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

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.