Comprehensive multiprocessor cache miss rate generation using multivariate models

Abstract

This article presents a technique for taking a sparse set of cache simulation data and fitting a multivariate model to fill in the missing points over a broad region of cache configurations. We extend previous work by its applicability to multiple miss rate components and its ability to model a wide range of cache parameters, including size, associativity and sharing. Miss rate models are useful for broad design exploration in which many cache configurations cannot be simulated directly due to limitations of trace collection setups or available resources. We show the effectiveness of the technique by applying it to two commercial workloads and presenting miss rate data for a broad design space with cache size, associativity, sharing and number of processors as variables. The fitted data match the simulation data very well. The various curves show how a miss rate model is useful for not only estimating the performance of specific configurations, but also for providing insight into miss rate trends. Furthermore, this modeling methodology is robust in the presence of corrupted simulation data and variations in simulation data from multiple sources.