Profiling

This document demonstrates practical profiling and optimization techniques using a real-world email reminder script. It covers measuring execution time with the time command, using pprofile and kcachegrind for performance analysis, identifying expensive operations in loops, and optimizing code by replacing repeated file operations with dictionary-based caching.

This document explores principles of code efficiency, including when to optimize, cost-benefit analysis of performance improvements, profiling tools and strategies for reducing expensive operations through caching and proper data structures.

This document demonstrates practical memory leak diagnosis and resolution through real-world examples using Python memory profilers. It covers identifying memory consumption patterns in applications, analyzing memory usage with tools like memory_profiler, and fixing code that unnecessarily retains data in memory causing resource exhaustion.

This document examines memory leaks in applications, covering how unreleased memory chunks cause system performance issues. It explores memory management in C/C++ versus garbage-collected languages, profiling tools like Valgrind for detecting leaks, and strategies for identifying and resolving memory consumption problems before they exhaust system resources.