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5 results for “Runtime modeling”
Automatic time step adjustment for shortening the runtime of the simulation of marine ecosystem models
<p><strong>Abstract:</strong></p> <p>In investigating the global carbon cycle, shortening the runtime of the simulation of marine ecosystem models is an important issue. More specifically, steady annual cycles mostly are used to assess and validate the models against<br> observational data and to identify relevant biogeochemical processes. Offline simulations based on the transport matrix method already reduce the high computational effort significantly. Furthermore, they facilitate the application<br> of larger time steps in a simple way. In this paper, we present two different methods that automatically adjust the time step during the simulation of a steady state using transport matrices. The algorithms use either an adaptive<br> step size control or decreasing time steps. Their aim is to apply always the time step as large as possible but without any manual selection. We applied the methods for a variety of ecosystem models of different complexity, using Latin<br> hypercube samples of size 100 for the model parameters of each model. We showed that both methods computed an approximation of the steady annual cycle that was of the same accuracy as solutions obtained with a fixed time step. Both algorithms lowered the runtime of the steady annual cycle computation significantly. The performance gain depended on the complexity of the models. Moreover, the adaptive method has a certain overhead that might lead to higher computational cost in special cases.</p> <p><strong>Content:</strong></p> <ul> <li>Tracer concentrations of a reference solution for all parameter vectors and biogeochemical models</li> <li>SQLite database including the results using the decreasing time steps algorithm</li> <li>Tracer concentrations of the results using the decreasing time steps algorithm</li> <li>SQLite database including the results using the step size control algorithm</li> <li>Tracer concentrations of the results using the step size control algorithm</li> </ul>
Dataset for "The AllScale Runtime Application Model" publication
<p>The experimental data collected for the associated paper.</p> <p>Each file covers the throughput measures for the three evaluated applications. The first column is the number of nodes, the second the throughput achieved by the AllScale runtime system, and the last column the throughput of a reference MPI version.</p>
Shortening the runtime using larger time steps for the simulation of marine ecosystem models
<p><strong>Abstract:</strong></p> <p>The reduction of computational costs for marine ecosystem models is important for the investigation and detection of the relevant biogeochemical processes because such models are computationally expensive. In order to lower these computational costs by means of larger time steps we investigated the accuracy of steady annual cycles (i.e., an annual periodic solution) calculated with different time steps. We compared the accuracy for a hierarchy of biogeochemical models showing an increasing complexity and computed the steady annual cycles with offline simulations that are based on the transport matrix approach. For each of these biogeochemical models, we obtained practically the same solution even though larger time steps. This indicates that larger time steps shortened the runtime with an acceptable loss of accuracy.</p> <p><strong>Content:</strong></p> <ul> <li>SQLite database including the obtained results</li> <li>Tracer concentrations obtained with different time steps for all used models and parameter vectors</li> </ul>
Replication Package: Anomaly detection via runtime monitoring data for structural equation modeling
<p>This replication package contains the following information:</p> <ul> <li><strong>Data extraction from literature & interviews: </strong><em>Generation Structural & Measurement Model via literature and interviews.xlsx</em> - here you can find the mapping of the extracted phrases to inductively summarise information regarding the structural and measurement models.</li> <li><strong>Dataset</strong> of runtime monitoring data extracted from TrainTicket via EvoMaster: <br> <ul> <li><em>TrainTicket faults classification.xlxs:</em> Describes the datasets and their faults, in which microservice the fault is injected for better explainability of the obtained results</li> <li><em>IndicatorDescriptionbasedonAnomalyDetectionToolsInterviews.xlsx:</em> description and mapping of selected indicators to the defined parameters from <a href="https://arxiv.org/abs/2408.07816" target="_blank" rel="noopener">previous work </a></li> <li>Unfortunately, the size of the datasets generated via EvoMaster and their injected faults are too big to upload here, thus, they will be available here: <a href="https://uibkacat-my.sharepoint.com/:f:/g/personal/monika_steidl_uibk_ac_at/EjLMt8SYWwtJtp2YuSaqavcBKJoCQ3b5H_l_OY0ifbVRCA?e=fatKyD" target="_blank" rel="noopener">Datasets with injected anomalies</a><br> <ul> <li>the error description can be found <a href="https://github.com/FudanSELab/train-ticket/wiki/Fault-Description" target="_blank" rel="noopener">here</a></li> <li>the datasets are named ts-error-<em>indicatorOfError</em>-reset.zip because the databases are getting reset so that no anomalies are introduced with wrong database entries</li> </ul> </li> </ul> </li> <li><strong>Code</strong> for handling and transforming data to extract indicators describing the whole system's and microservices' behavior from the collected runtime monitoring data collected from TrainTicket:<br> <ul> <li><a href="https://github.com/moniSt13/ConTest-Parsing" target="_blank" rel="noopener">link to the Github repository</a></li> </ul> </li> <li><strong>reports</strong> regarding the established PLS-SEM model using previously handled and transformed runtime monitoring data. Please be aware that opening the reports can leas to out of memory due to their size: <ul> <li><em>Assessment of Measurement Model: MeasurementModel_TrainTicket_erorcleaned.zip & MeasurementModel_Bootstrap_ALL_TrainTicket_errorcleaned.zip</em> </li> <li><em>Assessment of Structural Model: StructuralModel_TrainTicket_errorcleaned.zip & StructuralModel_Bootstrap_ALL_TrainTicket_errorcleaned.zip</em></li> </ul> </li> </ul> <p><br><br>---------------------------------------</p> <p><em>Future work </em>not elaborated in the associated paper due to space restrictions:</p> <ul> <li><strong>reports regarding F5 error</strong>: PLS-SEM model results without interpretation and further mediating effects between microservices included: F5_error.zip</li> </ul> <p> </p> <p> </p> <p> </p>
Runtime Monitoring of Refinement Progress in CEGAR-based Model Checking: Dataset
<p>Dataset used in the evaluation of Part II of the MSc thesis titled "Extending the Capabilities of the CEGAR Model Checking Algorithm"</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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OpenNeuro
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