@InProceedings{CacheAdaptive_SPAA2016,
AUTHOR = {Michael A. Bender and Erik D. Demaine and Roozbeh Ebrahimi and Jeremy T. Fineman and Rob Johnson and Andrea Lincoln and Jayson Lynch and Samuel McCauley},
TITLE = {Cache-Adaptive Analysis},
BOOKTITLE = {Proceedings of the 28th ACM Symposium on Parallelism in Algorithms and Architectures (SPAA 2016)},
bookurl = {https://spaa.acm.org/2016/index.html},
ADDRESS = {Pacific Grove, California},
YEAR = 2016,
PAGES = {135--144},
doi = {https://dx.doi.org/10.1145/2935764.2935798},
dblp = {https://dblp.org/rec/conf/spaa/BenderDEFJLLM16},
comments = {The paper is also available from the <A HREF="http://doi.acm.org/10.1145/2935764.2935798">ACM Digital Library</A>.},
withstudent = 1,
}
This paper presents techniques for designing and analyzing algorithms in a cache-adaptive setting, where the RAM available to the algorithm changes over time. These techniques make analyzing algorithms in the cache-adaptive model almost as easy as in the external memory, or DAM model. Our techniques enable us to analyze a wide variety of algorithms — Master-Method-style algorithms, Akra-Bazzi-style algorithms, collections of mutually recursive algorithms, and algorithms, such as FFT, that break problems of size N into subproblems of size Θ(Nc).