Multichannel Analysis of Surface Waves (MASW) is a technique frequently used in geotechnical engineering and engineering geophysics to infer layered models of seismic shear wave velocities in the top tens to hundreds of meters of the subsurface. We aim to accelerate MASW calculations by capitalizing on modern computer hardware available in the workstations of most engineers: multiple cores and graphics processing units (GPUs). We propose new parallel and GPU accelerated algorithms for evaluating MASW data, and provide software implementations in C using Message Passing Interface (MPI) and CUDA. These algorithms take advantage of sparsity that arises in the problem, and the work balance between processes considers typical data trends. We compare our methods to an existing open source Matlab MASW tool. Our serial C implementation achieves a 2x speedup over the Matlab software, and we continue to see improvements by parallelizing the problem with MPI. We see nearly perfect strong and weak scaling for uniform data, and improve strong scaling for realistic data by repartitioning the problem to process mapping. By utilizing GPUs available on most modern workstations, we observe an additional 1.3x speedup over the serial C implementation on the first use of the method. We typically repeatedly evaluate theoretical dispersion curves as part of an optimization procedure, and on the GPU the kernel can be cached for faster reuse on later runs. We observe a 3.2x speedup on the cached GPU runs compared to the serial C runs. This work is the first open-source parallel or GPU-accelerated software tool for MASW imaging, and should enable geotechnical engineers to fully utilize all computer hardware at their disposal.

中文翻译：

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表面波加速多通道分析 (MASWAccelerated)：使用 MPI 和 GPU 进行高效表面波反演的软件

表面波多道分析 (MASW) 是一种经常用于岩土工程和工程地球物理学的技术，用于推断地下数十至数百米的地震剪切波速度的分层模型。我们旨在通过利用大多数工程师工作站中可用的现代计算机硬件来加速 MASW 计算：多核和图形处理单元 (GPU)。我们提出了新的并行和 GPU 加速算法来评估 MASW 数据，并使用消息传递接口 (MPI) 和 CUDA 提供 C 语言的软件实现。这些算法利用了问题中出现的稀疏性，进程之间的工作平衡考虑了典型的数据趋势。我们将我们的方法与现有的开源 Matlab MASW 工具进行比较。我们的串行 C 实现比 Matlab 软件实现了 2 倍的加速，并且通过使用 MPI 并行化问题，我们继续看到改进。我们看到了统一数据的近乎完美的强缩放和弱缩放，并通过将问题重新划分为过程映射来改进现实数据的强缩放。通过利用大多数现代工作站上可用的 GPU，我们观察到在第一次使用该方法时，串行 C 实现的速度提高了 1.3 倍。作为优化过程的一部分，我们通常会反复评估理论色散曲线，并且可以在 GPU 上缓存内核，以便在以后的运行中更快地重用。我们观察到与串行 C 运行相比，缓存 GPU 运行速度提高了 3.2 倍。这项工作是第一个用于 MASW 成像的开源并行或 GPU 加速软件工具，