How Can We Profile Function Exit Time in Embedded Systems with Limited Profiling Support?

Capturing Function Exit Time with __gnu_mcount_nc

Embedded platform profiling often encounters limited support, including unavailable implementations for performance analysis tools. Understanding how to profile function exit time despite only having access to entry information can be challenging.

GCC's -pg flag inserts hooks to __gnu_mcount_nc at the start of each function, providing entry timing data. However, without exit point hooks, it's difficult to determine the time spent within function bodies.

A common approach involves maintaining a shadow callstack and modifying return addresses to trigger exit hooks. This method, while effective, has limitations, particularly in multithreaded environments and with recursion.

Alternative Profiling Approach

Existing profiling tools like gprof don't collect exit timing directly. Instead, they rely on self-time estimation and caller-callee count information to approximate function costs. This approach has limitations in terms of accuracy and overhead.

Stack-Sampling

A more efficient and flexible approach is stack-sampling. Rather than counting PC samples, stack-sampling captures a snapshot of the call stack at random intervals. This allows for more precise estimation of function self-time without the overhead associated with PC-sampling.

Stack-sampling techniques can reveal valuable insights into not only function costs but also the underlying reasons for those costs. It highlights problem areas that may not be evident in call graphs or hot-spots.

Limitations of Visualization

While flame graphs and other visual representations can aid in profiling analysis, it's important to recognize their limitations. They may not clearly expose functions that contribute significantly to performance due to being called multiple times from different locations.

Key Points

• gprof does not profile exit timing using __gnu_mcount_nc.
• Stack-sampling provides a more robust alternative for estimating function self-time.
• Analysis of raw stack samples is valuable for identifying performance bottlenecks.
• Visualizations can be misleading, so it's crucial to focus on uncovering hidden issues.

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