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((Short description|Program optimization approach in computing))
((Distinguish|Data-driven programming))
((Distinguish|Data-oriented programming))
((More citations needed|date=July 2020))
In [[computing]], '''data-oriented design''' is a [[program optimization]] approach motivated by efficient usage of the [[CPU cache]], often used in [[video game]] development.<ref name="Llopis">((cite web|url=http://gamesfromwithin.com/data-oriented-design|title=Data-oriented design|last=Llopis|first=Noel|date=December 4, 2009|website=Data-Oriented Design (Or Why You Might Be Shooting Yourself in The Foot With OOP)|archive-url=|archive-date=|access-date=April 17, 2020))</ref> The approach is to focus on the data layout, separating and sorting [[field (computing)|fields]] according to when they are needed, and to think about transformations of data. Proponents include Mike Acton,<ref>((cite web|title=CppCon 2014: Mike Acton "Data-Oriented Design and C++"|website = [[YouTube]]|url=https://www.youtube.com/watch?v=rX0ItVEVjHc))</ref> [[Scott Meyers]],<ref>((cite web|title=code::dive conference 2014 - Scott Meyers: Cpu Caches and Why You Care|website = [[YouTube]]|url=https://www.youtube.com/watch?v=WDIkqP4JbkE))</ref> and [[Jonathan Blow]].

The [[parallel array]] (or [[structure of arrays]]) is the main example of data-oriented design. It is contrasted with the ''array of structures'' typical of object-oriented designs.

The definition of data-oriented design as a [[programming paradigm]] can be seen as contentious as many believe that it can be used side by side with another paradigm,<ref>((cite web|access-date=2023-12-20|title=Data-Oriented Design|author=Richard Fabian|date=October 8, 2018|url=https://www.dataorienteddesign.com/dodbook/|website=www.dataorienteddesign.com))</ref> but due to the emphasis on data layout, it is also incompatible with most other paradigms. <ref name="Llopis"/>

== Motives ==
These methods became especially popular in the mid to late 2000s during the [[seventh generation of video game consoles]] that included the [[IBM]] [[PowerPC]] based [[PlayStation 3]] (PS3) and [[Xbox 360]] consoles. Historically, [[game console]]s often have relatively weak [[central processing unit]]s (CPUs) compared to the top-of-line desktop computer counterparts. This is a design choice to devote more power and [[transistor budget]] to the [[graphics processing unit]]s (GPUs). For example, the 7th generation CPUs were not manufactured with modern [[out-of-order execution]] processors, but instead use [[in-order processor]]s with high clock speeds and deep [[Pipeline (computing)|pipelines]]. In addition, most types of computing systems have [[main memory]] located hundreds of [[clock cycle]]s away from the [[processing element]]s. Furthermore, as CPUs have become faster alongside a large increase in main memory capacity, there is massive data consumption that increases the likelihood of  [[cache misses]] in the [[system bus|shared bus]], otherwise known as [[Von Neumann architecture#Von Neumann bottleneck|Von Neumann bottlenecking]]. Consequently, [[locality of reference]] methods have been used to control performance, requiring improvement of [[memory access pattern]]s to fix bottlenecking. Some of the software issues were also similar to those encountered on the [[Itanium]], requiring [[loop unrolling]] for upfront scheduling.

== Contrast with object orientation ==
((Original research section|date=September 2021))
The claim is that traditional [[object-oriented programming]] (OOP) design principles result in poor data locality,<ref>((cite web
 | title = INTEL ® HPC DEVELOPER CONFERENCE FUEL YOUR INSIGHT IMPROVE VECTORIZATION EFFICIENCY USING INTEL SIMD DATA LAYOUT TEMPLATE (INTEL SDLT)
 | url = https://www.intel.com/content/dam/www/public/us/en/documents/presentation/improving-vectorization-efficiency.pdf
))</ref><ref>((cite journal
 | title = SoAx: A generic C++ Structure of Arrays for handling particles in HPC codes
 | author1 = Holger Homann
 | author2 = Francois Laenen
 | journal = Computer Physics Communications
 | date = 2018
 | volume = 224
 | pages = 325–332
 | doi = 10.1016/j.cpc.2017.11.015
 | arxiv = 1710.03462
 | s2cid = 2878169
 | language = English
))</ref> more so if runtime polymorphism ([[dynamic dispatch]]) is used (which is especially problematic on some processors).<ref>((cite web|title=What's wrong with Object-Oriented Design? Where's the harm in it?|url=http://www.dataorienteddesign.com/dodmain/node17.html))describes the problems with virtual function calls, e.g., i-cache misses</ref><ref name="Llopis"/> Although OOP appears to "organise code around data", it actually organises [[source code]] around [[data type]]s rather than physically grouping individual fields and arrays in an efficient format for access by specific functions. Moreover, it often hides layout details under [[abstraction layer]]s, while a data-oriented programmer wants to consider this first and foremost.

== See also ==
* [[CPU cache]]
* [[Data-driven programming]]
* [[Entity component system]]
* [[Memory access pattern]]
* [[Video game development]]

==References==
((Reflist))

[[Category:Software optimization]]
[[Category:Video game development]]
[[Category:Programming paradigms]]

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