IDC Update on How Big Data Is Redefining High Performance Computing Earl Joseph ejoseph@idc.com Steve Conway sconway@idc.com Chirag Dekate cdekate@idc.com Bob Sorensen bsorensen@idc.com
IDC Has Over 1,000 Analysts In 62 Countries
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 Market Update
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 Servers $10.3B Supercomputers (Over $500K) $4.0B Workgroup (under $100K) $1.6B Divisional ($250K - $500K) $1.4B Departmental ($250K - $100K) $3.4B
HPC WW Market Trends: A 17 Year Perspective C
HPC Market Forecasts
HPC Forecasts Forecasting a 7.4% yearly growth from 2013 to 2018 2018 should reach $14.7 billion
HPC Forecasts: By Industry/Applications
The Broader HPC Market
Big Data Challenges And Shortcomings
Defining Big Data
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 The need to pose more intelligent questions Smarter mathematical models and algorithms 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
Top Drivers For Implementing Big Data
Organizational Challenges With Big Data: Government Compared To All Others
Big Data Meets HPC And Advanced Simulation
High Performance Data Analysis Needs HPC resources High complexity (algorithms) High time-criticality High variability (On premise or in cloud) Simulation & analytics Search, pattern discovery Iterative methods Established HPC users + new commercial users Data of all kinds The 4 V s: volume, variety, velocity, value Structured, unstructured Partitionable, non-partitionable Regular, irregular patterns
HPC Adoption Timeline (Examples) 1960 1970 1980 1990 2000 2012
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.) Today: only.0000005% of the data is used
NOAA
HPC Will Be Used More for Managing Mega-IT Infrastructures
Examples of Big Data Redefining HPC
Use Case: PayPal Fraud Detection The Problem Finding suspicious patterns that we don t even know exist in related data sets
What Kind of Volume? PayPal s Data Volumes And HPDA Requirements
Where Paypal Used HPC
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
U.S. Postal Service
U.S. Postal Service MCDB = memory-centric database
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 workloads on their own.
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 HPDA ecosystem >$2.6B in 2018
IDC HPDA Storage Forecast Storage is the fastest-growing HPC market (9% CAGR) And HPDA storage will grow even faster (26.5% CAGR)
In Summary
Summary: HPDA Market Opportunity HPDA: simulation + newer highperformance 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 The CEA-GENCI-Intel-UVSQ Exascale Computing Research Centre, Marie-Christine Sawley, Intel
Questions? ejoseph@idc.com sconway@idc.com cdekate@idc.com
Big Data Software Shortcomings -- Today
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Big Data Software Technology Stack