Typed Closure Conversion*

Total Page:16

File Type:pdf, Size:1020Kb

Typed Closure Conversion* Typ ed Closure Conversion y Yasuhiko Minamide Greg Morrisett Rob ert Harp er Research Institute for Mathematical Sciences Scho ol of Computer Science Scho ol of Computer Science Kyoto University Carnegie Mellon University Carnegie Mellon University Kyoto Japan Pittsburgh PA Pittsburgh PA nankurimskyotouacjp jgmorriscscmuedu rwhcscmuedu data Functions with free variables are replaced by co de ab Abstract stracted on an extra environment parameter Free variables Closureconversion is a program transformation used by in the b o dy of the function are replaced by references to the compilers to separate co de from data Previous accounts environment The abstracted co de is partially applied to an of closure conversion use only untyped target languages Re explicitly constructed environmentproviding the bindings cent studies show that translating to typed target languages for these variables This partial application of the co de to is a useful metho dology for building compilers b ecause a its environment is in fact susp ended until the function is ac compiler can use the typ es to implement ecient data rep tually applied to its argument the susp ended application resentations calling conventions and tagfree garbage col called a closure is a data structure consisting of a piece of lection Furthermore typ ebased translations facilitate se pure co de and a representation of its environment curity and debugging through automatic typ e checking as A critical decision in closure conversion is the choice well as correctness arguments through the metho d of logical of representation for the environment whether to use a relations at FAMlike linked CAMlikeorhybrid represen ta We present closure conversion as a typ edirected and tion The choice of representation is inuenced byade typ epreserving translation for b oth the simplytyp ed and sire to minimize closure creation time the space consumed the p olymorphic calculus Our translations are based on by the environment and the time to access a variable in a simple closures as ob jects principle higherorder func the environment An imp ortant prop erty of closure tions are viewed as ob jects consisting of a single metho d conversion is that the representation of the environmentis the co de and a single instance variable the environment private to the closure This aords considerable exibility In the simplytyp ed case the PierceTurner mo del of ob in the representation of environments and is thus exploited ject typing where ob jects are packages of existential typ e to go o d advantage by Shao and App el and Wand and suces In the p olymorphic case more careful tracking of Steckler typ e sharing is required We exploit a variant of the Harp er Previous accounts consider closure conversion as a trans Lillibri dge translucenttyp e formalism to characterize the formation to untyped terms even if the source language is typ es of p olymorphi c closures typ ed This is adequate for compilers that make little or no use of typ es in the back end or at run time However when compiling typ ed languages it is often Intro duction advantageous to propagate typ e information through each stage of the compiler and to make use of typ es at link or Closureconversion is a program even run time For example Leroys representation analy transformation that achieves a separation b etween co de and sis uses typ es to determine pro cedure calling conven This researchwas sp onsored in part by the Advanced Research tions and Ohoris record compilation uses a representa Pro jects Agency CSTO under the title The Fox Pro ject Advanced tion of typ es at run time to access comp onents of a record Languages for Systems Software ARPA Order No C issued Compilation strategies for p olymorphic languages suchas by ESCENS under Contract No FC and in part those prop osed by Morrison et al and Harp er and Mor by the National Science Foundation under Grant No CCR and in part by the Isaac Newton Institute for Mathematical Sciences risett rely on analyzing typ es at run time to supp ort Cambridge England The views and conclusions contained in this unboxed representations and nonparametric op erators in do cument are those of the authors and should not b e interpreted as cluding printing and structural equalityTagfree garbage representing ocial p olicies either expressed or implied of ARPA collection for b oth monomorphic and p olymor or the US Government Any opinions ndings and conclusions or recommendations expressed in this material are those of the au phic programming languages relies on analyzing typ es at thors and do not necessarily reect the views of the National Science run time to determine the size and la yout of ob jects in the Foundation heap To supp ort any of these implementation strategies y This researchwas p erformed while the rst author was visiting it is necessary to propagate typ e information through clo the Fox Pro ject at Carnegie Mellon University sure conversion and into the generated co de The purp ose of this pap er is to demonstrate how this can b e done in b oth a simplytyp ed and a p olymorphic setting We present closure conversion as an example of a type directed and typepreserving translation In general such the companion technical rep ort translations transform b oth a term and its typ e p ossibly relying on typ e information to guide the translation Thus Closure conversion is discussed in descriptions of various each stage of the compiler can b e viewed as a typ epreserving functional language compilers It is sim translation b etween typ ed intermediate languages Exam ilar to lifting in that it eliminates free variables in ples of such translations havebeengiven byLeroy the b o dies of abstractions but diers by making the rep Ohori Harp er and Lillibri dge and Harp er and Mor resentation of the environment explicit as a data structure risett In contrasttotyp efree compilation strategies Making the environment explicit is imp ortant b ecause it ex these translations make essential use of typ e information poses environment construction and variable lo okup to an during translation to increase the time or space eciency of optimizer Furthermore Shao and App el show that not all programs In addition to the practical advantages of this ap environment representations are safe for space and proach typ edirected translation also facilitates the work of thus cho osing a go o d environment representation is an im the compiler writer In particular the typing prop erties of p ortant part of compilation Wand and Steckler have the intermediate co de may b e exploited to give clear concise wo optimizations of the basic closure conversion consider t pro ofs of compiler correctness through the metho d of logical strategy called selective and lightweight closure conversion relations Furthermore the intermediate and provide a correctness pro of for each of these in an un co de of the compiler can b e mechanicall y typ edchecked typ ed setting Hannan recasts Wands work into a typ ed an imp ortant debugging to ol for the compiler writer Fi setting and provides correctness pro ofs for one of Wands nallycheckable typ ed intermediate languages are a promis optimizations Hannans translation like ours is given as a ing technique for ensuring safety prop erties of programs in deductive system but he do es not consider the imp ortant a distributed environment issue of environment representation preferring an abstract account instead nor do es he consider the typing prop erties We describ e closure conversion for the simplytyp ed of the closureconverted co de Finally neither Wand nor calculus and the predicative fragment of the p olymorphic Hannan consider closure conversion under a typ epassing calculus In eachcasewe present closure conversion in interpretation of p olymorphism two stages The rst stage called abstract closureconver yp edirected translation to an intermediate lan sionisa t The remainder of this pap er is organized as follows In guage with a primitive notion of closures We describ e the Section we giveanoverview of closure conversion and translation as a deductive system where the choice of en the typing issues involved for the simplytyp ed calculus vironment representations may b e indep endently made for In Section weprovide the details of our typ epreserving each closure We argue that various representations consid transform for the simplytyp ed case In Section we givean ered in the literature such as the FAM or CAM as overview of closure conversion and the typing issues involved well as hybrid representations can all b e explained in for the predicative fragment of the p olymorphic calculus this uniform framework We establish the correctness of the The formal development of this conversion is given in Section translation once for all environment representations The second stage called closurerepresentationisan other typ edirected translation where closures are imple mented in terms of generic typ ed calculus primitives The Overview of SimplyTyp ed Closure Con main idea is to represent closures as objects consisting of a single metho d the co de and a single instance variable version the environment We show that in the simplytyp ed case The main ideas of closure conversion are illustrated by con Pierce and Turners typ e discipli ne for ob jectoriented pro sidering the following ML program gramming may b e used to characterize the typ es of clo sures In particular we use existential typ e abstraction to let val x ensure the privacy of environment
Recommended publications
  • Lecture 4. Higher-Order Functions Functional Programming
    Lecture 4. Higher-order functions Functional Programming [Faculty of Science Information and Computing Sciences] 0 I function call and return as only control-flow primitive I no loops, break, continue, goto I (almost) unique types I no inheritance hell I high-level declarative data-structures I no explicit reference-based data structures Goal of typed purely functional programming Keep programs easy to reason about by I data-flow only through function arguments and return values I no hidden data-flow through mutable variables/state [Faculty of Science Information and Computing Sciences] 1 I (almost) unique types I no inheritance hell I high-level declarative data-structures I no explicit reference-based data structures Goal of typed purely functional programming Keep programs easy to reason about by I data-flow only through function arguments and return values I no hidden data-flow through mutable variables/state I function call and return as only control-flow primitive I no loops, break, continue, goto [Faculty of Science Information and Computing Sciences] 1 I high-level declarative data-structures I no explicit reference-based data structures Goal of typed purely functional programming Keep programs easy to reason about by I data-flow only through function arguments and return values I no hidden data-flow through mutable variables/state I function call and return as only control-flow primitive I no loops, break, continue, goto I (almost) unique types I no inheritance hell [Faculty of Science Information and Computing Sciences] 1 Goal
    [Show full text]
  • Higher-Order Functions 15-150: Principles of Functional Programming – Lecture 13
    Higher-order Functions 15-150: Principles of Functional Programming { Lecture 13 Giselle Reis By now you might feel like you have a pretty good idea of what is going on in functional program- ming, but in reality we have used only a fragment of the language. In this lecture we see what more we can do and what gives the name functional to this paradigm. Let's take a step back and look at ML's typing system: we have basic types (such as int, string, etc.), tuples of types (t*t' ) and functions of a type to a type (t ->t' ). In a grammar style (where α is a basic type): τ ::= α j τ ∗ τ j τ ! τ What types allowed by this grammar have we not used so far? Well, we could, for instance, have a function below a tuple. Or even a function within a function, couldn't we? The following are completely valid types: int*(int -> int) int ->(int -> int) (int -> int) -> int The first one is a pair in which the first element is an integer and the second one is a function from integers to integers. The second one is a function from integers to functions (which have type int -> int). The third type is a function from functions to integers. The two last types are examples of higher-order functions1, i.e., a function which: • receives a function as a parameter; or • returns a function. Functions can be used like any other value. They are first-class citizens. Maybe this seems strange at first, but I am sure you have used higher-order functions before without noticing it.
    [Show full text]
  • Clojure, Given the Pun on Closure, Representing Anything Specific
    dynamic, functional programming for the JVM “It (the logo) was designed by my brother, Tom Hickey. “It I wanted to involve c (c#), l (lisp) and j (java). I don't think we ever really discussed the colors Once I came up with Clojure, given the pun on closure, representing anything specific. I always vaguely the available domains and vast emptiness of the thought of them as earth and sky.” - Rich Hickey googlespace, it was an easy decision..” - Rich Hickey Mark Volkmann [email protected] Functional Programming (FP) In the spirit of saying OO is is ... encapsulation, inheritance and polymorphism ... • Pure Functions • produce results that only depend on inputs, not any global state • do not have side effects such as Real applications need some changing global state, file I/O or database updates side effects, but they should be clearly identified and isolated. • First Class Functions • can be held in variables • can be passed to and returned from other functions • Higher Order Functions • functions that do one or both of these: • accept other functions as arguments and execute them zero or more times • return another function 2 ... FP is ... Closures • main use is to pass • special functions that retain access to variables a block of code that were in their scope when the closure was created to a function • Partial Application • ability to create new functions from existing ones that take fewer arguments • Currying • transforming a function of n arguments into a chain of n one argument functions • Continuations ability to save execution state and return to it later think browser • back button 3 ..
    [Show full text]
  • Topic 6: Partial Application, Function Composition and Type Classes
    Recommended Exercises and Readings Topic 6: Partial Application, • From Haskell: The craft of functional programming (3rd Ed.) Function Composition and Type • Exercises: • 11.11, 11.12 Classes • 12.30, 12.31, 12.32, 12.33, 12.34, 12.35 • 13.1, 13.2, 13.3, 13.4, 13.7, 13.8, 13.9, 13.11 • If you have time: 12.37, 12.38, 12.39, 12.40, 12.41, 12.42 • Readings: • Chapter 11.3, and 11.4 • Chapter 12.5 • Chapter 13.1, 13.2, 13.3 and 13.4 1 2 Functional Forms Curried and Uncurried Forms • The parameters to a function can be viewed in two different ways • Uncurried form • As a single combined unit • Parameters are bundled into a tuple and passed as a group • All values are passed as one tuple • Can be used in Haskell • How we typically think about parameter passing in Java, C++, Python, Pascal, C#, … • Typically only when there is a specific need to do • As a sequence of values that are passed one at a time • As each value is passed, a new function is formed that requires one fewer parameters • Curried form than its predecessor • Parameters are passed to a function sequentially • How parameters are passed in Haskell • Standard form in Haskell • But it’s not a detail that we need to concentrate on except when we want to make use of it • Functions can be transformed from one form to the other 3 4 Curried and Uncurried Forms Curried and Uncurried Forms • A function in curried form • Why use curried form? • Permits partial application multiply :: Int ‐> Int ‐> Int • Standard way to define functions in Haskell multiply x y = x * y • A function of n+1
    [Show full text]
  • Functional Programming Lecture 1: Introduction
    Functional Programming Lecture 13: FP in the Real World Viliam Lisý Artificial Intelligence Center Department of Computer Science FEE, Czech Technical University in Prague [email protected] 1 Mixed paradigm languages Functional programming is great easy parallelism and concurrency referential transparency, encapsulation compact declarative code Imperative programming is great more convenient I/O better performance in certain tasks There is no reason not to combine paradigms 2 3 Source: Wikipedia 4 Scala Quite popular with industry Multi-paradigm language • simple parallelism/concurrency • able to build enterprise solutions Runs on JVM 5 Scala vs. Haskell • Adam Szlachta's slides 6 Is Java 8 a Functional Language? Based on: https://jlordiales.me/2014/11/01/overview-java-8/ Functional language first class functions higher order functions pure functions (referential transparency) recursion closures currying and partial application 7 First class functions Previously, you could pass only classes in Java File[] directories = new File(".").listFiles(new FileFilter() { @Override public boolean accept(File pathname) { return pathname.isDirectory(); } }); Java 8 has the concept of method reference File[] directories = new File(".").listFiles(File::isDirectory); 8 Lambdas Sometimes we want a single-purpose function File[] csvFiles = new File(".").listFiles(new FileFilter() { @Override public boolean accept(File pathname) { return pathname.getAbsolutePath().endsWith("csv"); } }); Java 8 has lambda functions for that File[] csvFiles = new File(".")
    [Show full text]
  • Notes on Functional Programming with Haskell
    Notes on Functional Programming with Haskell H. Conrad Cunningham [email protected] Multiparadigm Software Architecture Group Department of Computer and Information Science University of Mississippi 201 Weir Hall University, Mississippi 38677 USA Fall Semester 2014 Copyright c 1994, 1995, 1997, 2003, 2007, 2010, 2014 by H. Conrad Cunningham Permission to copy and use this document for educational or research purposes of a non-commercial nature is hereby granted provided that this copyright notice is retained on all copies. All other rights are reserved by the author. H. Conrad Cunningham, D.Sc. Professor and Chair Department of Computer and Information Science University of Mississippi 201 Weir Hall University, Mississippi 38677 USA [email protected] PREFACE TO 1995 EDITION I wrote this set of lecture notes for use in the course Functional Programming (CSCI 555) that I teach in the Department of Computer and Information Science at the Uni- versity of Mississippi. The course is open to advanced undergraduates and beginning graduate students. The first version of these notes were written as a part of my preparation for the fall semester 1993 offering of the course. This version reflects some restructuring and revision done for the fall 1994 offering of the course|or after completion of the class. For these classes, I used the following resources: Textbook { Richard Bird and Philip Wadler. Introduction to Functional Program- ming, Prentice Hall International, 1988 [2]. These notes more or less cover the material from chapters 1 through 6 plus selected material from chapters 7 through 9. Software { Gofer interpreter version 2.30 (2.28 in 1993) written by Mark P.
    [Show full text]
  • Currying and Partial Application and Other Tasty Closure Recipes
    CS 251 Fall 20192019 Principles of of Programming Programming Languages Languages λ Ben Wood Currying and Partial Application and other tasty closure recipes https://cs.wellesley.edu/~cs251/f19/ Currying and Partial Application 1 More idioms for closures • Function composition • Currying and partial application • Callbacks (e.g., reactive programming, later) • Functions as data representation (later) Currying and Partial Application 2 Function composition fun compose (f,g) = fn x => f (g x) Closure “remembers” f and g : ('b -> 'c) * ('a -> 'b) -> ('a -> 'c) REPL prints something equivalent ML standard library provides infix operator o fun sqrt_of_abs i = Math.sqrt(Real.fromInt(abs i)) fun sqrt_of_abs i = (Math.sqrt o Real.fromInt o abs) i val sqrt_of_abs = Math.sqrt o Real.fromInt o abs Right to left. Currying and Partial Application 3 Pipelines (left-to-right composition) “Pipelines” of functions are common in functional programming. infix |> fun x |> f = f x fun sqrt_of_abs i = i |> abs |> Real.fromInt |> Math.sqrt (F#, Microsoft's ML flavor, defines this by default) Currying and Partial Application 4 Currying • Every ML function takes exactly one argument • Previously encoded n arguments via one n-tuple • Another way: Take one argument and return a function that takes another argument and… – Called “currying” after logician Haskell Curry Currying and Partial Application 6 Example val sorted3 = fn x => fn y => fn z => z >= y andalso y >= x val t1 = ((sorted3 7) 9) 11 • Calling (sorted3 7) returns a closure with: – Code fn y => fn z
    [Show full text]
  • Lecture Notes on First-Class Functions
    Lecture Notes on First-Class Functions 15-411: Compiler Design Rob Simmons and Jan Hoffmann Lecture 25 Nov 29, 2016 1 Introduction In this lecture, we discuss two generalizations of C0: function pointers and nested, anonymous functions (lambdas). As a language feature, nested functions are a nat- ural extension of function pointers. However, because of the necessity of closures in the implementation of nested functions, the necessary implementation strategies are somewhat different. 2 Function pointers The C1 language includes a concept of function pointers, which are obtained from a function with the address-of operator &f. The dynamic semantics can treat &f as a new type of constant, which represents the memory address where the function f is stored. S; η ` (∗e)(e1; e2) B K −! S; η ` e B ((∗_)(e1; e2) ;K) S; η ` &f B ((∗_)(e1; e2);K) −! S; η ` e1 B (f(_; e2) ;K) Again, we only show the special case of evaluation function calls with two and zero arguments. After the second instruction, we continue evaluating the argu- ments to the function left-to-right and then call the function as in our previous dynamics. We do not have to model function pointers using a heap as we did for arrays and pointers since we are not able to change the functions that is stored at a given address. It is relatively straightforward to extend a language with function pointers, be- cause they are addresses. We can obtain that address at runtime by referring to the label as a constant. Any label labl in an assembly file represents an address in memory (since the program must be loaded into memory in order to run), and can LECTURE NOTES NOV 29, 2016 First-Class Functions L25.2 be treated as a constant by writing $labl.
    [Show full text]
  • Ml-Curry-4Up.Pdf
    CS 251 SpringFall 2019 2020 Principles of of Programming Programming Languages Languages Ben Wood More idioms for closures λ Ben Wood • Function composition Currying • Currying and partial application and Partial Application • Callbacks (e.g., reactive programming, later) and other tasty closure recipes • Functions as data representation (later) https://cs.wellesley.edu/~cs251/s20/ Currying and Partial Application 1 Currying and Partial Application 2 Function composition (right-to-left) Pipelines (left-to-right composition) fun compose (f,g) = fn x => f (g x) Common in functional programming. Closure “remembers” f and g : ('b -> 'c) * ('a -> 'b) -> ('a -> 'c) infix |> REPL prints something equivalent fun x |> f = f x fun sqrt_of_abs i = ML standard library provides infix operator o i |> abs |> Real.fromInt |> Math.sqrt fun sqrt_of_abs i = Math.sqrt(Real.fromInt(abs i)) fun sqrt_of_abs i = (Math.sqrt o Real.fromInt o abs) i val sqrt_of_abs = Math.sqrt o Real.fromInt o abs (F#, Microsoft's ML flavor, defines this by default) Right to left. Currying and Partial Application 3 Currying and Partial Application 4 Currying Example • Every ML function takes exactly one argument val sorted3 = fn x => fn y => fn z => z >= y andalso y >= x • Previously encoded n arguments via one n-tuple val t1 = ((sorted3 7) 9) 11 • Another way: 1. Calling (sorted3 7) returns closure #1 with: Take one argument and return a function that Code fn y => fn z => z >= y andalso y >= x takes another argument and… Environment: x ↦ 7 – Called “currying” after logician Haskell Curry 2. Calling closure #1 on 9 returns closure #2 with: Code fn z => z >= y andalso y >= x Environment: y ↦ 9, x ↦ 7 3.
    [Show full text]
  • CSE 341 : Programming Languages
    CSE 341 : Programming Languages Lecture 10 Closure Idioms Zach Tatlock Spring 2014 More idioms • We know the rule for lexical scope and function closures – Now what is it good for A partial but wide-ranging list: • Pass functions with private data to iterators: Done • Combine functions (e.g., composition) • Currying (multi-arg functions and partial application) • Callbacks (e.g., in reactive programming) • Implementing an ADT with a record of functions (optional) 2 Combine functions Canonical example is function composition: fun compose (f,g) = fn x => f (g x) • Creates a closure that “remembers” what f and g are bound to • Type ('b -> 'c) * ('a -> 'b) -> ('a -> 'c) but the REPL prints something equivalent • ML standard library provides this as infix operator o • Example (third version best): fun sqrt_of_abs i = Math.sqrt(Real.fromInt(abs i)) fun sqrt_of_abs i = (Math.sqrt o Real.fromInt o abs) i val sqrt_of_abs = Math.sqrt o Real.fromInt o abs 3 Left-to-right or right-to-left val sqrt_of_abs = Math.sqrt o Real.fromInt o abs As in math, function composition is “right to left” – “take absolute value, convert to real, and take square root” – “square root of the conversion to real of absolute value” “Pipelines” of functions are common in functional programming and many programmers prefer left-to-right – Can define our own infix operator – This one is very popular (and predefined) in F# infix |> fun x |> f = f x fun sqrt_of_abs i = i |> abs |> Real.fromInt |> Math.sqrt 4 Another example • “Backup function” fun backup1 (f,g) = fn x => case
    [Show full text]
  • Functional Programming at Work in Object-Oriented Programming
    Functional Programming at Work in Object-Oriented Programming Narbel version 2010 Narbel Functional Programming at Work in Object-Oriented Programming 1 A Claim about Programming Styles Claim: Adding functional programming capabilities to an object-oriented language leads to benefits in object-oriented programming design. Narbel Functional Programming at Work in Object-Oriented Programming 2 Existing Languages with a FP-OOP Mix Some old and less old languages with FP+OOP: For instance, Smalltalk, Common Lisp (CLOS). More recently, Python or Ruby. Notations: FP, Functional programming; OOP, Object-oriented programming, Narbel Functional Programming at Work in Object-Oriented Programming 3 FP techniques emulated in OOP Practices in OOP languages include emulations of FP techniques: C++ programmers: function pointers and overloadings of the () operator, i.e. “object-functions” or functors. Java programmers: anonymous classes and introspection/reflexion. Narbel Functional Programming at Work in Object-Oriented Programming 4 Existence of FP-OOP Comparison Points The idea of using FP to enrich OOP is old, see e.g. the discussions about the problem of the user-defined datatype extension: User-defined types and procedural data structures as complementary approaches to data abstraction. Reynolds. 1975. The Expression Problem. Wadler. 1998. Narbel Functional Programming at Work in Object-Oriented Programming 5 A Trend: FP Extensions for OO Languages A recent trend: to propose and include typed FP extensions in mainstream static OO languages. Extensions for C++ (see e.g. Laufer, Striegnitz, McNamara, Smaragdakis), and work in progress in the C++ standard committees. Java 7 expected to include FP constructs. C# offers FP constructs (even more in its 3.0 version).
    [Show full text]
  • Exploring Languages with Interpreters and Functional Programming Chapter 16
    Exploring Languages with Interpreters and Functional Programming Chapter 16 H. Conrad Cunningham 15 October 2018 Contents 16 Haskell Function Concepts 2 16.1 Chapter Introduction . .2 16.2 Strictness . .2 16.3 Currying and Partial Application . .3 16.4 Operator Sections . .4 16.5 Combinators . .5 16.6 Functional Composition . .7 16.7 Function Pipelines . .7 16.8 Lambda Expressions . .9 16.9 Application Operator $ ....................... 10 16.10Eager Evaluation Using seq and $! ................. 11 16.11What Next? . 12 16.12Exercises . 13 16.13Acknowledgements . 13 16.14References . 14 16.15Terms and Concepts . 14 Copyright (C) 2016, 2017, 2018, H. Conrad Cunningham Professor of Computer and Information Science University of Mississippi 211 Weir Hall P.O. Box 1848 University, MS 38677 (662) 915-5358 Browser Advisory: The HTML version of this textbook requires a browser that supports the display of MathML. A good choice as of October 2018 is a recent version of Firefox from Mozilla. 1 16 Haskell Function Concepts 16.1 Chapter Introduction The previous chapter introduced the concepts of first-class and higher-order functions and generalized common computational patterns to construct a library of useful higher-order functions to process lists. This chapter continues to examine those concepts and their implications for Haskell programming. It explores strictness, currying, partial application, com- binators, operator sections, functional composition, inline function definitions, evaluation strategies, and related methods. The Haskell module for this chapter is in FunctionConcepts.hs. 16.2 Strictness In the discussion of the fold functions, the previous chapter introduced the concept of strictness. In this section, we explore that in more depth.
    [Show full text]