通过共享资源管理在多核系统中提供高性能和可控性能
Providing High and Controllable Performance in Multicore Systems Through Shared Resource Management
摘要：
在多核系统上并发执行的多个应用程序在不同的共享资源（例如主存储器和共享高速缓存）处相互干扰。这种应用程序间干扰如果不受控制，会导致系统性能严重下降和不可预测的应用程序减速。虽然以前的工作已经提出了应用程序感知的内存调度作为一种解决方案，以减轻应用程序间的干扰和提高系统性能，以前提出的内存调度技术会导致高硬件复杂性和不公平地减慢一些应用程序。此外，先前提出的存储器干扰减轻技术没有被设计为精确地控制应用性能。本文旨在通过减轻和量化共享资源干扰的影响来实现多核系统中的高性能和可控性能。首先，为了减轻内存干扰，实现高性能，我们提出了黑名单内存调度，实现高性能和公平性，在低复杂度。接下来，为了量化内存干扰的影响，并实现可控的性能，在存在内存带宽干扰，我们提出了内存干扰引起的减速估计（MISE）模型。我们提出并演示了两个用例，可以利用MISE提供软性能保证和高的整体性能/公平性。最后，除了内存带宽干扰外，我们还试图量化共享缓存干扰对应用程序速度降低的影响。为此，我们提出了应用程序减速模型（ASM）。我们提出并演示了ASM的几个用例，这些用例利用ASM提供软性能保证并提高性能和公平性。
英文摘要：
Multiple applications executing concurrently on a multicore system interfere with each other at different shared resources such as main memory and shared caches. Such inter-application interference, if uncontrolled, results in high system performance degradation and unpredictable application slowdowns. While previous work has proposed application-aware memory scheduling as a solution to mitigate inter-application interference and improve system performance, previously proposed memory scheduling techniques incur high hardware complexity and unfairly slowdown some applications. Furthermore, previously proposed memory-interference mitigation techniques are not designed to precisely control application performance. This dissertation seeks to achieve high and controllable performance in multicore systems by mitigating and quantifying the impact of shared resource interference. First, towards mitigating memory interference and achieving high performance, we propose the Blacklisting memory scheduler that achieves high performance and fairness at low complexity. Next, towards quantifying the impact of memory interference and achieving controllable performance in the presence of memory bandwidth interference, we propose the Memory Interference induced Slowdown Estimation (MISE) model. We propose and demonstrate two use cases that can leverage MISE to provide soft performance guarantees and high overall performance/fairness. Finally, we seek to quantify the impact of shared cache interference on application slowdowns, in addition to memory bandwidth interference. Towards this end, we propose the Application Slowdown Model (ASM). We propose and demonstrate several use cases of ASM that leverage it to provide soft performance guarantees and improve performance and fairness.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
