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 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 Workflow Prerequisites Run the GPU-to-GPU Performance Modeling Method 1. Use Collection Preset Method 2. Use per-Analysis Collection Method 3. Use Python* Scripts View the Results See Also
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

Run GPU-to-GPU Performance Modeling from Command Line

With Intel® Advisor, you can model performance of SYCL, OpenCL™, or OpenMP* target application running on a graphics processing unit (GPU) for a different GPU device without its CPU version. For this, run the GPU-to-GPU modeling workflow of the Offload Modeling perspective.

The GPU-to-GPU modeling analyzes only GPU compute kernels and ignores the application parts executed on a CPU. As a result, there are several changes in the modeling flow:

  • Compute kernels characteristics are collected with the Intel Advisor GPU profiling capabilities.
  • High-overhead features, such as call stack handling, cache simulation, data transfer simulation, dependencies analysis, are disabled.
  • Instead of CPU-to-GPU data transfer simulation, memory objects transferred between host and device memory are traced.

Workflow

The GPU-to-GPU performance modeling workflow is similar to the CPU-to-GPU modeling and includes the following steps:

  1. Survey analysis measures execution time, cache, and GTI traffic using hardware counters for GPU-enabled kernels running on an Intel® Graphics.
  2. Characterization analysis measures the number of compute operations counting different GPU instructions separately for kernels running on a Intel Graphics. For example, it implements separate counters for hardware-implemented 32-bit math functions, such as SQRT, EXP, DIV.
  3. Performance Modeling analysis models performance of all kernels on a target GPU device, whether they are profitable or not.
NOTE:
For correct memory object tracing, GPU kernels should run with the oneAPI Level Zero back end.

Prerequisites

  1. Configure your system to analyze GPU kernels.
  2. Set Intel Advisor environment variables with an automated script to enable Intel Advisor command line interface.

Run the GPU-to-GPU Performance Modeling

To run the GPU-to-GPU performance modeling from command line, you can use one of the following methods:

  • Method 1. Run a collection preset using Intel Advisor command line interface (CLI) to execute multiple analyses with a single command and control modeling accuracy.
  • Method 2. Run analyses separately using Intel Advisor CLI if you need more advanced customization for each analysis.
  • Method 3. Run Python* scripts if you need more customization for collection and modeling steps.

You can also run the GPU-to-GPU Offload Modeling from Intel Advisor graphical user interface (GUI). See Run Offload Modeling Perspective from GUI.

After you run the Offload Modeling with any method above, you can view the results in Intel Advisor graphical user interface (GUI), command line interface (CLI), or an interactive HTML report. For example, the interactive HTML report is similar to the following:

TIP:
If you want to analyze an MPI application, you can generate pre-configured command lines, copy them, and run one by one. For details, see Generate Pre-configured Command Lines.

Method 1. Use Collection Preset

To run the collection preset for the GPU-to-GPU modeling, use the --gpu option with the --collect=offload action. When you run the collection, it sequentially runs data collection on a GPU and performance modeling steps. The specific analyses and options depend on the accuracy level you specify for the collection.

Note: In the commands below, make sure to replace the myApplication with your application executable path and name before executing a command. If your application requires additional command line options, add them after the executable name.

For example, to run the GPU-to-GPU modeling with the default (medium) accuracy level:

  • On Linux* OS: 
    advisor --collect=offload --gpu --project-dir=./advi_results -- ./myApplication
  • On Windows* OS: 
    advisor --collect=offload --gpu --project-dir=.\advi_results -- myApplication.exe

The collection progress and commands for each analysis executed will be printed to a terminal or a command prompt. When the collection is finished, you will see the result summary.

You can also specify a different accuracy level to change analyses to run and their options. Available accuracy levels are low, medium (default), and high.

For example, run the high accuracy level: 

advisor --collect=offload --accuracy=high --gpu --project-dir=./advi_results -- ./myApplication

If you want to see the commands that are executed at each accuracy level, you can run the collection with the --dry-run and --gpu options. The commands will be printed to a terminal or a command prompt.

For details about each accuracy level, see Offload Modeling Accuracy Levels in Command Line.

Method 2. Use per-Analysis Collection

You can collect data and model performance for your application by running each Offload Modeling analysis in a separate command for more advanced customization. To enable the GPU-to-GPU modeling, use the --profile-gpu option for each analysis you run.

Consider the following workflow example. Using this example, you can run the Survey, Trip Counts, and FLOP analyses to profile an application running on a GPU and the Performance Modeling to model its performance on Intel® Iris® Xe MAX graphics (gen12_dg1 configuration).

Note: In the commands below, make sure to replace the myApplication with your application executable path and name before executing a command. If your application requires additional command line options, add them after the executable name.

On Linux OS:

  1. Run the Survey analysis. 
    advisor --collect=survey --profile-gpu --project-dir=./advi_results -- ./myApplication
  2. Run the Trip Counts and FLOP analyses with data transfer estimation. 
    advisor --collect=tripcounts --profile-gpu --flop --target-device=gen12_dg1 --data-transfer=light --project-dir=./advi_results -- ./myApplication
  3. Run the Performance Modeling analysis. 
    advisor --collect=projection --profile-gpu --config=gen12_dg1 --project-dir=./advi_results

    You will see the result summary printed to the command prompt.

On Windows OS:

  1. Run the Survey analysis. 
    advisor --collect=survey --project-dir=.\advi_results -- myApplication.exe
  2. Run the Trip Counts and FLOP analyses with data transfer. 
    advisor --collect=tripcounts --profile-gpu --flop --stacks --target-device=gen12_dg1 --data-transfer=light --project-dir=.\advi_results -- myApplication.exe
  3. Run the Performance Modeling analysis. 
    advisor --collect=projection --profile-gpu --config=gen12_dg1 --project-dir=.\advi_results

    You will see the result summary printed to the command prompt.

Method 3. Use Python* Scripts

Intel Advisor has three scripts that use the Intel Advisor Python* API to run the Offload Modeling - run_oa.py, collect.py, analyze.py. You can run the scripts with the advisor-python command line interface of the Intel Advisor or with your local Python 3.6 or 3.7.

To enable the GPU-to-GPU modeling, use the --gpu option for each script you run.

NOTE:
The scripts do not support MPI applications. To analyze an MPI application, use the per-analysis collection with the Intel Advisor CLI.

You can run the Offload Modeling using different combinations of the scripts and/or the Intel Advisor CLI. For example:

  • Run run_oa.py to profile application and model its performance.
  • Run the collect.py to profile application and analyze.py to model its performance. Re-run analyze.py to remodel with a different configuration.
  • Run the Intel Advisor CLI to collect performance data and analyze.py to model performance. Re-run analyze.py to remodel with a different configuration.
  • Run run_oa.py to collect data and model performance for the first time and run analyze.py to remodel with a different configuration.

Consider the following examples of some typical scenarios with Python scripts.

Note: In the commands below, make sure to replace the myApplication with your application executable path and name before executing a command. If your application requires additional command line options, add them after the executable name.

Example 1. Run the run_oa.py script to profile an application on GPU and model its performance for Intel® Iris® Xe MAX graphics (gen12_dg1 configuration).

  • On Linux OS: 
    advisor-python $APM/run_oa.py ./advi_results --gpu --config=gen12_dg1 -- ./myApplication
  • On Windows OS: 
    advisor-python %APM%\run_oa.py .\advi_results --gpu --config=gen12_dg1 -- myApplication.exe

You will see the result summary printed to the command prompt.

Example 2. Run the collect.py to profile an application on GPU and run the analyze.py to model its performance for Intel® Iris® Xe MAX graphics (gen12_dg1 configuration).

  • On Linux OS:
    1. Collect performance data. 
      advisor-python $APM/collect.py ./advi_results --gpu --config=gen12_dg1 -- ./myApplication
    2. Model application performance. 
      advisor-python $APM/analyze.py ./advi --gpu --config=gen12_dg1

      You will see the result summary printed to the command prompt.

  • On Windows OS:
    1. Collect performance data. 
      advisor-python %APM%\collect.py .\advi_results --collect=basic --gpu --config=gen12_dg1 -- myApplication.exe
    2. Model application performance. 
      advisor-python %APM%\analyze.py .\advi_results --gpu --config=gen12_dg1

      You will see the result summary printed to the command prompt.

View the Results

Once the Intel Advisor finishes the analyses, the result is available in the following formats:

  • Review the result summary and a result file location printed to a command prompt or a terminal.
  • Review the project result in the Intel Advisor GUI generated to the project directory.
  • Review HTML reports generated to the <project-dir>/e<NNN>/report directory. View the detailed information about HTML reports in Work with Standalone HTML Reports.
  • Review a set of reports generated to the <project-dir>/e<NNN>/pp<NNN>/data.0 directory. The directory includes the main report in HTML format named as report.html and a set of CSV files with detailed metric tables.

For example, the result in the Intel Advisor GUI looks as follows:

To explore the interactive HTML report, you can download precollected Offload Modeling reports and examine the results and structure.

See Explore Offload Modeling Results for details about available result formats and Explore Performance Gain from GPU-to-GPU Modeling for details about the GPU-to-GPU modeling result.

Parent topic: Run Offload Modeling Perspective from Command Line

See Also

Offload Modeling Command Line Reference This reference section describes the command line options available for each of the Python* scripts that you can use to run the Offload Modeling perspective.
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