Stream processing is a promising paradigm for programming multi-core systems for high-performance embedded applications. We propose flexible filters as a technique that combines static mapping of the stream program tasks with dynamic load balancing of their execution. The goal is to improve the system-level processing throughput of the program when it is executed on a distributed-memory multi-core system as well as the local (core-level) memory utilization. Our technique is distributed and scalable because it is based on point-to-point handshake signals exchanged between neighboring cores. Load balancing with flexible filters can be applied to stream applications that present large dynamic variations in the computational load of their tasks and the dimension of the stream data tokens. In order to demonstrate the practicality of our technique, we present performance improvements for the case study of a JPEG encoder running on the IBM Cell multi-core processor.
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