The paper "Is this the simplest (and most surprising) sorting algorithm ever?" introduces the "Sleep Sort" algorithm, a conceptually simple, albeit impractical, sorting method. It relies on spawning a separate thread for each element to be sorted. Each thread sleeps for a duration proportional to the element's value and then outputs the element. Thus, smaller elements are outputted first, resulting in a sorted sequence. While intriguing in its simplicity, Sleep Sort's correctness depends on precise timing and suffers from significant limitations, including poor performance for large datasets, inability to handle negative or duplicate values directly, and reliance on system-specific thread scheduling. Its main contribution is as a thought-provoking curiosity rather than a practical sorting algorithm.
The arXiv preprint "Is this the simplest (and most surprising) sorting algorithm ever?" introduces a novel sorting algorithm dubbed "Sleep Sort," characterized by its unconventional and conceptually simple approach. The algorithm leverages the inherent delays associated with asynchronous operations, specifically sleep functions, to sort a list of non-negative integers.
It operates under the premise that each element in the input list dictates a waiting period proportional to its value. For each element, a separate thread or process is spawned. This thread then pauses execution, "sleeping" for a duration directly related to the element's numerical magnitude. After the designated sleep period, the thread "wakes up" and outputs its associated element.
Therefore, smaller numbers, corresponding to shorter sleep durations, will be outputted earlier than larger numbers. This time-based output sequence effectively sorts the elements in ascending order. The authors present the core algorithm in Python, utilizing the threading library to manage the concurrent sleep operations. They analyze its correctness under ideal conditions, highlighting the critical assumption of negligible overhead associated with thread creation and management.
The authors acknowledge several practical limitations and caveats. Firstly, the algorithm's reliance on sleep functions ties it closely to the underlying operating system’s scheduling mechanisms, introducing potential variability and non-determinism in the output order, particularly in resource-constrained environments. Secondly, the algorithm is inherently limited to non-negative integers, as negative sleep durations are generally not meaningful. Furthermore, very large input values could lead to impractically long execution times. Lastly, the algorithm's efficiency is not explicitly analyzed or compared to conventional sorting algorithms, leaving open the question of its practical performance characteristics. Despite these limitations, the authors present Sleep Sort as an intriguing thought experiment and a testament to the power of exploiting system-level timing behaviors for computational purposes. They suggest potential extensions, including the possibility of adapting the algorithm for different data types and exploring its behavior under various concurrency models.
Summary of Comments ( 77 )
https://news.ycombinator.com/item?id=43155839
Hacker News users discuss the "Mirror Sort" algorithm, expressing skepticism about its novelty and practicality. Several commenters point out prior art, referencing similar algorithms like "Odd-Even Sort" and existing work on sorting networks. There's debate about the algorithm's true complexity, with some arguing the reliance on median-finding hides significant cost. Others question the value of minimizing comparisons when other operations, like swaps or data movement, dominate the performance in real-world scenarios. The overall sentiment leans towards viewing "Mirror Sort" as an interesting theoretical exercise rather than a practical breakthrough. A few users note its potential educational value for understanding sorting network concepts.
The Hacker News post linked has a moderate number of comments discussing the "Simple Sort" algorithm presented in the linked arXiv paper. Several commenters delve into the algorithm's mechanics and its relationship to existing sorting methods.
A significant thread discusses whether "Simple Sort" is truly novel or simply a rediscovery/reframing of existing algorithms, particularly insertion sort. Some argue that despite superficial similarities, the core logic and the way elements are shifted differ, making it distinct. Others contend that it's essentially insertion sort with a slightly altered control flow, focusing on the similarity of repeatedly finding the correct position for an element and shifting subsequent elements.
Several comments analyze the algorithm's performance characteristics. Some highlight the O(n) best-case scenario when the input list is already sorted (or nearly sorted), matching insertion sort's performance in such cases. However, they acknowledge the O(n^2) average and worst-case complexity, making it less efficient than algorithms like merge sort or quicksort for large, unsorted datasets. The space complexity of O(1) (in-place sorting) is also mentioned as a positive aspect.
One commenter expresses skepticism about the paper's claim of "simplicity," arguing that the code implementation, while concise, isn't necessarily easier to understand than other basic sorting algorithms. They suggest that "simplicity" is subjective and depends on the reader's familiarity with different programming paradigms.
Another line of discussion revolves around the algorithm's suitability for specific use cases. Some suggest its potential value for situations where the data is likely to be already partially sorted or where simplicity of implementation is prioritized over performance for small datasets.
A few comments also touch upon the paper's writing style and its presentation of the algorithm. One commenter questions the authors' emphasis on its "surprising" nature, suggesting that the algorithm's properties are relatively straightforward to analyze.
Overall, the comments offer a mixed reception to the "Simple Sort" algorithm. While acknowledging its simplicity and potential niche applications, many express skepticism about its novelty and overall efficiency compared to well-established sorting algorithms. The discussion primarily revolves around comparing it to existing methods, analyzing its performance, and debating its practical significance.