SearcharxivSearch

arXiv · 2011.11144

Searching and Sorting with O(n^2) processors in O(1) time

Abstract

The proliferation of number of processing elements (PEs) in parallel computer systems, along with the use of more extensive parallelization of algorithms causes the interprocessor communications dominate VLSI chip space. This paper proposes a new architecture to overcome this issue by using simple crosspoint switches to pair PEs instead of a complex interconnection network. Based on the cyclic permutation wiring idea described in \cite{oruc2016self}, this pairing leads to a linear crosspoint array of $n(n-1)/2$ processing elements and as many crosspoints. We demonstrate the versatility of this new parallel architecture by designing fast searching and sorting algorithms for it. In particular, we show that finding a minimum, maximum, and searching a list of $n$ elements can all be performed in $O(1)$ time with elementary logic gates with $O(n)$ fan-in, and in $O(\lg n)$ time with $O(1)$ fan-in. We further show that sorting a list of $n$ elements can also be carried out in $O(1)$ time using elementary logic gates with $O(n)$ fan-in and threshold logic gates. The sorting time increases to $O(\lg n\lg\lg n)$ if only elementary logic gates with $O(1)$ fan-in are used. The algorithm can find the maximum among $n$ elements in $O(1)$ time, and sort $n$ elements in $O(\lg n (\lg\lg n))$ time. In addition, we show how other fundamental queries can be handled within the same order of time complexities.

Explore related subjects

Keep this discovery

BibTeXRIS

Taeyoung An, A. Yavuz Oruc. 2020-11-23. Searching and Sorting with O(n^2) processors in O(1) time. https://arxiv.org/abs/2011.11144

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Quasi-Monte Carlo Beyond Hardy-Krause II: $(1 + \varepsilon)n$ Samples Suffice

Numerical integration studies how well one can estimate the integral of a function $f$ over $[0,1)^d$ using $n$ sample points. The two classical methods, Monte Carlo (MC) and quasi-Monte Carlo (QMC), have complementary strengths and weaknesses, and a fundamental question is to design an approach that combines the benefits of both. Recently, building on the transference principle in discrepancy theory, Bansal and Jiang~\cite{BJ25a} gave a randomized QMC method that bridges MC and QMC guarantees using only i.i.d.\ samples. Their method also goes beyond the classical Koksma--Hlawka inequality: it achieves integration error $\widetilde{O}_d(\sigma_{\mathsf{SO}}(f)/n)$, where the smoothed-out variation $\sigma_{\mathsf{SO}}(f)$ can be substantially smaller than the Hardy--Krause variation that governs the classical bound. However, their algorithm requires $n^2$ i.i.d.\ samples as input, and this quadratic blowup is inherent to any method based on the transference principle. In this work, we bypass the quadratic blowup: for any constant $\varepsilon > 0$, we show that $(1+\varepsilon)n$ i.i.d.\ samples suffice to both obtain the beyond-Hardy--Krause guarantee of~\cite{BJ25a}, resolving an open problem posed there, and to produce low-discrepancy point sequences. Our algorithms are variants of the online Haar-thinning method of Dwivedi, Feldheim, Gurel-Gurevich, and Ramdas~\cite{DFG+19}.

cs.DS

Single-Exponential Algorithms and a Polynomial Kernel for Strong Connectivity Augmentation

Strong Connectivity Augmentation (SCA) asks whether a directed acyclic graph can be made strongly connected by adding at most $k$ prescribed links whose total weight is within a given budget. Klinkby, Misra, and Saurabh (SODA 2021) gave an $O^*(2^{O(k\log k)})$-time algorithm and asked whether the problem admits a single-exponential parameterized algorithm and a polynomial kernel. We answer both questions affirmatively: SCA can be solved in $O^*(9^k)$ time and admits a polynomial kernel with $O(k^4)$ vertices and $O(k^{16})$ bits. For unweighted SCA, we obtain $O^*(4^k)$ time and a kernel with $O(k^3)$ vertices. Our algorithms are based on a particularly simple reduction to Strongly Connected Spanning Subgraph with two edge costs.

cs.DS