Parallel implementation of GCM on GPUs
JaeSeok Lee, DongCheon Kim, Seog Chung Seo · ICT Express · 2025
This paper presents the first fully parallelized optimization of GCM in a GPU environment. As the era of IoT emerges, a large number of clients communicate with servers, necessitating encrypted communications for security. GCM is a type of AEAD and is currently used in various security protocols , including TLS 1.3 and IPsec . Due to the burden of performing encrypted communication with numerous clients, there has been significant research on utilizing GPUs for high-speed parallel processing in encryption. However, to date, there has been no fully parallelized implementation of GCM on GPUs. This paper proposes a method for parallelizing the challenging GHASH computation in GCM mode, leading to a high-speed parallel implementation of AES-GCM that can exceed 400Gb/s, meeting the requirements of next-generation communication systems . The proposed approach is algorithm-independent and can be applied to any block ciphers . Our implementation on an RTX 4090 demonstrates a performance improvement of × 15 . 38 compared to the maximum processing throughput of a multi-threaded Intel(R) Core(TM) i7-13700K. It also achieves a × 17 . 87 improvement compared to a hybrid CPU–GPU system. Compared to the most researched FPGA implementation for GCM, specifically Xilinx Ultrascale FPGA , our implementation achieves × 1 . 11 better performance. For not only throughput but also power efficiency also better than other implementation, it achieves × 3 . 33 compared to CPU implementation on Intel Xeon E3-1220, also it achieves × 21 . 09 compared to FPGA implementation for AES on Xilinx Virtex 7 series, which is not including full GCM.