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Dell HPC Community, March 28, 2017
Marc Hamilton, VP Solutions Architecture & Engineering
AI & THE GPU READY DATA CENTER
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Numerical models to understand and predict physical and biological behavior
Based on the laws of physics —motion, gravity, mass-energy, thermodynamics, electrostatics
Computational methods like PDE, FEM, MC, LA
Turbulent Flow
Structural Analysis
Molecular Dynamics
N-body Simulation
GPU COMPUTING FOR COMPUTATIONAL SCIENCE
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Combinatorial explosion
Incomplete information
No laws-of-physics equations exist
Deep learning extracts multi-dimensional features from data
Breakthrough for AI
“What’s the next move?” “Is there cancer?”
“What’s happening” “What does she mean?”
GPU COMPUTING FOR DATA SCIENCE
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A BRIEF HISTORY OF GPU COMPUTING
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2006: THE DAWN OF GPU COMPUTING
2006 2008 2010 2012 2014 2016 2017
CUDA Launched
62006 2008 2010 2012 2014 2016 2017
CUDA Launched
TSUBAME1.2First Top500 GPU Supercomputer
• TSUBAME1.2• 170 NVIDIA S1070• 680 Tesla T10 GPUs• 170 TF• Top500 #29
2008 HPC ADOPTS SCALE-UP GPU COMPUTING
72006 2008 2010 2012 2014 2016 2017
CUDA Launched
TSUBAME1.2First Top500 GPU Supercomputer
Top500 #30
• ORNL Titan, #1, 17.59 PF• 18,000 Tesla K20 GPUs
TSUBAME2.0TOp500 #4
2.4PF
ORNL TitanTop500 #117.59 PFCray XK7
AlexNet beats expert code by huge margin using 2
GPUs for 1 week
Stanford Builds 3 server AI Machine using 12 GPUs
Rivals 5000 server Google Brain
2012 GPUS ENTER AI WORLD
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IMAGENET 2012 WINNER
Our model is a large, deep convolutional neural network trained on raw RGB pixel values. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three globally-connected layers with a final 1000-way softmax. It was trained on two NVIDIA GPUs for about a week. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of convolutional nets.
Alex Krizhevsky, Iiya Sutskever, Geoffrey Hinton, University of Toronto
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PC INTERNETWinTel, Yahoo!1 billion PC users
AI & IOTDeep Learning, GPU100s of billions of devices
MOBILE-CLOUDiPhone, Amazon AWS2.5 billion mobile users
1995 2005 2015
A NEW ERA OF COMPUTING
“ It’s clear we’re moving
from a mobile first to an
AI-first world ”
Sundar Pichai, Google CEO
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Training
Device
Datacenter
GPU DEEP LEARNING IS A NEW COMPUTING MODEL FOR AI
TRAINING
Billions of Trillions of Operations
GPU train larger models, accelerate
time to market
10s of billions of image, voice, video
queries per day
GPU inference for fast response,
maximize datacenter throughput
DATACENTER INFERENCING
Billions of intelligent devices
& machines
Recognition, reasoning, problem solving
GPU inference: real-time
accurate response
DEVICE INFERENCING
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AI IS SWEEPING ACROSS INDUSTRIESInternet Services Medicine Media & Entertainment Security & Defense Autonomous Machines
➢ Cancer cell detection
➢ Diabetic grading
➢ Drug discovery
➢ Pedestrian detection
➢ Lane tracking
➢ Recognize traffic sign
➢ Face recognition
➢ Video surveillance
➢ Cyber security
➢ Video captioning
➢ Content based search
➢ Real time translation
➢ Image/Video classification
➢ Speech recognition
➢ Natural language processing
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TESLA GPU
ACCELERATED SYSTEMS
SYSTEM TOOLS
DEVELOPMENT TOOLS
INDUSTRY TOOLS
APPLICATIONS & SERVICES
TESLA P100 TESLA P100, P40, P4, M60, M10 NVLINK DATA CENTER FEATURES
NVIDIA DGX-1 OPEN COMPUTE SYSTEM OEM CLOUD
MONITORING & MNGMT ORCHESTRATION WORKLOAD MANAGER CONTAINER
COMPUTEWORKS DEEP LEARNING SDK DESIGNWORKS & GRID SDK
PARTNER TOOLS & LIBRARIES FRAMEWORKS VIRTUALIZATION SOLUTIONS
TESLA PLATFORM FOR THE ACCELERATED DATA CENTER
C/C++ CUDA Libraries
IndeX, nvGRAPH
Cognitive Services
HPC AI DESIGN & RENDERING
+400 More Applications
VASP
Dynamic Page RetirementECC ∙ GPUDirect RDMADC Management Tools
cuBLASNCCL
DeepStream SDK
NAMD
GAUSSIAN
DC GPU ManagerNVML Library
cuDNN
OptiX
TensorRT
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DEEP LEARNING IN ACTION
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NVIDIA Suggested Reference Links
NVIDIA Developer Sign Up Page https://developer.nvidia.com/join
NVIDIA Parallel Computing Blog https://devblogs.nvidia.com/parallelforall/
NVIDIA Deep Learning Home Page https://www.nvidia.com/en-us/deep-learning-ai/
NVIDIA AI News Center https://news.developer.nvidia.com/
NVIDIA Deep Learning Institute https://www.nvidia.com/en-us/deep-learning-ai/education/
NVIDIA Tesla P100 for Deep
Learning
http://www.nvidia.com/object/tesla-p100.html
NVIDIA DGX-1 http://www.nvidia.com/object/deep-learning-system.html
NVIDIA Academic GPU Teaching Kit https://developer.nvidia.com/teaching-kits
NVIDIA GPU Grant Program https://developer.nvidia.com/academic_gpu_seeding
NVIDIA Graduate Fellowship
Program
https://research.nvidia.com/relevant/graduate-fellowship-
program
NVIDIA Docker https://github.com/NVIDIA/nvidia-docker
NVIDIA PGI OpenACC Compiler https://www.pgroup.com/products/community.htm
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Q&A