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

Mexner, Wolfgang, Hrsg.; Bonn, Matthias; Kopmann, Andreas; Mauch, Viktor; Ressmann, Doris; Chilingaryan, Suren; Tan Jerome, Nicholoas; van de Kamp, Thomas; Heuveline, Vincent; Lösel, Philipp,; Schmelzle, Sebastian; Heethoff, Michael

Abstract (englisch):
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.

Zugehörige Institution(en) am KIT Institut für Beschleunigerphysik und Technologie (IBPT)
Institut für Prozessdatenverarbeitung und Elektronik (IPE)
Institut für Photonenforschung und Synchrotronstrahlung (IPS)
Publikationstyp Buchaufsatz
Jahr 2017
Sprache Englisch
Identifikator DOI: 10.4018/978-1-5225-2785-5
ISBN: 978152252785
ISSN: 1844-7600
KITopen ID: 1000071278
HGF-Programm 56.03.30; LK 01
Erschienen in Design and Use of Virtualization Technology in Cloud Computing. Ed.: P. Kumar Das
Verlag IGI Global, Hershey
Serie Advances in Computer and Electrical Engineering
Bemerkung zur Veröffentlichung Result of the BMB Collaboration ASTOR, finished 2016
Schlagworte VMware vSphere PCI-pass-through AaaS Scientific Cloud Astor GPU Virtualization Shibboleth
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