论文标题
通过张量网络计数并行加权模型
Parallel Weighted Model Counting with Tensor Networks
论文作者
论文摘要
在加权模型计数减少到张量 - 网络收缩之后，一种有希望的加权模型计数的新代数方法可利用张量网络。先前的工作重点是分析这种方法的单核性能，并证明它是当前加权模型计数算法组合的有效补充。 在这项工作中，我们探讨了多核和GPU使用对加权模型计数的张量网络收缩的影响。为了利用多个核心，我们实施了一个平行的树分解求解器组合，以找到收缩张量的订单。为了利用GPU，我们使用TensorFlow执行收缩。我们比较了1914年标准加权模型计数基准的产生加权模型计数器，并表明它显着改善了虚拟的最佳求解器。
A promising new algebraic approach to weighted model counting makes use of tensor networks, following a reduction from weighted model counting to tensor-network contraction. Prior work has focused on analyzing the single-core performance of this approach, and demonstrated that it is an effective addition to the current portfolio of weighted-model-counting algorithms. In this work, we explore the impact of multi-core and GPU use on tensor-network contraction for weighted model counting. To leverage multiple cores, we implement a parallel portfolio of tree-decomposition solvers to find an order to contract tensors. To leverage a GPU, we use TensorFlow to perform the contractions. We compare the resulting weighted model counter on 1914 standard weighted model counting benchmarks and show that it significantly improves the virtual best solver.
