Abstract
This paper presents a semantics of self-adjusting computation and proves that the semantics is correct and consistent. The semantics integrates change propagation with the classic idea of memoization to enable reuse of computations under mutation to memory. During evaluation, reuse of a computation via memoization triggers a change propagation that adjusts the reused computation to reflect the mutated memory. Since the semantics combines memoization and change-propagation, it involves both non-determinism and mutation. Our consistency theorem states that the non-determinism is not harmful: any two evaluations of the same program starting at the same state yield the same result. Our correctness theorem states that mutation is not harmful: self-adjusting programs are consistent with purely functional programming. We formalized the semantics and its meta-theory in the LF logical framework and machine-checked the proofs in Twelf.
Keywords
- Stable Expression
- Change Propagation
- Abstract Syntax
- Functional Programming
- Valid Evaluation
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References
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Acar, U.A., Blume, M., Donham, J. (2007). A Consistent Semantics of Self-adjusting Computation. In: De Nicola, R. (eds) Programming Languages and Systems. ESOP 2007. Lecture Notes in Computer Science, vol 4421. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71316-6_31
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