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Related Experiment Videos

Preserving provability over GPU program optimizations with annotation-aware transformations.

Ömer Şakar1, Mohsen Safari2, Marieke Huisman1

  • 1Formal Methods and Tools, University of Twente, Enschede, The Netherlands.

Formal Methods in System Design
|December 1, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces annotation-aware transformations to automatically optimize GPU programs. This method preserves code correctness and simplifies the verification process for developers.

Keywords:
Annotation-awareDeductive verificationGPUOptimizationProgram transformation

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Area of Science:

  • Computer Science
  • Software Engineering
  • High-Performance Computing

Background:

  • GPU programs require manual optimization for performance, which can introduce errors.
  • Annotations (pre- and postconditions) capture functional properties but are difficult to maintain during optimization.

Purpose of the Study:

  • To present an approach for automatically optimizing GPU programs while preserving their provability.
  • To introduce annotation-aware transformations that modify both code and annotations.

Main Methods:

  • Developed annotation-aware transformations to apply GPU optimizations.
  • Implemented the approach in the Alpinist tool.
  • Evaluated Alpinist with the VerCors program verifier on verified programs.

Main Results:

  • Demonstrated the ability to automatically apply optimizations to GPU programs.
  • Showcased the preservation of provability through transformed annotations.
  • Successfully reverified optimized programs using Alpinist and VerCors.

Conclusions:

  • Annotation-aware transformations offer an automated solution for optimizing GPU programs.
  • This approach reduces the labor and error-proneness associated with manual optimization and annotation maintenance.
  • Alpinist facilitates the development of correct and performant GPU software.