SLIPstream: scalable low-latency interactive perception on streaming data

A critical problem in implementing interactive perception applications is the considerable computational cost of current computer vision and machine learning algorithms, which typically run one to two orders of magnitude too slowly to be used interactively. Fortunately, many of these algorithms exhibit coarse-grained task and data parallelism that can be exploited across machines. The SLIPstream project focuses on building a highly-parallel runtime system called Sprout that can harness the computing power of a cluster to execute perception applications with low latency. This paper makes the case for using clusters for perception applications, describes the architecture of the Sprout runtime, and presents two computeintensive yet interactive applications. Categories and Subject Descriptors C.3 [Computer Systems Organization]: Special-Purpose and Application-Based Systems; D.2 [Software]: Software Engineering General Terms Algorithms Design Performance Keywords Parallel Computing, Clust...

• Type: Conference
• Year: 2009
• Where: NOSSDAV
• Authors: Padmanabhan Pillai, Lily B. Mummert, Steven W. Schlosser, Rahul Sukthankar, Casey Helfrich
• Keywords: Considerable Computational Cost, Interactive Perception Applications, NOSSDAV 2009, Operating System, Perception Applications