Abstract
This paper introduces ThreadMill – a distributed and parallel component architecture for applications that process large volumes of streamed (time-sequenced) data, such as is the case e.g. in speech and gesture recognition applications.
Many stream-oriented applications offer ample opportunity for enhanced performance via concurrent execution, exploring a wide variety of parallel paradigms, such as task, data and pipeline parallelism. ThreadMill addresses the challenges of development and evolution of parallel and distributed applications in this domain by offering a modeling formalism, a programming framework and a runtime infrastructure. Component development and reuse, and application evolution are facilitated by the isolation of communication, concurrency, and synchronization concerns promoted by ThreadMill. A direct consequence of the novel mechanisms introduced by ThreadMill is that applications composed of reusable components can be re-targeted, unchanged, and made to run efficiently on a variety of execution environments. These environments can range e.g. from a single machine with a single processor, to a cluster of heterogeneous computational nodes, to certain classes of supercomputers. Experimental results show an eight-fold speedup when using ten nodes of an AlphaServer DS20 cluster running a proof-of-concept 2D video-based tracker for hands and face of American Sign Language signers.
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Barthelmess, P., Ellis, C.A. (2004). A Distributed and Parallel Component Architecture for Stream-Oriented Applications. In: Meersman, R., Tari, Z. (eds) On the Move to Meaningful Internet Systems 2004: CoopIS, DOA, and ODBASE. OTM 2004. Lecture Notes in Computer Science, vol 3291. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30469-2_38
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