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MakoXC addresses the bottleneck in Density Functional Theory's exchange-correlation evaluation by reorganizing sparsity to better leverage modern AI accelerators. This approach overcomes challenges posed by irregular sparse workloads in linear-scaling methods.
MakoXC addresses the bottleneck in Density Functional Theory's exchange-correlation evaluation by reorganizing sparsity to better leverage modern AI accelerators. This approach overcomes challenges posed by irregular sparse workloads in linear-scaling methods.
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Density Functional Theory (DFT) is indispensable for materials science and drug discovery, yet the exchange--correlation (XC) evaluation remains a major bottleneck due to its cubic scaling. Although linear-scaling methods exploit electronic nearsightedness to reduce asymptotic complexity, they produce irregular...
Efficiently utilizing AI chips for DFT calculations can accelerate materials science and drug discovery research. MakoXC's method enables better performance by aligning computational sparsity with AI hardware capabilities.
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