Shared resource management in multi-core systems using LV competition model. (2016)

• Record Type: Journal Article
• Authors: Prabhu, Sapna; Daruwala, Rohin
• Is Part Of: International journal of high performance computing and networking. Volume 9:Number 3(2016)
• Page Start: 206
• Page End: 217
• Publication Date: 2016
• Subjects: workload characterisation -- inter-specific competition -- stability -- cross-core interference -- resource management -- LV competition model -- scalability -- cache partitioning -- shared resources -- multi-core systems -- high-performance computing -- hardware resources -- mathematical modelling -- competitive Lotka-Volterra model -- shared memory -- partition sizes -- performance evaluation; High performance computing -- Periodicals; Computer networks -- Periodicals; High performance computing; Periodicals; 004.05
• Languages: English
• ISSNs: 1740-0562

Abstract

In the age of high-performance computing, multi-core technology is seen to bridge the gap between good system throughput and increased power dissipation. Most multi-core architectures share hardware resources between cores, leading to cross-core interference which is non-trivial in many cases. Effective shared resource management is necessary to minimise the performance variability of threads due to cross-core interference as well as optimise resource usage. Many biological models have been proposed which model interactions between species. This paper uses a mathematical model, on the lines of the competitive Lotka-Volterra model for biological population growth, which models performance of the cores in a shared-memory multi-core system. It further formulates stability conditions for the multi-core system taking into account the workload characteristics and derives partition sizes to partition the shared cache to reduce the effect of cross-core interference for different workload types. Analysis of scalability of the model is also presented. Performance evaluations reveal that the LV-based mathematical model can be used to study the effects of resource sharing between cores, calculate partition sizes and is scalable.