““Workload Characterization of a Workload Characterization of a First Person Shooter”First Person Shooter”

Luka Spoljaric

Jeff Kwiat

The ProblemThe Problem

Problem: Today’s games consume enormous amounts of CPU resources.– Complex graphics and faster movement

require greater rendering speeds

Can anything be done about this??

SolutionSolution

We must form a better understanding of the workload generated by one these games– Increase efficiency within the game– Relieve the machine of excess burden

to allow other processes more CPU resources

Presentation OverviewPresentation Overview

Introduction to GamesMethodologyData AnalysisImplications, Conclusions, and

Future Research

IntroductionIntroduction

Several genres of video games– First Person Shooter

• Fast-paced, graphically enhanced• Focus of this presentation

– Role-Playing Games (RPGs)• Lower graphics and slower play

– Board Games• Just plain boring

MethodologyMethodology

Hardware TLAB setup– Dell Dimension 4100– 800 MHz Pentium III processor– 256 MB DRAM– Video Card: NVIDIA_GLX-0.9-5

Software– Instrumented version of Linux Doom– Running on Redhat Linux release 6.2

Testing PhaseTesting Phase

20 real-world tests were conducted using the Doom instrumentation

Each run lasted for 180 seconds Each player started at the same point in

the game– Shareware version - “Knee-deep in the Dead”

Skill level was set to “Ultimate Violence”– Player could interact with more enemies

Doom InstrumentationDoom Instrumentation

Incoming Data (3 Blocks)– Doom Loop wraps all of these– GetEvents()

• X Windows events Doom Events

– ProcessEvents()• Process Doom Events

– DisplayEvents()• Update Sounds• Display• Submit Sounds

Task SizesTask Sizes

Represent the amount of CPU time needed to perform a specific task.

We measured task sizes of each event block:– GetEvents()– ProcessEvents()– DisplayEvents

Empty Tasks Blocks where no events occurred– ~98.5% !!

GetEvents() BlockGetEvents() Block

Task Size– With empty tasks

• Mean: 0.00176 seconds• STD: 0.0050825 seconds• Range: 0.1 – 0.4 seconds

– Without empty tasks• Mean: 0.01390 seconds• STD: 0.00595 seconds• Range: 0.1 – 0.4 seconds

Task Sizes With Empty TasksTask Sizes With Empty Tasks

0.00 0.01 0.02 0.03

0

10000

20000

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Task Sizes (With 0s) - GEL

Fre

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Many cycles without events

Task Sizes Without Empty TasksTask Sizes Without Empty Tasks

0.01 0.02 0.03

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Task Size (Without 0s) GEL

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Inter-arrival TimesInter-arrival Times

Inter-arrival times show multi-modal distributions

Divided data into separate bins (short intervals and long intervals)

Plotted each separately to present more effectively

GetEvents() BlockGetEvents() Block

Inter-arrival times– Mean: 0.0159– STD: 0.0244– Distribution: Tri-modal Distribution

STD > Mean!! – Relates to multi-modal distribution

Inter-arrival Times vs. PercentInter-arrival Times vs. Percent

0.0 0.1 0.2 0.3

0

10

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Inter-arrival Times

Per

cent

Inter-arrival Times (Short)Inter-arrival Times (Short)

0.050.040.030.020.010.00

10000

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0

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Inter-arrival Times (Long)Inter-arrival Times (Long)

0.1 0.2 0.3

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ProcessEvents() BlockProcessEvents() Block

Task Size– With empty tasks

• Mean: 0.00387• STD: 0.00781

– Without empty task• Mean: 0.0166• STD: 0.00703

Task Sizes with Empty TasksTask Sizes with Empty Tasks

0.00 0.01 0.02 0.03 0.04

0

50000

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C5

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Task Sizes without Empty TasksTask Sizes without Empty Tasks

0.01 0.02 0.03 0.04

0

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C6

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ProcessEvents()ProcessEvents()

Inter-arrival times– Mean: 0.029 s– STD: 0.026 s– Distribution: Multi-Modal distribution

• Longer intervals are more popular than in the GetEvents() blocks

Inter-arrival Times vs. PercentInter-arrival Times vs. Percent

0.0 0.1 0.2 0.3

0

10

20

30

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60

Sorted_1

Per

cent

Inter-arrival Times (Short)Inter-arrival Times (Short)

0.00 0.01 0.02 0.03 0.04 0.05

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Short Inter-arrival Times

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Inter-arrival Times (Long)Inter-arrival Times (Long)

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Medium Inter-arrival Times

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Display() BlockDisplay() Block

Task Size– With empty tasks

• Mean: 0.0016• STD: 0.0050

– Without empty tasks• Mean: 0.0148• STD: 0.0065

Task Sizes with Empty TasksTask Sizes with Empty Tasks

0.00 0.01 0.02 0.03 0.04

0

100000

200000

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Sort

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Task Sizes without Empty TasksTask Sizes without Empty Tasks

0.01 0.02 0.03 0.04

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Task Size w/o 0s

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Display() BlockDisplay() Block

Inter-arrival Times– Mean: 0.0346– STD: 0.0307– Distribution: Multi-modal

Inter-arrival Times vs. PercentInter-arrival Times vs. Percent

0.0 0.1 0.2 0.3

0

10

20

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C3

Per

cent

Inter-arrival Times (Short)Inter-arrival Times (Short)

0.00 0.01 0.02 0.03 0.04 0.05

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Short Inter-arrival Times

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Inter-arrival Times (Long)Inter-arrival Times (Long)

0.1 0.2 0.3

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Long Inter-arrival Times

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Display() EventsDisplay() Events

Event 1: “UpdateSounds”

0.000001 0.000002 0.000003 0.000004 0.000005 0.000006

0

5000

10000

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Update

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Display() EventsDisplay() Events

Event 2: “Display”

0.0 0.1 0.2

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Display

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Display() EventsDisplay() Events

Event 3: “SubmitSounds”

0.0002 0.0003 0.0004 0.0005 0.0006

0

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Submit

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Future WorkFuture Work

Extend workload characterization to other games (especially first person shooters)

Participants with varying skill levels– Differences in task sizes– Differences in inter-arrival times

Develop software to dynamically decide how much resources are necessary at a given point in the game

ConclusionsConclusions

The task sizes of event blocks range from 0.0 – 0.4 CPU seconds with a large number of empty tasks

The inter-arrival times can be modeled as a multi-modal distribution

Thanks