Use of simple analytic performance models for streaming data applications deployed on diverse architectures

Modern hardware is often heterogeneous. With heterogeneity comes multiple abstraction layers that hide underlying complex systems. This complexity makes quantitative performance modeling a difficult task. Designers of high-performance streaming applications for heterogeneous systems must contend with unpredictable and often non-generalizable models to predict performance of a particular application and hardware mapping. This paper outlines a computationally simple approach that can be used to model the overall throughput and buffering needs of a streaming application on heterogeneous hardware. The model presented is based upon a hybrid maximum flow and decomposed discrete queueing model. The utility of the model is assessed using a set of real and synthetic benchmarks with model predictions compared to measured application performance.