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HEAR: Homomorphically Encrypted Allreduce
Marcin Chrapek, Mikhail Khalilov, Torsten Hoefler
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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- Up or Down? Adaptive Rounding for Post-Training QuantizationMarkus Nagel, Rana Ali Amjad, Mart van Baalen, Christos Louizos et al.ICML 2020 · 816 citations
- Enabling Rack-scale Confidential Computing using Heterogeneous Trusted Execution EnvironmentJianping Zhu, Rui Hou, XiaoFeng Wang, Wenhao Wang et al.S&P 2020 · 95 citations
- An In-Network Architecture for Accelerating Shared-Memory Multiprocessor CollectivesBenjamin Klenk, Nan Jiang, Greg Thorson, Larry DennisonISCA 2020 · 67 citations
- ReDMArk: Bypassing RDMA Security MechanismsBenjamin Rothenberger, Konstantin Taranov, Adrian Perrig, Torsten HoeflerUSENIX Security 2021 · 56 citations
- Flare: flexible in-network allreduceDaniele De Sensi, Salvatore Di Girolamo, Saleh Ashkboos, Shigang Li et al.SC 2021 · 49 citations
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