# Cheddar: A Swift Fully Homomorphic Encryption Library Designed for GPU Architectures

arXiv: https://arxiv.org/abs/2407.13055 · HTML: https://mlsanity.com/paper/2407.13055/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2024.

## Built on by (most cited first, 5 of 5)

- [Leveraging ASIC AI Chips for Homomorphic Encryption](https://mlsanity.com/paper/2501.07047/) (2025)
- [A Scalable Multi-GPU Framework for Encrypted Large-Model Inference](https://mlsanity.com/paper/2512.11269/) (2025)
- [CrypTorch: PyTorch-based Auto-tuning Compiler for Machine Learning with Multi-party Computation](https://mlsanity.com/paper/2511.19711/) (2025)
- [Securing Transfer-Learned Networks with Reverse Homomorphic Encryption](https://mlsanity.com/paper/2505.14323/) (2025)
- [Static Bootstrap Placement for Encrypted Language Model Decoding](https://mlsanity.com/paper/2610.04912/) (2026)

## Abstract (arXiv)

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× for representative FHE workloads compared to state-of-the-art GPU implementations.