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CUDACLAW : a data parallel solution framework for hyperbolic PDEs / by Hrag Gorune Krikor Ohannessian.

Show simple item record Ohannessian, Hrag Gorune Krikor. 2012-12-03T13:33:56Z 2012-12-03T13:33:56Z 2012
dc.description Thesis (M.S.)--American University of Beirut, Computational Science Program, 2012.;"Advisor : Dr. George Turkiyyah, Professor, Computer Science--Members of Committee : Dr. Paul Attie, Associate Professor, Computer Science Dr. Wassim El Hajj, Assistant Professor, Computer Science."
dc.description Includes bibliographical references (leaves 106-108)
dc.description.abstract We present CUDACLAW, a data-parallel solution framework for 2D and 3D hyperbolic partial differential equation (PDE) systems. CUDACLAW is a finite volume method based on time adaptive point-wise Riemann problem solvers, and can handle linear and nonlinear problems. The framework is tailored for the GPU architecture, optimized to take advantage of the powerful computational potential, high memory bandwidth and fast on-chip memories. Our solution approach minimizes total memory usage allowing large problems to run on commodity hardware. From the user's point of view, the framework is simple and its use does not require any knowledge of the underlying data structure, nor experience in CUDA programming. With the power of the graphics processor, the solution can be viewed in real time. We demonstrate the power of CUDACLAW on an acoustics wave equation solution in 2D and 3D. Sustained performance of more than a 150 GFlops-s is achieved on the Tesla C2050 Nvidia GPU.
dc.format.extent viii, 108 leaves : col. ill. 30 cm.
dc.relation.ispartof Theses, Dissertations, and Projects
dc.subject.classification T:005647 AUBNO
dc.subject.lcsh Parallel algorithms.;Graphics processing units.;Differential equations, Hyperbolic.;Differential equations, Partial.
dc.title CUDACLAW : a data parallel solution framework for hyperbolic PDEs / by Hrag Gorune Krikor Ohannessian.
dc.type Thesis
dc.contributor.department American University of Beirut. Faculty of Arts and Sciences. Computational Science Program.

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