Experience on Comparison of Operating Systems Scalability on the Multi-core Architecture

  • Authors:
  • Yan Cui;Yingxin Wang;Yu Chen;Yuanchun Shi

  • Affiliations:
  • -;-;-;-

  • Venue:
  • CLUSTER '11 Proceedings of the 2011 IEEE International Conference on Cluster Computing
  • Year:
  • 2011

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Abstract

Multi-core processor architectures have become ubiquitous in today's computing platforms, especially in parallel computing installations, with their power and cost advantages. While the technology trend continues towards having hundreds of cores on a chip in the foreseeable future, an urgent question posed to system designers as well as application users is whether applications can receive sufficient support on today's operating systems for them to scale to many cores. To this end, people need to understand the strengths and weaknesses on their support on scalability and to identify major bottlenecks limiting the scalability, if any. As open-source operating systems are of particular interests in the research and industry communities, in this paper we choose three operating systems (Linux, Solaris and FreeBSD) to systematically evaluate and compare their scalability by using a set of highly-focused micro benchmarks for broad and detailed understanding their scalability on an AMD 32-core system. We use system profiling tools and analyze kernel source codes to find out the root cause of each observed scalability bottleneck. Our results reveal that there is no single operating system among the three standing out on all system aspects, though some system(s) can prevail on some of the system aspects. For example, Linux outperforms Solaris and FreeBSD significantly for file-descriptor- and process-intensive operations. For applications with intensive sockets creation and deletion operations, Solaris leads FreeBSD, which scales better than Linux. With the help of performance tools and source code instrumentation and analysis, we find that synchronization primitives protecting shared data structures in the kernels are the major bottleneck limiting system scalability. Empowered by the knowledge obtained through targeted experiments and analysis on a small-scale system, we are able to project the scalability of an application on any of the investigated operating systems running on a system of a larger number of cores.