使用虚拟筛选应用的输入和体系结构特征提高计算效率
Improving computation efficiency using input and architecture features for a virtual screening application
摘要：
虚拟筛选是药物发现过程的早期阶段，选择最有前途的候选药物。在紧急计算场景中，在短时间内找到解决方案至关重要。在本文中，我们专注于一个真实世界的虚拟筛选应用程序来评估内核外优化，考虑输入和架构特征，以提高GPU上的计算效率。在一个现代超级计算机节点上的实验结果表明，我们几乎可以提高一倍的性能。此外，我们使用SYCL实现了优化，它提供了与CUDA优化一致的好处。虚拟筛选活动可以利用这种性能上的增益来增加被评估的候选药物的数量，从而提高找到药物的概率。
英文摘要：
Virtual screening is an early stage of the drug discovery process that selects the most promising candidates. In the urgent computing scenario it is critical to find a solution in a short time frame. In this paper, we focus on a real-world virtual screening application to evaluate out-of-kernel optimizations, that consider input and architecture features to improve the computation efficiency on GPU. Experiment results on a modern supercomputer node show that we can almost double the performance. Moreover, we implemented the optimization using SYCL and it provides a consistent benefit with the CUDA optimization. A virtual screening campaign can use this gain in performance to increase the number of evaluated candidates, improving the probability of finding a drug.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
