SearcharxivSearch

arXiv · 1409.1749

Faster Small-Constant-Periodic Merging Networks

Abstract

We consider the problem of merging two sorted sequences on a comparator network that is used repeatedly, that is, if the output is not sorted, the network is applied again using the output as input. The challenging task is to construct such networks of small depth (called a period in this context). In our previous paper entitled Faster 3-Periodic Merging Network we reduced twice the time of merging on $3$-periodic networks, i.e. from $12\log N$ to $6\log N$, compared to the first construction given by Kuty{\l}owski, Lory\'s and Oesterdikhoff. Note that merging on $2$-periodic networks require linear time. In this paper we extend our construction, which is based on Canfield and Williamson $(\log N)$-periodic sorter, and the analysis from that paper to any period $p \ge 4$. For $p\ge 4$ our $p$-periodic network merges two sorted sequences of length $N/2$ in at most $\frac{2p}{p-2}\log N + p\frac{p-8}{p-2}$ rounds. The previous bound given by Kuty{\l}owski at al. was $\frac{2.25p}{p-2.42}\log N$. That means, for example, that our $4$-periodic merging networks work in time upper-bounded by $4\log N$ and our $6$-periodic ones in time upper-bounded by $3\log N$ compared to the corresponding $5.67\log N$ and $3.8\log N$ previous bounds. Our construction is regular and follows the same periodification schema, whereas some additional techniques were used previously to tune the construction for $p \ge 4$. Moreover, our networks are also periodic sorters and tests on random permutations show that average sorting time is closed to $\log^2 N$.

Explore related subjects

Keep this discovery

BibTeXRIS

Marek Piotrów. 2014-09-05. Faster Small-Constant-Periodic Merging Networks. https://arxiv.org/abs/1409.1749

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