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首页> 外文期刊>Earth, planets and space: EPS >Accelerating large-scale simulation of seismic wave propagation by multi-GPUs and three-dimensional domain decomposition
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Accelerating large-scale simulation of seismic wave propagation by multi-GPUs and three-dimensional domain decomposition

机译：利用多GPU和三维域分解加速大规模地震波传播模拟

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We adopted the GPU (graphics processing unit) to accelerate the large-scale finite-difference simulation of seismic wave propagation. The simulation can benefit from the high-memory bandwidth of GPU because it is a "memory intensive" problem. In a single-GPU case we achieved a performance of about 56 GFlops, which was about 45-fold faster than that achieved by a single core of the host central processing unit (CPU). We confirmed that the optimized use of fast shared memory and registers were essential for performance. In the multi-GPU case with three-dimensional domain decomposition, the non-contiguous memory alignment in the ghost zones was found to impose quite long time in data transfer between GPU and the host node. This problem was solved by using contiguous memory buffers for ghost zones. We achieved a performance of about 2.2 TFlops by using 120 GPUs and 330 GB of total memory: nearly (or more than) 2200 cores of host CPUs would be required to achieve the same performance. The weak scaling was nearly proportional to the number of GPUs. We therefore conclude that GPU computing for large-scale simulation of seismic wave propagation is a promising approach as a faster simulation is possible with reduced computational resources compared to CPUs.
机译：我们采用了GPU（图形处理单元）来加速地震波传播的大规模有限差分模拟。该仿真可以受益于GPU的高内存带宽，因为它是一个“内存密集型”问题。在单GPU情况下，我们实现了约56 GFlop的性能，这比主机中央处理器（CPU）的单核所实现的性能快约45倍。我们确认快速共享内存和寄存器的优化使用对于性能至关重要。在具有三维域分解的多GPU情况下，发现幻影区域中的非连续内存对齐会给GPU和主机节点之间的数据传输带来相当长的时间。通过为幻影区域使用连续的内存缓冲区解决了此问题。通过使用120个GPU和330 GB的总内存，我们获得了约2.2 TFlops的性能：要达到相同的性能，将需要近（或超过）2200个主机CPU内核。弱缩放几乎与GPU的数量成正比。因此，我们得出的结论是，用于大规模模拟地震波传播的GPU计算是一种很有前途的方法，因为与CPU相比，通过减少计算资源可以实现更快的仿真。

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