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Dataset results
45 results for “GPU”
Supporting Data for "Extending GPU-Accelerated Gaussian Integrals in the TeraChem Software Package to f Type Orbitals: Implementation and Applications."
<p>Raw data and output files for "Extending GPU-Accelerated Gaussian Integrals in the TeraChem Software Package to f Type Orbitals: Implementation and Applications."</p>
Accelerate the Parameterization of Unified Microphysics Across Scales (PUMAS) on the graphics processing unit (GPU) with directive-based methods
<p>Code used to produce results of paper titled "Accelerate the Parameterization of Unified Microphysics Across Scales (PUMAS) on the graphics processing unit (GPU) with directive-based methods" by Sun et al.</p> <p>Includes:</p> <p>- Source code of CAM to perform a CPU or GPU simulation</p> <p>- Source code of PUMAS stand-alone kernel for GPU porting (OpenACC and OpenMP offload), an example batch script for build/run on Casper (NCAR's cluster) and the input dataset</p> <p>- Dataset to reproduce the figures in the paper</p> <p> </p> <p>Contact details: Jian Sun (sunjian@ucar.edu)</p>
AI-SPRINT GPU STochastic Scheduler
<p>This repository includes the source code and the datasets used to evaluate the GPU STochastic Scheduler developed in the context of the AI-SPRINT project. The corresponding results are included in the AI-SPRINT project deliverable "D3.3 - Second release and evaluation of the runtime environment".</p>
dataset for "GPU-enhanced DEM analysis of flow behaviour of irregularly shaped particles in a full-scale twin screw granulator"
<p>This dataset contains the numerical simulation data describing the conveying behaviour of shaped-particle systems. Detailed study on the conveying behaviour is presented in a journal paper entitled "GPU-enhanced DEM analysis of flow behaviour of irregularly shaped particles in a full-scale twin screw granulator". We provide all the essential data in a single excel file with the description below:</p> <p>Dataset 1- Average particle speed (Fig. 7a) and XYZ components (Fig. 7b) for: Sphere at t = 2.1 s, Cube at t = 2.85 s, Biluna at t = 3.1 s, HexP at t = 3.45 s. (Data of Fig. 7)</p> <p>Dataset 2 - XYZ coordinates of a typical particle trapped in the granulator. (Data of Fig. 9)</p> <p>Dataset 3 - Particle retention numbers for different shaped particles. (Data of Fig. 10).</p> <p>Dataset 4 - Average particle speeds for different shaped particles. (Data of Fig. 11).</p> <p>Dataset 5 - Average axial particle speeds for different shaped particles (Data of Fig. 12).</p> <p>Dataset 6 - Residence time distributions for various shaped particles. (Data of Fig. 13)</p> <p>Dataset 7 - Normalised residence time distributions for various shaped particles. (Data of Fig. 14)</p> <p>Dataset 8 - Mean residence time for various shaped particles. (Data of Fig. 15)</p> <p>Dataset 9 - The variance of RTD for different shaped particles. (Data of Fig. 16)</p> <p>Dataset 10 - Cumulative exit age distribution for various shaped particles. (Data of Fig. 17)</p> <p>Dataset 11 - Normalised cumulative exit age distribution for various shaped particles. (Data of Fig. 18)</p> <p>Dataset 12 - Evolution of the cumulative power consumption for various shaped particles. (Data of Fig.19)</p> <p>Dataset 13 - Power consumption distribution for various shaped particles. (Data of Fig.20)</p>
Optimizing Sparse Matrix-Matrix Multiplication for the GPU supplementary data
<p>This contains the matrices for the SpGEMM tests presented in "Optimizing Sparse Matrix-Matrix Multiplication for the GPU", by Steven Dalton, Nathan Bell, and Luke N. Olson.</p> <p>Each A matrix from Table 3 has an companion matrix P in the directory. The storage scheme appends "_P" to the end of the A matrix filename.<br> </p>
GPUHarbor: Testing GPU Memory Consistency At Large (Experience Paper): Artifact
<p>Artifact for the ISSTA 2023 paper "GPUHarbor: Testing GPU Memory Consistency At Large (Experience Paper)"</p>
ICPP23 Scalable Incremental Checkpointing using GPU-Accelerated De-Duplication Input Graphs
<p>Input graphs and checkpoints used for generating results in the ICPP 23 paper "Scalable Incremental Checkpointing using GPU-Accelerated De-Duplication". </p>
Supplemental materials for studying GPU programming with Stack Overflow posts
<p>It includes the data used in our study of GPU programming and the complete results obtained from the study.</p>
A dynamic ancestral graph model and GPU-based simulation of a community based on metagenomic sampling
Open the record for dataset details and reuse information.
Scalasca summary analysis of HemeLB_GPU application execution with 129 MPI processes on JUWELS/V100
<p>The CompBioMed HPC CoE flagship application HemeLB (prototype GPU version) was run with a (patched) arteries geometry dataset on JSC's JUWELS supercomputer, and its execution performance with 129 MPI processes on 32 dual 20-core CPU + quad V100 GPU compute nodes measured by Score-P and analysed by Scalasca (and Vampir)</p>
AI-SPRINT GPU Scheduler
<p>This repository includes the source code and the datasets used to evaluate the GPU Scheduler developed in the context of the AI-SPRINT project. The corresponding results are included in the AI-SPRINT project deliverable "D3.1 - First release and evaluation of the runtime environment".</p>
Hierarhical method and Dynamic Programming methods for AI-SPRINT GPU Scheduler
<p>This repository includes the source code and the datasets of the Hierarchical method and the Dynamic Programming methods used to obtain the results reported in the article "Scheduling Deep Learning Jobs Training in the Cloud: Comparing Multiple Approaches". The two methods constitute a part of the GPU Scheduler developed in the context of the AI-SPRINT project.</p>
LISFLOOD-FP 8.1: New GPU accelerated solvers for faster fluvial/pluvial flood simulations - video supplement
<p>These are video supplement files to Sharifian et al. (2022), to give step-by-step instructions on how to download and install LISFLOOD-FP8.2, and reproduce the simulations for representative case studies. For a full description of the methodology and case studies, please refer to the manuscript.</p>
LISFLOOD-FP 8.1: New GPU accelerated solvers for faster fluvial/pluvial flood simulations - simulation results
<p>Simulation result data for Sharifian et al. (2022) for the following case studies:</p> <p>1- Lower Triangle catchment</p> <p>2- Upper Lee catchment</p> <p>3- Eden catchment</p> <p>4- Glasgow urban area</p> <p>5- Cockermouth urban area</p>
A Layer-Elastic Scheduling System for DLT in GPU Clusters
<p>This artifact contains the simulator and K8S implementation for a layer-elastic scheduling system. </p>
Large SAT Benchmark Suite for Certified SAT Solving with GPU Accelerated Inprocessing
<p>This submission includes the SAT benchmark dataset for "Certified SAT Solving with GPU Accelerated Inprocessing" article. The dataset is intended to evaluate the performance of ParaFROST GPU solver and to compare with the state of the art.</p>
ParaFROST Proofs for Certified SAT Solving with GPU Accelerated Inprocessing
<p>This submission includes all the proofs of ParaFROST GPU SAT solver for the "Certified SAT Solving with GPU Accelerated Inprocessing" article.</p>
Performance data for GPU runs of the Islet SL method described in the Islet paper
<p>Performance data for GPU runs of the Islet SL method described in the Islet paper. The code that created this data is https://doi.org/10.5281/zenodo.5595508.</p>
Dataset to reproduce "SeisNoise.jl: Ambient Noise Cross-Correlation on the CPU and GPU in Julia"
<p>These data are the 188 LHZ channels from Jan 1, 2019- Jan 1, 2020 mentioned in "SeisNoise.jl: Ambient Noise Cross-Correlation on the CPU and GPU in Julia" by Clements et al. (2020), submitted to Seismological Research Letters.</p> <p>Waveform data used in this study was downloaded from IRIS. Data is from the Berkeley Digital Seismic Network (https://doi.org/10.7932/BDSN), Southern California Seismic Network (https://doi.org/10.7914/SN/CI), Caribbean USGS Network (https://doi.org/10.7914/SN/CU), GEOSCOPE network (https://doi.org/10.18715/GEOSCOPE.G), Global Seismograph Network - IRIS/IDA (https://doi.org/10.7914/SN/II), Global Seismograph Network - IRIS/USGS (https://doi.org/10.7914/SN/IU), United States National Seismic Network (https://doi.org/10.7914/SN/US) and the West Indies French Seismic Network (https://doi.org/10.18715/antilles.WI).</p>
Test set of 140 complexes for AutoDock-GPU
<p>Set of 140 protein-ligand complexes<br> ===================================<br> <br> # Overview<br> <br> The ligands herein vary in the number of atoms and number of rotatable bonds.<br> This is the full data set used in the following study:<br> Accelerating AutoDock4 with GPUs and Gradient-Based Local Search<br> https://dx.doi.org/10.26434/chemrxiv.9702389.v1<br> <br> # Warning<br> <br> These structures, both proteins and ligands, were prepared in an automated way<br> without manual inspection. The following is a non-comprehensive list of<br> issues that may exist:<br> <br> * missing water molecules that bridge ligand-receptor interactions,<br> * missing atoms in the proteins,<br> * non-integer sum of partial charges,<br> * incorrect protonation state,<br> * incorrect protein conformations.<br> <br> This intended use of this data is to evaluate the performance of docking with<br> regard to computational and algorithmic efficiency, but not the accuracy of<br> the scoring function.<br> <br> # Details<br> <br> In ligand\_properties.csv<br> * `pdb` Protein Data Bank accession code<br> * `n_atom` number of atoms in the ligand<br> * `n_tors` number of bonds in the ligand that can rotate during docking.<br> * `score_of_probable_global_minimum` lowest (best) score (score is the sum of intra- and inter-mo<br> leculer energy components). The best score did not improve with an increase in the search effort<br> * `RMSD_of_probable_global_minimum` RMSD from X-ray pose for the solution with the lowst score <br> * `best_score_so_far` lowest score ever found but even lower scores are likely to exist<br> <br> </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.