Paper by Erik D. Demaine

Reference:
Erik D. Demaine, Andrea Lincoln, Quanquan C. Liu, Jayson Lynch, and Virginia Vassilevska Williams, “Fine-grained I/O Complexity via Reductions: New Lower Bounds, Faster Algorithms, and a Time Hierarchy”, in Proceedings of the 9th Innovations in Theoretical Computer Science Conference (ITCS 2018), Cambridge, Massachusetts, 2018, 34:1–34:23.

Abstract:
This paper initiates the study of I/O algorithms (minimizing cache misses) from the perspective of fine-grained complexity (conditional polynomial lower bounds). Specifically, we aim to answer why sparse graph problems are so hard, and why the Longest Common Subsequence problem gets a savings of a factor of the size of cache times the length of a cache line, but no more. We take the reductions and techniques from complexity and fine-grained complexity and apply them to the I/O model to generate new (conditional) lower bounds as well as faster algorithms. We also prove the existence of a time hierarchy for the I/O model, which motivates the fine-grained reductions.

Comments:
The full version of this paper is available as arXiv:1711.07960.

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Last updated September 17, 2018 by Erik Demaine.