Publications of Torsten Hoefler

Marcin Chrapek, Mikhail Khalilov, Torsten Hoefler:

HEAR: Homomorphically Encrypted Allreduce

(In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC'23), presented in Denver, CO, USA, Association for Computing Machinery, ISBN: 979-8-400701-09-2, Nov. 2023)
SC23 Best student paper, SC23 Reproducibility Advancement Award

Publisher Reference

Abstract

Allreduce is one of the most commonly used collective operations. Its latency and bandwidth can be improved by offloading the calculations to the network. However, no way exists to conduct such offloading securely; in state-of-the-art solutions, the data is passed unprotected into the network. Security is a significant concern for High-Performance Computing applications, but achieving it while maintaining performance remains challenging. We present HEAR, the first high-performance system for securing in-network compute and Allreduce operations based on homomorphic encryption. HEAR implements carefully designed and modified encryption schemes for the most common Allreduce functions and leverages communication domain knowledge in MPI programs to obtain decryption and encryption routines with high performance. HEAR operates on integers and floats with no code base and no or little hardware changes. We design and evaluate HEAR, showing its minimal overhead, and open-source our implementation. HEAR represents the first step towards achieving confidential HPC.

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BibTeX

@inproceedings{,
  author={Marcin Chrapek and Mikhail Khalilov and Torsten Hoefler},
  title={{HEAR: Homomorphically Encrypted Allreduce}},
  year={2023},
  month={Nov.},
  booktitle={Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC'23)},
  location={Denver, CO, USA},
  publisher={Association for Computing Machinery},
  isbn={979-8-400701-09-2},
  source={http://www.unixer.de/~htor/publications/},
}


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