Enterprise-grade permissioned blockchain systems provide a promising infrastructure for data sharing and cooperation between different companies. However, performance bottlenecks seriously hinder the adoption of these systems in many industrial applications that process complex business logic and huge transaction volumes. Our research identifies two key factors that limit the system performance: 1) At the block level, the serial dependency of inter-block processing severely limits the system throughput. A new block must wait for the completion of all previous blocks. 2) At the transaction level, the lack of efficient intra-block transactions concurrency makes it difficult to achieve high performance, especially when dealing with multiple CPU-heavy contracts which are commonly used in industrial scenarios.

The Elliptic Curve Digital Signature Algorithm (ECDSA) is an essential building block of various cryptographic protocols. In particular, most blockchain systems adopt it to ensure transaction integrity. However, due to its high computational intensity, ECDSA is often the performance bottleneck in blockchain transaction processing. Recent work has accelerated ECDSA algorithms on the CPU; in contrast, success has been limited on the GPU, which has great potential for parallelization but is challenging for implementing elliptic curve functions. In this paper, we propose RapidEC, a GPU-based ECDSA implementation for SM2, a popular elliptic curve. Specifically, we design architecture-aware parallel primitives for elliptic curve point operations, and parallelize the processing of a single SM2 request as well as batches of requests. Consequently, our GPU-based RapidEC outperformed the state-of-the-art CPU-based algorithm by orders of magnitude. Additionally, our GPU-based modular arithmetic functions as well as point operation primitives can be applied to other computation tasks.