论文标题
快速并行精确推断贝叶斯网络：海报
Fast Parallel Exact Inference on Bayesian Networks: Poster
论文作者
论文摘要
贝叶斯网络（BNS）很有吸引力，因为它们是图形和可解释的机器学习模型。但是，对BNS的精确推断很耗时，尤其是对于复杂问题。为了提高效率，我们提出了一种在多核CPU上的快速BN精确推理解决方案。 Fast-BNI通过混合并行性，提高了精确推断的效率，从而紧密整合了粗粒和细粒并行性。我们还提出了技术，以进一步简化BN精确推断的瓶颈操作。 Fast-BNI源代码可在https://github.com/jjiantong/fastbn上免费获得。
Bayesian networks (BNs) are attractive, because they are graphical and interpretable machine learning models. However, exact inference on BNs is time-consuming, especially for complex problems. To improve the efficiency, we propose a fast BN exact inference solution named Fast-BNI on multi-core CPUs. Fast-BNI enhances the efficiency of exact inference through hybrid parallelism that tightly integrates coarse- and fine-grained parallelism. We also propose techniques to further simplify the bottleneck operations of BN exact inference. Fast-BNI source code is freely available at https://github.com/jjiantong/FastBN.
