GAPS: GPU-accelerated processing service for SM9

AbstractSM9 was established in 2016 as a Chinese official identity-based cryptographic (IBC) standard, and became an ISO standard in 2021. It is well-known that IBC is suitable for Internet of Things (IoT) applications, since a centralized processing of client data (e.g. IoT cloud) is often done by gateways. However, due to limited computation resources inside IoT devices, the performance of SM9 becomes a bottleneck in practical usage. The existing SM9 implementations are often CPU-based, with relatively low latency and low throughput. Consequently, a pivotal challenge for SM9 in large-scale applications is how to reduce the latency while maximizing throughput for numerous concurrent inputs. After a systematic analysis of the SM9 algorithms, we apply optimization techniques including precomputation, resource caching and parallelization to reduce the overhead of SM9. In this work, we introduce the first practical implementation of SM9 and its underlying curve on GPU. Our GPU implementation combines multiple algorithms and low-level optimizations tailored for GPU’s single instruction, multiple threads architecture in order to achieve high throughput for SM9. Based on these, we propo