In sensitive domains such as healthcare and finance, machine learning increasingly employs Fully Homomorphic Encryption (FHE) to secure both user data and models. Although FHE's intrinsic parallelism naturally aligns with GPU architectures, optimizing GPU kernels alone remains insufficient for efficient end-to-end FHE application development. The inherent complexity of FHE schemes and intricate GPU-specific details impede developers from focusing on high-level program logic. Additionally, FHE's high memory requirements, fine-grained memory operations, and redundant computations introduce further optimization challenges, resulting in inefficiencies even when GPU kernels are individually optimized. This paper introduces EasyFHE, a framework designed to simplify the development and optimization of GPU-accelerated FHE applications. Similar to PyTorch, EasyFHE provides high-level interfaces for defining computational logic while automatically handling low-level tasks, such as implementation selection and memory management. Furthermore, it incorporates an optimization framework that systematically addresses performance bottlenecks by applying tailored optimization passes during the lowering from high-level FHE programs to GPU kernels. Compared to state-of-the-art open-source GPU FHE libraries, EasyFHE uniquely supports FHE programs with memory requirements exceeding typical GPU capacities, achieving an average speedup of 2.88× with a peak of 4.39×.

Zero-knowledge proofs validate statements without revealing any information, pivotal for applications such as verifiable outsourcing and digital currencies. However, their broad adoption is limited by the prolonged proof generation times, mainly due to two operations: Multi-Scalar Multiplication (MSM) and Number Theoretic Transform (NTT). While MSM has been efficiently accelerated using multi-GPU systems, NTT has not, due to the high inter-GPU communication overhead incurred by its permutation data access pattern.

With the growth of digital data and rising security concerns, techniques for privacy-preserving computation have become increasingly essential. Big integer multiplication, pivotal for these applications, is compute-intensive but poses challenges for GPU acceleration due to its complexity and the need for application-specific tailored implementations. This paper presents IMCompiler, a compiler-like framework that automatically gen-erates optimized GPU kernels for integer multiplications used in cryptosystems. It features a frontend-IR-backend structure, where the Intermediate Representation (IR) employs a segmented integer multiplication algorithm to decouple architecture-specific optimizations from high-level parameters. The frontend can then easily translate integer multiplication with various high-level parameters into the IR, while the backend focuses on fine-tuning a single GPU kernel for each device, enabling automatic code generation. Moreover, we introduce a computation diagram to facilitate the analysis of parallelization strategies, inspiring many optimizations, including two-dimensional parallelization, tailored caching strategy, index transposing, and lazy carrying. Experiments show that IMCompiler achieves a 4.47× speedup compared to the widely used baseline and 1.42 × over Nvidia's official library. The speedup will be even higher for larger integers and higher-capacity GPUs.