Accelerating Drug Discovery in AutoDock-GPU with Tensor Cores
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866914971621785600 |
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| author | Schieffer, Gabin Peng, Ivy |
| author_facet | Schieffer, Gabin Peng, Ivy |
| contents | In drug discovery, molecular docking aims at characterizing the binding of a drug-like molecule to a macromolecule. AutoDock-GPU, a state-of-the-art docking software, estimates the geometrical conformation of a docked ligand-protein complex by minimizing a scoring function. Our profiling results indicate that the current reduction operation that is heavily used in the scoring function is sub-optimal. Thus, we developed a method to accelerate the sum reduction of four-element vectors using matrix operations on NVIDIA Tensor Cores. We integrated the new reduction operation into AutoDock-GPU and evaluated it on multiple chemical complexes on three GPUs. Our results show that our method for reduction operation is 4-7 times faster than the AutoDock-GPU baseline. We also evaluated the impact of our method on the overall simulation time in the real-world docking simulation and achieved a 27% improvement on the average docking time. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_10447 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Accelerating Drug Discovery in AutoDock-GPU with Tensor Cores Schieffer, Gabin Peng, Ivy Distributed, Parallel, and Cluster Computing In drug discovery, molecular docking aims at characterizing the binding of a drug-like molecule to a macromolecule. AutoDock-GPU, a state-of-the-art docking software, estimates the geometrical conformation of a docked ligand-protein complex by minimizing a scoring function. Our profiling results indicate that the current reduction operation that is heavily used in the scoring function is sub-optimal. Thus, we developed a method to accelerate the sum reduction of four-element vectors using matrix operations on NVIDIA Tensor Cores. We integrated the new reduction operation into AutoDock-GPU and evaluated it on multiple chemical complexes on three GPUs. Our results show that our method for reduction operation is 4-7 times faster than the AutoDock-GPU baseline. We also evaluated the impact of our method on the overall simulation time in the real-world docking simulation and achieved a 27% improvement on the average docking time. |
| title | Accelerating Drug Discovery in AutoDock-GPU with Tensor Cores |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2410.10447 |