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cuGimli: optimized implementation of the Gimli authenticated encryption and hash function on GPU for IoT applications

Abstract

Recently, National Institute of Standards and Technology (NIST) in the U.S. had initiated a global-scale competition to standardize the lightweight authenticated encryption with associated data (AEAD) and hash function. Gimli is one of the Round 2 candidates that is designed to be efficiently implemented across various platforms, including hardware (VLSI and FPGA), microprocessors, and microcontrollers. However, the performance of Gimli in massively parallel architectures like Graphics Processing Units (GPU) is still unknown. A high performance Gimli implementation on GPU can be especially useful to Internet of Things (IoT) applications, wherein the gateway devices and cloud servers need to handle a massive number of communications protected by AEAD. In this paper, we show that with careful optimization, Gimli can be efficiently implemented in desktop and embedded GPU to achieve extremely high throughput. Our experiments show that the proposed Gimli implementation can achieve 661.44 KB/s (encryption), 892.24 KB/s (decryption), and 4344.46 KB/s (hashing) in state-of-the-art GPUs.

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Data availibility

This paper uses the code from NIST Lightweight Cryptography Standardization as a starting point to develop the optimized implementation on GPU. The code for Gimli hash and authenticated encryption can be found here: https://csrc.nist.gov/projects/lightweight-cryptography/round-2-candidates.

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Acknowledgements

This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2019H1D3A1A01102607, 2020R1A2B5B01002145, 2021R1A6A3A13038773).

Funding

This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2019H1D3A1A01102607, 2020R1A2B5B01002145, 2021R1A6A3A13038773).

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All authors contributed equally to the final dissemination of the research investigation as a full article. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Seong Oun Hwang.

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Han, K., Lee, WK. & Hwang, S.O. cuGimli: optimized implementation of the Gimli authenticated encryption and hash function on GPU for IoT applications. Cluster Comput (2021). https://doi.org/10.1007/s10586-021-03415-z

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Keywords

  • CUDA
  • GPU
  • Gimli
  • Authenticated encryption