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4,694 results for “data analysis”
Realtime Data Collection and Analysis Framework for Collaboration and Co-presence in a Virtual Reality Environment
<p>Title: Realtime Data Collection and Analysis Framework for Collaboration and Co-presence in a Virtual Reality Environment</p> <p>Abstract:</p> <p>As VR technologies continue to evolve and gain popularity, one of their most notable features is connecting with the virtual presence of a person who is not physically present. Understanding the dynamics of user interaction within these environments is crucial, as they can be utilized in various ways, including collaboration, communication, social interactions, or games and entertainment. This paper presents a method for measuring collaboration and co-presence factors of users by developing a real-time data collection and analysis framework. The designed framework focuses on different collaboration and co-presence scenarios and evaluates a comprehensive system for monitoring and analyzing user interactions in VR, employing both physiological sensors and subjective feedback to assess the sense of presence, co-presence, and collaboration quality. Through an extensive literature review, the paper studies how various factors, including avatar realism and communication modalities, influence user engagement and interaction efficacy. The experiment framework’s capability to integrate qualitative and quantitative data provides a deeper understanding of the immersive experience and its impact on collaborative tasks. The results highlight the importance of design choices in VR environments and their implications for human-computer interaction, user performance, and satisfaction. The findings offer practical guidance for developing more effective VR systems for collaborative work and social interaction.</p> <p>Data Description:</p> <p>1. User Interaction Logs:</p> <p><span> </span>- Data Type: Quantitative</p> <p><span> </span>- Description: Timestamped logs of user actions and interactions within the VR environment, including movement data, interaction with objects, and communication instances.</p> <p><span> </span>- Format: CSV</p> <p><span> </span>- Variables: User ID, Timestamp, Action Type, Object Interacted, Coordinates, Duration</p> <p>2. Physiological Sensor Data:</p> <p><span> </span>- Data Type: Quantitative</p> <p><span> </span>- Description: Real-time physiological data collected from users during VR sessions, including heart rate, skin conductance, and EEG data.</p> <p><span> </span>- Format: CSV,</p> <p><span> </span>- Variables: User ID, Timestamp, Heart Rate, Skin Conductance, EEG Channels</p> <p>3. Avatar Realism and Communication Modalities Data:</p> <p><span> </span>- Data Type: Quantitative</p> <p><span> </span>- Description: Data evaluating the impact of avatar realism and communication methods (e.g., voice chat, text chat) on user engagement and interaction efficacy.</p> <p><span> </span>- Format: CSV, Text</p> <p><span> </span>- Variables: User ID, Avatar Type, Communication Modality, Engagement Score, Interaction Quality Feedback</p> <p>4. Collaboration and Co-presence Metrics:</p> <p><span> </span>- Data Type: Quantitative</p> <p><span> </span>- Description: Calculated metrics for collaboration efficiency and co-presence, derived from interaction logs and physiological data.</p> <p><span> </span>- Format: CSV</p> <p><span> </span>- Variables: User ID, Collaboration Efficiency Score, Co-presence Score, Task Performance</p> <p> </p> <p> </p>
Vegetation greenesss data for the Aït Benhaddou Catchment, Morocco. Includes: 1984-2019 NDVI time series, breakpoint analysis results, and resillience indicator results, among others.
<p>This dataset is comprised of two main parts, both originating from different but related works. </p> <p>The NDVI timeseries and breakpoint analysis were originally developed by Vermeer (2021) for the MSc thesis: Vermeer, A. L. (2021). <em>Ecological stability in the face of climatic disturbances: a case study of a dryland ecosystem in the Moroccan High Atlas Mountains</em>. These data include a harmonized timeseries of Normalized Difference Vegetation Index from different Landsat missions at 30x30 meter resolution for the Aït Benhaddou catchment in Morocco. It also includes the output of a breakpoint analysis that was conducted using this dataset, which showcases different statistical breakpoints in NDVI after a severe drought that occured between 1998 and 2002. Shapefiles, a DEM and masks of irrigiated areas for the catchment are also included. For more information about these data, consult Vermeer (2021).</p> <p>The secondary part of this dataset was produced by Grootoonk (2024) for the MSc thesis: Grootoonk, W. (2024). <em>Relations between temporal resilience indicators and trend breakpoints in a dryland high-mountain catchment, </em>drawing upon the original dataset from Vermeer (2021). These data include Kendall's tau values for the resillience indicators variance and lag-one autocorrelation, computed using a rolling window for each pixel. Results for differerent window sizes (WS) for both indicators are included. </p> <p>Beyond these main results, a number of additional data sources are provided. These are Kendall's tau for precipitation variance in the area, produced using CHIRPS data (https://www.chc.ucsb.edu/data/chirps) and a NSI soil salinity map produced from Landsat imagery. See Grootoonk (2024) for more information. </p>
Supplementary File 8; The full data set used for the analysis presented herein
Open the record for dataset details and reuse information.
Bayesian analysis of (3+1)D relativistic nuclear dynamics with the RHIC beam energy scan data
<p>This dataset contains the MCMC chain (LHD+HPP) without any constraints on the parameters for the (3+1)D Bayesian inference study for the RHIC beam energy scan program.<br>We also provide the nine trained emulator objects, which were generated with the code available at <a title="GPBayesTools-HIC: v1.1.0" href="https://doi.org/10.5281/zenodo.12807892" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12807892</a>.<br>The training data is given in pickle format as dictionaries for the training points. The first 1000 points in the files correspond to the Latin Hypercube design points and the last 100 points are points sampled from the posterior distribution.</p>
Data and analysis scripts for arXiv:2407.09605
<p>Ancillary files for the paper "Soft-gluon exchange matters: isotropic screening in QCD kinetic theory" by K. Boguslavski and F. Lindenbauer<br>[<a href="https://inspirehep.net/literature/2807689" target="_blank" rel="noopener">arXiv:2407.09605</a>]</p> <p>If the data or scripts provided here are used, please cite this paper.</p>
Methodology data of "A qualitative and quantitative citation analysis toward retracted articles: a case of study"
<p>This document contains the datasets and visualizations generated after the application of the methodology defined in our work: <em>"A qualitative and quantitative citation analysis toward retracted articles: a case of study"</em>. The methodology defines a citation analysis of the Wakefield et al. [1] retracted article from a quantitative and qualitative point of view. The data contained in this repository are based on the first two steps of the methodology. The first step of the methodology (i.e. “Data gathering”) builds an annotated dataset of the citing entities, this step is largely discussed also in [2]. The second step (i.e. "Topic Modelling") runs a topic modeling analysis on the textual features contained in the dataset generated by the first step. </p> <p><strong>Note:</strong> the data are all contained inside the "<strong><em>method_data.zip"</em> </strong>file. You need to unzip the file to get access to all the files and directories listed below.</p> <p> </p> <p><strong>Data gathering</strong></p> <p>The data generated by this step are stored in <strong>"<em>data/</em>"</strong>:</p> <ol> <li><em><strong>"cits_features.csv": </strong></em>a dataset containing all the entities (rows in the CSV) which have cited the Wakefield et al. retracted article, and a set of features characterizing each citing entity (columns in the CSV). The features included are: DOI ("doi"), year of publication ("year"), the title ("title"), the venue identifier ("source_id"), the title of the venue ("source_title"), yes/no value in case the entity is retracted as well ("retracted"), the subject area ("area"), the subject category ("category"), the sections of the in-text citations ("intext_citation.section"), the value of the reference pointer ("intext_citation.pointer"), the in-text citation function ("intext_citation.intent"), the in-text citation perceived sentiment ("intext_citation.sentiment"), and a yes/no value to denote whether the in-text citation context mentions the retraction of the cited entity ("intext_citation.section.ret_mention").<br> <strong>Note: </strong>this dataset is licensed under a <a href="https://creativecommons.org/publicdomain/zero/1.0/legalcode">Creative Commons public domain dedication (CC0)</a>.<br> </li> <li><em><strong>"cits_text.csv": </strong>this dataset stores the abstract ("abstract") and the in-text citations context ("intext_citation.context") </em>for each citing entity identified using the DOI value ("doi").<br> <strong>Note: </strong>the data keep their original license (the one provided by their publisher). This dataset is provided in order to favor the reproducibility of the results obtained in our work.</li> </ol> <p> </p> <p><strong>Topic modeling</strong><br> We run a topic modeling analysis on the textual features gathered (i.e. abstracts and citation contexts). The results are stored inside the <em><strong>"topic_modeling/"</strong></em> directory. The topic modeling has been done using MITAO, a tool for mashing up automatic text analysis tools, and creating a completely customizable visual workflow [3]. The topic modeling results for each textual feature are separated into two different folders, <em><strong>"abstracts/"</strong></em> for the abstracts, and <em><strong>"intext_cit/"</strong></em> for the in-text citation contexts. Both the directories contain the following directories/files: <strong> </strong></p> <ol> <li> <p><em><strong>"mitao_workflows/"</strong></em>: the workflows of MITAO. These are JSON files that could be reloaded in MITAO to reproduce the results following the same workflows.</p> </li> <li> <p><em><strong>"corpus_and_dictionary/": </strong></em>it contains the dictionary and the vectorized corpus given as inputs for the LDA topic modeling.</p> </li> <li> <p><em><strong>"coherence/coherence.csv":</strong></em> the coherence score of several topic models trained on a number of topics from 1 - 40.</p> </li> <li> <p><em><strong>"datasets_and_views/": </strong></em>the datasets and visualizations generated using MITAO. </p> </li> </ol> <p> </p> <p><strong>References</strong></p> <ol> <li>Wakefield, A., Murch, S., Anthony, A., Linnell, J., Casson, D., Malik, M., Berelowitz, M., Dhillon, A., Thomson, M., Harvey, P., Valentine, A., Davies, S., & Walker-Smith, J. (1998). RETRACTED: Ileal-lymphoid-nodular hyperplasia, non-specific colitis, and pervasive developmental disorder in children. <em>The Lancet</em>, <em>351</em>(9103), 637–641. <a href="https://doi.org/10.1016/S0140-6736(97)11096-0">https://doi.org/10.1016/S0140-6736(97)11096-0</a></li> <li> <p>Heibi, I., & Peroni, S. (2020). A methodology for gathering and annotating the raw-data/characteristics of the documents citing a retracted article v1 (protocols.io.bdc4i2yw) [Data set]. In protocols.io. ZappyLab, Inc. <a href="https://doi.org/10.17504/protocols.io.bdc4i2yw">https://doi.org/10.17504/protocols.io.bdc4i2yw</a></p> </li> <li> <p> </p> Ferri, P., Heibi, I., Pareschi, L., & Peroni, S. (2020). MITAO: A User Friendly and Modular Software for Topic Modelling [JD]. PuntOorg International Journal, 5(2), 135–149. <a href="https://doi.org/10.19245/25.05.pij.5.2.3">https://doi.org/10.19245/25.05.pij.5.2.3</a> <p> </p> </li> </ol>
Binary data file used for analysis_common_envelope unit testing in the Phantom smoothed particle hydrodynamics and magnetohydrodynamics code
<p>** this file is automatically downloaded as part of the Phantom github actions tests **</p> <p>This is an example snapshot from a Phantom simulation of a common envelope interaction, taken from the paper by <a href="https://ui.adsabs.harvard.edu/abs/2022MNRAS.517.3181G">González-Bolívar et al. (2022)</a>. It is posted here primarily in order to perform unit and regression testing on the <a href="https://github.com/danieljprice/phantom/blob/master/src/utils/analysis_common_envelope.f90">analysis_common_envelope</a> module in the Phantom smoothed particle hydrodynamics and magnetohydrodynamics code (<a href="http://adsabs.harvard.edu/abs/2018PASA...35...31P">Price et al. 2018</a>).</p>
Effect size data for the meta-analysis article ""Effects of vegetation management intensity on biodiversity and ecosystem services in vineyards: a meta-analysis"
<p>This Exel file includes the effect size dataset used for the statistical analysis for the paper "Effect of vegetation management intensity on biodiversity and ecosystem services in vineyards: a meta-analysis", which will be published in the Journal of Applied Ecology in 2018.</p> <p>This meta-analysis was conducted in the course of the project VineDivers (<a href="http://www.vinedivers.eu/">www.vinedivers.eu</a>) funded through the 2013-2014 BiodivERsA/FACCE-JPI joint call for research proposals, with the national funders: Austrian Science Fund (FWF), Spanish Ministry for Economy and Competitiveness (MINECO), French National Research Agency (ANR), Romanian Executive Agency for Higher Education, Research, Development and Innovation Funding (UEFISCDI) and Federal Ministry of Education and Research (BMBF/Germany). P. Batáry was supported by the German Research Foundation (DFG BA4438/2-1) and by the Economic Development and Innovation Operational Programme of Hungary (GINOP–2.3.2–15–2016–00019).</p> <p> </p>
On the Timed Analysis of Big-Data Applications - Experimental Data
<p>This archive includes experimental data associated to the paper:</p> <p>On the Timed Analysis of Big-Data Applications. Accepted in <em>Proceedings of Nasa Formal Methods</em> (NFM 2018). <br> Marconi, F., Quattrocchi, G., Baresi, L., Bersani, M.M., Rossi, M.. 2018</p> <p>Specifically, it includes detailed data regarding the verification tasks reported in Section 4 (Implementation and Validation of the Model).<br> In reference to Table 1 of the paper, the archive is organized in the following way: there is one folder for each case study (sort_by_key, pagerank, kmeans) and, for each of these folders, there is a subfolder for each configuration considered in the paper.</p> <p>Here we report an overview of the verification tasks performed. The name of the tables correspond to the code of the setting and to the name of the folder, while the id of each entry corresponds to the folder name of each experiment.</p> <pre><code class="language-markdown">## SortByKey Experiments ### sort_by_key_C12_T100_rec260000000 | Application | Cores | Deadline | Outcome | Verification Time | id | |-------|:-------:|:------:|:--------:|:--------:|:------:| | sort_by_key | 12 | 91000 | timeout | None | C1_t100_c12_c12_t100_nr260000000_tb20_no_l_d91000_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 91100 | timeout | None | C1_t100_c12_c12_t100_nr260000000_tb20_no_l_d91100_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 91200 | timeout | None | C1_t100_c12_c12_t100_nr260000000_tb20_no_l_d91200_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 91300 | timeout | None | C1_t100_c12_c12_t100_nr260000000_tb20_no_l_d91300_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 91360 | timeout | None | C1_t100_c12_c12_t100_nr260000000_tb20_no_l_d91360_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 91370 | timeout | None | C1_t100_c12_c12_t100_nr260000000_tb20_no_l_d91370_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 91380 | timeout | None | C1_t100_c12_c12_t100_nr260000000_tb20_no_l_d91380_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 91381 | timeout | None | C1_t100_c12_c12_t100_nr260000000_tb20_no_l_d91381_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 91382 | timeout | None | C1_t100_c12_c12_t100_nr260000000_tb20_no_l_d91382_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 91383 | timeout | None | C1_t100_c12_c12_t100_nr260000000_tb20_no_l_d91383_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 91384 | sat | 3.81 | C1_t100_c12_c12_t100_nr260000000_tb20_no_l_d91384_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 91385 | sat | 3.34 | C1_t100_c12_c12_t100_nr260000000_tb20_no_l_d91385_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 91386 | sat | 3.52 | C1_t100_c12_c12_t100_nr260000000_tb20_no_l_d91386_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 91387 | sat | 3.42 | C1_t100_c12_c12_t100_nr260000000_tb20_no_l_d91387_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 91388 | sat | 3.4 | C1_t100_c12_c12_t100_nr260000000_tb20_no_l_d91388_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 91389 | sat | 3.43 | C1_t100_c12_c12_t100_nr260000000_tb20_no_l_d91389_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 91390 | sat | 2.37 | C1_t100_c12_c12_t100_nr260000000_tb20_no_l_d91390_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 91400 | sat | 3.38 | C1_t100_c12_c12_t100_nr260000000_tb20_no_l_d91400_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 91500 | sat | 16.52 | C1_t100_c12_c12_t100_nr260000000_tb20_no_l_d91500_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 91600 | sat | 6.8 | C1_t100_c12_c12_t100_nr260000000_tb20_no_l_d91600_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 91700 | sat | 12.03 | C1_t100_c12_c12_t100_nr260000000_tb20_no_l_d91700_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 91800 | sat | 5.62 | C1_t100_c12_c12_t100_nr260000000_tb20_no_l_d91800_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 91900 | sat | 5.54 | C1_t100_c12_c12_t100_nr260000000_tb20_no_l_d91900_tc_12_8_n_rounds_by1_t_task | the minimum SAT deadline is: 91384 ### sort_by_key_C12_T100_rec280000000 | Application | Cores | Deadline | Outcome | Verification Time | id | |-------|:-------:|:------:|:--------:|:--------:|:------:| | sort_by_key | 12 | 98200 | timeout | None | C1_t100_c12_c12_t100_nr280000000_tb20_no_l_d98200_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 98300 | timeout | None | C1_t100_c12_c12_t100_nr280000000_tb20_no_l_d98300_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 98400 | timeout | None | C1_t100_c12_c12_t100_nr280000000_tb20_no_l_d98400_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 98402 | timeout | None | C1_t100_c12_c12_t100_nr280000000_tb20_no_l_d98402_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 98403 | timeout | None | C1_t100_c12_c12_t100_nr280000000_tb20_no_l_d98403_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 98404 | timeout | None | C1_t100_c12_c12_t100_nr280000000_tb20_no_l_d98404_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 98405 | timeout | None | C1_t100_c12_c12_t100_nr280000000_tb20_no_l_d98405_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 98406 | timeout | None | C1_t100_c12_c12_t100_nr280000000_tb20_no_l_d98406_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 98407 | timeout | None | C1_t100_c12_c12_t100_nr280000000_tb20_no_l_d98407_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 98408 | timeout | None | C1_t100_c12_c12_t100_nr280000000_tb20_no_l_d98408_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 98409 | timeout | None | C1_t100_c12_c12_t100_nr280000000_tb20_no_l_d98409_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 98410 | timeout | None | C1_t100_c12_c12_t100_nr280000000_tb20_no_l_d98410_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 98420 | sat | 3.48 | C1_t100_c12_c12_t100_nr280000000_tb20_no_l_d98420_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 98430 | sat | 3.57 | C1_t100_c12_c12_t100_nr280000000_tb20_no_l_d98430_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 98440 | sat | 6.68 | C1_t100_c12_c12_t100_nr280000000_tb20_no_l_d98440_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 98450 | sat | 7.1 | C1_t100_c12_c12_t100_nr280000000_tb20_no_l_d98450_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 98460 | sat | 37.07 | C1_t100_c12_c12_t100_nr280000000_tb20_no_l_d98460_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 98470 | sat | 10.33 | C1_t100_c12_c12_t100_nr280000000_tb20_no_l_d98470_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 98480 | sat | 18.14 | C1_t100_c12_c12_t100_nr280000000_tb20_no_l_d98480_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 98490 | sat | 10.78 | C1_t100_c12_c12_t100_nr280000000_tb20_no_l_d98490_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 98500 | sat | 3.43 | C1_t100_c12_c12_t100_nr280000000_tb20_no_l_d98500_tc_12_8_n_rounds_by1_t_task | the minimum SAT deadline is: 98420 ### sort_by_key_C12_T100_rec300000000 | Application | Cores | Deadline | Outcome | Verification Time | id | |-------|:-------:|:------:|:--------:|:--------:|:------:| | sort_by_key | 12 | 105200 | timeout | None | C1_t100_c12_c12_t100_nr300000000_tb20_no_l_d105200_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 105300 | timeout | None | C1_t100_c12_c12_t100_nr300000000_tb20_no_l_d105300_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 105400 | timeout | None | C1_t100_c12_c12_t100_nr300000000_tb20_no_l_d105400_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 105420 | timeout | None | C1_t100_c12_c12_t100_nr300000000_tb20_no_l_d105420_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 105430 | timeout | None | C1_t100_c12_c12_t100_nr300000000_tb20_no_l_d105430_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 105440 | timeout | None | C1_t100_c12_c12_t100_nr300000000_tb20_no_l_d105440_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 105441 | timeout | None | C1_t100_c12_c12_t100_nr300000000_tb20_no_l_d105441_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 105442 | timeout | None | C1_t100_c12_c12_t100_nr300000000_tb20_no_l_d105442_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 105443 | sat | 3.33 | C1_t100_c12_c12_t100_nr300000000_tb20_no_l_d105443_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 105444 | sat | 3.35 | C1_t100_c12_c12_t100_nr300000000_tb20_no_l_d105444_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 105445 | sat | 3.3 | C1_t100_c12_c12_t100_nr300000000_tb20_no_l_d105445_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 105446 | sat | 3.31 | C1_t100_c12_c12_t100_nr300000000_tb20_no_l_d105446_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 105447 | sat | 3.37 | C1_t100_c12_c12_t100_nr300000000_tb20_no_l_d105447_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 105448 | sat | 3.37 | C1_t100_c12_c12_t100_nr300000000_tb20_no_l_d105448_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 105449 | sat | 3.3 | C1_t100_c12_c12_t100_nr300000000_tb20_no_l_d105449_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 105450 | sat | 3.02 | C1_t100_c12_c12_t100_nr300000000_tb20_no_l_d105450_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 105460 | sat | 3.03 | C1_t100_c12_c12_t100_nr300000000_tb20_no_l_d105460_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 105470 | sat | 4.52 | C1_t100_c12_c12_t100_nr300000000_tb20_no_l_d105470_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 105480 | sat | 4.54 | C1_t100_c12_c12_t100_nr300000000_tb20_no_l_d105480_tc_12_8_n_rounds_by1_t_task | | sort_by_key | 12 | 105490 | sat | 9.48 | C1_t100_c12_c12_t100_nr300000000_tb20_no_l_d105490_tc_12_8_n_rounds_by1_t_task | the minimum SAT deadline is: 105443 ### sort_by_key_C22_T100_rec260000000 | Application | Cores | Deadline | Outcome | Verification Time | id | |-------|:-------:|:------:|:--------:|:--------:|:------:| | sort_by_key | 22 | 70000 | timeout | None | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d70000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 70500 | timeout | None | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d70500_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 71000 | timeout | None | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d71000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 71500 | timeout | None | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d71500_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72000 | timeout | None | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72250 | timeout | None | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72250_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72500 | timeout | None | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72500_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72750 | timeout | None | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72750_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72885 | timeout | None | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72885_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72890 | timeout | None | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72890_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72895 | timeout | None | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72895_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72898 | timeout | None | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72898_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72899 | timeout | None | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72899_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72900 | timeout | None | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72900_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72901 | timeout | None | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72901_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72902 | timeout | None | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72902_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72903 | timeout | None | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72903_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72904 | sat | 9.35 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72904_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72905 | sat | 8.16 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72905_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72910 | sat | 8.15 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72910_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72915 | sat | 7.72 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72915_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72920 | sat | 7.55 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72920_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72925 | sat | 5.29 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72925_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72930 | sat | 10.23 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72930_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72935 | sat | 11.21 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72935_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72940 | sat | 10.98 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72940_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72945 | sat | 12.62 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72945_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72950 | sat | 12.55 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72950_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72955 | sat | 3.19 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72955_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72960 | sat | 2.62 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72960_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72965 | sat | 2.62 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72965_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72970 | sat | 2.63 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72970_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72975 | sat | 2.62 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72975_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72980 | sat | 2.72 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72980_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72985 | sat | 2.6 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72985_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72990 | sat | 2.55 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72990_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72995 | sat | 2.56 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d72995_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 73000 | sat | 3.2 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d73000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 74000 | sat | 6.34 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d74000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 75000 | sat | 3.88 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d75000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 76000 | sat | 4.6 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d76000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 77000 | sat | 4.15 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d77000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 78000 | sat | 1.89 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d78000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 79000 | sat | 3.28 | C2_t100_c22_c22_t100_nr260000000_tb20_no_l_d79000_tc_22_10_n_rounds_by1_t_task | the minimum SAT deadline is: 72904 ### sort_by_key_C22_T100_rec280000000 | Application | Cores | Deadline | Outcome | Verification Time | id | |-------|:-------:|:------:|:--------:|:--------:|:------:| | sort_by_key | 22 | 78000 | timeout | None | C2_t100_c22_c22_t100_nr280000000_tb20_no_l_d78000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 78480 | timeout | None | C2_t100_c22_c22_t100_nr280000000_tb20_no_l_d78480_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 78485 | timeout | None | C2_t100_c22_c22_t100_nr280000000_tb20_no_l_d78485_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 78490 | timeout | None | C2_t100_c22_c22_t100_nr280000000_tb20_no_l_d78490_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 78492 | timeout | None | C2_t100_c22_c22_t100_nr280000000_tb20_no_l_d78492_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 78494 | timeout | None | C2_t100_c22_c22_t100_nr280000000_tb20_no_l_d78494_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 78495 | timeout | None | C2_t100_c22_c22_t100_nr280000000_tb20_no_l_d78495_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 78496 | timeout | None | C2_t100_c22_c22_t100_nr280000000_tb20_no_l_d78496_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 78497 | timeout | None | C2_t100_c22_c22_t100_nr280000000_tb20_no_l_d78497_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 78498 | timeout | None | C2_t100_c22_c22_t100_nr280000000_tb20_no_l_d78498_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 78499 | timeout | None | C2_t100_c22_c22_t100_nr280000000_tb20_no_l_d78499_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 78500 | sat | 40.11 | C2_t100_c22_c22_t100_nr280000000_tb20_no_l_d78500_tc_22_10_n_rounds_by1_t_task_num_v | | sort_by_key | 22 | 78750 | sat | 1.33 | C2_t100_c22_c22_t100_nr280000000_tb20_no_l_d78750_tc_22_10_n_rounds_by1_t_task_num_v | | sort_by_key | 22 | 79000 | sat | 9.14 | C2_t100_c22_c22_t100_nr280000000_tb20_no_l_d79000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 80000 | sat | 2.27 | C2_t100_c22_c22_t100_nr280000000_tb20_no_l_d80000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 81000 | sat | 1.98 | C2_t100_c22_c22_t100_nr280000000_tb20_no_l_d81000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 82000 | sat | 5.89 | C2_t100_c22_c22_t100_nr280000000_tb20_no_l_d82000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 83000 | sat | 8.81 | C2_t100_c22_c22_t100_nr280000000_tb20_no_l_d83000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84000 | sat | 1.86 | C2_t100_c22_c22_t100_nr280000000_tb20_no_l_d84000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 85000 | sat | 2.46 | C2_t100_c22_c22_t100_nr280000000_tb20_no_l_d85000_tc_22_10_n_rounds_by1_t_task | the minimum SAT deadline is: 78500 ### sort_by_key_C22_T100_rec300000000 | Application | Cores | Deadline | Outcome | Verification Time | id | |-------|:-------:|:------:|:--------:|:--------:|:------:| | sort_by_key | 22 | 5000 | unsat | 0.56 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d5000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 10000 | unsat | 0.44 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d10000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 12000 | unsat | 1.07 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d12000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 15000 | unsat | 1.72 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d15000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 20000 | unsat | 1.13 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d20000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 25000 | unsat | 3.71 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d25000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 30000 | unsat | 4.27 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d30000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 35000 | unsat | 15.73 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d35000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 40000 | unsat | 8.82 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d40000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 45000 | unsat | 15.27 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d45000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 50000 | unsat | 19.42 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d50000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 55000 | unsat | 39.81 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d55000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 60000 | unsat | 39.71 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d60000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 65000 | unsat | 87.48 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d65000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 70000 | unsat | 120.21 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d70000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 72500 | unsat | 172.18 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d72500_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 74000 | unsat | 186.82 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d74000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 75000 | unsat | 120.49 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d75000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 76000 | unsat | 1175.84 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d76000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 76250 | unsat | 1167.23 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d76250_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 77500 | unsat | 2091.15 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d77500_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 78250 | unsat | 3000.66 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d78250_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 80000 | unsat | 34239.41 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d80000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 81250 | unsat | 79433.55 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d81250_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 82500 | unsat | 198577.62 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d82500_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 83000 | unsat | 183847.9 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d83000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 83750 | unsat | 245288.49 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d83750_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84000 | unsat | 276617.22 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84000_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84100 | timeout | None | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84100_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84101 | unsat | 253379.92 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84101_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84102 | timeout | None | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84102_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84103 | unsat | 254808.3 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84103_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84104 | timeout | None | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84104_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84105 | unsat | 269304.76 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84105_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84106 | timeout | None | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84106_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84107 | unsat | 242278.6 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84107_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84108 | timeout | None | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84108_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84109 | unsat | 260411.26 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84109_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84110 | timeout | None | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84110_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84111 | unsat | 259311.3 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84111_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84112 | timeout | None | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84112_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84113 | timeout | None | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84113_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84114 | timeout | None | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84114_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84115 | unsat | 230687.19 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84115_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84116 | timeout | None | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84116_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84117 | unsat | 281773.79 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84117_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84118 | timeout | None | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84118_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84119 | unsat | 272785.32 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84119_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84120 | sat | 4.09 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84120_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84121 | sat | 3.98 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84121_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84122 | sat | 4.01 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84122_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84123 | sat | 4.28 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84123_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84124 | sat | 4.24 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84124_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84125 | sat | 4.22 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84125_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84126 | sat | 4.25 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84126_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84127 | sat | 4.39 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84127_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84128 | sat | 4.23 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84128_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84129 | sat | 4.14 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84129_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84130 | sat | 4.07 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84130_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84131 | sat | 4.24 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84131_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84132 | sat | 4.42 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84132_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84133 | sat | 4.32 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84133_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84134 | sat | 4.31 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84134_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84135 | sat | 4.33 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84135_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84136 | sat | 4.26 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84136_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84137 | sat | 4.01 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84137_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84138 | sat | 3.82 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84138_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84139 | sat | 4.07 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84139_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84140 | sat | 4.38 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84140_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84141 | sat | 8.74 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84141_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84142 | sat | 8.98 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84142_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84143 | sat | 9.1 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84143_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84144 | sat | 9.09 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84144_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84145 | sat | 9.19 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84145_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84146 | sat | 9.1 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84146_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84147 | sat | 8.82 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84147_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84148 | sat | 6.54 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84148_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84149 | sat | 6.56 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84149_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84150 | sat | 6.59 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84150_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84151 | sat | 6.84 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84151_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84152 | sat | 6.87 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84152_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84153 | sat | 6.97 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84153_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84154 | sat | 6.88 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84154_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84155 | sat | 6.98 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84155_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84156 | sat | 6.71 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84156_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84157 | sat | 6.75 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84157_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84158 | sat | 6.42 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84158_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84159 | sat | 6.62 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84159_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84160 | sat | 6.75 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84160_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84161 | sat | 6.95 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84161_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84162 | sat | 7.03 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84162_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84163 | sat | 6.98 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84163_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84164 | sat | 7.08 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84164_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84165 | sat | 6.93 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84165_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84166 | sat | 6.81 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84166_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84167 | sat | 6.47 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84167_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84168 | sat | 6.66 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84168_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84169 | sat | 6.73 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84169_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84171 | sat | 6.8 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84171_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84172 | sat | 6.81 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84172_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84173 | sat | 6.71 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84173_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84174 | sat | 3.56 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84174_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84175 | sat | 3.26 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84175_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84176 | sat | 3.52 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84176_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84177 | sat | 3.6 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84177_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84178 | sat | 3.02 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84178_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84179 | sat | 3.6 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84179_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84180 | sat | 3.62 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84180_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84181 | sat | 3.59 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84181_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84182 | sat | 3.62 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84182_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84183 | sat | 6.72 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84183_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84184 | sat | 6.58 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84184_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84186 | sat | 6.59 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84186_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84187 | sat | 6.85 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84187_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84188 | sat | 6.56 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84188_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84189 | sat | 6.88 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84189_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84190 | sat | 6.84 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84190_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84191 | sat | 6.86 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84191_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84192 | sat | 6.94 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84192_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84193 | sat | 6.93 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84193_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84194 | sat | 6.91 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84194_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84195 | sat | 6.84 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84195_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84196 | sat | 6.99 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84196_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84197 | sat | 6.89 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84197_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84198 | sat | 6.95 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84198_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84200 | sat | 8.04 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84200_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 | 84300 | sat | 11.06 | C2_t100_c22_c22_t100_nr300000000_tb20_no_l_d84300_tc_22_10_n_rounds_by1_t_task | | sort_by_key | 22 </code></pre> <p> </p> <pre><code class="language-markdown"> ## PageRank Experiments ### pagerank_C28_T128_rec200000000 | Application | Cores | Deadline | Outcome | Verification Time | id | |-------|:-------:|:------:|:--------:|:--------:|:------:| | pagerank | 28 | 61000 | timeout | None | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr200000000_tb45_no_l_d61000_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 61500 | timeout | None | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr200000000_tb45_no_l_d61500_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 62000 | timeout | None | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr200000000_tb45_no_l_d62000_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 62500 | sat | 7805.0 | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr200000000_tb45_no_l_d62500_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 63000 | sat | 330.87 | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr200000000_tb45_no_l_d63000_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 63500 | sat | 79.78 | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr200000000_tb45_no_l_d63500_tc_28_12_n_rounds_by1_t_task_num_v | the minimum SAT deadline is: 62500 ### pagerank_C28_T128_rec300000000 | Application | Cores | Deadline | Outcome | Verification Time | id | |-------|:-------:|:------:|:--------:|:--------:|:------:| | pagerank | 28 | 93000 | timeout | None | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr300000000_tb45_no_l_d93000_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 94000 | sat | 192.86 | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr300000000_tb45_no_l_d94000_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 94001 | sat | 7331.63 | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr300000000_tb45_no_l_d94001_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 94501 | sat | 1934.68 | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr300000000_tb45_no_l_d94501_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 94502 | sat | 2856.96 | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr300000000_tb55_no_l_d94502_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 95000 | sat | 221.22 | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr300000000_tb45_no_l_d95000_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 95001 | sat | 173.91 | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr300000000_tb45_no_l_d95001_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 96001 | sat | 87.89 | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr300000000_tb45_no_l_d96001_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 97001 | sat | 70.07 | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr300000000_tb45_no_l_d97001_tc_28_12_n_rounds_by1_t_task_num_v | the minimum SAT deadline is: 94000 ### pagerank_C28_T128_rec400000000 | Application | Cores | Deadline | Outcome | Verification Time | id | |-------|:-------:|:------:|:--------:|:--------:|:------:| | pagerank | 28 | 118000 | timeout | None | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr400000000_tb45_no_l_d118000_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 119000 | timeout | None | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr400000000_tb45_no_l_d119000_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 119001 | timeout | None | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr400000000_tb45_no_l_d119001_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 120000 | timeout | None | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr400000000_tb45_no_l_d120000_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 120001 | timeout | None | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr400000000_tb45_no_l_d120001_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 120002 | sat | 138.84 | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr400000000_tb45_no_l_d120002_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 120003 | sat | 272.19 | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr400000000_tb45_no_l_d120003_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 120004 | sat | 2072.06 | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr400000000_tb45_no_l_d120004_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 121000 | sat | 580.28 | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr400000000_tb45_no_l_d121000_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 121001 | sat | 55.8 | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr400000000_tb45_no_l_d121001_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 122000 | sat | 301.84 | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr400000000_tb45_no_l_d122000_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 122001 | sat | 187.93 | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr400000000_tb45_no_l_d122001_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 124000 | sat | 51.47 | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr400000000_tb45_no_l_d124000_tc_28_12_n_rounds_by1_t_task_num_v | | pagerank | 28 | 131000 | sat | 46.01 | exp_n4_c28_p140_SPARKSEQ_5_rounds_replica_c28_t128_nr400000000_tb45_no_l_d131000_tc_28_12_n_rounds_by1_t_task_num_v | the minimum SAT deadline is: 120002 ### pagerank_C48_T128_rec200000000 | Application | Cores | Deadline | Outcome | Verification Time | id | |-------|:-------:|:------:|:--------:|:--------:|:------:| | pagerank | 48 | 5000 | unsat | 20.91 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d5000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 10000 | unsat | 1002.53 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d10000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 15000 | unsat | 407.96 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d15000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 20000 | unsat | 667.32 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d20000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 25000 | unsat | 1263.14 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d25000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 30000 | unsat | 3561.83 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d30000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 35000 | unsat | 12742.87 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d35000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 37000 | unsat | 44051.41 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d37000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 40000 | timeout | None | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d40000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 43000 | timeout | None | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d43000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 45000 | timeout | None | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d45000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 46000 | timeout | None | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d46000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 46050 | timeout | None | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d46050_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 46100 | timeout | None | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d46100_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 46200 | timeout | None | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d46200_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 46250 | timeout | None | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d46250_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 46750 | timeout | None | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d46750_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 46900 | timeout | None | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d46900_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 47000 | sat | 59.41 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d47000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 48000 | sat | 39.88 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d48000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 49000 | sat | 31.35 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d49000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 50000 | sat | 45.02 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d50000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 51000 | sat | 43.73 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d51000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 52000 | sat | 36.25 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d52000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 52500 | sat | 40.5 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d52500_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 53500 | sat | 40.61 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d53500_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 54000 | sat | 39.9 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr200000000_tb45_no_l_d54000_tc_48_16_n_rounds_by1_t_task_num_v | the minimum SAT deadline is: 47000 ### pagerank_C48_T128_rec300000000 | Application | Cores | Deadline | Outcome | Verification Time | id | |-------|:-------:|:------:|:--------:|:--------:|:------:| | pagerank | 48 | 64000 | timeout | None | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr300000000_tb45_no_l_d64000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 65000 | timeout | None | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr300000000_tb45_no_l_d65000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 65050 | timeout | None | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr300000000_tb45_no_l_d65050_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 65100 | sat | 1282.46 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr300000000_tb45_no_l_d65100_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 65200 | sat | 102.8 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr300000000_tb45_no_l_d65200_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 65250 | sat | 75.45 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr300000000_tb45_no_l_d65250_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 65500 | sat | 39.15 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr300000000_tb45_no_l_d65500_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 65750 | sat | 78.42 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr300000000_tb45_no_l_d65750_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 65900 | sat | 33.61 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr300000000_tb45_no_l_d65900_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 66000 | sat | 96.91 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr300000000_tb45_no_l_d66000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 67000 | sat | 56.04 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr300000000_tb45_no_l_d67000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 68000 | sat | 65.88 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr300000000_tb45_no_l_d68000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 69000 | sat | 40.79 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr300000000_tb45_no_l_d69000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 70000 | sat | 51.82 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr300000000_tb45_no_l_d70000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 72000 | sat | 49.1 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr300000000_tb45_no_l_d72000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 73000 | sat | 26.86 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr300000000_tb45_no_l_d73000_tc_48_16_n_rounds_by1_t_task_num_v | the minimum SAT deadline is: 65100 ### pagerank_C48_T128_rec400000000 | Application | Cores | Deadline | Outcome | Verification Time | id | |-------|:-------:|:------:|:--------:|:--------:|:------:| | pagerank | 48 | 85000 | timeout | None | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr400000000_tb45_no_l_d85000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 86000 | sat | 278.68 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr400000000_tb45_no_l_d86000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 87000 | sat | 55.3 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr400000000_tb45_no_l_d87000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 88000 | sat | 55.59 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr400000000_tb45_no_l_d88000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 89000 | sat | 42.06 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr400000000_tb45_no_l_d89000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 90000 | sat | 51.77 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr400000000_tb45_no_l_d90000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 91000 | sat | 45.12 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr400000000_tb45_no_l_d91000_tc_48_16_n_rounds_by1_t_task_num_v | | pagerank | 48 | 92000 | sat | 38.55 | exp_n4_c48_p128_SPARKSEQ_3_rounds_c48_t128_nr400000000_tb45_no_l_d92000_tc_48_16_n_rounds_by1_t_task_num_v | the minimum SAT deadline is: 86000 </code></pre> <p> </p> <pre><code class="language-markdown">## K-Means Experiments ### kmeans_C24_T18_rec80000000 | Application | Cores | Deadline |Time Bound | Outcome | Verification Time | id | |-------|:-------:|:------:|:--------:|:--------:|:------:|:------:| | kmeans | 24 | 79000 | 50 | sat | 61854.62 | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr80000000_tb50_no_l_d79000_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 79500 | 50 | sat | 18414.51 | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr80000000_tb50_no_l_d79500_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 80000 | 50 | sat | 14840.75 | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr80000000_tb50_no_l_d80000_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 85000 | 50 | err/timeout | None | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr80000000_tb50_no_l_d85000_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 90000 | 50 | sat | 14590.33 | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr80000000_tb50_no_l_d90000_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 100000 | 50 | err/timeout | None | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr80000000_tb50_no_l_d100000_tc_24_6_n_rounds_by1_t_task_num_v | the minimum SAT deadline is: 79000 ### kmeans_C24_T18_rec120000000 | Application | Cores | Deadline |Time Bound | Outcome | Verification Time | id | |-------|:-------:|:------:|:--------:|:--------:|:------:|:------:| | kmeans | 24 | 105000 | 50 | sat | 152363.81 | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr120000000_tb50_no_l_d105000_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 105000 | 80 | err/timeout | None | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr120000000_tb80_no_l_d105000_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 106000 | 50 | sat | 43759.32 | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr120000000_tb50_no_l_d106000_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 106500 | 50 | sat | 22230.43 | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr120000000_tb50_no_l_d106500_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 107000 | 50 | sat | 25027.5 | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr120000000_tb50_no_l_d107000_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 108000 | 50 | err/timeout | None | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr120000000_tb50_no_l_d108000_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 110000 | 50 | sat | 40917.73 | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr120000000_tb50_no_l_d110000_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 110000 | 80 | err/timeout | None | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr120000000_tb80_no_l_d110000_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 115000 | 50 | sat | 12142.65 | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr120000000_tb50_no_l_d115000_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 115000 | 80 | err/timeout | None | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr120000000_tb80_no_l_d115000_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 120000 | 50 | err/timeout | None | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr120000000_tb50_no_l_d120000_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 120000 | 80 | err/timeout | None | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr120000000_tb80_no_l_d120000_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 150000 | 50 | sat | 14259.95 | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr120000000_tb50_no_l_d150000_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 150000 | 60 | err/timeout | None | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr120000000_tb60_no_l_d150000_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 150000 | 70 | err/timeout | None | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr120000000_tb70_no_l_d150000_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 150000 | 80 | sat | 14189.62 | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr120000000_tb80_no_l_d150000_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 150000 | 90 | err/timeout | None | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr120000000_tb90_no_l_d150000_tc_24_6_n_rounds_by1_t_task_num_v | the minimum SAT deadline is: 105000 ### kmeans_C24_T18_rec160000000 | Application | Cores | Deadline |Time Bound | Outcome | Verification Time | id | |-------|:-------:|:------:|:--------:|:--------:|:------:|:------:| | kmeans | 24 | 140000 | 50 | sat | 40930.77 | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr160000000_tb50_no_l_d140000_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 140000 | 80 | err/timeout | None | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr160000000_tb80_no_l_d140000_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 150000 | 50 | sat | 21741.22 | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr160000000_tb50_no_l_d150000_tc_24_6_n_rounds_by1_t_task_num_v | | kmeans | 24 | 150000 | 80 | err/timeout | None | kmeans_exp_n2_c24_p24_SPARK_FSEQ_4_rounds_c24_t18_nr160000000_tb80_no_l_d150000_tc_24_6_n_rounds_by1_t_task_num_v | the minimum SAT deadline is: 140000 ### kmeans_C32_T24_rec80000000 | Application | Cores | Deadline |Time Bound | Outcome | Verification Time | id | |-------|:-------:|:------:|:--------:|:--------:|:------:|:------:| | kmeans | 32 | 63000 | 80 | sat | 26400.12 | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr80000000_tb80_no_l_d63000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 65000 | 80 | sat | 10414.91 | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr80000000_tb80_no_l_d65000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 70000 | 80 | sat | 5554.03 | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr80000000_tb80_no_l_d70000_tc_32_8_n_rounds_by1_t_task_num_v | the minimum SAT deadline is: 63000 ### kmeans_C32_T24_rec120000000 | Application | Cores | Deadline |Time Bound | Outcome | Verification Time | id | |-------|:-------:|:------:|:--------:|:--------:|:------:|:------:| | kmeans | 32 | 82000 | 60 | sat | 2692.91 | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr120000000_tb60_no_l_d82000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 82000 | 100 | running | None | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr120000000_tb100_no_l_d82000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 83000 | 60 | err/timeout | None | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr120000000_tb60_no_l_d83000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 83000 | 100 | err/timeout | None | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr120000000_tb100_no_l_d83000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 90000 | 60 | err/timeout | None | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr120000000_tb60_no_l_d90000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 90000 | 100 | sat | 60535.12 | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr120000000_tb100_no_l_d90000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 150000 | 50 | err/timeout | None | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr120000000_tb50_no_l_d150000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 150000 | 60 | sat | 1240.52 | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr120000000_tb60_no_l_d150000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 150000 | 70 | err/timeout | None | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr120000000_tb70_no_l_d150000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 150000 | 80 | err/timeout | None | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr120000000_tb80_no_l_d150000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 150000 | 90 | err/timeout | None | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr120000000_tb90_no_l_d150000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 150000 | 100 | sat | 1957.42 | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr120000000_tb100_no_l_d150000_tc_32_8_n_rounds_by1_t_task_num_v | the minimum SAT deadline is: 82000 ### kmeans_C32_T24_rec160000000 | Application | Cores | Deadline |Time Bound | Outcome | Verification Time | id | |-------|:-------:|:------:|:--------:|:--------:|:------:|:------:| | kmeans | 32 | 103000 | 50 | sat | 47624.75 | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr160000000_tb50_no_l_d103000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 103000 | 80 | err/timeout | None | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr160000000_tb80_no_l_d103000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 105000 | 50 | sat | 38614.93 | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr160000000_tb50_no_l_d105000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 105000 | 80 | err/timeout | None | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr160000000_tb80_no_l_d105000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 110000 | 50 | sat | 11604.73 | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr160000000_tb50_no_l_d110000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 120000 | 50 | sat | 11559.37 | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr160000000_tb50_no_l_d120000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 120000 | 80 | sat | 27413.19 | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr160000000_tb80_no_l_d120000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 200000 | 50 | sat | 1811.52 | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr160000000_tb50_no_l_d200000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 200000 | 60 | err/timeout | None | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr160000000_tb60_no_l_d200000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 200000 | 70 | sat | 2423.68 | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr160000000_tb70_no_l_d200000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 200000 | 80 | sat | 952.42 | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr160000000_tb80_no_l_d200000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 200000 | 90 | sat | 1300.52 | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr160000000_tb90_no_l_d200000_tc_32_8_n_rounds_by1_t_task_num_v | | kmeans | 32 | 200000 | 100 | err/timeout | None | kmeans_exp_n2_c32_p32_SPARKSEQ_4_rounds_c32_t24_nr160000000_tb100_no_l_d200000_tc_32_8_n_rounds_by1_t_task_num_v | the minimum SAT deadline is: 103000 </code></pre> <p> </p>
Supplementary Data for risk analysis
<p>The files in this record contain data for risk analysis for real-time flood control operation of a multi-reservoir system using a dynamic bayesian network.</p> <p>The files consist of:</p> <ul> <li>Reservoir data and river flood routing parameters</li> <li>Code and results of the Monte Carlo simulations</li> <li>Code and results of the Bayesian network</li> </ul>
Test data for C-QTL analysis of barley Recombinant Chromosome Substitution Lines
<p>This data shows the testing of C-QTL approach to visualise the influence of ensembles of groups of genetic markers on plant traits from a selection of Recombinant Chromosome Substitution Lines. The analysis was performed on of 29 genotypes and two traits: Heading Date and plant height. For heading date, the two major QTLs on 2H and 7H associated with heading date using the REML approach (de la Fuente Canto 2016) are also detected using the CQTL analysis, getting the highest 'rank' or score with this approach. Similarly, marker main effect for plant height at the region of the sdw1 seems to be detected with the CQTL analysis.</p>
juliacarbajal/bilingual_assimilation: First release of data & analysis scripts for bilingual assimilation project.
<p>This release contains all the app data and vocabulary questionnaires collected for Carbajal et al. (in preparation)'s project on bilingual assimilation (OSF project <a href="https://osf.io/52z9g/">https://osf.io/52z9g/</a>). It also includes analysis scripts as of the 22nd of March, 2018.</p>
An Image-Based Gamut Analysis of Translucent Digital Ceramic Prints for Coloured Photovoltaic Modules: Supplementary Data
<p>Colouring the frontglass of PV modules via digital ceramic printing aids in concealing the PV when integrated into existing building façades as BIPV, while admitting sufficient light to produce electricity. This promotes the visual acceptance and adoption of PV as a source of renewable energy in urban environments. The effective colour of the PV laminate is a combination of the transparent colour on glass and the colour of the PV cells. This colour should ideally match the architect’s visual expectations in terms of fidelity, but also in terms of relative PV efficiency as a function of print density. In practice, these requirements are often contradictory, particularly for vivid colours, and the visual results may deviate significantly. This paper presents an objective analysis of how colours appear on PV frontglass laminated with a PV module, using an image-based colour acquisition process. Given a set of 1044 nominal colours uniformly distributed in the RGB colour space, each printed in 10 opacities, we quantify the range of effective colours observed when printed on glass and combined with PV, and their deviation from the nominals. Our results confirm that the effective colour gamuts are significantly constrainted and skewed, depending on the ink volume and glass finish used for printing. In particular, blue-magenta hues cannot be reliably rendered with this process. These insights can serve as guidelines for selecting target colours for BIPV that can be well approximated in practice.</p>
Summary-level data from meta-analysis of fat distribution phenotypes in UK Biobank and GIANT
<p>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~</p> <p>Summary-level data as presented in:</p> <p>"Meta-analysis of genome-wide association studies for body fat distribution in 694,649 individuals of European ancestry." Pulit, SL et al. bioRxiv, 2018. https://www.biorxiv.org/content/early/2018/04/18/304030</p> <p>**If you use these data, please cite the above preprint.</p> <p>If you have any questions or comments regarding these files, please contact me:</p> <p>Sara L Pulit<br> spulit@well.ox.ac.uk or s.l.pulit@umcutrecht.nl</p> <p>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~</p> <p><strong>(1) Data files</strong></p> <p><em>i. whradjbmi.giant-ukbb.meta-analysis.combined.23May2018.txt</em><br> Meta-analysis of waist-to-hip ratio adjusted for body mass index (whradjbmi) in UK Biobank and GIANT data. Combined set of samples, max N = 694,649.</p> <p><em>ii. whradjbmi.giant-ukbb.meta-analysis.females.23May2018.txt</em><br> Meta-analysis of whradjbmi in UK Biobank and GIANT data. Female samples only, max N = 379,501.</p> <p><em>iii. whradjbmi.giant-ukbb.meta-analysis.males.23May2018.txt</em><br> Meta-analysis of whradjbmi in UK Biobank and GIANT data. Male samples only, max N = 315,284.</p> <p><em>iv. whr.giant-ukbb.meta-analysis.combined.23May2018.txt</em><br> Meta-analysis of waist-to-hip ratio (whr) in UK Biobank and GIANT data. Combined set of samples, max N = 697,734.</p> <p><em>v. whr.giant-ukbb.meta-analysis.females.23May2018.txt</em><br> Meta-analysis of whr in UK Biobank and GIANT data. Female samples only, max N = 381,152.</p> <p><em>vi. whr.giant-ukbb.meta-analysis.males.23May2018.txt</em><br> Meta-analysis of whr in UK Biobank and GIANT data. Male samples only, max N = 316,772.</p> <p><em>vii. bmi.giant-ukbb.meta-analysis.combined.23May2018.txt</em><br> Meta-analysis of body mass index (bmi) in UK Biobank and GIANT data. Combined set of samples, max N = 806,834.</p> <p><em>viii. bmi.giant-ukbb.meta-analysis.females.23May2018.txt</em><br> Meta-analysis of bmi in UK Biobank and GIANT data. Female samples only, max N = 434,794.</p> <p><em>ix. bmi.giant-ukbb.meta-analysis.males.23May2018.txt</em><br> Meta-analysis of bmi in UK Biobank and GIANT data. Male samples only, max N = 374,756.</p> <p><strong>(2) Data file format</strong></p> <p>CHR: Chromosome</p> <p>POS: Chromosomal position of the SNP, build hg19</p> <p>SNP: the dbSNP151 identifier of the SNP, followed by the first allele and second allele of the SNP, delimited with a colon. A small number of SNPs (<9,000) from the GIANT data had no dbSNP151 identifier, and are left as just an rsID. Note that these SNPs are also missing chromosome and position information (not provided in the GIANT data).</p> <p>Tested_Allele: the allele for which all association statistics are reported</p> <p>Other_Allele: the other allele at the SNP</p> <p>Freq_Tested_Allele: frequency of the tested allele</p> <p>BETA: the effect size of the tested allele</p> <p>SE: the standard error of the beta</p> <p>P: the p-value of the SNP, as reported from the inverse variance-weighted fixed effects meta-analysis</p> <p>N: the total sample size for this SNP</p> <p>INFO: the imputation quality (info score) of the SNP, as reported by UK Biobank. A number between 0 and 1 indicating quality of imputation (0, poor quality; 1, high quality or genotyped). Note that the summary-level GIANT data does not report info score, so SNPs appearing only in the GIANT analysis do not have info scores.</p>
padpadpadpad/Padfield_2018_ELE_metab_size_struc: Archive of analysis and raw data for Padfield et al (2018) ELE
<p>This is an archived version of the data and analysis to go along with the paper:</p> <p>Padfield et al. (2018) Linking phytoplankton community metabolism to the individual size distribution. Ecology Letters. <a href="https://onlinelibrary.wiley.com/doi/full/10.1111/ele.13082">https://doi.org/10.1111/ele.13082</a>.</p>
Data from: Individual Movement - Sequence Analysis Method (IM-SAM): characterising spatio-temporal patterns of animal trajectories across scales and landscapes
<p>Dataset included in Zenodo supports the analyses performed in "<em>Individual Movement - Sequence Analysis Methods (IM-SAM) characterising spatio-temporal patterns of animal trajectories across scales and landscapes.</em>"</p> <p>The dataset includes one RDS file, that can be easily loaded into R using the readRDS function. The RDS file consists out of a list including two objects per animal:</p> <ul> <li>Object 1 contains a data frame with the real and simulated sequences for an animal. e.g., ls[[1]][[1]] </li> <li>Object 2 contains the home range in raster format of an animal. e.g., ls[[1]][[2]]</li> </ul> <p>The data frames in object 1 contain real habitat use sequences and corresponding simulated habitat use sequences generated in the home range of the specific individual (900 simulated sequences: 6 habitat selection rules x 3 selection coefficients x 50 repetitions). Open and closed habitats are respectively encoded by 0 and 1. The first 96 columns of each row in a data frame represent a 16-day habitat use sequence, with a fixed 4-hour relocation interval (0, 4, 8, 12, 16 and 20h). Column names are named as follows: Day_1_0h, Day_1_4h,..., Day_16_20h. In the next columns we provide the selection coefficients (columns 97-99), the habitat selection rules (or pattern, columns 100-102) and the number of missing values (mvs, columns, 103-104) for each of the real and simulated sequences. Note that simulated sequences have no missing values (i.e. values are always 0.00) and for real sequences there is no selection coefficient or habitat selection rule (i.e. values are always xxx).</p> <p>Rownames of simulated sequences are composed out of the habitat selection rule (c, o, a24, a33, a42 and u), the selection coefficient (5, 10, 50) and the replicate (1 to 50), separated by dashes. For example, the first simulated sequence in the first data frame (ls[[1]][[1]][1,]) is described as a24_10_1. The rownames of real sequences instead are composed out of the individuals' identifier, the biweekly period (1 to 23) and the year. For example, the first real sequence in the first data frame (ls[[1]][[1]][901,]) is described as 1_5_2006.</p> <p><br> </p>
SCID Multiomics Post-Processed Data and Analysis
<p>In this repository are the post-processed datasets and analytical code for the SCID Multiomics paper. The repository is structured as an installable R package for dependency management and dataset loading; it does not export any functions.</p> <p><strong>Installation</strong></p> <p>The easiest way to install this is to download the repository and install using `devtools::install()`. This will allow the import of various datasets using the `data()` function, upon which many of the analysis scripts depend.</p> <p><strong>Datasets</strong></p> <p>In no particular order, the important datasets are described below:</p> <p>- <strong>intsites</strong>: summary statistics from (Wang et al, Blood, 2010) for timepoints used in this study<br> - <strong>tcr</strong>: Aggregate TCR data from Adaptive Biotechnology's ImmunoSeq pipeline.<br> - <strong>mb</strong>: Metadata for the microbiome sampling timepoints, as well as species data from Metaphlan (not used)<br> - <strong>agg.mb.kz</strong>: Kraken species data for the microbiome samples, after low-complexity filtering<br> - <strong>agg.vp.kz</strong>: Kraken species data for the virome samples, after low-complexity filtering<br> - <strong>card</strong>: Antibiotic resistance gene data from CARD<br> - <strong>subject_ids.csv</strong>: Provides a mapping from the original sample IDs used in the datasets to the ones used in the manuscript.</p> <p>The code for creating these datasets from the original data files are in the `data-raw` directory.</p> <p><strong>Analysis/Figures</strong></p> <p>The analysis code is broken apart by subject and is largely concerned with figure generation. The R<br> scripts are all located in the `inst` folder. To generate all figures, you should run each script in the<br> order specified by the `GenerateFigures.R` file.</p> <p>Figures are output to the `figures` directory, while tables are output to the `tables` directory.</p> <p>Please note: many of the figures used in the manuscript were aesthetically modified after generation (text size, color palette, orientation), precluding exact figure replication</p>
Fine vertical structures at the cloud heights of Venus revealed by radio holographic analysis of Venus Express and Akatsuki radio occultation data -- dataset
<p>The data used in the figures in the paper "Fine vertical structures at the cloud heights of Venus revealed by radio holographic analysis of Venus Express and Akatsuki radio occultation data" by Imamura et al. (J. Geophys. Res)</p> <p>The description of the columns in the files are given in the header section.</p>
WSSSPE 5.1 - Data for speed blog analysis
<p>Data and coding for the thematic/framework analysis of speed blogs produced at WSSSPE 5.1.</p> <p>WSSSPE5.1_blogs.docx contains the original text of the speed blogs, obtained from the Software Sustainability Institute website. Authors of the individual speed blogs are credited in the text of each post.</p> <p>WSSSPE5.1_blogpost_coding.xlxs contains the results of the thematic analysis.</p>
Data from: Billeci et al. "Patient-specific seizure prediction based on heart rate variability and recurrence quantification analysis"
<p>Dataset of electrocardiogram and electroencephalogram signals (.edf) acquired in epileptic patients (N=15).</p> <p>All the patients were long-term monitored with a Video-EEG, with electrodes arranged on the basis of the international 10-20 system, and with ECG. ECG was measured simultaneously with a sampling rate of 512 Hz.</p> <p>Each data include a descriptor file (.txt) containing all the information related to the acquisition: data, registration start (time), registration end (time), seizure/s start, seizure/s end and the electrodes involved at the seizure onset.</p> <p> </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.