Higher Order Escape Analysis: Optimizing Stack Allocation In
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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 -
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. -
Incrementalized Pointer and Escape Analysis Frédéric Vivien, Martin Rinard
Incrementalized Pointer and Escape Analysis Frédéric Vivien, Martin Rinard To cite this version: Frédéric Vivien, Martin Rinard. Incrementalized Pointer and Escape Analysis. PLDI ’01 Proceedings of the ACM SIGPLAN 2001 conference on Programming language design and implementation, Jun 2001, Snowbird, United States. pp.35–46, 10.1145/381694.378804. hal-00808284 HAL Id: hal-00808284 https://hal.inria.fr/hal-00808284 Submitted on 14 Oct 2018 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Incrementalized Pointer and Escape Analysis∗ Fred´ eric´ Vivien Martin Rinard ICPS/LSIIT Laboratory for Computer Science Universite´ Louis Pasteur Massachusetts Institute of Technology Strasbourg, France Cambridge, MA 02139 [email protected]strasbg.fr [email protected] ABSTRACT to those parts of the program that offer the most attrac- We present a new pointer and escape analysis. Instead of tive return (in terms of optimization opportunities) on the analyzing the whole program, the algorithm incrementally invested resources. Our experimental results indicate that analyzes only those parts of the program that may deliver this approach usually delivers almost all of the benefit of the useful results. An analysis policy monitors the analysis re- whole-program analysis, but at a fraction of the cost. -
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 .. -
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 -
Generalized Escape Analysis and Lightweight Continuations
Generalized Escape Analysis and Lightweight Continuations Abstract Might and Shivers have published two distinct analyses for rea- ∆CFA and ΓCFA are two analysis techniques for higher-order soning about programs written in higher-order languages that pro- functional languages (Might and Shivers 2006b,a). The former uses vide first-class continuations, ∆CFA and ΓCFA (Might and Shivers an enrichment of Harrison’s procedure strings (Harrison 1989) to 2006a,b). ∆CFA is an abstract interpretation based on a variation reason about control-, environment- and data-flow in a program of procedure strings as developed by Sharir and Pnueli (Sharir and represented in CPS form; the latter tracks the degree of approxi- Pnueli 1981), and, most proximately, Harrison (Harrison 1989). mation inflicted by a flow analysis on environment structure, per- Might and Shivers have applied ∆CFA to problems that require mitting the analysis to exploit extra precision implicit in the flow reasoning about the environment structure relating two code points lattice, which improves not only the precision of the analysis, but in a program. ΓCFA is a general tool for “sharpening” the precision often its run-time as well. of an abstract interpretation by tracking the amount of abstraction In this paper, we integrate these two mechanisms, showing how the analysis has introduced for the specific abstract interpretation ΓCFA’s abstract counting and collection yields extra precision in being performed. This permits the analysis to opportunistically ex- ∆CFA’s frame-string model, exploiting the group-theoretic struc- ploit cases where the abstraction has introduced no true approxi- ture of frame strings. mation. -
Escape from Escape Analysis of Golang
Escape from Escape Analysis of Golang Cong Wang Mingrui Zhang Yu Jiang∗ KLISS, BNRist, School of Software, KLISS, BNRist, School of Software, KLISS, BNRist, School of Software, Tsinghua University Tsinghua University Tsinghua University Beijing, China Beijing, China Beijing, China Huafeng Zhang Zhenchang Xing Ming Gu Compiler and Programming College of Engineering and Computer KLISS, BNRist, School of Software, Language Lab, Huawei Technologies Science, ANU Tsinghua University Hangzhou, China Canberra, Australia Beijing, China ABSTRACT KEYWORDS Escape analysis is widely used to determine the scope of variables, escape analysis, memory optimization, code generation, go pro- and is an effective way to optimize memory usage. However, the gramming language escape analysis algorithm can hardly reach 100% accurate, mistakes ACM Reference Format: of which can lead to a waste of heap memory. It is challenging to Cong Wang, Mingrui Zhang, Yu Jiang, Huafeng Zhang, Zhenchang Xing, ensure the correctness of programs for memory optimization. and Ming Gu. 2020. Escape from Escape Analysis of Golang. In Software In this paper, we propose an escape analysis optimization ap- Engineering in Practice (ICSE-SEIP ’20), May 23–29, 2020, Seoul, Republic of proach for Go programming language (Golang), aiming to save Korea. ACM, New York, NY, USA, 10 pages. https://doi.org/10.1145/3377813. heap memory usage of programs. First, we compile the source code 3381368 to capture information of escaped variables. Then, we change the code so that some of these variables can bypass Golang’s escape 1 INTRODUCTION analysis mechanism, thereby saving heap memory usage and reduc- Memory management is very important for software engineering. -
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(".") -
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. -
Escape Analysis for Java
Escape Analysis for Java Jong-Deok Choi Manish Gupta Mauricio Serrano Vugranam C. Sreedhar Sam Midkiff IBM T. J. Watson Research Center P. O. Box 218, Yorktown Heights, NY 10598 jdchoi, mgupta, mserrano, vugranam, smidkiff ¡ @us.ibm.com ¢¤£¦¥¨§ © ¦ § § © § This paper presents a simple and efficient data flow algorithm Java continues to gain importance as a language for general- for escape analysis of objects in Java programs to determine purpose computing and for server applications. Performance (i) if an object can be allocated on the stack; (ii) if an object is an important issue in these application environments. In is accessed only by a single thread during its lifetime, so that Java, each object is allocated on the heap and can be deallo- synchronization operations on that object can be removed. We cated only by garbage collection. Each object has a lock asso- introduce a new program abstraction for escape analysis, the ciated with it, which is used to ensure mutual exclusion when connection graph, that is used to establish reachability rela- a synchronized method or statement is invoked on the object. tionships between objects and object references. We show that Both heap allocation and synchronization on locks incur per- the connection graph can be summarized for each method such formance overhead. In this paper, we present escape analy- that the same summary information may be used effectively in sis in the context of Java for determining whether an object (1) different calling contexts. We present an interprocedural al- may escape the method (i.e., is not local to the method) that gorithm that uses the above property to efficiently compute created the object, and (2) may escape the thread that created the connection graph and identify the non-escaping objects for the object (i.e., other threads may access the object). -
Making Collection Operations Optimal with Aggressive JIT Compilation
Making Collection Operations Optimal with Aggressive JIT Compilation Aleksandar Prokopec David Leopoldseder Oracle Labs Johannes Kepler University, Linz Switzerland Austria [email protected] [email protected] Gilles Duboscq Thomas Würthinger Oracle Labs Oracle Labs Switzerland Switzerland [email protected] [email protected] Abstract 1 Introduction Functional collection combinators are a neat and widely ac- Compilers for most high-level languages, such as Scala, trans- cepted data processing abstraction. However, their generic form the source program into an intermediate representa- nature results in high abstraction overheads – Scala collec- tion. This intermediate program representation is executed tions are known to be notoriously slow for typical tasks. We by the runtime system. Typically, the runtime contains a show that proper optimizations in a JIT compiler can widely just-in-time (JIT) compiler, which compiles the intermediate eliminate overheads imposed by these abstractions. Using representation into machine code. Although JIT compilers the open-source Graal JIT compiler, we achieve speedups of focus on optimizations, there are generally no guarantees up to 20× on collection workloads compared to the standard about the program patterns that result in fast machine code HotSpot C2 compiler. Consequently, a sufficiently aggressive – most optimizations emerge organically, as a response to JIT compiler allows the language compiler, such as Scalac, a particular programming paradigm in the high-level lan- to focus on other concerns. guage. Scala’s functional-style collection combinators are a In this paper, we show how optimizations, such as inlin- paradigm that the JVM runtime was unprepared for. ing, polymorphic inlining, and partial escape analysis, are Historically, Scala collections [Odersky and Moors 2009; combined in Graal to produce collections code that is optimal Prokopec et al. -
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