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

Authors: 

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

Abstract: 

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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BibTeX
@inproceedings {277816,
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,
}

Presentation Video