Computational Caches
View/ Open
Author
Appavoo, Jonathan
Published Version
https://doi.org/10.1145/2485732.2485749Metadata
Show full item recordCitation
Waterland, Amos, Elaine Angelino, Ekin D. Cubuk, Efthimios Kaxiras, Ryan P. Adams, Jonathan Appavoo, and Margo Seltzer. 2013. “Computational Caches.” In Proceedings of the 6th International Systems and Storage Conference on - SYSTOR ’13, June 30 - July 02, 2013, Haifa, Israel, 8. doi:10.1145/2485732.2485749.Abstract
Caching is a well-known technique for speeding up computation. We cache data from file systems and databases; we cache dynamically generated code blocks; we cache page translations in TLBs. We propose to cache the act of computation, so that we can apply it later and in different contexts. We use a state-space model of computation to support such caching, involving two interrelated parts: speculatively memoized predicted/resultant state pairs that we use to accelerate sequential computation, and trained probabilistic models that we use to generate predicted states from which to speculatively execute. The key techniques that make this approach feasible are designing probabilistic models that automatically focus on regions of program execution state space in which prediction is tractable and identifying state space equivalence classes so that predictions need not be exact.Terms of Use
This article is made available under the terms and conditions applicable to Open Access Policy Articles, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#OAPCitable link to this page
http://nrs.harvard.edu/urn-3:HUL.InstRepos:33921645
Collections
- FAS Scholarly Articles [18304]
Contact administrator regarding this item (to report mistakes or request changes)