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OpenGL® API-Based Analysis of Large Datasets in a Cloud Environment

OpenGL® API-Based Analysis of Large Datasets in a Cloud Environment

Wolfgang Mexner, Matthias Bonn, Andreas Kopmann, Viktor Mauch, Doris Ressmann, Suren A. Chilingaryan, Nicholas Tan Jerome, Thomas van de Kamp, Vincent Heuveline, Philipp Lösel, Sebastian Schmelzle, Michael Heethoff
Copyright: © 2018 |Pages: 21
ISBN13: 9781522527855|ISBN10: 1522527850|EISBN13: 9781522527862
DOI: 10.4018/978-1-5225-2785-5.ch006
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MLA

Mexner, Wolfgang, et al. "OpenGL® API-Based Analysis of Large Datasets in a Cloud Environment." Design and Use of Virtualization Technology in Cloud Computing, edited by Prashanta Kumar Das and Ganesh Chandra Deka, IGI Global, 2018, pp. 161-181. https://doi.org/10.4018/978-1-5225-2785-5.ch006

APA

Mexner, W., Bonn, M., Kopmann, A., Mauch, V., Ressmann, D., Chilingaryan, S. A., Jerome, N. T., van de Kamp, T., Heuveline, V., Lösel, P., Schmelzle, S., & Heethoff, M. (2018). OpenGL® API-Based Analysis of Large Datasets in a Cloud Environment. In P. Das & G. Deka (Eds.), Design and Use of Virtualization Technology in Cloud Computing (pp. 161-181). IGI Global. https://doi.org/10.4018/978-1-5225-2785-5.ch006

Chicago

Mexner, Wolfgang, et al. "OpenGL® API-Based Analysis of Large Datasets in a Cloud Environment." In Design and Use of Virtualization Technology in Cloud Computing, edited by Prashanta Kumar Das and Ganesh Chandra Deka, 161-181. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-2785-5.ch006

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Abstract

Modern applications for analysing 2D/3D data require complex visual output features which are often based on the multi-platform OpenGL® API for rendering vector graphics. Instead of providing classical workstations, the provision of powerful virtual machines (VMs) with GPU support in a scientific cloud with direct access to high performance storage is an efficient and cost effective solution. However, the automatic deployment, operation and remote access of OpenGL® API-capable VMs with professional visualization applications is a non-trivial task. In this chapter the authors demonstrate the concept of such a flexible cloud-like analysis infrastructure within the framework of the project ASTOR. The authors present an Analysis-as-a-Service (AaaS) approach based on VMware™-ESX for on demand allocation of VMs with dedicated GPU cores and up to 256 GByte RAM per machine.

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