Low-Level Scheduling Implications for Data-Intensive Cyclic Workloads on Modern Microarchitectures

Processing data intensive multimedia workloads is challenging, and scheduling and resource management are vitally important for the best possible utilization of machine resources. In earlier work, we have used work-stealing, which is frequently used today, and proposed improvements. We found already then that no singular work-stealing variant is ideally suited for all workloads. Therefore, we investigate in more detail in this paper how workloads consisting of various multimedia filter sequences should be scheduled on a variety of modern processor architectures to maximize performance. Our results show that a low-level scheduler additionally cannot achieve optimal performance without taking the specific micro-architecture, the placement of dependent tasks and cache sizes into account. These details are not generally available for application developers and they differ between deployments. Our proposal is therefore to use performance monitoring and dynamic adaption for the cyclic workloads of our target multimedia scenario, where operations are repeated cyclically on a stream of data.