Beckman Institute, University of Illinois at Urbana-Champaign, 405 N. Mathews Ave., Urbana, IL 61801, USA.
J Mol Graph Model. 2010 Sep;29(2):116-25. doi: 10.1016/j.jmgm.2010.06.010. Epub 2010 Jul 8.
Graphics processing units (GPUs) have traditionally been used in molecular modeling solely for visualization of molecular structures and animation of trajectories resulting from molecular dynamics simulations. Modern GPUs have evolved into fully programmable, massively parallel co-processors that can now be exploited to accelerate many scientific computations, typically providing about one order of magnitude speedup over CPU code and in special cases providing speedups of two orders of magnitude. This paper surveys the development of molecular modeling algorithms that leverage GPU computing, the advances already made and remaining issues to be resolved, and the continuing evolution of GPU technology that promises to become even more useful to molecular modeling. Hardware acceleration with commodity GPUs is expected to benefit the overall computational biology community by bringing teraflops performance to desktop workstations and in some cases potentially changing what were formerly batch-mode computational jobs into interactive tasks.
图形处理单元(GPU)在分子建模中传统上仅用于分子结构的可视化和分子动力学模拟轨迹的动画。现代 GPU 已经发展成为完全可编程的、大规模并行的协处理器,现在可以用来加速许多科学计算,通常比 CPU 代码快一个数量级,在特殊情况下可以快两个数量级。本文调查了利用 GPU 计算的分子建模算法的发展,已经取得的进展和仍然需要解决的问题,以及 GPU 技术的持续发展,这有望使分子建模变得更加有用。使用商品 GPU 的硬件加速预计将通过为台式工作站带来 teraflops 性能,在某些情况下可能将以前的批处理模式计算作业转变为交互式任务,从而使整个计算生物学社区受益。