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Abstract. Software performance evaluation relies on the ability of simple models to predict the performance of complex systems. Often, however, the models are not capturing potentially relevant effects in system behavior, such as sharing of memory caches or sharing of cores by hardware threads. The goal of this paper is to investigate whether and to what degree a simple linear adjustment of service demands in software performance models captures these effects and thus improves accuracy. Outlined experiments explore the limits of the approach on two hardware platforms that include shared caches and hardware threads, with results indicating that the approach can improve throughput prediction accuracy significantly, but can also lead to loss of accuracy when the performance models are otherwise defective.

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Abstract. The memory subsystems of contemporary computer architectures are increasingly complex – in fact, so much so that it becomes difficult to estimate the performance impact of many coding constructs, and some long known coding patterns are even discovered to be principally wrong. In contrast, many researchers still reason about algorithmic complexity in simple terms, where memory operations are sequential and of equal cost. The goal of this talk is to give an overview of some memory subsystem features that violate this assumption significantly, with the ambition to motivate development of algorithms tailored to contemporary computer architectures. 1

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.