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45 results for “GPU”
Dataset and scripts for the paper with title Evaluating Programming Models for the HPC GPU Ecosystem
<p>Dataset and scripts for the paper with title Evaluating Programming Models for the HPC GPU Ecosystem</p>
The dataset of figures for "The GPU version of LICOM3 under HIP framework and its large-scale application" (updated)
<p>A high-resolution (1/20°) global ocean general circulation model with Graphics processing units (GPUs) code implementations is developed based on the LASG/IAP Climate system Ocean Model version 3 (LICOM3) under Heterogeneous-compute Interface for Portability (HIP) framework. The dynamic core and physics package of LICOM3 are both ported to the GPU, and 3-dimensional parallelization is applied. The HIP version of the LICOM3 (LICOM3-HIP) is 42 times faster than what the same number of CPU cores dose, when 384 AMD GPUs and CPU cores are used. The LICOM3-HIP has excellent scalability; it can still obtain speedup of more than four on 9216 GPUs comparing to 384 GPUs. In this phase, we successfully performed a test of 1/20° LICOM3-HIP using 6550 nodes and 26200 GPUs, and at the grand scale, the model’s time to solution can still obtain an increasing, about 2.72 simulated years per day (SYPD). The high performance was due to putting almost all of computation processes inside GPUs, and thus greatly reduces the time cost of data transfer between CPUs and GPUs. At the same time, a 14-year spin-up integration following the phase 2 of Ocean Model Intercomparison Project (OMIP-2) protocol of surface forcing has been conducted, and the preliminary results have been evaluated. We found that the model results have little differences from the CPU version. Further comparison with observations and lower-resolution LICOM3 results suggests that the 1/20° LICOM3-HIP can not only reproduce the observations, but also produce much smaller scale activities, such as submesoscale eddies and frontal scales structures.</p>
Dataset for "In silico Positional Analogue Scanning with Amber GPU-TI"
<p>This repository contains the full data set and analysis scripts to reproduce all results for the manuscript "<a href="https://pubs.acs.org/doi/10.1021/acs.jcim.2c00860"><strong>In silico Positional Analogue Scanning with Amber GPU-TI</strong>, <em>J. Chem. Inf. Model.</em> 2022, 62, 18, 4448–4459</a>".</p> <p><a href="https://doi.org/10.1021/acs.jcim.2c00860">https://doi.org/10.1021/acs.jcim.2c00860</a></p> <p><br> The repository contains the following data:</p> <ul> <li><strong>20_PDB_66_MOL2_input_coordinates_for_PAS.tar.gz (7.7MB)</strong> <ul> <li>input structures of proteins (pdb format), ligands (mol2 format), experimental data and GPU-TI maps for all the 20 scans including Br-Scan, Cl-Scan, F-Scan, HO-Scan, MeO-Scan, Me-Scan, N-Scan.</li> </ul> </li> <li><strong>AMBER18.GPUTI.scripts.tar.gz (597.2 M)</strong> <ul> <li>Scripts and example of CDK8 TI output for GPU TI ddG calculation and cycle closure correlation.</li> </ul> </li> <li><strong>Supporting_Information_Tables_dG_ddG_small_big_change.xlsx (60kB)</strong></li> <li>AMBER18_input_fort files, parameter and topology files, first 500 ps equilibrated restart files, amber TI input files for each TI pair calculations (total 12.9 GB) <ul> <li><strong>AMBER-GPUTI_PAS_input.01.N-Scan.CDK8.tar.gz (736.2MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.02.N-Scan.Tankyrase.tar.gz (266.1 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.03.N-Scan.HCV_NS5B_gt1b.tar.gz (722.6 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.04.F-Scan.ox1r_antagonist.tar.gz (549.4 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.05.F-Scan.ox2r_antagonist.tar.gz (768.4 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.06.F-Scan.KAT6A.tar.gz (407.9 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.07.F-Scan.PDE1B.tar.gz (470.7 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.08.F-Scan.Akt1_kinase.tar.gz (390.6 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.09.Cl-Scan.PPAR_Gama.tar.gz (412.9 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.10.Cl-Scan.erk12.tar.gz (352.4 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.11.Cl-Scan.KAT6A.tar.gz (408.2 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.12.Br-Scan.PRMT4.tar.gz (522.1 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.13.Me-Scan.BD1_scaffold_thiophene.tar.gz (187.6 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.14.Me-Scan.BD1_scaffold_furan.tar.gz (187.3 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.15.Me-Scan.HIV-1.tar.gz (740.2 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.16.Me-Scan.PPAR_Gama.tar.gz (412.8 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.17.Me-Scan.avb6.tar.gz (2.2 GB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.18.MeO-Scan.KAT6A.tar.gz (409.3 MB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.19.MeO-Scan.ox2r_agonist.tar.gz (1.1 GB)</strong></li> <li><strong>AMBER-GPUTI_PAS_input.20.HO-Scan.ox2r_agonist.tar.gz (1.1 GB)</strong><br> </li> </ul> </li> <li><strong>Data Structures inside each files:</strong> <ul> <li><strong>directory_tree.20_PDB_66_MOL2_input_coordinates_for_PAS.txt (5 kB)</strong></li> <li><strong>directory_tree.AMBER18.GPUTI.scripts.txt (91 kB)</strong></li> <li><strong>directory_tree.AMBER18_input_for_PAS_GPUTI_simulations.txt (1.5 MB)</strong></li> </ul> </li> </ul>
hiperc-gpu-cuda-spinodal
<p>Solution to the CHiMaD Phase Field benchmark problem on spinodal decomposition using CUDA, with a 9-point discrete Laplacian stencil</p>
Dynamic and thermodynamic crossover scenarios in the Kob-Andersen mixture: Insights from multi-CPU and multi-GPU simulations
<p>This dataset is associated with "Dynamic and thermodynamic crossover scenarios in the Kob-Andersen mixture: Insights from multi-CPU and multi-GPU simulations", Daniele Coslovich, Misaki Ozawa, and Walter Kob, Eur. Phys. J. E 62, 41 (2018) [<a href="https://doi.org/10.1140/epje/i2018-11671-2">doi:10.1140/epje/i2018-11671-2</a> <a href="https://arxiv.org/abs/1804.04559">arXiv:1804.04559</a>]</p> <p>It includes scripts and data files to allow for the replication of the figures. EPS figures were generated using gnuplot version 5.0.</p> <p>Notes:</p> <ul> <li>Small differences in the dynamic data for the N=3600 dataset obtained with the MD protocol reflect additional statistics gathered since acceptance of the paper.</li> <li>Figure 6(b) in the published version of the manuscript was obtained using slightly incorrect values of the parameters J, T_0 entering equation 10. This minor issue has been fixed in this dataset.</li> </ul>
Efficient GPU Offloading with OpenMP for a Hyperbolic Finite Volume Solver on Dynamically Adaptive Meshes
<p>We identify and show how to overcome an OpenMP bottleneck in the administration of GPU memory. It arises for a wave equation solver on dynamically adaptive block-structured Cartesian meshes, which keeps all CPU threads busy and allows all of them to offload sets of patches to the GPU. Our studies show that multithreaded, concurrent, non-deterministic access to the GPU leads to performance breakdowns, since the GPU memory bookkeeping as offered through OpenMP's map clause, i.e., the allocation and freeing, becomes another runtime challenge besides expensive data transfer and actual computation. We, therefore, propose to retain the memory management responsibility on the host: A caching mechanism acquires memory on the accelerator for all CPU threads, keeps hold of this memory and hands it out to the offloading threads upon demand. We show that this user-managed, CPU-based memory administration helps us to overcome the GPU memory bookkeeping bottleneck and speeds up the time-to-solution of Finite Volume kernels by more than an order of magnitude.</p>
Dataset: Formulation and Implementation of Frequency-Dependent Linear Response Properties with Relativistic Coupled Cluster Theory for GPU-accelerated Computer Architectures
<p>This dataset collects the data (outputs, coordinate files) for the calculations presented in the manuscript "Formulation and Implementation of Frequency-Dependent Linear Response Properties with Relativistic Coupled<br> Cluster Theory for GPU-accelerated Computer Architectures".</p>
The dataset of figures for "The GPU version of LICOM3 under HIP framework and its large-scale application"
<p>A high-resolution (1/20°) global ocean general circulation model with Graphics processing units (GPUs) code implementations is developed based on the LASG/IAP Climate system Ocean Model version 3 (LICOM3) under Heterogeneous-compute Interface for Portability (HIP) framework. The dynamic core and physics package of LICOM3 are both ported to the GPU, and 3-dimensional parallelization is applied. The HIP version of the LICOM3 (LICOM3-HIP) is 42 times faster than what the same number of CPU cores dose, when 384 AMD GPUs and CPU cores are used. The LICOM3-HIP has excellent scalability; it can still obtain speedup of more than four on 9216 GPUs comparing to 384 GPUs. In this phase, we successfully performed a test of 1/20° LICOM3-HIP using 6550 nodes and 26200 GPUs, and at the grand scale, the model’s time to solution can still obtain an increasing, about 2.72 simulated years per day (SYPD). The high performance was due to putting almost all of computation processes inside GPUs, and thus greatly reduces the time cost of data transfer between CPUs and GPUs. At the same time, a 14-year spin-up integration following the phase 2 of Ocean Model Intercomparison Project (OMIP-2) protocol of surface forcing has been conducted, and the preliminary results have been evaluated. We found that the model results have little differences from the CPU version. Further comparison with observations and lower-resolution LICOM3 results suggests that the 1/20° LICOM3-HIP can not only reproduce the observations, but also produce much smaller scale activities, such as submesoscale eddies and frontal scales structures.</p>
Dataset for "Studying the effect of membrane permeability with a GPU-based Bloch-Torrey simulator"
<p>This dataset contains the results of the GPU-based simulations of diffusion in cardiac tissue. The data was used for the work presented at the ISMRM 27th Annual Meeting in 2019.</p>
Research data accompanying the paper 'GPU-Accelerated Exploration of Biomolecular Energy Landscapes'
<p>Input and output files for the results presented in Tables 1-8 of the paper 'GPU-Accelerated Exploration of Biomolecular Energy Landscapes'. Further information on these files can be found in the README files within the tar file.</p>
Latency and energy characterization of 5G LDPC FEC Decoding on CPU and GPU
<p>CloudRIC is a system that meets specific reliability targets in 5G FEC processing while sharing pools of heterogeneous processors among DUs, which leads to more cost- and energy-efficient vRANs. The details of the solution are presented in <a title="CloudRIC: Open Radio Access Network (O-RAN) Virtualization with Shared Heterogeneous Computing" href="https://doi.org/10.1145/3636534.3649381">CloudRIC: Open Radio Access Network (O-RAN) Virtualization with Shared Heterogeneous Computing</a>. These repository provides a dataset, analyzed therein, with experiments carried out with different 5G LDPC decoding processors: (i) Intel FlexRAN library and two open-source alternative libraries on an Intel Xeon Gold 6240R CPU, and (ii) a proprietary driver on an NVIDIA GPU V100.</p> <p>See README file for a description of the dataset.</p>
Precise and efficient modeling of stellar-activity-affected solar spectra using SOAP-GPU
<p>The simulated spectral time series generated using SOAP-GPU is based on the extraction of active regions from Solar Dynamics Observatory (SDO) data. The positions of sunspots and faculae on the solar disk at each time step are obtained from magnetogram and intensity maps, which are then input into SOAP-GPU to simulate the corresponding spectra. Comparison with HARPS-N solar spectra demonstrates that SOAP-GPU can accurately model the solar RV time series with a precision of 0.9 m/s.</p>
Code and data for Porting the Meso-NH Atmospheric Model on Different GPU Architectures for the Next Generation of Supercomputers (version MESONH-v55-OpenACC)
<p>GeometricMG.pdf (source: https://bitbucket.org/em459/tensorproductmultigrid/src/master/Documentation/)<br>MESONH_Bench_HECTOR_ADASTRA_LEONARDO.tar.gz: code and data for Meso-NH bench<br>Performance.zip: code and data for figures related to performance<br>WeatherApplications.zip: namelists for running weather applications<br>OASIS3_WW3.tar.gz: OASIS and WW3 codes for running the Meso-NH WWW3 coupled simulation</p>
A dynamic ancestral graph model and GPU-based simulation of a community based on metagenomic sampling
<p>In this paper we present an ancestral graph model of the evolution of a guild in an ecological community. The model is based on a metagenomic sampling design in that a random sample is taken at the community, as opposed the taxon, level and species are discovered by genetic sequencing. The specific implementation of the model envisions an ecological guild that was founded by colonization at some point in the past that then potentially undergoes diversification by natural selection. Within the graph, species emerge and evolve through the diversification process and their densities in the graph are dynamic and governed by both ecological drift and random genetic drift, as well as differential viability. We employ the 3% sequence divergence rule at a marker locus to identify Operational Taxonomic Units. We then explore approaches to see if there are indirect signals of the diversification process, including population genetic and ecological approaches. In terms of population genetics, we study the joint site frequency spectrum of OTUs, as well its associated statistics. In terms of ecology, we study the species (or OTU) abundance distribution. For both we observe deviations from neutrality, which indicates that there may be signals of diversifying selection in metagenomic studies under certain conditions. The model is available as a GPU-based computer program in C/C++ and using OpenCL, with the long-term goal of adding functionality iteratively to model large-scale eco-evolutionary processes for metagenomic data.</p>
High-resolution GPU-accelerated Superstrings Simulation
<p>Small clip from a 4096^3 matter era simulation of U(1)xU(1) cosmic strings, a simple proxy for cosmic superstrings. Cells pierced by p-strings are colour coded in blue, those pierced by q-strings in red and pq-strings in green.</p> <p>This work was financed by FEDER---Fundo Europeu de Desenvolvimento Regional funds through the COMPETE 2020-POCI, and by Portuguese funds through FCT in the framework of the project POCI-01-0145-FEDER-028987 and PTDC/FIS-AST/28987/2017. J.R.C. is supported by an FCT fellowship (SFRH/BD/130445/2017). We acknowledge PRACE for awarding us access to Piz Daint at CSCS, Switzerland, through Preparatory Access proposal 2010PA4610, Project Access proposal 2019204986 and Project Access proposal 2020225448. Technical support from Jean Favre at CSCS is gratefully acknowledged.</p>
Porting the WAVEWATCH III Wave Action Source Terms to GPU - WaveWatchIII configuration files
<p>This dataset contains the mesh and model configuration information for a WaveWatchIII run using a global ustructured grid.</p> <ul> <li>global_refined_59K.msh <ul> <li>Unstructured mesh file in gmsh format. An unstructured mesh of 1 degree global resolution and 0.25 degree in regions with depth less than 4km e.g. 1 degree at the equator and 0.25 degree at the coastal regions </li> </ul> </li> <li>global_refined_228K.msh <ul> <li>Unstructured mesh file in gmsh format. An unstructured mesh of 0.5 degree global resolution and 0.125 degree in regions with depth less than 4km.</li> </ul> </li> <li>ww3_grid.inp <ul> <li>The input file for the ww3_grid pre-processing program. This file contains many of the model configuration settings.</li> </ul> </li> <li>ww3_shel.inp <ul> <li>Input file for the ww3_shel program.</li> </ul> </li> </ul>
Understanding challenges of GPU programming by classifying and analyzing Stack Overflow posts
<p>This dataset includes a dataset of posts related to GPU programming and supplemental materials including the complete analyzed results of our paper "Understanding challenges of GPU programming by classifying and analyzing Stack Overflow posts".</p>
Supplementary materials: "Synthesizing Particle-in-Cell Simulations Through Learning and GPU Computing for Hybrid Particle Accelerator Beamlines"
<p>Data archive for stage 3 of manuscript for PASC24. See Readme stage 3.txt for more details.</p> <p> </p> <p>This work was supported by the Laboratory Directed Research and Development Program of Lawrence Berkeley National Laboratory under U.S. Department of Energy Contract No. DE-AC02-05CH11231 and by LLNL under Contract DE-AC52-07NA27344. This material is based upon work supported by the U.S. Department of Energy, OFfice of Science, Office of High Energy Physics, General Accelerator R&D (GARD), under contract number DE-AC02-05CH11231. This material is based upon work supported by the CAMPA collaboration, a project of the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research and Office of High Energy Physics, Scientific Discovery through Advanced Computing (SciDAC) program. This research was supported by the Exascale Computing Project (17-SC-20-SC), a joint project of the U.S. Department of Energy's Office of Science and National Nuclear Security Administration, responsible for delivering a capable exascale ecosystem, including software, applications, and hardware technology, to support the nation's exascale computing imperative.This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 using NERSC award HEP-ERCAP0023719.</p>
Front Data for Scalable GPU-Enabled Creation of Three Dimensional Weather Fronts
<p>Frontal Polylines for 2016 created with the network described in:</p> <p>https://doi.org/10.5194/wcd-3-113-2022</p> <p> </p> <p>The work is supported by the project ``Big Data in Atmospheric Physics<br>(BINARY)'', funded by the Carl Zeiss Foundation (grant P2018-02-003).</p>
The dataset of the manuscript "GPU-HADVPPM4HIP V1.0: higher model accuracy on China's domestically GPU-like accelerator using heterogeneous compute interface for portability (HIP) technology to accelerate the piecewise parabolic method (PPM) in an air quality model (CAMx V6.10)"
<p><strong>bcfile.zip:</strong> the clean boundary condition files.</p> <p><strong>CAMxv6x_cpp.zip: </strong>the source code of CAMx-HIP version which coupled with HIP-HADVPPM scheme.</p> <p><strong>data.zip:</strong> final data tables used to plot figures.</p> <p><strong>emisfile.zip: </strong>the emission files.</p> <p><strong>icfile.zip:</strong> the clean initial condition files.</p> <p><strong>tuvfile.zip </strong>and <strong>o3mapfile.zip:</strong> the photolysis files.</p> <p><strong>outputfile.zip:</strong> the computation results outputted by CAMx model for Fortran version on the Intel Xeon E5-2682 v4 CPU, CUDA version on the NVIDIA K40m and V100 clusters, and HIP version on the China' s domestically heterogeneous cluster A.</p> <p><strong>wrfcamx.zip:</strong> the meteorological files.</p> <p><strong>offline_test_cuda.zip: </strong>the advection module code written in CUDA C language</p> <p><strong>offline_test_fortran.zip:</strong> the advection module code written in Fortran language</p> <p><strong>offline_test_hip.zip: </strong>the advection module code written in HIP C language</p>
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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)
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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.