DPF-ECC: A Framework for Efficient ECC With Double Precision Floating-Point Computing Power
Lili Gao, Fangyu Zheng, Rong Wei, Jiankuo Dong, Niall Emmart, Yuan Ma, Jingqiang Lin, Charles Weems
Abstract
Used ubiquitously in a huge amount of security protocols or applications, elliptic curve cryptography (ECC) is one of the most important cryptographic primitives, featuring efficiency and short key size compared with other public-key cryptosystems such as DSA and RSA. However, as a computation-intensive public-key cryptographic primitive, ECC arithmetic is still the bottleneck that restrains the overall performance of the end applications. In this paper, instead of the conventional and straightforward integer-based methods, we present a general framework to accelerate ECC schemes over prime field, called DPF-ECC, that deeply exploits double precision floating-point (DPF) computing power. The DPF-ECC framework finely manages each bit of the DPF numbers and minimizes the overhead brought by additional data format conversion, by making use of the DPF representation, the rounding operations, and fused multiply-add instruction supported by the IEEE 754 floating point standard. We also conduct two comprehensive case studies on Crandall primes and Solinas primes to demonstrate how the DPF-ECC framework is applied to the prevailing ECC schemes. To evaluate the proposed DPF-ECC framework in the real world, leveraging the floating-point computing power of GPUs, we implement Curve25519/448 and Edwards25519/448, the popular ECC schemes widely used in TLS 1.3, SSH, etc. The experimental result in Tesla P100 achieves a record-setting performance that outperforms the existing fastest integer work with 2x to 3x throughput. With dependency only on the very commonly supported IEEE 754 floating point standard, DPF-ECC framework can be a very competent and promising candidate for ECC implementation in most of general-purpose platforms.