More than three years ago, Lode and I started thinking about parallel event processing for realtime systems. The main use case back then was gesture and motion detection based on cameras such as the Kinect. Thierry created the first fully functional prototype called PARTE, and in addition to his master thesis, we wrote a workshop paper about it. Now, we finally got also the revised and extended version of this paper accepted.
Below, you can find preprint and abstract:
Using imperative programming to process event streams, such as those generated by multi-touch devices and 3D cameras, has significant engineering drawbacks. Declarative approaches solve common problems but so far, they have not been able to scale on multicore systems while providing guaranteed response times.
We propose PARTE, a parallel scalable complex event processing engine that allows for a declarative definition of event patterns and provides soft real-time guarantees for their recognition. The proposed approach extends the classical Rete algorithm and maps event matching onto a graph of actor nodes. Using a tiered event matching model, PARTE provides upper bounds on the detection latency by relying on a combination of non-blocking message passing between Rete nodes and safe memory management techniques.
The performance evaluation shows the scalability of our approach on up to 64 cores. Moreover, it indicates that PARTE’s design choices lead to more predictable performance compared to a PARTE variant without soft real-time guarantees. Finally, the evaluation indicates further that gesture recognition can benefit from the exposed parallelism with superlinear speedups.