HI-Kyber:基于 GPU 的 Kyber 新型高性能实施方案

IF 5.6 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS IEEE Transactions on Parallel and Distributed Systems Pub Date : 2024-03-20 DOI:10.1109/TPDS.2024.3379734
Xinyi Ji;Jiankuo Dong;Tonggui Deng;Pinchang Zhang;Jiafeng Hua;Fu Xiao
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引用次数: 0

摘要

CRYSTALS-Kyber是美国国家标准与技术研究院(NIST)在第三轮评选中唯一入选的公钥加密(PKE)算法,被认为是最有前途的后量子加密(PQC)方案之一。基于网格的密码学利用网格上复杂的离散算法问题来构建安全的加密和解密系统,以抵御来自量子计算的攻击。性能是影响后量子密码学推广的重要瓶颈。本文提出了在英伟达™(NVIDIA®)GPU上的Kyber高性能实现(名为HI-Kyber),可以将Kyber的密钥交换性能提高到百万级别。首先,我们提出了一种基于网格的 PQC 实现架构,该架构基于内核融合,可以避免多余的全局内存访问操作。其次,我们优化并实现了 CRYSTALS-Kyber 的核心操作,包括数论变换(NTT)、逆数论变换(INTT)、点乘等。特别是针对计算瓶颈NTT操作,我们提出了三种新方法来探索极限性能:切片层合并(SLM)、切片深度优先搜索(SDFS-NTT)和整体深度优先搜索(EDFS-NTT),与原生实现相比,它们的速度分别提高了7.5%、28.5%和41.6%。第三,我们在上述优化的基础上进行了不同并行维度的综合性能实验。最后,我们的密钥交换性能达到了 1,664 kops/s。具体来说,在同一平台上,我们的 HI-Kyber 是基于相同指令集的 GPU 实现的 3.52 倍,是基于人工智能加速张量核的最先进实现的 1.78 倍。
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HI-Kyber: A Novel High-Performance Implementation Scheme of Kyber Based on GPU
CRYSTALS-Kyber, as the only public key encryption (PKE) algorithm selected by the National Institute of Standards and Technology (NIST) in the third round, is considered one of the most promising post-quantum cryptography (PQC) schemes. Lattice-based cryptography uses complex discrete algorithm problems on lattices to build secure encryption and decryption systems to resist attacks from quantum computing. Performance is an important bottleneck affecting the promotion of post quantum cryptography. In this paper, we present a High-performance Implementation of Kyber (named HI-Kyber) on the NVIDIA GPUs, which can increase the key-exchange performance of Kyber to the million-level. Firstly, we propose a lattice-based PQC implementation architecture based on kernel fusion, which can avoid redundant global-memory access operations. Secondly, We optimize and implement the core operations of CRYSTALS-Kyber, including Number Theoretic Transform (NTT), inverse NTT (INTT), pointwise multiplication, etc. Especially for the calculation bottleneck NTT operation, three novel methods are proposed to explore extreme performance: the sliced layer merging (SLM), the sliced depth-first search (SDFS-NTT) and the entire depth-first search (EDFS-NTT), which achieve a speedup of 7.5%, 28.5%, and 41.6% compared to the native implementation. Thirdly, we conduct comprehensive performance experiments with different parallel dimensions based on the above optimization. Finally, our key exchange performance reaches 1,664 kops/s. Specifically, based on the same platform, our HI-Kyber is 3.52× that of the GPU implementation based on the same instruction set and 1.78× that of the state-of-the-art one based on AI-accelerated tensor core.
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来源期刊
IEEE Transactions on Parallel and Distributed Systems
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.
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