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|a QA76.76.C65
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|a Supalov, Alexander.
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|a Optimizing HPC Applications with Intel Cluster Tools :
|b Hunting Petaflops.
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| 250 |
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|a 1st ed.
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| 264 |
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|a Berkeley, CA :
|b Apress L. P.,
|c 2014.
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| 264 |
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|c Ã2014.
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| 300 |
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|a 1 online resource (291 pages)
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| 336 |
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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|a Intro -- Contents at a Glance -- Contents -- About the Authors -- About the Technical Reviewers -- Acknowledgments -- Foreword -- Introduction -- Chapter 1: No Time to Read This Book? -- Using Intel MPI Library -- Using Intel Composer XE -- Tuning Intel MPI Library -- Gather Built-in Statistics -- Optimize Process Placement -- Optimize Thread Placement -- Tuning Intel Composer XE -- Analyze Optimization and Vectorization Reports -- Use Interprocedural Optimization -- Summary -- References -- Chapter 2: Overview of Platform Architectures -- Performance Metrics and Targets -- Latency, Throughput, Energy, and Power -- Peak Performance as the Ultimate Limit -- Scalability and Maximum Parallel Speedup -- Bottlenecks and a Bit of Queuing Theory -- Roofline Model -- Performance Features of Computer Architectures -- Increasing Single-Threaded Performance: Where You Can and Cannot Help -- Process More Data with SIMD Parallelism -- Distributed and Shared Memory Systems -- Use More Independent Threads on the Same Node -- Don't Limit Yourself to a Single Server -- HPC Hardware Architecture Overview -- A Multicore Workstation or a Server Compute Node -- Coprocessor for Highly Parallel Applications -- Group of Similar Nodes Form an HPC Cluster -- Other Important Components of HPC Systems -- Summary -- References -- Chapter 3: Top-Down Software Optimization -- The Three Levels and Their Impact on Performance -- System Level -- Application Level -- Working Against the Memory Wall -- The Magic of Vectors -- Distributed Memory Parallelization -- Shared Memory Parallelization -- Other Existing Approaches and Methods -- Microarchitecture Level -- Addressing Pipelines and Execution -- Closed-Loop Methodology -- Workload, Application, and Baseline -- Iterating the Optimization Process -- Summary -- References -- Chapter 4: Addressing System Bottlenecks.
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|a Classifying System-Level Bottlenecks -- Identifying Issues Related to System Condition -- Characterizing Problems Caused by System Configuration -- Understanding System-Level Performance Limits -- Checking General Compute Subsystem Performance -- Testing Memory Subsystem Performance -- Testing I/O Subsystem Performance -- Characterizing Application System-Level Issues -- Selecting Performance Characterization Tools -- Monitoring the I/O Utilization -- Analyzing Memory Bandwidth -- Summary -- References -- Chapter 5: Addressing Application Bottlenecks: Distributed Memory -- Algorithm for Optimizing MPI Performance -- Comprehending the Underlying MPI Performance -- Recalling Some Benchmarking Basics -- Gauging Default Intranode Communication Performance -- Gauging Default Internode Communication Performance -- Discovering Default Process Layout and Pinning Details -- Gauging Physical Core Performance -- Doing Initial Performance Analysis -- Is It Worth the Trouble? -- Example 1: Initial HPL Performance Investigation -- Getting an Overview of Scalability and Performance -- Learning Application Behavior -- Example 2: MiniFE Performance Investigation -- Choosing Representative Workload(s) -- Example 2 (cont.): MiniFE Performance Investigation -- Balancing Process and Thread Parallelism -- Example 2 (cont.): MiniFE Performance Investigation -- Doing a Scalability Review -- Example 2 (cont.): MiniFE Performance Investigation -- Analyzing the Details of the Application Behavior -- Example 2 (cont.): MiniFE Performance Investigation -- Choosing the Optimization Objective -- Detecting Load Imbalance -- Example 2 (cont.): MiniFE Performance Investigation -- Dealing with Load Imbalance -- Classifying Load Imbalance -- Addressing Load Imbalance -- Example 2 (cont.): MiniFE Performance Investigation -- Example 3: MiniMD Performance Investigation.
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|a Optimizing MPI Performance -- Classifying the MPI Performance Issues -- Addressing MPI Performance Issues -- Mapping Application onto the Platform -- Understanding Communication Paths -- Selecting Proper Communication Fabrics -- Using Scalable Datagrams -- Specifying a Network Provider -- Using IP over IB -- Controlling the Fabric Fallback Mechanism -- Using Multirail Capabilities -- Detecting and Classifying Improper Process Layout and Pinning Issues -- Controlling Process Layout -- Controlling the Global Process Layout -- Controlling the Detailed Process Layout -- Setting the Environment Variables at All Levels -- Controlling the Process Pinning -- Controlling Memory and Network Affinity -- Example 4: MiniMD Performance Investigation on Xeon Phi -- Example 5: MiniGhost Performance Investigation -- Tuning the Intel MPI Library -- Tuning Intel MPI for the Platform -- Tuning Point-to-Point Settings -- Adjusting the Eager and Rendezvous Protocol Thresholds -- Changing DAPL and DAPL UD Eager Protocol Threshold -- Bypassing Shared Memory for Intranode Communication -- Bypassing the Cache for Intranode Communication -- Choosing the Best Collective Algorithms -- Tuning Intel MPI Library for the Application -- Using Magical Tips and Tricks -- Disabling the Dynamic Connection Mode -- Applying the Wait Mode to Oversubscribed Jobs -- Fine-Tuning the Message-Passing Progress Engine -- Reducing the Pre-reserved DAPL Memory Size -- What Else? -- Example 5 (cont.): MiniGhost Performance Investigation -- Optimizing Application for Intel MPI -- Avoiding MPI_ANY_SOURCE -- Avoiding Superfluous Synchronization -- Using Derived Datatypes -- Using Collective Operations -- Betting on the Computation/Communication Overlap -- Replacing Blocking Collective Operations by MPI-3 Nonblocking Ones -- Using Accelerated MPI File I/O.
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|a Example 5 (cont.): MiniGhost Performance Investigation -- Using Advanced Analysis Techniques -- Automatically Checking MPI Program Correctness -- Comparing Application Traces -- Instrumenting Application Code -- Correlating MPI and Hardware Events -- Collecting and Analyzing Hardware Counter Information in ITAC -- Collecting and Analyzing Hardware Counter Information in VTune -- Summary -- References -- Chapter 6: Addressing Application Bottlenecks: Shared Memory -- Profiling Your Application -- Using VTune Amplifier XE for Hotspots Profiling -- Hotspots for the HPCG Benchmark -- Compiler-Assisted Loop/Function Profiling -- Sequential Code and Detecting Load Imbalances -- Thread Synchronization and Locking -- Dealing with Memory Locality and NUMA Effects -- Thread and Process Pinning -- Controlling OpenMP Thread Placement -- Thread Placement in Hybrid Applications -- Summary -- References -- Chapter 7: Addressing Application Bottlenecks: Microarchitecture -- Overview of a Modern Processor Pipeline -- Pipelined Execution -- Data Conflicts -- Control Conflicts -- Structural Conflicts -- Out-of-order vs. In-order Execution -- Superscalar Pipelines -- SIMD Execution -- Speculative Execution: Branch Prediction -- Memory Subsystem -- Putting It All Together: A Final Look at the Sandy Bridge Pipeline -- A Top-down Method for Categorizing the Pipeline Performance -- Intel Composer XE Usage for Microarchitecture Optimizations -- Basic Compiler Usage and Optimization -- Using Optimization and Vectorization Reports to Read the Compiler's Mind -- Optimizing for Vectorization -- The AVX Instruction Set -- Why Doesn't My Code Vectorize in the First Place? -- Data Dependences -- Data Aliasing -- Array Notations -- Vectorization Directives -- ivdep -- vector -- simd -- Understanding AVX: Intrinsic Programming -- What Are Intrinsics?.
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|a First Steps: Loading and Storing -- Arithmetic -- Data Rearrangement -- Dealing with Disambiguation -- Dealing with Branches -- __builtin_expect -- Profile-Guided Optimization -- Pragmas for Unrolling Loops and Inlining -- unroll/nounroll -- unroll_and_jam/nounroll_and_jam -- inline, noinline, forceinline -- Specialized Routines: How to Exploit the Branch Prediction for Maximal Performance -- When Optimization Leads to Wrong Results -- Using a Standard Library Method -- Using a Manual Implementation in C -- Vectorization with Directives -- Analyzing Pipeline Performance with Intel VTune Amplifier XE -- Summary -- References -- Chapter 8: Application Design Considerations -- Abstraction and Generalization of the Platform Architecture -- Types of Abstractions -- Levels of Abstraction and Complexities -- Raw Hardware vs. Virtualized Hardware in the Cloud -- Questions about Application Design -- Designing for Performance and Scaling -- Designing for Flexibility and Performance Portability -- Data Layout -- Structured Approach to Express Parallelism -- Understanding Bounds and Projecting Bottlenecks -- Data Storage or Transfer vs. Recalculation -- Total Productivity Assessment -- Summary -- References -- Index.
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|a Description based on publisher supplied metadata and other sources.
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| 590 |
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|a Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2023. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
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|a Electronic books.
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| 700 |
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|a Semin, Andrey.
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| 700 |
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|a Dahnken, Christopher.
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| 700 |
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|a Klemm, Michael.
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|i Print version:
|a Supalov, Alexander
|t Optimizing HPC Applications with Intel Cluster Tools
|d Berkeley, CA : Apress L. P.,c2014
|z 9781430264965
|
| 797 |
2 |
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|a ProQuest (Firm)
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| 856 |
4 |
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|u https://ebookcentral.proquest.com/lib/matrademy/detail.action?docID=6422827
|z Click to View
|
