HMC-FHE: A Heterogeneous Near Data Processing Framework for Homomorphic Encryption

Abstract

Fully homomorphic encryption (FHE) offers a promising solution to ensure data privacy by enabling computations directly on encrypted data. However, its notorious performance degradation severely limits the practical application, due to the explosion of both the ciphertext volume and computation. In this article, leveraging the diversity of computing power and memory bandwidth requirements of FHE operations, we present HMC-FHE, a robust acceleration framework that combines both GPU and hybrid memory cube (HMC) processing engines to accelerate FHE applications cooperatively. HMC-FHE incorporates four key hardware/software co-design techniques: 1) a fine-grained kernel offloading mechanism to efficiently offload FHE operations to relevant processing engines; 2) a ciphertext partitioning scheme to minimize data transfer across decentralized HMC processing engines; 3) an FHE operation pipeline scheme to facilitate pipelined execution between GPU and HMC engines; and 4) a kernel tuning scheme to guarantee the parallelism of GPU and HMC engines. We demonstrate that the GPU-HMC architecture with proper resource management serves as a promising acceleration scheme for memory-intensive FHE operations. Compared with the state-of-the-art GPU-based acceleration scheme, the proposed framework achieves up to $2.65\times $ performance gains and reduces $1.81\times $ energy consumption with the same peak computation capacity.