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arXiv · 2402.02320

Spin: An Efficient Secure Computation Framework with GPU Acceleration

Wuxuan Jiang·Xiangjun Song·Shenbai Hong·Haijun Zhang·Wenxin Liu·Bo Zhao·Wei Xu·Yi Li
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Abstract

Accuracy and efficiency remain challenges for multi-party computation (MPC) frameworks. Spin is a GPU-accelerated MPC framework that supports multiple computation parties and a dishonest majority adversarial setup. We propose optimized protocols for non-linear functions that are critical for machine learning, as well as several novel optimizations specific to attention that is the fundamental unit of Transformer models, allowing Spin to perform non-trivial CNNs training and Transformer inference without sacrificing security. At the backend level, Spin leverages GPU, CPU, and RDMA-enabled smart network cards for acceleration. Comprehensive evaluations demonstrate that Spin can be up to $2\times$ faster than the state-of-the-art for deep neural network training. For inference on a Transformer model with 18.9 million parameters, our attention-specific optimizations enable Spin to achieve better efficiency, less communication, and better accuracy.

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Wuxuan Jiang, Xiangjun Song, Shenbai Hong, Haijun Zhang, Wenxin Liu, Bo Zhao, Wei Xu, Yi Li. 2024-02-04. Spin: An Efficient Secure Computation Framework with GPU Acceleration. https://arxiv.org/abs/2402.02320

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