Xiaolong Xie
ABSTRACT
GPUs have benefited many ML algorithms. However, we observe that the performance of existing Latent Dirichlet Allocation(LDA) solutions on GPUs are not satisfying. We present CuLDA_CGS, an efficient approach to accelerate large-scale LDA problems. We delicately design workload partition and synchronization mechanism to exploit multiple GPUs. We also optimize the algorithm from the sampling algorithm, parallelization, and data compression perspectives. Experiment evaluations show that compared with the state-of-the-art LDA solutions, CuLDA_CGS outperforms them by a large margin (up to 7.3X) on a single GPU.
- Jianfei Chen, Kaiwei Li, Jun Zhu, and Wenguang Chen. 2016. WarpLDA: A Cache Efficient O(1) Algorithm for Latent Dirichlet Allocation. Proc. VLDB Endow. (2016). Google Scholar
Digital Library
- James Foulds, Levi Boyles, Christopher DuBois, Padhraic Smyth, and Max Welling. 2013. Stochastic Collapsed Variational Bayesian Inference for Latent Dirichlet Allocation (KDD '13). Google ScholarDigital Library
- Kaiwei Li, Jianfei Chen, Wenguang Chen, and Jun Zhu. 2017. SaberLDA: Sparsity-Aware Learning of Topic Models on GPUs (ASPLOS '17). Google ScholarDigital Library
- Xiaolong Xie, Yun Liang, Guangyu Sun, and Deming Chen. 2013. An efficient compiler framework for cache bypassing on GPUs (ICCAD'13). Google ScholarDigital Library
- Limin Yao, David Mimno, and Andrew McCallum. 2009. Efficient Methods for Topic Model Inference on Streaming Document Collections (KDD '09). Google ScholarDigital Library
Index Terms
- CuLDA_CGS: solving large-scale LDA problems on GPUs
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