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    Big Data and Implications on Platform Architecture Big Data and Implications on Platform Architecture Presentation Transcript

    • Big Data and Implications onPlatform ArchitectureFayé A Briggs, PhDIntel Fellow and Chief Server Platform Architect, Intel BIGS002
    • Agenda • What is Big Data? • Big Data use cases • What does Big Data mean for the data center? • Call to action The PDF for this Session presentation is available from our Technical Session Catalog at the end of the day at: intel.com/go/idfsessionsBJ URL is on top of Session Agenda Pages in Pocket Guide2
    • Agenda • What is Big Data? • Big Data use cases • What does Big Data mean for the data center? • Call to action3
    • What is Big Data? Unstructured size is beyond volume, variety, value and velocity datasets whose the ability of typical database software tools to capture, store, manage and analyze† Unstructured Data Analyze Big Sensed Data Manage Volume Big Corporate Data Big Web Data Store Structured Data Corporate Data Time Capture4 †”Big data: The next frontier for innovation, competition, and productivity”, McKinsey Global Institute
    • The Four Pillars and Associated Challenges of Big Data Massive scale and growth of unstructured data  80%~90% of total data Volume  Growing 10x~50x faster than structured (relational) data  10x~100x of traditional data warehousing Heterogeneity and variable nature of Big Data  Many different forms (text, document, image, video, ...) Variety  No schema or weak schema  Inconsistent syntax and semantics Predictive analytics for future trends and patterns Value  Deep, complex analysis (machine learning, statistic modeling, graph algorithms, …), versus  Traditional business intelligence (querying, reporting, …) Real-time rather than batch-style analysis Velocity  Data streamed in, tortured, and discarded  Making impact on the spot rather than after-the-fact5
    • Big Data Has Gone Mainstream “ . . . . every large company is working on Big Data projects “at a furious pace.” - Gartner analyst Merv Adrian “. . . the use of Big Data will be effective for every segment of the economy.” - Michael Chui, McKinsey & Co. analyst Behold the Big Data sign Greeting commuters on Highway 101 in Silicon gracing Times Square Valley, a giant Walmart* Big Data sign6
    • Big Data Usages Examples (Telecom/Financial/Search) • Telecom – Calling patterns, signal processing, forecasting  Analyze switches/routers data for quality of call, frequency of calls, region loads, etc. – Act before problems happen. Act before customer calls arrive. • Financial – Trading behaviour  Analyze real-time data to understand market behavior, role of individual institution/investor – Detect fraud, detect impact of an event, detect the players • Search Engines – Process the data collected by Web bot in multiple dimensions – Enhance relevance of search Big Data impacts e-connected businesses through capture, processing and storage of huge amount of data efficiently7
    • Big Data Usages Examples (E-Biz, Media) • Click Stream Analysis – Analysis of online users behavior – Develop comprehensive insight (Business Intelligence) to run effective strategies in real time • Graph analysis – Term for discovering the online influencers in various walks of life – Enables a business to understand key players and devise effective strategies • Lifecycle Marketing – Strategies to move away from spam/mass mail – Enables a business to spend money on high probable customers only • Revenue Attribution – Term for analyzing the data to accurately attribute revenue back to various marketing investments – Business can identify effectiveness of campaign to control expenses Big Data phenomenon allows businesses to know, predict and influence customer behaviors!!!8
    • Big Data in Health Care Cancer Care: American Society of Clinical Oncologists “learning health system”, CancerLinQ • Benefits: Collects and analyzes de-anonymized cancer care data from millions of patient visits Genome Sequencing: Improvements in DNA sequencing driving down costs of processing complete set of genomes • Benefits: Saving lives through better identification and treatment of diseases GenBank* is the NIH genetic sequence database, an annotated collection of all publicly available DNA sequences Sequencing costs approaches “$1000.” Analytics, compute, networking, & storage to improve affordability still challenging.9
    • Big Data Analytics Processing • “Batch”: Sophisticated data processing: enable “better” decisions – Analyze, transform, scan, etc. large amount of data – E.g., ETL, graph construction, anomaly detection, trend analysis • “Real-time”: Queries on historical data: enable “faster” decisions – Data at rest but needs to be served in real-time – E.g., Facebook* uses HBase* “messaging” App serving real-time data to its users • “Streaming”: Queries on live data: enable “instantaneous” decisions on real-time streaming data – Large volume of data being ingested and analyzed in real-time – E.g., detect and block worms in real-time (a worm may infect 1mil hosts in 1.3sec)10 Adapted from Ion Stoica, UC Berkeley
    • Big Data Converted to Knowledge in an Iterative Cycle† Knowledge Deliver Visualization Presentation Tableau*, R, Progress Software*, Pentaho*, IBM*, others Transact Batch Analytics Real-time Analytics Streaming Analytics Call Data Records Intelligent Transport Twitter* Spam Analytics Gene Seq & Analysis Home Energy Video meta-data GraphLab*-MLearning, Financial Analytics Traffic Modeling ETL, Sensor Network Database Virus Intrusion Detection Search, Time Series Stock Ticks Stock trading Index Creation, Customer Behavior Ads Video Surveillance Analytics Click Stream Analysis, Retail – Video, PoS data BI Analytics Compute Batch Processing Real-Time Processing Stream Processing MR-Hadoop*, HBase*, SAP* HANA, Spark, Spark Streaming, Storm, GraphLabs,Giraph Shark, Cassandra*, S4–Simple Scalable Stream. mongoDB, Drill, Impala Sys Data Management External Unstructured/Semi-structured on Dist. Parallel File System (HDFS*, Lustre*, CloudStore*, GPFS, GlusterFS*, etc.) Archive Filtering, Cleansing, ETL, Scribe, etc. Big Data Ingest †Based on Frog’s & IDC’s Layered & Iterative Approach11
    • Agenda • What is Big Data? • Big Data use cases • What does Big Data mean for the data center? • Call to action12
    • Telco Usage - China Mobile Group Guangdong Hadoop* Big Data storage and analytics Analytics13
    • Telco Usage - China Mobile Group Guangdong Hadoop* Big Data storage and analyticsUsage Model: Deliver real-time access to Call Data Records (CDR)for billing self service• Solution: Hadoop* + Intel® Xeon® processors over RDBMS to remove data access bottlenecks, increase storage, and scale system• Benefits: Lower TCO, up to 30x performance increase, stable operation, analytics on subscriber usage for targeted promotions• Characteristics: – 30TB billing data/month; real-time retrieval of 30 days CDRs – 300k records/sec, 800k insert speed/sec; 15 analytics queries , 133 server nodes+Platform and Cluster Architectural Attributes:• Compute: – Scale-out Intel Xeon processor E5 based platform for fast analytic query processing – Memory: 2-4GB/Core for Data Management Hadoop JVM – PCI Express* Gen3• Storage: Scale-out Storage (HDD) for capacity; SSD Cache• I/O: High bandwidth and IOPS to Storage (HDD + SSD cache) for fast record inserts and reads• Network: High network bandwidth for Hadoop Shuffle data; 10GbE TORS; 10-40GbE14 Inter-Rack Switch
    • Real-Time Analytics: DuPont*– Crop Genetics HBase* Big Data analytics with “BLAST” to compare protein in genomic dataUsage Model: Comparative genomic research. Run “BLAST” to compareprotein with every other protein in the genomic code.• Solution: The current RDBMS didn’t scale to planned data growth. HBase*/HDFS* proved a reduction in processing time from over 30 days to less than 7 hours.• Characteristics: – Genetic data for 4 million organisms – 1TB in size; scale to 14 million – 12 Trillion HBase rows; single record search takes 1.2 seconds Platform and Cluster Architectural Attributes: • Compute: – Scaleout Intel® Xeon® processor E5 base platform for real-time data serving – Memory: Higher Memory Capacity 4GB/Core for HBase Memstores – PCI Express* Gen3 • Storage: Scaleout Storage (HDD) for capacity; SSD Cache • I/O: High bandwidth and IOPS to Storage (HDD + SSD cache) for fast data access to HBase tables • Network: High network bandwidth for HBase region servers; 10GbE TORS; 10- 40GbE Inter-Rack Switch15
    • Telco - China Unicom Hadoop* & HBase* for Behavioral Analysis Subscriber Usage & Billing ETL Storage, Analytics • Log Analysis • Daily Reports New Customer Segmentation & Insights16
    • Telco - China Unicom Hadoop* & HBase* for Behavioral Analysis Usage Model: Analyze subscriber Web usage and billing to derive new information products • Solution: Scale out storage based on Hadoop* & HBase* with network optimization based on Web traffic, log analysis for daily reporting • Benefits: New customer segmentation • Characteristics: – 188 nodes, 14TB/server – 2.5PB raw disk capacity – High speed data loading – Real-time query (latency <1s)  Daily statistics & reports (sum, count, join, etc.) Platform and Cluster Architectural Attributes: • Compute: – Scaleout Intel® Xeon® processor E5 based platform for real-time data serving in < 1 sec – Memory: Higher Memory Capacity 4GB/Core for HBase Memstores – PCI Express* Gen3 • Storage: Scaleout Storage (HDD) for capacity; SSD Cache • I/O: High bandwidth and IOPS to Storage (HDD + SSD cache) for fast data access to HBase tables • Network: High network bandwidth for HBase region servers; 10GbE TORS; 10- 40GbE Inter-Rack Switch17
    • In-Memory GraphLab* Analytics: PageRank Big Data analytics with GraphLab18
    • In-Memory GraphLab* Analytics: PageRank Big Data analytics with GraphLab Usage Model: Deliver Page Ranking for search • Solution: Hadoop* + Intel® Xeon® processors: Large number of ML, Genomics, Web, etc. applications can be efficiently run in Graph Parallel solution • Benefits: Significantly faster solutions to Graph(Vertices, Edges) domain problems • Characteristics: – XML docs, News Feeds, Web Pages – Data collected from Web Pages for Page Ranking Platform and Cluster Architectural Attributes: • Compute: – Scaleout Intel Xeon processor E5 based platform for fast analytic in-memory processing – Memory: 2-4GB/Core for Data Management Hadoop JVM; For graph data – PCI Express* Gen3 • Storage: Scaleout Storage (HDD) for capacity; SSD Cache • I/O: High bandwidth and IOPS to Storage (HDD + SSD cache) for fast record inserts and reads • Network: High network bandwidth for Hadoop Shuffle data during graph construction; 10GbE TORS; 10-40GbE Inter-Rack Switch19
    • Pipelined In-Memory Analytics: Twitter* Feed Spam Analytics Spark Streaming Big Data Analytics • Run a streaming computation as a series of extremely small, deterministic batch jobs • Batch sizes as low as ½ second, latency ~ 1 second *20
    • Pipelined In-Memory Analytics: Twitter* Feed Spam Analytics Spark Streaming Big Data Analytics Usage Model: Process and filter out Twitter* feed spam as tweets are ingested • Solution: Spark Streaming from Berkeley • Characteristics: – Data ingested from Twitter feeds Platform and Cluster Architectural Attributes: • Compute: – Scaleout Intel® Xeon® processor E5 based platform for fast analytic query processing – Memory: Very large Memory Capacity close to the CPU; High memory bandwidth – PCI Express* Gen3 • Storage: Scaleout Storage (HDD) for capacity; SSD Cache • I/O: High bandwidth and IOPS to Storage (HDD + SSD cache) for fast record inserts and reads • Network: High network bandwidth; 10GbE TORS; 10-40GbE Inter-Rack Switch21
    • Smart City Sensor Model Embedded Smart Smart Grid building sensors Cloud Industrial sensors automation Pollution sensors sensors Dedicated Meteorological Smart sensors meters HPC INTELLIGENT INTELLIGENT CITY FACTORY Transactional INTELLIGENT INTELLIGENT HOSPITAL HIGHWAY Social Sensors on Inductive TrafficPortable medical Medical smartphones Sensors on sensors camerasimaging services sensors on vehicles Location ambulances22
    • The Fusion of Internet of Things and Big Data Planogram monitoring Real-time transaction (Real-time stock level) Dynamic pricing Real-time, personalized ad Auto promotion/coupon Social network connection Interactive display (Behavioral marketing) RFID RFID Store heat map (hot merchandise, browsing Surveillance camera (store statistics) history, conversion rate)23
    • Smart Traffic Intelligent Transport System HBase* Application for Predictive Analytics24
    • Smart Traffic Intelligent Transport System HBase* Application for Predictive Analytics Usage Model: Analyze city traffic to derive statistics for crime prevention, info sharing, and predictive traffic analysis • Solution: Embed HBase* client in camera for real-time inserts of structured/unstructured data • Benefits: Automated queries for traffic violation, data mining of fake licenses <1 minute for all data captured for a week, predictive traffic forecasting • Characteristics: – 30000 + camera data collection points – Petabytes of traffic data & terabytes of images – 2 billion HBase records Platform and Cluster Architectural Attributes: • Compute: – Scaleout Intel® Xeon® processor E5 based platform for real-time data serving – Memory: Higher Memory Capacity 4GB/Core for HBase Memstores – PCI Express* Gen3 • Storage: Scaleout Storage (HDD) for capacity; SSD Cache • I/O: High bandwidth and IOPS to Storage (HDD + SSD cache) for fast data access to HBase tables • Network: High network bandwidth for HBase region servers; 10GbE TORS; 10- 40GbE Inter-Rack Switch25
    • Video created Video analyzed Video Cold storage E2E Analytics Use Case - Safe City Video metadata stored Camera Video Storage (Edge or Centralized) Private Cloud Public Cloud 2-3 TB Video data/Camera/Month Management System Police Car 300 GB Edge Device metadata/Camera/Month Edge Client Video (Video capture) Data Center/ Data Services Indexer/Analyzer/Transcoder Cloud (VSaaS, VAaaS) Smart (Image extraction & Metadata Creation) (Private/Public) Checkpoint District City  State  Country By end of 2017 Edge & Backend VA’s Value By end of 2017 457 PB Metadata tagging and compression at the edge 76 PB Raw Video per Metadata per Enables 10s of new use models for traffic mgmt Day Day Traffic video People, cars, Fastest time to information and public safety streamed by HD geospatial cameras information Typical Scenario : Automated traffic violations26 E.g., 70% Traffic violation detection by video in 2011
    • Smart City Big Data Architecture Framework Intel® Many Visualization & Interpretation Integrated Core Architecture Vertical Scale Based on Intel® Horizontal & Streaming [Un]Structured Batch microarchitecture-EX Analytics Data Analytics Based on Intel Data Acquisition microarchitecture-EP Microserver Local Analytics Based on Intel Complex Event Processing microarchitecture-EN Analytics Processing Horizontal Preprocessing/ Storage Cleansing/Filtering/ Intel® Core™ Scale Aggregation Data Acquisition Video Analytics Sensors Cameras Core System- on-a-Chip27
    • Agenda • What is Big Data? • Big Data use cases • What does Big Data mean for the data center? • Call to action28
    • Choice of Compute Platforms Optimized for Big Data Intel® Xeon® processor E5 Family Intel Xeon processor E7 Family RAM QPI 1 Xeon E7-4800 QPI 2 CORE 1 CORE 2 QPI 3 CORE 3 CORE 4 CORE 5 CORE 6 QPI 4 Up to 4 channels CORE 7 CORE 8 Up to 8 channels Integrated DDR3 1600 MHz DDR3 1066 MHz PCI Express* memory CORE 9 CORE 10 memory 3.0 4 QPI 1.0 Up to 40 Up to 8 cores Lanes for robust CACHE Up to 10 cores Up to 20 MB Up to 30 MB lanes cache scalability per socket cache • Preferred solution for Hadoop* and scale- • Preferred solution for in-memory analytic out analytic/DW engines engines and enterprise databases • Up to 80%** performance boost compared • Highest cache and thread performance for to prior generation large-dataset processing • Intel® Integrated I/O with PCI Express* • Up to 2TB memory footprint (4-socket 3.0 provides more bandwidth for large platform) for in-memory apps data sets • Highest reliability and 8-socket+ scalability • Latest DDR3 memory technology/capacity for reduced memory latency Right Analytic Platforms begins with Intel Xeon processors29 QPI = Intel® QuickPath Interconnect. **See backup slides for 80% claim
    • Platform and Software Optimizations for Hadoop* Integrated Up to four channels PCI Express* DDR3 1600 MHz 3.0 memory Up to 40 Up to eight lanes cores per socket Up to 20 MB cache • • Up to 80%** performance boost vs. prior generation – Intel® Advanced Vector Extensions - reduce compute time – Intel® Turbo Boost Technology - increased performance • Intel® Distribution for Apache Hadoop* software – Built on open source releases – Custom tuning for data types and scaling approaches ** See backup slides for 80% claim 1 Performance comparison using best submitted/published 2-socket server results on the SPECfp*_rate_base2006 benchmark as of 6 March 2012. 2 Source: Intel internal measurements of average time for an I/O device read to local system memory under idle conditions comparing Intel® Xeon® processor E5-2600 product family (230 ns) vs.. QPI = Intel® QuickPath Interconnect Intel® Xeon® processor 5500 series (340 ns). See notes in backup for configuration details Intel® Xeon® processor E530 * Other names and brands may be claimed as the property of others
    • Low Density Servers Intel® Xeon® processor Intel Xeon processor 4S 2S concept concept Delivering Performance/Power Efficiency31
    • Microserver: High Density, Low Power System Innovations• Addressing the low power, high density packaging• Based on Intel® Atom™ processors – Next generation Intel Atom processor codename Avoton – Workloads  Web tier, SaaS, IaaS, PaaS and light data analytics – For scale-out apps32
    • Transforming Storage Data explosion … Driving storage opportunity 690% Distributed 30% CAGR Growth in storage capacity 2010- Storage 2015+ Traditional 16% Volume Unstructured Data Storage CAGR Big Sensed Data Intel® Xeon® processors provide Big Corp Data storage intelligence Big Web Data • Deduplication Structured Data • Thin provisioning Corporate Data • Erasure code • MapReduce Time • Encryption 690 percent growth in storage capacity based off Intel analysis and IDC data, between 2010 (26,066 petabytes) to 2015 (179,327) which is ~690% Source: Intel33
    • Intelligent Distributed Storage Optimizations Intelligent pattern matching reduces large blocks of repeated data BEFORE AFTER TRADITIONAL Real-Time Data Analytics THIN PROVISIONING ALLOCATION ALLOCATED -FREE On-demand utilization of available storage – APPLI 2 SYSTEM-WIDE APPLI 1 ALLOCATED - USED ALLOCATED -FREE CAPACITY RESERVED APPLI 2 virtual and real capacity ALLOCATED -USED APPLI 1 Analysis of real-time storage determines extent and nature of compression Strategic positioning of faster storage devices, improves storage performance34
    • Agenda • What is Big Data? • Big Data use cases • What does Big Data mean for the data center? • Call to action35
    • Call to Action • Big Data represents a huge industry opportunity for innovation – get involved! – New solutions for analytics – Hardware infrastructure innovation – across the platform • Customers from across enterprise, cloud, government, HPC and telecom are looking to improve decision making with big data – If you are a developer of solutions: Understand the market opportunities – If you are a manager of solutions: Understand where big data can help your organization • Intel is deeply engaged in big data – Work with us on delivery of big data solutions36
    • Legal DisclaimerINFORMATION IN THIS DOCUMENT IS PROVIDED IN CONNECTION WITH INTEL PRODUCTS. NO LICENSE, EXPRESS OR IMPLIED,BY ESTOPPEL OR OTHERWISE, TO ANY INTELLECTUAL PROPERTY RIGHTS IS GRANTED BY THIS DOCUMENT. EXCEPT ASPROVIDED IN INTELS TERMS AND CONDITIONS OF SALE FOR SUCH PRODUCTS, INTEL ASSUMES NO LIABILITY WHATSOEVERAND INTEL DISCLAIMS ANY EXPRESS OR IMPLIED WARRANTY, RELATING TO SALE AND/OR USE OF INTEL PRODUCTS INCLUDINGLIABILITY OR WARRANTIES RELATING TO FITNESS FOR A PARTICULAR PURPOSE, MERCHANTABILITY, OR INFRINGEMENT OF ANYPATENT, COPYRIGHT OR OTHER INTELLECTUAL PROPERTY RIGHT.• A "Mission Critical Application" is any application in which failure of the Intel Product could result, directly or indirectly, in personal injury or death. SHOULD YOU PURCHASE OR USE INTELS PRODUCTS FOR ANY SUCH MISSION CRITICAL APPLICATION, YOU SHALL INDEMNIFY AND HOLD INTEL AND ITS SUBSIDIARIES, SUBCONTRACTORS AND AFFILIATES, AND THE DIRECTORS, OFFICERS, AND EMPLOYEES OF EACH, HARMLESS AGAINST ALL CLAIMS COSTS, DAMAGES, AND EXPENSES AND REASONABLE ATTORNEYS FEES ARISING OUT OF, DIRECTLY OR INDIRECTLY, ANY CLAIM OF PRODUCT LIABILITY, PERSONAL INJURY, OR DEATH ARISING IN ANY WAY OUT OF SUCH MISSION CRITICAL APPLICATION, WHETHER OR NOT INTEL OR ITS SUBCONTRACTOR WAS NEGLIGENT IN THE DESIGN, MANUFACTURE, OR WARNING OF THE INTEL PRODUCT OR ANY OF ITS PARTS.• Intel may make changes to specifications and product descriptions at any time, without notice. Designers must not rely on the absence or characteristics of any features or instructions marked "reserved" or "undefined". Intel reserves these for future definition and shall have no responsibility whatsoever for conflicts or incompatibilities arising from future changes to them. The information here is subject to change without notice. Do not finalize a design with this information.• The products described in this document may contain design defects or errors known as errata which may cause the product to deviate from published specifications. Current characterized errata are available on request.• Intel product plans in this presentation do not constitute Intel plan of record product roadmaps. Please contact your Intel representative to obtain Intels current plan of record product roadmaps.• Intel processor numbers are not a measure of performance. Processor numbers differentiate features within each processor family, not across different processor families. Go to: http://www.intel.com/products/processor_number.• Contact your local Intel sales office or your distributor to obtain the latest specifications and before placing your product order.• Copies of documents which have an order number and are referenced in this document, or other Intel literature, may be obtained by calling 1-800-548-4725, or go to: http://www.intel.com/design/literature.htm• Avoton and other code names featured are used internally within Intel to identify products that are in development and not yet publicly announced for release. Customers, licensees and other third parties are not authorized by Intel to use code names in advertising, promotion or marketing of any product or services and any such use of Intels internal code names is at the sole risk of the user• Intel, Xeon, Atom, Core, Sponsors of Tomorrow and the Intel logo are trademarks of Intel Corporation in the United States and other countries.• *Other names and brands may be claimed as the property of others.• Copyright ©2013 Intel Corporation.37
    • Legal Disclaimer• Intel® Turbo Boost Technology requires a system with Intel Turbo Boost Technology. Intel Turbo Boost Technology and Intel Turbo Boost Technology 2.0 are only available on select Intel® processors. Consult your PC manufacturer. Performance varies depending on hardware, software, and system configuration. For more information, visit http://www.intel.com/go/turbo.38
    • Risk Factors The above statements and any others in this document that refer to plans and expectations for the first quarter, the year and the future are forward-looking statements that involve a number of risks and uncertainties. Words such as “anticipates,” “expects,” “intends,” “plans,” “believes,” “seeks,” “estimates,” “may,” “will,” “should” and their variations identify forward-looking statements. Statements that refer to or are based on projections, uncertain events or assumptions also identify forward-looking statements. Many factors could affect Intel’s actual results, and variances from Intel’s current expectations regarding such factors could cause actual results to differ materially from those expressed in these forward-looking statements. Intel presently considers the following to be the important factors that could cause actual results to differ materially from the company’s expectations. Demand could be different from Intels expectations due to factors including changes in business and economic conditions; customer acceptance of Intel’s and competitors’ products; supply constraints and other disruptions affecting customers; changes in customer order patterns including order cancellations; and changes in the level of inventory at customers. Uncertainty in global economic and financial conditions poses a risk that consumers and businesses may defer purchases in response to negative financial events, which could negatively affect product demand and other related matters. Intel operates in intensely competitive industries that are characterized by a high percentage of costs that are fixed or difficult to reduce in the short term and product demand that is highly variable and difficult to forecast. Revenue and the gross margin percentage are affected by the timing of Intel product introductions and the demand for and market acceptance of Intels products; actions taken by Intels competitors, including product offerings and introductions, marketing programs and pricing pressures and Intel’s response to such actions; and Intel’s ability to respond quickly to technological developments and to incorporate new features into its products. The gross margin percentage could vary significantly from expectations based on capacity utilization; variations in inventory valuation, including variations related to the timing of qualifying products for sale; changes in revenue levels; segment product mix; the timing and execution of the manufacturing ramp and associated costs; start-up costs; excess or obsolete inventory; changes in unit costs; defects or disruptions in the supply of materials or resources; product manufacturing quality/yields; and impairments of long-lived assets, including manufacturing, assembly/test and intangible assets. Intels results could be affected by adverse economic, social, political and physical/infrastructure conditions in countries where Intel, its customers or its suppliers operate, including military conflict and other security risks, natural disasters, infrastructure disruptions, health concerns and fluctuations in currency exchange rates. Expenses, particularly certain marketing and compensation expenses, as well as restructuring and asset impairment charges, vary depending on the level of demand for Intels products and the level of revenue and profits. Intel’s results could be affected by the timing of closing of acquisitions and divestitures. Intel’s current chief executive officer plans to retire in May 2013 and the Board of Directors is working to choose a successor. The succession and transition process may have a direct and/or indirect effect on the business and operations of the company. In connection with the appointment of the new CEO, the company will seek to retain our executive management team (some of whom are being considered for the CEO position), and keep employees focused on achieving the company’s strategic goals and objectives. Intels results could be affected by adverse effects associated with product defects and errata (deviations from published specifications), and by litigation or regulatory matters involving intellectual property, stockholder, consumer, antitrust, disclosure and other issues, such as the litigation and regulatory matters described in Intels SEC reports. An unfavorable ruling could include monetary damages or an injunction prohibiting Intel from manufacturing or selling one or more products, precluding particular business practices, impacting Intel’s ability to design its products, or requiring other remedies such as compulsory licensing of intellectual property. A detailed discussion of these and other factors that could affect Intel’s results is included in Intel’s SEC filings, including the company’s most recent Form 10-Q, report on Form 10-K and earnings release. Rev. 1/17/1339
    • Backup40
    • Disclaimer for “Up to 80% performance boost compared to prior generation” • Performance comparison using best submitted/published 2-socket server results on the SPECfp*_rate_base2006 benchmark as of 6 March 2012. Baseline score of 271 published by Itautec on the Servidor Itautec MX203* and Servidor Itautec MX223* platforms based on the prior generation Intel® Xeon® processor X5690. New score of 492 submitted for publication by Dell on the PowerEdge T620 platform and Fujitsu on the PRIMERGY RX300 S7* platform based on the Intel® Xeon® processor E5-2690. For additional details, please visit www.spec.org. Intel does not control or audit the design or implementation of third party benchmark data or Web sites referenced in this document. Intel encourages all of its customers to visit the referenced Web sites or others where similar performance benchmark data are reported and confirm whether the referenced benchmark data are accurate and reflect performance of systems available for purchase.41