  Processing large graphs with memory-limited GPU needs to resolve issues of
host-GPU data transfer, which is a key performance bottleneck. Existing
GPU-accelerated graph processing frameworks reduce the data transfers 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 access with zero-copy or unified-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.
  In this work, we propose a hybrid transfer management approach to take the
merits of both the two approaches at runtime, with an objective to achieve the
shortest execution time in each iteration. Based on the hybrid approach, we
present HytGraph, a GPU-accelerated graph processing framework, which is
empowered by a set of effective task scheduling optimizations to improve the
performance. Our experimental results on real-world and synthesized graphs
demonstrate that HyTGraph achieves up to 10.27X speedup over existing
GPU-accelerated graph processing systems including Grus, Subway, and EMOGI.
