Broken Call Tree
Symptoms
After executing the
, you see one of the following:
Offload Modeling
perspective, in the
Accelerated Regions
tab- A code region is duplicated.
- A code region is located at a wrong place.
- A code region has incorrect number of trip counts reported in any column of the Trip Counts column group.
- A code region with your code has aSystem Modulediagnostics messageandCannot be modeled: System Modulereason for not offloading.
Any of these symptoms mean that the
detected the application call tree incorrectly during Survey.
Intel® Advisor
Details
A broken call tree often happens if you use a program model with Data Parallel C++ (DPC++) or
Intel® oneAPI Threading Building Blocks
. These program models run code in many threads using a complicated scheduler, and the
Intel Advisor
sometimes cannot correctly detect their call stacks. As a result, some code instances might have no metrics or incorrect metrics in a report and a call tree is broken.
Cause
This can happen due to the following reasons:
- Call stacks were detected incorrectly.
- A heavy optimization was used.
- Debug information has issues.
Possible Solution
This is not an issue if all hotspots and code you are interested in are outside of the broken part of the call tree. You can ignore it in this case.
To fix a broken call tree, do the following:
- Make sure you compiled binary with-goption.You can recompile it with the-debug inline-debug-infooption to get enhanced debug information.
- Recompile the binary with a lower optimization level: use-O2.
- If you collect performance metrics withWhen running the Survey analysis, try the following:advisorCLI:
- Remove--stackwalk-mode=onlineoption.
- Add--no-stack-stitchingoption.
- Offload only specific code regions if their estimated execution time on a target device is greater than or equal to the original execution time. Rerun the performance modeling with--select-loopsto specify loops of interest and--enforce-offloadsto make sure all of them are offloaded. For example:advisor-python <APM>/analyze.py <project-dir> --select-loops=[<file-name1>:<line-number1>,<file-name1>:<line-number2>,<file-name2>:<line-number3>] –-enforce-offloadsReplace<APM>with$APMon Linux* OS or%APM%on Windows* OS.For details, see Enforce Offloading for Specific Loops
- If you model a multithreaded code that runs with a complicated scheduler, you might see a code region with suspiciously low trip counts and multiple instances of the same region loop present in the scheduler. This means that theOffload Modelingcould not correctly detect the call stacks. Use the--enable-batchingoption to artificially increase the number of trip counts by using total number of executions instead of average number trip counts.