Data Parallel Three-Dimensional Cahn-Hilliard Field Equation Simulation on GPUs with CUDA
Data Parallel Three-Dimensional Cahn-Hilliard Field Equation Simulation on GPUs with CUDA
复制标题
使用 CUDA 在 GPU 上进行数据并行三维 Cahn-Hilliard 场方程仿真
DOI：
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发表时间：
2009
期刊：
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通讯作者：
K. Hawick
中科院分区：
文献类型：
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作者：
D. Playne;K. Hawick
计算科学模拟长期以来一直使用并行计算机来提高其性能。最近，图形卡已被用来提供此功能。GPGPU API（如NVidia的CUDA）可用于将GPU的功能用于计算机图形以外的用途。GPU被设计用于处理二维数据。在之前的工作中，我们展示了几个二维Cahn-Hilliard模拟，每个模拟都使用不同的CUDA内存类型，并比较了它们的结果。在本文中，我们将这些想法扩展到三个维度。由于GPU不用于处理三维数据阵列，因此内存优化的性能预计会发生变化。在这里，我们提出了几个三维的Cahn-Hilliard模拟，以探讨不同的内存类型在三维空间中的挑战和性能。结果表明，三维仿真设计的最优性能与二维仿真设计的最优性能采用了不同的存储器类型。
Computational scientific simulations have long used parallel computers to increase their performance. Recently graphics cards have been utilised to provide this functionality. GPGPU APIs such as NVidia’s CUDA can be used to harness the power of GPUs for purposes other than computer graphics. GPUs are designed for processing twodimensional data. In previous work we have presented several two-dimensional Cahn-Hilliard simulations that each utilise different CUDA memory types and compared their results. In this paper we extend these ideas to three dimensions. As GPUs are not intended for processing threedimensional data arrays, the performance of the memory optimisations is expected to change. Here we present several three-dimensional Cahn-Hilliard simulations to explore the challenges and the performance of the different memory types in three-dimensions. The results show that the simulation design with the best performance in threedimensions uses a different memory type to the optimal two-dimensional simulation.