Gauss-Seidel (GS) is a widely used iterative method for solving sparse linear sys-tems of equations and also known to be effective as a smoother in algebraic multigrid methods. Parallelization of GS is a challenging task since solving the sparse lower triangular system in GS constitutes a sequential bottleneck at each iteration. We propose a distributed-memory parallel GS (dmpGS) by implementing a parallel sparse triangular solver (stSpike) based on the Spike algorithm. stSpike decouples the global triangular system into smaller systems that can be solved concurrently and requires the solution of a much smaller reduced sparse lower triangular system which constitutes a sequential bottleneck. In order to alleviate this bottleneck and to reduce the communication over-head of dmpGS, we propose a partitioning and reordering model consisting of two phases. The first phase is a novel hypergraph partitioning model whose partitioning objective simultaneously encodes minimizing the reduced system size and the communication volume. The second phase is an in-block row reordering method for decreasing the nonzero count of the reduced system. Extensive experi-ments on a dataset consisting of 359 sparse linear systems verify the effectiveness of the proposed partitioning and reordering model in terms of reducing the communication and the sequential com-putational overheads. Parallel experiments on 12 large systems using up to 320 cores demonstrate that the proposed model significantly improves the scalability of dmpGS.