MakoXC: Rearchitecting DFT Exchange-Correlation with Matrix-Aligned and Knowledge-Organized Sparsity

Abstract: Density Functional Theory (DFT) is essential to materials science and drug discovery, yet exchange-correlation (XC) evaluation remains a major bottleneck. MakoXC is a matrix-aligned XC engine that restructures nearsightedness-induced sparsity into regular, accelerator-friendly computation. It combines Matrix-Aligned Cells, Sparsity-Guided Activation, and a Kernel-Fused Pipeline to expose implicit sparsity while consolidating fragmented workloads into a compute-intensive execution path. MakoXC achieves average speedups of 67.8x over standard XC evaluation and 4.7x over state-of-the-art linear-scaling methods. Integrated into a production-grade commercial DFT package, it scales to ubiquitin on 64 GPUs and completes end-to-end DFT in under five minutes.

@inproceedings{scmakoxc,
  title={MakoXC: Rearchitecting DFT Exchange-Correlation with Matrix-Aligned and Knowledge-Organized Sparsity},
  author={Haozhi Han and Fusong Ju and Jing Bai and Ruge Zhang and Xiang Zhao and Liang Yuan and Yunquan Zhang and Ting Cao and Yunxin Liu and Yifeng Chen and Kun Li},
  booktitle={International Conference for High Performance Computing, Networking, Storage, and Analysis (SC)},

  year={2026}
}