Data-oriented design: Difference between revisions

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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)|url-status=live|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]]| date=29 September 2014 |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]]| date=5 January 2015 |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/dodmaindodbook/node3|website=www.htmldataorienteddesign.com}}</ref>, but due to the emphasis on data layout, it is also incompatible with most paradigmother paradigms. <ref>{{cite web|url=http://gamesfromwithin.com/data-oriented-design|title=Data-oriented design|lastname="Llopis|first=Noel|date=December 4, 2009|website=Data-Oriented Design (Or Why You Might Be Shooting Yourself in The Foot With OOP)|url-status=live|archive-url=|archive-date=|access-date=April 17, 2020}}<"/ref>.

== Motives ==

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== 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

The claim is that traditional [[object-oriented programming]] (OOP) design principles result in poor data locality <ref>https://www.intel.com/content/dam/www/public/us/en/documents/presentation/improving-vectorization-efficiency.pdf</ref><ref>https://arxiv.org/pdf/1710.03462.pdf</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>{{cite web|title=Data-oriented design - why you might be shooting yourself in the foot with OOP|url=http://gamesfromwithin.com/data-oriented-design}}</ref> 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.

| 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

| bibcode = 2018CoPhC.224..325H

| s2cid = 2878169

| language = English

The claim is that traditional [[object-oriented programming]] (OOP) design principles result in poor data locality <ref>https://www.intel.com/content/dam/www/public/us/en/documents/presentation/improving-vectorization-efficiency.pdf}}</ref><ref>https://arxiv.org/pdf/1710.03462.pdf</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>{{cite web|titlename=Data-oriented design - why you might be shooting yourself in the foot with OOP|url=http:"Llopis"//gamesfromwithin.com/data-oriented-design}}</ref> 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 ==