Selection of Supervised Learning-based Sparse Matrix Reordering Algorithms
Tao Tang, Youfu Jiang, Yingbo Cui, Jianbin Fang, Peng Zhang, Lin Peng, Chun Huang
稀疏矩阵排序是一种重要的优化技术,通常用于解决大规模稀疏矩阵。 它的目标是通过重组其行和列来最小化矩阵带宽,从而提高效率。 算法选择的常规方法通常依赖于蛮力搜索或经验知识,缺乏适应各种稀疏矩阵结构的能力。因此,我们引入了一个基于监督学习的模型,用于选择稀疏矩阵重排序算法。 该模型掌握了矩阵特征与常用再排序算法之间的相关性,促进了合适的稀疏矩阵重排序算法的自动化和智能选择。 对佛罗里达稀疏矩阵数据集进行的实验表明,我们的模型可以准确预测各种矩阵的最佳重新排序算法,与仅使用AMD再排序算法相比,解决方案时间减少了55.37%,平均加速比为1.45。
Sparse matrix ordering is a vital optimization technique often employed for solving large-scale sparse matrices. Its goal is to minimize the matrix bandwidth by reorganizing its rows and columns, thus enhancing efficiency. Conventional methods for algorithm selection usually depend on brute-force search or empirical knowledge, lacking the ability to adjust to diverse sparse matrix structures.As a result, we have introduced a supervised learning-based model for choosing sparse matrix reordering a...