High performance computing : 31st International Conference, ISC High Performance 2016, Frankfurt, Germany, June 19-23, 2016, Proceedings. ISC High Performance (Conference) (31st : 2016 : Frankfurt, Germany)最新文献

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Early Experiences Porting the NAMD and VMD Molecular Simulation and Analysis Software to GPU-Accelerated OpenPOWER Platforms. 将NAMD和VMD分子模拟和分析软件移植到gpu加速的OpenPOWER平台的早期经验。

High performance computing : 31st International Conference, ISC High Performance 2016, Frankfurt, Germany, June 19-23, 2016, Proceedings. ISC High Performance (Conference) (31st : 2016 : Frankfurt, Germany)

Pub Date : 2016-06-01 Epub Date: 2016-10-06 DOI: 10.1007/978-3-319-46079-6_14

John E Stone, Antti-Pekka Hynninen, James C Phillips, Klaus Schulten

All-atom molecular dynamics simulations of biomolecules provide a powerful tool for exploring the structure and dynamics of large protein complexes within realistic cellular environments. Unfortunately, such simulations are extremely demanding in terms of their computational requirements, and they present many challenges in terms of preparation, simulation methodology, and analysis and visualization of results. We describe our early experiences porting the popular molecular dynamics simulation program NAMD and the simulation preparation, analysis, and visualization tool VMD to GPU-accelerated OpenPOWER hardware platforms. We report our experiences with compiler-provided autovectorization and compare with hand-coded vector intrinsics for the POWER8 CPU. We explore the performance benefits obtained from unique POWER8 architectural features such as 8-way SMT and its value for particular molecular modeling tasks. Finally, we evaluate the performance of several GPU-accelerated molecular modeling kernels and relate them to other hardware platforms.

生物分子的全原子分子动力学模拟为探索现实细胞环境中大型蛋白质复合物的结构和动力学提供了有力的工具。不幸的是，这样的模拟在计算需求方面要求极高，并且在准备、模拟方法、结果分析和可视化方面提出了许多挑战。我们描述了我们将流行的分子动力学模拟程序NAMD和模拟准备、分析和可视化工具VMD移植到gpu加速的OpenPOWER硬件平台的早期经验。我们报告了使用编译器提供的自动矢量化的经验，并与POWER8 CPU的手工矢量特性进行了比较。我们将探讨独特的POWER8体系结构特性(如8路SMT)所带来的性能优势及其对特定分子建模任务的价值。最后，我们评估了几个gpu加速的分子建模内核的性能，并将它们与其他硬件平台相关联。

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