Performance Analysis of Cache Memory Optimization Techniques in Modern Computer Architectures
TL;DR
Results indicate that techniques such as Least Recently Used (LRU) policies improve hit rates by 10-25% compared to FIFO, while set-associative mapping reduces miss rates by 15-30% relative to direct mapping.
Abstract
Cache memory plays a vital role in improving computer system performance by reducing the speed gap between the processor and main memory. This study provides a comparative analysis of various cache memory optimization techniques, including cache replacement policies, mapping methods, multi-level cache architectures, prefetching, and cache partitioning. Using a literature-based approach, the research reviews and evaluates findings from existing studies and scholarly publications. Results indicate that techniques such as Least Recently Used (LRU) policies improve hit rates by 10-25% compared to FIFO, while set-associative mapping reduces miss rates by 15-30% relative to direct mapping. Multi-level cache reduces average memory access latency by up to 50%, and prefetching can boost performance by 20-40% in data-intensive workloads. This study provides a concise overview of cache optimization techniques and their impact on system performance, contributing to a better understanding of cache memory design in modern computer architectures.