High Performance Computing

RSVP: I/O staging for extreme scale data

RSVP: Runtime System for I/O staging in support of Voluminous in situ Processing of extreme scale data

At these extreme scales, online data processing pipelines will need to be easily and dynamically composed, efficiently executed alongside the scientific simulations producing the data, and support reuse of computation and data. Furthermore, the need to seamlessly integrate experimental data is imposing additional demands on extreme-scale datamanagement solutions. The overarching goal of the RSVP project is to fundamentally address these challenges by developing model in which computational, data transformation and data analytic services can be easily and efficiently associated with and applied to science data as part of an end-to-end, in situ “process flow.”


GlassBox is a National Science Foundation-funded project with a goal of enabling application developers to improve performance on future high end computing (HEC) machines for their scientific and engineering processes. The basic approach of the project is to offer an open, transparent software infrastructure - a Glass Box system - for creating and tuning large-scale, parallel applications. “Opening up” the tools and services used to create and evaluate peta- and exa-scale codes will involve developing interfaces and methods that make available tool-internal information and the tools will be accessible for new performance management services that improve developer productivity and code efficiency.


Monitoring Analytics for In Situ Workflows at the Exascale

The Mona project is a collaboration between Georgia Tech, University of Oregon, Oak Ridge National Laboratory, and Princeton Plasma Physics Laboratory. Led by Dr. Greg Eisenhauer after Dr. Schwan's passing, the project is aimed at providing scalable platform and application monitoring for in situ workflows at the exascale.

The MONA(lytics) project seeks to understand, evaluate, and ultimately, control the online data flows generated by future exascale applications and the analytics processing applied to those flows: their volumes, speeds, and processing needs; the energy saved by online vs. offline data processing; the effects of next generation computer hardware and of the new ways of performing data management; and the tradeoffs in how well data is analyzed vs. the costs of doing so, when approximate methods are sufficient for the immediate scientific insights being sought.

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