Acceleration of conjugate gradient method for circuit simulation using CUDA
Acceleration of conjugate gradient method for circuit simulation using CUDA
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使用 CUDA 进行电路仿真的共轭梯度法加速
DOI：
10.1109/hipc.2009.5433184
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
S. Patkar
中科院分区：
文献类型：
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作者：
Anirudh Maringanti;V. Athavale;S. Patkar
共轭梯度法是求解线性方程组的一种常用迭代方法，用于各种应用。DC Analyser是印度理工学院孟买分校建立的电路模拟器，用于解决包含电阻，电压和电流源的大型电路，并采用共轭梯度法。与传统CPU相比，当前一代的图形卡提供了极高的原始处理能力和内存带宽。我们使用Nvidia GTX 280 GPU和新的CUDA技术加速了DC Analyser的共轭梯度部分，并成功地获得了CG方法超过10倍的加速比，与单线程CPU实现相比，整个应用程序的超大型电路加速比超过4倍。
The Conjugate Gradient method is a popular iterative method to solve a system of linear equations and is used in a variety of applications. The DC Analyser is a circuit simulator built at IIT Bombay to solve large circuits containing resistances, voltage and current sources and which employs the conjugate gradient method. Current generation of graphics cards offer extremely high raw processing power and memory bandwidths compared to conventional CPUs. We have accelerated the conjugate gradient part of the DC Analyser using an Nvidia GTX 280 GPU and the new CUDA technology and successfully obtained a speedup of over 10x for the CG method and more than 4x for the entire application for very large circuits when compared to a single-threaded CPU implementation.