Evaluating Resource Centric Behavior of Workloads and Performance Analysis in CMPs due to Shared Resources
Preeti Jain1, Sunil K Surve2

1Preeti Jain*, Fr Conceicao Rodrigues College of Engineering Department of Electronics Engineering, Bandra, Mumbai, India.
2Dr. Sunil K Surve, Fr Conceicao Rodrigues College of Engineering Department of Computer Engineering, Bandra, Mumbai, India.
Manuscript received on July 12, 2019. | Revised Manuscript received on August 26, 2019. | Manuscript published on August 30, 2019. | PP: 4974-4981 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8872088619/2019©BEIESP | DOI: 10.35940/ijeat.F8872.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Multicore systems are achieving new dimensions to meet the pace between processor technology and high processing demands. These chip multiprocessors (CMPs) share some on- chip and off-chip resources to achieve higher performance. With the increase in core integration, the performance of workloads is dependent on the allocation of resources. Though the CMPs elevate performance, but the challenge imposed due to subtle interactions of several applications contending for resources leads to performance degradation by several magnitudes. In this direction the work performs a two-fold evaluation of application programs. The applications running on CMP cores depict distinct behavior towards consumption of shared resources. It characterizes the resource centric nature of SPEC CPU2006 benchmarks based on their resource consumption behavior. Secondly the work aims to evaluate the effect of inter-core interference on the performance of application programs based on the characterization obtained and the potential contention caused on performance due to corunners. Lastly we place significant remarks on the impact on performance due to resource sharing and its implication on resource contention.
Keywords: Multicore, Shared resource, Contention, characterization, Inter-core interference.