Abstrakt
Memory caches significantly improve the performance of workloads that have temporal and spatial locality by providing faster access to data. Current processor designs have multiple cores sharing a cache.
To accurately model a workload performance and to improve system throughput by intelligently scheduling workloads on cores, we need to understand how sharing caches between workloads affects their data accesses. Past research has developed analytical models that estimate the cache behavior for combined workloads given the stack distance profiles describing these workloads.
We extend this research by presenting an analytical model with contributions to accuracy and composability -- our model makes fewer simplifying assumptions than earlier models, and its output is in the same format as its input, which is an important property for hierarchical composition during software performance modeling. To compare the accuracy of our analytical model with earlier models, we attempted to reproduce the reported accuracy of those models.
This proved to be difficult. We provide additional insight into the major factors that influence analytical model accuracy.