声学和弹性模型节块间断Galerkin方法的GPU性能分析
GPU performance analysis of a nodal discontinuous Galerkin method for acoustic and elastic models
摘要：
基于间断Galerkin方法的有限元格式具有适合于用通用图形处理单元（GPU）加速的大规模并行计算的特征。然而，这种方案的计算性能强烈地依赖于它们的实现。过去曾提出过若干实施战略。它们完全基于为每个操作调整的专用计算内核，或者它们可以利用为基本线性代数操作提供优化例程的BLAS库。在本文中，我们介绍并分析了不同实现的最新性能结果，并在单个NVIDIA GTX980 GPU上的统一框架中进行了测试。我们表明，专门的内核编写的一个节点，每个线程的战略是竞争力的多项式基地的第五和第七度的声学和弹性模型，分别。对于更高的度数，使用NVIDIA cuBLAS库的策略可以提供更好的结果，能够达到理论峰值的35.7%的净算术吞吐量。
英文摘要：
Finite element schemes based on discontinuous Galerkin methods possess features amenable to massively parallel computing accelerated with general purpose graphics processing units (GPUs). However, the computational performance of such schemes strongly depends on their implementation. In the past, several implementation strategies have been proposed. They are based exclusively on specialized compute kernels tuned for each operation, or they can leverage BLAS libraries that provide optimized routines for basic linear algebra operations. In this paper, we present and analyze up-to-date performance results for different implementations, tested in a unified framework on a single NVIDIA GTX980 GPU. We show that specialized kernels written with a one-node-per-thread strategy are competitive for polynomial bases up to the fifth and seventh degrees for acoustic and elastic models, respectively. For higher degrees, a strategy that makes use of the NVIDIA cuBLAS library provides better results, able to reach a net arithmetic throughput 35.7% of the theoretical peak value.
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。
