“Optimizing Algorithmic Efficiency: Exploring Advanced Data Structures for High-Performance Computing”

This work explores advanced data structures as a means of optimizing algorithmic efficiency in high-performance computing. The search for faster and more scalable algorithms becomes essential as computing demands rise further. This paper explores state-of-the-art data structures intended to improve overall performance, memory usage, and computational speed. We hope to offer insights into the applicability and impact of various advanced data structures on high-performance computing tasks through a thorough review and analysis of them, including but not limited to self-balancing trees, hash tables, and advanced graph structures. The results of this study not only advance our theoretical knowledge of these data structures but also provide developers and researchers with useful advice on how to utilize algorithmic efficiency in quickly fully. It is important to remember that algorithmic urban planning is an interdisciplinary field that encompasses data science, computer science, urban planning, and many other fields. The goal of integrating algorithms and smart technologies is to build more efficient, sustainable, and liveable urban environments.[8].