Large-scale computational analysis of National Animal Identification System mock data, including traceback and trace forward
Date
2008
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Abstract
Cattle production is the single largest segment of U.S. agriculture. Animal disease, whether a single incident or a full-scale outbreak, can result in significantly restricted access to both foreign and domestic markets. Regaining consumer confidence is difficult. If a disease cannot be traced back to a common source, then only time can tell whether or not eradication and containment efforts have been successful. Simply "waiting it out" can result in long-term economic losses on a National scale especially when diseases which are prone to epizootic outbreaks or those with long incubation periods are involved. The United States Department of Agriculture (USDA) maintains that traceability is the key to protecting animal health and marketability: The National Animal Identification System (NAIS) is a voluntary disease traceability framework released by the USDA. Many of the efforts surrounding the development of the NAIS have encompassed the identification of livestock production and handling premises as well as individuals or herds of animals, whereas little effort has been directed toward the ultimate goal of animal traceback in 48 hours. In this dissertation, computational science is applied to the problem of animal disease traceability. In particular, a computational model is developed for the purpose of conducting large-scale traceability simulations. The model consists of two components; the first being a parallel, Monte Carlo discrete events simulator capable of generating large, NAIS-compliant, mock datasets representative of the processing requirements of actual NAIS data. The second component is a large-scale, parallel disease tracing algorithm that is mapped onto an SMP supercomputer where high-performance is achieved by adopting a hybrid parallel programming model that mixes a shared memory multi-threading model (OpenMP) with a distributed memory message passing model (MPI). The objectives of this dissertation are to characterize the computational requirements of the NAIS, identify computational platforms and programming paradigms well suited to this effort, and to identify and address computational performance bottlenecks associated with large-scale tracing algorithms.
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Subject
high-performance computing
hybrid OpenMP/MPI
NAIS mock data processing
parallel random number generators
mathematics