MISE: Providing performance predictability and improving fairness in shared main memory systems

Authors:
Lavanya Subramanian;Vivek Seshadri;Yoongu Kim;Ben Jaiyen;Onur Mutlu
Affiliations:
Carnegie Mellon University, USA;Carnegie Mellon University, USA;Carnegie Mellon University, USA;Carnegie Mellon University, USA;Carnegie Mellon University, USA
Venue:
HPCA '13 Proceedings of the 2013 IEEE 19th International Symposium on High Performance Computer Architecture (HPCA)
Year:
2013

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Abstract

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.