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Details of Grant 

EPSRC Reference: EP/I030638/1
Title: Enabling High-performance Statistical Computing in R on Hybrid GPU and Multicore Architectures
Principal Investigator: Montana, Professor G
Other Investigators:
Dongarra, Professor J Guo, Professor Y
Researcher Co-Investigators:
Project Partners:
Department: Mathematics
Organisation: Imperial College London
Scheme: Standard Research
Starts: 01 October 2011 Ends: 30 September 2013 Value (£): 346,273
EPSRC Research Topic Classifications:
Computer Sys. & Architecture High Performance Computing
Statistics & Appl. Probability
EPSRC Industrial Sector Classifications:
No relevance to Underpinning Sectors
Related Grants:
Panel History:
Panel DatePanel NameOutcome
02 Mar 2011 HPC Software Development 2010-11 Announced
Summary on Grant Application Form
The R system for statistical computing is a popular, open-source platform used world-wide by a very large community of statisticians, engineers, physicists and other scientists. R is used across a diverse range of applications areas including bioinformatics/genomics, cosmology, particle physics, astronomy and image processing. A key reason for R's popularity is its high-level, easy to learn language for programming with data , that enables users to perform many common data analytic tasks, including organization and manipulation of data sets, fitting statistical models, producing graphics, and executing a vast range of numerical computations and simulations. Although R's impact on many data-driven applications is undeniable, the recent huge increase in the size of modern data sets, coupled with the continuing development of highly sophisticated, but computationally intensive data analytic techniques, has led to a serious data analysis bottleneck.This project aims to enhance the capabilities of the R system for statistical computing by developing a framework for high-performance computing that takes full advantage of the specialized processing capabilities offered by the latest generation of 'commodity' hardware. Specifically, we recognize that almost all of today's statistical and analytical approaches rely heavily on the performing of common mathematical computations such as matrix algebra operations, and we seek to contribute to the development of a new generation of extremely fast mathematical libraries that will utilize the special processing capabilities of the modern multicore and graphical processing units (GPU), that are now found in all personal computers and laptops. Given the widespread use of R across many scientific disciplines, we believe that this project will have immediate and far reaching consequences, enabling high-performance statistical computing for the masses.
Key Findings
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Further Information:  
Organisation Website: http://www.imperial.ac.uk