Data-oriented design: Difference between revisions

Revision as of 16:52, 2 December 2023

Latest revision as of 12:57, 10 January 2025

(12 intermediate revisions by 7 users not shown)

Line 1:

{{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/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 ==

Line 12 ⟶ 13:

== 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,{{Clarify|reason=What? Sorry, but OOP doesn't have to do anything with data layouts or design. Data are instances of the Objects and can be organized irrelevantly to OOP itself. The whole paragraph make no sense.|date=September 2021}} 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,{{Clarify|reason=What? Sorry, but OOP doesn't have to do anything with data layouts or design. Data are instances of the Objects and can be organized irrelevantly to OOP itself. The whole paragraph make no sense.|date=September 2021}}</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 ==