A Mathematical Framework for Optimizing Lexical Analysis on Multi-Core Architectures

In the field of multi-core computing systems, optimizing the distribution of tasks across multiple cores is essential to harness maximum performance potential. Traditional static task allocation methods, often employed in lexical analysis-the initial and vital phase of compiling and interpreting source code-frequently result in imbalanced workloads and suboptimal resource utilization. This paper introduces an innovative and sophisticated mathematical model for dynamic task allocation specifically tailored to lexical analysis, aiming to revolutionize how tasks are distributed across multiple cores. By employing advanced dynamic allocation techniques, our proposed model not only ensures balanced workload distribution but also scales efficiently with varying computational demands. This novel approach significantly enhances the efficiency of lexical analysis in multi-core environments, leading to substantial improvements in processing times and resource utilization. Extensive experimental results validate the superiority of our method, showcasing dramatic reductions in processing latency and a more effective exploitation of multi-core capabilities. This dynamic model not only advances the field of lexical analysis but also sets a new benchmark for task allocation strategies in multi-core computing systems, promising broader applicability and future innovations in compiler design and resource management.