SRP: Symbiotic Resource Partitioning of the Memory Hierarchy in CMPs

Abstract

There have been many recent works in the context of Chip Multiprocessors (CMPs) investigating the need of intelligent shared cache partitioning which is believed to reduce the pressure on the off-chip bandwidth. Management of the off-chip memory bandwidth to improve system performance and/or mitigate performance volatility of applications has itself received considerable attention. Coordinated resource management schemes treat the interactions between cache allocation and bandwidth management as a black-box. This hinders the ability of these schemes from exploiting the intricate inter-relationships between the resource management strategies. In a multiprogrammed scenario, given the limited availability of the on-chip cache, it is not feasible to entirely eliminate off-chip accesses. However, it is possible to mitigate the impact of additional queueing delays associated with the memory controller by avoiding multiple applications from exercising the off-chip bandwidth simultaneously. Therefore, from the point of view of improving system performance, it is more important to have a symbiotic resource partitioning scheme that performs partitioning of each resource based on feedback it receives from the partitioning of the other.

Symbiotic resource partitioning (SRP) proposed in this paper avoids the scenarios of multiple applications exercising the off-chip memory bandwidth simultaneously by appropriately controlling the cache partitioning. In order to control the cache partitioning, SRP employs an empirical model that relies on a metric (last level cache misses per cycle) that represents the off-chip memory bandwidth demand of the applications and models the impact of cache partitioning on bandwidth demand by representing the last level cache misses per cycle metric as a function of the cache allocation per application. This model is dynamically updated to account for the phase behavior of the applications. Moreover, SRP is an iterative approach wherein each iteration of the approach consists of an update to the model, cache partitioning and bandwidth partitioning with a feedback from bandwidth partitioning that updates the model. Extensive simulations with a full system simulator and applications from the MiBench benchmark suite shows that SRP leads to a significant overall improvement in system performance as compared to a state-of the-art cache and bandwidth management schemes.

This research is supported in part by NSF grants CNS #0720645, CCF #0811687, CCF #0702519, CNS #0202007 and CNS #0509251, a grant from Microsoft Corporation and support from the Gigascale Systems Research Focus Center, one of the five research centers funded under SRC’s Focus Center Research Program.