| 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: |
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| Researcher Co-Investigators: |
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| Project Partners: |
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| Department: |
Mathematics |
| Organisation: |
Imperial College London |
| Scheme: |
Standard Research |
| Starts: |
01 October 2011 |
Ends: |
30 September 2013 |
Value (£): |
346,273
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| EPSRC Research Topic Classifications: |
| Computer Sys. & Architecture |
High Performance Computing |
| Statistics & Appl. Probability |
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| EPSRC Industrial Sector Classifications: |
| No relevance to Underpinning Sectors |
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| Related Grants: |
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| Panel History: |
| Panel Date | Panel Name | Outcome |
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02 Mar 2011
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HPC Software Development 2010-11
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Announced
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Summary on Grant Application Form |
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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.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.imperial.ac.uk |