Ruixiang Huang

Ruixiang Huang, Weifan Liu

Simulating large-scale microswimmer dynamics in viscous fluid poses significant challenges due to the coupled high spatial and temporal complexity. Conventional high-performance computing (HPC) methods often address these two dimensions in isolation, leaving a critical gap for synergistic acceleration. This paper introduces a heterogeneous CPU--GPU computing framework specifically optimized for the long-time simulation of filamentous microswimmers in viscous fluid. We propose a two-level parallelization strategy: (1) high-intensity GPU kernels to resolve the quadratic spatial interactions given by the Method of Regularized Stokeslets (MRS), and (2) a distributed MPI-GPU pipelined Parareal architecture to exploit temporal concurrency. By mapping the asynchronous pipeline onto multiple GPU devices, our framework effectively overlaps coarse and fine propagators, overcoming the serial bottlenecks of traditional Parareal method. Furthermore, we employ a GPU-optimized numerical routine for computing the matrix square root arising in the numerical scheme of the filamentous microswimmer simulations. Theoretical analysis of the efficiency improvement of the pipelined Parareal is presented. Numerical experiments demonstrate that the proposed framework achieves order-of-magnitude speedups over CPU-only methods, providing a scalable pathway for simulating complex emergent behaviors in large-scale biology and physics systems.

Peijin Guo, Minghui Li, Hewen Pan et al.

While deep learning models play a crucial role in predicting antibody-antigen interactions (AAI), the scarcity of publicly available sequence-structure pairings constrains their generalization. Current AAI methods often focus on residue-level static details, overlooking fine-grained structural representations of antibodies and their inter-antibody similarities. To tackle this challenge, we introduce a multi-modality representation approach that integates 3D structural and 1D sequence data to unravel intricate intra-antibody hierarchical relationships. By harnessing these representations, we present MuLAAIP, an AAI prediction framework that utilizes graph attention networks to illuminate graph-level structural features and normalized adaptive graph convolution networks to capture inter-antibody sequence associations. Furthermore, we have curated an AAI benchmark dataset comprising both structural and sequence information along with interaction labels. Through extensive experiments on this benchmark, our results demonstrate that MuLAAIP outperforms current state-of-the-art methods in terms of predictive performance. The implementation code and dataset are publicly available at https://github.com/trashTian/MuLAAIP for reproducibility.