KIT, Eggenstein-Leopoldshafen, Germany
Modern data analysis applications for 2D/3D data samples apply complex visual output features which are often based on OpenGL, a multi-platform API for rendering vector graphics. They demand special computing workstations with a corresponding CPU/GPU power, enough main memory and fast network interconnects for a performant remote data access. For this reason, users depend heavily on available free workstations, both temporally and locally. The provision of virtual machines (VMs) accessible via a remote connection could avoid this inflexibility. However, the automatic deployment, operation and remote access of OpenGL-capable VMs with professional visualization applications is a non-trivial task. In this paper, we discuss a concept for a flexible analysis infrastructure that will be part in the project ASTOR, which is the abbreviation for “Arthropod Structure revealed by ultra-fast Tomography and Online Reconstruction”. We present an Analysis-as-a-Service (AaaS) approach based on the on-demand allocation of VMs with dedicated GPU cores and a corresponding analysis environment to provide a cloud-like analysis service for scientific users.
