Special Session 63: Interdisciplinary Applications of Traditional Numerical Methods, Deep Learning Methods, and Statistical Approaches
A GPU-Accelerated Matrix-Free FAS Multigrid Solver with Memory-Efficient Implementations
Zhenlin Guo
Beijing Computational Science Research Center
Peoples Rep of China
Co-Author(s):    
Abstract:
We develop a matrix-free Full Approximation Storage (FAS) multigrid solver based on staggered finite differences and implemented on GPU in MATLAB. To enhance performance, intermediate variables are reused, and an X-shape Multi-Color Gauss-Seidel (X-MCGS) smoother is introduced. This scheme eliminates the conditional branching required to distinguish red and black nodes in the standard two-color Red-Black Gauss-Seidel (RBGS) method by partitioning the grid into four submatrices according to row-column parity. In addition, restriction and prolongation operators are implemented with GPU acceleration.
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