High-Throughput Elliptic Curve Cryptography Using AVX2 Vector Instructions

Cheng, Hongbo; Großschädl, Johann; Tian, J.; Rønne, Peter B.; Ryan, Peter Y. A.

doi:10.1007/978-3-030-81652-0_27

Cited by 7 publications

(2 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Faz-Hernández et al [13] harnessed AVX2 SIMD vector instructions, yielding substantial performance enhancements for ECC on contemporary Intel x86-64 architectures. In a similar vein, Cheng et al [14] introduced a throughput-optimised AVX2 implementation of variable-base scalar multiplication. It is important to note that none of these references involve the utilisation of the parallel formulae of Hisil et al [6].…”

Section: Related Workmentioning

confidence: 99%

Armed with Faster Crypto: Optimizing Elliptic Curve Cryptography for ARM Processors

De Smet,

Blancquaert,

Godden

et al. 2024

Sensors

View full text Add to dashboard Cite

Elliptic curve cryptography is a widely deployed technology for securing digital communication. It is the basis of many cryptographic primitives such as key agreement protocols, digital signatures, and zero-knowledge proofs. Fast elliptic curve cryptography relies on heavily optimised modular arithmetic operations, which are often tailored to specific micro-architectures. In this article, we study and evaluate optimisations of the popular elliptic curve Curve25519 for ARM processors. We specifically target the ARM NEON single instruction, multiple data (SIMD) architecture, which is a popular architecture for modern smartphones. We introduce a novel representation for 128-bit NEON SIMD vectors, optimised for SIMD parallelisation, to accelerate elliptic curve operations significantly. Leveraging this representation, we implement an extended twisted Edwards curve Curve25519 back-end within the popular Rust library “curve25519-dalek”. We extensively evaluate our implementation across multiple ARM devices using both cryptographic benchmarks and the benchmark suite available for the Signal protocol. Our findings demonstrate a substantial back-end speed-up of at least 20% for ARM NEON, along with a noteworthy speed improvement of at least 15% for benchmarked Signal functions.

show abstract

Section: Related Workmentioning

confidence: 99%

Armed with Faster Crypto: Optimizing Elliptic Curve Cryptography for ARM Processors

De Smet,

Blancquaert,

Godden

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…The results of optimization research using AVX for various cryptographic algorithms (hash function, block cipher, Elliptic Curve Cryptography, PQC, etc.) have been published [19][20][21][22][23]. The study by Kim et al is an optimal LSH implementation method using AVX-2 [19].…”

Section: Introductionmentioning

confidence: 99%

Parallel Implementation of Lightweight Secure Hash Algorithm on CPU and GPU Environments

Choi,

Seo

2024

Electronics

View full text Add to dashboard Cite

Currently, cryptographic hash functions are widely used in various applications, including message authentication codes, cryptographic random generators, digital signatures, key derivation functions, and post-quantum algorithms. Notably, they play a vital role in establishing secure communication between servers and clients. Specifically, servers often need to compute a large number of hash functions simultaneously to provide smooth services to connected clients. In this paper, we present highly optimized parallel implementations of Lightweight Secure Hash (LSH), a hash algorithm developed in Korea, on server sides. To optimize LSH performance, we leverage two parallel architectures: AVX-512 on high-end CPUs and NVIDIA GPUs. In essence, we introduce a word-level parallel processing design suitable for AVX-512 instruction sets and a data parallel processing design appropriate for the NVIDIA CUDA platform. In the former approach, we parallelize the core functions of LSH using AVX-512 registers and instructions. As a result, our first implementation achieves a performance improvement of up to 50.37% compared to the latest LSH AVX-2 implementation. In the latter approach, we optimize the core operation of LSH with CUDA PTX assembly and apply a coalesced memory access pattern. Furthermore, we determine the optimal number of blocks/threads configuration and CUDA streams for RTX 2080Ti and RTX 3090. Consequently, in the RTX 3090 architecture, our optimized CUDA implementation achieves about a 180.62% performance improvement compared with the initially ported LSH implementation to the CUDA platform. As far as we know, this is the first work on optimizing LSH with AVX-512 and NVIDIA GPU. The proposed implementation methodologies can be used alone or together in a server environment to achieve the maximum throughput of LSH computation.

show abstract

SMCOS: Fast and Parallel Modular Multiplication on ARM NEON Architecture for ECC

Wang

Lin

et al. 2021

Information Security and Cryptology

View full text Add to dashboard Cite

High-Throughput Elliptic Curve Cryptography Using AVX2 Vector Instructions

Cited by 7 publications

References 19 publications

Armed with Faster Crypto: Optimizing Elliptic Curve Cryptography for ARM Processors

Armed with Faster Crypto: Optimizing Elliptic Curve Cryptography for ARM Processors

Parallel Implementation of Lightweight Secure Hash Algorithm on CPU and GPU Environments

SMCOS: Fast and Parallel Modular Multiplication on ARM NEON Architecture for ECC

Contact Info

Product

Resources

About