EZLDA: Efficient and Scalable LDA on GPUs
This work addresses the bottleneck of inefficient LDA training on GPUs for applications in topic modeling, representing an incremental improvement with specific optimizations.
The paper tackles the problem of accelerating Latent Dirichlet Allocation (LDA) topic modeling on GPUs by introducing EZLDA, which achieves superior performance over state-of-the-art methods with lower memory consumption through efficient sampling, sparsity-aware formats, and workload balancing.
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.