Algebraic multigrid (AMG) is widely used to accelerate largescale sparse linear solvers.In distributed environments, neighboring communication overhead in AMG significantly impacts overall solution time.We propose Communication-Reduced Algebraic Multigrid (CRAMG) methods to minimize inter-process data exchange and message count by fusing interpolation/restriction operators with residual computations.This reduces communication frequency from four per level to as few as two.Experiments show up to 45% reduction in data exchange and 35% fewer messages.Performance evaluations on an Intel platform demonstrate significant improvements

This paper presents DAS-ILU, a Distributed Asynchronous parallel Incomplete LU factorization method based on domain decomposition. DAS-ILU partitions the computational domain into independently processed interior nodes and asynchronously updated separator nodes, thereby reducing cross-processor dependencies and halving the separator size compared to conventional methods. To further improve performance, it employs optimized data exchange patterns to minimize communication overhead and extends support to block-structured sparse matrices via exact block inversions. Comprehensive evaluations on a range of problem types—including structural mechanics, computational fluid dynamics, and reservoir simulation demonstrate the superior performance of DAS-ILU. Compared to state-of-the-art ILU implementations, DAS-ILU achieves solve time speedups of up to 2.07 × over Chow-Patel’s fine-grained parallel ILU and up to 4.11 × over HYPRE’s ILU. Moreover, DAS-ILU exhibits strong robustness when applied to challenging nonsymmetric and indefinite systems.

The traditional auto-calibration of synthetic aperture radar (SAR) images based on the range-Doppler (RD) model couples the coordinates of ground target points (GTPs) and the slant range correction to solve, resulting in unstable solutions. This paper proposes a twofold positioning method (TPM) for multiview spaceborne SAR images to solve this problem. In order to obtain the more precise coordinates of GTPs and the stability of the solution, the conjugate gradient method (CGM) is performed to the initial and secondary positioning for the normalized RD model. Compared with the traditional least squares method (LSM), the accuracy of TPM is on average 13.47% higher with YaoGan-SAR satellite (150MHz and 24.4us), and 44.38% higher with YaoGan-SAR satellite (200MHz and 24.4us). In addition, the stability of the method is improved. The experimental results based on the YaoGan-SAR satellite images verify the effectiveness of the method.