Portable Acceleration of Learning With Errors KEMs for Post-Quantum Cryptography
2026-07-10 • Cryptography and Security
Cryptography and Security
AI summaryⓘ
The authors developed a way to speed up a certain type of quantum-secure encryption called Learning With Errors (LWE) using graphics cards (GPUs). Instead of using code specific to one GPU brand, they wrote a single code that works on both NVIDIA and AMD GPUs. They tested this method and found it runs much faster than normal computer processors and works well on different GPUs. Their work shows it’s possible to make these future-proof encryptions faster without being restricted to one hardware brand.
Post-quantum cryptographyLearning With Errors (LWE)Key Encapsulation Mechanism (KEM)GPU accelerationOpenMP TargetCUDANVIDIA GH200AMD MI300XMemory bound workloadCryptographically secure random number generation
Authors
Tiziana Liberati, Nitin Shukla, Simone Rizzo, Elisabetta Boella, Matteo Barbieri, Gabriella Bettonte, Daniele Gregori, Marco Pedicini
Abstract
The transition to post-quantum cryptography (PQC) is driving demand for implementations that can meet the computational requirements of real-world applications. Among the proposed PQC constructions, Learning With Errors (LWE) based key encapsulation mechanisms (KEMs) are particularly attractive due to their strong security foundations, but they incur substantial computational costs from matrix operations and large-scale cryptographically secure random number generation. These characteristics position GPU acceleration as an effective approach for lowering the computational overhead of lattice based cryptographic schemes. In this work, we present a portable GPU implementation of a plain LWE based KEM using OpenMP Target offloading. Unlike most existing GPU implementations, which rely on CUDA specific optimizations, our approach uses a single source code base that executes on both NVIDIA and AMD accelerators. We evaluate the proposed implementation on different accelerator architectures, analyzing performance benchmarking, runtime profiling, scalability analysis, and energy to solution measurements. Experimental results show that OpenMP Target offloading delivers substantial acceleration over a multicore CPU baseline while preserving source level portability across heterogeneous GPU ecosystems. Cross platform analysis identifies NVIDIA GH200 and AMD MI300X as the most effective platforms for this memory bound workload, while profiling indicates that memory system organization and CPU GPU interaction play a more critical role than peak compute capability alone. These findings demonstrate that portable GPU acceleration can significantly reduce the computational overhead of PQC while avoiding vendor lock in, thereby facilitating the deployment of quantum resistant cryptographic infrastructures.