A fast parallel graph partitioner can benefit many applications by reducing data transfers. The online methods for partitioning graphs have to be fast and they often rely on simple one-pass streaming algorithms, while the offline methods for partitioning graphs contain more involved algorithms and the most successful methods in this category belong to the multilevel approaches. In this work, we assess the feasibility of using streaming graph partitioning algorithms within the multilevel framework. Our end goal is to come up with a fast parallel offline multilevel partitioner that can produce competitive cutsize quality. We rely on a simple but fast and flexible streaming algorithm throughout the entire multilevel framework. This streaming algorithm serves multiple purposes in the partitioning process: a clustering algorithm in the coarsening, an effective algorithm for the initial partitioning, and a fast refinement algorithm in the uncoarsening. Its simple nature also lends itself easily for parallelization. The experiments on various graphs show that our approach is on the average up to 5.1x faster than the multi-threaded MeTiS, which comes at the expense of only 2x worse cutsize.

快速并行图分区程序可以减少数据传输，从而使许多应用程序受益。在线划分图的方法必须快速，并且它们通常依赖于简单的一次通过流算法，而离线划分图的方法则包含更多的算法，并且该类别中最成功的方法属于多级方法。在这项工作中，我们评估了在多级框架中使用流图分区算法的可行性。我们的最终目标是提出一种快速并行的脱机多级分区器，该分区器可以产生具有竞争力的裁切质量。在整个多级框架中，我们依靠一种简单但快速而灵活的流算法。这种流传输算法在分区过程中有多种用途：粗化中的聚类算法，一种有效的初始分区算法，以及粗化中的快速优化算法。它的简单性质也很容易实现并行化。在各种图形上进行的实验表明，我们的方法平均比多线程MeTiS快5.1倍，而牺牲的裁剪尺寸只有2倍。