Evaluation of memory performance in NUMA architectures using Stochastic Reward Nets

Abstract

Understanding memory performance in multi-core platforms is a prerequisite to perform optimizations. To this end, this paper presents analytical models based on Stochastic Reward Nets (SRNs) to model and evaluate the memory performance of Non-Uniform Memory Access (NUMA) multi-core architectures. The approach considers the details of the architecture and first proposes a monolithic SRN model that evaluates the memory performance in terms of the mean memory response time. Since the monolithic model incurs a state space explosion with an increasing number of cores and memory controllers, two approximate models are presented that are able to evaluate large-scale NUMA architectures. The SRNs are validated through measurements on two NUMA multi-core platforms, a 64-core AMD Opteron server and a 72-core Intel system. The results demonstrate the ability of the proposed models to accurately compute the mean memory response time on NUMA architectures. The results also provide valuable information that runtime systems and application designers can use to optimize execution of parallel applications on such architectures.