Label: performance profiling
This tag covers practical approaches to performance profiling in real-world software systems.
The focus is not limited to finding the function with the highest CPU time. Effective profiling also requires understanding why that function became expensive, what workload triggered it, how threads interact, where synchronization occurs, and whether the observed cost comes from computation, memory management, logging, networking, or file I/O.
Articles in this section examine profiler output, call stacks, hotspots, thread activity, system calls, allocation patterns, and asynchronous processing. They also show how to trace an expensive library function or backend operation back to the specific source-code path that created the workload.
The main goal is to turn raw profiling data into actionable engineering decisions. Instead of applying broad fixes such as disabling diagnostics or reducing functionality, the profiling process should identify the small number of operations that actually dominate execution time.
You will find practical investigations, performance measurements, optimization techniques, and examples from C and C++ applications, including multithreaded services and event-driven systems.
Log Source Profiling: Which Log Statement Is Responsible?
