gECC: A GPU-based high-throughput framework for Elliptic Curve Cryptography

TL;DR

gECC is a GPU-optimized framework for elliptic curve cryptography that significantly enhances throughput by employing batch processing, microarchitecture optimization, and novel instruction reduction techniques, outperforming existing systems in cryptographic tasks.

Contribution

The paper introduces gECC, a GPU-based ECC framework with innovative batch execution and instruction optimization, achieving high throughput and efficiency improvements over prior GPU and CPU systems.

Findings

Achieves 5.56x speedup in ECDSA operations

Achieves 4.94x speedup in ECDH operations

Provides 1.56x performance improvement in blockchain applications

Abstract

Elliptic Curve Cryptography (ECC) is an encryption method that provides security comparable to traditional techniques like Rivest-Shamir-Adleman (RSA) but with lower computational complexity and smaller key sizes, making it a competitive option for applications such as blockchain, secure multi-party computation, and database security. However, the throughput of ECC is still hindered by the significant performance overhead associated with elliptic curve (EC) operations. This paper presents gECC, a versatile framework for ECC optimized for GPU architectures, specifically engineered to achieve high-throughput performance in EC operations. gECC incorporates batch-based execution of EC operations and microarchitecture-level optimization of modular arithmetic. It employs Montgomery's trick to enable batch EC computation and incorporates novel computation parallelization and memory management techniques to maximize the computation parallelism and minimize the access overhead of GPU global memory. Also, we analyze the primary bottleneck in modular multiplication by investigating how the user codes of modular multiplication are compiled into hardware instructions and what these instructions' issuance rates are. We identify that the efficiency of modular multiplication is highly dependent on the number of Integer Multiply-Add (IMAD) instructions. To eliminate this bottleneck, we propose techniques to minimize the number of IMAD instructions by leveraging predicate registers to pass the carry information and using addition and subtraction instructions (IADD3) to replace IMAD instructions. Our results show that, for ECDSA and ECDH, gECC can achieve performance improvements of 5.56x and 4.94x, respectively, compared to the state-of-the-art GPU-based system. In a real-world blockchain application, we can achieve performance improvements of 1.56x, compared to the state-of-the-art CPU-based system.