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Latent Dirichlet Allocation(LDA) is a popular topic model. Given the factthat the input corpus of LDA algorithms consists of millions to billions oftokens, the LDA training process is very time-consuming, which may prevent theusage of LDA in many scenarios, e.g., online service. GPUs have benefitedmodern machine learning algorithms and big data analysis as they can providehigh memory bandwidth and computation power. Therefore, many frameworks, e.g.Ten- sorFlow, Caffe, CNTK, support to use GPUs for accelerating the popularmachine learning data-intensive algorithms. However, we observe that LDAsolutions on GPUs are not satisfying.  In this paper, we present CuLDA_CGS, a GPU-based efficient and scalableapproach to accelerate large-scale LDA problems. CuLDA_CGS is designed toefficiently solve LDA problems at high throughput. To it, we first delicatelydesign workload partition and synchronization mechanism to exploit the benefitsof mul- tiple GPUs. Then, we offload the LDA sampling process to eachindividual GPU by optimizing from the sampling algorithm, par- allelization,and data compression perspectives. Evaluations show that compared withstate-of-the-art LDA solutions, CuLDA_CGS outperforms them by a large margin(up to 7.3X) on a single GPU. CuLDA_CGS is able to achieve extra 3.0X speedupon 4 GPUs. The source code is publicly available on https://github.com/cuMF/CuLDA_CGS.

CuLDA_CGS: Solving Large-scale LDA Problems on GPUs