Abstract
This paper presents a framework based on a flow-based programming paradigm to design data-stream processing applications for NP. The developed framework encourages a functional decomposition of the overall data processing application into small mono-functional artifacts that are easy to understand, develop and debug. The fact that these artifacts (actors) are programmatically independent means they can be scaled and optimized independently, which is difficult for monolithic application components. One of the advantages of this approach is fault tolerance, where independent actors can come and go in the data stream without stopping or crashing the entire application. Because actors are loosely coupled, and data carries context, they can run in heterogeneous environments and utilize wide-ranging accelerators. This paper describes the main design concepts of this framework, presenting a proof-of-concept application and the results of processing on-beam calorimeter streaming data.
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