Stampede: a cluster programming middleware for interactive stream-oriented applications

Ramachandran, Umakishore; Nikhil, Rishiyur S.; Rehg, James M.; Angelov, Yavor; Paul, Arnab; Adhikari, Subodh; MacKenzie, Kenneth; Harel, Nissim; Knobe, Kathleen

doi:10.1109/tpds.2003.1247674

Cited by 20 publications

(17 citation statements)

References 19 publications

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“…FlowVR [3] and Stampede [33] both provide support for distributed execution of interactive multimedia applications on compute clusters. An application is structured as a data flow of processing modules and explicit data dependencies.…”

Section: Stream Processing Systemsmentioning

confidence: 99%

Exploiting multi-level parallelism for low-latency activity recognition in streaming video

Chen

Mummert

Pillai

et al. 2010

Proceedings of the First Annual ACM SIGMM Conference on Multimedia Systems

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Video understanding is a computationally challenging task that is critical not only for traditionally throughput-oriented applications such as search but also latency-sensitive interactive applications such as surveillance, gaming, videoconferencing, and vision-based user interfaces. Enabling these types of video processing applications will require not only new algorithms and techniques, but new runtime systems that optimize latency as well as throughput. In this paper, we present a runtime system called Sprout that achieves low latency by exploiting the parallelism inherent in video understanding applications. We demonstrate the utility of our system on an activity recognition application that employs a robust new descriptor called MoSIFT, which explicitly augments appearance features with motion information. MoSIFT outperforms previous recognition techniques, but like other state-of-the-art techniques, it is computationally expensive -a sequential implementation runs 100 times slower than real time. We describe the implementation of the activity recognition application on Sprout, and show that it can accurately recognize activities at full frame rate (25 fps) and low latency on a challenging airport surveillance video corpus.

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Section: Stream Processing Systemsmentioning

confidence: 99%

Exploiting multi-level parallelism for low-latency activity recognition in streaming video

Chen

Mummert

Pillai

et al. 2010

Proceedings of the First Annual ACM SIGMM Conference on Multimedia Systems

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“…In fact, ThreadMill could be used to transparently implement the concurrency mechanisms within individual components of these coarser-grained architectures, complementing their functionality. An exception to the above is Stampede [18]. Stampede shares ThreadMill's goal of facilitating the development of applications that deal with time-sequenced data.…”

Section: Related Literaturementioning

confidence: 99%

The ThreadMill architecture for stream-oriented human communication analysis applications

Barthelmess

Ellis

2004

Proceedings of the 6th International Conference on Multimodal Interfaces

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This work introduces a new component software architecture -ThreadMill -whose main purpose is to facilitate the development of applications in domains where high volumes of streamed data need to be efficiently analyzed. It focuses particularly on applications that target the analysis of human communication e.g. in speech and gesture recognition. Applications in this domain usually employ costly signal processing techniques, but offer in many cases ample opportunities for concurrent execution in many different phases. ThreadMill's abstractions facilitate the development of applications that take advantage of this potential concurrency by hiding the complexity of parallel and distributed programming. As a result, ThreadMill applications can be made to run unchanged on a wide variety of execution environments, ranging from a single-processor machine to a cluster of multi-processor nodes. The architecture is illustrated by an implementation of a tracker for hands and face of American Sign Language signers that uses a parallel and concurrent version of the Joint Likelihood Filter method.

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“…An exception to the above is Stampede [22]. Stampede shares ThreadMill's goal of facilitating the development of applications that deal with time-sequenced data.…”

Section: Related Workmentioning

confidence: 99%

A Distributed and Parallel Component Architecture for Stream-Oriented Applications

Barthelmess

Ellis

2004

On the Move to Meaningful Internet Systems 2004: CoopIS, DOA, and ODBASE

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Abstract. This paper introduces ThreadMill -a distributed and parallel component architecture for applications that process large volumes of streamed (timesequenced) 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 eightfold 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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Stampede: a cluster programming middleware for interactive stream-oriented applications

Cited by 20 publications

References 19 publications

Exploiting multi-level parallelism for low-latency activity recognition in streaming video

Exploiting multi-level parallelism for low-latency activity recognition in streaming video

The ThreadMill architecture for stream-oriented human communication analysis applications

A Distributed and Parallel Component Architecture for Stream-Oriented Applications

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