Parallel Graph Partitioning on a CPU-GPU Architecture

Graph partitioning has important applications in multiple areas of computing, including scheduling, social networks, and parallel processing. In recent years, GPUs have proven successful at accelerating several graph algorithms. However, the irregular nature of the real-world graphs poses a problem for GPUs, which favor regularity. In this paper, we discuss the design and implementation of a parallel multilevel graph partitioner for a CPU-GPU system. The partitioner aims to overcome some of the challenges arising due to memory constraints on GPUs and maximizes the utilization of GPU threads through suitable load-balancing schemes. We present a lock-free shared-memory scheme since fine-grained synchronization among thousands of threads imposes too high a performance overhead. The partitioner, implemented in CUDA, outperforms serial Metisand parallel MPI-based ParMetis. It performs similar to theshared-memory CPU-based parallel graph partitioner mt-metis.

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