lib gECC

Introduction

This project presents gECC, a versatile framework for ECC optimized for GPU architectures, specifically engineered to achieve high-throughput performance in EC operations. To maximize throughput, gECC incorporates batch-based execution (using Montgomery’s trick) of EC operations and microarchitecture-level optimization of modular arithmetic.

Copyright (C) 2024, BDTS/STCS/CGCL and Huazhong University of Science and Technology.

📄 Publication

Our work on gECC has been accepted to appear in ACM Transactions on Architecture and Code Optimization (TACO).

• Title: "gECC: A GPU-based high-throughput framework for Elliptic Curve Cryptography"
• Authors: Qian Xiong, Weiliang Ma, Xuanhua Shi, Yongluan Zhou, Hai Jin, Kaiyi Huang, Haozhou Wang, Zhengru Wang
• Journal: ACM Transactions on Architecture and Code Optimization (TACO)
• Status: Accepted, to appear
• Preprint: gECC available on arXiv

📢 We will update the final version and BibTeX entry once the paper is published online.

Files

| Files | description | | -------- | -------- | | test | all performance analysis benchmarks | | scripts | define finite field related parameters, generate test data for benchmark. | | gecc/arith | implemente ec operation (with multiple coordinate system) and modular operation on finite filed| | gecc/ecdsa | implemente ECDSA algorithm|

Prerequisites

GTest

wget https://github.com/google/googletest/archive/release-1.10.0.tar.gz tar xzvf release-1.10.0.tar.gz cmake -BBuild -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=${HOME}/.local/opt/gtest . cmake --build Build --target install export GTEST_ROOT=${HOME}/.local/opt/gtest

Build and Run

To evaluate performance: python3 ./dev-support/build.py -R -A 80 ./bench.sh

Support or Contact

gECC is developed in National Engineering Research Center for Big Data Technology and System, Cluster and Grid Computing Lab, Services Computing Technology and System Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China by Qian Xiong(qianxiong@hust.edu.cn), Weiliang Ma(weiliangma@hust.edu.cn) and Xuanhua Shi(xhshi@hust.edu.cn).

If you have any questions, please contact Qian Xiong(qianxiong@hust.edu.cn), Weiliang Ma(weiliangma@hust.edu.cn) and Xuanhua Shi(xhshi@hust.edu.cn). We welcome you to commit your modification to support our project.