Predicting the throughput of multiprocessor applications under dynamic workload

This work contributes to throughput calculation for real-time multiprocessor applications experiencing dynamic workload variations. We focus on a method to predict the system throughput when processing an arbitrarily long data frame given the meta-characteristics of the workload in that frame. This is useful for different purposes, such as resource allocation or dynamic voltage scaling in embedded systems. An accurate enough analysis is not trivial when two factors are combined: parallelism and dynamic workload variations. In earlier work, two analysis methods showed good accuracy for several application examples, but no comparative experiments were carried out. In this work, we contribute new propositions to the theoretical basis of the previous methods. Based on these propositions, we remove a potential problem in a common subroutine and propose a new analysis method.We compare the methods experimentally. The new method provides a significant reduction of the throughput prediction error, up to 12%.