DOCSLIB.ORG

Factor: a Dynamic Stack-Based Programming Language

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Factor: a Dynamic Stack-Based Programming Language Factor: A Dynamic Stack-based Programming Language Slava Pestov Daniel Ehrenberg Joe Groff Stack Effects LLC Carleton College Durian Software [email protected] [email protected] [email protected] Abstract high-performance implementation and interactive develop- Factor is a new dynamic object-oriented programming lan- ment environment make it notable. guage. It began as an embedded scripting language and Factor programs look very different from programs in evolved to a mature application development language. The most other programming languages. At the most basic level, language has a simple execution model and is based on the function calls and arithmetic use postfix syntax, rather than manipulation of data on a stack. An advanced metaprogram- prefix or infix as in most programming languages. Factor ming system provides means for easily extending the lan- provides local variables, but they are used in only a small guage. Thus, Factor allows programmers to use the right minority of procedures because its language features allow features for their problem domain. The Factor implemen- most code to be comfortably written in a point-free style. tation is self-hosting, featuring an interactive development Factor is an object-oriented language with an object sys- environment and an optimizing compiler. In this paper, the tem centered around CLOS-inspired generic functions in language and its implementation are presented. place of traditional message passing. To make Factor suit- able for development of larger applications, it has a robust Categories and Subject Descriptors D.3.3 [Programming module system. Factor’s metaprogramming system allows Languages]: Language Constructs and Features; D.3.4 for arbitrary extension of syntax and for compile-time com- [Programming Languages]: Processors — Compilers, Op- putation. Factor allows the clean integration of high-level timization and low-level code with extensive support for calling li- braries in other languages and for efficient manipulation of General Terms Languages, Design, Performance binary data. Keywords Factor, dynamic languages, stack-based lan- Factor has an advanced, high-performance implementa- guages tion. We believe good support for interactive development is invaluable, and for this reason Factor allows programmers to 1. Introduction test and reload code as it runs. Our ahead-of-time optimiz- ing compiler and efficient runtime system can remove much Factor is a dynamic stack-based programming language. It of the overhead of high-level language features. Together, was originally conceived as an experiment to create a stack- these features make Factor a useful language for writing both based language practical for modern programming tasks. quick scripts and large programs in a high-level way. It was inspired by earlier stack-based languages like Forth Factor is an open-source project and the product of [33] and Joy [44]. The stack-based model allows for con- many contributors. It is available for free download from cise and flexible syntax with a high degree of factoring and http://factorcode.org. code reuse. Driven by the needs of its users, Factor gradu- This paper contributes the following: ally evolved from this base into a dynamic, object-oriented programming language. Although there is little truly novel to the Factor language or implementation, Factor’s combi- nation of the stack-based paradigm with functional, object- • Abstractions and checking for managing the flow of data oriented, and low-level programming features alongside its in stack-based languages (Section 2.1) • A CLOS- and Dylan-inspired object system, featuring generic functions built upon a metaobject protocol and a flexible type system (Section 2.2) Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed • An expressive and easy-to-use system for staged metapro- for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute gramming (Section 2.3) to lists, requires prior specific permission and/or a fee. • DLS 2010, October 18, 2010, Reno/Tahoe, Nevada, USA. The design of a foreign function interface and low-level Copyright c 2010 ACM 978-1-4503-0405-4/10/10. $10.00 capabilities in a dynamic language (Section 2.4) 43 "data.txt" utf8 file-lines On the second line, the standard library word head pops 10 head two objects from the stack, the array of lines of text result- ing from file-lines and the integer 10, and pushes a new Figure 1: Retrieving the first ten lines of a file array containing a fixed number of elements from the be- ginning of the input array. The final result of the example is an array containing the first ten lines of text from the file • The design of an effective optimizing compiler for a "data.txt" as strings. dynamic language (Section 3) The two lines of code in Figure 1 can be understood • A case study in the evolution of a dynamic language either as separate programs or concatenated to form a single (Secton 4) program. The latter case has the effect of first running the Much of Factor’s features and implementation are not rad- first program and then the second. Note that in Factor source ically different from previous dynamic languages. It is the code, newlines between words are interpreted the same as combination of these features into a useful system that make spaces. Factor notable. 2.1.2 Higher-order Programming 2. Language Design Factor supports higher-order functions (functions which take Factor combines features from existing languages with new functions as arguments). In the Joy tradition, we refer to innovations. We focus here on the prominent unique aspects higher-order functions as combinators and to anonymous of Factor’s design: our contributions to the stack-based lan- functions as quotations. Quotation literals are pushed on the guage paradigm, the module and object systems, tools for stack by surrounding a series of tokens with [ and ], which metaprogramming, and low-level binary data manipulation delays the evaluation of the surrounded code and stores it support. in a quotation object. Combinators are invoked like any other word, with quotation objects as parameters. In Factor, 2.1 Stack-based Programming Language all control flow is expressed using combinators, including In Factor, as in Forth and Joy [44], function parameters are common branching and looping constructs usually given passed by pushing them on an operand stack prior to per- special syntax in other languages. Some examples: forming the function call. We introduce two original contri- • if is a combinator taking three inputs from the stack: a butions: a set of combinators which replace the stack shuf- boolean value, a “then” branch quotation, and an “else” fling words found in other stack languages, and a syntax for branch quotation. For example, partial application. 2 even? [ "OK" ] [ "Cosmic rays detected" ] 2.1.1 Postfix Syntax if tests whether the value 2 is even, placing the string "OK" In stack-based languages, a function call is written as a sin- on the stack if so or "Cosmic rays detected" if not. gle token, which leads to the term “word” being used in • place of “function”, in the Forth tradition. This contrasts The each standard library word implements what other with mainstream programming languages, in which func- languages call a “for-each” loop. It iterates in order over tion call syntax combines the function name with a list of the elements of an array (or other sequence, see 2.2.2), parameters. Symbols that traditionally behave as operators invoking a quotation with each element as the input pa- with infix syntax in other languages, such as +, -, *, and /, rameter on each iteration. For example, { } are normal postfix words in Factor and receive no special "veni" "vidi" "vici" [ print ] each syntactic treatment. Languages which use an operand stack will print the strings "veni", "vidi", and "vici" to the { } in this manner have been called concatenative, because they console in order. (The and delimiters create a literal have the property that programs are created by “concatenat- array object in the same manner [ and ] create a quota- ing” (or “composing”) smaller programs. In this way, words tion.) can be seen as functions which take and return a stack [30]. • reduce is a variation of each that accumulates a value Literals in a stack language, such as "data.txt" and 10 between iterations. From the top of the stack, it takes an in Figure 1, can be thought of as functions that push them- array, an initial accumulator value, and a quotation. On selves on the stack, making the values available to subse- each iteration, it passes to the provided quotation the next quent word calls which pop them off the stack. Some words, item in the array along with the accumulated value thus such as utf8, can also behave as literal symbols that push far. For example, themselves on the stack. file-lines consumes two objects { 1234} 0 [ + ] reduce from the stack, the filename string "data.txt" and the en- will sum the numbers 1, 2, 3, and 4, and coding symbol utf8, and pushes back an array of strings, { 1234} 1 [ * ] reduce the contents of the specified file broken into lines of text. will multiply them. 44 [ "#" head? not ] filter : tail-factorial ( accumulator n -- n! ) [ string>number ] map dup 0 = 0 [ + ] reduce [ drop ] [[*][1-]bitail-factorial ] Figure 2: Summing the numerical value of array elements if ; not beginning with # : factorial ( n -- n! ) 1 swap (factorial) ; • map iterates over its input array like each but addition- ally collects the output value from each invocation of the quotation into a new array.
Load more

Recommended publications
  • A New Macro-Programming Paradigm in Data Collection Sensor Networks
    SpaceTime Oriented Programming: A New Macro-Programming Paradigm in Data Collection Sensor Networks Hiroshi Wada and Junichi Suzuki Department of Computer Science University of Massachusetts, Boston Program Insert This paper proposes a new programming paradigm for wireless sensor networks (WSNs). It is designed to significantly reduce the complexity of WSN programming by providing a new high- level programming abstraction to specify spatio-temporal data collections in a WSN from a global viewpoint as a whole rather than sensor nodes as individuals. Abstract SpaceTime Oriented Programming (STOP) is new macro-programming paradigm for wireless sensor networks (WSNs) in that it supports both spatial and temporal aspects of sensor data and it provides a high-level abstraction to express data collections from a global viewpoint for WSNs as a whole rather than sensor nodes as individuals. STOP treats space and time as first-class citizens and combines them as spacetime continuum. A spacetime is a three dimensional object that consists of a two special dimensions and a time playing the role of the third dimension. STOP allows application developers to program data collections to spacetime, and abstracts away the details in WSNs, such as how many nodes are deployed in a WSN, how nodes are connected with each other, and how to route data queries in a WSN. Moreover, STOP provides a uniform means to specify data collections for the past and future. Using the STOP application programming interfaces (APIs), application programs (STOP macro programs) can obtain a “snapshot” space at a given time in a given spatial resolutions (e.g., a space containing data on at least 60% of nodes in a give spacetime at 30 minutes ago.) and a discrete set of spaces that meet specified spatial and temporal resolutions.
    [Show full text]
  • SQL Processing with SAS® Tip Sheet
    SQL Processing with SAS® Tip Sheet This tip sheet is associated with the SAS® Certified Professional Prep Guide Advanced Programming Using SAS® 9.4. For more information, visit www.sas.com/certify Basic Queries ModifyingBasic Queries Columns PROC SQL <options>; SELECT col-name SELECT column-1 <, ...column-n> LABEL= LABEL=’column label’ FROM input-table <WHERE expression> SELECT col-name <GROUP BY col-name> FORMAT= FORMAT=format. <HAVING expression> <ORDER BY col-name> <DESC> <,...col-name>; Creating a SELECT col-name AS SQL Query Order of Execution: new column new-col-name Filtering Clause Description WHERE CALCULATED new columns new-col-name SELECT Retrieve data from a table FROM Choose and join tables Modifying Rows WHERE Filter the data GROUP BY Aggregate the data INSERT INTO table SET column-name=value HAVING Filter the aggregate data <, ...column-name=value>; ORDER BY Sort the final data Inserting rows INSERT INTO table <(column-list)> into tables VALUES (value<,...value>); INSERT INTO table <(column-list)> Managing Tables SELECT column-1<,...column-n> FROM input-table; CREATE TABLE table-name Eliminating SELECT DISTINCT CREATE TABLE (column-specification-1<, duplicate rows col-name<,...col-name> ...column-specification-n>); WHERE col-name IN DESCRIBE TABLE table-name-1 DESCRIBE TABLE (value1, value2, ...) <,...table-name-n>; WHERE col-name LIKE “_string%” DROP TABLE table-name-1 DROP TABLE WHERE col-name BETWEEN <,...table-name-n>; Filtering value AND value rows WHERE col-name IS NULL WHERE date-value Managing Views “<01JAN2019>”d WHERE time-value “<14:45:35>”t CREATE VIEW CREATE VIEW table-name AS query; WHERE datetime-value “<01JAN201914:45:35>”dt DESCRIBE VIEW view-name-1 DESCRIBE VIEW <,...view-name-n>; Remerging Summary Statistics DROP VIEW DROP VIEW view-name-1 <,...view-name-n>; SELECT col-name, summary function(argument) FROM input table; Copyright © 2019 SAS Institute Inc.
    [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]
  • Knowledge Based Engineering: Between AI and CAD
    Advanced Engineering Informatics 26 (2012) 159–179 Contents lists available at SciVerse ScienceDirect Advanced Engineering Informatics journal homepage: www.elsevier.com/locate/aei Knowledge based engineering: Between AI and CAD. Review of a language based technology to support engineering design ⇑ Gianfranco La Rocca Faculty of Aerospace Engineering, Delft University of Technology, Chair of Flight Performance and Propulsion, Kluyverweg 1, 2629HS Delft, The Netherlands article info abstract Article history: Knowledge based engineering (KBE) is a relatively young technology with an enormous potential for Available online 16 March 2012 engineering design applications. Unfortunately the amount of dedicated literature available to date is quite low and dispersed. This has not promoted the diffusion of KBE in the world of industry and acade- Keywords: mia, neither has it contributed to enhancing the level of understanding of its technological fundamentals. Knowledge based engineering The scope of this paper is to offer a broad technological review of KBE in the attempt to fill the current Knowledge engineering information gap. The artificial intelligence roots of KBE are briefly discussed and the main differences and Engineering design similarities with respect to classical knowledge based systems and modern general purpose CAD systems Generative design highlighted. The programming approach, which is a distinctive aspect of state-of-the-art KBE systems, is Knowledge based design Rule based design discussed in detail, to illustrate its effectiveness in capturing and re-using engineering knowledge to automate large portions of the design process. The evolution and trends of KBE systems are investigated and, to conclude, a list of recommendations and expectations for the KBE systems of the future is provided.
    [Show full text]
  • Rexx to the Rescue!
    Session: G9 Rexx to the Rescue! Damon Anderson Anixter May 21, 2008 • 9:45 a.m. – 10:45 a.m. Platform: DB2 for z/OS Rexx is a powerful tool that can be used in your daily life as an z/OS DB2 Professional. This presentation will cover setup and execution for the novice. It will include Edit macro coding examples used by DBA staff to solve everyday tasks. DB2 data access techniques will be covered as will an example of calling a stored procedure. Examples of several homemade DBA tools built with Rexx will be provided. Damon Anderson is a Senior Technical DBA and Technical Architect at Anixter Inc. He has extensive experience in DB2 and IMS, data replication, ebusiness, Disaster Recovery, REXX, ISPF Dialog Manager, and related third-party tools and technologies. He is a certified DB2 Database Administrator. He has written articles for the IDUG Solutions Journal and presented at IDUG and regional user groups. Damon can be reached at [email protected] 1 Rexx to the Rescue! 5 Key Bullet Points: • Setting up and executing your first Rexx exec • Rexx execs versus edit macros • Edit macro capabilities and examples • Using Rexx with DB2 data • Examples to clone data, compare databases, generate utilities and more. 2 Agenda: • Overview of Anixter’s business, systems environment • The Rexx “Setup Problem” Knowing about Rexx but not knowing how to start using it. • Rexx execs versus Edit macros, including coding your first “script” macro. • The setup and syntax for accessing DB2 • Discuss examples for the purpose of providing tips for your Rexx execs • A few random tips along the way.
    [Show full text]
  • The Semantics of Syntax Applying Denotational Semantics to Hygienic Macro Systems
    The Semantics of Syntax Applying Denotational Semantics to Hygienic Macro Systems Neelakantan R. Krishnaswami University of Birmingham <[email protected]> 1. Introduction body of a macro definition do not interfere with names oc- Typically, when semanticists hear the words “Scheme” or curring in the macro’s arguments. Consider this definition of and “Lisp”, what comes to mind is “untyped lambda calculus a short-circuiting operator: plus higher-order state and first-class control”. Given our (define-syntax and typical concerns, this seems to be the essence of Scheme: it (syntax-rules () is a dynamically typed applied lambda calculus that sup- ((and e1 e2) (let ((tmp e1)) ports mutable data and exposes first-class continuations to (if tmp the programmer. These features expose a complete com- e2 putational substrate to programmers so elegant that it can tmp))))) even be characterized mathematically; every monadically- representable effect can be implemented with state and first- In this definition, even if the variable tmp occurs freely class control [4]. in e2, it will not be in the scope of the variable definition However, these days even mundane languages like Java in the body of the and macro. As a result, it is important to support higher-order functions and state. So from the point interpret the body of the macro not merely as a piece of raw of view of a working programmer, the most distinctive fea- syntax, but as an alpha-equivalence class. ture of Scheme is something quite different – its support for 2.2 Open Recursion macros. The intuitive explanation is that a macro is a way of defining rewrites on abstract syntax trees.
    [Show full text]
  • Lisp: Final Thoughts
    20 Lisp: Final Thoughts Both Lisp and Prolog are based on formal mathematical models of computation: Prolog on logic and theorem proving, Lisp on the theory of recursive functions. This sets these languages apart from more traditional languages whose architecture is just an abstraction across the architecture of the underlying computing (von Neumann) hardware. By deriving their syntax and semantics from mathematical notations, Lisp and Prolog inherit both expressive power and clarity. Although Prolog, the newer of the two languages, has remained close to its theoretical roots, Lisp has been extended until it is no longer a purely functional programming language. The primary culprit for this diaspora was the Lisp community itself. The pure lisp core of the language is primarily an assembly language for building more complex data structures and search algorithms. Thus it was natural that each group of researchers or developers would “assemble” the Lisp environment that best suited their needs. After several decades of this the various dialects of Lisp were basically incompatible. The 1980s saw the desire to replace these multiple dialects with a core Common Lisp, which also included an object system, CLOS. Common Lisp is the Lisp language used in Part III. But the primary power of Lisp is the fact, as pointed out many times in Part III, that the data and commands of this language have a uniform structure. This supports the building of what we call meta-interpreters, or similarly, the use of meta-linguistic abstraction. This, simply put, is the ability of the program designer to build interpreters within Lisp (or Prolog) to interpret other suitably designed structures in the language.
    [Show full text]
  • Concatenative Programming
    Concatenative Programming From Ivory to Metal Jon Purdy ● Why Concatenative Programming Matters (2012) ● Spaceport (2012–2013) Compiler engineering ● Facebook (2013–2014) Site integrity infrastructure (Haxl) ● There Is No Fork: An Abstraction for Efficient, Concurrent, and Concise Data Access (ICFP 2014) ● Xamarin/Microsoft (2014–2017) Mono runtime (performance, GC) What I Want in a ● Prioritize reading & modifying code over writing it Programming ● Be expressive—syntax closely Language mirroring high-level semantics ● Encourage “good” code (reusable, refactorable, testable, &c.) ● “Make me do what I want anyway” ● Have an “obvious” efficient mapping to real hardware (C) ● Be small—easy to understand & implement tools for ● Be a good citizen—FFI, embedding ● Don’t “assume you’re the world” ● Forth (1970) Notable Chuck Moore Concatenative ● PostScript (1982) Warnock, Geschke, & Paxton Programming ● Joy (2001) Languages Manfred von Thun ● Factor (2003) Slava Pestov &al. ● Cat (2006) Christopher Diggins ● Kitten (2011) Jon Purdy ● Popr (2012) Dustin DeWeese ● … History Three ● Lambda Calculus (1930s) Alonzo Church Formal Systems of ● Turing Machine (1930s) Computation Alan Turing ● Recursive Functions (1930s) Kurt Gödel Church’s Lambdas e ::= x Variables λx.x ≅ λy.y | λx. e Functions λx.(λy.x) ≅ λy.(λz.y) | e1 e2 Applications λx.M[x] ⇒ λy.M[y] α-conversion (λx.λy.λz.xz(yz))(λx.λy.x)(λx.λy.x) ≅ (λy.λz.(λx.λy.x)z(yz))(λx.λy.x) (λx.M)E ⇒ M[E/x] β-reduction ≅ λz.(λx.λy.x)z((λx.λy.x)z) ≅ λz.(λx.λy.x)z((λx.λy.x)z) ≅ λz.z Turing’s Machines ⟨ ⟩ M
    [Show full text]
  • Metaprogramming with Julia
    Metaprogramming with Julia https://szufel.pl Programmers effort vs execution speed Octave R Python Matlab time, time, log scale - C JavaScript Java Go Julia C rozmiar kodu Sourcewego w KB Source: http://www.oceanographerschoice.com/2016/03/the-julia-language-is-the-way-of-the-future/ 2 Metaprogramming „Metaprogramming is a programming technique in which computer programs have the ability to treat other programs as their data. It means that a program can be designed to read, generate, analyze or transform other programs, and even modify itself while running.” (source: Wikipedia) julia> code = Meta.parse("x=5") :(x = 5) julia> dump(code) Expr head: Symbol = args: Array{Any}((2,)) 1: Symbol x 2: Int64 5 3 Metaprogramming (cont.) julia> code = Meta.parse("x=5") :(x = 5) julia> dump(code) Expr head: Symbol = args: Array{Any}((2,)) 1: Symbol x 2: Int64 5 julia> eval(code) 5 julia> x 5 4 Julia Compiler system not quite accurate picture... Source: https://www.researchgate.net/ publication/301876510_High- 5 level_GPU_programming_in_Julia Example 1. Select a field from an object function getValueOfA(x) return x.a end function getValueOf(x, name::String) return getproperty(x, Symbol(name)) end function getValueOf2(name::String) field = Symbol(name) code = quote (obj) -> obj.$field end return eval(code) end function getValueOf3(name::String) return eval(Meta.parse("obj -> obj.$name")) end 6 Let’s test using BenchmarkTools struct MyStruct a b end x = MyStruct(5,6) @btime getValueOfA($x) @btime getValueOf($x,"a") const getVal2 = getValueOf2("a") @btime
    [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]
  • ASDF 3, Or Why Lisp Is Now an Acceptable Scripting Language (Extended Version)
    ASDF 3, or Why Lisp is Now an Acceptable Scripting Language (Extended version) François-René Rideau Google [email protected] Abstract libraries, or network services; one can scale them into large, main- ASDF, the de facto standard build system for Common Lisp, has tainable and modular systems; and one can make those new ser- been vastly improved between 2009 and 2014. These and other im- vices available to other programs via the command-line as well as provements finally bring Common Lisp up to par with "scripting via network protocols, etc. languages" in terms of ease of writing and deploying portable code The last barrier to making that possible was the lack of a that can access and "glue" together functionality from the underly- portable way to build and deploy code so a same script can run ing system or external programs. "Scripts" can thus be written in unmodified for many users on one or many machines using one or Common Lisp, and take advantage of its expressive power, well- many different compilers. This was solved by ASDF 3. defined semantics, and efficient implementations. We describe the ASDF has been the de facto standard build system for portable most salient improvements in ASDF and how they enable previ- CL software since shortly after its release by Dan Barlow in 2002 ously difficult and portably impossible uses of the programming (Barlow 2004). The purpose of a build system is to enable divi- language. We discuss past and future challenges in improving this sion of labor in software development: source code is organized key piece of software infrastructure, and what approaches did or in separately-developed components that depend on other compo- didn’t work in bringing change to the Common Lisp community.
    [Show full text]
  • Dynamic Economics Quantitative Methods and Applications to Macro and Micro
    Dynamic Economics Quantitative Methods and Applications to Macro and Micro J¶er^ome Adda and Nicola Pavoni MACT1 2003-2004. I Overview Dynamic Programming Theory ² { Contraction mapping theorem. { Euler equation Numerical Methods ² Econometric Methods ² Applications ² MACT1 2003-2004. I Numerical Methods Examples: Cake Eating Deterministic Cake eating: ² V (K) = max u(c) + ¯V (K c) c ¡ with { K: size of cake. K 0 ¸ { c: amount of cake consumed. c 0 ¸ Stochastic Cake eating: ² V (K; y) = max u(c) + ¯Ey0 V (K0; y0) c K0 = K c + y ¡ Discrete Cake eating: ² V (K; ") = max[u(K; "); ¯E 0 V (½K; "0)] ½ [0; 1] " 2 MACT1 2003-2004. I How do we Solve These Models? Not necessarily a closed form solution for V (:). ² Numerical approximations. ² MACT1 2003-2004. I Solution Methods Value function iterations. (Contraction Mapping Th.) ² Policy function iterations. (Contraction Mapping Th.) ² Projection methods. (Euler equation) ² MACT1 2003-2004. I Value Function Iterations Value Function Iterations Vn(S) = max u(action; S) + ¯EVn 1(S0) action ¡ Vn(:) = T Vn 1(:) ¡ Take advantage of the Contraction Mapping Theorem. If T is ² the contraction operator, we use the fact that d(Vn; Vn 1) ¯d(Vn 1; Vn 2) ¡ · ¡ ¡ n Vn(:) = T V0(:) This guarantee that: ² 1. successive iterations will converge to the (unique) ¯xed point. 2. starting guess for V0 can be arbitrary. Successive iterations: ² { Start with a given V0(:). Usually V0(:) = 0. { Compute V1(:) = T V0(:) { Iterate Vn(:) = T Vn 1(:) ¡ { Stop when d(Vn; Vn 1) < ". ¡ MACT1 2003-2004. I Value Function Iterations: Deterministic Cake Eating Model: ² V (K) = max u(c) + ¯V (K c) c ¡ Can be rewritten as: ² V (K) = max u(K K0) + ¯V (K0) K0 ¡ The iterations will be on ² Vn(K) = max u(K K0) + ¯Vn 1(K0) K0 ¡ ¡ example: take u(c) = ln(c).
    [Show full text]