Bootstrap Estimation and Model Selection for Multivariate Normal Mixtures using Parallel Computing with Graphics Processing Units
Bootstrap Estimation and Model Selection for Multivariate Normal Mixtures using Parallel Computing with Graphics Processing Units
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使用图形处理单元并行计算进行多元正态混合物的自举估计和模型选择
DOI：
10.1080/03610918.2017.1311916
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
T.
中科院分区：
文献类型：
--
作者：
Iida;M.;Miata;Y.;and Shiohama;T.
在多元有限混合模型的应用中，未知成分的个数估计往往是一个难题。我们提出了一个自助信息准则，据此我们计算预期的对数似然最大后验估计模型选择。使用bootstrap的准确估计需要大量的bootstrap重复。我们通过在计算统一设备架构（CUDA）平台上使用图形处理单元（GPU）进行并行处理来加速此计算。我们进行了CUDA算法在GPU上的实现和在CPU上的实现之间的运行时比较。结果表明，所提出的CUDA算法在多线程CPU上具有显着的性能增益。
In applications of multivariate finite mixture models, estimating the number of unknown components is often difficult. We propose a bootstrap information criterion, whereby we calculate the expected log-likelihood at maximum a posteriori estimates for model selection. Accurate estimation using the bootstrap requires a large number of bootstrap replicates. We accelerate this computation by employing parallel processing with graphics processing units (GPUs) on the Compute Unified Device Architecture (CUDA) platform. We conducted a runtime comparison of CUDA algorithms between implementation on the GPU and that on a CPU. The results showed significant performance gains in the proposed CUDA algorithms over multithread CPUs.