A view of unconstrained optimization
Optimization
Text Categorization Based on Regularized Linear Classification Methods
Information Retrieval
Efficient computation of sum-products on GPUs through software-managed cache
Proceedings of the 22nd annual international conference on Supercomputing
Temporal pattern discovery for trends and transient effects: its application to patient records
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Many-core algorithms for statistical phylogenetics
Bioinformatics
Parallel multivariate slice sampling
Statistics and Computing
Mining Unexpected Temporal Associations: Applications in Detecting Adverse Drug Reactions
IEEE Transactions on Information Technology in Biomedicine
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Following a series of high-profile drug safety disasters in recent years, many countries are redoubling their efforts to ensure the safety of licensed medical products. Large-scale observational databases such as claims databases or electronic health record systems are attracting particular attention in this regard, but present significant methodological and computational concerns. In this article we show how high-performance statistical computation, including graphics processing units, relatively inexpensive highly parallel computing devices, can enable complex methods in large databases. We focus on optimization and massive parallelization of cyclic coordinate descent approaches to fit a conditioned generalized linear model involving tens of millions of observations and thousands of predictors in a Bayesian context. We find orders-of-magnitude improvement in overall run-time. Coordinate descent approaches are ubiquitous in high-dimensional statistics and the algorithms we propose open up exciting new methodological possibilities with the potential to significantly improve drug safety.