DOCSLIB.ORG

CMU Common Lisp User's Manual

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
CMU Common Lisp User's Manual CMU Common Lisp Users Manual Rob ert A MacLachlan Editor July CMUCS Scho ol of Computer Science Carnegie Mellon University Pittsburgh PA Sup ersedes Technical Rep orts CMUCS and CMUCS Abstract CMU Common Lisp is an implementation of that Common Lisp is currently supp orted on MIPSpro cessor DECstations Sparcbased workstations from Sun and the IBM RT PC and other p orts are planned All architectures are supp orted under MachaBerkeley Unix binary compatible op erating system The Sparc is also supp orted under SunOS The largest single part of this do cument describ es the Python compiler and the programming styles and techniques that the compiler encourages The rest of the do cument describ es extensions and the implementation dep endentchoices made in developing this implementation of Common Lisp Wehave added several extensions including a source level debugger an interface to Unix system calls a foreign function call interface supp ort for interpro cess communication and remote pro cedure call and other features that provide agoodenvironment for developing Lisp co de This research was sp onsored by The Defense Advanced Research Pro jects Agency Information Science and Technology Oce under the title Research on Parallel Computing ARPA Order No issued byDARPACMO under Contract MDAC The views and conclusions contained in this do cument are those of the author and should not b e interpreted as represen ting the ocial p olicies either expressed or implied of the US government Keywords lisp Common Lisp manual compiler programming language implementation programming environment Contents Introduction Supp ort Lo cal Distribution of CMU Common Lisp Net Distribution of CMU Common Lisp Source Availability Command Line Options Credits Design Choices and Extensions Data Typ es Symb ols Integers Floats Characters Array Initialization Default Interrupts for Lisp Packages The Editor Garbage Collection Describ e The Insp ector The Graphical Interface The TTY Insp ector Load The Reader Running Programs from Lisp Pro cess Accessors Saving a Core Image Pathnames Unix Pathnames Wildcard Pathnames Logical Pathnames Search Lists Predened SearchLists SearchList Op erations Search List Example Filesystem Op erations Wildcard Matching File Name Completion Miscellaneous Filesystem Op erations Time Parsing and Formatting Lisp Library i CONTENTS ii The Debugger Debugger Intro duction The Command Lo op StackFrames Stack Motion How Arguments are Printed Function Names FunnyFrames Debug Tail Recursion Unknown Lo cations and Interrupts Variable Access Variable Value Availability Note On Lexical Variable Access Source Lo cation Printing How the Source is Found Source Lo cation Availability Compiler Policy Control Exiting Commands Information Commands Breakp oint Commands Breakp oint Example Function Tracing Encapsulation Functions Sp ecials The Compiler Compiler Intro duction Calling the Compiler Compilation Units Undened Warnings Interpreting Error Messages The Parts of the Error Message The Original and Actual Source The Pro cessing Path Error Severity Errors During Macro expansion Read Errors Error Message Parameterization Typ es in Python Compile Time Typ e Errors Precise Typ e Checking Weakened Typ e Checking Getting Existing Programs to Run Compiler Policy The Optimize Declaration The OptimizeInterface Declaration Op en Co ding and Inline Expansion Advanced Compiler Use and Eciency Hints Advanced Compiler Intro duction Typ es Optimization Function Call Represen tation of Ob jects Writing Ecient Code More Ab out Typ es in Python CONTENTS iii More Typ es Meaningful Canonicalization Member Typ es Union Typ es The EmptyTyp e Function Typ es The Values Declaration Structure Typ es The FreezeT yp e Declaration Typ e Restrictions Typ e Style Recommendations Typ e Inference Variable Typ e Inference Lo cal Function Typ e Inference Global Function Typ e Inference Op eration Sp ecic Typ e Inference Dynamic Typ e Inference Typ e Check Optimization Source Optimization Let Optimization ConstantFolding Unused Expression Elimination Control Optimization Unreachable Co de Deletion Multiple Values Optimization Source to Source Transformation Style Recommendations Tail Recursion Tail Recursion Exceptions Lo cal Call SelfRecursive Calls Let Calls Closures Lo cal Tail Recursion Return Values Blo ck Compilation Blo ck Compilation Semantics Blo ck Compilation Declarations Compiler Arguments Practical Diculties Context Declarations Context Declaration Example Inline Expansion Inline Expansion Recording SemiInline Expansion The Mayb eInline Declaration Byte Co ded Compilation Ob ject Represen tation Think Before You Use a List Structure Representation Arrays Vectors BitVectors Hashtables Numb ers CONTENTS iv Descriptors NonDescriptor Representations Variables Generic Arithmetic Fixnums Word Integers Floating Point Eciency Sp ecialized Arrays Sp ecialized Structure Slots Interactions With Lo cal Call Represen tation of Characters General Eciency Hints Compile Your Co de Avoid Unnecessary Consing Complex ArgumentSyntax Mapping and Iteration Trace Files and Disassembly Eciency Notes Typ e Uncertainty Eciency Notes and Typ e Checking Represen tation Eciency Notes Verb osityControl Proling Prole Interface Proling Techniques Nested or Recursive Calls Clock resolution Proling overhead Additional Timing Utilities A Note on Timing Benchmarking Techniques UNIX Interface Reading the Command Line Useful Variables Lisp Equivalents for C Routines Typ e Translations System Area Pointers Unix System Calls File Descriptor Streams Making Sense of Mach Return Co des Unix Interrupts Changing Interrupt Handlers Examples of Signal Handlers Event Dispatching with SERVEEVENT Ob ject Sets The SERVEEVENT Function Using SERVEEVENT with Unix File Descriptors .
Load more

Recommended publications
  • Speech Understanding Systems: Summary of Results of the Five-Year Research Effort at Carnegie-Mellon University
    Carnegie Mellon University Research Showcase Computer Science Department School of Computer Science 1-1-1977 Speech understanding systems: summary of results of the five-year research effort at Carnegie-Mellon University. Carnegie-Mellon University.Computer Science Dept. Carnegie Mellon University Follow this and additional works at: http://repository.cmu.edu/compsci Recommended Citation Carnegie-Mellon University.Computer Science Dept., "Speech understanding systems: summary of results of the five-year research effort at Carnegie-Mellon University." (1977). Computer Science Department. Paper 1529. http://repository.cmu.edu/compsci/1529 This Technical Report is brought to you for free and open access by the School of Computer Science at Research Showcase. It has been accepted for inclusion in Computer Science Department by an authorized administrator of Research Showcase. For more information, please contact research- [email protected]. NOTICE WARNING CONCERNING COPYRIGHT RESTRICTIONS: The copyright law of the United States (title 17, U.S. Code) governs the making of photocopies or other reproductions of copyrighted material. Any copying of this document without permission of its author may be prohibited by law. "7? • 3 SPEECH UNDERSTANDING SYSTEMS Summary of Results of the Five-Year Research Effort at Carnegie-Mellon University Carnegie-Mellon University Department of Computer Science Pittsburgh, Pennsylvania 15213 First Version printed September 1976 Present Version printed August 1977 M d R— <h Projects A en, un^coX^no. TSS^l MiF*"™ ™™ B Scientific Research. ™ and monitored by ,h. Air Z^0^!7, PREFACE This report is an augmented version of a report originally issued in September of 1976, during the demonstration at the end of the five-year speech effort.
    [Show full text]
  • An Implementation of Python for Racket
    An Implementation of Python for Racket Pedro Palma Ramos António Menezes Leitão INESC-ID, Instituto Superior Técnico, INESC-ID, Instituto Superior Técnico, Universidade de Lisboa Universidade de Lisboa Rua Alves Redol 9 Rua Alves Redol 9 Lisboa, Portugal Lisboa, Portugal [email protected] [email protected] ABSTRACT Keywords Racket is a descendent of Scheme that is widely used as a Python; Racket; Language implementations; Compilers first language for teaching computer science. To this end, Racket provides DrRacket, a simple but pedagogic IDE. On the other hand, Python is becoming increasingly popular 1. INTRODUCTION in a variety of areas, most notably among novice program- The Racket programming language is a descendent of Scheme, mers. This paper presents an implementation of Python a language that is well-known for its use in introductory for Racket which allows programmers to use DrRacket with programming courses. Racket comes with DrRacket, a ped- Python code, as well as adding Python support for other Dr- agogic IDE [2], used in many schools around the world, as Racket based tools. Our implementation also allows Racket it provides a simple and straightforward interface aimed at programs to take advantage of Python libraries, thus signif- inexperienced programmers. Racket provides different lan- icantly enlarging the number of usable libraries in Racket. guage levels, each one supporting more advanced features, that are used in different phases of the courses, allowing Our proposed solution involves compiling Python code into students to benefit from a smoother learning curve. Fur- semantically equivalent Racket source code. For the run- thermore, Racket and DrRacket support the development of time implementation, we present two different strategies: additional programming languages [13].
    [Show full text]
  • Bringing GNU Emacs to Native Code
    Bringing GNU Emacs to Native Code Andrea Corallo Luca Nassi Nicola Manca [email protected] [email protected] [email protected] CNR-SPIN Genoa, Italy ABSTRACT such a long-standing project. Although this makes it didactic, some Emacs Lisp (Elisp) is the Lisp dialect used by the Emacs text editor limitations prevent the current implementation of Emacs Lisp to family. GNU Emacs can currently execute Elisp code either inter- be appealing for broader use. In this context, performance issues preted or byte-interpreted after it has been compiled to byte-code. represent the main bottleneck, which can be broken down in three In this work we discuss the implementation of an optimizing com- main sub-problems: piler approach for Elisp targeting native code. The native compiler • lack of true multi-threading support, employs the byte-compiler’s internal representation as input and • garbage collection speed, exploits libgccjit to achieve code generation using the GNU Com- • code execution speed. piler Collection (GCC) infrastructure. Generated executables are From now on we will focus on the last of these issues, which con- stored as binary files and can be loaded and unloaded dynamically. stitutes the topic of this work. Most of the functionality of the compiler is written in Elisp itself, The current implementation traditionally approaches the prob- including several optimization passes, paired with a C back-end lem of code execution speed in two ways: to interface with the GNU Emacs core and libgccjit. Though still a work in progress, our implementation is able to bootstrap a func- • Implementing a large number of performance-sensitive prim- tional Emacs and compile all lexically scoped Elisp files, including itive functions (also known as subr) in C.
    [Show full text]
  • The Evolution of Lisp
    1 The Evolution of Lisp Guy L. Steele Jr. Richard P. Gabriel Thinking Machines Corporation Lucid, Inc. 245 First Street 707 Laurel Street Cambridge, Massachusetts 02142 Menlo Park, California 94025 Phone: (617) 234-2860 Phone: (415) 329-8400 FAX: (617) 243-4444 FAX: (415) 329-8480 E-mail: [email protected] E-mail: [email protected] Abstract Lisp is the world’s greatest programming language—or so its proponents think. The structure of Lisp makes it easy to extend the language or even to implement entirely new dialects without starting from scratch. Overall, the evolution of Lisp has been guided more by institutional rivalry, one-upsmanship, and the glee born of technical cleverness that is characteristic of the “hacker culture” than by sober assessments of technical requirements. Nevertheless this process has eventually produced both an industrial- strength programming language, messy but powerful, and a technically pure dialect, small but powerful, that is suitable for use by programming-language theoreticians. We pick up where McCarthy’s paper in the first HOPL conference left off. We trace the development chronologically from the era of the PDP-6, through the heyday of Interlisp and MacLisp, past the ascension and decline of special purpose Lisp machines, to the present era of standardization activities. We then examine the technical evolution of a few representative language features, including both some notable successes and some notable failures, that illuminate design issues that distinguish Lisp from other programming languages. We also discuss the use of Lisp as a laboratory for designing other programming languages. We conclude with some reflections on the forces that have driven the evolution of Lisp.
    [Show full text]
  • Lisp Tutorial
    Gene Kim 9/9/2016 CSC 2/444 Lisp Tutorial About this Document This document was written to accompany an in-person Lisp tutorial. Therefore, the information on this document alone is not likely to be sufficient to get a good understanding of Lisp. However, the functions and operators that are listed here are a good starting point, so you may look up specifications and examples of usage for those functions in the Common Lisp HyperSpec (CLHS) to get an understanding of their usage. Getting Started Note on Common Lisp Implementations I will use Allegro Common Lisp since it is what is used by Len (and my research). However, for the purposes of this class we won’t be using many (if any) features that are specific to common lisp implementations so you may use any of the common lisp implementations available in the URCS servers. If you are using any libraries that are implementation-specific for assignments in this class, I would strongly urge you to rethink your approach if possible. If we (the TAs) deem any of these features to be solving to much of the assigned problem, you will not receive full credit for the assignment. To see if a function is in the Common Lisp specifications see the Common Lisp HyperSpec (CLHS). Simply google the function followed by “clhs” to see if an entry shows up. Available Common Lisp implementations (as far as I know): - Allegro Common Lisp (acl or alisp will start an Allegro REPL) - CMU Common Lisp (cmucl or lisp will start a CMUCL REPL) - Steel Bank Common Lisp (sbcl will start an SBCL REPL) IDE/Editor It is not important for this class to have an extensive IDE since we will be working on small coding projects.
    [Show full text]
  • Artificial Intelligence Research at Carnegie
    ARTIFICIAL INTELLIGENCE production systems as programming languages have been important in AI work at CMU. RESEARCH AT CARNEGIE-MELLON UNIVERSITY The first production systems used at CMU were Newell’s PS and PSG systems. Rychener developed his PSNLST system soon after PSG came into use. In 1975, Edited by Jaime Carbonell Forgy, McDermott, Newell, and Rychener developed the AI research at CMU is closely integrated with other first system in the OPS family. OPS has continued to activities in the Computer Science Department, and to a evolve since that time, the latest version, OPS5, having major degree with ongoing research in the Psychology been completed in 1979. OPS has a served as a basis for Department. Although there are over 50 faculty, staff and many programs, including Langley and Simon’s BACON graduate students involved in various aspects of AI systems and McDermott’s RI expert system. research, there is no administratively (or physically) separate AI laboratory. To underscore the interdisciplinary During the last few years, many production systems nature of much of our AI research, a significant fraction of have been developed to serve as psychological models. (It the projects listed below are joint ventures between is characteristic of psychological modeling that many computer science and psychology. systems must be implemented, for the testing of each new architectural feature requires that a system exhibiting that The history of AI research at Carnegie-Mellon goes back feature be implemented and tested.) Anderson combines production rules with a semantic-network memory in his twenty-five years. The early work was characterized by a ACT systems to model human memory phenomena.
    [Show full text]
  • Emoticons All Over Your Essay? It ● Which Ones Do You Like the Most / Least? Why? Looks Ridiculous
    StarterStStatarrttteerr A LISTENINGLISTENING 1 Work in pairs.p ai rs. LookLo ok ata t theth e peoplepe op le ini n the picturespi ct ur es and discussdis cuss whatwh at youy ou thinkt hink theirt he ir attitudesa tt it ud es tot o the followingfo ll ow in g mightmigh t be:be : ● personalpe rs on al appearancea pp eara nce ● clothescl ot he s ● cosmeticco sm et ic surgerys ur ge ry Hannah, UK Hiro, Japan Marielena, Venezuela 2 Listen to the interviews from a radio programme. 4 Work in pairs. Discuss the questions. Were you right? ● How would you describe young people’s attitudes to appearance, dress and cosmetic surgery in your country? 3 Listen again and answer the following questions. ● How do you think your generation’s attitudes are 1 What does the presenter say about the effect of different from your parents’ or your grandparents’ globalisation on young people around the world? attitudes? 2 What two things does Chris say still influence young ● Would you ever have cosmetic surgery? people’s attitudes to dress and appearance? 3 What does Chris say that young people in the UK have traditionally been? 4 According to Chris, what type of cosmetic surgery has become more popular in Venezuela in recent years? 5 In Japanese working environments, what is expected of employers in terms of dress and appearance? Grammar VOCABULARY People / Travel and GRAMMAR Present perfect presentation adventure simple and continuous 5 Match the words in the box with the definitions. We use the present perfect simple for: events or situations within an unfinished or competitor economist employee unspecified time period.
    [Show full text]
  • “ My Heart Is in the Work.” Businesses
    Carnegie Mellon University has been a birthplace of innovation since its founding in 1900. Today, CMU is a global leader bringing groundbreaking ideas to market and creating successful startup “ My Heart is in the Work.” businesses. Our award-winning faculty are renowned for working closely with students to solve major scientific, Andrew Carnegie, Founder technological and societal challenges. We put a strong November 15, 1900 emphasis on creating things — from art to robots. We have become a model for economic development in forming partnerships with companies such as Uber, Google and Disney. Our students are recruited by some of the world’s most innovative companies. 13,961 37% U.S. 63% International Graduate GLOBAL COMMUNITY STUDENTS 77% U.S. 23% International Undergraduate Students representing 109 countries 1,391 87% U.S. 13% International FACULTY Faculty representing 42 countries 105,255+ 89% U.S. 11% International Alumni representing ALUMNI (LIVING) 145 countries # SCHOOL OF # TIME-BASED/ # INFORMATION 1 COMPUTER 1 NEW MEDIA 1 & TECHNOLOGY SCIENCE U.S. News & World Report, 2016 MANAGEMENT U.S. News & World Report, 2014 U.S. News & World Report, 2016 # SCHOOL OF # COLLEGE OF # BEST FOR 2 DRAMA 5 ENGINEERING 10 NEW HIRES1 The Hollywood Reporter, 2017 U.S. News & World Report, 2017 Wall Street Journal, 2010 # AMONG U.S. # UNIVERSITY % OF COMPUTER 17 UNIVERSITIES 24 IN THE WORLD 49.8 SCIENCE’S FIRST- Times Higher Education Times Higher Education YEAR STUDENTS of London, 2017-18 of London, 2017-18 WERE WOMEN IN 2017 Nearly triple the national average 1 The Wall Street Journal’s poll asked recruiters what schools are tops when looking for new hires.
    [Show full text]
  • A Lisp Oriented Architecture by John W.F
    A Lisp Oriented Architecture by John W.F. McClain Submitted to the Department of Electrical Engineering and Computer Science in partial fulfillment of the requirements for the degrees of Master of Science in Electrical Engineering and Computer Science and Bachelor of Science in Electrical Engineering at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY September 1994 © John W.F. McClain, 1994 The author hereby grants to MIT permission to reproduce and to distribute copies of this thesis document in whole or in part. Signature of Author ...... ;......................... .............. Department of Electrical Engineering and Computer Science August 5th, 1994 Certified by....... ......... ... ...... Th nas F. Knight Jr. Principal Research Scientist 1,,IA £ . Thesis Supervisor Accepted by ....................... 3Frederic R. Morgenthaler Chairman, Depattee, on Graduate Students J 'FROM e ;; "N MfLIT oARIES ..- A Lisp Oriented Architecture by John W.F. McClain Submitted to the Department of Electrical Engineering and Computer Science on August 5th, 1994, in partial fulfillment of the requirements for the degrees of Master of Science in Electrical Engineering and Computer Science and Bachelor of Science in Electrical Engineering Abstract In this thesis I describe LOOP, a new architecture for the efficient execution of pro- grams written in Lisp like languages. LOOP allows Lisp programs to run at high speed without sacrificing safety or ease of programming. LOOP is a 64 bit, long in- struction word architecture with support for generic arithmetic, 64 bit tagged IEEE floats, low cost fine grained read and write barriers, and fast traps. I make estimates for how much these Lisp specific features cost and how much they may speed up the execution of programs written in Lisp.
    [Show full text]
  • 2015 Meeting of the Minds Program [Pdf]
    2 MEETING OF THE MINDS 2015 WELCOME WELCOME TO OUR 20TH MEETING OF THE MINDS. That ‘s right—it is our 20th anniversary and we are especially happy to celebrate this milestone with all of you. Meeting of the Minds is a true campus-wide event that touches all faculty, staff and students who are associated with our university. Twenty years ago, a far-thinking Associate Vice Provost, Barbara Lazarus—joined by an adventuresome graduate of Carnegie Mellon and founding director of the URO, Jessie Ramey— created our campus-wide research symposium aptly named Meeting of the Minds. It started very small with a handful of students. It has grown to be a hallmark of Carnegie Mellon and a model for others. There is a great deal to see and hear today. The abstracts in this booklet provide a good map to begin your journey. Be prepared for the descriptions to come alive in novel and interesting ways. Whether you travel through the poster displays or attend a few oral presentations, watch a performance or contemplate our art installations, you will be dazzled by the diversity and quality of the projects our undergraduates are showcasing. Feel free to visit people you know and those you don’t know. This is a chance to introduce yourself to different academic parts of our campus. There are two important times to keep in mind. At 2:30, President Subra Suresh will deliver a short keynote address in the first floor Kirr Commons area. We will also hold a drawing for participating students for a smart watch and a Fitbit, and make announcements for the final rounds of particular competitions.
    [Show full text]
  • GNU/Linux AI & Alife HOWTO
    GNU/Linux AI & Alife HOWTO GNU/Linux AI & Alife HOWTO Table of Contents GNU/Linux AI & Alife HOWTO......................................................................................................................1 by John Eikenberry..................................................................................................................................1 1. Introduction..........................................................................................................................................1 2. Traditional Artificial Intelligence........................................................................................................1 3. Connectionism.....................................................................................................................................1 4. Evolutionary Computing......................................................................................................................1 5. Alife & Complex Systems...................................................................................................................1 6. Agents & Robotics...............................................................................................................................1 7. Programming languages.......................................................................................................................2 8. Missing & Dead...................................................................................................................................2 1. Introduction.........................................................................................................................................2
    [Show full text]
  • Episodic Memory Representation in a Knowledge Base, with Application to Event Monitoring and Event Detection
    Episodic Memory Representation in a Knowledge Base, with Application to Event Monitoring and Event Detection Master’s Thesis Engin C¸ınar S¸ahin Committee Members Scott E. Fahlman Eric Nyberg Stephen F. Smith Language Technologies Institute School of Computer Science Carnegie Mellon University Pittsburgh, PA, 15213 Abstract The thesis explores the use of a knowledge-based AI system to assist people in executing proce- dures and detecting events in an observed world. We extend the Scone knowledge-base system with open-ended facilities for representing time and events. Then we use this episodic knowledge repre- sentation as the foundation for our event monitoring and event detection algorithms. This approach lets us represent and reason about three fundamental aspects of the observed events: 1. their ontological character and what entities take part in these events (e.g. buying is a kind of transaction that involves an agent, a seller, money and goods) 2. how events change the world over time (e.g. after a buy event the agent has the goods rather than the money) 3. how events may be composed of other subevents (i.e. a buy event may be composed of giving money and receiving goods) We illustrate knowledge-based solutions to the event monitoring problem in the conference organization domain and to the event detection problem in the national security domain. i Acknowledgements First, I would like to thank my advisor Scott Fahlman for letting me learn so much from him. His invaluable experience and constant support helped me go through the ups and downs of research. Working with him, his passion and excitement for knowledge representation and reasoning has passed on to me.
    [Show full text]