| Abstract: |
Modern embedded systems are typically integrated as multi processor system on chips, and are often characterized by the complex behaviors and dependencies that system components exhibit.
Different events that trigger such systems normally cause different execution demands, depending on their event type as well as on the task they are processed by, leading to complex workload
correlations. For example in data processing systems, the size of an events payload data will typically determine its execution demand on most or all system components, leading to highly
correlated workloads. Performance analysis of such complex system is often very difficult, and conventional analysis methods have no means to capture the possible existence of workload
correlations. This leads to overly pessimistic analysis results, and thus to too expensive system designs with considerable performance reserves. We propose an abstract model to characterize and
capture workload correlations present in a system architecture, and we show how the captured additional system information can be incorporated into an existing framework for modular performance
analysis of embedded systems. We also present a method to analytically obtain the proposed abstract workload correlation model from a typical system specification. The applicability of our
approach and its advantages over conventional performance analysis methods is shown in a detailed case study of a multi processor system on chip, where the analysis results obtained with our
approach are considerably improved compared to the results obtained with conventional analysis methods. |
