Towards Communication-Efficient Out-of-Core Graph Processing on the GPU

The key performance bottleneck of large-scale graph processing on memory-limited GPUs is the host-GPU graph data transfer. Existing GPU-accelerated graph processing frameworks address this issue by managing the active subgraph transfer at runtime. Some frameworks adopt explicit transfer management approaches based on explicit memory copy with filter or compaction. In contrast, others adopt implicit transfer management approaches based on on-demand accesses with the zero-copy mechanism or unified virtual memory. Having made intensive analysis, we find that as the active vertices evolve, the performance of the two approaches varies in different workloads. Due to heavy redundant data transfers, high CPU compaction overhead, or low bandwidth utilization, adopting a single approach often results in suboptimal performance. Moreover, these methods lack effective cache management methods to address the irregular and sparse memory access pattern of graph processing. In this work, we propose a hybrid transfer management approach that takes the merits of both two transfer approaches at runtime. Moreover, we present an efficient vertex-centric graph caching framework that minimizes CPU-GPU communication by caching frequently accessed graph data at runtime. Based on these techniques, we present HytGraph, a GPU-accelerated graph processing framework, which is empowered by a set of effective task-scheduling optimizations to improve performance. Experiments on real-world and synthetic graphs show that HytGraph achieves average speedups of 2.5 ×, 5.0 ×, and 2.0 × compared to the state-of-the-art GPU-accelerated graph processing systems, Grus, Subway, and EMOGI, respectively.