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.

中文翻译：

---

使用张量网络进行并行加权模型计数

在从加权模型计数减少到张量网络收缩之后，一种有前途的加权模型计数新代数方法利用了张量网络。之前的工作重点是分析这种方法的单核性能，并证明它是对当前加权模型计数算法组合的有效补充。在这项工作中，我们探索了多核和 GPU 使用对加权模型计数的张量网络收缩的影响。为了利用多个内核，我们实现了一个并行的树分解求解器组合，以找到收缩张量的顺序。为了利用 GPU，我们使用 TensorFlow 来执行收缩。我们在 1914 年标准加权模型计数基准上比较所得加权模型计数器，并表明它显着提高了虚拟最佳求解器。