Scalable Analysis Methods and In Situ Infrastructure for Extreme Scale Knowledge Discovery
This project addresses a set of research challenges for enabling scientific knowledge discovery within the context of in situ processing at extreme-scale concurrency. This work is motivated by a widening gap between FLOPs and I/O capacity that will make full-resolution, I/O-intensive post hoc analysis prohibitively expensive, if not impossible.
The project focuses on new algorithms for analysis, and visualization – topological, geometric, statistical analysis, flow field analysis, pattern detection and matching – suitable for use in an in situ context aimed specifically at enabling scientific knowledge discovery in several exemplar application areas of importance to DOE. Complementary to the in situ algorithmic work is a focus on several leading in situ infrastructures, with the aim of tackling research questions germane to enabling new algorithms to run at scale across a diversity of existing in situ implementations.
The intent is to move the field of in situ processing in a direction where it may ultimately be possible to write an algorithm once, and then have it execute in one of several different in situ software implementations. The combination of algorithmic and infrastructure work is grounded in direct interactions with specific application code teams, all of which are engaged in their own R&D aimed at evolving to the exascale.