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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. To enable fully GPU-resident execution on heterogeneous platforms, we extend the `RNGonGPU` library with the Heterogeneous-computing Interface for Portability (HIP) support and integrate it into the OpenMP Target workflow. 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 CPUGPU 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.