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Modern Programming Models for Gpu-Accelerated Heterogeneous Supercomputers: Computational Fluid Dynamics and in Situ Data Compression.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Modern Programming Models for Gpu-Accelerated Heterogeneous Supercomputers: Computational Fluid Dynamics and in Situ Data Compression./
Author:	Pacella, Heather Elizabeth.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	112 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-05, Section: B.
Contained By:	Dissertations Abstracts International83-05B.
Subject:	Decomposition. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28688330
ISBN:	9798544203544

Modern Programming Models for Gpu-Accelerated Heterogeneous Supercomputers: Computational Fluid Dynamics and in Situ Data Compression.
Pacella, Heather Elizabeth.

Modern Programming Models for Gpu-Accelerated Heterogeneous Supercomputers: Computational Fluid Dynamics and in Situ Data Compression. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 112 p.

Source: Dissertations Abstracts International, Volume: 83-05, Section: B.

Thesis (Ph.D.)--Stanford University, 2021.

This item must not be sold to any third party vendors.

This work presented in this thesis is concerned with large-scale computational fluid dynamics simulations and can be divided into two main parts. The first is an investigation into the suitability of asynchronous, task-based parallelism for computational fluid dynamics simulations targeting exascale, heterogeneous architectures. The second part is concerned with the incorporation of data compression algorithms into computational fluid dynamics simulations using interpolative decomposition methods.The first chapter is a brief introduction to computational fluid dynamics, as well as an overview of traditional parallel computing practices in the field. The second chapter details the Legion programming system, which is used in this work to develop asynchronous, task-parallel solvers. Chapter Three is a case study on the implementation of a high-order, Navier-Stokes solver using the Regent programming language and the Legion runtime; this is compared to an existing, dataparallel MPI+Fortran version of the solver. The fourth chapter provides an in-depth discussion of interpolative decomposition methods for data compression, and the fifth chapter presents results for the incorporation of a novel interpolative decomposition algorithm into the Legion flow solver described in Chapter Three. Finally, Chapter Six concludes with "lessons learned" and possible avenues for future work.

ISBN: 9798544203544Subjects--Topical Terms:

3561186
Decomposition.

Modern Programming Models for Gpu-Accelerated Heterogeneous Supercomputers: Computational Fluid Dynamics and in Situ Data Compression.
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500 $a Advisor: Iaccarino, Gianluca; Aiken, Alexander; Darve, Eric.
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506 $a This item must not be sold to any third party vendors.
520 $a This work presented in this thesis is concerned with large-scale computational fluid dynamics simulations and can be divided into two main parts. The first is an investigation into the suitability of asynchronous, task-based parallelism for computational fluid dynamics simulations targeting exascale, heterogeneous architectures. The second part is concerned with the incorporation of data compression algorithms into computational fluid dynamics simulations using interpolative decomposition methods.The first chapter is a brief introduction to computational fluid dynamics, as well as an overview of traditional parallel computing practices in the field. The second chapter details the Legion programming system, which is used in this work to develop asynchronous, task-parallel solvers. Chapter Three is a case study on the implementation of a high-order, Navier-Stokes solver using the Regent programming language and the Legion runtime; this is compared to an existing, dataparallel MPI+Fortran version of the solver. The fourth chapter provides an in-depth discussion of interpolative decomposition methods for data compression, and the fifth chapter presents results for the incorporation of a novel interpolative decomposition algorithm into the Legion flow solver described in Chapter Three. Finally, Chapter Six concludes with "lessons learned" and possible avenues for future work.
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