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Robert W. Senser, Jr.
This dissertation presents our novel declarative framework, called the Declarative Framework for GPUs (DEFG). GPUs are highly sophisticated computing devices, capable of computing at very high speeds. The framework makes the development of OpenCL-based GPU applications less complex, and less time consuming. The framework’s approach is two-fold. First, we developed the DEFG domain-specific language in […]
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Robert Senser, Tom Altman
DEFG is our declarative language and framework for the efficient generation of OpenCL GPU applications. Using our new DEFG implementation, run-time and lines-of-code comparisons are provided for three well-known algorithms: Sobel image filtering, breadth-first search and all-pairs shortest path. The DEFG declarative language and corresponding OpenCL kernels provide complete OpenCL applications. The lines-of-code comparison demonstrates […]
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Robert Senser, Tom Altman
DEF-G is a declarative language and framework for the efficient generation of OpenCL GPU applications. Using our proof-of-concept DEF-G implementation, run-time and lines-of-code comparisons are provided for three well-known algorithms (Sobel image filtering, breadth-first search and all-pairs shortest path), each evaluated on three different platforms. The DEF-G declarative language and corresponding OpenCL kernels generated complete […]
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G Dada Khalandhar, V Sai Ram, M Srinivasa Rao, Lalith Srikanth C, Pallav Kumar Baruah, Balasubramanian S, R Raghunatha Sarma
Image reconstruction is a process of obtaining the original image from corrupted data. Applications of image reconstruction include Computer Tomography, radar imaging, weather forecasting etc. Recently steering kernel regression method has been applied for image reconstruction [1]. There are two major drawbacks in this technique. Firstly, it is computationally intensive. Secondly, output of the algorithm […]
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V.Sairam, M.Srinivasa Rao, G.Dada Khalandhar, L.Srikanth, P.K.Baruah, R.R.Sarma
Image reconstruction is a method by which the underlying images, hidden in blurry and noisy data, can be retrieved. This is used in applications such as computer tomography (CT), magnetic resonance and radio astronomy. In recent times, a non-parametric adaptive regression method called steering kernel regression was proposed and proved to be effective. This method […]
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Xavier Lacoste, Pierre Ramet, Mathieu Faverge, Yamazaki Ichitaro, Jack Dongarra
The current trend in the high performance computing shows a dramatic increase in the number of cores on the shared memory compute nodes. Algorithms, especially those related to linear algebra, need to be adapted to these new computer architectures in order to be efficient. PASTIX is a sparse parallel direct solver, that incorporates a dynamic […]
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Praveen K., Vamshi Krishna K., Anil Sri Harsha B., S. Balasubramanian, P.K. Baruah
The PageRank algorithm for determining the "importance" of Web pages forms the core component of Google’s search technology. As the Web graph is very large, containing over a billion nodes, PageRank is generally computed offline, during the preprocessing of the Web crawl, before any queries have been issued. Viewed mathematically, PageRank is nothing but the […]
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