Advanced search options

Advanced Search Options 🞨

Browse by author name (“Author name starts with…”).

Find ETDs with:

in
/  
in
/  
in
/  
in

Written in Published in Earliest date Latest date

Sorted by

Results per page:

You searched for +publisher:"University of North Carolina" +contributor:("Kimbell, Julia"). One record found.

Search Limiters

Last 2 Years | English Only

No search limiters apply to these results.

▼ Search Limiters


University of North Carolina

1. Malahe, Michael. PDE Solvers for Hybrid CPU-GPU Architectures.

Degree: Mathematics, 2016, University of North Carolina

Many problems of scientific and industrial interest are investigated through numerically solving partial differential equations (PDEs). For some of these problems, the scope of the investigation is limited by the costs of computational resources. A new approach to reducing these costs is the use of coprocessors, such as graphics processing units (GPUs) and Many Integrated Core (MIC) cards, which can execute floating point operations at a higher rate than a central processing unit (CPU) of the same cost. This is achieved through the use of a large number of processors in a single device, each with very limited dedicated memory per thread. Codes for a number of continuum methods, such as boundary element methods (BEM), finite element methods (FEM) and finite difference methods (FDM) have already been implemented on coprocessor architectures. These methods were designed before the adoption of coprocessor architectures, so implementing them efficiently with reduced thread-level memory can be challenging. There are other methods that do operate efficiently with limited thread-level memory, such as Monte Carlo methods (MCM) and lattice Boltzmann methods (LBM) for kinetic formulations of PDEs, but they are not competitive on CPUs and generally have poorer convergence than the continuum methods. In this work, we introduce a class of methods in which the parallelism of kinetic formulations on GPUs is combined with the better convergence of continuum methods on CPUs. We first extend an existing Feynman-Kac formulation for determining the principal eigenpair of an elliptic operator to create a version that can retrieve arbitrarily many eigenpairs. This new method is implemented for multiple GPUs, and combined with a standard deflation preconditioner on multiple CPUs to create a hybrid concurrent method with superior convergence to that of the deflation preconditioner alone. The hybrid method exhibits good parallelism, with an efficiency of 80% on a problem with 300 million unknowns, run on a configuration of 324 CPU cores and 54 GPUs. Advisors/Committee Members: Malahe, Michael, Mitran, Sorin, Griffith, Boyce, Huang, Jingfang, Kimbell, Julia, McLaughlin, Richard.

Subjects/Keywords: College of Arts and Sciences; Department of Mathematics

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Malahe, M. (2016). PDE Solvers for Hybrid CPU-GPU Architectures. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:89ea54ad-e4b9-4b5f-b1ab-a9c6958fc421

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Malahe, Michael. “PDE Solvers for Hybrid CPU-GPU Architectures.” 2016. Thesis, University of North Carolina. Accessed November 24, 2020. https://cdr.lib.unc.edu/record/uuid:89ea54ad-e4b9-4b5f-b1ab-a9c6958fc421.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Malahe, Michael. “PDE Solvers for Hybrid CPU-GPU Architectures.” 2016. Web. 24 Nov 2020.

Vancouver:

Malahe M. PDE Solvers for Hybrid CPU-GPU Architectures. [Internet] [Thesis]. University of North Carolina; 2016. [cited 2020 Nov 24]. Available from: https://cdr.lib.unc.edu/record/uuid:89ea54ad-e4b9-4b5f-b1ab-a9c6958fc421.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Malahe M. PDE Solvers for Hybrid CPU-GPU Architectures. [Thesis]. University of North Carolina; 2016. Available from: https://cdr.lib.unc.edu/record/uuid:89ea54ad-e4b9-4b5f-b1ab-a9c6958fc421

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

.