Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
9
datasets available to search
ShareScore release 0.9.0
Dataset results
9 results for “Parallel Algorithm”
Files and plotting scripts for "Parallel tridiagonal matrix inversion with a hybrid multigrid--Thomas algorithm method"
<p>This archive contains the files required to reproduce the results and figures presented in <em>Parallel tridiagonal matrix inversion with a hybrid multigrid--Thomas algorithm method</em>, J. T. Parker, P. A. Hill, D. Dickinson and B. D. Dudson.</p> <p>Also available at the repository: https://gitlab.com/JosephThomasParker/files-and-plotting-scripts-for-parallel-tridiagonal-matrix-inversion-with-a-hybrid-multigrid-thomas-algorithm-method/</p> <p>Questions to joseph.parker@ukaea.uk.</p> <p>This version is before submission to journal.</p>
Construction of Parallel Addition Algorithms by the Extending Window Method - results
<p>An algebraic number <span class="math-tex">\(\beta \in \mathbb{C}\)</span> with no conjugate of modulus 1 can serve as the base of a numeration system <span class="math-tex">\((\beta, \mathcal{A})\)</span> with parallel addition, i.e., the sum of two operands represented in base <span class="math-tex">\(\beta\)</span> with digits from <span class="math-tex">\(\mathcal{A}\)</span> is calculated in constant time, irrespective of the length of the operands.</p> <p>In the paper <a href="https://arxiv.org/abs/1801.01062">Construction of Algorithms for Parallel Addition</a>, a so-called <em>Extending Window Method </em>is introduced. This method is an algorithm to construct Parallel Addition algorithms. See the paper for the details, or the <a href="https://jan.legersky.cz/project/paralleladdition/">project website</a>.</p> <p>We present here the results of this method for selected numeration systems, see the <a href="http://doi.org/10.5281/zenodo.1542942">implementation</a>.</p>
Simulation results for "Localized statistics decoding: A parallel decoding algorithm for quantum low-density parity-check codes"
<p>This dataset contains simulations results presented in the paper "Localized statistics decoding: A parallel decoding algorithm for quantum low-density parity-check codes".</p> <p>The files are in `csv` file format, with data easily processable using the python library `sinter`.</p>
A Reconfiguration Algorithm for Power-Aware Parallel Applications
<p><strong><em>Abstract: </em></strong><em>In current computing systems, many applications require guarantees on their maximum power consumption to not exceed the available power budget. On the other hand, for some applications, it could be possible to decrease their performance, yet maintaining an acceptable level, in order to reduce their power consumption. To provide such guarantees, a possible solution consists in changing the number of cores assigned to the application, their clock frequency and the placement of application threads over the cores. However, power consumption and performance have different trends depending on the application considered and on its input. Finding a configuration of resources satisfying user requirements is in the general case a challenging task. In this paper we propose Nornir, an algorithm to automatically derive, without relying on historical data about previous executions, performance and power consumption models of an application in different configurations. By using these models, we are able to select a close to optimal configuration for the given user requirement, either performance or power consumption. The configuration of the application will be changed on-the-fly throughout the execution to adapt to workload fluctuations, external interferences and/or application's phase changes. We validate the algorithm by simulating it over the applications of the PARSEC benchmark suite. Then, we implement our algorithm and we analyse its accuracy and overhead over some of these applications on a real execution environment. Eventually, we compare the quality of our proposal with that of the optimal algorithm and of some state of the art solutions.</em></p> <p>This dataset contains the raw data of the experiments and the scripts used to plot them.</p> <p> </p>
Benchmarking splice variant prediction algorithms using massively parallel splicing assays
<p>Dataset, jupyter notebooks, and support python modules for "Benchmarking splice variant prediction algorithms using massively parallel splicing assays" (Smith and Kitzman, 2023)</p>
Data Used for Article: Speeding up large wind farms layout optimization using gradients, parallelization, and a heuristic algorithm for the initial layout
Open the record for dataset details and reuse information.
"SOME ISSUES OF IMPLEMENTATION OF PARALLEL ALGORITHMS BASED ON CUBIC BASED SPLINES ON MULTI-CORE PROCESSORS."
Open the record for dataset details and reuse information.
Supplementary Data: Cloud-based multi-dimensional parallel dynamic programming algorithm for a hydropower station system
<p>The files in this record contain data for cloud-based multi-dimensional parallel dynamic programming algorithm for a hydropower station system considered for publication in Water Resources Research.</p> <p>The files consist of:</p> <ul> <li>cascade reservoir system data;</li> <li>Source code and results of the parallel dynamic programming algorithm program on the physical machine;</li> <li>Source code and results of the parallel dynamic programming algorithm program on the cloud virtual machine;</li> </ul>
Parallel, Portable Algorithms for Distance-2 Maximal Independent Set and Graph Coarsening: Rebuttal Material
<p>Additional material with rebuttal of IPDPS22 submission, "Parallel, Portable Algorithms for Distance-2 Maximal Independent Set and Graph Coarsening".</p>
ScienceDex guides
Understand access before you commit
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.