This talk focuses on the newest release in RenderMan* 22.5 and its adoption at Pixar Animation Studios* for rendering future movies. With native support for Intel® Advanced Vector Extensions, Intel® Advanced Vector Extensions 2, and Intel® Advanced Vector Extensions 512, it includes enhanced library features, debugging support, and an extensive test framework.
Learn how Intel worked with Pixar Animation Studios* and Sony Imageworks* to realize dynamic SIMD code generation of Open Shading Language shader networks, achieving 3-9x speedups with Intel® AVX-512.
Learn how Intel worked with Pixar Animation Studios* and Sony Imageworks* to realize dynamic SIMD code generation of Open Shading Language shader networks, achieving 3-9x speedups with Intel® AVX-512.
Embree Ray Tracing Kernels | Overview and New Features | SIGGRAPH 2018 Tech S...Intel® Software
Overview of the new Embree 3 ray tracing framework, including how to use the new API, supported geometry types, and ray intersection methods. Includes a look at new features like normal oriented curves, vertex grids, etc.
Ray Tracing with Intel® Embree and Intel® OSPRay: Use Cases and Updates | SIG...Intel® Software
Explore practical examples of Intel® Embree and Intel® OSPRay in production rendering and the best practices of using the kernels in typical rendering pipelines.
Open Source Interactive CPU Preview Rendering with Pixar's Universal Scene De...Intel® Software
Universal Scene Description* (USD) is an open source initiative developed by Pixar for fast, large scale, and universal asset management across multiple programs including Maya, Houdini, and others.
Christchurch Embedded .NET User Group - Introduction to Microsoft Embedded pl...christopherfairbairn
Part 1 of the first session of the newly formed Christchurch Embedded .NET User Group.
Introduces the range of embedded platforms and technologies offered by Microsoft. Covers the .NET Micro and Compact Frameworks as well as operating systems such as Windows Embedded CE and Windows Mobile.
Presented by Andrew Leckie, Bryn Lewis and myself.
Efficient and Advanced Omniscient Debugging for xDSMLs (SLE 2015)Benoit Combemale
Talk given at the 8th ACM SIGPLAN Int'l Conf. on Software Language Engineering (SLE 2015), Pittsburgh, PA, USA on October 27, 2015. Preprint available at https://hal.inria.fr/hal-01182517
Optimizing Direct X On Multi Core Architecturespsteinb
This slide set covers best practices in designing threaded rendering in PC games. Examples of current PC titles will be used throughout the talk to highlight the various points.
Simulating Networks Using Cisco Modeling Labs (TechWiseTV Workshop)Robb Boyd
These are the slides....but you need to watch the replay so you can benefit from the demo! This was easily one of our most popular sessions with a HUGE number of attendees smashing previous records. REPLAY: cs.co/9001BMjah
Q&A Added on Nov 3, 2015: http://www.slideshare.net/robboyd/qa-from-cisco-modeling-labs-workshop
DESCRIPTION:
Building physical networks can be a slow, painful, and repetitive task. We often spend more time building the physical aspects of rack, cabling, and configuring and end up rushing the actual work.
In Simulating Networks using Cisco Modeling Labs, we will look at how you can simplify the building of labs using network virtualization. What is the architecture behind the system, the type of routers and switches you can use, the performance and capacity considerations, a demonstration of the product, and finally the gotchas for planning and building a virtual network.
John Healy
GM, Software Defined Networking Division
Intel Corporation
Plenaries Session
ONS2015: http://bit.ly/ons2015sd
ONS Inspire! Webinars: http://bit.ly/oiw-sd
Watch the talk (video) on ONS Content Archives: http://bit.ly/ons-archives-sd
AI for All: Biology is eating the world & AI is eating Biology Intel® Software
Advances in cell biology and creation of an immense amount of data are converging with advances in Machine learning to analyze this data. Biology is experiencing its AI moment and driving the massive computation involved in understanding biological mechanisms and driving interventions. Learn about how cutting edge technologies such as Software Guard Extensions (SGX) in the latest Intel Xeon Processors and Open Federated Learning (OpenFL), an open framework for federated learning developed by Intel, are helping advance AI in gene therapy, drug design, disease identification and more.
Python Data Science and Machine Learning at Scale with Intel and AnacondaIntel® Software
Python is the number 1 language for data scientists, and Anaconda is the most popular python platform. Intel and Anaconda have partnered to bring scalability and near-native performance to Python with simple installations. Learn how data scientists can now access oneAPI-optimized Python packages such as NumPy, Scikit-Learn, Modin, Pandas, and XGBoost directly from the Anaconda repository through simple installation and minimal code changes.
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Similar to RenderMan*: The Role of Open Shading Language (OSL) with Intel® Advanced Vector Extensions | SIGGRAPH 2019 Technical Sessions
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Overview of the new Embree 3 ray tracing framework, including how to use the new API, supported geometry types, and ray intersection methods. Includes a look at new features like normal oriented curves, vertex grids, etc.
Ray Tracing with Intel® Embree and Intel® OSPRay: Use Cases and Updates | SIG...Intel® Software
Explore practical examples of Intel® Embree and Intel® OSPRay in production rendering and the best practices of using the kernels in typical rendering pipelines.
Open Source Interactive CPU Preview Rendering with Pixar's Universal Scene De...Intel® Software
Universal Scene Description* (USD) is an open source initiative developed by Pixar for fast, large scale, and universal asset management across multiple programs including Maya, Houdini, and others.
Christchurch Embedded .NET User Group - Introduction to Microsoft Embedded pl...christopherfairbairn
Part 1 of the first session of the newly formed Christchurch Embedded .NET User Group.
Introduces the range of embedded platforms and technologies offered by Microsoft. Covers the .NET Micro and Compact Frameworks as well as operating systems such as Windows Embedded CE and Windows Mobile.
Presented by Andrew Leckie, Bryn Lewis and myself.
Efficient and Advanced Omniscient Debugging for xDSMLs (SLE 2015)Benoit Combemale
Talk given at the 8th ACM SIGPLAN Int'l Conf. on Software Language Engineering (SLE 2015), Pittsburgh, PA, USA on October 27, 2015. Preprint available at https://hal.inria.fr/hal-01182517
Optimizing Direct X On Multi Core Architecturespsteinb
This slide set covers best practices in designing threaded rendering in PC games. Examples of current PC titles will be used throughout the talk to highlight the various points.
Simulating Networks Using Cisco Modeling Labs (TechWiseTV Workshop)Robb Boyd
These are the slides....but you need to watch the replay so you can benefit from the demo! This was easily one of our most popular sessions with a HUGE number of attendees smashing previous records. REPLAY: cs.co/9001BMjah
Q&A Added on Nov 3, 2015: http://www.slideshare.net/robboyd/qa-from-cisco-modeling-labs-workshop
DESCRIPTION:
Building physical networks can be a slow, painful, and repetitive task. We often spend more time building the physical aspects of rack, cabling, and configuring and end up rushing the actual work.
In Simulating Networks using Cisco Modeling Labs, we will look at how you can simplify the building of labs using network virtualization. What is the architecture behind the system, the type of routers and switches you can use, the performance and capacity considerations, a demonstration of the product, and finally the gotchas for planning and building a virtual network.
John Healy
GM, Software Defined Networking Division
Intel Corporation
Plenaries Session
ONS2015: http://bit.ly/ons2015sd
ONS Inspire! Webinars: http://bit.ly/oiw-sd
Watch the talk (video) on ONS Content Archives: http://bit.ly/ons-archives-sd
AI for All: Biology is eating the world & AI is eating Biology Intel® Software
Advances in cell biology and creation of an immense amount of data are converging with advances in Machine learning to analyze this data. Biology is experiencing its AI moment and driving the massive computation involved in understanding biological mechanisms and driving interventions. Learn about how cutting edge technologies such as Software Guard Extensions (SGX) in the latest Intel Xeon Processors and Open Federated Learning (OpenFL), an open framework for federated learning developed by Intel, are helping advance AI in gene therapy, drug design, disease identification and more.
Python Data Science and Machine Learning at Scale with Intel and AnacondaIntel® Software
Python is the number 1 language for data scientists, and Anaconda is the most popular python platform. Intel and Anaconda have partnered to bring scalability and near-native performance to Python with simple installations. Learn how data scientists can now access oneAPI-optimized Python packages such as NumPy, Scikit-Learn, Modin, Pandas, and XGBoost directly from the Anaconda repository through simple installation and minimal code changes.
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Preprocess, visualize, and Build AI Faster at-Scale on Intel Architecture. Develop end-to-end AI pipelines for inferencing including data ingestion, preprocessing, and model inferencing with tabular, NLP, RecSys, video and image using Intel oneAPI AI Analytics Toolkit and other optimized libraries. Build at-scale performant pipelines with Databricks and end-to-end Xeon optimizations. Learn how to visualize with the OmniSci Immerse Platform and experience a live demonstration of the Intel Distribution of Modin and OmniSci.
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oneDNN Graph API extends oneDNN with a graph interface which reduces deep learning integration costs and maximizes compute efficiency across a variety of AI hardware including AI accelerators. Get started on your AI Developer Journey @ software.intel.com/ai.
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Scale your research workloads faster with Intel on AWS. Learn how the performance and productivity of Intel Hardware and Software help bridge the gap between ideation and results in Data Science. Get started on your AI Developer Journey @ software.intel.com/ai.
Whether you are an AI, HPC, IoT, Graphics, Networking or Media developer, visit the Intel Developer Zone today to access the latest software products, resources, training, and support. Test-drive the latest Intel hardware and software products on DevCloud, our online development sandbox, and use DevMesh, our online collaboration portal, to meet and work with other innovators and product leaders. Get started by joining the Intel Developer Community @ software.intel.com.
Advanced Single Instruction Multiple Data (SIMD) Programming with Intel® Impl...Intel® Software
Explore practical elements, such as performance profiling, debugging, and porting advice. Get an overview of advanced programming topics, like common design patterns, SIMD lane interoperability, data conversions, and more.
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Review state-of-the-art techniques that use neural networks to synthesize motion, such as mode-adaptive neural network and phase-functioned neural networks. See how next-generation CPUs with reinforcement learning can offer better performance.
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Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
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Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
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Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
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Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
4. Shading Network
• Multiple reusable shading
nodes
• Connect nodes to define
complex materials
• Production shading
networks can grow very
large to 100s, 1000s of
nodes.
4
5. C++ Shader Limitations
• Lack of context at compile time
• Input parameters unknown
• Geometry being shaded
unknown
• Mode of shading unknown
• Surrounding shading
network unknown
• Branchy testing required
• Lack of portability
• Requires “Performance Ninjas”
Image Credit: Ninja Working AT Desk from Vector.me (by Hector Gomez)
5
6. Open Shading
Language
• Developed by Sony Pictures Imageworks*
• C-like DSL for programmable shading
• API to connect shaders into networks
• Open source
• http://github.com/imageworks/OpenShadingLanguage
• Sci-Tech Award* in 2017
Logo owned by Academy of Motion Picture Arts and Sciences for Infobox
*Other names and brands may be claimed as the property of others.
6
7. Poster images (c) Sony Pictures*, Paramount*, Warner
Brothers*, Disney*, Fox*, Universal*
7
8. Example OSL Shader
shader marble (color Cin = .5,
float freq = 1.0,
output color Cout = 0)
{
float sum = 0;
float freqVal = freq;
point Pshad = transform ("object", P);
for (int i = 0; i < 6; i++)
{
sum = sum + 1/freqVal * abs(.5 - noise( 4 * freqVal * Pshad)) ;
freqVal = 2 * freqVal;
}
Cout = Cin * sum;
}
Shader
Globals
(input set by renderer)
Library Calls
8
10. oslc
Offline
compiler
Shader
Written in OSL
Intermediate OSO
(Instructions + operands)
Renderer
(Pixar’s RenderMan*, Autodesk Arnold*, Blender*)
Scene Management
Ray Tracing/Path Tracing
Light Integration
OSL Runtime
Build
Shading
Network
callbacks
Execute
Shading
Network
(per Point)
Optimized
x86-64
QueryOutputs
*Other names and brands may be claimed as the property of others.
Render Time
Optimization
With
LLVM* JIT
(Just In Time Compilation)
Pre-
compiled
library
functions
OSL Framework
12. Renderer
(Pixar’s RenderMan*, Autodesk Arnold*, Blender*)
Scene Management
Ray Tracing/Path Tracing
Light Integration
SIMD OSL Runtime
callbacks
Execute
Shading
Network
(per Point)
Optimized Intel®
AVX-512, AVX2,
or AVX
QueryOutputs
*Other names and brands may be claimed as the property of others.
Render Time
Optimization
With
LLVM* Wide JIT
(Just In Time Compilation)
Pre-compiled
library
functions
Intel® AVX-
512
SIMD OSL Framework
Pre-compiled
library
functions
Intel® AVX2
Pre-compiled
library
functions
Intel® AVX
12
13. Components in
SIMD OSL Render-time
Optimized x86-64
Render Time
Optimization
With
LLVM* JIT
(Just In Time Compilation)
Wide Library
Wizard Oz Castle Clipart: https://www.clipart.email/clipart/wizard-of-oz-castle-clipart-18891.html;
<a href="https://www.clipart.email/download/374139.html" title="Image from clipart.email"><img src="https://cdn.clipart.email/e173b51872baa07a65151101799b4f7d_wizard-of-oz-clipart-emerald-castle-pencil-and-in-color-wizard-_1300-1390.jpeg" width="350" alt="Wizard Of Oz Castle Clipart" /></a>
13
*Other names and brands may be claimed as the property of others.
14. my_callback(void *wS, void *wM, void *wVec, void *wVS, void *wVT, unsigned int
mask_value)
{
Mask mask (mask_value);
ASSERT(mask.any_on());
Wide<const float> wScale (wS);
Wide<const Vec3> wVec (wVec);
Wide<const Matrix44> wMat (wM);
Masked<Vec3> wVT_result (wVT, mask);
Masked<Vec3> wVS_result (wVS, mask);
for(int lane = 0; lane < __OSL_WIDTH; ++lane) {
Vec3 V = wVec[lane];
Float F = wScale[lane];
Matrix M = wMat[lane];
wVS_result[lane] = V*F;
wVT_result[lane] = transform(M,V);
}
}
Accessors
transparent
AOS view of SOA
SIMD OSL’s Wide Library
14
15. my_callback(void *wS, void *wM, void *wVec, void *wVS, void *wVT, unsigned int
mask_value)
{
Mask mask (mask_value);
ASSERT(mask.any_on());
Wide<const float> wScale (wS);
Wide<const Vec3> wVec (wVec);
Wide<const Matrix44> wMat (wM);
Masked<Vec3> wVT_result (wVT, mask);
Masked<Vec3> wVS_result (wVS, mask);
for(int lane = 0; lane < __OSL_WIDTH; ++lane) {
Vec3 V = wVec[lane];
Float F = wScale[lane];
Matrix M = wMat[lane];
wVS_result[lane] = V*F;
wVT_result[lane] = transform(M,V);
}
}
Accessors
transparent
AOS view of SOA
Extract data
from a lane
of the SOA
SIMD OSL’s Wide Library
15
16. my_callback(void *wS, void *wM, void *wVec, void *wVS, void *wVT, unsigned int
mask_value)
{
Mask mask (mask_value);
ASSERT(mask.any_on());
Wide<const float> wScale (wS);
Wide<const Vec3> wVec (wVec);
Wide<const Matrix44> wMat (wM);
Masked<Vec3> wVT_result (wVT, mask);
Masked<Vec3> wVS_result (wVS, mask);
for(int lane = 0; lane < __OSL_WIDTH; ++lane) {
Vec3 V = wVec[lane];
Float F = wScale[lane];
Matrix M = wMat[lane];
wVS_result[lane] = V*F;
wVT_result[lane] = transform(M,V);
}
}
Array subscript returns a
proxy object to that lane
Accessors
transparent
AOS view of SOA
Extract data
from a lane
of the SOA
SIMD OSL’s Wide Library
16
17. my_callback(void *wS, void *wM, void *wVec, void *wVS, void *wVT, unsigned int
mask_value)
{
Mask mask (mask_value);
ASSERT(mask.any_on());
Wide<const float> wScale (wS);
Wide<const Vec3> wVec (wVec);
Wide<const Matrix44> wMat (wM);
Masked<Vec3> wVT_result (wVT, mask);
Masked<Vec3> wVS_result (wVS, mask);
for(int lane = 0; lane < __OSL_WIDTH; ++lane) {
Vec3 V = wVec[lane];
Float F = wScale[lane];
Matrix M = wMat[lane];
wVS_result[lane] = V*F;
wVT_result[lane] = transform(M,V);
}
}
Array subscript returns a
proxy object to that lane
Accessors
transparent
AOS view of SOA
Extract data
from a lane
of the SOA
Skips assignment if lane masked off
SIMD OSL’s Wide Library
17
18. Components in
SIMD OSL Render-time
Render Time
Optimization
With
LLVM* JIT
(Just In Time Compilation)
Wide Library
Divergent
Control Flows
Optimized x86-64
Wizard Oz Castle Clipart: https://www.clipart.email/clipart/wizard-of-oz-castle-clipart-18891.html;
<a href="https://www.clipart.email/download/374139.html" title="Image from clipart.email"><img src="https://cdn.clipart.email/e173b51872baa07a65151101799b4f7d_wizard-of-oz-clipart-emerald-castle-pencil-and-in-color-wizard-_1300-1390.jpeg" width="350" alt="Wizard Of Oz Castle Clipart" /></a>
18
*Other names and brands may be claimed as the property of others.
19. if (x > 0.5)
{
...
if (y > 0.5)
{
…
if (powB > 0.23)
{
…
}
else
{
…
}
} //y
} //x
Stack of masks
Effective mask
(result of combining stack)
Divergent Control Flows
19
20. Stack of masks
PUSH
Effective mask
(result of combining stack)
if (x > 0.5)
{
...
if (y > 0.5)
{
…
if (powB > 0.23)
{
…
}
else
{
…
}
} //y
} //x
Divergent Control Flows
20
21. if (x > 0.5)
{
...
if (y > 0.5)
{
…
if (powB > 0.23)
{
…
}
else
{
…
}
} //y
} //x
Stack of masks
PUSH
Effective mask
(result of combining stack)
Divergent Control Flows
21
22. if (x > 0.5)
{
...
if (y > 0.5)
{
…
if (powB > 0.23)
{
…
}
else
{
…
}
} //y
} //x
Stack of masks
PUSH
Effective mask
(result of combining stack)
Divergent Control Flows
22
23. if (x > 0.5)
{
...
if (y > 0.5)
{
…
if (powB > 0.23)
{
…
}
else
{
…
}
} //y
} //x
Stack of masks
POP
Effective mask
(result of combining stack)
Divergent Control Flows
23
24. if (x > 0.5)
{
...
if (y > 0.5)
{
…
if (powB > 0.23)
{
…
}
else
{
…
}
} //y
} //x
NEGATE
Stack of masks
Effective mask
(result of combining stack)
PUSH
Divergent Control Flows
24
25. if (x > 0.5)
{
...
if (y > 0.5)
{
…
if (powB > 0.23)
{
…
}
else
{
…
}
} //y
} //x
Stack of masks
POP
Effective mask
(result of combining stack)
Divergent Control Flows
25
26. if (x > 0.5)
{
...
if (y > 0.5)
{
…
if (powB > 0.23)
{
…
}
else
{
…
}
} //y
} //x
Stack of masks
POP
Effective mask
(result of combining stack)
Divergent Control Flows
26
27. if (x > 0.5)
{
...
if (y > 0.5)
{
…
if (powB > 0.23)
{
…
}
else
{
…
}
} //y
} //x
Stack of masks
POP
Effective of mask
(result of combining stack)
Divergent Control Flows
27
28. Components in
SIMD OSL Render-time
Render Time
Optimization
With
LLVM* JIT
(Just In Time Compilation)
Wide Library
Divergent
Control Flow
Vectorized IR
Generation
Optimized x86-64
Wizard Oz Castle Clipart: https://www.clipart.email/clipart/wizard-of-oz-castle-clipart-18891.html;
<a href="https://www.clipart.email/download/374139.html" title="Image from clipart.email"><img src="https://cdn.clipart.email/e173b51872baa07a65151101799b4f7d_wizard-of-oz-clipart-emerald-castle-pencil-and-in-color-wizard-_1300-1390.jpeg" width="350" alt="Wizard Of Oz Castle Clipart" /></a>
28
*Other names and brands may be claimed as the property of others.
29. General LLVM Code Flow for
OSL Operations
OSL
Retrieve symbols for
Operands
Emit LLVM-defined operations
OR
Call appropriate functions
Store Result
29
30. What changes in SIMD OSL
OSL
Retrieve symbols for
Operands
Load values
Initialize values
Emit LLVM-defined operations
OR
Call appropriate functions
Store Result
30
OperandsàUniform
ResultsàUniform
OperandsàUniform
ResultsàVarying
OperandsàVarying
ResultsàUniform
OperandsàVarying
ResultsàVarying
31. What changes in SIMD OSL
31
SIMD OSL
Retrieve symbols for
Operands
Call uniform
function
Store Result
OperandsàUniform
ResultsàUniform
32. What changes in SIMD OSL
32
SIMD OSL
Retrieve symbols for
Operands
Call uniform
function
Widen Result
Store Result
OperandsàUniform
ResultsàVarying
33. What changes in SIMD OSL
33
SIMD OSL
Retrieve symbols for
Operands
Add effective mask to
arguments
Call varying function
Add address for
Results to arguments
OperandsàVarying
ResultsàVarying
34. What changes in SIMD OSL
34
SIMD OSL
Retrieve symbols for
Operands
Add effective mask to
all arguments
Call varying function
Add address for
Results to arguments
Allocate a varying
temp
Widen uniform
Operands and store to
varying temp
OperandsàUniform,
and Varying
ResultsàVarying
35. What changes in SIMD OSL
35
Unreachable
OperandsàVarying
ResultsàUniform
36. Components in
SIMD OSL Render-time
Render Time
Optimization
With
LLVM* JIT
(Just In Time Compilation)
Wide Library
Divergent
Control Flow
Vectorized IR
Generation
“For-each-
unique”
algorithm
Optimized x86-64
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<a href="https://www.clipart.email/download/374139.html" title="Image from clipart.email"><img src="https://cdn.clipart.email/e173b51872baa07a65151101799b4f7d_wizard-of-oz-clipart-emerald-castle-pencil-and-in-color-wizard-_1300-1390.jpeg" width="350" alt="Wizard Of Oz Castle Clipart" /></a>
36
*Other names and brands may be claimed as the property of others.
41. Components in
SIMD OSL Render-time
Optimized x86
Render Time
Optimization
With
LLVM* JIT
(Just In Time Compilation)
Wide Library
Divergent
Control Flows
Vectorized IR
Generation
“For-each-
unique”
algorithm
SIMD OSL
built-ins
41
Wizard Oz Castle Clipart: https://www.clipart.email/clipart/wizard-of-oz-castle-clipart-18891.html;
<a href="https://www.clipart.email/download/374139.html" title="Image from clipart.email"><img src="https://cdn.clipart.email/e173b51872baa07a65151101799b4f7d_wizard-of-oz-clipart-emerald-castle-pencil-and-in-color-wizard-_1300-1390.jpeg" width="350" alt="Wizard Of Oz Castle Clipart" /></a>
*Other names and brands may be claimed as the property of others.
43. OSL Microbenchmarks: Speedup of
SIMD AVX-512 OSL over Scalar OSL
0.125
0.25
0.5
1
2
4
8
16
null
sin cos tan
asin
acos
atan
sinh
cosh
tanh
atan2
sincos
log
log2
log10
logb
exp
exp2
expm1
pow
erf
erfc
radians
degrees
sqrt
inversesqrt
hypot
abs
fabs
sign
floor
ceil
roundtruncmod
min
maxclampmix
isnan
isfinite
select
dot
cross
length
distance
normalize
reflect
fresnel
rotate
transform
transform_matrix
matrix_object_camera
determinant
transpose
linearstep
smooth_linearstep
noise_perlin
noise_cell
noise_simplex
noise_gabor
pnoise_perlin
pnoise_cell
pnoise_gabor
spline_bezier
spline_bspline
spline_catmull-rom
spline_hermitespline_linearspline_constant
48 threads on Intel(R) Xeon(R) Platinum 8260L CPU @2.30GHz (config 2)
Average: 6.9x
Geomean: 6.14x
43
For more complete information about performance and benchmark results, visit www.intel.com/benchmarks.
44. OSL SIMD Performance at Maximum
Batch Utilization
OSL’s testshade running Intel® AVX-512® on 48 threads of
Intel(R) Xeon(R) Platinum 8260L CPU @2.40 Ghz (config 1)
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
leopard concrete diamond oak marble
Speedupatmaxbatchsize
5.2x
6x
10x
12x
15x
44
*Other names and brands may be claimed as the property of others.
For more complete information about performance and benchmark results, visit www.intel.com/benchmarks.
45. SIMD OSL Intel® AVX-512 VS AVX2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
leopard concrete diamond plate oak marble thread donut
Speedup
1.6x 1.9x
1.1x
OSL’s testshade running Intel® AVX-512 and AVX2 on 48 threads of
Intel(R) Xeon(R) Platinum 8260L CPU @2.40 Ghz (config 1)
1.3x 1.3x
1.4x
1.8x
45
*Other names and brands may be claimed as the property of others.
For more complete information about performance and benchmark results, visit www.intel.com/benchmarks.
46. Evolution of SIMD OSL—Proof of
Concept to Production 2016‒2019
SIMD OSL
Library
SIMD OSL
Framework
SIMD OSL
Performance
Intel® AVX-512,
AVX2, AVX-specific
libraries
Masking and scatter-
gather
17k+ tests
Improved
performance on
built-in functions
Compiler + platform
support
Reduction in JIT
time
Coverage for built-in
function variants
Handling
treacherous control
flows
Noise functions
with options
LLVM optimization
passes to improve
AVX2
46
47. SIMD Open Shading
Language
Open Shading
Language
https://github.com/imageworks/OpenShadingLanguage
https://gitlab.com/intel-osl/BatchedOSL
47
51. 22.4’s Overall Rendering
Speedup with SIMD OSL
51
1
1.05
1.1
1.15
1.2
1.25
1.3
Bonnie’s room Fillmore Bonnie
Speedup
CLX8260L (24c, 2.3GHz)
1.11x
1.17x
1.27x
*Other names and brands may be claimed as the property of others.
Run on 48 threads of 24-core Intel(R) Xeon(R) Platinum 8260L CPU @ 2.30GHz (config 2)
For more complete information about performance and benchmark results, visit www.intel.com/benchmarks.