Intel® Advisor User Guide

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Date 11/07/2023
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Document Table of Contents
Document Table of Contents x
Intel® Advisor User Guide
Intel® Advisor User Guide x
Introduction Install and Launch Intel® Advisor Set Up Project Analyze Vectorization Perspective Analyze CPU Roofline Model Threading Designs Model Offloading to a GPU Analyze GPU Roofline Design and Analyze Flow Graphs Minimize Analysis Overhead Analyze MPI Applications Manage Results Command Line Interface Troubleshooting Reference Appendix
Introduction x
Design and Optimization Methodology Visualize Performance Bottlenecks with Roofline Chart Model Offloading to Accelerator Check Vectorization Efficiency Prototype Threading Designs Tutorials and Samples Get Help and Support
Install and Launch Intel® Advisor x
Install Intel® Advisor Set Up Environment Variables Set Up System to Analyze GPU Kernels Set Up Environment to Offload SYCL, OpenMP* target, and OpenCL™ Applications to CPU Launch Intel® Advisor GUI Navigation Quick Start
Set Up Project x
Configure Target Application Build Target Application Create Project
Configure Target Application x
Limit the Number of Threads Used by Parallel Frameworks Choose a Small, Representative Data Set
Create Project x
Configure Project Configure Binary/Symbol Search Directories Configure Source Search Directory Binary/Symbol Search and Source Search Locations
Analyze Vectorization Perspective x
Run Vectorization and Code Insights Perspective from GUI Run Vectorization and Code Insights Perspective from Command Line Explore Vectorization and Code Insights Results
Run Vectorization and Code Insights Perspective from GUI x
Vectorization Accuracy Presets Customize Vectorization and Code Insights Perspective
Run Vectorization and Code Insights Perspective from Command Line x
Vectorization Accuracy Levels in Command Line
Explore Vectorization and Code Insights Results x
Vectorization Report Overview Examine Not-Vectorized and Under-Vectorized Loops Analyze Loop Call Count Investigate Memory Usage and Traffic Find Data Dependencies
Analyze CPU Roofline x
Run CPU / Memory Roofline Insights Perspective from GUI Run CPU / Memory Roofline Insights Perspective from Command Line Explore CPU/Memory Roofline Results
Run CPU / Memory Roofline Insights Perspective from GUI x
CPU Roofline Accuracy Presets Customize CPU / Memory Roofline Insights Perspective
Run CPU / Memory Roofline Insights Perspective from Command Line x
CPU Roofline Accuracy Levels in Command Line
Explore CPU/Memory Roofline Results x
CPU Roofline Report Overview Examine Bottlenecks on CPU Roofline Chart Examine Relationships Between Memory Levels Compare CPU Roofline Results
Model Threading Designs x
Run Threading Perspective from GUI Run Threading Perspective from Command Line Annotate Code for Deeper Analysis Explore Threading Results Model Threading Parallelism Check for Dependencies Issues Add Parallelism to Your Program
Run Threading Perspective from GUI x
Customize Threading Perspective
Run Threading Perspective from Command Line x
Threading Accuracy Levels in Command Line
Annotate Code for Deeper Analysis x
Annotate Code to Model Parallelism Annotations Annotation Report
Annotate Code to Model Parallelism x
Before Annotating Code for Deeper Analysis Use Amdahl's Law and Measure the Program Task Organization and Annotations Annotate Parallel Sites and Tasks Task Patterns Choose the Tasks Use Partially Parallel Programs with Intel® Advisor
Task Patterns x
Multiple Parallel Sites Data and Task Parallelism Mix and Match Tasks
Choose the Tasks x
Task Interactions and Suitability How Big Should a Task Be?
Annotations x
Annotation Types Annotation Definitions Files Add Annotations into Your Source Code Tips for Annotation Use with C/C++ Programs
Annotation Types x
Annotation Types Summary Annotation General Characteristics Site and Task Annotations for Simple Loops With One Task Site and Task Annotations for Parallel Sites with Multiple Tasks Lock Annotations Pause Collection and Resume Collection Annotations Special-purpose Annotations
Annotation Definitions Files x
Reference the Annotations Definitions Directory Include the Annotations Header File in C/C++ Sources
Add Annotations into Your Source Code x
Copy Annotations and Build Settings Using the Annotation Assistant Pane Insert Annotations in a Text Editor
Tips for Annotation Use with C/C++ Programs x
Control the Expansion of advisor-annotate.h Handle Compilation Issues that Appear After Adding advisor-annotate.h
Annotation Report x
Annotation Report, Clear Description of Storage Row Annotation Report, Disable Observations in Region Row Annotation Report, Pause Collection Row Annotation Report, Inductive Expression Row Annotation Report, Lock Row Annotation Report, Observe Uses Row Annotation Report, Reduction Row Annotation Report, Re-enable Observations at End of Region Row Annotation Report, Resume Collection Row Annotation Report, Site Row Annotation Report, Task Row Annotation Report, User Memory Allocator Use Row Annotation Report, User Memory Deallocator Use Row
Model Threading Parallelism x
Suitability Report Overview Choose Modeling Parameters in the Suitability Report Fix Annotation-related Errors Detected by the Suitability Tool Advanced Modeling Options Reduce Parallel Overhead, Lock Contention, and Enable Chunking
Reduce Parallel Overhead, Lock Contention, and Enable Chunking x
Reduce Site Overhead Reduce Task Overhead Reduce Lock Overhead Reduce Lock Contention Enable Task Chunking
Check for Dependencies Issues x
Code Locations Pane Filter Pane (Dependencies Report) Problems and Messages Pane Dependencies Source Window
Dependencies Source Window x
Code Locations Pane (Dependencies Source Window) Focus Code Location Pane Focus Code Location Call Stack Pane Related Code Locations Pane Related Code Location Call Stack Pane Relationship Diagram Pane
Add Parallelism to Your Program x
Before You Add Parallelism: Choose a Parallel Framework Add the Parallel Framework to Your Build Environment Annotation Report Replace Annotations with Intel® oneAPI Threading Building Blocks (oneTBB) Code Replace Annotations with OpenMP* Code Next Steps for the Parallel Program
Before You Add Parallelism: Choose a Parallel Framework x
Parallel Frameworks Intel® oneAPI Threading Building Blocks (oneTBB) OpenMP* Microsoft Task Parallel Library* (TPL) Other Parallel Frameworks
Add the Parallel Framework to Your Build Environment x
Enable Intel® oneAPI Threading Building Blocks (oneTBB) in your Build Environment Define the TBBROOT Environment Variable Enable C++11 Lambda Expression Support with Intel® oneAPI Threading Building Blocks (oneTBB) Enable OpenMP* in your Build Environment
Annotation Report x
Annotation Report Overview Locate Annotations with the Annotation Report
Replace Annotations with Intel® oneAPI Threading Building Blocks (oneTBB) Code x
Intel® oneAPI Threading Building Blocks (oneTBB) Mutexes Intel® oneAPI Threading Building Blocks (oneTBB) Simple Mutex - Example Test the Intel® oneAPI Threading Building Blocks (oneTBB) Synchronization Code Parallelize Functions - Intel® oneAPI Threading Building Blocks (oneTBB) Tasks Parallelize Data - Intel® oneAPI Threading Building Blocks (oneTBB) Counted Loops Parallelize Data - Intel® oneAPI Threading Building Blocks (oneTBB) Loops with Complex Iteration Control
Replace Annotations with OpenMP* Code x
Add OpenMP Code to Synchronize the Shared Resources OpenMP Critical Sections Basic OpenMP Atomic Operations Advanced OpenMP Atomic Operations OpenMP Reduction Operations OpenMP Locks Test the OpenMP Synchronization Code Parallelize Functions - OpenMP Tasks Parallelize Data - OpenMP Counted Loops Parallelize Data - OpenMP Loops with Complex Iteration Control
Next Steps for the Parallel Program x
Use Intel® Inspector and Intel® VTune™ Profiler Debug Parallel Programs
Model Offloading to a GPU x
Run Offload Modeling Perspective from GUI Run Offload Modeling Perspective from Command Line Explore Offload Modeling Results Advanced Modeling Configuration
Run Offload Modeling Perspective from GUI x
Offload Modeling Accuracy Presets Customize Offload Modeling Perspective
Run Offload Modeling Perspective from Command Line x
Offload Modeling Accuracy Levels in Command Line Run GPU-to-GPU Performance Modeling from Command Line
Explore Offload Modeling Results x
Offload Modeling Report Overview Examine Regions Recommended for Offloading Examine Data Transfers for Modeled Regions Check for Dependency Issues Explore Performance Gain from GPU-to-GPU Modeling Investigate Non-Offloaded Code Regions
Advanced Modeling Configuration x
Model Application Performance on a Custom Target GPU Device Check How Assumed Dependencies Affect Modeling Manage Invocation Taxes Enforce Offloading for Specific Loops
Analyze GPU Roofline x
Run GPU Roofline Insights Perspective from GUI Run GPU Roofline Insights Perspective from Command Line Explore GPU Roofline Results
Run GPU Roofline Insights Perspective from GUI x
GPU Roofline Accuracy Presets Customize GPU Roofline Insights Perspective
Run GPU Roofline Insights Perspective from Command Line x
GPU Roofline Accuracy Levels in Command Line
Explore GPU Roofline Results x
Examine GPU Roofline Summary Examine Bottlenecks on GPU Roofline Chart Examine Kernel Details Compare GPU Roofline Results
Design and Analyze Flow Graphs x
Where to Find the Flow Graph Analyzer Launching the Flow Graph Analyzer Flow Graph Analyzer GUI Overview Flow Graph Analyzer Workflows Designer Workflow Generating C++ Stubs Preferences Scalability Analysis Collecting Traces from Applications Nested Parallelism in Flow Graph Analyzer Analyzer Workflow Experimental Support for OpenMP* Applications Sample Trace Files Additional Resources
Flow Graph Analyzer GUI Overview x
Menus Toolbars Tabs Main Canvas Charts
Designer Workflow x
Adding Nodes, Edges, and Ports Modifying Node Properties Viewing Edge Properties Validating a Graph Saving a Graph to a File
Scalability Analysis x
Activating the Graph Scalability Analysis Prerequisites Running the Scalability Analysis Exploring the Parallelism in a Concurrent Node Showing Non-Parallel Nature of a Serial Node Explore Parallelism Provided by the Topology of a Graph Understanding Analysis Color Codes
Scalability Analysis Prerequisites x
Setting Concurrency Specification Setting Data Count Setting Node Weight
Collecting Traces from Applications x
Building an Application for Trace Collection Collecting Trace Files
Building an Application for Trace Collection x
Building an Application on Windows* OS Building an Application on Linux* OS Building an Application on macOS*
Collecting Trace Files x
Collect Traces In the Flow Graph Analyzer GUI Collect Traces Outside the Flow Graph Analyzer GUI
Collect Traces Outside the Flow Graph Analyzer GUI x
Collecting Trace Files with fgtrun Script Collecting Trace Files without fgtrun Script
Analyzer Workflow x
Find Time Regions of Low Concurrency and Their Cause Finding a Critical Path Finding Tasks with Small Durations Reduce Scheduler Overhead using Lightweight Policy Identifying Tasks that Operate on Common Input Support for SYCL
Support for SYCL x
Collect SYCL Application Traces Examine a SYCL Application Graph Find Issues Using Static Rule-check Engine
Examine a SYCL Application Graph x
Hotspot View View Performance Inefficiencies of Data-parallel Constructs
Find Issues Using Static Rule-check Engine x
Issue: Const Reference to a Host Pointer Used to Initialize a Buffer Issue: Host Pointer Accessor Used in a Loop Issue: Data Parallel Construct Inefficiency
Experimental Support for OpenMP* Applications x
Collecting Traces for OpenMP* Applications OpenMP* Constructs in the Per-Thread Task View OpenMP* Constructs in the Graph Canvas
Sample Trace Files x
code_generation Samples performance_analysis Samples
Minimize Analysis Overhead x
Collection Controls to Minimize Analysis Overhead Loop Markup to Minimize Analysis Overhead Filtering to Minimize Analysis Overhead Execution Speed/Duration/Scope Properties to Minimize Analysis Overhead Miscellaneous Techniques to Minimize Analysis Overhead
Analyze MPI Applications x
Model MPI Application Performance on GPU Control Collection with an MPI_Pcontrol Function
Manage Results x
Open a Result Rename an Existing Result Delete a Result Save Results to a Custom Location Work with Standalone HTML Reports Create a Read-only Result Snapshot Create a Result Snapshot Dialog Box
Command Line Interface x
advisor Command Line Interface Reference Offload Modeling Command Line Reference Generate Pre-configured Command Lines
advisor Command Line Interface Reference x
advisor Command Action Reference advisor Command Option Reference
advisor Command Action Reference x
collect command create-project help import-dir mark-up-loops report snapshot version workflow
advisor Command Option Reference x
accuracy append app-working-dir assume-dependencies assume-hide-taxes assume-ndim-dependency assume-single-data-transfer auto-finalize batching benchmarks-sync bottom-up cache-binaries cache-binaries-mode cache-config cache-simulation cache-sources cachesim cachesim-associativity cachesim-cacheline-size cachesim-mode cachesim-sampling-factor cachesim-sets check-profitability clear config count-logical-instructions count-memory-instructions count-memory-objects-accesses count-mov-instructions count-send-latency cpu-scale-factor csv-delimiter custom-config data-limit data-reuse-analysis data-transfer data-transfer-histogram data-transfer-page-size data-type delete-tripcounts disable-fp64-math-optimization display-callstack dry-run duration dynamic enable-cache-simulation enable-data-transfer-analysis enable-task-chunking enforce-baseline-decomposition enforce-fallback enforce-offloads estimate-max-speedup evaluate-min-speedup exclude-files executable-of-interest exp-dir filter filter-by-scope filter-reductions flop force-32bit-arithmetics force-64bit-arithmetics format gpu gpu-carm gpu-kernel-of-interest gpu-sampling-interval hide-data-transfer-tax ignore ignore-app-mismatch ignore-checksums instance-of-interest integrated interval limit loop-call-count-limit loop-filter-threshold loops mark-up mark-up-list memory-level memory-operation-type mix mkl-user-mode model-baseline-gpu model-children model-extended-math model-system-calls module-filter module-filter-mode mpi-rank mrte-mode ndim-depth-limit option-file overlap-taxes pack profile-gpu profile-intel-perf-libs profile-jit profile-python profile-stripped-binaries project-dir quiet recalculate-time record-mem-allocations record-stack-frame reduce-lock-contention reduce-lock-overhead reduce-site-overhead reduce-task-overhead refinalize-survey remove report-output report-template result-dir resume-after return-app-exitcode search-dir search-n-dim select set-dependency set-parallel set-parameter show-all-columns show-all-rows show-functions show-loops show-not-executed show-report small-node-filter sort-asc sort-desc spill-analysis stack-access-granularity stack-stitching stack-unwind-limit stacks stackwalk-mode start-paused static-instruction-mix strategy support-multi-isa-binaries target-device target-gpu target-pid target-process target-system threading-model threads top-down trace-mode trace-mpi track-memory-objects track-stack-accesses track-stack-variables trip-counts verbose with-stack
Offload Modeling Command Line Reference x
run_oa.py Options collect.py Options analyze.py Options
Troubleshooting x
Error Message: Application Sets Its Own Handler for Signal Error Message: Cannot Collect GPU Hardware Metrics for the Selected GPU Adapter Error Message: Memory Model Cache Hierarchy Incompatible Error Message: No Annotations Found Error Message: No Data Is Collected Error Message: Stack Size Is Too Small Error Message: Undefined Linker References to dlopen or dlsym Problem: Broken Call Tree Problem: Code Region is not Marked Up Problem: Debug Information Not Available Problem: No Data Problem: Source Not Available Problem: Stack in the Top-Down Tree Window Is Incorrect Problem: Survey Tool does not Display Survey Report Problem: Unexpected C/C++ Compilation Errors After Adding Annotations Problem: Unexpected Unmatched Annotations in the Dependencies Report Warning: Analysis of Debug Build Warning: Analysis of Release Build
Reference x
Data Reference Dependencies Problem and Message Types Recommendation Reference User Interface Reference
Data Reference x
CPU Metrics Accelerator Metrics
Dependencies Problem and Message Types x
Dangling Lock Data Communication Data Communication, Child Task Inconsistent Lock Use Lock Hierarchy Violation Memory Reuse Memory Reuse, Child Task Memory Watch Missing End Site Missing End Task Missing Start Site Missing Start Task No Tasks in Parallel Site One Task Instance in Parallel Site Orphaned Task Parallel Site Information Thread Information Unhandled Application Exception
Recommendation Reference x
Vectorization Recommendations for C++ Vectorization Recommendations for Fortran
User Interface Reference x
Dialog Box: Corresponding Command Line Dialog Box: Create a Project Dialog Box: Create a Result Snapshot Dialog Box: Options - Assembly Editor Tab Dialog Box: Options - General Dialog Box: Options - Result Location Dialog Box: Project Properties - Analysis Target Dialog Box: Project Properties - Binary/Symbol Search Dialog Box: Project Properties - Source Search Pane: Advanced View Pane: Analysis Workflow Pane: Roofline Chart Pane: GPU Roofline Chart Project Navigator Pane Toolbar: Intel Advisor Annotation Report Window: Dependencies Source Window: GPU Roofline Regions Window: GPU Roofline Insights Summary Window: Memory Access Patterns Source Window: Offload Modeling Summary Window: Offload Modeling Report - Accelerated Regions Window: Perspective Selector Window: Refinement Reports Window: Suitability Report Window: Suitability Source Window: Survey Report Window: Survey Source Window: Threading Summary Window: Vectorization Summary
Window: Refinement Reports x
Tab: Dependencies Report Tab: Memory Access Patterns Report
Appendix x
Data Sharing Problems Notational Conventions Key Concepts Related Information
Data Sharing Problems x
Data Sharing Problem Types Problem Solving Strategies
Data Sharing Problem Types x
Incidental Sharing Independent Updates
Problem Solving Strategies x
Eliminate Incidental Sharing Synchronize Independent Updates Difficult Problems: Choosing a Different Set of Tasks Fix Problems in Code Used by Multiple Parallel Sites Memory That is Accessed Through a Pointer
Eliminate Incidental Sharing x
Examine the Task's Static and Dynamic Extent Verify Whether Incidental Sharing Exists Create the Private Memory Location Pointer Dereferences
Synchronize Independent Updates x
Synchronization Explicit Locking Assign Locks to Transactions Pitfalls from Using Synchronization
Key Concepts x
Glossary Parallelism
Parallelism x
Parallel Processing Terminology Add Parallelism Common Issues When Adding Parallelism Parallel Programming Implementations
Intel® Advisor User Guide

Design and Optimization Methodology

Intel® Advisor helps you to design and optimize high-performing Fortran, C, C++, SYCL, OpenMP*, and OpenCL™ code to realize full performance potential on modern computer architecture. You can measure your application performance, collect required data, and look at your code from different perspectives depending on your goal to dig deeper and get hints for optimization.

Visualize Performance Bottlenecks with Roofline Chart

When optimizing your C, C++, SYCL, or Fortran application, it is useful to know application's current and potential performance in relation to hardware-imposed limitations like memory bandwidth and compute capacity of a target platform that it runs on - a CPU or a GPU.

Roofline model of the Intel Advisor visualizes actual performance against hardware-imposed performance ceilings and helps you determine the main limiting factor (memory bandwidth or compute capacity) to provide an ideal road map of potential optimization steps. This analysis highlights loops that have the most headroom for improvement, which allows you to focus on areas that deliver the biggest performance payoff.

To generate a Roofline report, the Intel Advisor:

  • Collects loop/function (for CPU) or OpenCL™ kernels (for GPU) timings and memory data.
  • Measures the hardware limitations and collects floating-point and integer operations data.

The Roofline chart plots an application achieved performance and arithmetic intensity against the hardware maximum achievable performance:

  • Arithmetic intensity (x axis) - measured in number of floating-point operations (FLOPs) and/or integer operations (INTOPs) per byte, based on the loop/function algorithm, transferred between CPU/VPU/GPU and memory.
  • Performance (y axis) - measured in billions of floating-point operations per second (GFLOPS) and/or billions of integer operations per second (GINTOPS).

With the data collected, the Intel Advisor plots the Roofline chart:

  • Execution time of each loop/function/kernel is reflected in the size and color of each dot. The dots on the chart correspond to OpenCL kernels for GPU Roofline, while for the CPU Roofline, they correspond to individual loops/functions.
  • Memory bandwidth limitations are plotted as diagonal lines.
  • Compute capacity limitations are plotted as horizontal lines.

For details on how to get the Roofline report and read the results, see CPU / Memory Roofline Insights Perspective or GPU Roofline Insights Perspective.

Model Offloading to Accelerator

When designing your application to offload to an accelerator, you might first want to:

  • Estimate the offload benefit and overhead for each loop/function in your original C++ or Fortran code to make better decisions on which parts of code to offload

  • Check performance gain for a SYCL , OpenCL™, or OpenMP* target application if you offload it to a different accelerator

Offload Modeling perspective of the Intel® Advisor can identify high-impact portions of a code that are profitable to offload to a target platform (for example, to a GPU) as well as the code regions that are not advantageous to offload. It can also predict the code performance if run on the target platform and lets you experiment with accelerator configuration parameters.

Offload Modeling takes measured baseline metrics and application characteristics as an input and applies an analytical model to estimate execution time and characteristics on a target platform.

Offload Modeling is based on three models:

  • Compute throughput model counts arithmetic operations in a region on a baseline platform and estimates the execution time on a target platform required to achieve the same mix of arithmetic operations, considering it as bound by compute engines only.
  • Memory sub-system throughput model traces memory accesses inside a region on a baseline platform and estimates the execution time on a target platform needed to transfer the same amount of memory. Memory traffic is measured using a cache simulator that reflects the target platform's memory configuration.
  • Offload data transfer analysis measures memory accesses that are read from or written to a region and will need to be sent over a PCIe* if the region is offloaded to a target platform.

For details on how to run the Offload Modeling perspective and read the reports, see Offload Modeling Perspective.

Check Vectorization Efficiency

Modern Intel® processors have extensions that support SIMD (single instruction, multiple data) parallelism with Intel® Streaming SIMD Extensions (Intel® SSE), Intel® Advanced Vector Extensions 2 (Intel® AVX2), Intel® Advanced Vector Extensions 512 (Intel® AVX-512) . To take advantage of SIMD instructions with the expanded vector width and achieve higher performance, applications need to be vectorized.

You can rely on your desired compiler - Intel® Fortran Compiler Classic, Intel® oneAPI DPC++/C++ Compiler, GNU Compiler Collection (GCC)* - to auto-vectorize some loops, but serial constraints of programming languages limit the compiler's ability to vectorize some loops. The need arose for explicit vector programming methods to extend vectorization capability for supporting reductions, vectorizing:

  • Outer loops
  • Loops with user-defined functions
  • Loops that the compiler assumes to have data dependencies

To improve the performance of CPU-bound applications on modern processors with vector processing units, you might use explicit vector programming apply structural changes for thread-level parallelism and SIMD-level parallelism.

Use the Vectorization and Code Insights perspective of the Intel Advisor to analyze your application run time behavior and identify application parts that will benefit most from vectorization. Vectorization and Code Insights perspective helps you to achieve the best performance using vectorization and identify:

  • Where vectorization, or parallelization with threads, will pay off the most
  • If vectorized loops are providing benefit, and if not, why not
  • Un-vectorized loops and why they are not vectorized
  • Performance problems in general

For details on how to run the perspective and read the reports, see Vectorization and Code Insights Perspective.

Prototype Threading Designs

The best performance improvements from adding parallel execution (parallelism) to a program occur when many cores are busy most of the time doing useful work. Achieving this requires a lot of analysis, knowledge, and testing.

Because your serial program was not designed to allow parallel execution, as you convert parts of it to use parallel execution, you may encounter unexpected errors that occur only during parallel execution. Instead of wasting effort on portions of the program that use almost no CPU time, you should focus on the hotspots, and the functions between the main entry point and each hotspot.

If you add parallel execution to a program without proper preparation, unpredictable crashes, program hangs, and wrong answers can result from incorrect parallel task interactions. For example, you may need to add synchronization to avoid incorrect parallel task interactions, but this must be done carefully because locking overhead and serial synchronization can reduce the benefits of the parallel execution.

Threading perspective of the Intel Advisor helps you quickly prototype multiple threading options, project scaling on larger systems, optimize faster, and implement with confidence.

  • Identify issues and fix them before implementing parallelism
  • Add threading to C, C++, and Fortran code
  • Prototype the performance impact of different threaded designs and project scaling on systems with larger core counts without disrupting development or implementation
  • Find and eliminate data-sharing issues during design (when they're less expensive to fix)

The high-level parallel frameworks available for each programming language include:

Language

Available High-Level Parallel Frameworks

C

OpenMP

C++

Intel® oneAPI Threading Building Blocks (oneTBB)

OpenMP

Fortran

OpenMP

NOTE:
C# and .NET support is deprecated starting Intel® Advisor 2021.1.

For details on how to run the perspective and read the reports, see Threading Perspective.

Using Amdahl's Law and Measuring the Program

There are two rules of optimization that apply to parallel programming:

  • Focus on the part of the program that uses the most time.

  • Do not guess, measure.

Amdahl's Law

In the context of parallel programming, Gene Amdahl formalized a rule called Amdahl's Law, which states that the speed-up that is possible from parallelizing one part of a program is limited by the portion of the program that still runs serially.

The consequence may be surprising: parallelizing the part of your program where it spends 80% of its time cannot speed it up by more than a factor of five, no matter how many cores you run it on.

Therefore, to get maximum benefit from parallelizing your program, you could add parallelism to all parts of your program as suggested by Amdahl's Law. However, it is more practical to find where it spends most of its time and focus on areas that can provide the most benefit.

Do Not Guess - Measure

This leads to another rule of optimization: Do Not guess - Measure. Programmers' intuitions about where their programs are spending time are notoriously inaccurate. Intel® Advisor includes a Survey tool you can use to profile your running program and measure where it spends it time.

After you add Intel® Advisor annotations to your program to mark the proposed parallel code regions, run the Suitability tool to predict the approximate maximum performance gain for the program and the annotated sites. These estimated performance gain values are based on a model of parallel execution that reflects the impact of Amdahl's law.

Parent topic: Introduction
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