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1
ALISON B LOWNDES
AI DevRel | EMEA
@alisonblowndes
Obtaining &
accelerating insight
from downstream data
with GPUs
Phi Week 2019
Key ”Key decisions
and investments
are needed in the
next 12 months”
David Jensen, UN
4
AI CENTERS OF EXCELLENCE
StartingtheAI economyrequiresknowledgeandskills,
which requirecomputinginfrastructure,which canbe
afforded only ifthereisdemand.Governmentcan kick-
startthisvirtuouscycleby providingaccessto large-scale
AI computinginfrastructureto its universities,startups,
and industry.
SKILLINGAND RESKILLING
AI will acceleratejob growth,butharnessingitrequires
specific skillsandexpertise.Countriesneed to investin
trainingandeducation programsthatadd AI skillsto their
workforce.
INDUSTRY SOLUTIONS
By investinginkey industries,countries havea unique
opportunity to drivegrowth and solvetheir greatestsocial
and economicchallenges.
Reskilling
Workforce
Basic ResearchIndustry-Standard Platform
Rich Software
Ecosystem
State-of-the-Art
AI Computing
University-Industry
Collaboration
Vibrant Startup
Community
Hyper-Productive
Manufacturing
Safe, Efficient
Transportation
Affordable, Accessible
Healthcare
Smart, Safe
Cities
PILLARS OF NATIONAL AI INIATIVES
Phi Week 2019
Phi Week 2019
7
8
Detection Planning
Acceleration Assimilation
Enhancement Parametrization
AugmentationPrediction
Optimize Disaster Planning
9
Detection Planning
Acceleration Assimilation
Enhancement Parametrization
AugmentationPrediction
Develop Autonomous Agents for Vehicles and Sensors
10
Detection Planning
Acceleration Assimilation
Enhancement Parametrization
AugmentationPrediction
Monitor EnvironmentalChange
drought flooding deforestation urbanification melting glaciers sea-level change
11
12
CONDITIONAL GANS
Generates output consistent with the training set
Generator
(Regressor)
Discriminator
(Classifier)
Generated
Target
• If the output is under-constrained your output will look fuzzy.
• It won’t capture the true peaks and valleys
• We can overcome this problem using a conditional GAN
• Generates output matching the training set
Loss fcn
13
NOISE ELIMINATION IN
GLOBAL CLIMATE
OBSERVATIONS
HadCRUT4 data is one of the
most widely used long term
data sets for climate. But it is
filled with missing values. By
using a noise2noise algorithm,
we can fill in the missing
values, making a complete,
gridded product, far more
accurately than the usual
approach of linear
interpolation.
Freie Universität Berlin & NVIDIA
14
MINIMISE ENERGY
FOOTPRINT
MINIMISE TIME TO
COMPUTE
MAINTAIN ACCURACY
& NUMERICAL
STABILITY
Results with
NVLINK/NVSWITCH/DGX-2/V100
andreas.mueller@ecmwf.int
Global Modeling and Assimilation Office
gmao.gsf c.nasa.govGMAO
National Aeronautics and Space Administration
XGBoost for simulating atmospheric chemistry
christoph.a.keller@nasa.gov
Phi Week 2019
ONNX
https://github.com/onnx/tutorials
18
DESIGNING SPACECRAFTS
WITH GPU ACCELERATED
SIMULATIONS
Large scale aerodynamic simulations are necessary to help
define the shape and performance of aircraft, spacecrafts,
automotive vehicles and others. NASA Langley Research
Center develops FUN3D computational fluid dynamics
software to simulate fluid flow for a broad range of
aerodynamics applications. This single application consumes
more cycles at NASA’s supercomputers than any other
application. GPU acceleration enables over 23X higher
performance than CPU servers while running these
simulations.
The performance on GPUs scales very well to enable
efficient computation of the largest and the most complex
simulations. NASA has shown that a thousand GPU servers on
Summit supercomputer can do the work of over a million
CPU cores for a fraction of the energy costs.
Images: SLS)Block 1B booster separation flowfield simulated using NASA's FUN3D code. . Credit: Jamie Meeroff, Henry Lee, NASA/Ames
Phi Week 2019
20
www.FrontierDevelopmentLab.org
22
2323
https://karpathy.github.io/2019/04/25/recipe/
24NVIDIA CONFIDENTIAL. DO NOT DISTRIBUTE.
LEARNING STRATEGY
• Evolutionary algorithms (EA): population-based optimizer for black-box functions,
combination and mutation to generatenew architectures
• Gradient-based: based on continuousrelaxation of the architecture representation,
allowing search of the architecture with gradient descent
• Reinforcement learning (RL): an agent (network generator) interacts with an
environment (search space) with the goal of maximizingrewards (accuracy on
validation data)
25
KAOLIN
- A Pytorch library for 3D DL
- Supports a wide range of 3D data representations
- Convenient dataloading/preprocessing/conversions
- Large collection of 3D neural nets to choose from
- Optimized implementations
- Omniverse-Kit integration for easy rendering,
interactive visualization, and much more.
https://gitlab-
master.nvidia.com/Toronto_
DL_Lab/kaolin
RAPIDS
RAPIDS.ai
GPU Accelerated End-to-End Data Science
RAPIDS is a set of open source libraries for GPU accelerating
data preparation and machine learning.
GPU Memory
Data Preparation VisualizationModel Training
cuGraph
Graph Analytics
cuML
Machine Learning
cuDF
Data Preparation
27
28
ISAAC OPEN SDK
Isaac Robot Engine – Modular robot framework | Isaac Sim - Virtual roboticslaboratory
Isaac Gym – Reinforcement learning simulator | Isaac Robot Apps – Kaya, Carter and Link
Available at developer.nvidia.com/isaac-sdk
CARTER (Xavier)KAYA (Nano) LINK (Multi Xavier)
JETSONNANO
ISAAC OPEN TOOLBOX
Sensor and
Actuator Drivers Core Libraries GEMS Reference DNN Tools
CUDA-X
Isaac Robot Engine
JETSONTX2 JETSONAGX XAVIER
Isaac Sim Isaac Gym
Phi Week 2019
30
JETSON NANO
€99 NVIDIA CUDA-X AI Computer
CUDA-X acceleration stack | High-resolution sensor support | Runs all CUDA-X AI models
31
Jetson AGX
Xavier
developer.nvidia.com/jetson-
xavier
32
A WORKSTATION FOR DATA SCIENTISTS
Powered by NVIDIA GPU and CUDA-X AI
Dual Quadro RTX 8000 with 96 GB Memory | Pre-installed for CUDA-X Accelerated DataScience —
RAPIDS, TensorFlow, PyTorch, Caffe, AnacondaDistribution | 10X Faster
0 2 4 6 8 10
Data Prep
Training
End to end
Acceleration of FSI Data Science Workflow
2x RTX8000 1x RTX8000 CPU
33
2 PFLOPS | 512GB HBM2 | 10 kW | 350 lbs
NVIDIA DGX-2
35
NVIDIA GPUs IN THE CLOUD
AVAILABLE ON-DEMANDFROM THE TOP CLOUD SERVICE PROVIDERS
• Immediate access to NVIDIA GPU
infrastructure for data science in the
cloud
• Wide variety of deployment and
management options using
containers, Kubernetes, Kubeflow,
support for cloud native services, and
more
Phi Week 2019
37
FIVE ROADS TO GPU COMPUTING
GPU Libraries
______________
Drop-in replacement for
existing libraries
cuBLAS, CUDA Math,
cuSPARSE, cuRAND,
cuSOLVER, nvGRAPH, cuDNN,
cuFFT, Thrust
OPEN-ACC
______________
Comment-based
directives in
C / C++ / Fortran
Single source code
parallelization for
multiple architectures
CUDA
______________
Parallel Programming
Model for GPUs in C, C++,
Fortran, Python, MATLAB
Specialized Kernels for
general purpose GPU
RAPIDS
______________
GPU Acceleration of
Traditional Machine
Learning
Accelerate Scikit-Learn
style ML algorithms
DEEP LEARNING
______________
GPU accelerated deep
learning frameworks
TensorFlow, Pytorch
Build GPU-accelerated
functions directly
from data
38
PURPOSE-BUILT AI SUPERCOMPUTERS
AI WORKSTATION AI DATA CENTER
Universal SW for Deep Learning
Predictable execution across
platforms
Pervasive reach
NGC DL SOFTWARE STACK
The Essential
Instrument for AI
Research
DGX-1
The Personal
AI Supercomputer
DGX Station
The World’s Most Powerful
AI System for the Most
Complex AI Challenges
DGX-2
39
40
GET STARTED WITH NGC
Deploy containers:
ngc.nvidia.com
Learn more about NGC offering:
nvidia.com/ngc
Technical information:
developer.nvidia.com
Explore the NGC Registry for DL, ML & HPC
41
Available to Instructors Now!
developer.nvidia.com/teaching-kits
Robotics Teaching
Kit with ‘Jet’ - ServoCity
D E E P L E A R N I N G
Fundamentals
Accelerated Computing
Game Development &
Digital Content
Finance
NVIDIA DEEP LEARNING
INSTITUTE
Online self-paced labs and instructor-led
workshops on deep learning and
accelerated computing
Take self-paced labs at
www.nvidia.co.uk/dlilabs
View upcoming workshops and request a
workshop onsite at www.nvidia.co.uk/dli
Educators can join the University
Ambassador Program to teach DLI courses
on campus and access resources. Learn
more at www.nvidia.com/dli
Intelligent Video
Analytics
Healthcare
Robotics
Autonomous Vehicles
Virtual Reality
43
CONNECT
Connect with experts from
NVIDIA, GE Healthcare, NSF
Carnegie Mellon, Google, and
other leading organizations
LEARN
Gain insight and valuable
hands-on training through
over 100 sessions
DISCOVER
See how GPU technologies
are creating amazing
breakthroughs in important
fields such as deep learning
INNOVATE
Explore disruptive
innovations that can
transform your work
Don’t miss the premier AI conference.
nvidia.com/en-us/gtc-dc/
Join us at GTC DC | Use VIP code NVALOWNDES for 25% off | Govt. attends free
November 4 - 6, 2019 | Washington, D.C.
44
www.nytimes.com/2019/08/08/climate/climate-change-food-supply.html
Luis Tato/AFP

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Phi Week 2019

  • 1. 1 ALISON B LOWNDES AI DevRel | EMEA @alisonblowndes Obtaining & accelerating insight from downstream data with GPUs
  • 3. Key ”Key decisions and investments are needed in the next 12 months” David Jensen, UN
  • 4. 4 AI CENTERS OF EXCELLENCE StartingtheAI economyrequiresknowledgeandskills, which requirecomputinginfrastructure,which canbe afforded only ifthereisdemand.Governmentcan kick- startthisvirtuouscycleby providingaccessto large-scale AI computinginfrastructureto its universities,startups, and industry. SKILLINGAND RESKILLING AI will acceleratejob growth,butharnessingitrequires specific skillsandexpertise.Countriesneed to investin trainingandeducation programsthatadd AI skillsto their workforce. INDUSTRY SOLUTIONS By investinginkey industries,countries havea unique opportunity to drivegrowth and solvetheir greatestsocial and economicchallenges. Reskilling Workforce Basic ResearchIndustry-Standard Platform Rich Software Ecosystem State-of-the-Art AI Computing University-Industry Collaboration Vibrant Startup Community Hyper-Productive Manufacturing Safe, Efficient Transportation Affordable, Accessible Healthcare Smart, Safe Cities PILLARS OF NATIONAL AI INIATIVES
  • 7. 7
  • 8. 8 Detection Planning Acceleration Assimilation Enhancement Parametrization AugmentationPrediction Optimize Disaster Planning
  • 9. 9 Detection Planning Acceleration Assimilation Enhancement Parametrization AugmentationPrediction Develop Autonomous Agents for Vehicles and Sensors
  • 10. 10 Detection Planning Acceleration Assimilation Enhancement Parametrization AugmentationPrediction Monitor EnvironmentalChange drought flooding deforestation urbanification melting glaciers sea-level change
  • 11. 11
  • 12. 12 CONDITIONAL GANS Generates output consistent with the training set Generator (Regressor) Discriminator (Classifier) Generated Target • If the output is under-constrained your output will look fuzzy. • It won’t capture the true peaks and valleys • We can overcome this problem using a conditional GAN • Generates output matching the training set Loss fcn
  • 13. 13 NOISE ELIMINATION IN GLOBAL CLIMATE OBSERVATIONS HadCRUT4 data is one of the most widely used long term data sets for climate. But it is filled with missing values. By using a noise2noise algorithm, we can fill in the missing values, making a complete, gridded product, far more accurately than the usual approach of linear interpolation. Freie Universität Berlin & NVIDIA
  • 14. 14 MINIMISE ENERGY FOOTPRINT MINIMISE TIME TO COMPUTE MAINTAIN ACCURACY & NUMERICAL STABILITY Results with NVLINK/NVSWITCH/DGX-2/V100 andreas.mueller@ecmwf.int
  • 15. Global Modeling and Assimilation Office gmao.gsf c.nasa.govGMAO National Aeronautics and Space Administration XGBoost for simulating atmospheric chemistry christoph.a.keller@nasa.gov
  • 18. 18 DESIGNING SPACECRAFTS WITH GPU ACCELERATED SIMULATIONS Large scale aerodynamic simulations are necessary to help define the shape and performance of aircraft, spacecrafts, automotive vehicles and others. NASA Langley Research Center develops FUN3D computational fluid dynamics software to simulate fluid flow for a broad range of aerodynamics applications. This single application consumes more cycles at NASA’s supercomputers than any other application. GPU acceleration enables over 23X higher performance than CPU servers while running these simulations. The performance on GPUs scales very well to enable efficient computation of the largest and the most complex simulations. NASA has shown that a thousand GPU servers on Summit supercomputer can do the work of over a million CPU cores for a fraction of the energy costs. Images: SLS)Block 1B booster separation flowfield simulated using NASA's FUN3D code. . Credit: Jamie Meeroff, Henry Lee, NASA/Ames
  • 20. 20
  • 22. 22
  • 24. 24NVIDIA CONFIDENTIAL. DO NOT DISTRIBUTE. LEARNING STRATEGY • Evolutionary algorithms (EA): population-based optimizer for black-box functions, combination and mutation to generatenew architectures • Gradient-based: based on continuousrelaxation of the architecture representation, allowing search of the architecture with gradient descent • Reinforcement learning (RL): an agent (network generator) interacts with an environment (search space) with the goal of maximizingrewards (accuracy on validation data)
  • 25. 25 KAOLIN - A Pytorch library for 3D DL - Supports a wide range of 3D data representations - Convenient dataloading/preprocessing/conversions - Large collection of 3D neural nets to choose from - Optimized implementations - Omniverse-Kit integration for easy rendering, interactive visualization, and much more. https://gitlab- master.nvidia.com/Toronto_ DL_Lab/kaolin
  • 26. RAPIDS RAPIDS.ai GPU Accelerated End-to-End Data Science RAPIDS is a set of open source libraries for GPU accelerating data preparation and machine learning. GPU Memory Data Preparation VisualizationModel Training cuGraph Graph Analytics cuML Machine Learning cuDF Data Preparation
  • 27. 27
  • 28. 28 ISAAC OPEN SDK Isaac Robot Engine – Modular robot framework | Isaac Sim - Virtual roboticslaboratory Isaac Gym – Reinforcement learning simulator | Isaac Robot Apps – Kaya, Carter and Link Available at developer.nvidia.com/isaac-sdk CARTER (Xavier)KAYA (Nano) LINK (Multi Xavier) JETSONNANO ISAAC OPEN TOOLBOX Sensor and Actuator Drivers Core Libraries GEMS Reference DNN Tools CUDA-X Isaac Robot Engine JETSONTX2 JETSONAGX XAVIER Isaac Sim Isaac Gym
  • 30. 30 JETSON NANO €99 NVIDIA CUDA-X AI Computer CUDA-X acceleration stack | High-resolution sensor support | Runs all CUDA-X AI models
  • 32. 32 A WORKSTATION FOR DATA SCIENTISTS Powered by NVIDIA GPU and CUDA-X AI Dual Quadro RTX 8000 with 96 GB Memory | Pre-installed for CUDA-X Accelerated DataScience — RAPIDS, TensorFlow, PyTorch, Caffe, AnacondaDistribution | 10X Faster 0 2 4 6 8 10 Data Prep Training End to end Acceleration of FSI Data Science Workflow 2x RTX8000 1x RTX8000 CPU
  • 33. 33
  • 34. 2 PFLOPS | 512GB HBM2 | 10 kW | 350 lbs NVIDIA DGX-2
  • 35. 35 NVIDIA GPUs IN THE CLOUD AVAILABLE ON-DEMANDFROM THE TOP CLOUD SERVICE PROVIDERS • Immediate access to NVIDIA GPU infrastructure for data science in the cloud • Wide variety of deployment and management options using containers, Kubernetes, Kubeflow, support for cloud native services, and more
  • 37. 37 FIVE ROADS TO GPU COMPUTING GPU Libraries ______________ Drop-in replacement for existing libraries cuBLAS, CUDA Math, cuSPARSE, cuRAND, cuSOLVER, nvGRAPH, cuDNN, cuFFT, Thrust OPEN-ACC ______________ Comment-based directives in C / C++ / Fortran Single source code parallelization for multiple architectures CUDA ______________ Parallel Programming Model for GPUs in C, C++, Fortran, Python, MATLAB Specialized Kernels for general purpose GPU RAPIDS ______________ GPU Acceleration of Traditional Machine Learning Accelerate Scikit-Learn style ML algorithms DEEP LEARNING ______________ GPU accelerated deep learning frameworks TensorFlow, Pytorch Build GPU-accelerated functions directly from data
  • 38. 38 PURPOSE-BUILT AI SUPERCOMPUTERS AI WORKSTATION AI DATA CENTER Universal SW for Deep Learning Predictable execution across platforms Pervasive reach NGC DL SOFTWARE STACK The Essential Instrument for AI Research DGX-1 The Personal AI Supercomputer DGX Station The World’s Most Powerful AI System for the Most Complex AI Challenges DGX-2
  • 39. 39
  • 40. 40 GET STARTED WITH NGC Deploy containers: ngc.nvidia.com Learn more about NGC offering: nvidia.com/ngc Technical information: developer.nvidia.com Explore the NGC Registry for DL, ML & HPC
  • 41. 41 Available to Instructors Now! developer.nvidia.com/teaching-kits Robotics Teaching Kit with ‘Jet’ - ServoCity D E E P L E A R N I N G
  • 42. Fundamentals Accelerated Computing Game Development & Digital Content Finance NVIDIA DEEP LEARNING INSTITUTE Online self-paced labs and instructor-led workshops on deep learning and accelerated computing Take self-paced labs at www.nvidia.co.uk/dlilabs View upcoming workshops and request a workshop onsite at www.nvidia.co.uk/dli Educators can join the University Ambassador Program to teach DLI courses on campus and access resources. Learn more at www.nvidia.com/dli Intelligent Video Analytics Healthcare Robotics Autonomous Vehicles Virtual Reality
  • 43. 43 CONNECT Connect with experts from NVIDIA, GE Healthcare, NSF Carnegie Mellon, Google, and other leading organizations LEARN Gain insight and valuable hands-on training through over 100 sessions DISCOVER See how GPU technologies are creating amazing breakthroughs in important fields such as deep learning INNOVATE Explore disruptive innovations that can transform your work Don’t miss the premier AI conference. nvidia.com/en-us/gtc-dc/ Join us at GTC DC | Use VIP code NVALOWNDES for 25% off | Govt. attends free November 4 - 6, 2019 | Washington, D.C.