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MISE: Providing Performance Predictability and Improving Fairness in Shared Main Memory Systems

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posted on 01.02.2013, 00:00 authored by Lavanya Subramanian, Vivek Seshadri, Yoongu Kim, Ben Jaiyen, Onur Mutlu

Applications running concurrently on a multicore system interfere with each other at the main memory. This interference can slow down different applications differently. Accurately estimating the slow down of each application in such a system can enable mechanisms that can enforce quality-of-service. While much prior work has focused on mitigating the performance degradation due to inter-application interference, there is little work on estimating slow down of individual applications in a multi-programmed environment. Our goal in this work is to build such an estimation scheme. To this end, we present our simple Memory-Interference-induced Slowdown Estimation (MISE) model that estimates slowdowns caused by memory interference. We build our model based on two observations. First, the performanceof a memory-bound application is roughly proportional to the rate at which its memory requests are served, suggesting that request-service-rate can be used as a proxy for performance. Second, when an application's requests are prioritized over all other applications' requests, the application experiences very little interference from other applications. This provides a means for estimating the uninterfered request-service-rate of an application while it is run alongside other applications. Using the above observations, our model estimates the slowdown of an application as the ratio of its uninterfered and interfered request service rates. We propose simple changes to the above model to estimate the slowdown of non-memory-bound applications. We demonstrate the effectiveness of our model by developing two new memory scheduling schemes: 1) one that provides soft quality-of-service guarantees and 2) another that explicitly attempts to minimize maximum slowdown (i.e., unfairness) in the system. Evaluations show that our techniques perform significantly better than state-of-the-artmemory scheduling approaches to address the above problems.


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