High-Performance Implementations of Classic McEliece KEM on GPUs

This paper studies the high-performance implementation of Classic McEliece Key Encapsulation Mechanism (KEM), one of the candidates in the fourth round NIST Post-Quantum Cryptography competition. We focus on batch processing scenarios and improve the performance of key components in Classic McEliece through loop unrolling optimization. The study reveals that while loop unrolling is effective in improving performance, it may lead to performance degradation during the key encapsulation phase under high task loads. To address this issue, we introduce inline expansion optimization. We evaluate our approach on NVIDIA GeForce GTX 1660S and RTX 3090 platforms. The results show that loop unrolling optimization in the key encapsulation phase reduces latency by up to 54.09% (resp. 74.53%) at low task loads, while inline expansion optimization increases throughput by up to 16.17% (resp. 30.31%) at high task loads. In the key decapsulation phase, loop unrolling optimization reduces latency by up to 96.97% (resp. 96.54%) at low task loads and increases throughput by up to 58.64% (resp. 96.54%) at high task loads.