Lightweight Formal Methods for Correct, Efficient Systems Programming

Compilers are foundational to everything we ask our computers to do—applications can only be as efficient and reliable as the underlying compiler stack that translates their logic to machine code. But compiler expertise is a finite resource, and engineers may have to choose whether to prioritize adding optimizations for efficiency or validating their existing features for reliability. This dissertation presents three systems that use lightweight, practical formal methods to push past this tension between performance and correctness. The Diospyros compiler combines an efficient term-rewriting strategy, equality saturation, with translation validation to find correct, fast vectorizations for specialized linear algebra tasks on digital signal processors. The Kani verifier for Rust leverages compiler invariants to improve the performance of dynamically dispatched methods in a satisfiability-solver-based model checker for low-level systems code. Finally, the VeriISLE engine uses annotations to automatically verify machine code generation in Cranelift, a popular production compiler infrastructure for WebAssembly where miscompilation bugs can cause serious security vulnerabilities. In sum, these projects point to a future where lightweight formal methods help us build compilers for fast and reliable computer systems.

Name thesis:degree_name

Ph. D., Computer Science

Level thesis:degree_level

Doctor of Philosophy

Discipline thesis:degree_discipline

Computer Science

Grantor

Cornell University

Year dc:date.issued

2023

Author dc:creator

• VanHattum, Alexa

Committee members dc:contributor.committeemember

• Dell, Nicola
• Myers, Andrew

• Compilers
• Formal methods
• Programming languages

Statement dc:rights

• Attribution 4.0 International

Licence dc:rights.uri

• https://creativecommons.org/licenses/by/4.0/

Language dc:language.iso

en

DOI

https://doi.org/10.7298/3dd3-da81

Dc Identifier Other

ProQuest Submission ID: 13765
ProQuest Publication ID: 30567888

OAI identifier oai:identifier

oai:ecommons.cornell.edu:1813/114787