Towards high-throughput gibbs sampling at scale: a study across storage managers

Factor graphs and Gibbs sampling are a popular combination for Bayesian statistical methods that are used to solve diverse problems including insurance risk models, pricing models, and information extraction. Given a fixed sampling method and a fixed amount of time, an implementation of a sampler that achieves a higher throughput of samples will achieve a higher quality than a lower-throughput sampler. We study how (and whether) traditional data processing choices about materialization, page layout, and buffer-replacement policy need to be changed to achieve high-throughput Gibbs sampling for factor graphs that are larger than main memory. We find that both new theoretical and new algorithmic techniques are required to understand the tradeoff space for each choice. On both real and synthetic data, we demonstrate that traditional baseline approaches may achieve two orders of magnitude lower throughput than an optimal approach. For a handful of popular tasks across several storage backends, including HBase and traditional unix files, we show that our simple prototype achieves competitive (and sometimes better) throughput compared to specialized state-of-the-art approaches on factor graphs that are larger than main memory.