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

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Title: Preserving provability over GPU program optimizations with annotation-aware transformations.
Authors: Şakar, Ömer1 (AUTHOR) o.f.o.sakar@utwente.nl, Safari, Mohsen2 (AUTHOR) mohsen.safari@surf.nl, Huisman, Marieke1 (AUTHOR) m.huisman@utwente.nl, Wijs, Anton3 (AUTHOR) a.j.wijs@tue.nl
Source: Formal Methods in System Design. Dec2025, Vol. 67 Issue 3, p316-372. 57p.
Subjects: Program transformation, Software verification, Optimization algorithms, Parallel programs (Computer programs), Scientific observation
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. [ABSTRACT FROM AUTHOR]
Copyright of Formal Methods in System Design is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="DE" term="%22Program+transformation%22">Program transformation</searchLink><br /><searchLink fieldCode="DE" term="%22Software+verification%22">Software verification</searchLink><br /><searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Parallel+programs+%28Computer+programs%29%22">Parallel programs (Computer programs)</searchLink><br /><searchLink fieldCode="DE" term="%22Scientific+observation%22">Scientific observation</searchLink>
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  Data: 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Formal Methods in System Design is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Text: English
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      – SubjectFull: Software verification
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      – SubjectFull: Optimization algorithms
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              Text: Dec2025
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