ProXplore: A GPU-Enhanced Protein Discovery Engine

Language models have achieved unprecedented success in natural language processing tasks and have recently been adapted for biological sequences. However, GPUs still encounter significant performance bottlenecks when running BERT-style natural language models. Moreover, due to the vastly greater number of tokens in protein sequences compared to human languages (as illustrated in Figure 1), directly transferring language models from human languages to proteins results in significant changes in runtime behavior. Consequently, optimizations designed for short-input BERT models are less effective for protein language models.In this paper, we propose a novel GPU-based cross-layer optimization strategy. From an architectural perspective, our approach leverages intra- and inter-operator parallelism, multilevel data computation and warp parallelism to fully utilize GPU features. From an algorithmic perspective, we address the issue of varying lengths in protein input sequences and reduce memory overhead through padding removal. Experimental results demonstrate that ProXplore achieves a 1.8× increase in inference speed on the TAPE benchmark without a significant loss in prediction accuracy. This improvement effectively overcomes performance bottlenecks and provides substantial benefits for protein sequence analysis and other bioinformatics applications.