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{{Short description|Applying operations to whole sets of values simultaneously}}
In [[computer science]], '''array programming''' refers to solutions that allow the application of operations to an entire set of values at once. Such solutions are commonly used in [[computational science|scientific]] and
{{Programming paradigms}}▼
▲In [[computer science]], '''array programming''' refers to solutions that allow the application of operations to an entire set of values at once. Such solutions are commonly used in [[computational science|scientific]] and [[engineering]] settings.
Modern programming languages that support array programming (also known as [[vector (data structure)|vector]] or [[multidimensional analysis|multidimensional]] languages) have been engineered specifically to generalize operations on [[scalar (computing)|scalar]]s to apply transparently to [[vector (geometric)|vector]]s, [[matrix (mathematics)|matrices]], and higher-dimensional arrays. These include [[APL (programming language)|APL]], [[J (programming language)|J]], [[Fortran
==Concepts of array==
The fundamental idea behind array programming is that operations apply at once to an entire set of values. This makes it a [[high-level programming language|high-level programming]] model as it allows the programmer to think and operate on whole aggregates of data, without having to resort to explicit loops of individual scalar operations.
[[Kenneth E. Iverson]] described the rationale behind array programming (actually referring to APL) as follows:<ref>{{cite journal |
{{quote|most programming languages are decidedly inferior to mathematical notation and are little used as tools of thought in ways that would be considered significant by, say, an applied mathematician.
The thesis is that the advantages of executability and universality found in programming languages can be effectively combined, in a single coherent language, with the advantages offered by mathematical notation. it is important to distinguish the difficulty of describing and of learning a piece of notation from the difficulty of mastering its implications. For example, learning the rules for computing a matrix product is easy, but a mastery of its implications (such as its associativity, its distributivity over addition, and its ability to represent linear functions and geometric operations) is a different and much more difficult matter.
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==Uses==
Array programming is very well suited to [[implicit parallelization]]; a topic of much research nowadays. Further, [[Intel]] and compatible CPUs developed and produced after 1997 contained various instruction set extensions, starting from [[MMX (instruction set)|MMX]] and continuing through [[SSSE3]] and [[3DNow!]], which include rudimentary [[Single instruction, multiple data|SIMD]] array capabilities. This has continued into the 2020s with instruction sets such as [[AVX-512]], making modern CPUs sophisticated vector processors. Array processing is distinct from [[parallel computing|parallel processing]] in that one physical processor performs operations on a group of items simultaneously while parallel processing aims to split a larger problem into smaller ones ([[Multiple instruction, multiple data|MIMD]]) to be solved piecemeal by numerous processors. Processors with
==Languages==
The canonical examples of array programming languages are [[Fortran]], [[APL (programming language)|APL]], and [[J (programming language)|J]]. Others include: [[A+ (programming language)|A+]], [[Analytica (software)|Analytica]], [[Chapel (programming language)|Chapel]], [[IDL (programming language)|IDL]], [[Julia (programming language)|Julia]], [[K (programming language)|K]], Klong, [[Q (programming language from Kx Systems)|Q]], [[MATLAB]], [[GNU Octave]], [[Scilab]], [[FreeMat]], [[Perl Data Language
===Scalar languages===
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[1,] 30 21
[2,] 42 30
</syntaxhighlight>
====Raku====
Raku supports the array paradigm via its Metaoperators.<ref>{{cite web |url=https://docs.raku.org/language/operators#Metaoperators |title=Metaoperators section of Raku Operator documentation}}</ref> The following example demonstrates the addition of arrays @a and @b using the Hyper-operator in conjunction with the plus operator.
<syntaxhighlight lang="raku">
[0] > my @a = [[1,1],[2,2],[3,3]];
[[1 1] [2 2] [3 3]]
[1] > my @b = [[4,4],[5,5],[6,6]];
[[4 4] [5 5] [6 6]]
[2] > @a »+« @b;
[[5 5] [7 7] [9 9]]
</syntaxhighlight>
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==Third-party libraries==
The use of specialized and efficient libraries to provide more terse abstractions is also common in other programming languages. In [[C++]] several linear algebra libraries exploit the language's ability to [[operator overloading|overload operators]]. In some cases a very terse abstraction in those languages is explicitly influenced by the array programming paradigm, as the [[NumPy]] extension library to [[Python (programming language)|Python]], [[Armadillo (C++ library)|Armadillo]] and [[Blitz++]] libraries do.<ref>{{cite web |title= Reference for Armadillo 1.1.8. Examples of Matlab/Octave syntax and conceptually corresponding Armadillo syntax. |url=
==See also==
* [[Array slicing]]
* [[List of programming languages by type#Array languages|List of array programming languages]]
* [[Automatic vectorization]]
==References==
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*[http://www.nsl.com/ "No stinking loops" programming]
*[https://web.archive.org/web/20110227013846/http://www.vector.org.uk/archive/v223/smill222.htm Discovering Array Languages]
*[http://www.zareenacademy.com/ "Types of Arrays" programming]
▲{{Programming paradigms navbox}}
{{Types of programming languages}}
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