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LDA is a statistical approach for topic modeling with a wide range ofapplications. However, there exist very few attempts to accelerate LDA on GPUswhich come with exceptional computing and memory throughput capabilities. Tothis end, we introduce EZLDA which achieves efficient and scalable LDA trainingon GPUs with the following three contributions: First, EZLDA introducesthree-branch sampling method which takes advantage of the convergenceheterogeneity of various tokens to reduce the redundant sampling task. Second,to enable sparsity-aware format for both D and W on GPUs with fast sampling andupdating, we introduce hybrid format for W along with corresponding tokenpartition to T and inverted index designs. Third, we design a hierarchicalworkload balancing solution to address the extremely skewed workload imbalanceproblem on GPU and scaleEZLDA across multiple GPUs. Taken together, EZLDAachieves superior performance over the state-of-the-art attempts with lowermemory consumption.

EZLDA: Efficient and Scalable LDA on GPUs