On Data Structures and Memory Models
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Benchmarks for IP Forwarding Tables
Reviewers James Abello Richard Cleve Vassos Hadzilacos Dimitris Achilioptas James Clippinger Jim Hafner Micah Adler Anne Condon Torben Hagerup Oswin Aichholzer Stephen Cook Armin Haken William Aiello Tom Cormen Shai Halevi Donald Aingworth Dan Dooly Eric Hansen Susanne Albers Oliver Duschka Refael Hassin Eric Allender Martin Dyer Johan Hastad Rajeev Alur Ran El-Yaniv Lisa Hellerstein Andris Ambainis David Eppstein Monika Henzinger Amihood Amir Jeff Erickson Tom Henzinger Artur Andrzejak Kousha Etessami Jeremy Horwitz Boris Aronov Will Evans Russell Impagliazzo Sanjeev Arora Guy Even Piotr Indyk Amotz Barnoy Ron Fagin Sandra Irani Yair Bartal Michalis Faloutsos Ken Jackson Julien Basch Martin Farach-Colton David Johnson Saugata Basu Uri Feige John Jozwiak Bob Beals Joan Feigenbaum Bala Kalyandasundaram Paul Beame Stefan Felsner Ming-Yang Kao Steve Bellantoni Faith Fich Haim Kaplan Micahel Ben-Or Andy Fingerhut Bruce Kapron Josh Benaloh Paul Fischer Michael Kaufmann Charles Bennett Lance Fortnow Michael Kearns Marshall Bern Steve Fortune Sanjeev Khanna Nikolaj Bjorner Alan Frieze Samir Khuller Johannes Blomer Anna Gal Joe Kilian Avrim Blum Naveen Garg Valerie King Dan Boneh Bernd Gartner Philip Klein Andrei Broder Rosario Gennaro Spyros Kontogiannis Nader Bshouty Ashish Goel Gilad Koren Adam Buchsbaum Michel Goemans Dexter Kozen Lynn Burroughs Leslie Goldberg Dina Kravets Ran Canetti Paul Goldberg S. Ravi Kumar Pei Cao Oded Goldreich Eyal Kushilevitz Moses Charikar John Gray Stephen Kwek Chandra Chekuri Dan Greene Larry Larmore Yi-Jen Chiang -
Persistent Predecessor Search and Orthogonal Point Location on the Word
Persistent Predecessor Search and Orthogonal Point Location on the Word RAM∗ Timothy M. Chan† August 17, 2012 Abstract We answer a basic data structuring question (for example, raised by Dietz and Raman [1991]): can van Emde Boas trees be made persistent, without changing their asymptotic query/update time? We present a (partially) persistent data structure that supports predecessor search in a set of integers in 1,...,U under an arbitrary sequence of n insertions and deletions, with O(log log U) { } expected query time and expected amortized update time, and O(n) space. The query bound is optimal in U for linear-space structures and improves previous near-O((log log U)2) methods. The same method solves a fundamental problem from computational geometry: point location in orthogonal planar subdivisions (where edges are vertical or horizontal). We obtain the first static data structure achieving O(log log U) worst-case query time and linear space. This result is again optimal in U for linear-space structures and improves the previous O((log log U)2) method by de Berg, Snoeyink, and van Kreveld [1995]. The same result also holds for higher-dimensional subdivisions that are orthogonal binary space partitions, and for certain nonorthogonal planar subdivisions such as triangulations without small angles. Many geometric applications follow, including improved query times for orthogonal range reporting for dimensions 3 on the RAM. ≥ Our key technique is an interesting new van-Emde-Boas–style recursion that alternates be- tween two strategies, both quite simple. 1 Introduction Van Emde Boas trees [60, 61, 62] are fundamental data structures that support predecessor searches in O(log log U) time on the word RAM with O(n) space, when the n elements of the given set S come from a bounded integer universe 1,...,U (U n). -
Arxiv:2008.08417V2 [Cs.DS] 3 Nov 2020
Modular Subset Sum, Dynamic Strings, and Zero-Sum Sets Jean Cardinal∗ John Iacono† Abstract a number of operations on a persistent collection strings, e modular subset sum problem consists of deciding, given notably, split and concatenate, as well as nding the longest a modulus m, a multiset S of n integers in 0::m − 1, and common prex (LCP) of two strings, all in at most loga- a target integer t, whether there exists a subset of S with rithmic time with updates being with high probability. We elements summing to t (mod m), and to report such a inform the reader of the independent work of Axiotis et + set if it exists. We give a simple O(m log m)-time with al., [ABB 20], contains much of the same results and ideas high probability (w.h.p.) algorithm for the modular sub- for modular subset sum as here has been accepted to ap- set sum problem. is builds on and improves on a pre- pear along with this paper in the proceedings of the SIAM vious O(m log7 m) w.h.p. algorithm from Axiotis, Backurs, Symposium on Simplicity in Algorithms (SOSA21). + Jin, Tzamos, and Wu (SODA 19). Our method utilizes the As the dynamic string data structure of [GKK 18] is ADT of the dynamic strings structure of Gawrychowski quite complex, we provide in §4 a new and far simpler alter- et al. (SODA 18). However, as this structure is rather com- native, which we call the Data Dependent Tree (DDT) struc- plicated we present a much simpler alternative which we ture. -
The Best Nurturers in Computer Science Research
The Best Nurturers in Computer Science Research Bharath Kumar M. Y. N. Srikant IISc-CSA-TR-2004-10 http://archive.csa.iisc.ernet.in/TR/2004/10/ Computer Science and Automation Indian Institute of Science, India October 2004 The Best Nurturers in Computer Science Research Bharath Kumar M.∗ Y. N. Srikant† Abstract The paper presents a heuristic for mining nurturers in temporally organized collaboration networks: people who facilitate the growth and success of the young ones. Specifically, this heuristic is applied to the computer science bibliographic data to find the best nurturers in computer science research. The measure of success is parameterized, and the paper demonstrates experiments and results with publication count and citations as success metrics. Rather than just the nurturer’s success, the heuristic captures the influence he has had in the indepen- dent success of the relatively young in the network. These results can hence be a useful resource to graduate students and post-doctoral can- didates. The heuristic is extended to accurately yield ranked nurturers inside a particular time period. Interestingly, there is a recognizable deviation between the rankings of the most successful researchers and the best nurturers, which although is obvious from a social perspective has not been statistically demonstrated. Keywords: Social Network Analysis, Bibliometrics, Temporal Data Mining. 1 Introduction Consider a student Arjun, who has finished his under-graduate degree in Computer Science, and is seeking a PhD degree followed by a successful career in Computer Science research. How does he choose his research advisor? He has the following options with him: 1. Look up the rankings of various universities [1], and apply to any “rea- sonably good” professor in any of the top universities. -
Kurt Mehlhorn, and Monique Teillaud
Volume 1, Issue 3, March 2011 Reasoning about Interaction: From Game Theory to Logic and Back (Dagstuhl Seminar 11101) Jürgen Dix, Wojtek Jamroga, and Dov Samet .................................... 1 Computational Geometry (Dagstuhl Seminar 11111) Pankaj Kumar Agarwal, Kurt Mehlhorn, and Monique Teillaud . 19 Computational Complexity of Discrete Problems (Dagstuhl Seminar 11121) Martin Grohe, Michal Koucký, Rüdiger Reischuk, and Dieter van Melkebeek . 42 Exploration and Curiosity in Robot Learning and Inference (Dagstuhl Seminar 11131) Jeremy L. Wyatt, Peter Dayan, Ales Leonardis, and Jan Peters . 67 DagstuhlReports,Vol.1,Issue3 ISSN2192-5283 ISSN 2192-5283 Published online and open access by Aims and Scope Schloss Dagstuhl – Leibniz-Zentrum für Informatik The periodical Dagstuhl Reports documents the GmbH, Dagstuhl Publishing, Saarbrücken/Wadern, program and the results of Dagstuhl Seminars and Germany. Dagstuhl Perspectives Workshops. Online available at http://www.dagstuhl.de/dagrep In principal, for each Dagstuhl Seminar or Dagstuhl Perspectives Workshop a report is published that Publication date contains the following: August, 2011 an executive summary of the seminar program and the fundamental results, Bibliographic information published by the Deutsche an overview of the talks given during the seminar Nationalbibliothek (summarized as talk abstracts), and The Deutsche Nationalbibliothek lists this publica- summaries from working groups (if applicable). tion in the Deutsche Nationalbibliografie; detailed This basic framework can be extended by suitable bibliographic data are available in the Internet at contributions that are related to the program of the http://dnb.d-nb.de. seminar, e.g. summaries from panel discussions or open problem sessions. License This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported license: CC-BY-NC-ND. -
Lecture 1 — 24 January 2017 1 Overview 2 Administrivia 3 The
CS 224: Advanced Algorithms Spring 2017 Lecture 1 | 24 January 2017 Prof. Jelani Nelson Scribe: Jerry Ma 1 Overview We introduce the word RAM model of computation, which features fixed-size storage blocks (\words") similar to modern integer data types. We also introduce the predecessor problem and demonstrate solutions to the problem that are faster than optimal comparison-based methods. 2 Administrivia • Personnel: Jelani Nelson (instructor) and Tom Morgan (TF) • Course site: people.seas.harvard.edu/~cs224 • Course staff email: [email protected] • Prerequisites: CS 124/125 and probability. • Coursework: lecture scribing, problem sets, and a final project. Students will also assist in grading problem sets. No coding. • Topics will include those from CS 124/125 at a deeper level of exposition, as well as entirely new topics. 3 The Predecessor Problem In the predecessor, we wish to maintain a ordered set S = fX1;X2; :::; Xng subject to the prede- cessor query: pred(z) = maxfx 2 Sjx < zg Usually, we do this by representing S in a data structure. In the static predecessor problem, S does not change between pred queries. In the dynamic predecessor problem, S may change through the operations: insert(z): S S [ fzg del(z): S S n fzg For the static predecessor problem, a good solution is to store S as a sorted array. pred can then be implemented via binary search. This requires Θ(n) space, Θ(log n) runtime for pred, and Θ(n log n) preprocessing time. 1 For the dynamic predecessor problem, we can maintain S in a balanced binary search tree, or BBST (e.g. -
THE INVARIANCE THESIS 1. Introduction in 1936, Turing [47
THE INVARIANCE THESIS NACHUM DERSHOWITZ AND EVGENIA FALKOVICH-DERZHAVETZ School of Computer Science, Tel Aviv University, Ramat Aviv, Israel e-mail address:[email protected] School of Computer Science, Tel Aviv University, Ramat Aviv, Israel e-mail address:[email protected] Abstract. We demonstrate that the programs of any classical (sequential, non-interactive) computation model or programming language that satisfies natural postulates of effective- ness (which specialize Gurevich’s Sequential Postulates)—regardless of the data structures it employs—can be simulated by a random access machine (RAM) with only constant factor overhead. In essence, the postulates of algorithmicity and effectiveness assert the following: states can be represented as logical structures; transitions depend on a fixed finite set of terms (those referred to in the algorithm); all atomic operations can be pro- grammed from constructors; and transitions commute with isomorphisms. Complexity for any domain is measured in terms of constructor operations. It follows that any algorithmic lower bounds found for the RAM model also hold (up to a constant factor determined by the algorithm in question) for any and all effective classical models of computation, what- ever their control structures and data structures. This substantiates the Invariance Thesis of van Emde Boas, namely that every effective classical algorithm can be polynomially simulated by a RAM. Specifically, we show that the overhead is only a linear factor in either time or space (and a constant factor in the other dimension). The enormous number of animals in the world depends of their varied structure & complexity: — hence as the forms became complicated, they opened fresh means of adding to their complexity. -
Integer Sorting in O ( N //Spl Root/Log Log N/ ) Expected Time and Linear Space
p (n log log n) Integer Sorting in O Expected Time and Linear Space Yijie Han Mikkel Thorup School of Interdisciplinary Computing and Engineering AT&T Labs— Research University of Missouri at Kansas City Shannon Laboratory 5100 Rockhill Road 180 Park Avenue Kansas City, MO 64110 Florham Park, NJ 07932 [email protected] [email protected] http://welcome.to/yijiehan Abstract of the input integers. The assumption that each integer fits in a machine word implies that integers can be operated on We present a randomized algorithm sorting n integers with single instructions. A similar assumption is made for p (n log log n) O (n log n) in O expected time and linear space. This comparison based sorting in that an time bound (n log log n) improves the previous O bound by Anderson et requires constant time comparisons. However, for integer al. from STOC’95. sorting, besides comparisons, we can use all the other in- As an immediate consequence, if the integers are structions available on integers in standard imperative pro- p O (n log log U ) bounded by U , we can sort them in gramming languages such as C [26]. It is important that the expected time. This is the first improvement over the word-size W is finite, for otherwise, we can sort in linear (n log log U ) O bound obtained with van Emde Boas’ data time by a clever coding of a huge vector-processor dealing structure from FOCS’75. with n input integers at the time [30, 27]. At the heart of our construction, is a technical determin- Concretely, Fredman and Willard [17] showed that we n (n log n= log log n) istic lemma of independent interest; namely, that we split can sort deterministically in O time and p p n (n log n) integers into subsets of size at most in linear time and linear space and randomized in O expected time space. -
CS Cornell 40Th Anniversary Booklet
Contents Welcome from the CS Chair .................................................................................3 Symposium program .............................................................................................4 Meet the speakers ..................................................................................................5 The Cornell environment The Cornell CS ambience ..............................................................................6 Faculty of Computing and Information Science ............................................8 Information Science Program ........................................................................9 College of Engineering ................................................................................10 College of Arts & Sciences ..........................................................................11 Selected articles Mission-critical distributed systems ............................................................ 12 Language-based security ............................................................................. 14 A grand challenge in computer networking .................................................16 Data mining, today and tomorrow ...............................................................18 Grid computing and Web services ...............................................................20 The science of networks .............................................................................. 22 Search engines that learn from experience ..................................................24 -
Optimal Multithreaded Batch-Parallel 2-3 Trees Arxiv:1905.05254V2
Optimal Multithreaded Batch-Parallel 2-3 Trees Wei Quan Lim National University of Singapore Keywords Parallel data structures, pointer machine, multithreading, dictionaries, 2-3 trees. Abstract This paper presents a batch-parallel 2-3 tree T in an asynchronous dynamic multithreading model that supports searches, insertions and deletions in sorted batches and has essentially optimal parallelism, even under the restrictive QRMW (queued read-modify-write) memory contention model where concurrent accesses to the same memory location are queued and serviced one by one. Specifically, if T has n items, then performing an item-sorted batch (given as a leaf-based balanced binary tree) of b operations · n ¹ º ! 1 on T takes O b log b +1 +b work and O logb+logn span (in the worst case as b;n ). This is information-theoretically work-optimal for b ≤ n, and also span-optimal for pointer-based structures. Moreover, it is easy to support optimal intersection, n union and difference of instances of T with sizes m ≤ n, namely within O¹m·log¹ m +1ºº work and O¹log m + log nº span. Furthermore, T supports other batch operations that make it a very useful building block for parallel data structures. To the author’s knowledge, T is the first parallel sorted-set data structure that can be used in an asynchronous multi-processor machine under a memory model with queued contention and yet have asymptotically optimal work and span. In fact, T is designed to have bounded contention and satisfy the claimed work and span bounds regardless of the execution schedule. -
Algorithms: a Quest for Absolute Definitions∗
Algorithms: A Quest for Absolute De¯nitions¤ Andreas Blassy Yuri Gurevichz Abstract What is an algorithm? The interest in this foundational problem is not only theoretical; applications include speci¯cation, validation and veri¯ca- tion of software and hardware systems. We describe the quest to understand and de¯ne the notion of algorithm. We start with the Church-Turing thesis and contrast Church's and Turing's approaches, and we ¯nish with some recent investigations. Contents 1 Introduction 2 2 The Church-Turing thesis 3 2.1 Church + Turing . 3 2.2 Turing ¡ Church . 4 2.3 Remarks on Turing's analysis . 6 3 Kolmogorov machines and pointer machines 9 4 Related issues 13 4.1 Physics and computations . 13 4.2 Polynomial time Turing's thesis . 14 4.3 Recursion . 15 ¤Bulletin of European Association for Theoretical Computer Science 81, 2003. yPartially supported by NSF grant DMS{0070723 and by a grant from Microsoft Research. Address: Mathematics Department, University of Michigan, Ann Arbor, MI 48109{1109. zMicrosoft Research, One Microsoft Way, Redmond, WA 98052. 1 5 Formalization of sequential algorithms 15 5.1 Sequential Time Postulate . 16 5.2 Small-step algorithms . 17 5.3 Abstract State Postulate . 17 5.4 Bounded Exploration Postulate and the de¯nition of sequential al- gorithms . 19 5.5 Sequential ASMs and the characterization theorem . 20 6 Formalization of parallel algorithms 21 6.1 What parallel algorithms? . 22 6.2 A few words on the axioms for wide-step algorithms . 22 6.3 Wide-step abstract state machines . 23 6.4 The wide-step characterization theorem . -
A Satisfiability Algorithm for AC0 961 Russel Impagliazzo, William Matthews and Ramamohan Paturi
A Satisfiability Algorithm for AC0 Russell Impagliazzo ∗ William Matthews † Ramamohan Paturi † Department of Computer Science and Engineering University of California, San Diego La Jolla, CA 92093-0404, USA January 2011 Abstract ing the size of the search space. In contrast, reducing We consider the problem of efficiently enumerating the problems such as planning or model-checking to the spe- satisfying assignments to AC0 circuits. We give a zero- cial cases of cnf Satisfiability or 3-sat can increase the error randomized algorithm which takes an AC0 circuit number of variables dramatically. On the other hand, as input and constructs a set of restrictions which cnf-sat algorithms (especially for small width clauses partitions {0, 1}n so that under each restriction the such as k-cnfs) have both been analyzed theoretically value of the circuit is constant. Let d denote the depth and implemented empirically with astounding success, of the circuit and cn denote the number of gates. This even when this overhead is taken into account. For some algorithm runs in time |C|2n(1−µc,d) where |C| is the domains of structured cnf formulas that arise in many d−1 applications, state-of-the-art SAT-solvers can handle up size of the circuit for µc,d ≥ 1/O[lg c + d lg d] with probability at least 1 − 2−n. to hundreds of thousands of variables and millions of As a result, we get improved exponential time clauses [11]. Meanwhile, very few results are known ei- algorithms for AC0 circuit satisfiability and for counting ther theoretically or empirically about the difficulty of solutions.