profilers

Profilers are software tools designed to measure the runtime behavior and resource consumption of a program. They help developers identify performance bottlenecks by collecting data on where time is spent, how memory is allocated, and how input/output is performed during execution. Profilers typically support different data collection methods, such as sampling (periodically recording the active execution context) and instrumentation (inserting code to record events).

Common categories include CPU profilers, which report time spent in functions or methods; memory profilers, which track allocations, deallocations, and garbage collection; and I/O or network profilers, which monitor file, disk, or network activity. Some profilers combine several capabilities. Many languages have dedicated profilers for C/C++, Java, or Python, and some general-purpose debuggers include profiling features.

Results are presented as summaries, call graphs, flame graphs, or charts showing percentile distributions. Flame graphs visualize stack traces with proportional width to time spent in each function, aiding quick identification of hot paths.

Profilers are used during development, testing, and occasionally in production for performance monitoring. However, profiling introduces overhead and can change timing and behavior, so results should be interpreted with this caveat. For accurate results, profiling should reflect typical workloads; multiple runs may be necessary.

Typical workflow: choose a profiler, run the program under profiling, collect data, analyze hotspots, optimize code, and re-profile to validate improvements. Limitations include overhead, path coverage, and multi-threading effects; some optimizations may shift bottlenecks rather than remove them.