Run Generation Revisited: What Goes Up May or May Not Come Down

Michael A. Bender; Samuel McCauley; Andrew McGregor; Shikha Singh; Hoa T. Vu

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arXiv · 1504.06501

Run Generation Revisited: What Goes Up May or May Not Come Down

Michael A. Bender·Samuel McCauley·Andrew McGregor·Shikha Singh·Hoa T. Vu

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Abstract

In this paper, we revisit the classic problem of run generation. Run generation is the first phase of external-memory sorting, where the objective is to scan through the data, reorder elements using a small buffer of size M , and output runs (contiguously sorted chunks of elements) that are as long as possible. We develop algorithms for minimizing the total number of runs (or equivalently, maximizing the average run length) when the runs are allowed to be sorted or reverse sorted. We study the problem in the online setting, both with and without resource augmentation, and in the offline setting. (1) We analyze alternating-up-down replacement selection (runs alternate between sorted and reverse sorted), which was studied by Knuth as far back as 1963. We show that this simple policy is asymptotically optimal. Specifically, we show that alternating-up-down replacement selection is 2-competitive and no deterministic online algorithm can perform better. (2) We give online algorithms having smaller competitive ratios with resource augmentation. Specifically, we exhibit a deterministic algorithm that, when given a buffer of size 4M , is able to match or beat any optimal algorithm having a buffer of size M . Furthermore, we present a randomized online algorithm which is 7/4-competitive when given a buffer twice that of the optimal. (3) We demonstrate that performance can also be improved with a small amount of foresight. We give an algorithm, which is 3/2-competitive, with foreknowledge of the next 3M elements of the input stream. For the extreme case where all future elements are known, we design a PTAS for computing the optimal strategy a run generation algorithm must follow. (4) Finally, we present algorithms tailored for nearly sorted inputs which are guaranteed to have optimal solutions with sufficiently long runs.

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Michael A. Bender, Samuel McCauley, Andrew McGregor, Shikha Singh, Hoa T. Vu. 2015-04-24. Run Generation Revisited: What Goes Up May or May Not Come Down. https://arxiv.org/abs/1504.06501

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