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ShareScore release 0.9.0
Dataset results
32 results for “performance benchmarking”
Synthetic data set to evaluate and benchmark the performance of multiple linear regression algorithms in Scikit-Learn and SANElib
<p>The datasets respresent different numbers of columns and rows to measure the scalability of linear regression algorihms in terms of columns and rows.</p>
Benchmarking data for studying order-related performance effects
<p>This dataset includes over 2.2M performance measurements collected on the CloudLab testbed. Performance tests from CPU and memory benchmarks were run in fixed and random orders allowing comprehensive comparisons. The analysis and the outcomes are summarized in the paper "Avoiding the Ordering Trap in Systems Performance Measurement", which will become available online in the near future and will be presented at USENIX ATC'23.</p> <p>--------------</p> <p>Contributors:</p> <ul> <li> <p>Dmitry Duplyakin (University of Utah)</p> </li> <li> <p>Nikhil Ramesh (University of Utah) </p> </li> <li> <p>Carina Imburgia (University of Washington) </p> </li> <li> <p>Hamza Fathallah Al Sheikh (University of Utah) </p> </li> <li> <p>Semil Jain (University of Utah) </p> </li> <li> <p>Prikshit Tekta (University of Utah) </p> </li> <li> <p>Aleksander Maricq (University of Utah) </p> </li> <li> <p>Gary Wong (University of Utah) </p> </li> <li> <p>Robert Ricci (University of Utah) </p> </li> </ul>
A Generic Model for Benchmark Aerodynamic Analysis of Fifth-Generation High-Performance Aircraft (CGNS grid files)
<p>Openly available supplementary data to accompany paper https://doi.org/10.3390/aerospace10090746. This data set includes unstructured CGNS grid files to facilitate code comparison. When using this data, please cite:</p> <p>Giannelis, N.F.; Bykerk, T.; Vio, G.A. A Generic Model for Benchmark Aerodynamic Analysis of Fifth-Generation High-Performance Aircraft. Aerospace 2023, 10, 746.</p>
Performance data for 10 DE variants on CEC2014 benchmark suite in 10 dimensions (30 problems)
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Data for CASP15 performance benchmarking of the state-of-the-art protein structure prediction methods
<p>CASP15 performance benchmarking of the state-of-the-art protein structure prediction methods</p>
Data from: Dental data perform relatively poorly in reconstructing mammal phylogenies: morphological partitions evaluated with molecular benchmarks
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Benchmarking the Performance of Healthcare Systems in the Provision of Childhood Immunization Services
<p>This dataset was used to conduct data analysis of a research paper with a title "Bechmarking the Performance of Healthcare Systems in the Provision of Childhood Services".</p>
First Experiences in Performance Benchmarking with the New SPEChpc 2021 Suites - Measurement Data
<p>Modern High Performance Computing (HPC) systemsare built with innovative system architectures and novelprogramming models to further push the speed limit of computing.The increased complexity poses challenges for performanceportability and performance evaluation. The Standard PerformanceEvaluation Corporation (SPEC) has a long history ofproducing industry-standard benchmarks for modern computersystems. SPEC’s newly released SPEChpc 2021 benchmark suites,developed by the High Performance Group, are a bold attempt toprovide a fair and objective benchmarking tool designed for state-HPC, SPEC, HPG, SPEChpc 2021, benchmarks,performance benchmarking and analysis, heterogeneity, offloading,MPI, MPI+X, OpenMP, OpenACCof-the-art HPC systems. With the support of multiple host andaccelerator programming models, the suites are portable acrossboth homogeneous and heterogeneous architectures. Differentworkloads are developed to fit system sizes ranging from a fewcompute nodes to a few hundred compute nodes. In this work wepresent our first experiences in performance benchmarking thenew SPEChpc2021 suites and evaluate their portability and basicperformance characteristics on various popular and emergingHPC architectures, including x86 CPU, NVIDIA GPU, and AMDGPU. This study provides a first-hand experience of executingthe SPEChpc 2021 suites at scale on production HPC systems,discusses real-world use cases, and serves as an initial guidelinefor using the benchmark suites</p> <p>This deposit contains all measurement data for the results shown in the paper as well as all platform setups (compilers and flags) and also the errors encountered on the exascale test system "Spock".</p>
Performance data for 10 DE variants on BBOB benchmark suite in 10 dimensions (1-12 problems)
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Clinical Validation and Benchmarking of Top Performing ctDNA Diagnostics - Stage III NSCLC
ClinicalTrials.gov study NCT06111807. IPD Sharing: UNDECIDED. Countries: 3. Publications: 0.
GUIDE.MRD-01-CRC: Clinical Validation and Benchmarking of Top Performing CtDNA Diagnostics - Colorectal Cancer
ClinicalTrials.gov study NCT06111105. IPD Sharing: Not stated. Countries: 5. Publications: 0.
SNIAPE: Sensor Network and IoT Application Performance Evaluation Benchmark
<p>This repository is currently anonymised for submission. It contains the code, data, questionnaire and the full report version of SNIAPE, which is a sensor network and IoT application performance evaluation benchmark</p>
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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.