Ali Can Demiralp, M. Sc.
Room K111
Phone: +49 241 80 29732
Fax: +49 241 80 22134
Email: demiralp@vis.rwth-aachen.de

Visualization Team


Performance Assessment of Diffusive Load Balancing for Distributed Particle Advection

Ali Can Demiralp, Dirk Norbert Helmrich, Joachim Protze, Torsten Wolfgang Kuhlen, Tim Gerrits
30. International Conference in Central Europe on Computer Graphics, Visualization, and Computer Vision 2022 (WSCG2022)

Particle advection is the approach for the extraction of integral curves from vector fields. Efficient parallelization of particle advection is a challenging task due to the problem of load imbalance, in which processes are assigned unequal workloads, causing some of them to idle as the others are performing computing. Various approaches to load balancing exist, yet they all involve trade-offs such as increased inter-process communication, or the need for central control structures. In this work, we present two local load balancing methods for particle advection based on the family of diffusive load balancing. Each process has access to the blocks of its neighboring processes, which enables dynamic sharing of the particles based on a metric defined by the workload of the neighborhood. The approaches are assessed in terms of strong and weak scaling as well as load imbalance. We show that the methods reduce the total run-time of advection and are promising with regard to scaling as they operate locally on isolated process neighborhoods.

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

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.

Voxel-Based Edge Bundling Trough Direction-Aware Kernel Smoothing

Daniel Zielasko, Xiaoqing Zhao, Ali Can Demiralp, Torsten Wolfgang Kuhlen, Benjamin Weyers
Computers & Graphics

Relational data with a spatial embedding and depicted as node-link diagram is very common, e.g., in neuroscience, and edge bundling is one way to increase its readability or reveal hidden structures. This article presents a 3D extension to kernel density estimation-based edge bundling that is meant to be used in an interactive immersive analysis setting. This extension adds awareness of the edges’ direction when using kernel smoothing and thus implicitly supports both directed and undirected graphs. The method generates explicit bundles of edges, which can be analyzed and visualized individually and as sufficient as possible for a given application context, while it scales linearly with the input size.

» Show BibTeX

title = "Voxel-based edge bundling through direction-aware kernel smoothing",
journal = "Computers & Graphics",
volume = "83",
pages = "87 - 96",
year = "2019",
issn = "0097-8493",
doi = "https://doi.org/10.1016/j.cag.2019.06.008",
url = "http://www.sciencedirect.com/science/article/pii/S0097849319301025",
author = "Daniel Zielasko and Xiaoqing Zhao and Ali Can Demiralp and Torsten W. Kuhlen and Benjamin Weyers"}

Interactive Level-of-Detail Visualization of 3D-Polarized Light Imaging Data Using Spherical Harmonics

Claudia Hänel, Ali Can Demiralp, Markus Axer, David Gräßel, Bernd Hentschel, Torsten Wolfgang Kuhlen
19th EG/VGTC Conference on Visualization (EuroVis 2017)

3D-Polarized Light Imaging (3D-PLI) provides data that enables an exploration of brain fibers at very high resolution. However, the visualization poses several challenges. Beside the huge data set sizes, users have to visually perceive the pure amount of information which might be, among other aspects, inhibited for inner structures because of occlusion by outer layers of the brain. We propose a clustering of fiber directions by means of spherical harmonics using a level-of-detail structure by which the user can interactively choose a clustering degree according to the zoom level or details required. Furthermore, the clustering method can be used for the automatic grouping of similar spherical harmonics automatically into one representative. An optional overlay with a direct vector visualization of the 3D-PLI data provides a better anatomical context.

Honorable Mention for Best Short Paper!

» Show BibTeX

@inproceedings {Haenel2017Interactive,
booktitle = {EuroVis 2017 - Short Papers},
editor = {Barbora Kozlikova and Tobias Schreck and Thomas Wischgoll},
title = {{Interactive Level-of-Detail Visualization of 3D-Polarized Light Imaging Data Using Spherical Harmonics}},
author = {H\”anel, Claudia and Demiralp, Ali C. and Axer, Markus and Gr\”assel, David and Hentschel, Bernd and Kuhlen, Torsten W.},
year = {2017},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-043-7},
DOI = {10.2312/eurovisshort.20171145}

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