  Graph neural networks (GNNs) process large-scale graphs consisting of a
hundred billion edges. In contrast to traditional deep learning, unique
behaviors of the emerging GNNs are engaged with a large set of graphs and
embedding data on storage, which exhibits complex and irregular preprocessing.
  We propose a novel deep learning framework on large graphs, HolisticGNN, that
provides an easy-to-use, near-storage inference infrastructure for fast,
energy-efficient GNN processing. To achieve the best end-to-end latency and
high energy efficiency, HolisticGNN allows users to implement various GNN
algorithms and directly executes them where the actual data exist in a holistic
manner. It also enables RPC over PCIe such that the users can simply program
GNNs through a graph semantic library without any knowledge of the underlying
hardware or storage configurations.
  We fabricate HolisticGNN's hardware RTL and implement its software on an
FPGA-based computational SSD (CSSD). Our empirical evaluations show that the
inference time of HolisticGNN outperforms GNN inference services using
high-performance modern GPUs by 7.1x while reducing energy consumption by
33.2x, on average.
