Many scientific computing problems, including circuit simulations, rely on efficient lower–upper (LU) decomposition of sparse matrices. Prior studies took advantage of GPUs to parallelize LU decomposition, but they suffer from nontrivial data dependencies. This article presents a new method, called GLU3.0, to accelerate GPU-based sparse LU factorization. — Umit Ogras, Arizona State University

中文翻译：

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GLU3.0：用于电路仿真的基于GPU的快速并行稀疏LU分解

许多科学计算问题，包括电路仿真，都依赖于稀疏矩阵的有效上下限（LU）分解。先前的研究利用GPU来并行化LU分解，但是它们具有非平凡的数据依赖性。本文介绍了一种称为GLU3.0的新方法，可以加速基于GPU的稀疏LU分解。— Umit Ogras，亚利桑那州立大学