Dell HPC Community, March 28, 2017 Marc Hamilton, VP ... · tesla gpu accelerated systems system...

Dell HPC Community, March 28, 2017

Marc Hamilton, VP Solutions Architecture & Engineering

AI & THE GPU READY DATA CENTER

2

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

3

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

4

A BRIEF HISTORY OF GPU COMPUTING

5

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

8

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

9

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

10

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

11

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

12

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

13

DEEP LEARNING IN ACTION

14

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

15

Q&A