Algorithms Design Performance

A critical problem in implementing interactive perception applica-tions 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. For-tunately, many of these algorithms exhibit coarse-grained task and data parallelism that can be exploited across machines. The SLIP-stream 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 compute-intensive yet interactive applications