Gradual Typing for Smalltalk Esteban Allende, Oscar Callau, Johan Fabry, Eric´ Tanter, Marcus Denker
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Create Union Variables Difference Between Unions and Structures
Union is a user-defined data type similar to a structure in C programming. How to define a union? We use union keyword to define unions. Here's an example: union car { char name[50]; int price; }; The above code defines a user define data type union car. Create union variables When a union is defined, it creates a user-define type. However, no memory is allocated. To allocate memory for a given union type and work with it, we need to create variables. Here's how we create union variables: union car { char name[50]; int price; } car1, car2, *car3; In both cases, union variables car1, car2, and a union pointer car3 of union car type are created. How to access members of a union? We use . to access normal variables of a union. To access pointer variables, we use ->operator. In the above example, price for car1 can be accessed using car1.price price for car3 can be accessed using car3->price Difference between unions and structures Let's take an example to demonstrate the difference between unions and structures: #include <stdio.h> union unionJob { //defining a union char name[32]; float salary; int workerNo; } uJob; struct structJob { char name[32]; float salary; int workerNo; } sJob; main() { printf("size of union = %d bytes", sizeof(uJob)); printf("\nsize of structure = %d bytes", sizeof(sJob)); } Output size of union = 32 size of structure = 40 Why this difference in size of union and structure variables? The size of structure variable is 40 bytes. It's because: size of name[32] is 32 bytes size of salary is 4 bytes size of workerNo is 4 bytes However, the size of union variable is 32 bytes. -
[Note] C/C++ Compiler Package for RX Family (No.55-58)
RENESAS TOOL NEWS [Note] R20TS0649EJ0100 Rev.1.00 C/C++ Compiler Package for RX Family (No.55-58) Jan. 16, 2021 Overview When using the CC-RX Compiler package, note the following points. 1. Using rmpab, rmpaw, rmpal or memchr intrinsic functions (No.55) 2. Performing the tail call optimization (No.56) 3. Using the -ip_optimize option (No.57) 4. Using multi-dimensional array (No.58) Note: The number following the note is an identification number for the note. 1. Using rmpab, rmpaw, rmpal or memchr intrinsic functions (No.55) 1.1 Applicable products CC-RX V2.00.00 to V3.02.00 1.2 Details The execution result of a program including the intrinsic function rmpab, rmpaw, rmpal, or the standard library function memchr may not be as intended. 1.3 Conditions This problem may arise if all of the conditions from (1) to (3) are met. (1) One of the following calls is made: (1-1) rmpab or __rmpab is called. (1-2) rmpaw or __rmpaw is called. (1-3) rmpal or __rmpal is called. (1-4) memchr is called. (2) One of (1-1) to (1-3) is met, and neither -optimize=0 nor -noschedule option is specified. (1-4) is met, and both -size and -avoid_cross_boundary_prefetch (Note 1) options are specified. (3) Memory area that overlaps with the memory area (Note2) read by processing (1) is written in a single function. (This includes a case where called function processing is moved into the caller function by inline expansion.) Note 1: This is an option added in V2.07.00. -
Dotty Phantom Types (Technical Report)
Dotty Phantom Types (Technical Report) Nicolas Stucki Aggelos Biboudis Martin Odersky École Polytechnique Fédérale de Lausanne Switzerland Abstract 2006] and even a lightweight form of dependent types [Kise- Phantom types are a well-known type-level, design pattern lyov and Shan 2007]. which is commonly used to express constraints encoded in There are two ways of using phantoms as a design pattern: types. We observe that in modern, multi-paradigm program- a) relying solely on type unication and b) relying on term- ming languages, these encodings are too restrictive or do level evidences. According to the rst way, phantoms are not provide the guarantees that phantom types try to en- encoded with type parameters which must be satised at the force. Furthermore, in some cases they introduce unwanted use-site. The declaration of turnOn expresses the following runtime overhead. constraint: if the state of the machine, S, is Off then T can be We propose a new design for phantom types as a language instantiated, otherwise the bounds of T are not satisable. feature that: (a) solves the aforementioned issues and (b) makes the rst step towards new programming paradigms type State; type On <: State; type Off <: State class Machine[S <: State] { such as proof-carrying code and static capabilities. This pa- def turnOn[T >: S <: Off] = new Machine[On] per presents the design of phantom types for Scala, as im- } new Machine[Off].turnOn plemented in the Dotty compiler. An alternative way to encode the same constraint is by 1 Introduction using an implicit evidence term of type =:=, which is globally Parametric polymorphism has been one of the most popu- available. -
Coredx DDS Type System
CoreDX DDS Type System Designing and Using Data Types with X-Types and IDL 4 2017-01-23 Table of Contents 1Introduction........................................................................................................................1 1.1Overview....................................................................................................................2 2Type Definition..................................................................................................................2 2.1Primitive Types..........................................................................................................3 2.2Collection Types.........................................................................................................4 2.2.1Enumeration Types.............................................................................................4 2.2.1.1C Language Mapping.................................................................................5 2.2.1.2C++ Language Mapping.............................................................................6 2.2.1.3C# Language Mapping...............................................................................6 2.2.1.4Java Language Mapping.............................................................................6 2.2.2BitMask Types....................................................................................................7 2.2.2.1C Language Mapping.................................................................................8 2.2.2.2C++ Language Mapping.............................................................................8 -
Julia's Efficient Algorithm for Subtyping Unions and Covariant
Julia’s Efficient Algorithm for Subtyping Unions and Covariant Tuples Benjamin Chung Northeastern University, Boston, MA, USA [email protected] Francesco Zappa Nardelli Inria of Paris, Paris, France [email protected] Jan Vitek Northeastern University, Boston, MA, USA Czech Technical University in Prague, Czech Republic [email protected] Abstract The Julia programming language supports multiple dispatch and provides a rich type annotation language to specify method applicability. When multiple methods are applicable for a given call, Julia relies on subtyping between method signatures to pick the correct method to invoke. Julia’s subtyping algorithm is surprisingly complex, and determining whether it is correct remains an open question. In this paper, we focus on one piece of this problem: the interaction between union types and covariant tuples. Previous work normalized unions inside tuples to disjunctive normal form. However, this strategy has two drawbacks: complex type signatures induce space explosion, and interference between normalization and other features of Julia’s type system. In this paper, we describe the algorithm that Julia uses to compute subtyping between tuples and unions – an algorithm that is immune to space explosion and plays well with other features of the language. We prove this algorithm correct and complete against a semantic-subtyping denotational model in Coq. 2012 ACM Subject Classification Theory of computation → Type theory Keywords and phrases Type systems, Subtyping, Union types Digital Object Identifier 10.4230/LIPIcs.ECOOP.2019.24 Category Pearl Supplement Material ECOOP 2019 Artifact Evaluation approved artifact available at https://dx.doi.org/10.4230/DARTS.5.2.8 Acknowledgements The authors thank Jiahao Chen for starting us down the path of understanding Julia, and Jeff Bezanson for coming up with Julia’s subtyping algorithm. -
Cross-Platform Language Design
Cross-Platform Language Design THIS IS A TEMPORARY TITLE PAGE It will be replaced for the final print by a version provided by the service academique. Thèse n. 1234 2011 présentée le 11 Juin 2018 à la Faculté Informatique et Communications Laboratoire de Méthodes de Programmation 1 programme doctoral en Informatique et Communications École Polytechnique Fédérale de Lausanne pour l’obtention du grade de Docteur ès Sciences par Sébastien Doeraene acceptée sur proposition du jury: Prof James Larus, président du jury Prof Martin Odersky, directeur de thèse Prof Edouard Bugnion, rapporteur Dr Andreas Rossberg, rapporteur Prof Peter Van Roy, rapporteur Lausanne, EPFL, 2018 It is better to repent a sin than regret the loss of a pleasure. — Oscar Wilde Acknowledgments Although there is only one name written in a large font on the front page, there are many people without which this thesis would never have happened, or would not have been quite the same. Five years is a long time, during which I had the privilege to work, discuss, sing, learn and have fun with many people. I am afraid to make a list, for I am sure I will forget some. Nevertheless, I will try my best. First, I would like to thank my advisor, Martin Odersky, for giving me the opportunity to fulfill a dream, that of being part of the design and development team of my favorite programming language. Many thanks for letting me explore the design of Scala.js in my own way, while at the same time always being there when I needed him. -
Blossom: a Language Built to Grow
Macalester College DigitalCommons@Macalester College Mathematics, Statistics, and Computer Science Honors Projects Mathematics, Statistics, and Computer Science 4-26-2016 Blossom: A Language Built to Grow Jeffrey Lyman Macalester College Follow this and additional works at: https://digitalcommons.macalester.edu/mathcs_honors Part of the Computer Sciences Commons, Mathematics Commons, and the Statistics and Probability Commons Recommended Citation Lyman, Jeffrey, "Blossom: A Language Built to Grow" (2016). Mathematics, Statistics, and Computer Science Honors Projects. 45. https://digitalcommons.macalester.edu/mathcs_honors/45 This Honors Project - Open Access is brought to you for free and open access by the Mathematics, Statistics, and Computer Science at DigitalCommons@Macalester College. It has been accepted for inclusion in Mathematics, Statistics, and Computer Science Honors Projects by an authorized administrator of DigitalCommons@Macalester College. For more information, please contact [email protected]. In Memory of Daniel Schanus Macalester College Department of Mathematics, Statistics, and Computer Science Blossom A Language Built to Grow Jeffrey Lyman April 26, 2016 Advisor Libby Shoop Readers Paul Cantrell, Brett Jackson, Libby Shoop Contents 1 Introduction 4 1.1 Blossom . .4 2 Theoretic Basis 6 2.1 The Potential of Types . .6 2.2 Type basics . .6 2.3 Subtyping . .7 2.4 Duck Typing . .8 2.5 Hindley Milner Typing . .9 2.6 Typeclasses . 10 2.7 Type Level Operators . 11 2.8 Dependent types . 11 2.9 Hoare Types . 12 2.10 Success Types . 13 2.11 Gradual Typing . 14 2.12 Synthesis . 14 3 Language Description 16 3.1 Design goals . 16 3.2 Type System . 17 3.3 Hello World . -
Type-Check Python Programs with a Union Type System
日本ソフトウェア科学会第 37 回大会 (2020 年度) 講演論文集 Type-check Python Programs with a Union Type System Senxi Li Tetsuro Yamazaki Shigeru Chiba We propose that a simple type system and type inference can type most of the Python programs with an empirical study on real-world code. Static typing has been paid great attention in the Python language with its growing popularity. At the same time, what type system should be adopted by a static checker is quite a challenging task because of Python's dynamic nature. To exmine whether a simple type system and type inference can type most of the Python programs, we collected 806 Python repositories on GitHub and present a preliminary evaluation over the results. Our results reveal that most of the real-world Python programs can be typed by an existing, gradual union type system. We discovered that 82.4% of the ex- pressions can be well typed under certain assumptions and conditions. Besides, expressions of a union type are around 3%, which indicates that most of Python programs use simple, literal types. Such preliminary discovery progressively reveals that our proposal is fairly feasible. while the effectiveness of the type system has not 1 Introduction been empirically studied. Other works such as Py- Static typing has been paid great attention in type implement a strong type inferencer instead of the Python language with its growing popularity. enforcing type annotations. At the same time, what type system should be As a dynamically typed language, Python excels adopted by a static checker is quite a challenging at rapid prototyping and its avail of metaprogram- task because of Python's dynamic nature. -
Webassembly Specification
WebAssembly Specification Release 1.1 WebAssembly Community Group Andreas Rossberg (editor) Mar 11, 2021 Contents 1 Introduction 1 1.1 Introduction.............................................1 1.2 Overview...............................................3 2 Structure 5 2.1 Conventions.............................................5 2.2 Values................................................7 2.3 Types.................................................8 2.4 Instructions.............................................. 11 2.5 Modules............................................... 15 3 Validation 21 3.1 Conventions............................................. 21 3.2 Types................................................. 24 3.3 Instructions.............................................. 27 3.4 Modules............................................... 39 4 Execution 47 4.1 Conventions............................................. 47 4.2 Runtime Structure.......................................... 49 4.3 Numerics............................................... 56 4.4 Instructions.............................................. 76 4.5 Modules............................................... 98 5 Binary Format 109 5.1 Conventions............................................. 109 5.2 Values................................................ 111 5.3 Types................................................. 112 5.4 Instructions.............................................. 114 5.5 Modules............................................... 120 6 Text Format 127 6.1 Conventions............................................ -
Software II: Principles of Programming Languages
Software II: Principles of Programming Languages Lecture 6 – Data Types Some Basic Definitions • A data type defines a collection of data objects and a set of predefined operations on those objects • A descriptor is the collection of the attributes of a variable • An object represents an instance of a user- defined (abstract data) type • One design issue for all data types: What operations are defined and how are they specified? Primitive Data Types • Almost all programming languages provide a set of primitive data types • Primitive data types: Those not defined in terms of other data types • Some primitive data types are merely reflections of the hardware • Others require only a little non-hardware support for their implementation The Integer Data Type • Almost always an exact reflection of the hardware so the mapping is trivial • There may be as many as eight different integer types in a language • Java’s signed integer sizes: byte , short , int , long The Floating Point Data Type • Model real numbers, but only as approximations • Languages for scientific use support at least two floating-point types (e.g., float and double ; sometimes more • Usually exactly like the hardware, but not always • IEEE Floating-Point Standard 754 Complex Data Type • Some languages support a complex type, e.g., C99, Fortran, and Python • Each value consists of two floats, the real part and the imaginary part • Literal form real component – (in Fortran: (7, 3) imaginary – (in Python): (7 + 3j) component The Decimal Data Type • For business applications (money) -
1 Intro to ML
1 Intro to ML 1.1 Basic types Need ; after expression - 42 = ; val it = 42: int -7+1; val it =8: int Can reference it - it+2; val it = 10: int - if it > 100 then "big" else "small"; val it = "small": string Different types for each branch. What will happen? if it > 100 then "big" else 0; The type checker will complain that the two branches have different types, one is string and the other is int If with then but no else. What will happen? if 100>1 then "big"; There is not if-then in ML; must use if-then-else instead. Mixing types. What will happen? What is this called? 10+ 2.5 In many programming languages, the int will be promoted to a float before the addition is performed, and the result is 12.5. In ML, this type coercion (or type promotion) does not happen. Instead, the type checker will reject this expression. div for integers, ‘/ for reals - 10 div2; val it =5: int - 10.0/ 2.0; val it = 5.0: real 1 1.1.1 Booleans 1=1; val it = true : bool 1=2; val it = false : bool Checking for equality on reals. What will happen? 1.0=1.0; Cannot check equality of reals in ML. Boolean connectives are a bit weird - false andalso 10>1; val it = false : bool - false orelse 10>1; val it = true : bool 1.1.2 Strings String concatenation "University "^ "of"^ " Oslo" 1.1.3 Unit or Singleton type • What does the type unit mean? • What is it used for? • What is its relation to the zero or bottom type? - (); val it = () : unit https://en.wikipedia.org/wiki/Unit_type https://en.wikipedia.org/wiki/Bottom_type The boolean type is inhabited by two values: true and false. -
Distributive Disjoint Polymorphism for Compositional Programming
Distributive Disjoint Polymorphism for Compositional Programming Xuan Bi1, Ningning Xie1, Bruno C. d. S. Oliveira1, and Tom Schrijvers2 1 The University of Hong Kong, Hong Kong, China fxbi,nnxie,[email protected] 2 KU Leuven, Leuven, Belgium [email protected] Abstract. Popular programming techniques such as shallow embeddings of Domain Specific Languages (DSLs), finally tagless or object algebras are built on the principle of compositionality. However, existing program- ming languages only support simple compositional designs well, and have limited support for more sophisticated ones. + This paper presents the Fi calculus, which supports highly modular and compositional designs that improve on existing techniques. These im- provements are due to the combination of three features: disjoint inter- section types with a merge operator; parametric (disjoint) polymorphism; and BCD-style distributive subtyping. The main technical challenge is + Fi 's proof of coherence. A naive adaptation of ideas used in System F's + parametricity to canonicity (the logical relation used by Fi to prove co- herence) results in an ill-founded logical relation. To solve the problem our canonicity relation employs a different technique based on immediate substitutions and a restriction to predicative instantiations. Besides co- herence, we show several other important meta-theoretical results, such as type-safety, sound and complete algorithmic subtyping, and decidabil- ity of the type system. Remarkably, unlike F<:'s bounded polymorphism, + disjoint polymorphism in Fi supports decidable type-checking. 1 Introduction Compositionality is a desirable property in programming designs. Broadly de- fined, it is the principle that a system should be built by composing smaller sub- systems.