Data Management Chairs: This email address is being protected from spambots. You need JavaScript enabled to view it., This email address is being protected from spambots. You need JavaScript enabled to view it. Experts in our field expect that, as concurrency grows, there will be a widening gap between computational and I/O capacity, and this will be further stressed by energy demands. Our approach is to perform as much work as possible while the data is still resident in application memory, a use model often referred to as “in-situ.” Data Analysis Chairs: This email address is being protected from spambots. You need JavaScript enabled to view it., This email address is being protected from spambots. You need JavaScript enabled to view it. Early analysis of the data generated by large scale simulations is crucial to effectively monitor the correct progress of simulations and the understanding of simulation results. Such early analysis capability requires an in-situ monitoring framework that allows analysis tasks to take place “on-the-fly” while data is generated. Visualization Chairs: This email address is being protected from spambots. You need JavaScript enabled to view it., This email address is being protected from spambots. You need JavaScript enabled to view it. While the idea of a single library upon which applications are built is well grounded in software engineering principles, the current ubiquitous library was designed during the era of the single core processor. Our plan is to engage a wide segment of leading visualization and system researchers and developers to produce a new library that is architecture-aware and can serve as the basis for future visualization applications. Scientific Software Tools Chairs: This email address is being protected from spambots. You need JavaScript enabled to view it., This email address is being protected from spambots. You need JavaScript enabled to view it., This email address is being protected from spambots. You need JavaScript enabled to view it. A sustainable software infrastructure requires quality assurance, regression testing, distribution, and tracking feedback from the users. Our intent is to deliver a software infrastructure to the scientific community that couples the best practices from both research and development.
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Final Report
SDAV Research Highlights
DIY Block-Parallel Data Analysis[View PDF] Dmitriy Morozov (LBNL) Tom Peterka (ANL) Objectives DIY is a programming model and runtime for block-parallel analytics on DOE leadership machines.… Read more
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