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“Big Data” at the Natural Resource Ecology Laboratory Randall B. Boone

“Big Data” at the Natural Resource Ecology Laboratory Randall B. Boone Research Scientist, Natural Resource Ecology Laboratory and Associate Professor, Department of Ecosystem Science and Sustainability ISTeC Big Data Forum Colorado State University April 18, 2013. Department of Ecosystem

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“Big Data” at the Natural Resource Ecology Laboratory Randall B. Boone

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  1. “Big Data” at the Natural Resource Ecology Laboratory Randall B. Boone Research Scientist, Natural Resource Ecology Laboratory and Associate Professor, Department of Ecosystem Science and Sustainability ISTeC Big Data Forum Colorado State University April 18, 2013 Department of Ecosystem Science and Sustainability

  2. Integrating across scales through Top-down and Bottom-up approaches D. Ojima

  3. Agent-based Modeling and Big Data

  4. G-Range, A Global Rangeland Model

  5. T. Hilinski

  6. DayCent and Century Simulations T. Hilinski Potential Vegetation: Mean Annual NPP (gC/m2) 1961-2006 Mean

  7. Managing Big Data at NREL Our data are big, but well described Unlike some industrial applications, the system must be exceedingly flexible Must be responsive to a variety of users (e.g., more diverse uses than the ISTeC Cray) Calculation-intensive uses Users are unlikely to have the ability to parallelize tools

  8. Rubel Cluster – The Backbone of NREL’s “Mid-Performance Computing” Distributed memory computer cluster 256 processors 500 Gflops 463 GB memory 2 TB immediate storage 50-60 TB extended storage, plus 120 TB coming online Private 1 Gbps Ethernet interconnect, with 10 Gbps access to storage

  9. Rubel Cluster Provides a testing platform for the CRAY for some Others use alternatives, such as R, which can make use of multiple processors Good, but not high performance computing A mix of desktop and cluster analyses yield “mid-performance computing” But it is effective!

  10. Tom Hilinski

  11. Rubel – Past, Present, and Future

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