Fully homomorphic encryption (FHE) frees cloud computing from privacy concerns by enabling secure computation on encrypted data. However, its substantial computational and memory overhead results in significantly slower performance compared to unencrypted processing. To mitigate this overhead, we present Cheddar, a high-performance FHE library for GPUs, achieving substantial speedups over previous GPU implementations. We systematically enable 32-bit FHE execution, leveraging the 32-bit integer datapath within GPUs. We optimize GPU kernels using efficient low-level primitives and algorithms tailored to specific GPU architectures. Further, we alleviate the memory bandwidth burden by adjusting common FHE operational sequences and extensively applying kernel fusion. Cheddar delivers performance improvements of 2.18--4.45$\times$ for representative FHE workloads compared to state-of-the-art GPU implementations.

翻译：全同态加密（FHE）通过对加密数据进行安全计算，使云计算摆脱了隐私问题的困扰。然而，其巨大的计算与内存开销导致其性能显著慢于非加密处理。为缓解此开销，我们提出了Cheddar——一个面向GPU的高性能FHE库，相比先前的GPU实现获得了显著的加速效果。我们系统性地实现了32位FHE运算，充分利用GPU内部的32位整数数据通路。通过采用针对特定GPU架构优化的高效底层原语和算法，我们对GPU内核进行了优化。此外，我们通过调整常见FHE操作序列并广泛运用内核融合技术，减轻了内存带宽压力。与最先进的GPU实现相比，Cheddar在典型FHE工作负载上实现了2.18–4.45倍的性能提升。