NDLI: Fast, Processor-Cardinality Agnostic PRNG with a Tracking Application

Content Provider	IEEE Xplore Digital Library
Author	Janowczyk, A. Chandran, S. Aluru, S.
Copyright Year	2008
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Indian Insitute of Technol. Bombay, Mumbai (Janowczyk, A.; Chandran, S.)
Abstract	As vision algorithms mature with increasing inspiration from the learning community, statistically independent pseudo random number generation (PRNG) becomes increasingly important. At the same time, execution time demands have seen algorithms being implemented on evolving parallel hardware such as GPUs. The Mersenne Twister (MT) has proven to be the current state of the art for generating high quality random numbers, and the Nvidia provided software for parallel MT is in widespread use. While execution time is important, development time is also critical. As processor cardinality changes, a foundation for generating simulations that will vary only in execution time and not in the actual result is useful; otherwise the development time will be impacted. In this paper, we present an implementation of the Lagged Fibonacci Generator (LFG) considered to be of quality equal to MT on the GPU. Unlike MT, LFG has this important processor-cardinality agnostic capability -- that is -- as the number of processing resources changes, the overall sequence of random numbers remains the same. This feature not withstanding, our basic implementation is roughly as fast as the parallel MT; an in-memory version is actually 25% faster in execution time. Both parallel MT as well as parallel LFG show enormous speed up over their sequential counterparts. Finally, a prototype particle filter tracking application shows that our method works not just in parallel computing theory, but also in practice for vision applications, providing a decrease of 60% in execution time.
Starting Page	171
Ending Page	178
File Size	619248
Page Count	8
File Format	PDF
ISBN	9780769534763
DOI	10.1109/ICVGIP.2008.90
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-16
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer vision Tracking Image processing Pseudo random number Application software Parallel algorithms GPU Computer science Parallel computing Reproducible Monte-carlo Processor-cardinality agnostic Computer graphics Parallel processing Hardware Random number generation Principal component analysis
Content Type	Text
Resource Type	Article

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