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cs.DC2026
Scalable High-Fidelity Macromolecular Docking for GPU-Accelerated Supercomputers
Xiangyu Meng, Peng Chen, Mingzhen Li +7
Flexible macromolecular docking offers high-fidelity predictions of biomolecular interactions, but remains prohibitively expensive at scale. Among existing approaches, LightDock le…
cs.DC2026
SHIRO: Near-Optimal Communication Strategies for Distributed Sparse Matrix Multiplication
Chen Zhuang, Lingqi Zhang, Benjamin Brock +5
Distributed Sparse Matrix-Matrix Multiplication (SpMM) is a fundamental operation in high-performance computing and deep learning applications. The major performance bottleneck in…
cs.DC2025
Scaling Large-scale GNN Training to Thousands of Processors on CPU-based Supercomputers
Chen Zhuang, Lingqi Zhang, Du Wu +8
Graph Convolutional Networks (GCNs), particularly for large-scale graphs, are crucial across numerous domains. However, training distributed full-batch GCNs on large-scale graphs s…