IDC Update on How Big Data Is Redefining High Performance Computing Earl Joseph Steve Conway

IDC Update on How Big Data Is

Redefining High Performance

Computing

Earl Joseph – [email protected] Steve Conway – [email protected] Chirag Dekate – [email protected] Bob Sorensen – [email protected]

Agenda



A Short HPC Market Update



Big Data Challenges and Short Comings



The High End of Big Data

•

Examples of Very Large Big Data



Examples of How Big Data is Redefining

High Performance Computing

HPC

5

What Is HPC?



IDC uses these terms to cover all technical

servers used by scientists, engineers, financial

analysts and others:

•

HPC

•

HPTC

•

Technical Servers

•

Highly computational servers



HPC covers all servers that are used for

computational or data intensive tasks

• From a $5,000 deskside server up to over $550 million dollar supercomputer

Top Trends in HPC

2013 declined overall – by $800 million

• For a total of $10.3 billion

• Mainly due to a few very large systems sales in 2012 that weren’t repeated in 2013

• We expect growth in 2015 to 2018

Software issues continue to grow

The worldwide Petascale Race is at full speed

GPUs and accelerators are hot new technologies

Big data combined with HPC is creating new solutions in new areas

IDC HPC Competitive Segments: 2013

HPC WW Market Trends:

A 17 Year Perspective



HPC

HPC Forecasts

• Forecasting a 7.4% yearly growth from 2013 to 2018 • 2018 should reach $14.7 billion

Big Data

Challenges And

Shortcomings

HPDA Market Drivers

 More input data (ingestion)

• More powerful scientific instruments/sensor networks • More transactions/higher scrutiny (fraud, terrorism)

 More output data for integration/analysis

• More powerful computers

• More realism

• More iterations in available time

 Real time, near-real time requirements

• Catch fraud before it hits credit cards

• Catch terrorists before they strike

• Diagnose patients before they leave the office

• Provide insurance quotes before callers leave the phone

 The need to pose more intelligent questions

Organizational Challenges With Big Data:

Government Compared To All Others

Big Data Meets

HPC And

High Performance Data Analysis

Needs HPC resources

• High complexity (algorithms)

• High time-criticality

• High variability

• (On premise or in cloud)

Data of all kinds

• The 4 V’s: volume, variety, velocity, value

• Structured, unstructured

• Partitionable, non-partitionable

• Regular, irregular patterns

Simulation & analytics

• Search, pattern discovery

• Iterative methods

• Established HPC users + new

commercial users

HPC Adoption Timeline (Examples)

Very Large

Big Data

Examples

NASA

Square Kilometre Arrary – Radio

Astronomy for Astrophysics

CERN

• LHC: the world’s leading accelerator -- Multiple Nobel Prizes for particle physics work

• Innovation driven by the need to distribute massive data sets and the accompanying applications

• Altas, one of CERN’s two detectors, generates 1PB of data per second when running! (Not all of this is distributed).

• Private cloud distribution to scientists in 20 EU member states plus observer states (single largest user is the U.S.)

NOAA

HPC Will Be Used More for Managing Mega-IT

Infrastructures

Examples of

Big Data

Finding suspicious patterns that we don’t even know exist in related data sets

Use Case: PayPal Fraud Detection

The Problem

What Kind of Volume?

The Results

 $710 million saved in fraud that they wouldn’t have been able to detect before (in the first year)

There Are New Technologies That Will Likely

Cause A Mass Explosion In Data – Requiring

HPDA Solutions

GEICO: Real-Time Insurance Quotes



Problem: Need accurate automated phone quotes

in 100ms. They couldn’t do these calculations

nearly fast enough on the fly.



Solution: Each weekend, use a new HPC cluster to

pre-calculate quotes for every American adult and

household (60 hour run time)

Something To Think About -- GEICO: Changing

The Way One Approaches Solving a Problem

• Instead of processing each event one-at-a-time,

process it for everyone on a regular basis

 It can be dramatically cheaper, faster and offers additional ways to be more accurate

 But most of all it can create new and more powerful capabilities

• Examples:

 For home loan applications – calculate for every adult in the US and every home in the US

 For health insurance fraud – track every procedure done on every US person by every doctor – and find patterns

Something To Think About -- GEICO: Changing

The Way One Approaches Solving a Problem

• Future Examples (continued):

 If you add-in large scale data collection via sensors like GPS, drones and RFID tags:

• New car insurance rules – The insurance company doesn’t have to pay if you break the law -- like

speeding and having an accident

• You could track every car at all times – then charge $2 to see where the in-laws are in traffic if they are late for a wedding

• Google maps could show in real-time where every letter and package is located

• But crooks could also use it in many ways – e.g. watching ATM machines, looking for when guards are on break, …

U.S. Postal Service

CMS: Government Health Care Fraud

 5 separate databases for the big USG health care programs under Centers for Medicare and Medicaid Services (CMS)

 Estimated fraud: $150B-$450B <$5B caught today)

 ORNL, SDSC have evaluation contracts to unify the databases and perform fraud detection on various architectures

Schrödinger: Cloud-based Lead

Discovery for Drug Design

NOVARTIS/SCHROEDINGER:

 Pharmaceutical company Novartis increased resolution of drug discovery algorithm 10x and wanted to use it to test 21 million small molecules as drug candidates

 Novartis used the Schroedinger drug discovery app in AWS public cloud, with the help of Cycle Computing

 Initial run used 51,000 AWS cores and took $14,000 and <4 hours

 … and its getting cheaper  Later run used 156,000 AWS cores with comparable costs and time

Schrödinger: Cloud-based Lead

Discovery for Drug Design

Global Financial Services: Company X

 One of the most respected firms in the global financial services industry updates detailed information daily on several million companies around the world.

 Clients use the firm's credit ratings and other company information in making lending decisions and for other planning, marketing, and business decision making.

 The firm uses statistical models to develop a company's scores and ratings, and for years, the ratings have been prepared and analyzed locally in near real time by the firm's personnel around the world.

• This practice is a major competitive advantage but resulted in the creation of hundreds of distinct databases and more than a dozen scoring environments.

• Several years ago, the company established a goal of centralizing these resources and chose SAS as the centralization mechanism, including SAS Grid Manager as part of the software stack.

Global Financial Services: Company X

 The centralized IT infrastructure created using SAS preserves the advantages of the company's locally created ratings and reports. The new infrastructure provides an effective environment for

analytics development and accommodates multiple testing, debt, and production environments in a single stack.

 It is flexible enough to allow dynamic prioritization among these environments, according to a company executive. With help from SAS Grid Manager, the company can maximize the use of its

computing resources. The software automatically assigns jobs to server nodes with available capacity, instead of having users wait in queue for time on fully utilized nodes.

 The company executive estimates that it might cost 30% more to purchase servers with enough capacity to handle these peak

Global Financial Services: Company X

 Several million clients use the firm’s credit ratings to help make lending

decisions

 Goal: increase efficiency for updating ratings

 Result: HPC multi-cluster grid boosted efficiency 30% -- no need to buy additional clusters yet.

Real Estate

 Worldwide vacation exchange & rental leader

 Goal: Update property valuations several times per day (not possible with enterprise servers)

 Results:

• HPC technology enabled all updates in 8-9 hours

• Avoided move to heuristics

• Allowed company to focus on rental side

Outcomes-Based Medical Diagnosis and

Treatment Planning

 Enter the patient’s history and symptomology.

 While the patient is still in the office, sift through millions of archived patient records for relevant outcomes.

 Provider considers the efficacies of various treatments for “similar” patients (but is not bound by the findings).

 Ergo, this functions as a powerful decision-support tool.

 Benefits: better outcomes + rein in costly outlier practices.

Digital Television Services



A global leader with 30 million subscribers



Goal: maximize revenue & customer satisfaction

during high-growth period



Result: HPC has added €7.5 million in annual

revenue while increasing satisfaction

IDC HPDA Server Forecast

 Fast growth from a small starting point: $933 M

IDC HPDA Storage Forecast

 Storage is the fastest-growing HPC market (9% CAGR)

In

Summary: HPDA Market Opportunity

 HPDA: simulation + newer high-performance analytics

• IDC predicts fast growth from a small starting point

 HPC and high-end commercial analytics are converging

• Algorithmic complexity is the common denominator

 Economically important use cases are emerging

 No single HPC solution is best for all problems

HPDA User Talks: HPC User Forums, UK, Germany, France, China and U.S. …

• HPC in Evolutionary Biology, Andrew Meade, University of Reading

• HPC in Pharmaceutical Research: From Virtual Screening to All-Atom Simulations of Biomolecules, Jan Kriegl,

Boehringer-Ingelheim

• European Exascale Software Initiative, Jean-Yves Berthou, Electricite de France

• Real-time Rendering in the Automotive Industry, Cornelia Denk, RTT-Munich

• Data Analysis and Visualization for the DoD HPCMP, Paul Adams, ERDC

• Why HPCs Hate Biologists, and What We're Doing About It, Titus Brown, Michigan State University

• Scalable Data Mining and Archiving in the Era of the Square Kilometre Array, the Square Kilometre Array Telescope

Project, Chris Mattmann, NASA/JPL

• Big Data and Analytics in HPC: Leveraging HPC and Enterprise Architectures for Large Scale Inline Transactional Analytics

in Fraud Detection at PayPal, Arno Kolster, PayPal, an eBay Company

• Big Data and Analytics Vendor Panel: How Vendors See Big Data Impacting the Markets and Their Products/Services,

Panel Moderator: Chirag Dekate, IDC

• Data Analysis and Visualization of Very Large Data, David Pugmire, ORNL

• The Impact of HPC and Data-Centric Computing in Cancer Research, Jack Collins, National Cancer Institute

• Urban Analytics: Big Cities and Big Data, Paul Muzio, City University of New York

• Stampede: Intel MIC And Data-Intensive Computing, Jay Boisseau, Texas Advanced Computing Center

• Big Data Approaches at Convey, John Leidel

• Cray Technical Perspective On Data-Intensive Computing, Amar Shan

• Data-intensive Computing Research At PNNL, John Feo, Pacific Northwest National Laboratory

• Trends in High Performance Analytics, David Pope, SAS

• Processing Large Volumes of Experimental Data, Shane Canon, LBNL

• SGI Technical Perspective On Data-Intensive Computing, Eng Lim Goh, SGI

• Big Data and PLFS: A Checkpoint File System For Parallel Applications, John Bent, EMC

• HPC Data-intensive Computing Technologies, Scott Campbell, Platform/IBM

Questions?

[email protected]

[email protected]

[email protected]

Big Data Software Shortcomings --

Today