arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~
arXiv 0901.1024math.NAcs.NA
收藏

图形处理器上的节点间断伽辽金方法

Nodal Discontinuous Galerkin Methods on Graphics Processors

Brown University(布朗大学);Rice University(莱斯大学)

Andreas Klöckner, Tim Warburton, Jeffrey Bridge, Jan S. Hesthaven

AI总结:

利用节点间断伽辽金方法的元素局部性和高阶特性,在GPU上实现了对三维非结构网格上麦克斯韦方程求解器40-60倍的加速,并达到200 gigaflops/s以上的性能。

摘要:

间断伽辽金(DG)方法用于偏微分方程的数值求解取得了显著成功,因为它既灵活又稳健:允许任意非结构化几何形状,且在不牺牲模拟稳定性的情况下易于控制精度。最近,DG的另一个特性变得越来越重要:DG算子的大部分应用是元素局部的,具有基于弱惩罚的元素间耦合。由此产生的内存访问局部性是使DG能够在现成的、大规模并行图形处理器(GPU)上运行的因素之一。此外,DG的高阶性质使其每个表示波长所需的数据点更少,因此内存访问更少,但算术强度更高。这两个因素都有利于DG在GPU上的实现。使用单个价值400美元的Nvidia GTX 280 GPU,我们将一般三维非结构网格上的麦克斯韦方程求解器加速了40到60倍,相对于当前一代CPU上的串行计算。在许多情况下,我们的算法充分利用了设备的可用内存带宽。示例计算达到并超过了200 gigaflops/s的净应用级浮点运算。在本文中,我们描述并推导了达到这一性能水平所使用的技术。此外,我们提供了关于该方法精度和运行时行为的全面数据。

英文摘要:

Discontinuous Galerkin (DG) methods for the numerical solution of partial differential equations have enjoyed considerable success because they are both flexible and robust: They allow arbitrary unstructured geometries and easy control of accuracy without compromising simulation stability. Lately, another property of DG has been growing in importance: The majority of a DG operator is applied in an element-local way, with weak penalty-based element-to-element coupling. The resulting locality in memory access is one of the factors that enables DG to run on off-the-shelf, massively parallel graphics processors (GPUs). In addition, DG's high-order nature lets it require fewer data points per represented wavelength and hence fewer memory accesses, in exchange for higher arithmetic intensity. Both of these factors work significantly in favor of a GPU implementation of DG. Using a single US$400 Nvidia GTX 280 GPU, we accelerate a solver for Maxwell's equations on a general 3D unstructured grid by a factor of 40 to 60 relative to a serial computation on a current-generation CPU. In many cases, our algorithms exhibit full use of the device's available memory bandwidth. Example computations achieve and surpass 200 gigaflops/s of net application-level floating point work. In this article, we describe and derive the techniques used to reach this level of performance. In addition, we present comprehensive data on the accuracy and runtime behavior of the method.

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。

↑