Hardware/Software Co-Programmable Framework for Computational SSDs to Accelerate Deep Learning Service on Large-Scale Graphs


Miryeong Kwon, Donghyun Gouk, Sangwon Lee, and Myoungsoo Jung, Computer Architecture and Memory Systems Laboratory, Korea Advanced Institute of Science and Technology (KAIST)


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.

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This content is available to:

@inproceedings {277816,
author = {Miryeong Kwon and Donghyun Gouk and Sangwon Lee and Myoungsoo Jung},
title = {{Hardware/Software} {Co-Programmable} Framework for Computational {SSDs} to Accelerate Deep Learning Service on {Large-Scale} Graphs},
booktitle = {20th USENIX Conference on File and Storage Technologies (FAST 22)},
year = {2022},
isbn = {978-1-939133-26-7},
address = {Santa Clara, CA},
pages = {147--164},
url = {https://www.usenix.org/conference/fast22/presentation/kwon},
publisher = {USENIX Association},
month = feb,

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