University of Illinois Urbana-Champaign

Program transformation and code generation for developing, modeling, and optimizing GPU programs

Stevens, James D

Content Files
STEVENS-DISSERTATION-2021.pdf

Permalink

https://hdl.handle.net/2142/113011

Description

Title
Program transformation and code generation for developing, modeling, and optimizing GPU programs
Author(s)
Stevens, James D
Issue Date
2021-07-13
Director of Research (if dissertation) or Advisor (if thesis)
Klöckner, Andreas
Doctoral Committee Chair(s)
Klöckner, Andreas
Committee Member(s)
  • Gropp, William
  • Solomonik, Edgar
  • Owens, John
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Date of Ingest
2022-01-12T21:45:33Z
Keyword(s)
  • Code generation
  • OpenCL
  • Program transformation
  • Polyhedral model
  • Dependencies
  • Performance model
  • GPU
  • Microbenchmark
  • Black box
  • Performance optimization
  • Visual programming interface
  • Human-guided transformation
Abstract
The ability to efficiently optimize or re-optimize an algorithm for high performance on a particular processor architecture is crucial in a wide spectrum of engineering and scientific applications. To help bridge the gap between fully automated optimization, which does not reliably produce optimal code, and fully manual optimization, which is inefficient and error-prone, we present three mechanisms facilitating a human-guided, partially automated process for production, optimization, and analysis of high-performance code: (1) formal mechanisms for describing and reasoning about program dependencies and loop nest structure within a programming system providing transformation-based code generation for GPUs and CPUs; (2) a system for constructing customizable, black-box performance models for GPUs; and (3) a visual user interface for program optimization and analysis. We demonstrate how our human-accessible dependency and loop-nest semantics enable reasoning about program correctness in a transformation and optimization procedure for two example scientific computing computations. We show how models created using our hardware-agnostic performance modeling approach can predict execution times accurately enough to select the best-performing of multiple implementation variants of a given computation, demonstrating results for three computations on five different GPUs. We show how our program transformation interface enables access to and navigation of a comprehensive search space of implementation variants for a given computation, as well as comparisons of multiple optimization paths. In doing so, we demonstrate how these architecture-agnostic tools, some of which are already being utilized by a persistent user base, further the goal of facilitating production, optimization, and analysis of high-performance code.
Graduation Semester
2021-08
Type of Resource
Thesis
Permalink
http://hdl.handle.net/2142/113011
Copyright and License Information
Copyright 2021 James D. Stevens

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