Work

Preserving provability over GPU program optimizations with annotation-aware transformations

Year: 2025

Type: article

Abstract: GPU programs are widely used in industry. To obtain the best performance, a typical development process involves the manual or semi-automatic application of optimizations prior to compiling the code. Such optimizations can introduce errors. To avoid the introduction of errors, we can augment GPU programs with (pre- and postcondition-style) annotations to capture functional properties. However, keeping these annotations correct when optimizing GPU programs is labor-intensive and error-prone. This paper presents an approach to automatically apply optimizations to GPU programs while preserving provability by defining annotation-aware transformations . It applies frequently-used GPU optimizations, but besides transforming code, it also transforms the annotations. The approach has been implemented in the Alpinist tool and we evaluate Alpinist in combination with the VerCors program verifier, to automatically apply optimizations to a collection of verified programs and reverify them.

Source: Formal Methods in System Design

DOI: 10.1007/s10703-025-00480-7

Authors: Ömer Şakar, Mohsen Safari, Marieke Huisman, Anton Wijs

Institutions: University of Twente, SURF, Eindhoven University of Technology

Language: English

Topic: Logic, programming, and type systems

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,390

Funder: Nederlandse Organisatie voor Wetenschappelijk Onderzoek