# A Comparative Analysis of Energy Consumption Between The Widespread Unreal
and Unity Video Game Engines

Carlos Pérez  Javier Verón  Félix García  MªÁngeles Moraga  Coral Calero
Carlos Cetina

###### Abstract

The total energy cost of computing activities is steadily increasing and
projections indicate that it will be one of the dominant global energy
consumers in the coming decades. However, perhaps due to its relative youth,
the video game sector has not yet developed the same level of environmental
awareness as other computing technologies despite the estimated three billion
regular video game players in the world. This work evaluates the energy
consumption of the most widely used industry-scale video game engines: Unity
and Unreal Engine. Specifically, our work uses three scenarios representing
relevant aspects of video games (Physics, Statics Meshes, and Dynamic Meshes)
to compare the energy consumption of the engines. The aim is to determine the
influence of using each of the two engines on energy consumption. Our research
has confirmed significant differences in the energy consumption of video game
engines: 351% in Physics in favor of Unity, 17% in Statics Meshes in favor of
Unity, and 26% in Dynamic Meshes in favor of Unreal Engine. These results
represent an opportunity for worldwide potential savings of at least 51 TWh
per year, equivalent to the annual consumption of nearly 13 million European
households, that might encourage a new branch of research on energy-efficient
video game engines.

###### keywords:

Energy consumption , Video Games , Green software , Green Video Games ,
Software sustainability , Game Engines , Game Software Engineering ,

###### PACS:

0000 , 1111

###### MSC:

0000 , 1111

††journal: Sustainable Computing: Informatics and Systems

[inst1]organization=SVIT Research Group,addressline=Universidad San Jorge,
Autovía Mudéjar, km. 299, city=Villanueva de Gállego, postcode=50830,
state=Zaragoza, country=Spain

[inst2]organization=Alarcos Research Group,addressline=University of Castilla-
La Mancha, Paseo de la Universidad, 4, city=Ciudad Real, postcode=13071,
state=Ciudad Real, country=Spain

##  1 Introduction

An increasing number of human activities, even those that we traditionally
associate with the physical world, rely to a large extent on software for
their development. A paradigmatic example is the automotive industry, where a
significant part of the value chain is moving to software, a trend that the
current electrification of the industry is likely to accelerate. The
transformation of traditional industries through software is happening on a
global scale.

This massive deployment of software in all our activities is accompanied by a
sharp increase in the energy costs required to run it. The total energy cost
of computing activities is steadily increasing, and projections indicate that
it will be one of the dominant global energy consumers in the coming decades
[1]. Therefore, there is a growing interest in understanding the environmental
impact of software in the various activities in which it is relevant and how
this impact can be evaluated and decreased.

One of the sectors where the growth has been most evident in recent years is
video games. It is estimated that there are more than three billion regular
video game players in the world, whether on PCs, mobile devices, dedicated
consoles, or other types of devices [2, 3]. The development of many video
games is characterized by, among other things, greater graphical realism and
an increase in the number of interactions and players, which requires more
powerful equipment capable of managing this complexity without degrading the
user experience. All these factors suggest that the energy consumption of
video games and their environmental impact are becoming increasingly
important.

We have estimated the global video game’s energy consumption per year to range
between 230 TWh (just considering PC gamers) and 347 TWh (including gaming
consoles). The estimation was made by measuring the average power required
when playing to Baldur’s Gate III, one of the most popular PC games released
in 2023 [4], which was was 358.6 W by just the video game execution (removing
the consumption by the PC background processes), and assuming all gamers
played the same game. A recent global estimation of average weekly hours spent
playing was of 8.45 hours [5], and the global estimation of people who played
video games was of 3.38 billion [6]. 43% of the total players are estimated to
play in PC and 65% are estimated to play in PC or console [7]. Gaming consoles
are supposed to be similar to gaming PCs in terms of power, and mobile devices
are not considered in the estimation, as their power is typically one or two
orders of magnitude smaller.

To put these numbers into perspective, the average annual amount of
electricity purchased by a EU member residential electric-utility customer was
3900 kWh[8]. Therefore, those figures are equivalent to the annual energy
consumption of approximately between 59 million (just PC gamers) and 89
million households (including gaming consoles) in the European Union. This is
all without taking into account the consumption of the additional
infrastructure apart of the player’s computer that games like Baldur’s Gate
III require; e.g., servers for multiplayer gameplay. Also not counting the new
trend of cloud gaming (e.g., GeForce Now), where the game is run on remote
servers and then played on the local device which can increase the overall
energy consumption [6].

However, perhaps due to its relative youth the video game sector has not yet
developed the same level of environmental awareness as other computing
technology sectors. In the case of computing technologies in general, there
are research efforts in the area of green cloud computing [9], green mobile
computing [10], green software [11], and green data centers [12] among others.
However, in the case of video games, efforts are particularly concentrated on
what is called Green-by computing. There are two perspectives for analyzing
the sustainability of software engineering in general and its energy
consumption in particular: Green-in and Green-by [13]. The first one deals
with improving the energy consumption of a field per se, e.g., video games,
whereas the second one is focused on the application of a field (video games)
to improve sustainability in any context. For example, [14] conducted a
systematic review to assess the effectiveness of gamification and serious
games in impacting domestic energy consumption.

Modern video games are highly complex products of software that rely on
interrelated technologies to function. It is common to use a video game engine
to build video games. The video game engine enables developers to create the
different games in a much more agile way, without having to repeat or code
low-level elements whose design from scratch would be very costly and
incompatible with the pace of development of these products. The most well-
known and widespread video game engines are Unity and Unreal Engine [15] [16].
Comparable video games in terms of visuals and Physics can be achieved with
both Unity and Unreal engine, however, the developers do not know the impact
of the engines in terms of energy consumption because it has not been explored
so far.

This work constitutes a first approach to the evaluation of energy consumption
of a video game using the two most widely used industry-scale video game
engines: Unity and Unreal Engine. Specifically, our work uses three scenarios
representing relevant aspects of video game development (Physics, Static
Meshes, and Dynamic Meshes) to compare the energy consumption of the engines.
The aim is to determine the influence of using each of the two engines on
energy consumption. The results show that there are significant differences
between the versions coded with Unity and Unreal Engine, depending on the
scenario. These results can be revulsive for the industry, triggering a race
in which manufacturers will make their engines better in terms of efficiency,
approaching the best values identified. Furthermore, these results might
encourage a new branch of research on energy-efficient video game engines.

The paper is structured as follows: Section 2 presents a brief overview of
video game engines: what they are and how they are used within a video game.
Section 3 discusses previous work on assessing software energy consumption and
in Section 4 the experiment is explained in detail. Once the foundations of
the experiment have been presented, the procedure to carry it out is analyzed
in Section 5. The threats to the validity of the experiment are explained in
Section 6 and, lastly, conclusions are summarized and future work is outlined
in Section 7.

##  2 A brief overview on video games engines

Video game engines, often simply referred to as a ”game engines”, are software
products designed to ease the development of video games. A game engine
provides game developers with essential tools and functionalities, enabling
them to focus on the creative and unique aspects of the game rather than
having to program all the basic functionalities from scratch. The
functionalities that a game engine usually implements are:

  * 1.

Rendering engine: The rendering engine is responsible for the graphical
display and renders game elements on the screen. It employs advanced computer
graphics techniques, including 2D and 3D rendering, lighting, and shading.

  * 2.

Physics engine: Simulates the laws of Physics within the game world, adding
realism to the movement and interactions of objects. This includes the
simulation of gravity, collisions, and other forces.

  * 3.

Sound system: Manages all aspects of audio in the game, which is essential for
including music, sound effects, and voice acting. This system aids in creating
an immersive experience for the player and in providing feedback for the
player’s actions.

  * 4.

Input system: This component handles how the game is going to receive the user
input from the different external devices supported by the system (e.g.,
mouse, keyboard, or controllers).

  * 5.

Game logic: This component handles how the game is going to work and be
played, including scripting systems.

Game engines usually include level editors, graphical user interface (GUI)
editors, animation systems, and profiling tools; allowing developers to
design, test, and polish faster their games. A game engine also usually offers
other functionalities that greatly shorten the development time, such as
cross-platform support, which enables the game to run on multiple devices and
platforms just by building it for different target devices.

Most game developers use game engines, 60% as of 2022 [17]. There are multiple
industry-scale engines, but Unity has the largest share of the game engine
market: 38% of game developers who use game engines use Unity as their primary
engine [17]. The next most popular game engine, Unreal Engine, has a 15% of
usage as a primary engine [17]. Normally, Unreal Engine is the preferred
choice for high budget developments. In fact, nearly 80 of the most highly
anticipated games released in 2023 are powered by Unreal Engine [18].

On the one hand, Unity is very flexible in performance depending on the target
platform, allowing the creation of mobile games and XR games usually easier
than Unreal Engine. It uses C# as scripting language and it is considered to
be easier to use by beginners because of its user-friendly interface, but it
usually can get as complex as needed.

On the other hand Unreal Engine is renowned for its advanced graphics and
high-fidelity rendering capabilities. It offers C++ as scripting language, and
it also offers Blueprint Visual Scripting. This visual scripting system allows
developers to create game logic and behaviors using a node-based interface
without traditional programming, to prototype or create game logic and
interactions without deep programming knowledge; but, even with the visual
scripting, Unreal Engine is considered to have a steeper learning curve than
other alternatives as Unity.

On an industry scale, both Unity and Unreal Engine have been used to develop
indie and AAA games. ”Indie” is short for ”independent”. An indie game is
typically developed by solo developers or small teams without the financial
support for a large development. Indie games are known for their innovative
and creative design. E.g., A Short Hike is an indie game developed with Unity
and RiME is an indie game developed with Unreal Engine. Both games are shown
in Figure 1. AAA (also called ”triple-A”) games are the video game equivalent
of blockbuster movies. They are high-budget, high-profile games that are
typically produced and distributed by large, well-established gaming
companies. AAA games are known for their impressive visuals and high
production values. E.g., Escape from Tarkov is a AAA game developed with Unity
and The Calisto Protocol is a AAA game developed with Unreal Engine. Both
games can also be seen in Figure 1.

Figure 1: Indie and AAA games made in Unity and Unreal Engine: A Short Hike
(indie, Unity), RiME (indie, Unreal Engine), Escape from Tarkov (AAA, Unity),
and The Calisto Protocol (AAA, Unreal Engine).

Rendering and Physics are key functionalities of video game engines that are
widely present in almost all video games. Both Unity and Unreal Engine can be
used to make games with simple or very complex Physics. Kerbal Space Program
and Rocket League, are two examples of well-known games with complex Physics
made with Unity and Unreal Engine respectively. Kerbal Space Program simulates
real-world orbital mechanics and space travel Physics. It includes elements
like gravitational forces from planets and moons, orbital trajectories, the
Physics of thrust and propulsion in a vacuum, atmospheric drag, and re-entry
heat. Rocket League combines the Physics of driving with the Physics of a
bouncing, spinning soccer ball. The game realistically simulates the momentum
and inertia of both the cars and the ball and accurately simulates the
interactions between the cars and the ball, including how the ball bounces and
reacts to hits. All in all, comparable results of the key elements of visuals
and Physics can be achieved with both Unity and Unreal engine, however, the
developers do not know the impact of the engines in terms of energy
consumption because it has not been explored so far.

##  3 Measuring the energy consumption of software

The environmental impact of information and communication technologies is a
growing concern in society. According to some estimates, by 2025, 20% of the
world’s energy consumption will be due to information and communication
technologies [19]. The environmental impact of this industry is therefore very
relevant and will continue to grow in the coming years. As a result of this
growing environmental awareness, the term ”green computing” was coined.

The term green computing encompasses two different perspectives: on the one
hand it can refer to the use of technology as a tool to achieve environmental
objectives. The use of information and communication technologies to monitor
and possibly reduce the environmental impact of another activity, to raise
awareness or train users in their environmental responsibility is an example
of this type of study. As they aim to achieve environmental objectives through
technology, they are often referred to as “green-by computing”. An example of
green-by computing is the implementation of smart grid technology in the
energy sector. Smart grids use digital communication technology to control
appliances at consumers’ homes to save energy and reduce costs.

On the other hand, if the objective is specifically the reduction of the
environmental impact of the technology itself in various fields of
application, the expression “green-in computing” is used. An example of green-
in computing is the design and operation of energy-efficient data centers.
Companies like Google [20] and Microsoft [21] have invested heavily in
creating data centers that use significantly less energy than traditional
ones.

Within the latter subcategory (”green-in” computing), the academic focus in
the past years has been mainly on studies that analyze the impact that
hardware improvements can have on the environmental impact generated. However,
in recent years, the impact that the proper use of software (green-in
software) has on the environment has been recognized and more effort is being
devoted to its study [13].

It is worth noting that the two pathways (hardware improvements and software
refinements) are by no means mutually exclusive - quite the contrary, the
maximum reduction in environmental impact is likely to be achieved through the
combined and judicious use of software and hardware improvements. In any case,
if we want to rigorously study the environmental impact of software, it is
essential to know as precisely as possible its energy consumption while in
operation.

There are two different methodologies for this purpose: The first one resorts
to specialized software tools that can estimate the energy consumed by the
hardware when a given software is being used, such as PowerAPI or Joulemeter.
These tools do not directly measure the energy consumed but estimate it from a
model fed with parameters that the software collects while the object of study
is being executed. These methods have the advantage that they are generally
effortless, easy to apply, and produce valuable results at different levels of
granularity. However, they only estimate the energy consumed, so the accuracy
of the results depends on the fit of the model to the situation being
analyzed.

The other family of methodologies available are those that use measuring
equipment connected to a suitably instrumented computer to measure the energy
consumed by the computer as a whole or by the different components to be
studied while the software under study is running. This approach has the
advantage over the previous one of producing real energy consumption results,
whose accuracy depends only on the quality of the equipment and the
measurement procedure. However, it requires instrumentation of the computer on
which the test is to be performed and can be more costly and complicated to
implement.

Having a physical device that allows us to measure the energy consumption of a
computer running a certain software is not enough to guarantee that the
results are sufficient to draw conclusions about it. It is necessary to define
a complete process that guarantees the rigor and consistency of the studies so
that the conclusions obtained are scientifically relevant. In this work, we
use the process defined by Mancebo et al. [22], the stages of which will be
described in Section 5.

##  4 Experiment description

The goal of this study is to assess the influence of the use of different
video game engines on the energy consumption of a commercial video game. There
are no public video games that have two identical versions programmed with
Unity in one of them and Unreal Engine in the other to perform a comparison.
In practice, studios decide on the engine at the beginning of development and
rarely change engines during development because it is a very costly decision.
Furthermore, if we were to take a complete video game, it would not be
possible to isolate engine performance for key aspects such as visuals and
Physics. Video game scenes combine simultaneously multiple aspects such as
Physics simulations or meshes rendering, among others. For example, in the
video games displayed in Figure 1 lots of the video game engine‘s
functionalities are working simultaneously with all the needed calculations
derived from Physics simulations (e.g., when the character moves, jumps, or
shoots Physics simulations have to be performed), meshes rendering (e.g.,
every 3D visual element needs the rendering of its mesh with its textures),
animation system operations (e.g., when the character moves, it needs to look
like it is walking) and others.

Consequently, to compare video game engines, we have developed for both
engines the same scenarios representing key aspects of the video games. Video
games might differ one from another in interaction and mechanic aspects, but
they require common aspects which include 2D and 3D rendering, Physics, sound,
and Artificial Intelligence [23, 24, 25]. Currently, the most developed type
of game is 3D games (47% against 36% of 2D games [17]). Three of the most
common systems in 3D video games are Physics, 3D rendering, and animation
systems. That is why we decided to create three scenarios focusing on these
systems for this consumption comparison between Unity and Unreal Engine.

The first scenario (Physics scenario) is built for evaluating the Physics
aspects of the engine. The scenario instantiates 1000 cubes. Each cube has a
mass of 50 kilograms, a size of 1 meter on each axis (X, Y, Z), and Physics
simulation and collision detection enabled. A random force of between 1 and 80
Newtons is applied to each cube on each frame, pushing them continuously
toward the origin position. That will make the cubes collide among themselves
while they keep receiving the force toward the origin. Collisions of this
nature are common in video games such as the popular Doom or the Battlefield
series, where there are shots and explosions affecting players, non-player
characters (NPCs), and other scene elements.

The second and third scenarios are built for evaluating the visual aspects of
the engine: Static Meshes rendering and animated Dynamic Meshes rendering.
Static Meshes are 3D models that do not change shape or move during gameplay.
They are often used for environment and background elements, such as buildings
and furniture. Dynamic Meshes are 3D models that can change shape and move in
real time. They are often used for characters and interactive elements, such
as NPCs and vehicles.

In the second scenario (implementing Static Meshes), 1000 Static Meshes are
instantiated and stay in place, making the engine render them for the duration
of the scenario. In the third scenario (implementing Dynamic Meshes), 1000
Dynamic Meshes with a simple animation are instantiated and stay in place. The
animation consists on the character stabbing with both hands, which is a
common action in lots of fantasy video games, such as the popular video game
Baldur’s Gate III.

The mesh and textures for both the second and third scenarios, and the
animation for only the third scenario have all been downloaded from Mixamo
(https://www.mixamo.com/). Mixamo, owned by Adobe Inc., allows the downloading
of ready-to-use professional 3D characters meshes and animations for personal,
commercial, and non-profit projects, including video games.

Images of the implementation of all three scenarios is shown in Figure 2. Each
row of the figure shows a different scenario. The first row contains two
images from the execution of the Physics scenario, where 1000 cubes start
scattered and then receive forces towards the center. The second row contains
an image from the execution of the Static Meshes scenario, followed by a
zoomed in image of it so the static meshes and their textures can be better
seen. Lastly, the third row contains an image from the execution of the
animated Dynamic Meshes scenario, followed by nine zoomed in captures in
different times of the execution which show the animation of the dynamic
meshes.

The implementations of the scenarios in both engines have the same number of
elements and complexity between the engines. The scenarios in both engines are
built from an empty level where any skybox or atmosphere of any type have been
removed, along with other elements that might have been in the default scene
or level. The only elements in the scenarios are:

  1. 1.

One directional light with default configuration values except for the
intensity, which has been modified to have the light in both engines with a
similar intensity.

  2. 2.

The scenario element (i.e., an empty GameObject placed in the Scene in Unity
or a Pawn instantiated in the PlayerStart on play in Unreal Engine) with
default values and containing both the camera and a script that generates all
the objects (i.e., Prefab instances in Unity or Blueprint Actors instances in
Unreal Engine) needed for each scenario (i.e., the cubes for the Physics
scenario and the Static and Dynamic Meshes for the other two scenarios). This
script in Unity is programmed in C# with a MonoBehaviour class of Unity
inheritor, as expected to be done in this engine. In Unreal Engine, it is
programmed with a combination of Blueprints visual scripting and C++ with an
APawn class of Unreal Engine inheritor, as expected to be done in this engine.

Figure 2: The three scenarios implementing Physics, Static Meshes rendering,
and Dynamic Meshes rendering.

We have used the latest available versions of both engines at the time when
the different scenarios were implemented: 2022.3.6f1 for Unity and 5.2.1 for
Unreal Engine. The scenarios have been implemented by the second author, who
happens to be a professional game developer himself, and have been validated
as equivalent between the two engines by two different professionals in the
industry (not involved in this paper).

All six applications; i.e., the versions of the three scenarios in the two
engines; are available in the following URL for replication purposes:
https://svit.usj.es/unreal-vs-unity-energy-comparative-analysis/

As our aim is to compare the energy consumption of these two commercial game
engines, the Research Questions for this work are built around this goal:

  * 1.

RQ1 – Is there a relationship between the energy consumption needed by a
Physics-related task at run time and the engine used during the development?

  * 2.

RQ2 – Is there a relationship between the energy consumption needed by a
Static mesh rendering-related task at run time and the engine used during the
development?

  * 3.

RQ3 – Is there a relationship between the energy consumption needed by an
animated Dynamic mesh rendering-related task at run time and the engine used
during the development?

##  5 Experimental procedure

The process we follow in the execution of our experiment is defined by [22].
Based on a review of good practices in measuring software energy consumption,
the process defined in [22] improves the reliability and consistency of the
measurements, so that reproducibility and comparison of different studies is
facilitated.

###  5.1 Phases of the procedure

We followed the Green Software Measurement Process (GSMP), as proposed by
Mancebo et al. [26]. This process is composed of a set of phases displayed in
Figure 3 that cover all the steps needed to carry out a correct analysis of
the energy consumption of software while it is being executed.

Figure 3: Process for evaluating the energy efficiency of software

####  5.1.1 Phase I. Scope Definition

In this phase, a complete specification of requirements for the evaluation of
energy efficiency is obtained. In addition, the software subject of the study
and the test cases to be analyzed must be clearly defined.

Our study aims to compare the energy consumption during the execution of three
representative scenarios of real video games, programmed with two different
game engines. It is worth emphasizing that our objective is to find out if
there is any significant difference in energy consumption between scenarios
implemented with different engines. The goal is not to discover what this
difference is exactly because this will always depend on the environment in
which the applications are run.

Special care has to be taken to actively ensure that there are no background
processes beyond those required by the operative system. The consumption of
the processes required by Windows needs to be measured separately and
discounted from the results as a baseline.

####  5.1.2 Phase II. Measurement Environment Settings

The purpose of this phase is the definition of the measurement environment
that will be used in the software energy consumption assessment.

The measurement environment of the Framework for Energy Efficiency Testing to
Improve Environmental Goals of the Software (FEETINGS) [26] (to which GSMP
also belongs) is used to evaluate the energy efficiency of the software. It is
based on actual measurements using a device connected to a computer.
Specifically, in FEETINGS there are two elements for consumption measurement:

  1. 1.

The Efficient Energy Tester (EET), which is a device that measures the power
demand of a set of hardware components and the execution time when the
Software Entity is executed in the Device Under Test (DUT); and

  2. 2.

The ELLIOT, which is the software application that processes and analyses the
data collected by the EET.

The DUT is a desktop computer that allows the execution of the test cases to
carry out the time and power measurements. In our experiment, the DUT had the
specifications displayed in Table 1.

Monitor | Falkon Q2702S 27” 2K |  |  |
---|---|---|---|---
Motherboard | Asus Prime B460-Plus |  |  |
Processor | i7 10700 |  |  |
RAM | 4 modules of 32GB DDR4 Kingston 2666MHz CL16 |  |  |
Graphics card | Zotac Gaming GForce RTX 3060 12 GB GDDR6 |  |  |
Hard Disk | Hard Disk & Kingston SSD A400 – 480GB SATA |  |  |
Power supply | Energy PS901SX 900W |  |  |
O.S. | Windows 11 Pro |  |  |

Table 1: DUT specifications

The energy consumption of the processor, the HDD and the overall energy
consumption of the device are accepted measurements in green software studies
[27]. However, in the case of video games, it is also necessary to measure the
energy consumption of the graphics card as video games use them intensively.

The final power measurements are computed by subtracting the baseline power
required (background processes before launching the scenarios) from the real
measurements recorded during the experiment (after launching the scenarios).

####  5.1.3 Phase III. Measurement Environment Preparation

The focus of this phase is on the preparation of the power measurements to be
performed and on the configuration of the measurement environment.

EET has a power supply that must be connected to the device under test where
the software is executed, replacing the power supply of the DUT.

The power of the computer cooling system may influence the power required by
the whole system if, in one of the scenarios, the computational demand causes
the processor to overheat. To verify that this effect does not occur, two runs
of the three scenarios with the two engines in different order have been
performed to check if the order in which the different cases are run alters
the results. The order of the two runs, labeled V1 and V2, is as follows:

  * 1.

V1: Unreal Engine-Dynamic Meshes, Unreal Engine-Static Meshes, Unreal Engine-
Physics, Unity-Dynamic Meshes, Unity-Static Meshes, Unity-Physics

  * 2.

V2: Unity-Physics, Unity-Static Meshes, Unity-Dynamic Meshes, Unreal Engine-
Physics, Unreal Engine-Static Meshes, Unreal Engine-Dynamic Meshes

The power required for each scenario and the execution time are measured and
recorded. Energy consumption can then be calculated by multiplying the average
power times the execution time. All the scenarios were executed during the
same time (60 seconds) for the different scenarios to be comparable in terms
of energy consumption.

The executable files of the six scenarios for the engines are copied to the
DUT. After checking that no other software is running in the background, the
runs are then executed through a .BAT file. In the .BAT file both the sequence
of executions and the number of repetitions have been detailed.

To avoid undesirable effects, and to guarantee the reliability of the analysis
and the statistical results obtained, both runs (V1 and V2) were recorded 30
times. By repeating the measurement 30 times it is possible to detect if there
is any other software running in the background, as the results would then
present differences between them. All the power measurements, that have been
collected by the EET, are saved onto a file for further processing.

####  5.1.4 Phase IV. Perform the Measurements

During this phase, power measurements are carried out and raw power data taken
from the measuring instrument is collected. A sample plot of the raw data as
collected by the EET is shown in Figure 4. Approximately 6000 individual
measurements are recorded, which corresponds to 60 seconds of recording at a
sampling frequency of 100 Hz.

Figure 4: Plot of the power measurements of one scenario as collected by the
EET.

####  5.1.5 Phase V. Test Case Data Analysis

On this phase, the raw data of power obtained by the measuring instrument is
processed using ELLIOT. As we mentioned before, all the measurements are
repeated 30 times. Then, this raw dataset is cleaned by removing invalid
measurements. A measurement is considered invalid if it corresponds to a wrong
execution (detected because all of the values obtained are not consistent with
the rest of the results for the other executions) or an outlier, in which one
or more values are either too high or too low compared to the rest of the
results for the other executions. The number of executions removed from the
exercise is shown in Table 2.

| Dynamic Meshes | Static Meshes | Physics
---|---|---|---
| Unity | UE | Unity | UE | Unity | UE
Invalid measurements | 4 | 7 | 5 | 9 | 4 | 9
Valid executions | 26 | 23 | 25 | 21 | 26 | 21

Table 2: Number of valid and invalid executions per scenario in Unity and UE
(Unreal Engine)

Once the data have been processed and prepared with ELLIOT, it also calculates
the descriptive statistics of the values obtained. These statistics are shown
in Table 3, Table 4, and Table 5.

Average without baseline (W) | HDD | Graphics Card | Processor | DUT
---|---|---|---|---
Video Game Engine | UE | Unity | UE | Unity | UE | Unity | UE | Unity
Mean | 1,95 | 1,98 | 104,53 | 93,20 | 22,75 | 19,26 | 422,51 | 377,18
Mean StandardDeviation (W) (Time in s) | 1,27 | 1,09 | 38,21 | 37,11 | 15,80 | 18,42 | 33,55 | 20,74
Mean Median (W) (Time in s) | 1,65 | 1,84 | 102,71 | 71,08 | 17,61 | 11,92 | 426,31 | 378,55
Min Record (W) | 0,03 | 0,03 | 30,94 | 30,68 | 0,3 | 0,3 | 166,277 | 235,15
Max Record (W) | 8,16 | 7,47 | 194,44 | 193,5 | 147,22 | 144,25 | 490,963 | 457,789
Table 3: Comparison between Unity and UE (Unreal Engine). Static Meshes scenario Average without baseline (W) | HDD | Graphics Card | Processor | DUT
---|---|---|---|---
Video Game Engine | UE | Unity | UE | Unity | UE | Unity | UE | Unity
Mean | 1,87 | 1,87 | 67,46 | 86,45 | 24,80 | 29,53 | 218,74 | 296,59
Mean StandardDeviation (W) (Time in s) | 1,28 | 1,16 | 22,88 | 38,33 | 33,50 | 30,17 | 21,26 | 24,14
Mean Median (W) (Time in s) | 1,54 | 1,71 | 64,53 | 65,60 | 11,26 | 16,98 | 327,57 | 404,98
Min Record (W) | 0,03 | 0,03 | 28,90 | 28,15 | 0,30 | 0,30 | 168,24 | 252,97
Max Record (W) | 7,22 | 7,22 | 188,37 | 190,72 | 157,32 | 154,35 | 397,96 | 499,35
Table 4: Comparison between Unity and UE (Unreal Engine). Dynamic Meshes scenario Average without baseline (W) | HDD | Graphics Card | Processor | DUT
---|---|---|---|---
Video Game Engine | UE | Unity | UE | Unity | UE | Unity | UE | Unity
Mean | 1,91 | 1,37 | 62,39 | 21,28 | 63,49 | 18,80 | 413,58 | 175,32
Mean StandardDeviation (W) (Time in s) | 1,23 | 0,81 | 28,78 | 4,63 | 29,38 | 22,78 | 30,65 | 9,39
Mean Median (W) (Time in s) | 1,65 | 1,26 | 48,08 | 21,07 | 58,17 | 9,22 | 417,15 | 174,97
Min Record (W) | 0,03 | 0,03 | 27,52 | 2,87 | 0,41 | 0,30 | 165,70 | 125,15
Max Record (W) | 7,03 | 6,88 | 179,11 | 63,99 | 178,15 | 142,46 | 469,77 | 244,23
Table 5: Comparison between Unity and UE (Unreal Engine). Physics scenario

Boxplots allow us to study the distribution of a dataset. The dataset is
divided into four parts, each of them with an equal number of measurements.
The lines dividing the groups are called quartiles, and the groups are
referred to as quartile groups. In our case the raw dataset is made up of 30
measurements, before deleting the invalid measurements. Energy consumption is
calculated for each measurement by multiplying the average power by the
execution time. The boxplots of the consumption data for our study are shown
in Figure 8, Figure 9, and Figure 10.

If the four sections of a boxplot are uneven in size this means that, while
many measurements have similar values in certain parts of the scale, in other
parts the measurements are more variable. In our case, if we look at the
boxplots of each of the three scenarios, comparing those run with Unity and
Unreal Engine, we can see that there are no outstanding asymmetries. Boxplots
are skewed left for the Dynamic Mesh scenario in Unity (Graphics Card), for
the Static Mesh scenario (Graphics Card), and for the Physics scenario with
Unity (HDD) and Unreal Engine (DUT). Boxplots are skewed right for the Dynamic
mesh scenario (HDD), for the Dynamic Mesh scenario with Unreal Engine (HDD)
and the Physics scenario with Unity (Graphics Card and Processor). The rest of
the scenarios show relatively symmetrical distributions.

If we compare the boxplots for each of the video game engines for the same
component and scenario we can observe that the median line lies outside the
box of the comparison boxplot for all the simulations except for the
comparison between HDD consumption with Unity versus Unreal Engine in the
Static Meshes scenario. This means that there are significant differences
between the consumption of the scenarios executed with between Unity and
Unreal Engine in almost all situations.

Finally, we can analyze the interquartile range to examine how the data is
dispersed. As is shown in Figs. 8, 9, and 10, in general the box length is
small, therefore the data are not greatly dispersed.

####  5.1.6 Phase VI. Software Entity Data Analysis

In this phase, the power required by the DUT during the executions of the
different scenarios will be presented, comparing the values of the scenarios
executed with Unity and those executed with the Unreal Engine.

Figure 11 shows the comparison between the power in each of the three
scenarios of both engines of the exercise run according to the sequences
described in Section 5.1.3. That is, sequences V1 and V2 run the same cases
but in reverse order. This exercise aims to check if the order of execution of
the different cases has any effect on the power required by the device for
each of the scenarios. As shown in the graph, the differences are very small
(between 1 and 3%), so we consider that the order in which the cases are
executed does not alter the representativeness of the comparison between the
Unity and Unreal Engine versions of the three scenarios.

We now move on to analyze the power required of the computer running each of
the three scenarios with the two engines that constitute the main goal of the
work. Table 6, Table 7, and Table 8 show the mean power values obtained in
each of the three scenarios of both engines. The columns show the power
required by the hard disk, the graphics card and the processor, and the last
column reflects the power of the DUT as a whole.

Table 6 shows the results of the comparison for the Static Meshes scenario. In
this case, Unreal Engine is approximately 17% less efficient in terms of power
required by the DUT than Unity. If we look at the power required by the
different components we see that the difference is more than 18% in processor
power and more than 12% in the case of the graphics card. There are no
appreciable differences in hard disk data.

Unreal Engine is known for its high-fidelity graphics and advanced rendering
features. These results might be caused because these features can be more
power-intensive and might favor higher visual quality at the cost of increased
energy consumption. It is important to stress that the Static Mesh scenario in
Unreal Engine and the one in Unity have the same visuals, that is, the very
same meshes. Therefore, the rendering of Unreal Engine comes at an energy cost
even though there is no visual benefit.

Table 7 shows the comparison between Unity and Unreal Engine for the Dynamic
Meshes scenario. In this scenario, the results of the previous scenario are
reversed and Unreal Engine is more energy efficient than Unity, with a
difference of more than 26% in the power required by the DUT in this scenario
in its favour. If we look at the power required by the different components we
see that the difference is 16% in processor power and almost 22% in the case
of the graphics card. There is also no appreciable difference in hard disk
data for this scenario.

These results denote that the animation of Unity might be less optimized for
handling complex animations compared to the one used by Unreal Engine. In
addition, it is possible that Unreal Engine might have rendering optimizations
that specifically benefit Dynamic Meshes, as the consumption in the Static
Meshes scenario is also greater than in the Dynamic Meshes scenario for this
engine.

The Physics scenario comparison, shown in Table 8, is the one that shows the
greatest percentage differences in power between the Unity and Unreal Engine
executions. In this case, there are significant differences in hard disk
power, which in the previous cases had not shown any discrepancy. The version
developed with Unity demands 41% less power than the version developed with
Unreal Engine. This significant difference is amplified when we analyze the
consumption of the graphics card and processor, where the power demand of the
Unreal Engine version is more than 3 times bigger than the power demand of the
Unity version (218% and 241%, respectively), and 4.5 times bigger (351%) when
considering the power required by the complete DUT.

The origin of this difference in consumption might be in the Physics engine
employed by default in each of the game engines: Unity uses NVIDIA PhysX while
Unreal Engine uses Chaos. To date, there has been no study on the energy
efficiency on these Physics engines.

Finally, if we compare the power required by each of the different elements of
the computer in the different scenarios, we can see that the power required by
the hard disk is almost two orders of magnitude lower than that of the other
elements included in the study (graphics card and processor) and with small
differences between the two engines, while the power of the graphics card,
shown in Figure 5; and the processor, shown in Figure 6, are comparable (same
order of magnitude). The graphics card requires more power in the two
rendering cases, Static and Dynamic, while the processor should be dominant in
the case of Physics and it is so in the case of Unreal Engine, but in the case
of Unity the power required by the processor for the three cases is quite
similar. The power required by the complete DUT for the three scenarios in
both engines is also displayed in Figure 7.

Figure 5: Graphic card power required for the three scenarios Figure 6:
Processor power required for the three scenarios Figure 7: DUT power required
for the three scenarios Figure 8: Energy consumption boxplots for Dynamic
Meshes Scenario Figure 9: Energy consumption boxplots for Static Meshes
Scenario Figure 10: Energy consumption boxplots for Physics Scenario Figure
11: DUT power required for sequences v1 and v2

Test case | Engine | HDD | GraphicsCard | Processor | DUT
---|---|---|---|---|---
StaticMesh (W) | Unity | 1,95 | 90,74 | 18,96 | 269,78
StaticMesh (W) | UE | 1,92 | 102,07 | 22,45 | 315,11
Power diff (Unity-UE) | 1,36% | -12,49% | -18,41% | -16,80%

Table 6: Power required (W) for Unity and UE (Unreal Engine). Static Meshes

Test case | Engine | HDD | GraphicsCard | Processor | DUT
---|---|---|---|---|---
DynamicMesh (W) | Unity | 1,87 | 86,45 | 29,53 | 296,59
DynamicMesh (W) | UE | 1,87 | 67,46 | 24,80 | 218,74
Power diff (Unity-UE) | 0,01% | 21,97% | 16,02% | 26,25%

Table 7: Power required (W) for Unity and UE (Unreal Engine). Dynamic Meshes

Test case | Engine | HDD | GraphicsCard | Processor | DUT
---|---|---|---|---|---
Physics (W) | Unity | 1,34 | 18,82 | 18,50 | 67,92
Physics (W) | UE | 1,88 | 59,93 | 63,19 | 306,17
Power diff (Unity-UE) | -40,92% | -218,45% | -241,49% | -350,78%

Table 8: Power required (W) for Unity and UE (Unreal Engine). Physics

###  5.2 Answering the Research Questions

We can now answer the research questions we posed at the beginning of the
article. Although all graphs are presented in power, we can conclude about
energy consumption since the run time was the same in all scenarios (60
seconds).

####  5.2.1 Answering RQ1 - Physics

When asked whether there is a relationship between the energy consumption of
the Physics-related scenario between the versions coded in the two engines
(RQ1), it is observed that there is a significant difference in consumption,
with the DUT consumption of the Unreal Engine version being almost 4,5 times
higher than that of the Unity version (351 %). This difference in the overall
consumption of the device also appears in the consumption of the different
components, 218 % for the graphics card and 241 % for the processor. For the
hard disk consumption, the difference between the two engines is smaller
(about 41%), although significant.

####  5.2.2 Answering RQ2 - Static Meshes rendering

When asked if there is a relationship between the energy consumption of the
DUT in a Static mesh rendering task and the game engine used (RQ2), the answer
is positive. There is a difference of 17% in favour of the Unity version in
this type of task in the overall consumption of the device. Once again, the
differences in hard disk consumption are very small, less than 2%, while the
differences in this case for both the consumption of the graphics card and the
processor show differences ranging from 12% to 18%.

####  5.2.3 Answering RQ3 - Animated Dynamic Meshes rendering

Finally, to the third research question about whether in the rendering of
Dynamic Meshes there are differences in consumption between the versions coded
in Unity and real, the results indicate that in this case, it is the Unreal
Engine that has a 26% lower energy consumption than the Unity engine. This
difference in consumption is evident in the consumption of the graphics card,
22% lower in the case of Unreal Engine and 16% lower in the processor, while
the consumption of the hard disk is virtually identical between the two
versions.

###  5.3 Putting the results in context

We can state that in light of the results obtained, there are significant
differences between the versions using one or the other video game engine in
the case of the scenarios analyzed. Our research has confirmed significant
differences in the energy consumption of video game engines: 351% in Physics
in favor of Unity, 17% in Statics Meshes in favor of Unity, and 26% in Dynamic
Meshes in favor of Unreal Engine.

By combining both engines and theoretically choosing the most efficient engine
technology for each scenario (assuming the simplification that the three
components have a similar weight in the context of a video game), it would be
possible to outperform any of the two engines, as presented in Table 9.
Comparing these data with a weighted average accounting for the market share
of the video game engines, a global power saving (from the current situation)
of 22% can be estimated, as can be seen in Table 10.

| Power measurements DUT ( W)
---|---
| Static Meshes | Dynamic Meshes | Physics | Average power
Unity | 269,8 | 296,6 | 67,9 | 211,4
Unreal Engine | 315,1 | 218,7 | 306,2 | 280,0
Best case | 269,8 | 218,7 | 67,9 | 185,5

Table 9:  Power estimation. Average of scenarios. The optimum per scenario is
highlighted

According to the worldwide consumption figures presented in Section 1 the 22%
represents a potential saving of between 51 TWh and 76 TWh per year,
equivalent to the annual consumption of at least 13 million European
households. There is so much opportunity for improvement because of the huge
popularity of video games. More than 40% of the world’s population plays
games, and in recent years it has grown at a rate of 15% per year. Moreover,
if in the future teleworking leads to virtual environments made with video
game engines, these numbers will grow even more. We expect these results to
raise awareness on the energy consumption of video game engines and motivate a
new branch of research in that direction.

Engine | Market share | Average power (W) | Optimum power (W)
---|---|---|---
Unity | 0,38 | 211,4 | 185,5
Unreal Engine | 0,15 | 280,0 | 185,5
Others | 0,47 | 245,7 | 185,5
Average per user(W) |  | 237,8 | 185,5
|  | Potential savings | -22,0%

Table 10: Global estimated power savings (The average of the measured power of
Unity and Unreal Engine is used as an estimation for the ”Others” category).

##  6 Threats to validity

In this section, we use the classification of threats to validity of Wohlin et
al. [28].

Construct validity: This aspect of validity reflects ”the degree to which the
independent and the dependent variables are accurately measured by the
measurement instruments used in the experiment”.

  * 1.

Dependent variables In this experiment, the dependent variables are the
consumption of energy required for executing the test scenarios; these have
been objectively measured by the Efficient Energy Tester which has been
validated as a reliable device for measuring the energy efficiency of running
software. [27] Moreover, all the executions have been repeated 30 times, and
all the scenarios have run on the same physical machine, operating system, and
configuration.

  * 2.

Independent variable The independent variables are the use of Unity or Unreal
Engine as the video game engine of the application and the selection of the
three scenarios to represent the consumption behavior of a typical video game.

Internal Validity: This aspect of validity is of concern when causal relations
are examined. There is a risk that the factor being investigated may be
affected by other neglected factors.

Due to the nature of both experiments, all variables have been controlled, so
threats to internal validity are minimized. In particular, the test cases are
the same and run the same scenarios in both video game engines (Unity and
Unreal Engine). It is worth noting again that the repetition of the
experiments 30 times during the same time also helps to minimize any possible
bias. In addition, subtracting the baseline power in the measurements and
removing invalid measurements (wrong executions or outliers) also help to
ensure that the dataset is valid for analyzing and answering the Research
Questions.

External Validity: This aspect of validity is concerned with to what extent it
is possible to generalize the findings, and to what extent the findings are of
relevance for other cases. The greater the external validity, the more the
results of an empirical study can be generalized to actual software
engineering practice.

To minimize the risk of the Unity and Unreal Engine implementations being not
comparable, the scenarios have been validated as equivalent between the two
engines by two different professionals in the industry. Furthermore, the
scenarios are representative of key aspects of video games (visual and
physical) that are present in the vast majority of video games being marketed
today.

Another threat to the external validity is the fact that the measurements are
obtained for a specific computer (the DUT being used). However, we believe
that using an already validated process and measurement device makes the
results more reliable because, although the actual consumption data may be
different in another computer, the relative differences between those values
would be similar to those obtained in this case. The study has been carried
out with a computer that can be considered ”standard” for gaming, although
there could be more significant changes if a particularly powerful computer
were chosen in some aspect.

##  7 Conclusions

The video games sector has experienced strong growth in recent years, both in
terms of the number of users and the complexity of the games, which
necessarily implies a significant increase in the energy consumed by this
activity. However, the environmental awareness of this sector, and in
particular the efforts to reduce the energy consumption of the billions of
gamers are still far from maturity, and the magnitude of the energy consumed
makes this a relevant issue for the research community.

Within the set of tools available to developers, game engines provide game
developers with essential tools and functionalities, enabling them to focus on
the creative and unique aspects of the game rather than having to program all
the basic functionalities from scratch.

In this work, the influence of the game engine on the energy consumption of
the application has been studied. We selected the most popular video game
engines (Unity and Unreal engine) and three representative scenarios of common
tasks relevant to the majority of video games (Static Meshes rendering,
Animated Dynamic Meshes rendering, and Physics simulations). The power
required for the different implementations has been carefully measured in a
controlled environment. Since the execution time was the same for all the
scenarios, power measurements and energy consumption are strictly
proportional.

The results obtained show that the different engines show clear differences in
energy consumption in the different scenarios. These differences can be
summarized as follows:

Unreal Engine has a lower consumption than Unity in the Dynamic Meshes
rendering scenario (17%).

Unity has a lower consumption than Unreal Engine in the Static Meshes scenario
(26%).

Unity has a lower consumption than Unreal Engine in the Physics simulation
scenario (351%).

The differences are significant, both in the consumption of individual
components such as the graphics card or the processor, as well as in the
overall power of the computer, so the selection of an energy-efficient engine
for the tasks included in the game could have a direct impact on the overall
consumption.

The fact that Unity and Unreal Engine outperform each other in energy
efficiency depending on the selected scenario, indicates that there is
potential interest in investigating into most energy-efficient video game
engines, identifying best practices and implementing them into a more refined,
energy-efficient video game engine technology.

There are still relevant functionalities implemented in both engines and
common in a lot of different video games to analyze, such as the sound system
and the navigation system for AI path-finding. Also, one of the reasons to
choose the usage of commercial engines such as Unity or Unreal Engine is that
they allow the development and building of a video game for multiple target
devices (PC, mobile, consoles, etc.). By targeting multiple platforms,
developers can reach a broader user base. New differences between the builds
for different devices in both engines can arise, thus making the analysis of
the same game in different platforms our future work.

## Declaration of Competing Interest

The authors do not have any conflict of interest.

## Acknowledgements

This work was supported in part by the following projects:

  * 1.

Project VARIATIVA (Ministry of Economy and Competitiveness (MINECO) through
the Spanish National R+D+i Plan and ERDF funds under Grant
PID2021-128695OB-I00)

  * 2.

Research Group S05_20D. Gobierno de Aragón (Spain).

  * 3.

OASSIS (PID2021-122554OBC31/AEI/10.13039/ 501100011033/FEDER, UE)

  * 4.

EMMA (Project SBPLY/21/180501/000115, funded by CECD (JCCM) and FEDER funds)

  * 5.

SEEAT (PDC2022-133249-C31 funded by MCIN/AEI/ 10.13039/501100011033 and
European Union NextGenerationEU/

  * 6.

PLAGEMIS (TED2021-129245B-C22 funded by MCIN/AEI/ 10.13039/501100011033 and
European Union NextGenerationEU/PRTR) PRTR)

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