  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.
