14003
Guy L. Steele Jr. (Oracle Labs), Jean-Baptiste Tristan
We describe a technique for drawing values from discrete distributions, such as sampling from the random variables of a mixture model, that avoids computing a complete table of partial sums of the relative probabilities. A table of alternate ("butterfly-patterned") form is faster to compute, making better use of coalesced memory accesses. From this table, complete […]
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Mian Lu, Ge Bai, Qiong Luo, Jie Tang, Jiuxin Zhao
We present the design and implementation of GLDA, a library that utilizes the GPU (Graphics Processing Unit) to perform Gibbs sampling of Latent Dirichlet Allocation (LDA) on a single machine. LDA is an effective topic model used in many applications, e.g., classification, feature selection, and information retrieval. However, training an LDA model on large data […]
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Tomonari Masada, Tsuyoshi Hamada, Yuichiro Shibata, Kiyoshi Oguri
In this paper, we propose an acceleration of collapsed variational Bayesian (CVB) inference for latent Dirichlet allocation (LDA) by using Nvidia CUDA compatible devices. While LDA is an efficient Bayesian multi-topic document model, it requires complicated computations for parameter estimation in comparison with other simpler document models, e.g. probabilistic latent semantic indexing, etc. Therefore, we […]
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