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SParSH-AMG

swMATH ID: 33759
Software Authors: Sashikumaar Ganesan, Manan Shah
Description: SParSH-AMG: A library for hybrid CPU-GPU algebraic multigrid and preconditioned iterative methods. Hybrid CPU-GPU algorithms for Algebraic Multigrid methods (AMG) to efficiently utilize both CPU and GPU resources are presented. In particular, hybrid AMG framework focusing on minimal utilization of GPU memory with performance on par with GPU-only implementations is developed. The hybrid AMG framework can be tuned to operate at a significantly lower GPU-memory, consequently, enables to solve large algebraic systems. Combining the hybrid AMG framework as a preconditioner with Krylov Subspace solvers like Conjugate Gradient, BiCG methods provides a solver stack to solve a large class of problems. The performance of the proposed hybrid AMG framework is analysed for an array of matrices with different properties and size. Further, the performance of CPU-GPU algorithms are compared with the GPU-only implementations to illustrate the significantly lower memory requirements.
Homepage: https://arxiv.org/abs/2007.00056
Source Code:  https://github.com/cmgcds/SParSH-AMG
Keywords: arXiv_cs.MS; MSC; 65F10; 65F50; 65N55; 65Y05; Algebraic Multigrid methods; AMG; Hybrid CPU-GPU; Iterative Solvers; Aggregation coarsening
Related Software: PARDISO; ParMooN; MKL; CUDA; CUSPARSE; AmgX; hypre; BootCMatch; SparseMatrix
Cited in: 0 Publications

Standard Articles

1 Publication describing the Software Year
SParSH-AMG: A library for hybrid CPU-GPU algebraic multigrid and preconditioned iterative methods
Sashikumaar Ganesan, Manan Shah
2020