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Stream computing has emerged as an important model of computation for embedded system applications particularly in the multimedia and network processing domains. In recent past several programming languages and embedded multi-core processors have been proposed for streaming applications. This paper examines the execution and dynamic scheduling of stream programs on embedded multi-core processors. The paper addresses the problem in the context of a multi-tasking environment with a time varying allocation of processing elements for a particular streaming application. As a solution the paper proposes a two step approach where the stream program is first compiled to gather key application information, and to generate re-targetable code. A light weight dynamic scheduler incorporates the second stage of the approach. The dynamic scheduler utilizes the static information and available resources to assign or partition the application across the multi-core architecture. The objective of the dynamic scheduler is to maximize the throughput of the application, and it is sensitive to the resource (processing elements, scratch-pad memory, DMA bandwidth) constraints imposed by the target architecture. We evaluate the proposed approach by compiling and scheduling benchmark stream programs on a representative embedded multi-core processor. We present experimental results that evaluate the quality of the solutions generated by the proposed approach by comparisons with existing techniques.