameter settings.</p> <p>This dataset provides researchers with measurement data to develop their own multi-layer algorithms and contribute to the ongoing development of great 3D ToF cameras.</p>
Preprocessed MPI System Matrix Data in HDF5 Files
<p>This data repository contains preprocessed system matrix data from magnetic particle imaging. The example files are hdf5. The data is used in this code example:</p> <p><a href="https://github.com/Ivo-B/3dSMRnet">github.com/Ivo-B/3dSMRnet</a></p>
Data and scripts for: Bayesian Phylogenetic Analysis on multi-core Compute Architectures: Implementation and evaluation of BEAGLE in RevBayes with MPI
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The MPI-Mainz UV/VIS Spectral Atlas of Gaseous Molecules
This archive contains a frozen snapshot of all cross section and quantum yield data files from the MPI-Mainz UV/VIS Spectral Atlas of Gaseous Molecules. To view the data, open the files cross_sections.html and quantum_yields.html in your browser. The up-to-date version of the Spectral Atlas is available at: http://www.uv-vis-spectral-atlas-mainz.org
Time independent traces from NAS MPI benchmarks (LU,IS,FT)
<p>Time independent traces from NAS MPI benchmarks (LU,IS,FT) that runs on Grid'5000 testbed on the graphene cluster (node 105 to143) using a dedicated switch (no network contention) and with 1 MPI process per node.</p> <p>These traces are made to be used by a distributed system simulator to replay the jobs executions.</p>
MPI-ESM1.2-LR P2k+ with a deep version of JSBACH (MPI-ESM P2k+d)
<p>This dataset corresponds to the following publication:</p><p>García-Pereira, F. et al. (2023): "First comprehensive assessment of industrial era land heat uptake from multiple sources". Submittted to Earth System Dynamics.</p><p>The dataset includes surface air temperature at 2 m (SAT) and subsurface temperature (soilTs) yearly information for a Past2k (0-1850) simulation extended to the historical period (1850-2014) and SSP585 climate change scenario (2015-2100, P2k+) conducted with a modified version of the CMIP6 low-resolution Earth System Model of the Max Planck Institute for Meteorology, the MPI-ESM1.2-LR (Mauritsen et al., 2019). MPI-ESM1.2 comprises the ocean model MPIOM1.6 and the atmosphere model ECHAM6.3 (Stevens et al., 2013). The latter is directly coupled to the Land Surface Model JSBACH3.2 (Reick et al., 2021) through the surface exchange of mass, momentum, and heat. The low-resolution configuration (MPI-ESM1.2-LR) has an atmospheric horizontal resolution truncated to T63, corresponding approximately to a 200-km grid cell size. This resolution is shared by the LSM. The standard shallow 5-layer vertical discretization of JSBACH3.2 (bottom boundary ca. 10 m) is modified to a much deeper 12-layer configuration (bottom boundary ca. 1400 m), following González-Rouco et al. (2021).</p><p>Additionally to the SAT and soilTs data, the land mask, and the global gridded maps of soil volumetric heat capacity (soilhcap) and diffusivity (soilhdiff) used by JSBACH are included.</p><p> </p><p>REFERENCES</p><p>González-Rouco, J. F., Steinert, N. J., García-Bustamante, E., Hagemann, S., de Vrese, P., Jungclaus, J. H., Lorenz, S. J., Melo-Aguilar, C., García-Pereira, F., and Navarro, J.: Increasing the Depth of a Land Surface Model. Part I: Impacts on the Subsurface Thermal Regime and Energy Storage, Journal of Hydrometeorology, 22, 3211 – 3230, https://doi.org/10.1175/JHM-D-21-0024.1, 2021.</p><p>Mauritsen, T., Bader, J., Becker, T., Behrens, J., Bittner, M., Brokopf, R., Brovkin, V., Claussen, M., Crueger, T., Esch, M., Fast, I., Fiedler, S., Fläschner, D., Gayler, V., Giorgetta, M., Goll, D. S., Haak, H., Hagemann, S., Hedemann, C., Hohenegger, C., Ilyina, T., Jahns, T., Jimenéz-de-la Cuesta, D., Jungclaus, J., Kleinen, T., Kloster, S., Kracher, D., Kinne, S., Kleberg, D., Lasslop, G., Kornblueh, L., Marotzke, J., Matei, D., Meraner, K., Mikolajewicz, U., Modali, K., Möbis, B., Müller, W. A., Nabel, J. E. M. S., Nam, C. C. W., Notz, D., Nyawira, S.-S., Paulsen, H., Peters, K., Pincus, R., Pohlmann, H., Pongratz, J., Popp, M., Raddatz, T. J., Rast, S., Redler, R., Reick, C. H., Rohrschneider, T., Schemann, V., Schmidt, H., Schnur, R., Schulzweida, U., Six, K. D., Stein, L., Stemmler, I., Stevens, B., von Storch, J.-S., Tian, F., Voigt, A., Vrese, P., Wieners, K.-H., Wilkenskjeld, S., Winkler, A., and Roeckner, E.: Developments in the MPI-M Earth System Model version 1.2 (MPI-ESM1.2) and Its Response to Increasing CO2, Journal of Advances in Modeling Earth Systems, 11, 998–1038, https://doi.org/10.1029/2018MS001400, 2019.</p><p>Reick, C. H., Gayler, V., Goll, D., Hagemann, S., Heidkamp, M., Nabel, J. E. M. S., Raddatz, T., Roeckner, E., Schnur, R., and Wilkenskjeld, S.: JSBACH 3 - The land component of the MPI Earth System Model: documentation of version 3.2, Hamburg: MPI für Meteorologie, https://doi.org/10.17617/2.3279802, 2021.</p><p>Stevens, B., Giorgetta, M., Esch, M., Mauritsen, T., Crueger, T., Rast, S., Salzmann, M., Schmidt, H., Bader, J., Block, K., Brokopf, R., Fast, I., Kinne, S., Kornblueh, L., Lohmann, U., Pincus, R., Reichler, T., and Roeckner, E.: Atmospheric component of the MPI-M Earth System Model: ECHAM6, Journal of Advances in Modeling Earth Systems, 5, 146–172, https://doi.org/10.1002/jame.20015, 2013.</p><p> </p><p>For any questions regarding the dataset, please free feel to contact Félix García-Pereira (felgar03@ucm.es)</p><p>Acknowledgement</p><p>We thank the Deutsches Klimarechenzentrum (DKRZ) for the resources granted by its Scientific Steering Committee (WLA) to run MPI-ESM1.2-LR P2k+ deep simulation under project ID bm1026 (PI: Johann Jungclaus).</p>
Runtime over 100 iterations with varying number of MPI processes
<p>Runtime over 100 iterations with varying number of MPI processes (np) and fixed block size <span class="math-tex">\(k=192\)</span> for matrix M5.</p>
Building pseudo latewood tree ring records for De Soto National Forest using MPI-ESM synthetic storms for the past millennium
<p>This release contains the past millennium synthetic storm dataset (MPI-ESM) passing De Soto National Forest (31.08<span>°</span>,-89.08<span>°</span>) and scripts needed to develop pseudo tree ring and sediment records used in Wallace et al. (2024).</p>
Dataset for Paper "An Event Model for Trace-Based Performance Analysis of MPI Partitioned Point-to-Point Communication"
<p>Test cases, measurement and processing scripts, and measurement data for the referenced paper. </p>
MPI-ESM1.2 data for Aerosol Forcing Does Not Sufficiently Explain the Suppressed Mid-20th Century Warming in CMIP6
<p>This dataset contains results from simulations with the MPI-ESM1.2-CR, created by Linnea Huusko and used in the following paper: Flynn, C. M., Huusko, L., Modak, A., and Mauritsen, T.: Strong aerosol cooling alone does not explain cold-biased mid-century temperatures in CMIP6 models, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-1613, 2023.</p>
Caffeine's Effect on Regadenoson Administration With Single Photon Emission Computed Tomography (SPECT) Myocardial Perfusion Imaging (MPI)
ClinicalTrials.gov study NCT00826280. IPD Sharing: YES. Countries: 1. Publications: 1.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.