HPC DAY 2017 - http://www.hpcday.eu/ HPE Strategy And Portfolio for AI, BigData and HPC Volodymyr Saviak | CEE HPC & POD Sales Manager at HPE

1. HPE HPC Strategy
Volodymyr Saviak, HPE HPC Sales Manager
HPE HPC Business Unit, October the 17th, 2017

2. Agenda
1. HPC Inspirational Video – 7 minutes
2. HPC BU Introduction - HPE Leadership in HPC – 5 minutes
3. HPC Strategy – 5 minutes
4. HPC Current Portfolio – 5 minutes
5. Memory Driven Computing – 10 minutes
6. AI Inspirational Video – 3 minutes
7. AI... more about AI – 15 minutes
2

3. HPC Inspirational Video
3

4. HPE Leadership in HPC
4

5. Technology features – key points to remember
HPC – High Performance Computing
High Performance
High Density
Fast
Interconnects
Scalable
Storage
Highly
Efficient
Infrastructure

6. HPC Solutions Business Unit
HPE HPC BU Solutions Areas
HighPerformance
Computing
- Monte Carlo simulations
- Oil & Gas computations
- Manufacturing, Intelligence
- Life sciences (Bio, Chem,…)
AI&BigDataapplications
- Deep Learning, AI
- HPDA (Hadoop, SPARK)
- In memory compute & DB
- Rendering, Content
Scale-OUTStorage
- Scale out Storage
- Media assets archives
- High Performance Storage
- Video surveillance
PerformanceOptimized
Datacenters
- Modular datacenters
- Mobile datacenters
- Green DC (low PUE)
- EMI/EMR protected DC

7. We design and deliver
a complete customer-specified solution,
including application software if needed
(often we stop with middleware),
delivered pre-built and tested to the highest level of quality,
ready to plug in and switch on with the shortest time to
acceptance.
What do deliver?

8. Hewlett Packard Enterprise Completes Acquisition of SGI
8

9. HPE is a proven leader in the high end Supercomputing Segment
9
Analysis – Summary
– HPE again #1 position - 128 systems (26%)
– Lenovo #2, with 84 systems (17%)
– Cray, #3, with 60 systems (12%)
– SGI, #7 with 25 systems (5%)
Vendor Top 25 Top 50 Top 100 Top 500
HPE 0 2 3 127
SGI 4 5 10 26
Cray 11 18 30 60
Lenovo 0 1 6 84
Comparison - # of Systems
TOP 500: 47th Edition
Top 500 – Vendor Comparison enhanced Top 100 HPE + SGI leadership

10. HPC Strategy
10

11. HPE Strategy –
Accelerate HPC leadership today and into the future
– NRE Efforts
– Forward Selling
– Early Ships
– Time To Market
Solutions
– Risk Compliant Archive
– Trade and Match
– Quantitative Finance Library
– Next Gen Sequencing
– CAE Solution
HPC Advanced Technology and Development
HPC and AI
Compute Solutions
HPC and AI
Storage Solutions
Deep Learning
Solutions
Optimized Platforms
Horizon 2
next 12 to 24 months
– Software Stacks
– Lustre
– Remote Graphics
– Cognitive Toolkit
Metrics
– Domain Expertise
– Customer Loyalty
– Share Growth
– Innovation
HPC and AI
Market
Leadership
Horizon 1
6 to 12 months
HPC and AI
Markets / Industries
Financial
Services
Industry
Life Sciences, Health
Oil & Gas,
Energy
ManufacturingEDA / CAE
Academia,
Research
Government
Weather
We are in the high performance computing solutions business

12. PathForward Exascale program
Ensures US competitiveness in the global market
– PathForward is a Department of Energy (DOE) Non-Recurring Engineering (NRE) Initiative
– Central element of DOE’s Exascale Compute Program (ECP) Hardware Technology effort
– Funding for R&D of technologies to develop the next generation compute infrastructure; includes open architectures
and alternative processors
– Cornerstone of U.S. scientific progress, technological innovation, economic vitality, and a strong national defense

13. Solving complex HPC and AI challenges with Hybrid Cluster
New Tokyo Institute of Technology Supercomputer
Key features
− 540 Compute Nodes
− Two (2) Intel® Xeon® E5-2680 v4 processors
− Four (4) NVIDIA TESLA P100 NVLink GPUs
− NVMe-compatible, high-speed 1.08 PB SSDs
− Four (4) Intel Omni-Path connectors/node
− Rich Fat Tree configuration
− 400 Gb/s bandwidth /node
TSUBAME 3.0 Supercomputer
− Available for outside researchers in private sector through
JHPCN1 and HPCI2
− Ranked #1 on Green500 List – most energy efficient
supercomputer in the world, running on HPE infrastructure.
− Supports significant AI and scientific HPC workloads,
providing unprecedented ability to analyze large data sets.
− Largest Tesla P100 SXM2 deployment to date with 2,160
NVLink-enabled GPUs
“Through our partnership with SGI, and now HPE, the
Tokyo Institute of Technology has worked successfully
to deliver a converged world-leading HPC and Deep
Learning platform….”
- Satoshi Matsuoka, Professor and TSUBAME Leader, Tokyo
Institute of Technology..
1, 2 Reference Information provided in speaker notes

14. World’s largest chemical company creates chemistry with HPC
HPE supercomputer enables global digital transformation at BASF
Key features
− HPE Apollo 6000 Gen10
− > 1 Petaflop using Next Gen platform
− Multitude nodes
− Work simultaneously on highly complex
tasks
− Dramatically reduce processing time
BASF Supercomputer
− Designed to be one of the world’s largest supercomputer
− Drive digitalization of BASF's worldwide research
− Shorten modeling / simulation times (months to days)
− Solve complex problems while decreasing discovery time
− Run virtual experiments to reduce time-to-market, lower costs
“The new supercomputer will promote the
application and development of complex
modeling and simulation approaches,
opening up completely new avenues for our
research at BASF.”
− Dr. Martin Brudermueller, Vice Chairman of
the Board of Executive Directors and CTO,
BASFBASF Cluster - HPE Factory Build in Houston, TX, May 2017

15. Exascale required to solve the world’s most complex problems
Life Sciences
Weather
Deep Learning, IoT and Artificial Intelligence systems will need Exascale computing
Material
Sciences
Manufacturing
Today’s top 500 systems
Consume 650MW of
power – (> ½ a Gigawatt)
Huge CO2 Footprint
Aggregated compute
power of ~1 ExaFLOPS
Accurate regional impact assessment of climate change
Accelerate and translate cancer research in RAS pathways,
drug responses, and treatment strategies
Additive manufacturing process design for qualifiable metal
components
Efficiency and performance characteristics of materials for
batteries, solar cells, and optoelectronics

16. HPC Current Portfolio
16

17. − Deliver more choice / flexibility for HPC
− ARM processor based system
− Proof of concepts with select customers
Accelerating HPC innovation for today and tomorrow
New HPE SGI 8600 Next
gen petaflop scale, liquid cooled
supercomputer
– Greater performance, scale
and efficiency
New HPE Apollo 6000 Gen10
Next gen air cooled, purpose built
enterprise HPC solution
– Best in class performance, rack
scale efficiency
New HPE Apollo 10
Series
– Cost effective platforms
for AI and emerging
applications
A new experience in IT
security and protection
New HPE Performance
Software Suite: Out-of-the-
box HPC stack, enhanced
cluster system management
and acceleration tools
New Services and
Consumption Model
– New Advisory, Professional and
Operational Services
– HPE Flexible Capacity for HPC
DoE PathForward Exascale
Program
− New Exascale program to create
reference designs
− Inspired by Memory-Driven Computing
and Hewlett Packard Labs technologies
New disruptive technology based
system architecture
– ARM processor based
system
– Proof of concepts with
select customers
1 Substantiation for quantifiable benefits in speaker notes
Workload optimized
for extreme performance
Secure, agile, flexible
Compute experience
Exascale and advanced
technology programs
– NEW collaboration
for AI application in
precision medicine
World’s Most Secure Servers1
for HPC and AI – HPE Apollo
6000 Gen10

18. HPE purpose-built portfolio for HPC
HPE
Apollo 6500 Gen9
Rack-scale GPU
Computing
HPE Integrity
Superdome X
HPE Integrity
MC990 X
Scale-up, shared
memory HPC, UV
Technologies
HPE
Apollo 6000
Gen9
Rack-scale HPC
HPE
Apollo 2000
Gen9
The bridge to enterprise
scale-out architecture
HPE
SGI 8600
Liquid cooled, delivering
industry leading performance,
density and efficiency
HPE
Apollo 6000
Gen10
Extreme Compute
Performance in High
Density
Supercomputing / Enterprise / Commercial HPC
Advisory, Professional and Operational Services – HPE Flexible Capacity for HPC, HPE Datacenter Care for Hyperscale
HPC Storage Choice of Fabrics
HPC Industry
Solutions
Weather and
Climate Research
Financial
Services
Life Sciences,
Health
Academia,
Research,
Gov’t
Oil and Gas,
Energy
EDA / CAE
Manufacturing
HPE
Software
Open
Source
Software
Commercial
HPC Software
− HPE Performance Software - Core Stack
− HPE Insight Cluster Management Utility
− HPE SGI Management Suite
− HPE Performance Software – Message
Passing Interface*
HPE Apollo
4520
Arista
Networking
– Intel® Omni-Path
Architecture
– Mellanox InfiniBand
– HPE FlexFabric
Network
HPC Data
Management
Framework
Software
Large-scale, storage
virtualization & tiered
data management
platform
HPE Performance Software Suite
Emerging HPC In-memory HPC
Additional Storage
Options available
* Available in August 2017

19. MDC Portfolio
Optimized Infrastructure
CAPACITY 150kW 300kW 500kW 900kW 1,100kW
DC8
200kW
368U
DC18
500kW
858U
ENTERPRISE
Availability
PERFORMANCE
Node Density
DC10
290kW
500U
DC21-600
580kW
1,050U
DC44
1,014kW
2,200U
DC4
50kW
200U
DC5
50kW
230U
DC5-50
50kW
250U
DC21-300
290kW
1050U
Max. Electrical Load
Max. Electrical Load

20. Memory Driven Computing
20

21. The New Normal: Compute is not keeping up
21
0,3 0,8 1,2
1,8
4,4
7,9
15,8
31,6
44
0
5
10
15
20
25
30
35
40
45
50
2006 2008 2010 2012 2014 2016 2018 2020
Data
(Zettabytes)
Data nearly doubles every two years
(2013-2020)
Data growth
Transistors
(thousands)
Single-thread
Performance
(SpecINT)
Frequency
(MHz)
Typical Power
(Watts)
Number of
Cores
1975 1980 1985 1990 1995 2000 2005 2010 2015
Microprocessors
107
106
105
104
103
102
101
100

22. We need new type of compute – Memory Driven Computing
Structured data
40 petabytes
Walmart’s transaction
database (2017)
Human interaction data
4 petabytes
Per-day posting to Facebook
across 1.1 billion active users
(May 2016)
4kB per active user
Digitization of Analog Reality
40,000 petabytes a day*
10m self-driving cars by 2020
Front camera
20MB / sec
Front ultrasonic sensors
10kB / sec
Infrared camera
20MB / sec
Side ultrasonic
sensors
100kB / sec
Front, rear and
top-view cameras
40MB / sec
Rear ultrasonic
cameras
100kB / secRear radar sensors
100kB / sec
Crash sensors
100kB / sec
Front radar
sensors
100kB / sec
* Driver assistance systems only

23. Key attributes of
Memory-Driven
Computing
Powerful
A quantum leap in performance,
beyond what you can imagine
Open
An open architecture designed to foster
a vibrant innovation ecosystem
Trusted
Always safe, always recoverable
All the benefits without asking for sacrifice
Simple
Structurally simple, manageable and
automatic, so that “it just works”
23

24. GPU
ASIC
Quantum
RISC
V
Memory
24
Memory
Memory
Memory
Memory
SoC
SoC
SoC
SoC
Future architecture
Memory-Driven Computing
Today’s architecture
From processor-centric computing

25. What are core Memory-Driven Computing components?
25
Combining memory and
storage in a stable
environment to increase
processing speed and
improve energy efficiency
Using photonics where
necessary to eliminate
distance and create
otherwise impossible
topologies
Optimizing processing from
general to specific tasks
Radically simplifying
programming and enabling
new applications that we
can’t even begin to build
today
Fast, persistent
memory
Fast memory fabric
Task-specific
processing
New and Adapted
software

26. GPU
ASIC
Quantum
RISC
V
Open architecture
Customize the hardware to the workload
26
DRAM
DRAM
NVRAM
Reduced cost
Less energy
Less space
Less complex

27. Memory-Driven Computing Developer Toolkit
Software already available to you
‒ Example Applications
‒ Programming and analytics
tools
‒ Operating system support
‒ Emulation/simulation tools
Get access to the toolkit:
https://www.labs.hpe.com/the-
machine/developer-toolkit
Open source components
Machine (Prototype) hardware
Node Operating System
Persistent Memory
Library (pmem.io)
Librarian File System (LFS)
Fabric attached memory
atomics library
Linux for
Memory-Driven
Computing
Example Applications
Management
Services
Librarian
Data Management & Programming Frameworks
Managed data
structures
Sparkle
Emulation/Simulation Tools
Performance
emulation for NVM
Fabric attached
memory emulation
X’86 emulation (Superdome X, MC990x,
ProLiant)
Fault-tolerant
programming
Fast
optimistic
engine
Image Search Large Scale Graph Inference
Persistent
memory toolkit

28. HPE introduces the world’s largest single-memory computer
The prototype contains 160 terabytes of memory
28
– 160 TB of shared memory spread across
40 physical nodes, interconnected using a
high-performance fabric protocol.
– An optimized Linux-based operating
system running on ThunderX2, Cavium’s
flagship second generation dual socket
capable ARMv8-A workload optimized
System on a Chip.
– Photonics/Optical communication links,
including the new X1 photonics module,
are online and operational.
– Software programming tools designed
to take advantage of abundant of
persistent memory.

29. Transform performance with Memory-Driven programming
29
In-memory analytics
15x
faster
New algorithms Completely rethink
Modify existing
frameworks
Similarity search
40x
faster
Financial models
10,000x
faster
Large-scale
graph inference
100x
faster

30. AI - Artificial intelligence
30

31. 31
We have large memory, memory driven computing,
its much faster, but there is a problem…

32. 32
We can’t scale people…easily.

33. AI Inspirational Video
– C:BACKUP_VideoGTC 2017- 'I Am AI' Opening in Keynote.mp4
33

34. Are we on the brink of a ….
34
Change 1:
Moving from gather and hunting
to settling down to farms and
ports
Change 2:
Developing the printing press
and industrial revolution
Latest Change:
The greatest change of our
lives. Artificial Intelligence

35. 0
10
20
30
40
50
60
70
Market in
billion US dollars
1.38
2016
2.24
2017
4.07
2018
6.63
2019
10.53
2020
16.24
2021
24.16
2022
34.38
2023
46.52
2024
59.75
2025
What is the size of the AI market?
35
1 Source : IDC IT Predictions 2017
Services
App
Advisory
Total AI TAM
2017 TAM 2021 TAM 4yr CAGR
$0.7B $2.3B 32%
$0.4B $1.6B 40%
$2.3B $18.5B 67%
Server- ML
$7.9B $31.3B 41%
$3.5B
$0.9BServer- DL
$4.6B
$4.4B
7%
48%
By 2019,
40% of all digital
transformation initiatives 100% of all effective IoT efforts will be
supported by AI capabilities1
andBy 2018,
75% of developer teams
will include AI functionality
in one or more applications1

36. 36

37. AI vs Brain?
AI – HPE, CMU Liberatus
Brain: Kim, Les, Chou, MCAulay
10160
Poker - 2017Checkers -1995
AI: UAlberta Chinook: white
Brain: Don Lafferty – red
1020
Chess -1997
AI: IBM Deep Blue: white
Brain: Garry Kasparov: black
1047
AI: Google AlphaGo - black
Brain: Lee Sedol - white
10171
Go - 2016

38. ‒ Search & information
extraction
‒ Security/Video
surveillance
‒ Self-driving cars
‒ Medical imaging
‒ Robotics
‒ Interactive voice
response (IVR)
systems
‒ Voice interfaces
(Mobile, Cars,
Gaming, Home)
‒ Security (speaker
identification)
‒ Health care
‒ People with disabilities
‒ Search and ranking
‒ Sentiment analysis
‒ Machine translation
‒ Question answering
‒ Recommendation
engines
‒ Advertising
‒ Fraud detection
‒ AI challenges
‒ Drug discovery
‒ Sensor data analysis
‒ Diagnostic support
Where can we use deep learning today?
Applications
38
TextVision Speech Other

39. Applications break down
39
Detection
Look for a known object/pattern
Classification
Assign a label from a predefined set of
labels
Generation
Generate content
Anomaly detection
Look for abnormal, unknown patterns
Images
Video
Text
Sensor
Other
Speech
Video surveillance
Speech recognition
Sentiment analysis
Predictive maintenance
Fraud detection
Image analysis

40. Where to start ?
Recommend DL stack by vertical application
40
Infrastructure
Frameworks
Typical layers
Data type
Data
ManufacturingVerticals Oil & gas
Connected
cars
Voice
interfaces
Social media
Speech Images Sensor dataVideo
Small Moderate Large
Convolutional
Fully-
connected
Recurrent
TensorFlow Caffe 2 CNTK …
x86 GPUs FPGAs TPU ? …
…
Torch
Neural Network sits here

41. AI expertise and solutions to “get started” with deep learning models
41
New foundation to “get started” with deep learning models
Enhance
employee productivity
Accelerate app development
with New deep learning
integrated solution
Pre-configured, proven
hardware & software solution
− Purpose-build platform
− Easy to use and install
− Simple management
− Automated framework updates
Train
your teams
Gain organizational
competencies with Enhanced
Deep Learning Institute
State of the art deep learning
training
− Latest techniques
− Software frameworks
− Infrastructure requirements
− Hands on, instructor led
HPE Fraud Detection Solution
with Kinetica
− Uses deep learning techniques
− Qualified with Kinetica in-
memory GPU database
− NVIDIA GPU accelerators
Leverage
“out of the box” solutions
Increase security of e-commerce
with Enhanced HPE Fraud
Detection solution
Get
Started
Select
ideal technologies & systems
Make Informed technology
decisions with New HPE
Deep Learning Cookbook
Comprehensive technology
selection tool
− Estimates & refines performance
− Characterizes frameworks
− Recommends ideal hardware
and software stacks
IT Expertise Solutions

42. 42
Conclusions

43. Where would the AI road take us?
43
Advances in artificial intelligence will transform modern life by reshaping transportation, health, science, finance, and the
military.
“High-level machine intelligence” (HLMI) is achieved when unaided machines can accomplish every task better and
more cheaply than human workers.
Grace et al , When Will AI Exceed Human Performance? Evidence from AI Experts
Writing a
bestseller –
2049
Driving a truck
- 2027
Math Research
- 2060
Surgeon -
2043
Retail - 2031 Full
Automation of
labor – 2140

44. Thank you