耦合流式AI和HPC集成，实现100- 1000倍的生物分子模拟速度
Coupling streaming AI and HPC ensembles to achieve 100-1000x faster biomolecular simulations
摘要：
基于机器学习（ML）的转向可以通过允许在线选择更具科学意义的计算来提高基于集成的模拟的性能。我们提出了DeepDriveMD，这是一个ML驱动的科学模拟转向框架，我们已经使用该框架通过在大型并行计算机上有效耦合ML和HPC来实现分子动力学（MD）性能的数量级改进。我们讨论了DeepDriveMD的设计并表征了其性能。我们证明，DeepDriveMD相对于其他方法可以实现100- 1000倍的蛋白质折叠模拟加速，如所执行的模拟时间量所测量的，同时覆盖模拟期间采样的状态所量化的相同构象景观。实验在多达1020个节点的领导级平台上进行。结果将DeepDriveMD确立为ML驱动的HPC模拟场景的高性能框架，支持各种MD模拟和ML后端，并通过改进当前计算能力可访问的长度和时间尺度来实现新的科学见解。
英文摘要：
Machine learning (ML)-based steering can improve the performance of ensemble-based simulations by allowing for online selection of more scientifically meaningful computations. We present DeepDriveMD, a framework for ML-driven steering of scientific simulations that we have used to achieve orders-of-magnitude improvements in molecular dynamics (MD) performance via effective coupling of ML and HPC on large parallel computers. We discuss the design of DeepDriveMD and characterize its performance. We demonstrate that DeepDriveMD can achieve between 100-1000x acceleration for protein folding simulations relative to other methods, as measured by the amount of simulated time performed, while covering the same conformational landscape as quantified by the states sampled during a simulation. Experiments are performed on leadership-class platforms on up to 1020 nodes. The results establish DeepDriveMD as a high-performance framework for ML-driven HPC simulation scenarios, that supports diverse MD simulation and ML back-ends, and which enables new scientific insights by improving the length and time scales accessible with current computing capacity.
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。
