GROMACS中分子动力学模拟的非均匀化与加速
Heterogeneous Parallelization and Acceleration of Molecular Dynamics Simulations in GROMACS
摘要：
诸如图形处理单元（GPU）之类的加速器设备的引入已经对分子动力学模拟产生了深远的影响，并且使得能够使用商品硬件实现数量级的性能进步。为了充分获得这些好处，有必要重新制定一些最基本的算法，包括Verlet列表，对搜索和截断。在这里，我们提出了异构并行化和加速设计的分子动力学实现的GROMACS代码库在过去的十年。该设置涉及一种基于集群的通用方法来实现对列表和非绑定对交互，该方法有效地利用了GPU和CPU SIMD加速，包括在CPU和GPU之间负载平衡任务的能力。算法的工作效率针对每种类型的硬件进行了调整，并且为了更有效地使用加速器，我们引入了具有滚动修剪更新的双对列表。结合新的直接GPU-GPU通信以及GPU集成，通过跨多个GPU的强大可扩展性和高效的多节点并行化，可实现单GPU模拟的卓越性能。
英文摘要：
The introduction of accelerator devices such as graphics processing units (GPUs) has had profound impact on molecular dynamics simulations and has enabled order-of-magnitude performance advances using commodity hardware. To fully reap these benefits, it has been necessary to reformulate some of the most fundamental algorithms, including the Verlet list, pair searching and cut-offs. Here, we present the heterogeneous parallelization and acceleration design of molecular dynamics implemented in the GROMACS codebase over the last decade. The setup involves a general cluster-based approach to pair lists and non-bonded pair interactions that utilizes both GPUs and CPU SIMD acceleration efficiently, including the ability to load-balance tasks between CPUs and GPUs. The algorithm work efficiency is tuned for each type of hardware, and to use accelerators more efficiently we introduce dual pair lists with rolling pruning updates. Combined with new direct GPU-GPU communication as well as GPU integration, this enables excellent performance from single GPU simulations through strong scaling across multiple GPUs and efficient multi-node parallelization.
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