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Section: About pbdR
What is this?
This is a website about ``Programming with Big Data in R'' and provide several
We develop pbdR packages and focus on analyzing big data by utilizing
Statistics methodology. All packages mainly support the
programming model with special focus on large scale computing clusters.
Packages are available in the
and instruction for
is also available.
Some basic examples can be found in the
The Programming with Big Data in R (pbdR) has an official website at http://r-pbd.org/ . The information remaining here is for internal development, technical discussion, and windows binary download. Other useful resources are all avaialbe via
When mentioning the Programming with Big Data in R,
George Ostrouchov and Wei-Chen Chen were supported in part by the project ``Visual Data Exploration and Analysis of Ultra-large Climate Data'' funded by U.S. DOE Office of Science under Contract No. DE-AC05-00OR22725. George Ostrouchov, Drew Schmidt, and Pragneshkumar Patel were supported in part by the project ``NICS Remote Data Analysis and Visualization Center'' funded by the Office of Cyberinfrastructure of the U.S. National Science Foundation under Award No. ARRA-NSF-OCI-0906324 for NICS-RDAV center.
This work used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. This work also used resources of National Institute for Computational Sciences at the University of Tennessee, Knoxville, which is supported by the Office of Cyberinfrastructure of the U.S. National Science Foundation under Award No. ARRA-NSF-OCI-0906324 for NICS-RDAV center. This work used resources of the Newton HPC Program at the University of Tennessee, Knoxville.
We thank our colleagues from the Scientific Data Group, Computer Science and Mathematics Division, Oak Ridge National Laboratory, Hasan Abbasi, Jong Youl Choi, Scott Klasky, and Nobert Podhorszki for discussing windows MPI systems, compiler issues, dynamic libraries, and generally improving our knowledge of MPI performance issues. We also thank our colleague, Ed D'Azevedo from the Computational Mathematics Group, Computer Science and Mathematics Division, Oak Ridge National Laboratory, for his discussions and illuminating advice using ScaLAPACK and distributed matrix computation.
We specially thank
Brian D. Ripley, Kurt Hornik, Uwe Ligges, and Simon Urbanek
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Created: Sep 23 2012 |
Last Revised: Feb 14 2014, 01:09 (CDT Ames, IA, USA)
Maintained: Wei-Chen Chen
E-Mail: wccsnow @ gmail.com
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