Parallel Graph Partitioning on Multicore Architectures

Graph partitioning is a common and frequent preprocessing step in many high-performance parallel applications on distributedand shared-memory architectures. It is used to distribute graphs across memory and to improve spatial locality. There are several parallel implementations of graph partitioning for distributed-memory architectures. In this paper, we present a parallel graph partitioner that implements a variation of the Metis partitioner for shared-memory, multicore architectures. We show that (1) the parallelism in this algorithm is an instance of the general amorphous data-parallelism pattern, and (2) a parallel implementation can be derived systematically from a sequential specification of the algorithm. The resulting program can be executed in parallel using the Galois system for optimistic parallelization. The scalability of this parallel implementation compares favorably with that of a publicly available, hand-parallelized C implementation of the algorithm, ParMetis, but absolute performance is lower because of missing sequential optimizations in our system. On a set of 15 large, publicly available graphs, we achieve an average scalability of 2.98X on 8 cores with our implementation, compared with 1.77X for ParMetis, and we achieve an average speedup of 2.80X over Metis, compared with 3.60X for ParMetis. These results show that our systematic approach for parallelizing irregular algorithms on multicore architectures is promising.