2017Proceedings of the ACM on Programming LanguagesOpen access

On automatically proving the correctness of math.h implementations

Wonyeol Lee, Rahul Sharma, Alex Aiken

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Abstract

Industry standard implementations of math.h claim (often without formal proof) tight bounds on floating-point errors. We demonstrate a novel static analysis that proves these bounds and verifies the correctness of these implementations. Our key insight is a reduction of this verification task to a set of mathematical optimization problems that can be solved by off-the-shelf computer algebra systems. We use this analysis to prove the correctness of implementations in Intel's math library automatically. Prior to this work, these implementations could only be verified with significant manual effort.

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Industry standard implementations of math.h claim (often without formal proof) tight bounds on floating-point errors. We demonstrate a novel static analysis that proves these bounds and verifies the correctness of these implementations. Our key insight is a reduction of this verification task to a set of mathematical optimization problems that can be solved by off-the-shelf computer algebra systems. We use this analysis to prove the correctness of implementations in Intel's math library automatically. Prior to this work, these implementations could only be verified with significant manual effort.

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Available abstract

Industry standard implementations of math.h claim (often without formal proof) tight bounds on floating-point errors. We demonstrate a novel static analysis that proves these bounds and verifies the correctness of these implementations. Our key insight is a reduction of this verification task to a set of mathematical optimization problems that can be solved by off-the-shelf computer algebra systems. We use this analysis to prove the correctness of implementations in Intel's math library automatically. Prior to this work, these implementations could only be verified with significant manual effort.

Key concepts: Correctness, Implementation, Computer science, Set (abstract data type), Task (project management), Theoretical computer science, Algorithm, Programming language

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