Intel® Advisor User Guide

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Date 10/31/2024
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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
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
Test Topic for Embedded Help 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 Model Performance of MPI Application Configure Performance Modeling for MPI Application Change the Number of MPI Processes per GPU Tile Ignore MPI Time View Results See Also 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-kernel-matching-mode 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

Model MPI Application Performance on GPU

You can model your MPI application performance on a target graphics processing unit (GPU) device to determine whether you can get a performance speedup from offloading the application to the GPU.

Offload Modeling perspective of the Intel® Advisor includes the following stages:

  1. Collecting the baseline performance data on a host device with the Survey, Characterization (Trip Counts, FLOP), and/or Dependencies analyses. You can collect data for one or more MPI ranks, where each rank corresponds to an MPI process.
  2. Modeling application performance on a target device with the Performance Modeling analysis. You can model performance only for one rank at a time. You can run performance modeling several times for different ranks analyzed to examine the potential performance difference between them, but the topic does not cover this case.

Model Performance of MPI Application

Prerequisite: Set up environment variables to enable Intel Advisor CLI.

In the commands below:

  • Data is collected remotely to a shared directory.
  • The analyses are performed for an application running in four processes.
  • Path to an application executable is ./mpi_sample.

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

  • Path to an Intel Advisor project directory is ./advi_results.
  • Performance is modeled for the default Intel® Arc™ graphics code-named Alchemist (xehpg_512xve configuration).

This example shows how to run Offload Modeling to model performance for the rank 1 of the MPI application. It uses the gtool option of the Intel® MPI Library to collect performance data on a baseline CPU. For other collection options, see Analyze MPI Applications.

  1. Optional, but recommended: Generate preconfigured command lines for your application using the --dry-run option. For example, generate the command lines using Intel Advisor CLI: 
    advisor --collect=offload --dry-run --project-dir=./advi_results -- ./mpi_sample

    After you run it, a list of analysis commands to run the Offload Modeling for the specified accuracy level is printed to the terminal/command prompt. For the command above, the commands are printed for the default medium accuracy: 

    advisor --collect=survey --auto-finalize --static-instruction-mix --project-dir=./advi_results -- ./mpi_sample
 advisor --collect=tripcounts --flop --stacks --auto-finalize --enable-cache-simulation --data-transfer=light --target-device=xehpg_512xve --project-dir=./advi_results -- ./mpi_sample
 advisor --collect=projection --no-assume-dependencies --config=xehpg_512xve --project-dir=./advi_results

    You need to modify the printed commands for the MPI syntax to use an MPI launcher. See Analyze MPI Applications for syntax details.

  2. Collect survey data for the rank 1 into the shared ./advi_results project directory. 
    mpirun -gtool "advisor --collect=survey --auto-finalize --static-instruction-mix --project-dir=./advi_results:1" -n 4 ./mpi_sample
  3. Collect trip counts and FLOP data for the rank 1. 
    mpirun -gtool "advisor --collect=tripcounts --flop --stacks --auto-finalize --enable-cache-simulation --data-transfer=light --target-device=xehpg_512xve --project-dir=./advi_results:1" -n 4 ./mpi_sample
  4. If you did not collect data to a shared location and need to copy the data to the local system to view the results, do it now.
  5. Model performance for the analyzed rank 1 of the MPI application that you ran the analyses for. 
    advisor --collect=projection --config=xehpg_512xve --mpi-rank=1 --project-dir=./advi_results

    You can only model performance for one rank at a time. The results are generated for the rank specified in a corresponding ./advi_results/rank.1 directory.

  6. If you did not collect data to a shared location and need to copy the data to the local system to view the results, do it now.
  7. On a local system, view the results with your preferred method.

Configure Performance Modeling for MPI Application

By default, Offload Modeling is optimized to model performance for a single-rank MPI application. For multi-rank MPI applications, you can apply additional configuration and settings to adjust the performance model for a specific hardware or application. You can adjust the number of MPI ranks to run per GPU tile and/or exclude MPI time from the report.

In the commands below:

  • Data is collected remotely to a shared directory.
  • The analyses are performed for an application running in four processes.
  • Path to an application executable is ./mpi_sample.

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

  • Path to an Intel Advisor project directory is ./advi_results.
  • Performance is modeled for Intel® Arc™ graphics code-named Alchemist (xehpg_512xve configuration).

Change the Number of MPI Processes per GPU Tile

Prerequisite: Set up environment variables to enable Intel Advisor CLI.

NOTE:
Families of Intel® Xe graphics products starting with Intel® Arc™ Alchemist (formerly DG2) and newer generations feature GPU architecture terminology that shifts from legacy terms. For more information on the terminology changes and to understand their mapping with legacy content, see GPU Architecture Terminology for Intel® Xe Graphics.

By default, Offload Modeling assumes that one MPI process, or rank, is mapped to one GPU tile. You can configure the performance model to adjust the number of MPI ranks to run per GPU tile to match your target device configuration.

To do this, you need to set the number of tiles per MPI process by scaling the Tiles_per_process target device parameter in a command line or a TOML configuration file. If you want to model performance for the Intel® Arc™ graphics code-named Alchemist, which is XeHPG 256 or XeHPG 512 configuration in Offload Modeling targets, use the Stack_per_process parameter. The parameter sets a fraction of a GPU tile that runs a single MPI process. For example, if you want to offload your MPI application with 8 processes to a target GPU device with 4 tiles, you need to adjust the performance model to run 2 MPI processes per tile, or to use 0.5 tile per process.

The number of tiles per process you set automatically adjusts:

  • the number of execution units (EU)
  • SLM, L1, L3 sizes and bandwidth
  • memory bandwidth
  • PCIe* bandwidth

The parameter accepts values from 0.01 to 12.0. Consider the following value examples:

Tiles_per_process/Stack_per_process Value

Number of MPI Ranks per Tile

1.0 (default)

1

12.0 (maximum)

1/12

0.25

4

0.125

8

To run the Offload Modeling with a custom tile-per-process parameter, you need to scale the parameter during the analysis. This change is one time and is applied only to the analysis you run it with. The commands below use the Tiles_per_process parameter for scaling. Replace it with Stack_per_process if needed.

  1. Generate pre-configured command lines for your application with the --set-parameter option to change the number of tiles per process. Use the --dry-run option of the collect.py script to generate commands to adjust cache configuration to the scaled parameter.

    For example, to generate commands for the ./advi_results project and model performance with 0.25 tiles per process, which corresponds to four MPI ranks per tile: 

    advisor-python $APM/collect.py ./advi_results --set-parameter scale.Tiles_per_process=0.25 --dry-run -- ./mpi_sample

    After you run it, a list of analysis commands to run the Offload Modeling with the specified accuracy level is printed to the terminal/command prompt similar to the following: 

    advisor --collect=survey --project-dir=./advi_results --static-instruction-mix -- ./mpi_sample
advisor --collect=tripcounts --project-dir=./advi_results --flop --ignore-checksums --data-transfer=medium --stacks --profile-jit --cache-sources --enable-cache-simulation --cache-config=8:64w:4k/1:192w:768k/1:4w:2m -- ./mpi_sample
python $APM/collect.py ./advi_results  -m generic
advisor --collect=dependencies --project-dir=./advi_results --filter-reductions --loop-call-count-limit=16 --ignore-checksums -- ./mpi_sample
  2. Copy the generated commands to your preferred text editor and modify them for the MPI-specific syntax. You need to add the following:
    • MPI launcher name and (optionally) gtool option for Intel® MPI Library
    • Number of MPI processes to launch
    • If you use gtool: MPI ranks to analyze

    See Analyze MPI Applications for syntax details.

    NOTE:
    You can skip the mark-up and Dependencies analysis step (the last two commands) because they add high overhead. See Check How Assumed Dependencies Affect Modeling for details.
  3. Run the modified commands for Survey, Trip Counts, and (optionally) Dependensies analyses one by one. For example, to run Survey and Trip Counts for the rank 1: 
    mpirun -gtool "advisor --collect=survey --static-instruction-mix -- ./mpi_sample --project-dir=./advi_results:1" -n 4 ./mpi_sample
    mpirun  -gtool "advisor --collect=tripcounts --flop --ignore-checksums --data-transfer=medium --stacks --profile-jit --cache-sources --enable-cache-simulation --cache-config=8:64w:4k/1:192w:768k/1:4w:2m --project-dir=./advi_results:1" -n 4 ./mpi_sample
  4. Run the performance modeling with the number of tiles per MPI processes specified using the --set-parameters option. For example, to model performance for the rank 1: 
    advisor --collect=projection --project-dir=./advi_results --set-parameter scale.Tiles_per_process=0.25 --mpi-rank=1
    NOTE:
    Make sure to specify the same value for the --set-parameter scale.Tiles_per_process as for the dry-run step.

    The result is generated for the rank specified in a corresponding ./advi_results/rank.1 directory. You can transfer them to the development system, if needed, and view the results.

When you open the result in the Intel Advisor GUI or an interactive HTML report, you should see the tiles per process or stack per process parameter in the Modeling Parameters pane with the value you set. The parameter is in a read-only format. Notice that tiles per process or stack per process parameter shows the value per process, while other parameters in the pane show the value per device.

Ignore MPI Time

Prerequisite: Set up environment variables to enable Intel Advisor CLI.

For multi-rank MPI workloads, time spent in MPI runtime can differ from rank to rank, which may cause significant performance imbalance. Because of this, the whole application time and Offload Modeling results may be different from rank to rank. If MPI time is large and differs between ranks, and the MPI code does not include many computations, you can exclude time spent in MPI routines from the analysis so that it does not affect modeling results.

  1. Collect Survey, Trip Counts, and (optionally) Dependencies data for your application. See Analyze MPI Applications for details.
  2. Run the performance modeling with time in MPI calls ignored using the --ignore=MPI option. 
    advisor --collect=projection --project-dir=./advi_results --ignore=MPI --mpi-rank=1

    The results are generated in a ./advi_results/rank.1 directory. You can transfer them to the development system and view the results.

In the report generated, all per-application performance modeling metrics are calculated based on application self-time with time spent in MPI calls excluded from the analysis. This should improve modeling across ranks.

NOTE:
This option affects only metrics for the whole program in the Summary tab. Metrics for individual regions are not recalculated.

View Results

Intel Advisor saves collection results into subdirectories for each rank analyzed under the project directory specified with --project-dir. The modeling results are available only for the ranks that you ran the Performance Modeling for, for example, as specified with the --mpi-rank option.

To view the performance or dependency results collected for a specific rank, you can do one of the following.

View Results in GUI

From the Intel Advisor GUI, open a result project file *.advixeproj that resides in the <project-dir> /rank.<n> directory.

You can also open the GUI from command line: 

advisor-gui ./advi_results/rank.1
NOTE:
If you used --no-auto-finalize when collecting data, make sure to set paths to application binaries and sources before viewing the result so that Intel Advisor can finalize it properly.

View Results in Command Line

After you run the Performance Modeling analysis, the summary result of the modeling is printed to a terminal/command prompt. Examine the data to learn the estimated speedup and top five offloaded regions.

View Results in an Interactive HTML Report

Open an interactive advisor-report HTML report generated in the respective rank directory at <project-dir>/rank.<n>/e<NNN>/report and a set of CSV reports in the respective rank directory at <project-dir>/rank.<n>/p<NNN>/data.0.

Parent topic: Analyze MPI Applications

See Also

Analyze MPI Applications With Intel® Advisor, you can analyze parallel tasks running on a cluster to examine performance of your MPI application.
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