DOCSLIB.ORG

Combinatorial and Algebraic Coding Techniques for Flash Memory Storage Kathryn A

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Combinatorial and Algebraic Coding Techniques for Flash Memory Storage Kathryn A University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Dissertations, Theses, and Student Research Papers Mathematics, Department of in Mathematics Spring 4-25-2014 Combinatorial and Algebraic Coding Techniques for Flash Memory Storage Kathryn A. Haymaker University of Nebraska-Lincoln, [email protected] Follow this and additional works at: http://digitalcommons.unl.edu/mathstudent Part of the Discrete Mathematics and Combinatorics Commons, and the Other Applied Mathematics Commons Haymaker, Kathryn A., "Combinatorial and Algebraic Coding Techniques for Flash Memory Storage" (2014). Dissertations, Theses, and Student Research Papers in Mathematics. 53. http://digitalcommons.unl.edu/mathstudent/53 This Article is brought to you for free and open access by the Mathematics, Department of at DigitalCommons@University of Nebraska - Lincoln. It has been accepted for inclusion in Dissertations, Theses, and Student Research Papers in Mathematics by an authorized administrator of DigitalCommons@University of Nebraska - Lincoln. COMBINATORIAL AND ALGEBRAIC CODING TECHNIQUES FOR FLASH MEMORY STORAGE by Kathryn Haymaker A DISSERTATION Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfilment of Requirements For the Degree of Doctor of Philosophy Major: Mathematics Under the Supervision of Professor Christine A. Kelley Lincoln, Nebraska May, 2014 COMBINATORIAL AND ALGEBRAIC CODING TECHNIQUES FOR FLASH MEMORY STORAGE Kathryn Haymaker, Ph. D. University of Nebraska, 2014 Adviser: Christine A. Kelley Error-correcting codes are used to achieve reliable and efficient transmission when storing or sending information across a noisy channel. This thesis investigates a mathematical approach to coding techniques for storage devices such as flash memory storage, although many of the resulting codes and coding schemes can be applied in other contexts. The main contributions of this work include the design of efficient codes and decoding algorithms using discrete structures such as graphs and finite geometries, and developing a variety of strategies for adapting codes to a multi-level setting. Information storage devices are prone to errors over time, and the frequency of such errors increases as the storage medium degrades. Flash memory storage tech- nology has become ubiquitous in devices that require high-density storage. In this work we discuss two methods of coding that can be used to address the eventual degradation of the memory. The first method is rewriting codes, a generalization of codes for write-once mem- ory (WOM), which can be used to prolong the lifetime of the memory. We present constructions of binary and ternary rewriting codes using the structure of finite Eu- clidean geometries. We also develop strategies for reusing binary WOM codes on multi-level cells, and we prove results on the performance of these strategies. The second method to address errors in memory storage is to use error-correcting iii codes. We present an LDPC code implementation method that is inspired by bit- error patterns in flash memory. Using this and the binary image mapping for non- binary codes, we design structured nonbinary LDPC codes for storage. We obtain performance results by analyzing the probability of decoding error and by using the graph-based structure of the codes. iv COPYRIGHT c 2014, Kathryn Haymaker v DEDICATION This dissertation is lovingly dedicated to my parents Thomas and Ann Haymaker, and in fond remembrance of Dr. Jenna Higgins. vi ACKNOWLEDGMENTS This dissertation would not have been possible without the work of Professor Christine Kelley, who has generously provided me with guidance and support since my first week at UNL. I was fortunate to have such a dedicated and insightful mentor. Working with Professor Kelley helped me grow mathematically and professionally, and I will be forever grateful for the many opportunities that she gave me. I would also like to thank the members of my doctoral committee: Professor Judy Walker and Professor Jamie Radcliffe, for providing me with valuable feedback at many points during my graduate career, and Professor Myra Cohen and Professor David Pitts, for showing consistent interest in my work. My family has given me immense support. This dissertation is dedicated to my parents, because their selfless love is beyond words. They would go to the end of the world for the sake of family, but instead they ended up repeatedly driving to Nebraska (and beyond). Thank you to my siblings Kelly and Joey, for always challenging me to improve by being better than me at many things. I am also grateful to my grandparents for providing me with a strong example of honest hard work, which has been particularly inspiring these past few years! I would like to acknowledge my officemates at UNL: Abby, Kaelly, and Molly, for being excellent friends and making our first year cozy in our three-person office, and Simone for many thoughtful conversations during my last year. Molly has also been a wonderful roommate, co-teacher, and work partner. Thank you to Ashley and Melanie, for work time and crossword time, and everything in between. Your friendship means so much to me. The UNL Department of Mathematics provides a wonderful environment for graduate students, and I have enjoyed learning and teaching alongside this group of talented individuals. I owe particular thanks to vii Amanda, Anisah, Ben, Courtney, James, Lauren, and Sara for being amazing, each in amazingly different ways. I would also like to acknowledge the other members of RAD for making our \recreational research" so much fun, even in the midst of realizing we had been scooped. Thank you to Bo Zhang and Angela Bliss for providing helpful comments throughout the past two years of the dissertation writing process. Professor Rhonda Hughes of Bryn Mawr College first inspired me to pursue math- ematics and continues to provide inspiration. I am also grateful to Professor Paul Melvin and Professor Leslie Cheng for their encouragement and support. Thank you to my dear friends from Bryn Mawr|particularly Alice, Talia, Nicole, and Jenna. Finally, I would like to thank Nathan Corwin for infusing my life with an abun- dance of humor and love. viii GRANT INFORMATION This work was supported in part by the National Security Agency under Grant Num- ber H98230-11-1-0156. The United States Government is authorized to reproduce and distribute reprints not-withstanding any copyright notation herein. This work was also supported in part by a University of Nebraska Presidential Fellowship, the United States Department of Education GAANN grant number P200A090002, and an NSF EPSCoR First Award. ix Contents Contents ix List of Figures xii List of Tables xv 1 Introduction1 2 Preliminaries5 2.1 Error-correcting codes .......................... 7 2.1.1 Hamming codes.......................... 9 2.1.2 Reed-Muller codes ........................ 10 2.1.3 LDPC codes............................ 11 2.2 Coding for flash memories........................ 15 2.2.1 Flash memory structure ..................... 15 2.2.2 WOM codes............................ 17 2.2.3 Flash Codes............................ 19 3 Write-once memory codes from finite geometries 21 3.1 Finite geometries............................. 21 3.2 The Merkx construction ......................... 23 x 3.3 WOM codes from EG(m; 2)....................... 25 3.3.1 Comparison............................ 29 3.4 Ternary flash codes from EG(m; 3) ................... 31 3.4.1 Encoding and decoding...................... 31 3.4.2 Examples ............................. 33 4 Coding methods for multi-level flash memories 38 4.1 Strategies for reusing binary WOM codes................ 39 4.1.1 Analysis of Strategies A and B ................. 42 4.2 Concatenation with WOM codes .................... 46 4.3 Generalized position modulation..................... 50 4.3.1 GPM-WOM code construction.................. 52 4.3.2 Examples and code performance................. 55 4.4 Coset encoding on multi-level cells ................... 58 4.4.1 Binary coset encoding ...................... 58 4.4.2 Construction I........................... 60 4.4.3 Construction II.......................... 63 4.4.4 Codes from Constructions I and II ............... 65 4.4.5 Error correction.......................... 68 5 Binary structured bit-interleaved LDPC codes 72 5.1 Motivation from MLC flash memory .................. 75 5.2 Bit assignments for binary regular LDPC codes ............ 76 5.2.1 Results for binary (j; k)-regular codes with Gallager A . 80 5.2.2 Results for binary (j; k)-regular codes with Gallager B . 84 5.3 More than two check node types..................... 86 5.4 Results in terms of noise variance and SNR thresholds . 88 xi 6 Nonbinary structured bit-interleaved LDPC codes 94 6.1 The binary image of a code ....................... 96 6.2 Implementing nonbinary codes in MLC flash.............. 97 6.3 Designing codes with nonbinary edge labels . 100 6.3.1 Performance of binary expanded graph decoding in terms of bi thesholds ............................. 102 6.3.2 Nonbinary performance in terms of noise variance and SNR thresholds............................. 108 6.4 Performance using nonbinary decoding . 109 7 Bounds on the covering radius of graph-based codes 119 7.1 Graph-based bound on covering radius . 120 7.2 LDPC codes from finite geometries ................... 121 7.3 Covering radius of Euclidean geometry LDPC codes . 124 7.4 Covering radius of projective geometry LDPC codes . 128 8 Conclusions 134 Bibliography
Load more

Recommended publications
  • Mathematics of Data Science
    SIAM JOURNAL ON Mathematics of Data Science Volume 2 • 2020 Editor-in-Chief Tamara G. Kolda, Sandia National Laboratories Section Editors Mark Girolami, University of Cambridge, UK Alfred Hero, University of Michigan, USA Robert D. Nowak, University of Wisconsin, Madison, USA Joel A. Tropp, California Institute of Technology, USA Associate Editors Maria-Florina Balcan, Carnegie Mellon University, USA Vianney Perchet, ENSAE, CRITEO, France Rina Foygel Barber, University of Chicago, USA Jonas Peters, University of Copenhagen, Denmark Mikhail Belkin, University of California, San Diego, USA Natesh Pillai, Harvard University, USA Robert Calderbank, Duke University, USA Ali Pinar, Sandia National Laboratories, USA Coralia Cartis, University of Oxford, UK Mason Porter, University of Califrornia, Los Angeles, USA Venkat Chandrasekaran, California Institute of Technology, Maxim Raginsky, University of Illinois, USA Urbana-Champaign, USA Patrick L. Combettes, North Carolina State University, USA Bala Rajaratnam, University of California, Davis, USA Alexandre d’Aspremont, CRNS, Ecole Normale Superieure, Philippe Rigollet, MIT, USA France Justin Romberg, Georgia Tech, USA Ioana Dumitriu, University of California, San Diego, USA C. Seshadhri, University of California, Santa Cruz, USA Maryam Fazel, University of Washington, USA Amit Singer, Princeton University, USA David F. Gleich, Purdue University, USA Marc Teboulle, Tel Aviv University, Israel Wouter Koolen, CWI, the Netherlands Caroline Uhler, MIT, USA Gitta Kutyniok, University of Munich, Germany
    [Show full text]
  • Digital Communication Systems 2.2 Optimal Source Coding
    Digital Communication Systems EES 452 Asst. Prof. Dr. Prapun Suksompong [email protected] 2. Source Coding 2.2 Optimal Source Coding: Huffman Coding: Origin, Recipe, MATLAB Implementation 1 Examples of Prefix Codes Nonsingular Fixed-Length Code Shannon–Fano code Huffman Code 2 Prof. Robert Fano (1917-2016) Shannon Award (1976 ) Shannon–Fano Code Proposed in Shannon’s “A Mathematical Theory of Communication” in 1948 The method was attributed to Fano, who later published it as a technical report. Fano, R.M. (1949). “The transmission of information”. Technical Report No. 65. Cambridge (Mass.), USA: Research Laboratory of Electronics at MIT. Should not be confused with Shannon coding, the coding method used to prove Shannon's noiseless coding theorem, or with Shannon–Fano–Elias coding (also known as Elias coding), the precursor to arithmetic coding. 3 Claude E. Shannon Award Claude E. Shannon (1972) Elwyn R. Berlekamp (1993) Sergio Verdu (2007) David S. Slepian (1974) Aaron D. Wyner (1994) Robert M. Gray (2008) Robert M. Fano (1976) G. David Forney, Jr. (1995) Jorma Rissanen (2009) Peter Elias (1977) Imre Csiszár (1996) Te Sun Han (2010) Mark S. Pinsker (1978) Jacob Ziv (1997) Shlomo Shamai (Shitz) (2011) Jacob Wolfowitz (1979) Neil J. A. Sloane (1998) Abbas El Gamal (2012) W. Wesley Peterson (1981) Tadao Kasami (1999) Katalin Marton (2013) Irving S. Reed (1982) Thomas Kailath (2000) János Körner (2014) Robert G. Gallager (1983) Jack KeilWolf (2001) Arthur Robert Calderbank (2015) Solomon W. Golomb (1985) Toby Berger (2002) Alexander S. Holevo (2016) William L. Root (1986) Lloyd R. Welch (2003) David Tse (2017) James L.
    [Show full text]
  • You and Your Research & the Elements of Style
    You and Your Research & The Elements of Style Philip Wadler University of Edinburgh Logic Mentoring Workshop Saarbrucken,¨ Monday 6 July 2020 Part I You and Your Research Richard W. Hamming, 1915–1998 • Los Alamos, 1945. • Bell Labs, 1946–1976. • Naval Postgraduate School, 1976–1998. • Turing Award, 1968. (Third time given.) • IEEE Hamming Medal, 1987. It’s not luck, it’s not brains, it’s courage Say to yourself, ‘Yes, I would like to do first-class work.’ Our society frowns on people who set out to do really good work. You’re not supposed to; luck is supposed to descend on you and you do great things by chance. Well, that’s a kind of dumb thing to say. ··· How about having lots of ‘brains?’ It sounds good. Most of you in this room probably have more than enough brains to do first-class work. But great work is something else than mere brains. ··· One of the characteristics of successful scientists is having courage. Once you get your courage up and believe that you can do important problems, then you can. If you think you can’t, almost surely you are not going to. — Richard Hamming, You and Your Research Develop reusable solutions How do I obey Newton’s rule? He said, ‘If I have seen further than others, it is because I’ve stood on the shoulders of giants.’ These days we stand on each other’s feet! Now if you are much of a mathematician you know that the effort to gen- eralize often means that the solution is simple.
    [Show full text]
  • Principles of Communications ECS 332
    Principles of Communications ECS 332 Asst. Prof. Dr. Prapun Suksompong (ผศ.ดร.ประพันธ ์ สขสมปองุ ) [email protected] 1. Intro to Communication Systems Office Hours: Check Google Calendar on the course website. Dr.Prapun’s Office: 6th floor of Sirindhralai building, 1 BKD 2 Remark 1 If the downloaded file crashed your device/browser, try another one posted on the course website: 3 Remark 2 There is also three more sections from the Appendices of the lecture notes: 4 Shannon's insight 5 “The fundamental problem of communication is that of reproducing at one point either exactly or approximately a message selected at another point.” Shannon, Claude. A Mathematical Theory Of Communication. (1948) 6 Shannon: Father of the Info. Age Documentary Co-produced by the Jacobs School, UCSD- TV, and the California Institute for Telecommunic ations and Information Technology 7 [http://www.uctv.tv/shows/Claude-Shannon-Father-of-the-Information-Age-6090] [http://www.youtube.com/watch?v=z2Whj_nL-x8] C. E. Shannon (1916-2001) Hello. I'm Claude Shannon a mathematician here at the Bell Telephone laboratories He didn't create the compact disc, the fax machine, digital wireless telephones Or mp3 files, but in 1948 Claude Shannon paved the way for all of them with the Basic theory underlying digital communications and storage he called it 8 information theory. C. E. Shannon (1916-2001) 9 https://www.youtube.com/watch?v=47ag2sXRDeU C. E. Shannon (1916-2001) One of the most influential minds of the 20th century yet when he died on February 24, 2001, Shannon was virtually unknown to the public at large 10 C.
    [Show full text]
  • MIMO Wireless Communications Ezio Biglieri, Robert Calderbank, Anthony Constantinides, Andrea Goldsmith, Arogyaswami Paulraj and H
    Cambridge University Press 978-0-521-13709-6 - MIMO Wireless Communications Ezio Biglieri, Robert Calderbank, Anthony Constantinides, Andrea Goldsmith, Arogyaswami paulraj and H. Vincent Poor Frontmatter More information MIMO Wireless Communications Multiple-input multiple-output (MIMO) technology constitutes a breakthrough in the design of wireless communication systems, and is already at the core of several wireless standards. Exploiting multi-path scattering, MIMO techniques deliver significant performance enhancements in terms of data transmission rate and interference reduction. This book is a detailed introduction to the analysis and design of MIMO wireless systems. Beginning with an overview of MIMO technology, the authors then examine the fundamental capacity limits of MIMO systems. Transmitter design, including precoding and space–time coding, is then treated in depth, and the book closes with two chapters devoted to receiver design. Written by a team of leading experts, the book blends theoretical analysis with physical insights, and highlights a range of key design challenges. It can be used as a textbook for advanced courses on wireless communications, and will also appeal to researchers and practitioners working on MIMO wireless systems. Ezio Biglieri is a professor in the Department of Technology at the Universitat Pompeu Fabra, Barcelona. Robert Calderbank is a professor in the Departments of Electrical Engineering and Mathematics at Princeton University, New Jersey. Anthony Constantinides is a professor in the Department of Electrical and Electronic Engineering at Imperial College of Science, Technology and Medicine, London. Andrea Goldsmith is a professor in the Department of Electrical Engineering at Stanford University, California. Arogyaswami Paulraj is a professor in the Department of Electrical Engineering at Stanford University, California.
    [Show full text]
  • History of Modern Applied Mathematics in Mathematics Education
    HISTORY OF MODERN APPLIED MATHEMATICS IN MATHEMATICS EDUCATION UFFE THOMAS JANKVISI [I] When conversations turn to using history of mathematics in in-issues, of mathematics. When using history as a tool to classrooms, the rnferent is typically the old, often antique, improve leaining or instruction, we may distinguish at least history of the discipline (e g, Calinger, 1996; Fauvel & van two different uses: history as a motivational or affective Maanen, 2000; Jahnke et al, 1996; Katz, 2000) [2] This tool, and histmy as a cognitive tool Together with history tendency might be expected, given that old mathematics is as a goal these two uses of histoty as a tool are used to struc­ often more closely related to school mathematics However, ture discussion of the educational benefits of choosing a there seem to be some clear advantages of including histo­ history of modern applied mathematics ries of more modern applied mathematics 01 histories of History as a goal 'in itself' does not refor to teaching his­ modem applications of mathematics [3] tory of mathematics per se, but using histo1y to surface One (justified) objection to integrating elements of the meta-aspects of the discipline Of course, in specific teach­ history of modetn applied mathematics is that it is often com­ ing situations, using histmy as a goal may have the positive plex and difficult While this may be so in most instances, it side effect of offering students insight into mathematical is worthwhile to search for cases where it isn't so I consider in-issues of a specific history But the impo1tant detail is three here.
    [Show full text]
  • Computing As Engineering
    Journal of Universal Computer Science, vol. 15, no. 8 (2009), 1642-1658 submitted: 14/3/09, accepted: 27/4/09, appeared: 28/4/09 © J.UCS Computing as Engineering Matti Tedre (Tumaini University, Iringa, Tanzania fi[email protected]) Abstract: Computing as a discipline is often characterized as a combination of three major traditions: theoretical, scientific, and engineering tradition. Although the three traditions are all considered equally necessary for modern computing, the engineering tradition is often considered to be useful but to lack intellectual depth. This article discusses the basic intellectual background of the engineering tradition of computing. The article depicts the engineering aims manifest in the academic field of computing, compares the engineering tradition with the other traditions of computing as a disci- pline, and presents some epistemological, ontological, and methodological views con- cerning the engineering tradition of computing. The article aims at giving the reader an overview of the engineering tradition in computing and of some open questions about the intellectual foundations and contributions of the engineering tradition in computing. Key Words: information technology, philosophy of computer science, philosophy of technology, computing, engineering Category: K.7, K.7.1, K.7.m 1 Introduction The juxtaposing of science and technology is perhaps nowhere else as marked as in the computing disciplines. The division of computing into its mathemat- ical/theoretical, scientific/empirical, and design/engineering traditions ([Weg- ner, 1976], [Denning et al., 1989]) has spurred fiery debates about the merits and shortcomings of each tradition. In those debates, the theoretical tradition leans on the recognition of mathematics and logic as the theoretical cornerstones of computing, the scientific tradition draws support from arguments from the philosophy of science, but the design/engineering tradition is usually only rec- ognized for its utility and not for its intellectual foundations.
    [Show full text]
  • OF the AMERICAN MATHEMATICAL SOCIETY 157 Notices February 2019 of the American Mathematical Society
    ISSN 0002-9920 (print) ISSN 1088-9477 (online) Notices ofof the American MathematicalMathematical Society February 2019 Volume 66, Number 2 THE NEXT INTRODUCING GENERATION FUND Photo by Steve Schneider/JMM Steve Photo by The Next Generation Fund is a new endowment at the AMS that exclusively supports programs for doctoral and postdoctoral scholars. It will assist rising mathematicians each year at modest but impactful levels, with funding for travel grants, collaboration support, mentoring, and more. Want to learn more? Visit www.ams.org/nextgen THANK YOU AMS Development Offi ce 401.455.4111 [email protected] A WORD FROM... Robin Wilson, Notices Associate Editor In this issue of the Notices, we reflect on the sacrifices and accomplishments made by generations of African Americans to the mathematical sciences. This year marks the 100th birthday of David Blackwell, who was born in Illinois in 1919 and went on to become the first Black professor at the University of California at Berkeley and one of America’s greatest statisticians. Six years after Blackwell was born, in 1925, Frank Elbert Cox was to become the first Black mathematician when he earned his PhD from Cornell University, and eighteen years later, in 1943, Euphemia Lofton Haynes would become the first Black woman to earn a mathematics PhD. By the late 1960s, there were close to 70 Black men and women with PhDs in mathematics. However, this first generation of Black mathematicians was forced to overcome many obstacles. As a Black researcher in America, segregation in the South and de facto segregation elsewhere provided little access to research universities and made it difficult to even participate in professional societies.
    [Show full text]
  • The Art of Proof
    Matthias Beck & Ross Geoghegan The Art Of Proof Basic Training For Deeper Mathematics August 8, 2011 Springer Great teachers introduced us to the arts of mathematics and writing: To Harald Kohl and Hartmut Stapf To the memory of Fr. Harry Lawlor, SJ and Fr. Joseph Veale, SJ Preface PEANUTS: c United Feature Syndicate, Inc. Reprinted with permission. We have written this book with several kinds of readers in mind: (a) Undergraduates who have taken courses such as calculus and linear algebra, but who are not yet prepared for upper-level mathematics courses. We cover mathematical topics that these students should know. The book also provides a bridge to the upper-level courses, since we discuss formalities and conventions in detail, including the axiomatic method and how to deal with proofs. (b) Mathematics teachers and teachers-in-training. We present here some of the foundations of mathematics that anyone teaching mathematics beyond the most elementary levels should know. (c) High-school students with an unusually strong interest in mathematics. Such students should find this book interesting and (we hope) unconventional. (d) Scientists and social scientists who have found that the mathematics they studied as undergraduates is not sufficient for their present needs. Typically, the problem here is not the absence of training in a particular technique, but rather a general feeling of insecurity about what is correct or incorrect in mathematics, a sense of material only partly understood. Scientists must be confident that they are using mathematics correctly: fallacious mathematics almost guarantees bad science. In so far as possible we try to “work in” the formal methods indirectly, as we take the reader through some interesting mathematics.
    [Show full text]
  • Naval Postgraduate School
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Calhoun, Institutional Archive of the Naval Postgraduate School Calhoun: The NPS Institutional Archive Faculty and Researcher Publications News Articles 2015-01-06 Iconic Researcher, Teacher Richard Hamming Maintains Lasting Legacy on Campus Stewart, Kenneth A. Monterey, California, Naval Postgraduate School http://hdl.handle.net/10945/44775 Naval Postgraduate School - Iconic Researcher, Teacher Richard Hamming Maintains Lasting Legacy on Campus Library Research Technology Services NPS Home About NPS Academics Administration Library Research Technology Services NPS Home About NPS Academics Administration Calendar | Directory SEARCH About NPS Academics Administration Library Research Technology Services Iconic Researcher, Teacher Richard Hamming Maintains Lasting Legacy on Campus NPS > About NPS > News Article By: Kenneth A. Stewart “The purpose of computing is insight, not numbers,” once noted renowned mathematician and former Naval Postgraduate School (NPS) Professor Richard W. Hamming. For the man who set aside a lifetime of groundbreaking discoveries for the love of teaching, at a self-imposed $1 per year salary, it would prove to be a prophetic statement indeed. Hamming held his final lecture at NPS in December of 1997, some 17 years ago, but his presence is as identifiable on campus as the vibrant red plaid sport coat he frequently donned to draw attention … “Because great ideas require an audience,” he would say. Hamming’s name is front and center each year, when the university honors its best teacher with the Richard W. Hamming Award for Teaching, and its top researcher with the Hamming Interdisciplinary Achievement Award.
    [Show full text]
  • Turing Award • John Von Neumann Medal • NAE, NAS, AAAS Fellow
    15-712: Advanced Operating Systems & Distributed Systems A Few Classics Prof. Phillip Gibbons Spring 2021, Lecture 2 Today’s Reminders / Announcements • Summaries are to be submitted via Canvas by class time • Announcements and Q&A are via Piazza (please enroll) • Office Hours: – Prof. Phil Gibbons: Fri 1-2 pm & by appointment – TA Jack Kosaian: Mon 1-2 pm – Zoom links: See canvas/Zoom 2 CS is a Fast Moving Field: Why Read/Discuss Old Papers? “Those who cannot remember the past are condemned to repeat it.” - George Santayana, The Life of Reason, Volume 1, 1905 See what breakthrough research ideas look like when first presented 3 The Rise of Worse is Better Richard Gabriel 1991 • MIT/Stanford style of design: “the right thing” – Simplicity in interface 1st, implementation 2nd – Correctness in all observable aspects required – Consistency – Completeness: cover as many important situations as is practical • Unix/C style: “worse is better” – Simplicity in implementation 1st, interface 2nd – Correctness, but simplicity trumps correctness – Consistency is nice to have – Completeness is lowest priority 4 Worse-is-better is Better for SW • Worse-is-better has better survival characteristics than the-right-thing • Unix and C are the ultimate computer viruses – Simple structures, easy to port, required few machine resources to run, provide 50-80% of what you want – Programmer conditioned to sacrifice some safety, convenience, and hassle to get good performance and modest resource use – First gain acceptance, condition users to expect less, later
    [Show full text]
  • A Debate on Teaching Computing Science
    Teaching Computing Science t the ACM Computer Science Conference last Strategic Defense Initiative. William Scherlis is February, Edsger Dijkstra gave an invited talk known for his articulate advocacy of formal methods called “On the Cruelty of Really Teaching in computer science. M. H. van Emden is known for Computing Science.” He challenged some of his contributions in programming languages and the basic assumptions on which our curricula philosophical insights into science. Jacques Cohen Aare based and provoked a lot of discussion. The edi- is known for his work with programming languages tors of Comwunications received several recommenda- and logic programming and is a member of the Edi- tions to publish his talk in these pages. His comments torial Panel of this magazine. Richard Hamming brought into the foreground some of the background received the Turing Award in 1968 and is well known of controversy that surrounds the issue of what be- for his work in communications and coding theory. longs in the core of a computer science curriculum. Richard M. Karp received the Turing Award in 1985 To give full airing to the controversy, we invited and is known for his contributions in the design of Dijkstra to engage in a debate with selected col- algorithms. Terry Winograd is well known for his leagues, each of whom would contribute a short early work in artificial intelligence and recent work critique of his position, with Dijkstra himself making in the principles of design. a closing statement. He graciously accepted this offer. I am grateful to these people for participating in We invited people from a variety of specialties, this debate and to Professor Dijkstra for creating the backgrounds, and interpretations to provide their opening.
    [Show full text]