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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
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Author(s): Wolfgang Mexner (Karlsruhe Institute of Technology (KIT), Germany), Matthias Bonn (Karlsruhe Institute of Technology (KIT), Germany), Andreas Kopmann (Karlsruhe Institute of Technology (KIT), Germany), Viktor Mauch (Karlsruhe Institute of Technology (KIT), Germany), Doris Ressmann (Karlsruhe Institute of Technology (KIT), Germany), Suren A. Chilingaryan (Karlsruhe Institute of Technology (KIT), Germany), Nicholas Tan Jerome (Karlsruhe Institute of Technology (KIT), Germany), Thomas van de Kamp (Karlsruhe Institute of Technology (KIT), Germany), Vincent Heuveline (Heidelberg University, Germany), Philipp Lösel (Heidelberg University, Germany), Sebastian Schmelzle (Technische Universität Darmstadt (TUD), Germany)and Michael Heethoff (Technische Universität Darmstadt (TUD), Germany)
Copyright: 2018
Pages: 21
Source title: Design and Use of Virtualization Technology in Cloud Computing
Source Author(s)/Editor(s): Prashanta Kumar Das (Government Industrial Training Institute Dhansiri, India)and Ganesh Chandra Deka (Government of India, India)
DOI: 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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