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Theodosian: A Deep Dive into Memory-Hierarchy-Centric FHE Acceleration
arXiv:2512.18345v1 Announce Type: new
Abstract: Fully homomorphic encryption (FHE) enables secure computation on encrypted data, mitigating privacy concerns in cloud and edge environments. However, due to its high compute and memory demands, extensive acceleration research has been pursued across diverse hardware platforms, especially GPUs. In this paper, we perform a microarchitectural analysis of CKKS, a popular FHE scheme, on modern GPUs. We focus on on-chip cache behavior, and show that the dominant kernels remain bound by memory bandwidth despite a high-bandwidth L2 cache, exposing a persistent memory wall. We further discover that the overall CKKS pipeline throughput is constrained by low per-kernel hardware utilization, caused by insufficient intra-kernel parallelism. Motivated by these findings, we introduce Theodosian, a set of complementary, memory-aware optimizations that improve cache efficiency and reduce runtime overheads. Our approach delivers consistent speedups across various CKKS workloads. On an RTX 5090, we reduce the bootstrapping latency for 32,768 complex numbers to 15.2ms with Theodosian, and further to 12.8ms with additional algorithmic optimizations, establishing new state-of-the-art GPU performance to the best of our knowledge.
Abstract: Fully homomorphic encryption (FHE) enables secure computation on encrypted data, mitigating privacy concerns in cloud and edge environments. However, due to its high compute and memory demands, extensive acceleration research has been pursued across diverse hardware platforms, especially GPUs. In this paper, we perform a microarchitectural analysis of CKKS, a popular FHE scheme, on modern GPUs. We focus on on-chip cache behavior, and show that the dominant kernels remain bound by memory bandwidth despite a high-bandwidth L2 cache, exposing a persistent memory wall. We further discover that the overall CKKS pipeline throughput is constrained by low per-kernel hardware utilization, caused by insufficient intra-kernel parallelism. Motivated by these findings, we introduce Theodosian, a set of complementary, memory-aware optimizations that improve cache efficiency and reduce runtime overheads. Our approach delivers consistent speedups across various CKKS workloads. On an RTX 5090, we reduce the bootstrapping latency for 32,768 complex numbers to 15.2ms with Theodosian, and further to 12.8ms with additional algorithmic optimizations, establishing new state-of-the-art GPU performance to the best of our knowledge.
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