  LDA is a statistical approach for topic modeling with a wide range of
applications. However, there exist very few attempts to accelerate LDA on GPUs
which come with exceptional computing and memory throughput capabilities. To
this end, we introduce EZLDA which achieves efficient and scalable LDA training
on GPUs with the following three contributions: First, EZLDA introduces
three-branch sampling method which takes advantage of the convergence
heterogeneity 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 and
updating, we introduce hybrid format for W along with corresponding token
partition to T and inverted index designs. Third, we design a hierarchical
workload balancing solution to address the extremely skewed workload imbalance
problem on GPU and scaleEZLDA across multiple GPUs. Taken together, EZLDA
achieves superior performance over the state-of-the-art attempts with lower
memory consumption.
