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Pthreads Performance Characteristics on Shared Cache CMP, Private Cache CMP and SMP

机译:Pthreads共享缓存CMP上的性能特征,私有缓存CMP和SMP

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With the wide availability of chip multi-processing (CMP), software developers are now facing the task of effectively parallelizing their software code. Once they have identified the areas of parallelization, they will need to know the level of code granularity needed to ensure profitable execution. Furthermore, this problem multiplies itself with different hardware available. In this paper, we present a novel approach for fair comparison of the hardware configuration by simulation through configuring a pair of quad-core processors. The simulated configuration represents shared cache CMP, private cache CMP and symmetrical multiprocessor (SMP) environment. We then present a modified lmbench micro-benchmark suite to measure the cost of threading on these different hardware configurations. In our empirical studies, we observe that shared cache CMP exhibits better performance when the operating systems load balancer is highly active. However, the measurements also indicate that thread size is an important consideration where potential cache trashing can occur when sharing a cache between processing cores. Private cache CMP and SMP do not exhibit significant difference in our measurements. The techniques presented can be incorporated into integrated development environment, compilers and potentially even other run-time environments.
机译:随着芯片多处理(CMP)的广泛可用性,软件开发人员现在正面临有效地并行化其软件代码的任务。一旦他们确定了并行化的区域,他们需要知道确保有利可图执行所需的代码粒度水平。此外,此问题乘以不同的硬件可用。在本文中,我们通过配置一对四核处理器来介绍一种用于公平比较硬件配置的公平比较方法。模拟配置表示共享高速缓存CMP,私有缓存CMP和对称多处理器(SMP)环境。然后,我们提出了一个修改的LMBench微基准套件,以测量这些不同的硬件配置上的线程成本。在我们的实证研究中,我们观察到,当操作系统负载均衡器非常有效时,共享缓存CMP表现出更好的性能。然而,测量还指示线程大小是在在处理核之间共享缓存时发生潜在的缓存垃圾的重要考虑因素。私有缓存CMP和SMP在我们的测量中没有表现出显着差异。呈现的技术可以合并到集成的开发环境,编译器和潜在的其他运行时环境中。

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