SlideShare a Scribd company logo
I don’t know anything about
wood carving, but…
Different problems,
different scales,
different tools.
Tools scale
• Chainsaw
• Dremel
• Laser
Tools scale
• Chainsaw
• Dremel <- Compiler
• Laser
To make best use of a tool (e.g. compiler)
• Solve the part of the problem the tool can’t help with.
• Prepare the area that the tool can help with.
• Solve details missed by using the tool as intended.
Part 1:
Solve the part of the problem
the tool can’t help with.
Approaches…
Approach #1
• Estimate resources available
• Triage based on cost and value estimates
• Collect data
• Adapt as cost and/or value predictions change
• AKA Engineering
Approach #2
• Don’t worry about resources
• Desperately scramble to fix when running out.
• Generously described as irrational
• Desperate scramble != “optimization”
The problems are there even
if you ignore them.
Estimate resources available
• What are the bottlenecks? i.e. most insufficient for the demand
Shell + Excel: test_0 vs. test_1 (W=2 -> W=512)
What can we infer?
0
5
10
15
20
25
30
35
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99
W=8
test_0 test_1
33ms = 30fps
Col W=8; Ea. R/W avg +/- 32B from next/prev
8192 * 1024 * 32bit = 3MB / frame
0
5
10
15
20
25
30
35
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99
W=8
test_0 test_1
33ms = 30fps
Col W=8; Ea. R/W avg +/- 32B from next/prev
8192 * 1024 * 32bit = 3MB / frame
Row, in order
8192 * 1024 * 32bit * 8.9x = 34.7MB / frame
0
10
20
30
40
50
60
70
80
90
100
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99
W=512
test_0 test_1
W=512;
> 20x diff (1.19MB/frame)
Algorithmic complexity equivalent
0
10
20
30
40
50
60
70
80
90
100
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99
W=512
test_0 test_1
Gap = doing nothing.
Why?
http://www.gameenginebook.com/SINFO.pdf
By comparison…
http://www.agner.org/optimize/instruction_tables.pdf
(AMD Piledriver)
http://www.agner.org/optimize/instruction_tables.pdf
(AMD Piledriver)
The Battle of North Bridge
L1
L2
RAM
Verify data…
Excel: W=64 (int32_t; mod 8)
0
10
20
30
40
50
60
70
80
90
100
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99
W=512
test_0 test_1
Make reasonable use of resources
0
10
20
30
40
50
60
70
80
90
100
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99
W=512
test_0 test_1
Make reasonable use of resources
(not the same as optimization)
Memory access is the most
significant component.
Bottleneck: most insufficient for the demand
• Q: Is everything always all about memory access / cache?
• A: No. It’s about whatever the most scarce resources are.
• E.g. Disk access seeks; GPU draw counts; etc.
Let’s look at it another way…
Simple, obvious things to look for
+ Back of the envelope calculations
= Substantial wins
http://deplinenoise.wordpress.com/2013/12/28/optimizable-code/
2 x 32bit read; same cache line = ~200
Waste 56 bytes / 64 bytes
Float mul, add = ~10
Let’s assume callq is replaced. Sqrt = ~30
Mul back to same addr; in L1; = ~3
Read+add from new line
= ~200
Waste 60 bytes / 64 bytes
90% waste!
Alternatively,
Only 10% capacity used*
* Not the same as “used well”, but we’ll start here.
Time spent waiting for L2 vs. actual work
~10:1
Time spent waiting for L2 vs. actual work
~10:1
This is the compiler’s space.
Time spent waiting for L2 vs. actual work
~10:1
This is the compiler’s space.
Compiler cannot solve the most
significant problems.
12 bytes x count(32) = 384 = 64 x 6
4 bytes x count(32) = 128 = 64 x 2
12 bytes x count(32) = 384 = 64 x 6
4 bytes x count(32) = 128 = 64 x 2
(6/32) = ~5.33 loop/cache line
12 bytes x count(32) = 384 = 64 x 6
4 bytes x count(32) = 128 = 64 x 2
Sqrt + math = ~40 x 5.33 = 213.33 cycles/cache line
(6/32) = ~5.33 loop/cache line
12 bytes x count(32) = 384 = 64 x 6
4 bytes x count(32) = 128 = 64 x 2
Sqrt + math = ~40 x 5.33 = 213.33 cycles/cache line
(6/32) = ~5.33 loop/cache line
+ streaming prefetch bonus
12 bytes x count(32) = 384 = 64 x 6
4 bytes x count(32) = 128 = 64 x 2
Sqrt + math = ~40 x 5.33 = 213.33 cycles/cache line
(6/32) = ~5.33 loop/cache line
+ streaming prefetch bonus
Using cache line to capacity* =
Est. 10x speedup
* Used. Still not necessarily as
efficiently as possible
0
0.5
1
1.5
2
2.5
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99
32K GameObjects
test_0 test_1
Measured = 6.8x
Part 2:
Prepare the area that the tool
can help with.
“The high-level design of the code generator”
• http://llvm.org/docs/CodeGenerator.html
• Instruction Selection
• Map data/code to instructions that exist
• Scheduling and Formation
• Give opportunity to schedule / estimate against data access latency
• SSA-based Machine Code Optimizations
• Manual SSA form
• Register Allocation
• Copy to/from locals in register size/types
• Prolog/Epilog Code Insertion
• Late Machine Code Optimizations
• Pay careful attention to inlining
• Code Emission
• Verify asm output regularly
“Compilers are good at applying
mediocre optimizations
hundreds of times.”
- @deplinenoise
Three easy tips to help compiler
• #1 Analyze one value
• #2 Hoist all loop-invariant reads and branches
• #3 Remove redundant transform redundancy
#1 Analyze one value
• Find any one value (or implicit value) you’re interested in, anywhere.
• Printf it out over time. (Helps if you tag it so you can find it.)
• Grep your tty and paste into excel. (Sometimes a quick and dirty
script is useful to convert to a more complex piece of data.)
• See what you see. You’re bound to be surprised by something.
e.g. SoundSourceBaseComponent::BatchUpdate
if ( g_DebugTraceSoundSource )
{
Printf("#SS-01 %d m_CountSourcesn”,i, component->m_CountSources);
}
Approximately 0% of the sound
source components have a
source. (i.e. not playing yet)
Approximately 0% of the sound
source components have a
source. (i.e. not playing yet)
 Switch to evaluating from
source->component not
component->source
 20K vs. 200 cache lines
#2 Hoist all loop-invariant reads and branches
• Don’t re-read member values or re-call functions when you already
have the data.
• Hoist all loop-invariant reads and branches. Even super-obvious ones
that should already be in registers (member fields especially.)
How is it used? What does it generate?
How is it used? What does it generate?
Equivalent to:
return m_NeedParentUpdate?count:0;
MSVC
MSVC
Re-read and re-test…
Increment and loop…
Re-read and re-test…
Increment and loop…
Why?
Super-conservative aliasing rules…?
Member value might change?
What about something more aggressive…?
Test once and return…
What about something more aggressive…?
Okay, so what about…
Okay, so what about…
Equivalent to:
return m_NeedParentUpdate?count:0;
…well at least it inlined it?
MSVC doesn’t fare any better…
Don’t re-read member values or re-call functions when
you already have the data.
Ghost reads and writes
BAM!
:(
Ghost reads and writes
Don’t re-read member values or re-call functions when
you already have the data.
Hoist all loop-invariant reads and branches. Even super-
obvious ones that should already be in registers.
:)
:)
A bit of unnecessary branching, but more-or-less equivalent.
#3 Remove redundant transform redundancy
• Often, especially with small functions, there is not enough context to
know when and under what conditions a transform will happen.
• It’s easy to fall into the over-generalization trap
• It’s only real cases, with real data, we’re concerned with.
A little gem…
wchar_t* -> char*
Suspicious. I know we don’t handle wide char
conversion consistently.
wchar_t* -> char* -> wchar_t*
it takes the char* we created and converts it
right back to a whar*
What’s happening?
• We convert from char* to wchar* to store it in m_DeleteFiles
• …So we can convert it from wchar* to char* to give to FileDelete
• …So we can convert it from char* to whcar* to give it to Windows
DeleteFile.
Where does the char* come
from in the first place?
Comes from argv. Never
needed to touch the memory!
Which turned out to be a
command line parameter that
was never used, anywhere.
Which brings us back to the
wood carving analogy…
Part 3:
Solve details missed by using
the tool as intended.
Optimization begins.
See: e.g.
Part 4: Practice
If you don’t practice,
you can’t do it when it matters.
Practice
https://oeis.org/A000081
What’s the cause?
http://www.insomniacgames.com/three-big-lies-typical-design-failures-in-game-programming-gdc10/
#GDC15 Code Clinic

More Related Content

What's hot

Frostbite Rendering Architecture and Real-time Procedural Shading & Texturing...
Frostbite Rendering Architecture and Real-time Procedural Shading & Texturing...Frostbite Rendering Architecture and Real-time Procedural Shading & Texturing...
Frostbite Rendering Architecture and Real-time Procedural Shading & Texturing...
Johan Andersson
 
Terrain Rendering in Frostbite using Procedural Shader Splatting (Siggraph 2007)
Terrain Rendering in Frostbite using Procedural Shader Splatting (Siggraph 2007)Terrain Rendering in Frostbite using Procedural Shader Splatting (Siggraph 2007)
Terrain Rendering in Frostbite using Procedural Shader Splatting (Siggraph 2007)
Johan Andersson
 
Hierachical z Map Occlusion Culling
Hierachical z Map Occlusion CullingHierachical z Map Occlusion Culling
Hierachical z Map Occlusion Culling
YEONG-CHEON YOU
 

What's hot (20)

Rendering Tech of Space Marine
Rendering Tech of Space MarineRendering Tech of Space Marine
Rendering Tech of Space Marine
 
Frostbite Rendering Architecture and Real-time Procedural Shading & Texturing...
Frostbite Rendering Architecture and Real-time Procedural Shading & Texturing...Frostbite Rendering Architecture and Real-time Procedural Shading & Texturing...
Frostbite Rendering Architecture and Real-time Procedural Shading & Texturing...
 
Terrain Rendering in Frostbite using Procedural Shader Splatting (Siggraph 2007)
Terrain Rendering in Frostbite using Procedural Shader Splatting (Siggraph 2007)Terrain Rendering in Frostbite using Procedural Shader Splatting (Siggraph 2007)
Terrain Rendering in Frostbite using Procedural Shader Splatting (Siggraph 2007)
 
Graphics Gems from CryENGINE 3 (Siggraph 2013)
Graphics Gems from CryENGINE 3 (Siggraph 2013)Graphics Gems from CryENGINE 3 (Siggraph 2013)
Graphics Gems from CryENGINE 3 (Siggraph 2013)
 
4K Checkerboard in Battlefield 1 and Mass Effect Andromeda
4K Checkerboard in Battlefield 1 and Mass Effect Andromeda4K Checkerboard in Battlefield 1 and Mass Effect Andromeda
4K Checkerboard in Battlefield 1 and Mass Effect Andromeda
 
Terrain in Battlefield 3: A Modern, Complete and Scalable System
Terrain in Battlefield 3: A Modern, Complete and Scalable SystemTerrain in Battlefield 3: A Modern, Complete and Scalable System
Terrain in Battlefield 3: A Modern, Complete and Scalable System
 
Siggraph2016 - The Devil is in the Details: idTech 666
Siggraph2016 - The Devil is in the Details: idTech 666Siggraph2016 - The Devil is in the Details: idTech 666
Siggraph2016 - The Devil is in the Details: idTech 666
 
Beyond porting
Beyond portingBeyond porting
Beyond porting
 
Dissecting the Rendering of The Surge
Dissecting the Rendering of The SurgeDissecting the Rendering of The Surge
Dissecting the Rendering of The Surge
 
SPU-Based Deferred Shading in BATTLEFIELD 3 for Playstation 3
SPU-Based Deferred Shading in BATTLEFIELD 3 for Playstation 3SPU-Based Deferred Shading in BATTLEFIELD 3 for Playstation 3
SPU-Based Deferred Shading in BATTLEFIELD 3 for Playstation 3
 
Five Rendering Ideas from Battlefield 3 & Need For Speed: The Run
Five Rendering Ideas from Battlefield 3 & Need For Speed: The RunFive Rendering Ideas from Battlefield 3 & Need For Speed: The Run
Five Rendering Ideas from Battlefield 3 & Need For Speed: The Run
 
Hierachical z Map Occlusion Culling
Hierachical z Map Occlusion CullingHierachical z Map Occlusion Culling
Hierachical z Map Occlusion Culling
 
Future Directions for Compute-for-Graphics
Future Directions for Compute-for-GraphicsFuture Directions for Compute-for-Graphics
Future Directions for Compute-for-Graphics
 
Vertex Shader Tricks by Bill Bilodeau - AMD at GDC14
Vertex Shader Tricks by Bill Bilodeau - AMD at GDC14Vertex Shader Tricks by Bill Bilodeau - AMD at GDC14
Vertex Shader Tricks by Bill Bilodeau - AMD at GDC14
 
Shiny PC Graphics in Battlefield 3
Shiny PC Graphics in Battlefield 3Shiny PC Graphics in Battlefield 3
Shiny PC Graphics in Battlefield 3
 
Parallel Graphics in Frostbite - Current & Future (Siggraph 2009)
Parallel Graphics in Frostbite - Current & Future (Siggraph 2009)Parallel Graphics in Frostbite - Current & Future (Siggraph 2009)
Parallel Graphics in Frostbite - Current & Future (Siggraph 2009)
 
쉐도우맵을 압축하여 대규모씬에 라이팅을 적용해보자
쉐도우맵을 압축하여 대규모씬에 라이팅을 적용해보자쉐도우맵을 압축하여 대규모씬에 라이팅을 적용해보자
쉐도우맵을 압축하여 대규모씬에 라이팅을 적용해보자
 
Frostbite on Mobile
Frostbite on MobileFrostbite on Mobile
Frostbite on Mobile
 
Taking Killzone Shadow Fall Image Quality Into The Next Generation
Taking Killzone Shadow Fall Image Quality Into The Next GenerationTaking Killzone Shadow Fall Image Quality Into The Next Generation
Taking Killzone Shadow Fall Image Quality Into The Next Generation
 
Anatomy of a Texture Fetch
Anatomy of a Texture FetchAnatomy of a Texture Fetch
Anatomy of a Texture Fetch
 

Viewers also liked

Great management of technical leads
Great management of technical leadsGreat management of technical leads
Great management of technical leads
Mike Acton
 
Nordic stockholm keynote
Nordic stockholm keynoteNordic stockholm keynote
Nordic stockholm keynote
Mike Acton
 
A beginner’s guide to programming GPUs with CUDA
A beginner’s guide to programming GPUs with CUDAA beginner’s guide to programming GPUs with CUDA
A beginner’s guide to programming GPUs with CUDA
Piyush Mittal
 

Viewers also liked (11)

#GDC15 Great Management of Technical Leads
#GDC15 Great Management of Technical Leads#GDC15 Great Management of Technical Leads
#GDC15 Great Management of Technical Leads
 
Great management of technical leads
Great management of technical leadsGreat management of technical leads
Great management of technical leads
 
Game tools as a webapp (2011)
Game tools as a webapp (2011)Game tools as a webapp (2011)
Game tools as a webapp (2011)
 
Nordic stockholm keynote
Nordic stockholm keynoteNordic stockholm keynote
Nordic stockholm keynote
 
A beginner’s guide to programming GPUs with CUDA
A beginner’s guide to programming GPUs with CUDAA beginner’s guide to programming GPUs with CUDA
A beginner’s guide to programming GPUs with CUDA
 
Rebooting the insomniac tools pax dev12
Rebooting the insomniac tools pax dev12Rebooting the insomniac tools pax dev12
Rebooting the insomniac tools pax dev12
 
Scene Graphs & Component Based Game Engines
Scene Graphs & Component Based Game EnginesScene Graphs & Component Based Game Engines
Scene Graphs & Component Based Game Engines
 
Cinematic quests
Cinematic questsCinematic quests
Cinematic quests
 
Low-level Shader Optimization for Next-Gen and DX11 by Emil Persson
Low-level Shader Optimization for Next-Gen and DX11 by Emil PerssonLow-level Shader Optimization for Next-Gen and DX11 by Emil Persson
Low-level Shader Optimization for Next-Gen and DX11 by Emil Persson
 
Making (console) games in the browser
Making (console) games in the browserMaking (console) games in the browser
Making (console) games in the browser
 
Deploying a Low-Latency Multiplayer Game Globally: Loadout
Deploying a Low-Latency Multiplayer Game Globally: Loadout Deploying a Low-Latency Multiplayer Game Globally: Loadout
Deploying a Low-Latency Multiplayer Game Globally: Loadout
 

Similar to #GDC15 Code Clinic

Solr Troubleshooting - Treemap Approach: Presented by Alexandre Rafolovitch, ...
Solr Troubleshooting - Treemap Approach: Presented by Alexandre Rafolovitch, ...Solr Troubleshooting - Treemap Approach: Presented by Alexandre Rafolovitch, ...
Solr Troubleshooting - Treemap Approach: Presented by Alexandre Rafolovitch, ...
Lucidworks
 
Csw2016 wheeler barksdale-gruskovnjak-execute_mypacket
Csw2016 wheeler barksdale-gruskovnjak-execute_mypacketCsw2016 wheeler barksdale-gruskovnjak-execute_mypacket
Csw2016 wheeler barksdale-gruskovnjak-execute_mypacket
CanSecWest
 
Memory Optimization
Memory OptimizationMemory Optimization
Memory Optimization
Wei Lin
 

Similar to #GDC15 Code Clinic (20)

Ot performance webinar
Ot performance webinarOt performance webinar
Ot performance webinar
 
Solr Troubleshooting - TreeMap approach
Solr Troubleshooting - TreeMap approachSolr Troubleshooting - TreeMap approach
Solr Troubleshooting - TreeMap approach
 
Solr Troubleshooting - Treemap Approach: Presented by Alexandre Rafolovitch, ...
Solr Troubleshooting - Treemap Approach: Presented by Alexandre Rafolovitch, ...Solr Troubleshooting - Treemap Approach: Presented by Alexandre Rafolovitch, ...
Solr Troubleshooting - Treemap Approach: Presented by Alexandre Rafolovitch, ...
 
Ekon24 from Delphi to AVX2
Ekon24 from Delphi to AVX2Ekon24 from Delphi to AVX2
Ekon24 from Delphi to AVX2
 
Blazing Performance with Flame Graphs
Blazing Performance with Flame GraphsBlazing Performance with Flame Graphs
Blazing Performance with Flame Graphs
 
PPU Optimisation Lesson
PPU Optimisation LessonPPU Optimisation Lesson
PPU Optimisation Lesson
 
Csw2016 wheeler barksdale-gruskovnjak-execute_mypacket
Csw2016 wheeler barksdale-gruskovnjak-execute_mypacketCsw2016 wheeler barksdale-gruskovnjak-execute_mypacket
Csw2016 wheeler barksdale-gruskovnjak-execute_mypacket
 
Brixton Library Technology Initiative Week0 Recap
Brixton Library Technology Initiative Week0 RecapBrixton Library Technology Initiative Week0 Recap
Brixton Library Technology Initiative Week0 Recap
 
Introduction to Parallelization ans performance optimization
Introduction to Parallelization ans performance optimizationIntroduction to Parallelization ans performance optimization
Introduction to Parallelization ans performance optimization
 
Parallel Programming: Beyond the Critical Section
Parallel Programming: Beyond the Critical SectionParallel Programming: Beyond the Critical Section
Parallel Programming: Beyond the Critical Section
 
Meetup Julio Algoritmos Genéticos
Meetup Julio Algoritmos GenéticosMeetup Julio Algoritmos Genéticos
Meetup Julio Algoritmos Genéticos
 
Quantifying the Performance of Garbage Collection vs. Explicit Memory Management
Quantifying the Performance of Garbage Collection vs. Explicit Memory ManagementQuantifying the Performance of Garbage Collection vs. Explicit Memory Management
Quantifying the Performance of Garbage Collection vs. Explicit Memory Management
 
How to make fewer errors at the stage of code writing. Part N3.
How to make fewer errors at the stage of code writing. Part N3.How to make fewer errors at the stage of code writing. Part N3.
How to make fewer errors at the stage of code writing. Part N3.
 
Address/Thread/Memory Sanitizer
Address/Thread/Memory SanitizerAddress/Thread/Memory Sanitizer
Address/Thread/Memory Sanitizer
 
Driving Moore's Law with Python-Powered Machine Learning: An Insider's Perspe...
Driving Moore's Law with Python-Powered Machine Learning: An Insider's Perspe...Driving Moore's Law with Python-Powered Machine Learning: An Insider's Perspe...
Driving Moore's Law with Python-Powered Machine Learning: An Insider's Perspe...
 
Introduction to Parallelization ans performance optimization
Introduction to Parallelization ans performance optimizationIntroduction to Parallelization ans performance optimization
Introduction to Parallelization ans performance optimization
 
Memory Optimization
Memory OptimizationMemory Optimization
Memory Optimization
 
CSS parsing: performance tips & tricks
CSS parsing: performance tips & tricksCSS parsing: performance tips & tricks
CSS parsing: performance tips & tricks
 
Performance and predictability (1)
Performance and predictability (1)Performance and predictability (1)
Performance and predictability (1)
 
Performance and Predictability - Richard Warburton
Performance and Predictability - Richard WarburtonPerformance and Predictability - Richard Warburton
Performance and Predictability - Richard Warburton
 

Recently uploaded

Recently uploaded (20)

UiPath Test Automation using UiPath Test Suite series, part 1
UiPath Test Automation using UiPath Test Suite series, part 1UiPath Test Automation using UiPath Test Suite series, part 1
UiPath Test Automation using UiPath Test Suite series, part 1
 
Where to Learn More About FDO _ Richard at FIDO Alliance.pdf
Where to Learn More About FDO _ Richard at FIDO Alliance.pdfWhere to Learn More About FDO _ Richard at FIDO Alliance.pdf
Where to Learn More About FDO _ Richard at FIDO Alliance.pdf
 
Syngulon - Selection technology May 2024.pdf
Syngulon - Selection technology May 2024.pdfSyngulon - Selection technology May 2024.pdf
Syngulon - Selection technology May 2024.pdf
 
Agentic RAG What it is its types applications and implementation.pdf
Agentic RAG What it is its types applications and implementation.pdfAgentic RAG What it is its types applications and implementation.pdf
Agentic RAG What it is its types applications and implementation.pdf
 
ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...
ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...
ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...
 
SOQL 201 for Admins & Developers: Slice & Dice Your Org’s Data With Aggregate...
SOQL 201 for Admins & Developers: Slice & Dice Your Org’s Data With Aggregate...SOQL 201 for Admins & Developers: Slice & Dice Your Org’s Data With Aggregate...
SOQL 201 for Admins & Developers: Slice & Dice Your Org’s Data With Aggregate...
 
10 Differences between Sales Cloud and CPQ, Blanka Doktorová
10 Differences between Sales Cloud and CPQ, Blanka Doktorová10 Differences between Sales Cloud and CPQ, Blanka Doktorová
10 Differences between Sales Cloud and CPQ, Blanka Doktorová
 
Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...
Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...
Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...
 
ECS 2024 Teams Premium - Pretty Secure
ECS 2024   Teams Premium - Pretty SecureECS 2024   Teams Premium - Pretty Secure
ECS 2024 Teams Premium - Pretty Secure
 
Salesforce Adoption – Metrics, Methods, and Motivation, Antone Kom
Salesforce Adoption – Metrics, Methods, and Motivation, Antone KomSalesforce Adoption – Metrics, Methods, and Motivation, Antone Kom
Salesforce Adoption – Metrics, Methods, and Motivation, Antone Kom
 
Optimizing NoSQL Performance Through Observability
Optimizing NoSQL Performance Through ObservabilityOptimizing NoSQL Performance Through Observability
Optimizing NoSQL Performance Through Observability
 
The Metaverse: Are We There Yet?
The  Metaverse:    Are   We  There  Yet?The  Metaverse:    Are   We  There  Yet?
The Metaverse: Are We There Yet?
 
A Business-Centric Approach to Design System Strategy
A Business-Centric Approach to Design System StrategyA Business-Centric Approach to Design System Strategy
A Business-Centric Approach to Design System Strategy
 
Extensible Python: Robustness through Addition - PyCon 2024
Extensible Python: Robustness through Addition - PyCon 2024Extensible Python: Robustness through Addition - PyCon 2024
Extensible Python: Robustness through Addition - PyCon 2024
 
Introduction to Open Source RAG and RAG Evaluation
Introduction to Open Source RAG and RAG EvaluationIntroduction to Open Source RAG and RAG Evaluation
Introduction to Open Source RAG and RAG Evaluation
 
Unpacking Value Delivery - Agile Oxford Meetup - May 2024.pptx
Unpacking Value Delivery - Agile Oxford Meetup - May 2024.pptxUnpacking Value Delivery - Agile Oxford Meetup - May 2024.pptx
Unpacking Value Delivery - Agile Oxford Meetup - May 2024.pptx
 
AI revolution and Salesforce, Jiří Karpíšek
AI revolution and Salesforce, Jiří KarpíšekAI revolution and Salesforce, Jiří Karpíšek
AI revolution and Salesforce, Jiří Karpíšek
 
Custom Approval Process: A New Perspective, Pavel Hrbacek & Anindya Halder
Custom Approval Process: A New Perspective, Pavel Hrbacek & Anindya HalderCustom Approval Process: A New Perspective, Pavel Hrbacek & Anindya Halder
Custom Approval Process: A New Perspective, Pavel Hrbacek & Anindya Halder
 
Powerful Start- the Key to Project Success, Barbara Laskowska
Powerful Start- the Key to Project Success, Barbara LaskowskaPowerful Start- the Key to Project Success, Barbara Laskowska
Powerful Start- the Key to Project Success, Barbara Laskowska
 
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdf
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdfThe Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdf
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdf
 

#GDC15 Code Clinic