Accelerating Programmable Bootstrapping Targeting Contemporary GPU Microarchitecture

Abstract

Fully homomorphic encryption (FHE) enables computation on encrypted data without privacy leakage, among which GSW-based schemes are notable for supporting the evaluation of arbitrary univariate functions using programmable bootstrapping (PBS). Despite their wide applicability, their computational complexity in a single PBS impedes widespread adoption. However, at the application level, there are enough number of independent PBSs to achieve high data-level parallelism, making them suitable for running on GPUs known for their high parallel computing capability. On contemporary GPUs, peak integer performance has steadily increased, and the sizes of L2 cache and shared memory have also grown rapidly since the Volta architecture. Prior attempts to accelerate PBS on GPUs have fallen short due to their outdated implementations that cannot leverage recent GPU advances. In this paper, we introduce a GPU implementation that supports the latest PBS algorithm and incorporates GPU-trend-aware optimizations. Our implementation achieves a 10.8× performance improvement over the state-of-the-art (SOTA) GPU implementations on RTX 4090 and even outperforms the SOTA ASIC implementation.