SC’14 HPC Dev. Workshop Session Presentation Template · Vector Parallel Vector. 1 core no SIMD....
Transcript of SC’14 HPC Dev. Workshop Session Presentation Template · Vector Parallel Vector. 1 core no SIMD....
Copyright © 2015, Intel Corporation. All rights reserved.
Herbert CorneliusIntel
Software Modernization
Taking full advantage of modern performance technologies
Copyright © 2015, Intel Corporation. All rights reserved.
#1 Supercomputer Scaling on Top500*
What if … the ‘2003-2013 CAGR held to 2023?
Source: TOP500 stats--www.top500.org1Projected flops and cores calculated using the CAGR in flops and cores on the #1 system on the TOP500 from 2003 to 2013 and extrapolating to 2023.
31.9 EF1?
33 PF
35 TF
LOG
SC
ALE
2003 2013 2023 2003 2013 2023
1.9B1?
3.12M
5.12K
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All products, computer systems, dates and figures specified are preliminary based on current expectations, and are subject to change without notice.
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Parallel is your path forward.
INDUSTRY TREND
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What are the chances?
A code (probably in FORTRAN) written for this CPU will perform well on these?
(die sizes not to scale, for illustration only)IBM* 5110 in 1978www.rugged-portable.com/history-portable-computers-rugged-bias/ibm-portable-pc-5110
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Many-Core & SIMD
Multi-Core & SIMD
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Intel® Xeon®
processor64-bit
Intel® Xeon®
processor 5100 series
Intel® Xeon®
processor 5500 series
Intel® Xeon®
processor 5600 series
Intel® Xeon®
processor code-named
Sandy Bridge EP
Intel® Xeon®
processor code-namedIvy Bridge EP
Intel® Xeon®
processor code-namedHaswell EP
Core(s) 1 2 4 6 8 12 18
Threads 2 2 8 12 16 24 36
SIMD Width 128 128 128 128 256 256 256
Intel® Xeon® and Intel® Xeon Phi™ Product Families are both going parallel
Intel® Xeon Phi™ coprocessorcode-named
Knights Corner
Intel® Xeon Phi™ processor & coprocessorcode-named
Knights Landing1
61 60+
244 240+
512 512
More Cores More Threads Wider Vectors
Product specification for launched and shipped products available on ark.intel.com. 1 Not launched or in planning.
Parallel is the Path Forward
(die sizes not to scale, for illustration only)
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“The Gap”
SOFTWARE MODERNIZATION
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The Urgency of Code Modernization
“This process is referred to as parallelization, code optimization or code modernization. As systems move toward exascale levels of performance, the problem of outdated code will only grow in urgency.
. . .
Driving the urgency for code modernization is recognition that it is both a technical and an economic imperative. Code not modernized equates to system performance and financial advantage left on the table. Outdated code equates to longer run times, more test and design iterations, more modeling within a given period of time, and slower simulations. Bottom line: higher operating costs and less revenue for organizations.”
- Doug Black
Source: www.scientificcomputing.com/articles/2014/12/hpc-community-experts-weigh-code-modernization
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Modernization is critical: But which path is best?
CUDA*, OpenACC* Investment locked into one architecture
OpenMP*, MPIReusable, Portable, Scalable
All products, computer systems, dates and figures specified are preliminary based on current expectations, and are subject to change without notice.
Device-Specific Applications (Accelerators)
Applications Suitable for Many Architectures (CPU)
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Modernization is critical: But which path is best?
CUDA*, OpenACC* Investment locked into one architecture
OpenMP*, MPIReusable, Portable, Scalable
Device-Specific Applications (Accelerators)
Applications Suitable for Many Architectures (CPU)
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Economical
Sustainable
Open
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Amdahl’s Law
Modern Version:
Enhancing a fraction f of a computation by speedup S,the overall speedup is:
Speedup S can be archived by both SIMD/Vectorization or Multi-Threading.
Speedupenhanced (f,S) = 1
1−𝑓𝑓 +𝑓𝑓𝑆𝑆
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Source: TR1593.pdf, http://minds.wisconsin.edu
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Modernize Your SoftwarePerformance = Parallelism on all Levels
NODES clustering
SIMD vectorization
CORES multi-threading
ILP instruction parallelism
CACHE optimization, data-layout
Simplified example for a 61 cores and 8-way SIMD processor -- for illustration only
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Analogy: Using all Resources:
Vector Parallel VectorSerial Scalar
1 core no SIMD 1 core with SIMD all cores with SIMD
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Impact of using the entire CPU
2012Intel® Xeon® Processor E5-2600 family(codenamed Sandy Bridge EP)
2013Intel® Xeon® ProcessorE5 2600 v2 family(codenamedIvy Bridge EP)
2010Intel® Xeon® Processor X5680(codenamedWestmere)
2007Intel® Xeon® Processor X5472(codenamedHarpertown)
2009Intel® Xeon® Processor X5570(codenamedNehalem)
2014Intel® Xeon® Processor E5-2600 v3 family(codenamed Haswell EP)
Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and MobileMark, are measured using specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. Performance measured in Intel Labs by Intel employees. For more information go to http://www.intel.com/performance .
SS: Single threaded and ScalarVS: Vectorized and Single threadedSP: Scalar and ParallelVP: Vectorized and Parallelized
179x
Example:
Vectorized & Parallelized
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4
512-bit
352 GB/s
Unlock Parallel PotentialConsider an Intel® Xeon Phi™ Coprocessor for code development today
Intel® Xeon Phi™ x100 Product Family
formerly codenamed
Knights Corner
Intel® Xeon Phi™ x200 Product Family
codenamed
Knights Landing
Cores up to
Threads/Core
Vector Width
Peak Memory Bandwidth
Future Xeon
The world is going parallel – stick with
sequential code and you will fall
behind.
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2
256-bit
68 GB/s
60+
4
512-bit
>350 GB/s
tba
tba
512-bit
tba
12
2
256-bit
59 GB/s
Intel® Xeon® Processor E5-2600 v2 Product
Family formerly codenamed
Ivy Bridge
Intel® Xeon® Processor E5-2600 v3 Product
Family formerly codenamed
Haswell
All products, computer systems, dates and figures specified are preliminary based on current expectations, and are subject to change without notice.
tba tba
…
Xeon Phi amplifies thread, core & wide vector scaling to be ready for tomorrow’s processors
Future(die sizes not to scale, for illustration only)
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Application Modernization
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Case study compliments of Hansen Experimental Physics Laboratory, Stanford University* and Colfax International*
HEATCODE – Cosmology ApplicationBuilding a Model of our Galaxy processing spectral data
Objective: Reduce days of compute to hours
Intel® Xeon® processors are parallelPrograms should be too …
• Parallel Tasks
• Utilize
• Cores
• Threads
• SIMD where possible
Case Study: Tapping the Performance Reserve
http://arxiv.org/pdf/1311.4627.pdf
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A real-life example
Colfax International* and Stanford* – HEATCODE application
• As advertised, the application ported quickly from Intel® Xeon® processors to the Intel® Xeon Phi™ coprocessor
• But, performance <1/3 of two Intel® Xeon® E5 Family CPUs
Why did performance decrease?
1. Move from >3 GHz out of order modern CPU to a low power 1 GHz in order core
2. Limited threading, no vectorization
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CPU = Xeon = Intel® Xeon ProcessorXeon Phi = Intel® Xeon Phi™ coprocessor
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What is the impact of modernization?
After optimization, the new many-core code:
• Runs up to 620x faster than it’s baseline!
• Runs up to 125x faster than the original Intel® Xeon® processor baseline!
• Well worth the time and effort
Results measured on Stanford University HEATCODE application in labs at Stanford University and Colfax International Configuration: Colfax CX2265i-XP5 server ( http://www.colfax-intl.com/nd/Servers/CX2265i-XP5.aspx ) based on two Intel Xeon E5-2680 processors with 64 GB of DDR3 memory at 1333 MHz. This server hosted two Intel Xeon Phi coprocessors B1QS-5110P, each with 60 cores and 8 GB of on-board GDDR5 memory. The server was running a Linux operating system CentOS6.4 on the host, and the MPSS (Intel MIC Platform Software Stack) version 2.1.4982-15 on coprocessors. Our tests were performed with one of the two compilers: Intel C++ compiler version 13.1.1 and GNU C++ compiler version 4.4.7.
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.For more complete information about performance and benchmark results, visit Performance Test Disclosure
CPU = Xeon = Intel® Xeon ProcessorXeon Phi = Intel® Xeon Phi™ coprocessor
Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and MobileMark, are measured using specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products.
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Still makes sense under Amdahl’s lawThe new, modern code
• Improves CPU-only performance up to 60x from baseline
• Has 1 coprocessor run ~2X the speed of 2 CPUs
A highly parallel processor will improve performance from 1-8x, and generally 1.5-3x
Any larger increase comes from software modernization -- extracting parallelism in the algorithm
Results measured on Stanford University HEATCODE application in labs at Stanford University and Colfax International Configuration: Colfax CX2265i-XP5 server ( http://www.colfax-intl.com/nd/Servers/CX2265i-XP5.aspx ) based on two Intel Xeon E5-2680 processors with 64 GB of DDR3 memory at 1333 MHz. This server hosted two Intel Xeon Phi coprocessors B1QS-5110P, each with 60 cores and 8 GB of on-board GDDR5 memory. The server was running a Linux operating system CentOS6.4 on the host, and the MPSS (Intel MIC Platform Software Stack) version 2.1.4982-15 on coprocessors. Our tests were performed with one of the two compilers: Intel C++ compiler version 13.1.1 and GNU C++ compiler version 4.4.7.
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.For more complete information about performance and benchmark results, visit Performance Test Disclosure
CPU = Xeon = Intel® Xeon ProcessorXeon Phi = Intel® Xeon Phi™ coprocessor
Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and MobileMark, are measured using specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products.
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Scaling Applicationson Intel® Xeon® and Intel® Xeon Phi™
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… and the performance on HEATCODE scales per node as you add an additional Intel Xeon Phi coprocessor.
Results measured on Stanford University HEATCODE application in labs at Stanford University and Colfax International Configuration: Colfax CX2265i-XP5 server ( http://www.colfax-intl.com/nd/Servers/CX2265i-XP5.aspx ) based on two Intel Xeon E5-2680 processors with 64 GB of DDR3 memory at 1333 MHz. This server hosted two Intel Xeon Phi coprocessors B1QS-5110P, each with 60 cores and 8 GB of on-board GDDR5 memory. The server was running a Linux operating system CentOS6.4 on the host, and the MPSS (Intel MIC Platform Software Stack) version 2.1.4982-15 on coprocessors. Our tests were performed with one of the two compilers: Intel C++ compiler version 13.1.1 and GNU C++ compiler version 4.4.7.
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.For more complete information about performance and benchmark results, visit Performance Test Disclosure
Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and MobileMark, are measured using specific computer systems, components, software, operations and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products.
CPU = Xeon = Intel® Xeon ProcessorXeon Phi = Intel® Xeon Phi™ coprocessor
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Performance & General Programmability
Source: http://research.colfaxinternational.com/file.axd?file=2013%2f11%2fSC13-Intel-Theater-Talk-Colfax.pdf(see optimization notice)
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Parallel Programming for Intel® Architecture(or, in general, for normal CPUs)
Cores, Threads
Vectors
Memory, Caches
Data Layout and Alignment
OpenMP* TBB Cilk™ Plus
Vectorloops
Vectorfunctions
Blocking algorithms
Manual layout, ugly code
AoS SoAlibrary
4 considerations when writing an efficient, unconstrained parallel program
Array notations
threads, locks
Intrinsics
Directives for alignment
23*Other logos, brands and names are the property of their respective owners.
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OpenMP* 4.0 Industry Standard
*Other logos, brands and names are the property of their respective owners.
www.openmp.org
One of the most-capable high-levelparallel languages, now supporting:
• SMP multi-threading• SIMD vectorization• Accelerator offloading
November 17, 2014: Technical Report 3 - Refinements for Accelerators and Task Creation Announcedhttp://openmp.org/wp/tr3-announced-refinements-for-accelerators-and-task-creation/
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Vector programming in OpenMP* 4.0Mandel example: vectorize an outer loop with function calls
#pragma omp declare simdint mandel(fcomplex c){ // Computes number of iterations for c to escape
fcomplex z = c; int iters = 0;while ((cabsf(z) < 2.0f) && (iters < LIMIT)) {
z = z * z + c; iters++;}return iters;
}
#pragma omp parallel forfor (int y = 0; y < ImageHeight; ++y) {
#pragma omp simdfor (int x = 0; x < ImageWidth; ++x) {
count[y][x] = mandel(in_vals[y][x]);}
}
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Intel® Software Development Tools“Faster Code Faster”
http://software.intel.com/en-us/intel-parallel-studio-xehttps://software.intel.com/en-us/developer-tools-technical-enterprise
Cluster EditionProfessional Edition
2015
Industry-leading performance from advanced compilers and toolsOptimizations for IA (incl. vector/SIMD and parallel/MT)Industry Standard Parallel programming modelsComprehensive optimized librariesInsightful analysis tools
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Intel® Software Development Tools
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Intel® Parallel Studio XE 2015 Professional Edition• Core and Node level (SMP)
Intel® Parallel Studio XE 2015 Cluster Edition• High Performance Cluster Development Tools for TC/HPC
Intel® Compilers• FORTRAN, C/C++, Cilk™ Plus, OpenMP*4.0
Intel® Libraries• Intel® Math Kernel Library (MKL)• Intel® Integrated Performance Primitives (IPP)• Intel® Threading Building Blocks (TBB)• Intel® MPI Library
Intel® Analysers
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Copyright © 2015, Intel Corporation. All rights reserved.
Growing Intel® Parallel Computing Centers Community
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https://software.intel.com/ipcc
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https://software.intel.com/en-us/articles/intel-xeon-phi-coprocessor-applications-and-solutions-catalog
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Further Readings
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https://software.intel.com/ipcc
https://software.intel.com/mic-developer
https://software.intel.com/en-us/server-developer
High Performance Parallelism Pearls: Multicore and Many-core Programming ApproachesJames Reinders, Jim Jeffers
Intel® Xeon Phi™ Coprocessor Architecture and Tools:The Guide for Application DevelopersRezaur Rahman
Intel Xeon Phi Coprocessor High Performance ProgrammingJim Jeffers, James Reinders
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Table of Contents• Foreword by Bronis de Supinski (CTO LLNL)• Preface• Chapter 1: No Time to Read this Book?• Chapter 2: Overview of Platform Architectures• Chapter 3: Top-Down Software Optimization• Chapter 4: Addressing System Bottlenecks• Chapter 5: Addressing Application Bottlenecks: Distributed Memory• Chapter 6: Addressing Application Bottlenecks: Shared Memory• Chapter 7: Addressing Microarchitecture Bottlenecks• Chapter 8: Application Design Implications
Meet “the book of the year”
ISBN-13 (pbk): 978-1-4302-6496-5ISBN-13 (electronic): 978-1-4302-6497-2
www.apress.com/9781430264965
Authors: Alexander Supalov, Andrey Semin, Michael Klemm, Chris Dahnken
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There is even more …
Intel® Technical Computing PortfolioIntel® based Workstations/Visualization (Embree)
Intel® Cluster Ready (ICR)
Intel® Data Center Manager (DCM)
Intel® SW Development Tools
Intel® Big Data Analytics Toolkit & Acceleration Library
Intel® Enterprise Edition Lustre* (Filesystem)
Intel® SSD (PCIe NVMe)
Intel® based Storage (NVM)
Intel® True Scale (IBA) and Omni-Path Fabric
Intel® Networking (10/40GbE)
Intel® Boards & Systems
Intel® Xeon Phi™ Processor and Coprocessor
Intel® Xeon® Processors
INTEL TECHNICAL COMPUTING & HPC PORTFOLIO
All components working “better together” for a comprehensive and high-performance end-to-end solution based on Intel technologies.
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Configuration Information
HEATCODE Example
Measurements taken by Colfax* International in September and October of 2013. System Configuration: Colfax CX2265i-XP5 server ( http://www.colfax-intl.com/nd/Servers/CX2265i-XP5.aspx ) based on two Intel® Xeon® E5-2680 processors with 64 GB of DDR3 memory at 1,333 MHz. This server hosted two Intel® Xeon Phi™ coprocessors B1QS-5110P, each with 60 cores and 8 GB of on-board GDDR5 memory. The server was running a Linux* operating system CentOS* 6.4 on the host, and the MPSS (Intel MIC Platform Software Stack) version 2.1.4982-15 on coprocessors. Our tests were performed with one of the two compilers: Intel C++ compiler version 13.1.1 and GNU C++ compiler version 4.4.7 .
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.
For more complete information about performance and benchmark results, visit Performance Test Disclosure
.
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Intel® Compilers – Performance Benefits
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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 Intel's internal code names is at the sole risk of the user.Intel, Intel® Xeon®, Intel Xeon Phi™, Intel® True Scale, Intel® Enterprise Edition Lustre, Intel® Data Center Manager, Intel® Cluster Ready 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 ©2014 Intel Corporation.
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Intel's compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include SSE2, SSE3, and SSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel.
Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice.
Notice revision #20110804
Optimization Notice
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Risk FactorsThe above statements and any others in this document that refer to plans and expectations for the second 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 important factors that could cause actual results to differ materially from the company’s expectations. Demand for Intel's products is highly variable and, in recent years, Intel has experienced declining orders in the traditional PC market segment. Demand could be different from Intel's expectations due to factors including changes in business and economic conditions; consumer confidence or income levels; customer acceptance of Intel’s and competitors’ products; competitive and pricing pressures, including actions taken by competitors; supply constraints and other disruptions affecting customers; changes in customer order patterns including order cancellations; and changes in the level of inventory at customers. Intel operates in highly competitive industries and its operations have high costs that are either fixed or difficult to reduce in the short term. Intel's 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; excess or obsolete inventory; changes in unit costs; defects or disruptions in the supply of materials or resources; and product manufacturing quality/yields. Variations in gross margin may also be caused by the timing of Intel product introductions and related expenses, including marketing expenses, and Intel's ability to respond quickly to technological developments and to introduce new products or incorporate new features into existing products, which may result in restructuring and asset impairment charges. Intel's 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. Intel’s results could be affected by the timing of closing of acquisitions, divestitures and other significant transactions. Intel's 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 Intel's SEC filings. 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 reports on Form 10-Q, Form 10-K and earnings release.
Rev. 4/15/14
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