Evaluating Multithreading in Classic C++, C++17, C++20 and C++23: Execution Policies, Compiler Efficiency, and Migration with Copilot

Abstract

This thesis evaluates the evolution and effectiveness of multithreading in the C++ pro- gramming language, specifically focusing on execution policies introduced in C++17, C++20, and C++23, compiler efficiency, and the role of AI-assisted coding through Git- Hub Copilot. The study compares traditional manual threading techniques against mod- ern parallel execution policies, analyzing their impact on the design, performance, and scalability of parallel algorithms. Through extensive performance benchmarking using profiling tools such as gprof, Linux perf, and Valgrind Massif, significant improvements in runtime and memory management are demonstrated when leveraging newer C++ stand- ards and execution policies. Compiler comparisons between GCC and Clang further reveal substantial differences in optimization capabilities, directly influencing multithreaded ap- plication performance. Additionally, the thesis assesses the readability, maintainability, and correctness of multithreaded code generated by GitHub Copilot, finding it largely ef- fective, though highlighting areas needing improvement in documentation and synchron- ization clarity. These insights collectively guide software developers in choosing efficient multithreading strategies, optimizing application performance, and facilitating smoother transitions to contemporary C++ standards