GIF-FHE: A Comprehensive Implementation and Evaluation of GPU-Accelerated FHE With Integer and Floating-Point Computing Power

IF 5.6 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

IEEE Transactions on Parallel and Distributed Systems Pub Date : 2025-03-28 DOI:10.1109/TPDS.2025.3574481

Fangyu Zheng;Guang Fan;Wenxu Tang;Yixuan Song;Tian Zhou;Yuan Zhao;Jiankuo Dong;Jingqiang Lin;Shoumeng Yan;Jiwu Jing

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引用次数: 0

Abstract

Fully Homomorphic Encryption (FHE) allows computations on encrypted data without revealing the plaintext, garnering significant interest from both academic and industrial communities. However, its broader adoption has been hindered by performance limitations. Consequently, researchers have turned to GPUs for efficient FHE implementation. Nevertheless, most have predominantly favored integer units due to their ease of use, overlooking the considerable computational potential of floating-point units in GPUs. Recognizing this untapped floating-point computational power, our article introduces GIF-FHE, an extensive exploration and implementation of FHE, leveraging GPUs’ integer and floating-point instructions for FHE acceleration. We develop a comprehensive suite of low-level and middle-level FHE primitives, offering multiple implementation variants with support for three word size configurations (

$64/52/32$

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GIF-FHE：具有整数和浮点计算能力的gpu加速FHE的综合实现和评估

完全同态加密（FHE）允许在不暴露明文的情况下对加密数据进行计算，引起了学术界和工业界的极大兴趣。然而，它的广泛采用受到性能限制的阻碍。因此，研究人员已经转向gpu高效的FHE实现。然而，由于易于使用，大多数人都倾向于整数单位，而忽略了gpu中浮点单位的巨大计算潜力。认识到这种未开发的浮点计算能力，我们的文章介绍了GIF-FHE，这是对FHE的广泛探索和实现，利用gpu的整数和浮点指令来加速FHE。我们开发了一套全面的低级和中级FHE原语，提供多种实现变体，支持三种字长配置（$64/52/32$）。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Parallel and Distributed Systems 工程技术-工程：电子与电气

CiteScore

11.00

自引率

9.40%

发文量

281

审稿时长

5.6 months

期刊介绍： IEEE Transactions on Parallel and Distributed Systems (TPDS) is published monthly. It publishes a range of papers, comments on previously published papers, and survey articles that deal with the parallel and distributed systems research areas of current importance to our readers. Particular areas of interest include, but are not limited to: a) Parallel and distributed algorithms, focusing on topics such as: models of computation; numerical, combinatorial, and data-intensive parallel algorithms, scalability of algorithms and data structures for parallel and distributed systems, communication and synchronization protocols, network algorithms, scheduling, and load balancing. b) Applications of parallel and distributed computing, including computational and data-enabled science and engineering, big data applications, parallel crowd sourcing, large-scale social network analysis, management of big data, cloud and grid computing, scientific and biomedical applications, mobile computing, and cyber-physical systems. c) Parallel and distributed architectures, including architectures for instruction-level and thread-level parallelism; design, analysis, implementation, fault resilience and performance measurements of multiple-processor systems; multicore processors, heterogeneous many-core systems; petascale and exascale systems designs; novel big data architectures; special purpose architectures, including graphics processors, signal processors, network processors, media accelerators, and other special purpose processors and accelerators; impact of technology on architecture; network and interconnect architectures; parallel I/O and storage systems; architecture of the memory hierarchy; power-efficient and green computing architectures; dependable architectures; and performance modeling and evaluation. d) Parallel and distributed software, including parallel and multicore programming languages and compilers, runtime systems, operating systems, Internet computing and web services, resource management including green computing, middleware for grids, clouds, and data centers, libraries, performance modeling and evaluation, parallel programming paradigms, and programming environments and tools.