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Architecting Tensor Core-Based Reductions for Irregular Molecular Docking Kernels

Workshop: IA^3 2025 — 15th Workshop on Irregular Applications: Architectures and Algorithms

Authors: Leonardo Solis-Vasquez (Technical University of Darmstadt), Andreas F. Tillack and Diogo Santos-Martins (Scripps Research), Andreas Koch (Technical University of Darmstadt), and Stefano Forli (Scripps Research)

Abstract: Tensor Cores (TCs) are specialized hardware units designed for efficient matrix multiplication and are widely utilized in deep learning workloads. However, their adoption in more irregular high- performance computing (HPC) applications remains limited. This paper presents a methodology for effectively integrating TCs into a representative HPC application: molecular docking with AutoDock-GPU. The irregular computational patterns and strict accuracy requirements of this application pose significant challenges for TC utilization. To address these, we adopt a twofold strategy: (i) accelerating sum reduction operations using TCs, and (ii) applying state-of-the-art numerical error correction (EC) techniques to maintain accuracy. Experimental evaluations on NVIDIA A100, H100, and B200 GPUs show that our CUDA-based implementation consistently outperforms the baseline while preserving algorithmic accuracy.

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