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The potential of graphical processing units to solve hydraulic network equations

机译：图形处理单元解决液压网络方程的潜力

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The Engineering discipline has relied on computers to perform numerical calculations in many of its sub-disciplines over the last decades. The advent of graphical processing units (GPUs), parallel stream processors, has the potential to speed up generic simulations that facilitate engineering applications aside from traditional computer graphics applications, using GPGPU (general purpose programming on the GPU). The potential benefits of exploiting the GPU for general purpose computation require the program to be highly arithmetic intensive and also data independent. This paper looks at the specific application of the Conjugate Gradient method used in hydraulic network solvers on the GPU and compares the results to conventional central processing unit (CPU) implementations. The results indicate that the GPU becomes more efficient as the data set size increases. However, with the current hardware and the implementation of the Conjugate Gradient algorithm, the application of stream processing to hydraulic network solvers is only faster and more efficient for exceptionally large water distribution models, which are seldom found in practice.
机译：在过去的几十年中，工程学科一直依靠计算机在其许多子学科中进行数值计算。图形处理单元（GPU）的出现，并行流处理器，具有使用GPGPU（GPU上的通用编程）来加速通用仿真的潜力，该仿真可以促进除传统计算机图形应用程序之外的工程应用程序。利用GPU进行通用计算的潜在好处要求程序具有很高的算术强度，并且与数据无关。本文着眼于在GPU上的液压网络求解器中使用的共轭梯度法的具体应用，并将结果与​​常规中央处理器（CPU）的实现方式进行比较。结果表明，随着数据集大小的增加，GPU变得更加高效。但是，使用当前的硬件和共轭梯度算法的实现，将流处理应用到液压网络求解器只会对在实践中很少发现的超大型水分配模型更快，更有效。

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