Parametric polymorphism

Résumé

In programming languages and type theory, parametric polymorphism allows a single piece of code to be given a "generic" type, using variables in place of actual types, and then instantiated with particular types as needed. Parametrically polymorphic functions and data types are sometimes called generic functions and generic datatypes, respectively, and they form the basis of generic programming. Parametric polymorphism may be contrasted with ad hoc polymorphism. Parametrically polymorphic definitions are uniform: they behave identically regardless of the type they are instantiated at. In contrast, ad hoc polymorphic definitions are given a distinct definition for each type. Thus, ad hoc polymorphism can generally only support a limited number of such distinct types, since a separate implementation has to be provided for each type. Basic definition It is possible to write functions that do not depend on the

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https://en.wikipedia.org/wiki/Parametric_polymorphism

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Miniboxing: An Encoding for Specialization

Cristian Talau

In the presence of parametric polymorphism, erasure-based languages such as Java and Scala handle primitives (boolean values, integers and floating point numbers) in a suboptimal way: in order to provide a uniform representation on the low level, all primitive values are stored in heap objects, in a process known as boxing. This leads to access overheads, wasteful usage of the heap space and broken cache locality. Specialization enables Scala to optimally handle primitive values in the context of generic classes, but this is done at the expense of duplicating classes up to 10 times for each type parameter, for each of the 9 Scala primitive types and heap objects. This prevents specialization of key classes in the Scala library, such as Function2, List, Map and so on. This project aims at testing the hypothesis that encoding several primitive types into a larger stack-based primitive type can maintain the performance of specialized code, while dramatically decreasing the generated bytecode size.

2012

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The Javadoc paradigm for displaying API documentation to users is quite popular, with similar variants existing for many mainstream languages. However, two user interface design properties of Javadoc may reduce its utility when displaying documentation for APIs that make use of inheritance and parametric polymorphism. First, Javadoc does not show a flattened view of all members of a class or interface, but rather only those defined directly in the type. Second, and as a consequence, any methods whose types contain type parameters of a superclass will always be shown in the context of the superclass. That is, if the method C.m returns type T, subclasses of C will always see this parent signature, even if they instantiate T to a concrete type such as Integer. We show that this situation arises often in some libraries, and present the results of a study that measures the usability consequences of these two Javadoc design decisions. Our results show that a user interface that shows instantiated type parameters for members is preferred over one that presents type parameters in the Javadoc style.

2010

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Miniboxing: Improving the Speed to Code Size Tradeoff in Parametric Polymorphism Translations

Martin Odersky, Cristian Talau, Vlad Ureche

Parametric polymorphism enables code reuse and type safety. Underneath the uniform interface exposed to programmers, however, its low level implementation has to cope with inherently non-uniform data: value types of different sizes and semantics (bytes, integers, floating point numbers) and reference types (pointers to heap objects). On the Java Virtual Machine, parametric polymorphism is currently translated to bytecode using two competing approaches: homogeneous and heterogeneous. Homogeneous translation requires boxing, and thus introduces indirect access delays. Heterogeneous translation duplicates and adapts code for each value type individually, producing more bytecode. Therefore bytecode speed and size are at odds with each other. This paper proposes a novel translation that significantly reduces the bytecode size without affecting the execution speed. The key insight is that larger value types (such as integers) can hold smaller ones (such as bytes) thus reducing the duplication necessary in heterogeneous translations. In our implementation, on the Scala compiler, we encode all primitive value types in long integers. The resulting bytecode approaches the performance of monomorphic code, matches the performance of the heterogeneous translation and obtains speedups of up to 22x over the homogeneous translation, all with modest increases in size.

2013

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