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.

中文翻译：

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SParSH-AMG：混合 CPU-GPU 代数多重网格和预处理迭代方法的库

提出了用于代数多重网格方法 (AMG) 的混合 CPU-GPU 算法，以有效利用 CPU 和 GPU 资源。特别是，开发了混合 AMG 框架，专注于 GPU 内存的最低利用率，其性能与仅 GPU 的实现相当。混合 AMG 框架可以调整为在显着较低的 GPU 内存下运行，因此能够解决大型代数系统。将混合 AMG 框架作为预处理器与 Krylov 子空间求解器（如共轭梯度）相结合，BiCG 方法提供了一个求解器堆栈来解决一大类问题。针对具有不同属性和大小的矩阵阵列分析了所提出的混合 AMG 框架的性能。更多，