User Guide

Contents

Problem: Code Region is not Marked Up

Symptoms

A code region of interest is not analyzed and has
Outside of Marked Region
why-not-offloaded message in the Details pane of the
Accelerated Regions
tab after you execute the
Offload Modeling
perspective
.

Details

To limit the scope of collections, the
Intel® Advisor
selects loops that match certain criteria and marks them up for analysis. By default, the
Intel Advisor
performs a smart region selection using the
generic
markup.
If a code region does not satisfy the markup criteria, you should see the
Outside of Marked Region
why-not-offloaded message
or the
System Module
diagnostics message in the Details pane of the
Accelerated Regions
tab
.

Cause

Your code region does not satisfy one or more markup rules for a specified markup mode. If you use the default generic mark-up strategy, make sure your loop of interest satisfies the following rules:
  • It is not a system module or a system function.
  • It has instruction mixes.
  • It is executed.
  • Its execution time is not less than 0.02 seconds, which is a sampling interval of the
    Intel Advisor
    . For more information about execution time limitations, see Total Time is Too Small for Reliable Modeling.

Possible Solution

If a code region does not satisfy the generic markup rules, but you want to analyze it, do one of the following:
  • You can change the markup strategy by using a
    --markup=<markup_mode>
    option of
    analyze.py
    or
    --select markup=<markup-mode>
    for
    --collect=performance
    . The following parameters select only loops inside regions that are already parallel:
    • generic
      or
      gpu_generic
      (default) - Select loops executed on a GPU.
    • omp
      - Select loops only in OpenMP parallel regions.
    • dpcpp
      - Select loops only in Data Parallel C++ parallel regions.
    • daal
      - Select loops only in
      Intel® oneAPI Data Analytics Library
      parallel regions.
    • tbb
      - Select loops only in
      Intel® oneAPI Threading Building Blocks
      parallel regions.
    omp
    ,
    dpcpp
    , and
    generic
    /
    gpu_generic
    select loops in the project so you can run another collection or performance modeling without markup or loop selection options.
  • If your loops of interest are not marked up because they have no static instruction mixes or not executed, you can limit the analysis to these specific loops by using the
    --select-loops
    option with the
    analyze.py
    script. With this option, only the loops specified are analyzed. For example:
    advisor-python <APM>/analyze.py <project-dir> --select-loops=[<file-name1>:<line-number1>,<file-name1>:<line-number2>,<file-name2>:<line-number3>]
    Replace
    <APM>
    with
    $APM
    on Linux* OS or
    %APM%
    on Windows* OS.
    With
    --collect=performance
    , use
    --select
    option to select specific loops to analyze by source location, ID, or other criteria.

Product and Performance Information

1

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.