Novel Cache Optimization Strategies for Multicore Processor Architectures

The rapid evolution of multicore processor architectures has intensified the demand for efficient cache management techniques to meet the growing computational and memory requirements of modern applications. Traditional cache optimization approaches often face challenges such as high latency, frequent cache misses, and scalability issues when applied to parallel workloads. This paper proposes novel cache optimization strategies that combine adaptive replacement policies, data prefetching mechanisms, and cooperative caching techniques tailored for multicore environments. Simulation studies conducted on benchmark workloads demonstrate that the proposed strategies reduce cache miss rates by up to 18 percent and improve execution time by nearly 12 percent compared to conventional policies such as LRU and FIFO. Furthermore, energy consumption is optimized through selective prefetching and intelligent block replacement, making the strategies suitable for power-constrained computing platforms. The findings highlight the potential of innovative cache management frameworks to enhance system performance, scalability, and energy efficiency in next-generation multicore processors