Paper by Erik D. Demaine
- 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.
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.
- Using fine-grained reductions, we give an algorithm for distinguishing 2 vs. 3 diameter and radius that runs in O(|E|2/(M B)) cache misses, which for sparse graphs improves over the previous O(|V|2/B) running time.
- We give new reductions from radius and diameter to Wiener index and median. These reductions are new in both the RAM and I/O models.
- We show meaningful reductions between problems that have linear-time solutions in the RAM model. The reductions use low I/O complexity (typically O(n/B)), and thus help to finely capture the relationship between “I/O linear time” Θ(n/B) and RAM linear time Θ(n).
- We generate new I/O assumptions based on the difficulty of improving sparse graph problem running times in the I/O model. We create conjectures that the current best known algorithms for Single Source Shortest Paths (SSSP), diameter, and radius are optimal.
- From these I/O-model assumptions, we show that many of the known reductions in the word-RAM model can naturally extend to hold in the I/O model as well (e.g., a lower bound on the I/O complexity of Longest Common Subsequence that matches the best known running time).
- We prove an analog of the Time Hierarchy Theorem in the I/O model, further motivating the study of fine-grained algorithmic differences.
- The full version of this paper is available as arXiv:1711.07960.
- The paper is available in PDF (502k).
- See information on file formats.
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Last updated September 17, 2018 by