  Not only with the large host memory for supporting large scale graph
processing, GPU-accelerated heterogeneous architecture can also provide a great
potential for high-performance computing. However, few existing heterogeneous
systems can exploit both hardware advantages to enable the scale-up performance
for graph processing due to the limited CPU-GPU transmission efficiency.
  In this paper, we investigate the transmission inefficiency problem of
heterogeneous graph systems. Our key insight is that the transmission
efficiency for heterogeneous graph processing can be greatly improved by simply
iterating each subgraph multiple times (rather than only once in prior work) in
the GPU, further enabling to obtain the improvable efficiency of heterogeneous
graph systems by enhancing GPU processing capability. We therefore present
Seraph, with the highlights of {\em pipelined} subgraph iterations and {\em
predictive} vertex updating, to cooperatively maximize the effective
computations of GPU on graph processing. Our evaluation on a wide variety of
large graph datasets shows that Seraph outperforms state-of-the-art
heterogeneous graph systems by 5.42x (vs. Graphie) and 3.05x (vs. Garaph).
Further, Seraph can be significantly scaled up over Graphie as fed with more
computing power for large-scale graph processing.
