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Computing Gamma Calculus on Computer Cluster

Computing Gamma Calculus on Computer Cluster

Hong Lin, Jeremy Kemp, Padraic Gilbert
Copyright: © 2012 |Pages: 12
ISBN13: 9781466617520|ISBN10: 1466617527|EISBN13: 9781466617537
DOI: 10.4018/978-1-4666-1752-0.ch020
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MLA

Lin, Hong, et al. "Computing Gamma Calculus on Computer Cluster." Knowledge and Technology Adoption, Diffusion, and Transfer: International Perspectives, edited by Ali Hussein Saleh Zolait, IGI Global, 2012, pp. 275-286. https://doi.org/10.4018/978-1-4666-1752-0.ch020

APA

Lin, H., Kemp, J., & Gilbert, P. (2012). Computing Gamma Calculus on Computer Cluster. In A. Zolait (Ed.), Knowledge and Technology Adoption, Diffusion, and Transfer: International Perspectives (pp. 275-286). IGI Global. https://doi.org/10.4018/978-1-4666-1752-0.ch020

Chicago

Lin, Hong, Jeremy Kemp, and Padraic Gilbert. "Computing Gamma Calculus on Computer Cluster." In Knowledge and Technology Adoption, Diffusion, and Transfer: International Perspectives, edited by Ali Hussein Saleh Zolait, 275-286. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-4666-1752-0.ch020

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Abstract

Gamma Calculus is an inherently parallel, high-level programming model, which allows simple programming molecules to interact, creating a complex system with minimum of coding. Gamma calculus modeled programs were written on top of IBM’s TSpaces middleware, which is Java-based and uses a “Tuple Space” based model for communication, similar to that in Gamma. A parser was written in C++ to translate the Gamma syntax. This was implemented on UHD’s grid cluster (grid.uhd.edu), and in an effort to increase performance and scalability, existing Gamma programs are being transferred to Nvidia’s CUDA architecture. General Purpose GPU computing is well suited to run Gamma programs, as GPU’s excel at running the same operation on a large data set, potentially offering a large speedup.

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