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Massively Parallel Algorithms for CFD Simulation and Optimization on Heterogeneous Many-Core Architectures

Title: Massively Parallel Algorithms for CFD Simulation and Optimization on Heterogeneous Many-Core Architectures.
Name(s): Duffy, Austen C., author
Sussman, Mark, professor directing dissertation
Hussaini, M. Yousuff, professor co-directing dissertation
Van Engelen, Robert, university representative
Cogan, Nick, committee member
Gallivan, Kyle, committee member
Department of Mathematics, degree granting department
Florida State University, degree granting institution
Type of Resource: text
Genre: Text
Issuance: monographic
Date Issued: 2011
Publisher: Florida State University
Place of Publication: Tallahassee, Florida
Physical Form: computer
online resource
Extent: 1 online resource
Language(s): English
Abstract/Description: In this dissertation we provide new numerical algorithms for use in conjunction with simulation based design codes. These algorithms are designed and best suited to run on emerging heterogenous computing architectures which contain a combination of traditional multi-core processors and new programmable many-core graphics processing units (GPUs). We have developed the following numerical algorithms (i) a new Multidirectional Search (MDS) method for PDE constrained optimization that utilizes a Multigrid (MG) strategy to accelerate convergence, this algorithm is well suited for use on GPU clusters due to its parallel nature and is more scalable than adjoint methods (ii) a new GPU accelerated point implicit solver for the NASA FUN3D code (unstructured Navier-Stokes) that is written in the Compute Unified Device Architecture (CUDA) language, and which employs a novel GPU sharing model, (iii) novel GPU accelerated smoothers (developed using PGI Fortran with accelerator compiler directives) used to accelerate the multigrid preconditioned conjugate gradient method (MGPCG) on a single rectangular grid, and (iv) an improved pressure projection solver for adaptive meshes that is based on the MGPCG method which requires fewer grid point calculations and has potential for better scalability on hetergeneous clusters. It is shown that a multigrid - multidirectional search (MGMDS) method can run up to 5.5X faster than the MDS method when used on a one dimensional data assimilation problem. It is also shown that the new GPU accelerated point implicit solver of FUN3D is up to 5.5X times faster than the CPU version and that the solver can perform up to 40% faster on a single GPU being shared by four CPU cores. It is found that GPU accelerated smoothers for the MGPCG method on uniform grids can run over 2X faster than the non-accelerated versions for 2D problems, and that the new MGPCG pressure projection solver for adaptive grids is up to 4X faster than the previous MG algorithm.
Identifier: FSU_migr_etd-0651 (IID)
Submitted Note: A Dissertation submitted to the Department of Mathematics in partial fulfillment of the requirements for the degree of Doctor of Philosophy.
Degree Awarded: Spring Semester, 2011.
Date of Defense: March 15, 2011.
Keywords: Adaptive Mesh Refinement, High Performance Computing, Computational Fluid Dynamics, Adjoint Methods, Multidirectional Search, Multigrid, Graphics Processing Unit
Bibliography Note: Includes bibliographical references.
Advisory Committee: Mark Sussman, Professor Directing Dissertation; M. Yousuff Hussaini, Professor Co-Directing Dissertation; Robert Van Engelen, University Representative; Nick Cogan, Committee Member; Kyle Gallivan, Committee Member.
Subject(s): Mathematics
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Owner Institution: FSU

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Duffy, A. C. (2011). Massively Parallel Algorithms for CFD Simulation and Optimization on Heterogeneous Many-Core Architectures. Retrieved from