This document summarizes an introduction to the Intel Graphics Performance Analyzers (Intel GPA) tool. The presentation provides an overview of Intel GPA's capabilities for optimizing game performance on Intel graphics through in-game analysis, frame capture, and trace analysis. It demonstrates Intel GPA's system analyzer, frame analyzer and trace analyzer features. The document also gives examples of optimizations that can be achieved through techniques like script culling, memory management, occlusion culling, level of detail modeling and terrain optimization.

1. Debug, Analyze and Optimize
Games with Intel Tools
Surviving the apocalypse on mainstream graphics
Matteo Valoriani, FifthIngenium CEO
Intel Software Innovator

2. Nice to Meet You
www.slideshare.net/MatteoValoriani
https://it.linkedin.com/in/matteovaloriani
http://fifthingenium.com/blog
https://github.com/mvaloriani
mvaloriani at gmail dot com
@MatteoValoriani
Matteo Valoriani
CEO of FifthIngenium
PhD at Politecnico of Milano
Speaker and Consultant
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3. Agenda
3
• Introduction
• Intel® Graphics Performance
Analyzers .. What ?
• Intel® GPA Live Demo
• Optimizations
• Conclusion

4. 4

5. PC Virtual Reality Mobile

6. 6
Getting Started with
Intel® Graphics
Performance Analyzers
(Intel® GPA)
https://goo.gl/2cKmMa

7. Intel® Graphics Performance Analyzers 2017 R1

8. 8
How to Get Started?
Download for FREE at https://software.intel.com/gpa/
• No Code Changes Needed
• No Environment Changes
• No IDE Necessary

9. 9
Intel® GPA - Optimize Your Graphics Applications!
High–End
GPU
Mainstream
Graphics
10fps
5fps
60+
fps
5fps
30 fps
30+
fps
Intel® Graphics
Performance
Analyzers

10. Host OS
▪ Win 7, Win 8.1, Win 10 (64-
bit)
DirectX*
▪ DX 9, 9EX, 10.x, 11.0, 11.1, 12
Target Hardware
▪ Intel, NVidia* & AMD* GPUs
▪ Windows x86 Tablets
▪ HTC Vive*, Oculus Rift*
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Optimize Windows*, Android*, and Ubuntu* Games!
Windows
Gaming
Android
Gaming
Host OS
▪ Windows, Ubuntu, Mac* OSX
OpenGL* ES
▪ 1.x, 2.x, 3.x
Target Hardware
▪ Intel Atom®
Android OS
▪ 4.x, 5.x, 6.x
Ubuntu
Gaming
Host OS
▪ Ubuntu 16.04
OpenGL
▪ 3.2, 3.3, 4.0, 4.1 (Core Profile)
Target Hardware
▪ Intel® HD Graphics 4k-6k
Target OS
▪ Ubuntu 16.04

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Host/Target Architecture
Host System Target System

12. 12
What’s Inside Intel® GPA?
System Analyzer / HUD
Graphics Frame
Analyzer
In-game analysis
Single frame analysisTimeline analysis
Graphics Monitor
Launch & config tool
Graphics Trace
Analyzer

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How to Use Intel® GPA?
HUD / System Analyzer:
Frame Analyzer:
Trace Analyzer:
CPU Limited
GPU Limited
Capture Frame
Capture Trace
?Run with
Intel® GPA
In-Game Analysis Offline Analysis

14. 14
Graphics Monitor
Launch & config tool
Define profiles & preferences

15. 15
System Analyzer / HUD
In-game analysis
• Get metrics for CPU, GPU, graphics drivers, DirectX*,
OpenGL*, or OpenGL* ES
• Experiment with override modes that quickly isolate
common performance bottlenecks
• Capture frames and traces for further analysis
• Display up to 16 performance metrics
simultaneously
• Monitor the current, minimum, and maximum frame
rate
• Use without code modifications or special libraries

16. 16
System Analyzer / HUD

17. Live Analysis
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18. 18
Graphics Frame Analyzer
In-game analysis
• Use the API log to identify visual errors by function and call
errors and warnings to graphics APIs
• Select a draw call and verify its contribution to the frame,
alpha channel, color, format, and depth buffers
• Quantify performance optimization opportunities with
render experiments per draw call
• Solve issues with shadowing, lighting, or color schemes by
locating misplaced objects

19. 19
Graphics Frame Analyzer

20. Performance Analysis with Hardware Metrics
Evaluation Flow to find 3D hotspots
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21. 21
Published in the 6th generation graphics API dev guide :
https://software.intel.com/en-us/articles/6th-gen-graphics-api-dev-guide

22. 22

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Graphics Trace Analyzer
Single frame analysis

24. 24
Optimizations

25. 25
Script Frustum Culling and Co-routines
Use the following Monobehavior callbacks to cull scripts
outside of the camera frustum that do not need to
update when not in focus.
Monobehavior callbacks which trigger when object with
script leaves / enters the camera frustum

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Script Frustum Culling and Co-routines (2)
Co-routines are essentially functions with the ability to
pause and resume execution.
The power of co-routines can be leveraged by
removing the original Update() function in your script
and replacing it with a co-routine.
You can then set how often you would like your co-
routine to execute using the yield command.

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Memory Management Optimization
A great way to get an overview of how you are managing memory is to check the ‘GC
Alloc’ section of the Overview window in Unity Profiler and step through your frames until
you see a significant allocation.
• To avoid frequent allocations, it is advantageous to use structs instead of classes to
have allocations be done on the stack, instead of in the heap.
• Multiple allocations to the heap can lead to significant memory fragmentation and
frequent garbage collections.

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Occlusion Culling
Occlusion culling is a feature available in Unity that enables you to cull out objects that
are occluded by other objects with respect to the camera.

29. 29
Occlusion Culling
1. Go through your entire scene to multi-select
any objects that should be included in
occlusion culling calculations and mark them
as “Occluder Static” and “Occludee Static”.
2. When setting up your occlusion culling system,
set your occlusion areas carefully.By default,
Unity uses the entire scene as the occlusion
area, which can lead to frivolous computation.
3. To make sure that the entire scene isn’t used,
create an occlusion area manually and
surround only the area to be included in the
calculation.

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LOD
Level of Detail (LOD) allows multiple meshes to attach to a game object and provides the
ability to switch between meshes the object uses based on camera distance. The LOD can
automatically simplify the mesh to compensate.
LOD L0 L1 L2
fps 160 180 220

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Terrain Optimization
• Sampler limited
• No dynamic branching
• Optimized for Legacy HW where sampling was faster than computing LODs
• Implementation of dynamic branching increased perf by 2x ( 3ms -> 1.5ms)
• Using samplegrad for dynamic LOD selection

32. Conclusion
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33. 33
Conclusion
With the right tools :
and the right methodology, finding performance bottlenecks is easy !

34. Questions ?
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35. References
https://software.intel.com/gpa/
https://software.intel.com/en-us/articles/6th-gen-graphics-api-dev-guide
https://software.intel.com/en-us/articles/how-to-plan-optimizations-with-unity
https://software.intel.com/en-us/android/articles/unity-optimization-guide-for-x86-android-part-2
https://software.intel.com/en-us/android/articles/unity-optimization-guide-for-x86-android-part-3
https://software.intel.com/en-us/android/articles/unity-optimization-guide-for-x86-android-part-4
https://x-team.com/blog/unity-3d-optimisation-and-best-practices-part-1/
http://docs.unity3d.com/Manual/class-OcclusionArea.html

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Intel technologies’ features and benefits depend on system configuration and may require enabled hardware, software or service activation. Performance varies depending on
system configuration. No computer system can be absolutely secure. Check with your system manufacturer or retailer or learn more at [intel.com].
Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and MobileMark,
are measured using specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results to vary. You should
consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other
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These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any
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