Whose cache line is it anyway?: operating system support for live detection and repair of false sharing

Mihir Nanavati, Mark Spear, Nathan Taylor, Shriram Rajagopalan, Dutch T. Meyer, W. Aiello, A. Warfield
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引用次数: 31

Abstract

As hardware parallelism continues to increase, CPU caches can no longer be considered as a transparent, hardware-level performance optimization. Cache impact on performance, in particular in the face of false sharing, is completely dependent on the software that is executing. To effectively support parallel workloads on cache coherent hardware, the operating system must begin to treat the CPU cache like other shared hardware resources, and manage it appropriately. We demonstrate a prototype example of such support by describing Plastic, a software-based system that detects, diagnoses, and transparently repairs false sharing as it occurs in running applications. Plastic solves two challenging problems. First, it is capable of rapid, low-overhead detection and diagnosis of false sharing in unmodified, running applications. Second, it resolves identified instances of false sharing by providing a sub-page granularity memory remapping facility within the system. Our implementation is capable of identifying and repairing pathological false sharing in under one second of execution and achieves speedups of 3-6x on known examples of false sharing in parallel benchmarks.
它到底是谁的缓存线?:操作系统支持实时检测和修复虚假共享
随着硬件并行性的不断提高,CPU缓存不再被视为透明的硬件级性能优化。缓存对性能的影响,特别是在面对虚假共享时,完全取决于正在执行的软件。为了有效地支持缓存一致硬件上的并行工作负载,操作系统必须开始像对待其他共享硬件资源一样对待CPU缓存,并对其进行适当的管理。我们通过描述Plastic来展示这种支持的原型示例,Plastic是一个基于软件的系统,可以检测、诊断并透明地修复在运行的应用程序中发生的错误共享。塑料解决了两个难题。首先,它能够在未修改的运行应用程序中快速、低开销地检测和诊断错误共享。其次,它通过在系统内提供子页面粒度内存重新映射功能来解决已识别的错误共享实例。我们的实现能够在不到一秒的执行时间内识别和修复病态虚假共享,并在并行基准测试中对已知的虚假共享示例实现3-6倍的加速。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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