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Efficient Algorithms with Asymmetric Read and Write Costs
Efficient Algorithms with Asymmetric Read and Write Costs Guy E. Blelloch1, Jeremy T. Fineman2, Phillip B. Gibbons1, Yan Gu1, and Julian Shun3 1 Carnegie Mellon University 2 Georgetown University 3 University of California, Berkeley Abstract In several emerging technologies for computer memory (main memory), the cost of reading is significantly cheaper than the cost of writing. Such asymmetry in memory costs poses a fun- damentally different model from the RAM for algorithm design. In this paper we study lower and upper bounds for various problems under such asymmetric read and write costs. We con- sider both the case in which all but O(1) memory has asymmetric cost, and the case of a small cache of symmetric memory. We model both cases using the (M, ω)-ARAM, in which there is a small (symmetric) memory of size M and a large unbounded (asymmetric) memory, both random access, and where reading from the large memory has unit cost, but writing has cost ω 1. For FFT and sorting networks we show a lower bound cost of Ω(ωn logωM n), which indicates that it is not possible to achieve asymptotic improvements with cheaper reads when ω is bounded by a polynomial in M. Moreover, there is an asymptotic gap (of min(ω, log n)/ log(ωM)) between the cost of sorting networks and comparison sorting in the model. This contrasts with the RAM, and most other models, in which the asymptotic costs are the same. We also show a lower bound for computations on an n × n diamond DAG of Ω(ωn2/M) cost, which indicates no asymptotic improvement is achievable with fast reads. -
Great Ideas in Computing
Great Ideas in Computing University of Toronto CSC196 Winter/Spring 2019 Week 6: October 19-23 (2020) 1 / 17 Announcements I added one final question to Assignment 2. It concerns search engines. The question may need a little clarification. I have also clarified question 1 where I have defined (in a non standard way) the meaning of a strict binary search tree which is what I had in mind. Please answer the question for a strict binary search tree. If you answered the quiz question for a non strict binary search tree withn a proper explanation you will get full credit. Quick poll: how many students feel that Q1 was a fair quiz? A1 has now been graded by Marta. I will scan over the assignments and hope to release the grades later today. If you plan to make a regrading request, you have up to one week to submit your request. You must specify clearly why you feel that a question may not have been graded fairly. In general, students did well which is what I expected. 2 / 17 Agenda for the week We will continue to discuss search engines. We ended on what is slide 10 (in Week 5) on Friday and we will continue with where we left off. I was surprised that in our poll, most students felt that the people advocating the \AI view" of search \won the debate" whereas today I will try to argue that the people (e.g., Salton and others) advocating the \combinatorial, algebraic, statistical view" won the debate as to current search engines. -
Efficient Algorithms with Asymmetric Read and Write Costs
Efficient Algorithms with Asymmetric Read and Write Costs Guy E. Blelloch1, Jeremy T. Fineman2, Phillip B. Gibbons1, Yan Gu1, and Julian Shun3 1 Carnegie Mellon University 2 Georgetown University 3 University of California, Berkeley Abstract In several emerging technologies for computer memory (main memory), the cost of reading is significantly cheaper than the cost of writing. Such asymmetry in memory costs poses a fun- damentally different model from the RAM for algorithm design. In this paper we study lower and upper bounds for various problems under such asymmetric read and write costs. We con- sider both the case in which all but O(1) memory has asymmetric cost, and the case of a small cache of symmetric memory. We model both cases using the (M, ω)-ARAM, in which there is a small (symmetric) memory of size M and a large unbounded (asymmetric) memory, both random access, and where reading from the large memory has unit cost, but writing has cost ω 1. For FFT and sorting networks we show a lower bound cost of Ω(ωn logωM n), which indicates that it is not possible to achieve asymptotic improvements with cheaper reads when ω is bounded by a polynomial in M. Moreover, there is an asymptotic gap (of min(ω, log n)/ log(ωM)) between the cost of sorting networks and comparison sorting in the model. This contrasts with the RAM, and most other models, in which the asymptotic costs are the same. We also show a lower bound for computations on an n × n diamond DAG of Ω(ωn2/M) cost, which indicates no asymptotic improvement is achievable with fast reads. -
P Versus NP Richard M. Karp
P Versus NP Computational complexity theory is the branch of theoretical computer science concerned with the fundamental limits on the efficiency of automatic computation. It focuses on problems that appear to Richard M. Karp require a very large number of computation steps for their solution. The inputs and outputs to a problem are sequences of symbols drawn from a finite alphabet; there is no limit on the length of the input, and the fundamental question about a problem is the rate of growth of the number of required computation steps as a function of the length of the input. Some problems seem to require a very rapidly growing number of steps. One such problem is the inde- pendent set problem: given a graph, consisting of points called vertices and lines called edges connect- ing pairs of vertices, a set of vertices is called independent if no two vertices in the set are connected by a line. Given a graph and a positive integer n, the problem is to decide whether the graph contains an independent set of size n. Every known algorithm to solve the independent set problem encounters a combinatorial explosion, in which the number of required computation steps grows exponentially as a function of the size of the graph. On the other hand, the problem of deciding whether a given set of vertices is an independent set in a given graph is solvable by inspection. There are many such dichotomies, in which it is hard to decide whether a given type of structure exists within an input object (the existence problem), but it is easy to decide whether a given structure is of the required type (the verification problem). -
Fiendish Designs
Fiendish Designs A Software Engineering Odyssey © Tim Denvir 2011 1 Preface These are notes, incomplete but extensive, for a book which I hope will give a personal view of the first forty years or so of Software Engineering. Whether the book will ever see the light of day, I am not sure. These notes have come, I realise, to be a memoir of my working life in SE. I want to capture not only the evolution of the technical discipline which is software engineering, but also the climate of social practice in the industry, which has changed hugely over time. To what extent, if at all, others will find this interesting, I have very little idea. I mention other, real people by name here and there. If anyone prefers me not to refer to them, or wishes to offer corrections on any item, they can email me (see Contact on Home Page). Introduction Everybody today encounters computers. There are computers inside petrol pumps, in cash tills, behind the dashboard instruments in modern cars, and in libraries, doctors’ surgeries and beside the dentist’s chair. A large proportion of people have personal computers in their homes and may use them at work, without having to be specialists in computing. Most people have at least some idea that computers contain software, lists of instructions which drive the computer and enable it to perform different tasks. The term “software engineering” wasn’t coined until 1968, at a NATO-funded conference, but the activity that it stands for had been carried out for at least ten years before that. -
The Humble Humorous Researcher: a Tribute to Michel Sintzoff
The Humble Humorous Researcher A Tribute to Michel Sintzoff Axel van Lamsweerde 1 Many of us lost a close colleague on November 28, 2010. More than that: we lost a friend. One that we used to meet regularly, all over the world, always listening to us, making interesting comments and suggestions on our work, joking on every occasion and making us discover how much fun our business is. Rather than reviewing his work in detail, this note puts more focus on Michel’s rich personality, with the hope that it will bring back fond memories among those who were lucky enough to share some good times with him. The style is intended to be personal and informal. Michel would have hated anything different, to be sure. Born in 1938 in Brussels, Michel completed his master’s degree in Mathematics at the Université catholique de Louvain (UCL) in 1962. After two years of civil service as a maths teacher in Katanga (Congo), he entered the MBLE Research Laboratory in Brussels in 1964 (MBLE stands for “ Manufacture Belge de Lampes et matériel Electrique”). This was a branch of Philips Research Labs specifically dedicated to background research in applied mathematics and computing science. After 18 years of research in programming languages, formal semantics, program analysis and concurrency at MBLE, he joined the newly founded department of Computing Science at UCL in 1982 with new interests in diverse areas such as proof systems, control theory and dynamical systems. Michel contributed to the PhD work of dozens of people in Belgium and France, as supervisor or contributor, without ever having been interested in getting a PhD himself. -
The P Versus Np Problem
THE P VERSUS NP PROBLEM STEPHEN COOK 1. Statement of the Problem The P versus NP problem is to determine whether every language accepted by some nondeterministic algorithm in polynomial time is also accepted by some (deterministic) algorithm in polynomial time. To define the problem precisely it is necessary to give a formal model of a computer. The standard computer model in computability theory is the Turing machine, introduced by Alan Turing in 1936 [37]. Although the model was introduced before physical computers were built, it nevertheless continues to be accepted as the proper computer model for the purpose of defining the notion of computable function. Informally the class P is the class of decision problems solvable by some algorithm within a number of steps bounded by some fixed polynomial in the length of the input. Turing was not concerned with the efficiency of his machines, rather his concern was whether they can simulate arbitrary algorithms given sufficient time. It turns out, however, Turing machines can generally simulate more efficient computer models (for example, machines equipped with many tapes or an unbounded random access memory) by at most squaring or cubing the computation time. Thus P is a robust class and has equivalent definitions over a large class of computer models. Here we follow standard practice and define the class P in terms of Turing machines. Formally the elements of the class P are languages. Let Σ be a finite alphabet (that is, a finite nonempty set) with at least two elements, and let Σ∗ be the set of finite strings over Σ. -
A Memorable Trip Abhisekh Sankaran Research Scholar, IIT Bombay
A Memorable Trip Abhisekh Sankaran Research Scholar, IIT Bombay It was my first trip to the US. It had not yet sunk in that I had been chosen by ACM India as one of two Ph.D. students from India to attend the big ACM Turing Centenary Celebration in San Francisco until I saw the familiar face of Stephen Cook enter a room in the hotel a short distance from mine; later, Moshe Vardi recognized me from his trip to IITB during FSTTCS, 2011. I recognized Nitin Saurabh from IMSc Chennai, the other student chosen by ACM-India; 11 ACM SIG©s had sponsored students and there were about 75 from all over the world. Registration started at 8am on 15th June, along with breakfast. Collecting my ©Student Scholar© badge and stuffing in some food, I entered a large hall with several hundred seats, a brightly lit podium with a large screen in the middle flanked by two others. The program began with a video giving a brief biography of Alan Turing from his boyhood to the dynamic young man who was to change the world forever. There were inaugural speeches by John White, CEO of ACM, and Vint Cerf, the 2004 Turing Award winner and incoming ACM President. The MC for the event, Paul Saffo, took over and the panel discussions and speeches commenced. A live Twitter feed made it possible for people in the audience and elsewhere to post questions/comments which were actually taken up in the discussions. Of the many sessions that took place in the next two days, I will describe three that I found most interesting. -
Alan Mathison Turing and the Turing Award Winners
Alan Turing and the Turing Award Winners A Short Journey Through the History of Computer TítuloScience do capítulo Luis Lamb, 22 June 2012 Slides by Luis C. Lamb Alan Mathison Turing A.M. Turing, 1951 Turing by Stephen Kettle, 2007 by Slides by Luis C. Lamb Assumptions • I assume knowlege of Computing as a Science. • I shall not talk about computing before Turing: Leibniz, Babbage, Boole, Gödel... • I shall not detail theorems or algorithms. • I shall apologize for omissions at the end of this presentation. • Comprehensive information about Turing can be found at http://www.mathcomp.leeds.ac.uk/turing2012/ • The full version of this talk is available upon request. Slides by Luis C. Lamb Alan Mathison Turing § Born 23 June 1912: 2 Warrington Crescent, Maida Vale, London W9 Google maps Slides by Luis C. Lamb Alan Mathison Turing: short biography • 1922: Attends Hazlehurst Preparatory School • ’26: Sherborne School Dorset • ’31: King’s College Cambridge, Maths (graduates in ‘34). • ’35: Elected to Fellowship of King’s College Cambridge • ’36: Publishes “On Computable Numbers, with an Application to the Entscheindungsproblem”, Journal of the London Math. Soc. • ’38: PhD Princeton (viva on 21 June) : “Systems of Logic Based on Ordinals”, supervised by Alonzo Church. • Letter to Philipp Hall: “I hope Hitler will not have invaded England before I come back.” • ’39 Joins Bletchley Park: designs the “Bombe”. • ’40: First Bombes are fully operational • ’41: Breaks the German Naval Enigma. • ’42-44: Several contibutions to war effort on codebreaking; secure speech devices; computing. • ’45: Automatic Computing Engine (ACE) Computer. Slides by Luis C. -
Computability and Complexity
Computability and Complexity Wolfgang Schreiner [email protected] Research Institute for Symbolic Computation (RISC) Johannes Kepler University, Linz, Austria http://www.risc.jku.at Wolfgang Schreiner http://www.risc.jku.at 1/11 Computability and Complexity Theoretical computer science: Eternal truths established by means of mathematics/logic. Independent of future technological advances. Core questions: Computability: what is computable and what not? Complexity: how efficiently can something be computed? Clarifies limits of the field. Physics: build a perpetual motion machine. Impossible: violates second law of thermodynamics. Mathematics: square a circle with compass and straightedge. Impossible: p is not an algebraic number. Computer science: write a reliable halting checker. Impossible: the language of the halting problem is not recursive. Fundamental knowledge for every decent computer scientist. Wolfgang Schreiner http://www.risc.jku.at 2/11 The Big Picture (Wikipedia) We will cover selected points of this picture. Wolfgang Schreiner http://www.risc.jku.at 3/11 Theoretical Skeleton Die Theoretische Informatik . bietet Grundlagen für die Definition, Verifikation und Ausführung der Programme von Programmiersprachen, den Bau der Compiler von Programmiersprachen – den Compilerbau – und die mathematische Formalisierung und Untersuchung von meist diskreten Problemstellungen und deren Modellen. Mit Hilfe mathematischer Abstraktion der Eigenschaften von gewonnenen Modellen ergaben sich nützliche Definitionen, Sätze, Beweise, Algorithmen, Anwendungen und Lösungen von bzw. zu Problemen. Die Theoretische Informatik bildet mit ihren zeitlosen, mathematischen Wahrheiten und Methoden ein formales Skelett, das die Informatik in der Praxis mit konkreten Implementierungen durchdringt. Die Theoretische Informatik identifizierte viele unlösbare Problemstellungen mittels der Berechenbarkeitstheorie und erlaubt, häufig mit konstruktiver Beweisführung der Komplexitätstheorie, die Abgrenzung der praktisch effizient lösbaren Probleme von denen, für die das Gegenteil gilt. -
The Psychology of Invention in Computer Science
In P. Romero, J. Good, E. Acosta Chaparro & S. Bryant (Eds). Proc. PPIG 17 Pages 131 - 144 The Psychology of Invention in Computer Science Ronald J. Leach, Caprice A. Ayers Department of Systems and Computer Science Howard University Washington, DC 20059 [email protected], [email protected] Abstract. Much of the existing work on the psychology of programmers has been experimental. The purpose of this initial study is to help provide some avenues for future experimental research by addressing creativity in computer scientists. We studied several writings and published interviews with a number of prominent computer scientists in an effort to understand the nature of creativity in computer science and to develop a set of research questions to be answered about the thought processes of programmers. The intent was to identify similar factors of their experiences that may have contributed to their success. Parallels to some existing work on the nature of creativity in mathematics and architecture are also made. The paper concludes with a discussion of how the study of common experiences of creative research computer scientists can be extended to study the creative processes of programmers. Introduction Creativity in fields such as astronomy, music, mathematics, and engineering has long been studied. In the nineteenth and early twentieth centuries, it was common for scientists and mathematicians to publish expository papers, educating the masses and discussing creativity in their domains. Computer science derived initially from a combination of mathematical and electrical engineering principles, so one might expect similar patterns of creativity. While the majority of computer scientists have devoted their attention to solving complex problems and introducing new technology, few have written on the thought process behind their creations. -
Laureates Meet Young Researchers in Heidelberg
Editorial Editorial: Laureates Meet Young Researchers In Heidelberg Helge Holden (Norwegian University of Science and Technology, Trondheim, Norway), Hans Munthe-Kaas (Uni- versity of Bergen, Norway) and Dierk Schleicher (Jacobs University Bremen, Germany) Young scientists have a chance to meet some of the and Adi Shamir (the “RS” from the “RSA algorithm”), most successful mathematicians and computer scientists as well as Stephen Cook and Richard Karp, pioneers of at the newly established “Heidelberg Laureate Forum” complexity theory. (HLF), a week-long event that brings together Abel lau- The plenary talks covered a broad range of themes. reates, Fields medallists and Turing awardees with “lead- Sir Michael Atiyah gave his advice to young mathemati- ing scientists of the next generation”, that is, university cians based on his experiences of a long life in mathemat- students, doctoral students and postdocs. We all know ics and continued his enthusiastic interaction with the that personal encounters can have a decisive impact on younger generation in the informal parts of the forum our personal lives – and what can be more exciting for a and the social events. young and aspiring scientist than a face-to-face meeting Curtis McMullen discussed new connections between with the most prominent scientists in their area? This is motions of billiard balls, Riemann surfaces and moduli the idea of the forum. It was launched in September 2013 spaces. Blogger Dana Mackenzie summarised his im- and will take place annually. pressions of McMullen’s talk in his Scientific American The highly successful Lindau Nobel Laureate Meet- blog Dances, Billiards and Pretzels: “When I came to the ings have taken place for more than 60 years, providing Heidelberg Laureate Forum, I expected a feast for my a forum where young researchers and Nobel Laureates mind.