Solving 3D anisotropic elastic wave equations on parallel GPU devices
Solving 3D anisotropic elastic wave equations on parallel GPU devices
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DOI：
10.1190/geo2012-0063.1
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发表时间：
2013-02
期刊：
影响因子：
3.3
通讯作者：
R. Weiss;J. Shragge
中科院分区：
文献类型：
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作者：
R. Weiss;J. Shragge
通过求解三维弹性波动方程对三维各向异性复杂介质中的地震数据进行有效建模是计算物理学的一个重要挑战。使用规则网格上的应力刚度公式，我们使用二阶时间和八阶空间精度模板测试了3D时域有限差分求解器，该模板利用图形处理单元（GPU）的大规模并行架构来加速关键内核的计算。单个GPU相对较小的内存限制了可以在单个设备上计算的模型域大小。为了规避这一约束，并转向建模行业规模的3D各向异性弹性数据集，我们通过使用区域分解，并在每个时间步，采用设备间通信协议，以交换数据值落入每个子域的内部边界跨多个GPU设备并行计算。对于单个计算节点内的两个或更多GPU设备，我们使用直接对等（即，GPU到GPU）通信，而对于网络节点，我们采用消息传递接口指令在网络上路由数据。我们的基于GPU的2D各向异性弹性建模测试相对于在八核机器上运行的OpenMP CPU实现了10倍的加速，而我们使用双GPU设备的3D测试产生了高达28倍的加速。GPU架构提供的性能提升使我们能够以更低的硬件成本和更短的时间为3D各向异性弹性模型建模地震数据。
Efficiently modeling seismic data sets in complex 3D anisotropic media by solving the 3D elastic wave equation is an important challenge in computational geophysics. Using a stress-stiffness formulation on a regular grid, we tested a 3D finite-difference time-domain solver using a second-order temporal and eighth-order spatial accuracy stencil that leverages the massively parallel architecture of graphics processing units (GPUs) to accelerate the computation of key kernels. The relatively small memory of an individual GPU limits the model domain sizes that can be computed on a single device. To circumvent this constraint and move toward modeling industry-sized 3D anisotropic elastic data sets, we parallelized computation across multiple GPU devices by using domain decomposition and, for each time step, employing an interdevice communication protocol to exchange data values falling within interior boundaries of each subdomain. For two or more GPU devices within a single compute node, we use direct peer-to-peer (i.e., GPU-to-GPU) communication, whereas for networked nodes we employed message-passing interface directives to route data over the network. Our 2D GPU-based anisotropic elastic modeling tests achieved a 10× speedup relative to an OpenMP CPU implementation run on an eight-core machine, whereas our 3D tests using dual-GPU devices produced up to a 28× speedup. The performance boost afforded by the GPU architecture allowed us to model seismic data for 3D anisotropic elastic models at lower hardware cost and in less time than has been previously possible.