hyperion research update: research highlights in hpc, … · 2020-01-09 · hyperion research...

HYPERION RESEARCH UPDATE:

Research Highlights In HPC, HPDA-AI, Cloud

Computing, Quantum Computing, and

Innovation Award Winners

Earl Joseph, Steve Conway,

Bob Sorensen, and Alex Norton

ISC19

Important Notes

▪ If you want a copy of the slides:• Please register at our homepage

(www.HyperionResearch.com ):

→ or leave a business card

▪ Check out our web sites:

• www.HyperionResearch.com

• www.hpcuserforum.com

© Hyperion Research 2

http://www.hyperionresearch.com/


http://www.hpcuserforum.com/

Agenda

1. Example Hyperion Research Projects

2. Update on the HPC Market

3. Cloud Computing Update and Our New

Scorecard Tool

4. Quantum Computing Update

5. The Exascale Race

6. The ISC19 Innovation Award Winners

7. Conclusions and Predictions


The Hyperion Research Team

Lloyd Coen

Earl Joseph

Steve Conway

Bob Sorensen

Alex Norton

Mike Thorp

Kurt Gantrish

Jean Sorensen

Tom Christian

Nishi Katsuya

Research studies & strategic consulting

Strategic consulting, HPC UF, Big Data, AI

Special studies, new data analysis, clouds

Business manager

Survey design & executive interviews

Japan research and studies

Global sales management

Global sales management

Strategic research, government studies, QC

4© Hyperion Research

Hyperion Research HPC Activities

• Track all HPC servers sold each quarter • By 28 countries

• 4 HPC User Forum meetings each year

• Publish 85 plus research reports each year

• Visit all major supercomputer sites & write reports

• Assist in collaborations between buyers/users and vendors

• Assist governments in HPC plans, strategies and direction

• Assist buyers/users in planning and procurements

• Maintain 5 year forecasts in many areas/topics

• Develop a worldwide ROI measurement system

• HPDA program (includes ML/DL/AI)

• HPC Cloud usage tracking

• Quarterly tracking of GPUs/accelerators

• Cyber Security

• Quantum Computing

• Map applications to algorithms to architectures


The HPC User Forum: www.hpcuserforum.com


HPC User Forum Steering Committee


Plan On Joining Us This Year:2019 HPC User Forum Meetings

(www.hpcuserforum.com)

Plus China in August

September 9-11

Argonne National Laboratory

(Chicago area)

© Hyperion Research 2019 8

October 7-8

CSCS: Swiss National

Supercomputing Center (Lugano)

October 10-11

EPCC

(Edinburgh)

9

Sample Projects

© Hyperion Research

Precision Medicine

Automated Driving Systems

Fraud and anomaly detection

Affinity Marketing

Business Intelligence

Cyber Security

IoT


HPDA & AI Key Takeaway:

Most Economically Important Use Cases

High Growth Areas:

HPDA-AI (May 2019)

11

• HPDA is growing faster than the overall HPC market

• The AI subset is growing faster than all HPDA


THE ROI From HPC & Success Storieswww.HyperionResearch.com/roi-with-hpc/


HPDA Algorithm Report: Mapping Algorithms

to Verticals & System Requirements https://hyperionresearch.com/proceed-to-download/?doctodown=hpda-algorithm-report


Finding HPC Centers (Map)www.HyperionResearch.com/us-hpc-centers-activity/

16© Hyperion Research B

Our Forecast On When & Where Exascale

Systems Will Be Installed


QC Documents are Starting to Roll Out


A New Cloud Application Assessment Toolhttps://hyperionresearch.com/cloud-application-assessment-tool/

19© Hyperion Research A

Price/Performance Trends of the

Largest Supercomputers


▪ This trendline projects

that the cost of a Linpack

PF will go from $100

million in 2009, to $110

thousand by 2028, a

roughly 1000X

improvement in a decade.

Tipping Points: How Quickly HPC

Buyers Can Change


Tracking New AI Hardware: Emergence

of AI-Specific Hardware Ecosystem

22© Hyperion Research 2019

23

HPC Market Update

© Hyperion Research E

Top Trends in HPC

2018 was a very strong year with over 15% growth --

$13.7 billion (US$) in revenues!

• Supercomputers grew 23% = $5.4 billion in 2018

The top systems have started growing again after

over 4 years of softness

• The profusion of Exascale announcements are

generating a lot of buzz

Big data combined with HPC is creating new solutions

• Adding many new users/buyers to the HPC space

• AI/ML/DL & HPDA are the hot new areas


The Worldwide HPC Server Market:$13.7 Billion in 2018

Departmental ($250K - $100K)

$3.9B

Divisional ($250K - $500K)

$2.5B

Supercomputers(Over $500K)

$5.4B

Workgroup(under $100K)

$2.0B

HPC

Servers

$13.7B

▪ Record revenues!


2018 HPC Market By Verticals


HPC Market By Vendor Shares


HPC Market By Regions (US $ Millions)


The Broader HPC Market Forecast


The Total HPC Market Including Public Cloud Spending ▪ TOTAL HPC spending grew from $22B in 2013 to $26B in 2017, and

is projected to reach $44B in 2022


31

HPC Cloud Forecasts

© Hyperion Research A

Major Trend

HPC in the Cloud

▪ Over 70% of HPC sites run some jobs in public clouds

• Up from 13% in 2011

▪ Over 10% of all HPC jobs are now running in clouds

• Public clouds are cost-effective for some jobs, but up to

10x more expensive for others

• Key concerns: security, data loss

▪ Private and hybrid cloud use is growing faster

▪ Large public clouds are going heterogeneous – and are

getting much better at running a broader set of HPC

workloads

• AWS with Ryft FPGAs, Google with NVIDIA GPGPUs,

Microsoft with Cray & Bright


Top Reasons for Using External Clouds

All Sites

1. Extra capacity (“surges”)

2. Cost-effectiveness

3. Isolate R&D projects

4. Special HW/SW features

5. Management decision

6. No on-premise data center

Government Only

1. Special HW/SW features

2. Extra capacity (“surges”)

3. No on-premise data center

4. Cost-effectiveness

5. Management decision

6. Isolate R&D projects


How we Developed the HPC Cloud

Forecast

▪ A public cloud, as we define it, is any large, third-

party cloud that sits outside of an organization's

firewall and is accessible in return for payment

by anyone over the public Internet

• And is owned by a different organization

• It excludes private clouds

▪ Our market forecast is from the point of view of

the end user

• The amount end users spend in public clouds

• It’s not a measure of the hardware within the clouds


HPC Public Cloud Forecast


What We Often Hear

36

“My boss says, why can’t you do everything in the cloud?”

“Clouds are only for embarrassingly parallel jobs.”

“It’s much more expensive to run jobs in the cloud.”

“It’s much cheaper to run jobs in the cloud.”

“I’m worried about data security and data loss in the cloud.”


So we created a tool/scorecard to help understand

the fit of public clouds for HPC applications…

37

The Hyperion Research

Cloud Friendliness

Scorecard Tool


About This Tool

▪ This tool is designed to help users decide between two popular environments for running HPC workloads: • On premise facilities or in public clouds.

▪ The metrics and observations are drawn from our global research and interactions with end users, CSPs and other members of the worldwide HPC community.

▪ The scorecard at the end of this slide deck allows users to record their responses concerning each application or workload they evaluate using this tool.

Hyperion Research offers this tool without charge to the HPC community, in the hope that it will be useful.

We welcome feedback on the tool: Contact Alex Norton, [email protected]


How to Use This Tool

▪ To use the tool, you select a number on each

scale (between 0 to 10) for each attribute, and

then average them for an overall score.

• The tool consists of a series of “sliders" that you can

select between the two end points for each workload

you use the tool to evaluate: “Public Cloud Favored”

or “On Premise Favored.”

• The scorecard at the end shows where your overall

evaluation falls:

▪ A lower score favors the on-prem option

▪ A higher score favors the public clouds option



Tool Scorecard

Next Steps

✓ Provided as an online tool ➢Go To: www.HyperionResearch.com (Special Projects)

▪ Collect data on a large, diverse set of HPC, HPDA & AI applications (just started) • And score them using this tool

▪ Then create a directory of HPC applications, and identify what are the major road blocks for each area:• Those that are public cloud friendly

• Those that fit better on-prem

• Those that are in the middle

▪ Then conduct custom studies for clients



© Hyperion Research 2018 PRIVATE INFORMATION FOR DESIGNATED U.S. GOVERNMENT RECIPIENTS 42

Quantum Computing

B

This Effort is an Amplification of Long-Term

Coverage of QC by Hyperion Research


The Promise of QC is Substantial…

QC systems have the potential to exceed the performance of conventional computers for problems of importance to humankind and businesses alike in areas such as:

• Cybersecurity

• Materials science

• Chemistry

• Pharmaceuticals

• Machine learning

• Optimization

And the list grows longer each day


… But So Are the Challenges Ahead

• Formidable technical issues in QC hardware and

software

• Uncertain performance gains

• Unclear time frames

• Wild West in algorithm/application progress

• Looming workforce issues

This complicates treating QC as a stable market along

side more traditional IT sectors

• Making a business case is tough


Errors … Errors Everywhere


Many Developers, Many Approaches


• D Wave

• IBM

• Google

• Quantum Circuits

• ionQ

• Intel

• Rigetti

• Microsoft

• Quantum Diamond

Technologies

• ATOS/Bull

• More….

▪ All are dealing with errors: NISQ is near-term

solution

Key Questions To Be Addressed

48© Hyperion Research 2019

• What are the leading development trends in QC hardware and software?

• Who are the most significant QC developers and how do they compare with counterpart efforts?

• What is the likelihood of success and time frame for leading QC development efforts?

• Which verticals can benefit most from near-term QC advances?

• What is the expected time frame/schedule for various QC hardware and software availability?

• What are some of the key technology drivers and inhibitors in QC development?

• What applications will benefit most from QC in both the near and far term?

What We Are Hearing, INPO

▪ Current business case -- FOMO

• Luckily, barrier to entry is low

• Divining ROI is not yet possible

▪ Provable algorithms may not be necessary

• ML/DL developers don’t lose sleep over this

• Best/worst case vs average case (GE)

▪ Quantum Supremacy/Advantage not necessary

• Demonstrated speed up over classical is all that matters

• The enterprise space doesn't want to have to look too far down the stack

▪ Living in the age of NISQ

• Pair with classical to get near-term results

• Concentrate on algorithms that are fault-tolerate


How This Plays Out Near-Term

▪ Near-term QC Ramp Up

• Many exploring applications/use cases and not just for traditional HPC but also in enterprise computing environments

• The QC/SME application developer may be the next unicorn

▪ Cloud Access Model looks to be short-term preference

▪ Crowd sourcing of algorithms/applications is current order of the day

▪ Will this succeed?

▪ We are not at the Moore’s Law stage yet

▪ National agendas are a double-edged sword

• As are national security considerations

▪ A rising tide lifts all boats

• For now


© Hyperion Research 2018 PRIVATE INFORMATION FOR DESIGNATED U.S. GOVERNMENT RECIPIENTS 51

The Exascale Race

Projected Exascale System Dates

U.S.

▪ Sustained ES*: 2022-2023

▪ Peak ES: 2021

▪ ES Vendors: U.S.

▪ Processors: U.S. (some ARM?)

▪ Cost: $500M-$600M per system (for early systems), plus heavy R&D investments

52

China▪ Sustained ES*: 2021-2022

▪ Peak ES: 2020

▪ Vendors: Chinese (multiple sites)

▪ Processors: Chinese (plus U.S.?)

▪ 13th 5-Year Plan

▪ Cost: $350-$500M per system, plus heavy R&D

EU▪ PEAK ES: 2023-2024

▪ Pre-ES: 2020-2022 (~$125M)

▪ Vendors: US and then European

▪ Processors: x86, ARM & RISC-V

▪ Initiatives: EuroHPC, EPI, ETP4HPC, JU

▪ Cost: Over $300M per system, plus heavy R&D investments

Japan

▪ Sustained ES*: ~2021/2022

▪ Peak ES: Likely as a AI/ML/DL system

▪ Vendors: Japanese

▪ Processors: Japanese ARM

▪ Cost: ~$1B, this includes both 1 system

and the R&D costs

▪ They will also do many smaller size

systems

* 1 exaflops on a 64-bit real application 52© Hyperion Research

Projected Exascale Investment Levels

(In Addition to System Purchases)

U.S. ▪ $1 to $2 billion a year in R&D

(around $10 billion over 7 years)

▪ Investments by both the government & vendors

▪ Plans are to purchases multiple exascale systems each year

53

China▪ Over $1billion a year in R&D (at

least $10 billion over 7 years)

▪ Investments by both governments & vendors

▪ Plans are to purchases multiple exascale systems each year

▪ Investing in 3 pre-exascale systems starting in late 2018

EU▪ About 5-6 billion euros in total (around

$1 billion a year)

▪ EU: 486M euros, Member States: 486M euros, Private sector: 422M euros

▪ Investments in multiple exascale and pre-exascale systems

▪ Large EU CPU funding

Japan▪ Planned investment of over

$1billion* (over 5 years) for both the R&D and purchase of 1 exascale system

▪ To be followed by a number of smaller systems ~$100M to $150M each

▪ Creating a new processor and a new software environment

* Note that this includes both the system and R&D53© Hyperion Research

US Exascale Plans

A21 A22 Frontier (OLCF5)

El Capitan (ATS-4) NERSC-10

NSF Frontera Follow-on

Location ANL ANL ORNL LLNL LBNL/NERSC TACC

Planned Delivery Date/ Estimated 2022 Q1 2022 2022 Q1 2022 2024 2024

Early Operation 2022, Q2 2023 2022, Q3 2023 2025 2025

Planned/Realized Performance (Pflops) ~1,000 1,300 or

higher1500-3000 4000-5000 8000-

12000 500

Linpack Performance (PFlops) 800-900 780-1040 900-2100 2000-3000 2000-3000

Linpack/Peak Performance Ratio

(%)80-90 (est.)

60-70 (est.)

60-70 (est.) 50-60 50-60 50-55

High Performance Conjugate Gradient

(PFlops/s)20.0-22.5 19.5-26.0 18-36 48-72 52-78 74

GF/Watt 40 60-100 134-200 266-480


Chinese Exascale Plans

Sunway 2020 Sugon Exascale NUDT 2020

Key User/Developer Sunway/NRCPC Sugon/AMD NUDT

Planned Delivery Date/ Estimated

2020, 4Q (could slip 1-1.5 years)



Planned/Realized Performance (Pflops) 1000 1024 1000

Linpack Performance (PFlops) 600-700 627-732 700-800

Linpack/Peak Performance Ratio (%) 60-70 60-70 (est.) 70-80

High Performance Conjugate Gradient

(Pflops/s)6-7 9.4-10.1 14-16

GF/Watt 30 34.13 20-30

Linpack GF/Watt 20-23 20.9 23.3-32.0


56

EU Exascale Plans


In Comparison: European Growth Compared To

USA Growth In Acquiring Supercomputers

▪ Europe has close to doubled their supercomputer purchases since 2005 (94.9% growth)

▪ Compared to the US growing by only 18.6% since 2005


The European JU Plan

New procurements for 8 systems: http://europa.eu/rapid/press-release_IP-19-2868_en.htm

The JU along with the hosting sites, plan to acquire 8 supercomputers: • 3 precursor to exascale machines (more than 150 Petaflops) • And 5 petascale machines (at least 4 Petaflops)

The precursor to exascale systems are expected to provide 4-5 times more computing power than the top PRACE supercomputers

• Together with the petascale systems, they will double the supercomputing resources available for European-level use

• In the next few months the Joint Undertaking will sign agreements with the selected hosting sites

• The supercomputers are expected to become operational during the second half of 2020


http://europa.eu/rapid/press-release_IP-19-2868_en.htm

New EU Processors


EPI Roadmap Targets Inclusion in Pre-

and Full-Exascale Supercomputers


EPI General Purpose Processor

(GPP) and Variants


Our Award Program


Examples Of Previous Winners


The Trophy For Winners

65

HPC Award Program Goals

▪ #1 Help to expand the use of HPC by showing real ROI

examples:

▪ Expand the “Missing Middle” – SMBs, SMSs, etc. by providing

examples of what can be done with HPC

▪ Show mainstream and leading edge HPC success stories

▪ #2 Create a large database of success stories across

many industries/verticals/disciplines

▪ To help justify investments and show non-users ideas on how

to adopt HPC in their environment

▪ Creating many examples for funding bodies and politicians to

use and better understand the value of HPC → to help grow

public interest in expanding HPC investments

▪ For OEMs to demonstrate success stories using their products


Users Must Show the Value of the

Accomplishment

▪ Users are required to submit the value achieved with their HPC system, using three broad categories: a) Dollar value of their HPC project

– e.g., made $$$ in new revenues

– Or saved $$$ in costs

– Or made $$$ in profits

b) Scientific or engineering accomplishment from their project

– e.g. discovered how xyz really works, develop a new drug that does xyz, etc.

c) The value to society as a whole from their project

– e.g. ended nuclear testing, made something safer, provided protection against xyz, etc.

… and the investment in HPC that was required


ISC19 Winners:

HPC User Innovation Awards


Massively Parallel Evolutionary Computation

for Empowering Electoral Reform: Quantifying

Gerrymandering via Multi-objective

Optimization and Statistical Analysis

▪ Wendy K. Tam Cho, NCSA

▪ Yan Liu, NCSA


Gerrymander: manipulate the

boundaries of electoral districts

so as to favor one party or group

Project DisCo (Discovery of Coherent

Structures)

▪ Adam Rupe, UC-Davis, NERSC

▪ Karthik Kashinath, NERSC

▪ Nalini Kumar, Intel


▪ James Crutchfield, UC-Davis

▪ Ryan James, UC-Davis

▪ Prabhat, NERSC

Can machine learning help find modes of

laser-driven fusion that have been

undiscovered by traditional methods?

▪ Brian Spears, et al., LLNL

© LLNL, 2019 71


Hidden Earthquake Research

Caltech team: Zachary Ross, Daniel Trugman, Egill Hauksson, Peter Shearer


We Invite Everyone

To Apply For The Next Round Of

Innovation Awards!


74

In Summary:

Some Predictions

For the Next Year


The Exascale Race

Will Drive New Technologies

▪ The global ES race is boosting funding for the Supercomputers market segment and creating widespread interest in HPC

▪ Exascale systems are being designed for HPC, AI, HPDA, etc.

• This will drive new processor types, new memories, new system designs, new software, etc.

▪ And (in some cases) that HPC is too strategic to depend on foreign sources

• This has led to indigenous technology initiatives


▪ Hyperion Research studies confirm that this

convergence is occurring and speeding up

▪ Competitive forces are driving companies to aim more-

complex questions at their data structures and push

business operations closer to real time

▪ Important HPC capabilities: scalable parallel processing,

ultrafast data movement and ultra-large, capable

memory systems

▪ The HPC and commercial sectors are also converging

around a shared need to extremely data-intensive AI-ML-

DL workloads, both on the simulation and analytics side


The HPC-Enterprise Market Convergence Will Drive HPC Products into the Broader Enterprise Market

▪ Choices of processing elements (CPUs, accelerators)

will increase

• x86 will remain the dominant HPC CPU, but indigenous

CPUs will gain ground

▪ In AI, startups and large companies are developing

processors designed for analytics workloads

▪ NVIDIA is the dominant accelerator, but three-quarters

of respondents in our recent survey expect NVIDIA

GPUs to face "serious competition" in the next 4-5 years

▪ Processors exploiting ARM IP are planned for Europe

(EPI), Japan (Post-K computer) and China


Many New Processors/Accelerators

Are on The Way

▪ Data-intensive HPC is driving new storage requirements

▪ Iterative methods will expand the size of data volumes needing to be stored

▪ Future architectures will allow computing and storage to happen more pervasively on the HPC infrastructure

▪ Metadata management will deal with data stored in multiple geographic locations and environments

▪ Physically distributed, globally shared memory will become more important

▪ More intelligence will need to be built into storage software


Storage Systems Will Increasingly

Become More Critical

▪ Running HPC workloads in CSP environments will grow

in step-function like leaps

• As clouds get better at running HPC workloads

▪ HPC on premise and cloud environments will more

closely resemble each other: the containerization of HPC

▪ 5G will be important for reducing latency in AI use cases

that rely on coupled local-cloud environments, such as

automated driving systems, precision medicine, fraud

detection, cyber security and Smart Cities/IoT

▪ Hybrid cloud environments will grow to be a highly viable

option for HPC users


Cloud Computing For HPC Workloads

Will Grow Fast, Via Tipping Points

▪ The AI market is at an early stage but already highly useful (e.g., visual and voice recognition)• Once better understood, there are many high value use cases

that will drive adoption

▪ Advances in inferencing will reduce the amount of training needed for today's AI tasks, but the need for training will grow to support more challenging tasks

▪ The trust (transparency) issue that strongly affects AI today will be overcome in time

▪ Learning models (ML, DL) have garnered most of the AI attention, but graph analytics will also play a crucial role with its unique ability to handle temporal and spatial relationships


Artificial Intelligence Will Grow

Faster Than Everything Else

▪ HPC is a high growth market• Growing recognition of HPC’s strategic value

• HPDA, including ML/DL, cognitive and AI

• HPC in the Cloud will lift the sector writ large

▪ HPDA, AI, ML & DL is growing very quickly • The HPDA, AI, ML & DL markets will expand

opportunities for vendors

▪ Vendor share positions shifted greatly in

2015, 2016, 2017 & again in 2019 and may

continue to shift • e.g., HPE acquisition of Cray

▪ Software continues to lag hardware and

systems designs/sizes

Conclusions

81©Hyperion Research

Important Dates For Your Calendar

▪ 2019 HPC USER FORUM MEETINGS:

• Late August, Hohhot and Beijing, China

• September 9 to 11, Chicago Illinois, Argonne National Laboratory

• October 7 to 8, Lugano, Switzerland at CSCS

• October 10 to 11, Edinburgh, Scotland at EPCC

▪ And Join us for the Hyperion SC19 Briefing


Thank You!

[email protected]


mailto:[email protected]

Visit Our Website: www.HyperionResearch.com

Twitter: HPC_Hyperion@HPC_Hyperion

Hyperion Research Holdings, LLC:▪ Owns 100% of the IDC HPC assets▪ Tracking the HPC market since 1986 ▪ Headquarters:

365 Summit Ave., St. Paul, MN 55102

