Framework for enabling system understanding

Abstract

Building the effective HPC resilience mechanisms required for viability of next generation supercomputers will require in depth understanding of system and component behaviors. Our goal is to build an integrated framework for high fidelity long term information storage, historic and run-time analysis, algorithmic and visual information exploration to enable system understanding, timely failure detection/prediction, and triggering of appropriate response to failure situations. Since it is unknown what information is relevant and since potentially relevant data may be expressed in a variety of forms (e.g., numeric, textual), this framework must provide capabilities to process different forms of data and also support the integration of new data, data sources, and analysis capabilities. Further, in order to ensure ease of use as capabilities and data sources expand, it must also provide interactivity between its elements. This paper describes our integration of the capabilities mentioned above into our OVIS tool. © 2012 Springer-Verlag Berlin Heidelberg.