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Simon Oehrl, M. Sc.
Room K111
Phone: +49 241 80 24890
Fax: +49 241 80 22134
Email: oehrl@vr.rwth-aachen.de

Virtual Reality Team



Publications


A Case Study on Providing Accessibility-Focused In-Transit Architectures for Neural Network Simulation and Analysis


Marcel Krüger, Simon Oehrl, Torsten Wolfgang Kuhlen, Tim Gerrits
ISC High Performance 2023

Due to the ever-increasing availability of high-performance computing infrastructure, developers can simulate increasingly complex models. However, the increased complexity comes with new challenges regarding data processing and visualization due to the sheer size of simulations. Exploring simulation results needs to be handled efficiently via in-situ/in-transit analysis during run-time. However, most existing in-transit solutions require sophisticated and prior knowledge and significant alteration to existing simulation and visualization code, which produces a high entry barrier for many projects. In this work, we report how Insite, a lightweight in-transit pipeline, provided in-transit visualization and computation capability to various research applications in the neuronal network simulation domain. We describe the development process, including feedback from developers and domain experts, and discuss implications.

» Show BibTeX

@inproceedings{kruger2023case,

title={A Case Study on Providing Accessibility-Focused In-Transit Architectures for Neural Network Simulation and Analysis},
author={Kr{\"u}ger, Marcel and Oehrl, Simon and Kuhlen, Torsten Wolfgang and Gerrits, Tim},
booktitle={International Conference on High Performance Computing},
pages={277--287},
year={2023},
organization={Springer}
}





Insite: A Pipeline Enabling In-Transit Visualization and Analysis for Neuronal Network Simulations


Marcel Krüger, Simon Oehrl, Ali Can Demiralp, Sebastian Spreizer, Jens Bruchertseifer, Torsten Wolfgang Kuhlen, Tim Gerrits, Benjamin Weyers
ISC High Performance 2022
pubimg

Neuronal network simulators are central to computational neuroscience, enabling the study of the nervous system through in-silico experiments. Through the utilization of high-performance computing resources, these simulators are capable of simulating increasingly complex and large networks of neurons today. Yet, the increased capabilities introduce a challenge to the analysis and visualization of the simulation results. In this work, we propose a pipeline for in-transit analysis and visualization of data produced by neuronal network simulators. The pipeline is able to couple with simulators, enabling querying, filtering, and merging data from multiple simulation instances. Additionally, the architecture allows user-defined plugins that perform analysis tasks in the pipeline. The pipeline applies traditional REST API paradigms and utilizes data formats such as JSON to provide easy access to the generated data for visualization and further processing. We present and assess the proposed architecture in the context of neuronal network simulations generated by the NEST simulator.

» Show BibTeX

@InProceedings{10.1007/978-3-031-23220-6_20,
author="Kr{\"u}ger, Marcel and Oehrl, Simon and Demiralp, Ali C. and Spreizer, Sebastian and Bruchertseifer, Jens and Kuhlen, Torsten W. and Gerrits, Tim and Weyers, Benjamin",
editor="Anzt, Hartwig and Bienz, Amanda and Luszczek, Piotr and Baboulin, Marc",
title="Insite: A Pipeline Enabling In-Transit Visualization and Analysis for Neuronal Network Simulations",
booktitle="High Performance Computing. ISC High Performance 2022 International Workshops",
year="2022",
publisher="Springer International Publishing",
address="Cham",
pages="295--305",
isbn="978-3-031-23220-6"
}





Talk: Insite: A Generalized Pipeline for In-transit Visualization and Analysis


Simon Oehrl, Jan Müller, Ali Can Demiralp, Marcel Krüger, Sebastian Spreizer, Benjamin Weyers, Torsten Wolfgang Kuhlen
NEST Conference 2020
pubimg

Neuronal network simulators are essential to computational neuroscience, enabling the study of the nervous system through in-silico experiments. Through utilization of high-performance computing resources, these simulators are able to simulate increasingly complex and large networks of neurons today. It also creates new challenges for the analysis and visualization of such simulations. In-situ and in-transport strategies are popular approaches in these scenarios. They enable live monitoring of running simulations and parameter adjustment in the case of erroneous configurations which can save valuable compute resources.

This talk will present the current status of our pipeline for in-transport analysis and visualization of neuronal network simulator data. The pipeline is able to couple with NEST along other simulators with data management (querying, filtering and merging) from multiple simulator instances. Finally, the data is passed to end-user applications for visualization and analysis. The goal is to be integrated into third party tools such as the multi-view visual analysis toolkit ViSimpl.




Streaming Live Neuronal Simulation Data into Visualization and Analysis


Simon Oehrl, Jan Müller, Jan Schnathmeier, Jochen M. Eppler, Alexander Peyser, Hans Ekkehard Plesser, Benjamin Weyers, Bernd Hentschel, Torsten Wolfgang Kuhlen, Tom Vierjahn
ISC High Performance 2018
pubimg

Neuroscientists want to inspect the data their simulations are producing while these are still running. This will on the one hand save them time waiting for results and therefore insight. On the other, it will allow for more efficient use of CPU time if the simulations are being run on supercomputers. If they had access to the data being generated, neuroscientists could monitor it and take counter-actions, e.g., parameter adjustments, should the simulation deviate too much from in-vivo observations or get stuck.

As a first step toward this goal, we devise an in situ pipeline tailored to the neuroscientific use case. It is capable of recording and transferring simulation data to an analysis/visualization process, while the simulation is still running. The developed libraries are made publicly available as open source projects. We provide a proof-of-concept integration, coupling the neuronal simulator NEST to basic 2D and 3D visualization.

» Show BibTeX

@InProceedings{10.1007/978-3-030-02465-9_18,
author="Oehrl, Simon
and M{\"u}ller, Jan
and Schnathmeier, Jan
and Eppler, Jochen Martin
and Peyser, Alexander
and Plesser, Hans Ekkehard
and Weyers, Benjamin
and Hentschel, Bernd
and Kuhlen, Torsten W.
and Vierjahn, Tom",
editor="Yokota, Rio
and Weiland, Mich{\`e}le
and Shalf, John
and Alam, Sadaf",
title="Streaming Live Neuronal Simulation Data into Visualization and Analysis",
booktitle="High Performance Computing",
year="2018",
publisher="Springer International Publishing",
address="Cham",
pages="258--272",
abstract="Neuroscientists want to inspect the data their simulations are producing while these are still running. This will on the one hand save them time waiting for results and therefore insight. On the other, it will allow for more efficient use of CPU time if the simulations are being run on supercomputers. If they had access to the data being generated, neuroscientists could monitor it and take counter-actions, e.g., parameter adjustments, should the simulation deviate too much from in-vivo observations or get stuck.",
isbn="978-3-030-02465-9"
}





Talk: Streaming Live Neuronal Simulation Data into Visualization and Analysis


Simon Oehrl, Jan Müller, Jan Schnathmeier, Benjamin Weyers, Jochen M. Eppler, Alexander Peyser, Hans Ekkehard Plesser, Bernd Hentschel, Torsten Wolfgang Kuhlen, Tom Vierjahn
NEST Conference 2018
pubimg

Being able to inspect neuronal network simulations while they are running provides new research strategies to neuroscientists as it enables them to perform actions like parameter adjustments in case the simulation performs unexpectedly. This can also save compute resources when such simulations are run on large supercomputers as errors can be detected and corrected earlier saving valuable compute time. This talk presents a prototypical pipeline that enables in-situ analysis and visualization of running simulations.




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