标题： 复杂广义线性模型的串行推理算法的大规模并行化 显示英文标题

标题： Massive parallelization of serial inference algorithms for a complex generalized linear model

摘要： 近年来发生了一系列引人注目的药品安全灾难，许多国家正在加倍努力确保已批准的医疗产品的安全性。大规模观察性数据库（如索赔数据库或电子健康记录系统）在这方面引起了特别的关注，但同时也带来了显著的方法学和计算上的担忧。 本文展示了高性能统计计算（包括图形处理单元，相对廉价的高度并行计算设备）如何能够在大型数据库中实现复杂方法。 我们专注于贝叶斯背景下拟合涉及数千万个观测值和数千个预测变量的条件广义线性模型的循环坐标下降法的优化和大规模并行化。我们发现总体运行时间提高了几个数量级。 坐标下降方法在高维统计中无处不在，我们提出的算法在药物安全性方面打开了令人兴奋的新方法可能性，并有可能显著改善药物安全性。 显示英文摘要

摘要： 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 paper 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.