Improved memory performance through the development of an energy efficient distributed memory Management System - Sundari, K.Siva; Narmadha, R. (2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COM-IT-CON), 2022)
In multiprocessor networks, shared distributed memory is a popular research subject. It has been studied by scientists and engineers from a variety of fields. To construct high-performance, large-scale multiprocessor systems, it is necessary to maximise distributed shared memory (SDM) performance. SDM algorithm, locking shared space, thrashing, concurrent access, page faults, extension, transparency, huge database support and cost have all been the subject of extensive investigation in the past. Memory structure has a substantial impact on the system's power consumption and performance; hence a low-power system is needed. There are a number of problems with the design of shared and private memory systems for multiple processors that this dissertation seeks to address. Using software parameters, these researchers developed a new distributed shared memory design that outperforms conventional structural design in managing a distributed shared address space. The primary goal of this study is to use a software method to create novel distributed shared memory architecture.