In this study, we present an efficient thread-adaptive sparse approximate inverse preconditioning algorithm on multiple GPUs, called GSPAI-Adaptive. For our proposed GSPAI-Adaptive, there are the following novelties: (1) a thread-adaptive allocation strategy is presented for each column of the preconditioner, and (2) a parallel framework of constructing the sparse approximate inverse preconditioner is proposed on multiple GPUs, and (3) each component of the preconditioner is computed in parallel inside a thread group of GPU. Experimental results show that GSPAI-Adaptive is effective, and is advantageous over the popular preconditioning algorithms in two public libraries, and a latest parallel sparse approximate inverse preconditioning algorithm.

在这项研究中，我们提出了一种在多个GPU上有效的线程自适应稀疏近似逆预处理算法，称为GSPAI-Adaptive。对于我们提出的GSPAI-Adaptive，有以下新颖之处：（1）为预处理器的每一列提供了一种线程自适应分配策略，（2）提出了在多个GPU上构造稀疏近似逆预处理器的并行框架。 ，以及（3）预调节器的每个组件都是在GPU线程组内并行计算的。实验结果表明，GSPAI-Adaptive是有效的，并且优于两个公共图书馆中流行的预处理算法，以及最新的并行稀疏近似逆预处理算法。