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When multi-core statistical computing fails for massive sample sizes

Banff International Research Station for Mathematical Innovation and Discovery

Much of statistical computing is memory-bandwidth limited, not floating-pointing operation throughput limited as commonly assumed. This often restricts the utility of multi-core computing techniques to improve statistical estimation run-time. I explore this conundrum in inference tools for a massive Bayesian model of sea-surface temperatures across the global. I describe approaches for computing the data likelihood that exploit fine-scale parallelization for potential scalability to real-time satellite surveillance data. These simple algorithmic changes open the door on using advancing computing technology involving many-core architectures. These architectures provide significantly higher memory-bandwidth and inexpensively afford order-of-magnitude run-time speed-ups.

Mathematics; Statistics; Biology and other natural sciences; Applied statistics

video/mp4

Author affiliation: University of California at Los Angeles

2014-08-07

Attribution-NonCommercial-NoDerivs 2.5 Canada

http://hdl.handle.net/2429/49831

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/2.5/ca/