对称多处理环境下调度策略的性能评估

J. Happe, Henning Groenda, Ralf H. Reussner
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引用次数: 4

摘要

硬件架构向并行执行的转变导致了多核处理器在桌面系统和服务器系统中的广泛使用。额外的处理器内核对软件性能的好处取决于软件的并行性以及操作系统调度器的能力。特别是,可用处理器(或核心)上的负载严重影响软件应用程序的响应时间和吞吐量。因此,对软件行为和操作系统调度器的相互影响的复杂理解对于准确的性能评估是必不可少的。多核系统对操作系统的性能分析和开发人员提出了新的挑战。例如,多服务器系统的最佳调度策略,如单服务器系统的最短剩余处理时间(SRPT),在排队理论中还不为人所知。本文对对称多处理(SMP)环境下通用操作系统(GPOS)调度器进行了详细的实验评估。我们特别感兴趣的是多处理器负载平衡对软件性能的影响。此外,评估还包括GPOS调度器的影响,这种影响也可能发生在单处理器环境中,例如任务的I/ o受限性和不同的优先级策略。本文提出的结果为今后开发更精确的软件系统性能模型提供了基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance evaluation of scheduling policies in symmetric multiprocessing environments
The shift of hardware architecture towards parallel execution led to a broad usage of multi-core processors in desktop systems and in server systems. The benefit of additional processor cores for software performance depends on the software's parallelism as well as the operating system scheduler's capabilities. Especially, the load on the available processors (or cores) strongly influences response times and throughput of software applications. Hence, a sophisticated understanding of the mutual influence of software behaviour and operating system schedulers is essential for accurate performance evaluations. Multi-core systems pose new challenges for performance analysis and developers of operating systems. For example, an optimal scheduling policy for multi-server systems, such as shortest remaining processing time (SRPT) for single-server systems, is not yet known in queueing theory. In this paper, we present a detailed experimental evaluation of general purpose operating system (GPOS) schedulers in symmetric multiprocessing (SMP) environments. In particular, we are interested in the influence of multiprocessor load balancing on software performance. Additionally, the evaluation includes effects of GPOS schedulers that can also occur in single-processor environments, such as I/O-boundedness of tasks and different prioritisation strategies. The results presented in this paper provide the basis for the future development of more accurate performance models of today's software systems.
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