EZLDA：GPU上高效且可扩展的LDA
EZLDA: Efficient and Scalable LDA on GPUs
摘要：
LDA是一种具有广泛应用的主题建模的统计方法。然而，很少有人尝试在GPU上加速LDA，这些GPU具有出色的计算和内存吞吐量能力。为此，我们引入了EZLDA，它在GPU上实现了高效和可扩展的LDA训练，具有以下三个贡献：首先，EZLDA引入了三分支采样方法，该方法利用各种令牌的收敛异构性来减少冗余采样任务。其次，为了在GPU上实现具有快速采样和更新的D和W的稀疏感知格式，我们引入了W的混合格式以及对应的令牌分区到T和倒排索引设计。第三，我们设计了一个分层的工作负载平衡解决方案，以解决GPU上的极端倾斜的工作负载不平衡问题和跨多个GPU的scaleEZLDA。总的来说，EZLDA实现了比最先进的尝试更低的内存消耗优越的性能。
英文摘要：
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.
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