How Can Stack Sampling Solve Function Exit Time Measurement Challenges in Embedded Systems Without __gnu_mcount_nc?

Function Exit Measurement with __gnu_mcount_nc

Understanding the Problem

Performance profiling on embedded platforms can be challenging, especially when an implementation of the __gnu_mcount_nc function is unavailable. This function is used to capture the entry time of functions when the -pg flag is enabled during compilation. However, it does not record exit times, making it difficult to determine the amount of time spent within a function.

Alternative Approach

The traditional profiling tools used by compilers do not rely on __gnu_mcount_nc for timing function entry or exit. Instead, they use a technique called stack sampling. This approach captures snapshots of the call stack at regular intervals and assigns a fraction of the total runtime to each function based on the frequency of its appearance in the sampled call stacks.

Stack sampling has several advantages over an approach that relies solely on __gnu_mcount_nc:

• It eliminates the need for modifying function entry and exit points.
• It is less sensitive to the presence of recursion and library calls made without the -pg flag.
• It can be implemented more efficiently in embedded environments with limited thread support.

Stack Sampling Implementation

To implement stack sampling, the following steps can be taken:

1. Choose a sampling rate (e.g., 10,000 samples per second).
2. Record the current call stack at each sample point.
3. Assign a fraction of the total runtime to each function based on the frequency of its appearance in the sampled call stacks.

Additional Insights

Beyond capturing function exit times, stack sampling also provides valuable insights into the program's overall behavior:

• Call graphs: Stack samples can be used to construct call graphs that show which functions are called from which callers.
• Hot paths: The hot path is the sequence of functions that are called most frequently. Stack sampling can identify these paths and help optimize them.
• Hot spots: The hot spots are the specific lines of code that take the most time. Stack sampling can reveal these hot spots and guide developers to areas for potential optimization.

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