DOE/ASCR Offers Grants for Extreme-Scale Scientific Data Management and Analysis

Inter-institutional collaborations are strongly encouraged to enhance and strengthen research capabilities as needed. Collaboration could include institutions such as universities, industry, non-profit organizations, federal laboratories and FFRDCs, including the DOE National Laboratories. Collaborations should be limited to filling critical voids in expertise and represent only a modest portion of the overall effort. Additionally, Federal agencies should not be included as participants or expect to receive funding under this Funding Opportunity Announcement.

Science has shifted from data scarcity to an overwhelming abundance of data, as simulations and experiments generate many petabytes of data, with some sciences facing exabytes of data near term. For example, a recent report states that climate model data are growing faster than the data set size for any other scientific discipline, with collections of hundreds of exabytes expected by 2020 (Challenges in Climate Change Science and the Role of Computing at the Extreme Scale, http://extremecomputing.labworks.org/climate/report.stm, and the Large Hadron Collider (LHC) is expected to produce roughly 15 petabytes of data annually over its estimated 15 year lifespan. (http://public.web.cern.ch/Public/en/LHC/Computing-en.html)

The value of scientific data is realized only when data are effectively analyzed and results are presented to the science community, policy makers, and the public in an understandable way. The challenges of analyzing massive scientific data sets are compounded by data complexity that results from heterogeneous methods and devices for data generation and capture and the inherently multi-scale, multi-physics nature of many sciences, resulting in data with hundreds of attributes or dimensions and spanning multiple spatial and temporal scales. The combination of massive scale and complexity is such that high performance computers will be needed to analyze data, as well as to generate it through modeling and simulation.

This Funding Opportunity Announcement (FOA) calls for innovative basic research in computer science for management and analysis of extreme-scale scientific data in the context of petascale computers and/or exascale computers with heterogeneous multi-core architectures. The activities supported by this FOA may be a combination of basic research, creation of algorithms for advanced architectures, and development of usable data management and analysis tools for scientific discovery. Partnerships among universities, National Laboratories, and industry are strongly encouraged.