Using Parallel MultiCore and HPC Systems for Dynamical Visualisation

Rückemann, Claus-Peter

doi:10.1109/geows.2009.14

Cited by 6 publications

(2 citation statements)

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“…The new implementation has been successfully used for loosely parallel processing and on the other hand has been integrated in Information Systems for calculating tenthousands of points and traces, with thousands of quasi redundant stack sets. The algorithm has been tested and used for supporting dynamical applications and cartography, and applications with operational and development collaboration scenarios, e.g., in the field of advanced IICS [16,17]. All these were using dynamical integration of resources in distributed computing and capacity computing scenarios, in High Performance Computing scenarios, using batch resources for processing real seismic data and modelling Fresnel Sections.…”

Section: Sst(i) = Nrmpos(i) * Meanpos(i) + Nrmneg(i) * Meanneg(i)mentioning

confidence: 99%

Supercomputing resources empowering superstack with interactive and integrated systems

Rückemann¹

2012

AIP Conference Proceedings

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Interactions in metallization systems for integrated circuitsAbstract. This paper presents the results from the development and implementation of Superstack algorithms to be dynamically used with integrated systems and supercomputing resources. Processing of geophysical data, thus named geoprocessing, is an essential part of the analysis of geoscientific data. The theory of Superstack algorithms and the practical application on modern computing architectures was inspired by developments introduced with processing of seismic data on mainframes and within the last years leading to high end scientific computing applications. There are several stacking algorithms known but with low signal to noise ratio in seismic data the use of iterative algorithms like the Superstack can support analysis and interpretation. The new Superstack algorithms are in use with wave theory and optical phenomena on highly performant computing resources for huge data sets as well as for sophisticated application scenarios in geosciences and archaeology.

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Section: Sst(i) = Nrmpos(i) * Meanpos(i) + Nrmneg(i) * Meanneg(i)mentioning

confidence: 99%

Supercomputing resources empowering superstack with interactive and integrated systems

Rückemann¹

2012

AIP Conference Proceedings

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“…In recent years, research on driving behavior based on vehicle motion states has become a hot topic with the maturity of in‐vehicle data collection devices. It is common to identify aggressive driving behavior using speed, acceleration, and distance to the vehicle in front (Kluger et al, 2016; Lee & Jang, 2019; Rückemann & Doytsher, 2017). To improve the accuracy and recall of recognition, researchers have used machine learning, deep learning, and other methods for driving behavior recognition and prediction (Lattanzi & Freschi, 2021; Shangguan et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Identification of aggressive driving behavior of online car‐hailing drivers based on association classification

Wu,

Chen,

et al. 2023

Hum Ftrs & Erg Mfg Svc

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With the rapid development of online car‐hailing, the related crashes have become a key issue with public concern. Identifying and predicting aggressive driving behaviors is critical to reduce traffic crashes. In this study, we propose a method to recognize aggressive driving behavior based on association classification, with multisource features being employed, including driver emotion, vehicle kinematic characteristics, and road environment. The model performs best in a 10‐fold cross‐test when the minimum support and minimum confidence are set as 0.01 and 0.8, respectively. Besides, we also compare the performance of aggressive driving behavior recognition classifiers constructed using association classification with other rule‐based classification methods, including ID3, C4.5, CART, and Random Forest. The results show that association classification performs better than other classification competitors. Thirty‐six if–then rules generated by the association classification are used to analyze the influencing factors and associated mechanisms of aggressive driving behavior. It is found that aggressive driving behavior is highly correlated with driver anger and disgust emotions. Aggressive driving behavior is more likely to occur when no passengers are in the car than the case with passengers. Driver entertainment behavior and passenger interference also affect driving behavior. Moreover, drivers are prone to aggressive driving when making a U‐turn. This research not only proposed a new identification method for aggressive driving behavior but also provided a comprehensive understanding of the associated influencing factors which thus benefit the further research and development of safety assistance driving devices.

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Constraint Programming-Based Job Dispatching for Modern HPC Applications

Galleguillos

Kızıltan

Sîrbu

et al. 2019

Lecture Notes in Computer Science

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Constraint Programming (CP) is a wellestablished area in AI as a programming paradigm for modelling and solving discrete optimization problems, and it has been been successfully applied to tackle the on-line job dispatching problem in HPC systems including those running modern applications. The limitations of the available CP-based job dispatchers may hinder their practical use in today's systems that are becoming larger in size and more demanding in resource allocation. In an attempt to bring basic AI research closer to a deployed application, we present a new CP-based on-line job dispatcher for modern HPC systems and applications. Unlike its predecessors, our new dispatcher tackles the entire problem in CP and its model size is independent of the system size. Experimental results based on a simulation study show that with our approach dispatching performance increases significantly in a large system and in a system where allocation is nontrivial.

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Using Parallel MultiCore and HPC Systems for Dynamical Visualisation

Cited by 6 publications

References 0 publications

Supercomputing resources empowering superstack with interactive and integrated systems

Supercomputing resources empowering superstack with interactive and integrated systems

Identification of aggressive driving behavior of online car‐hailing drivers based on association classification

Constraint Programming-Based Job Dispatching for Modern HPC Applications

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