论文标题
Sparsh-AMG：用于混合CPU-GPU代数多移民和预处理的迭代方法的库
SParSH-AMG: A library for hybrid CPU-GPU algebraic multigrid and preconditioned iterative methods
论文作者
论文摘要
介绍了用于有效利用CPU和GPU资源的代数多机方法（AMG）的混合CPU-GPU算法。特别是，开发了侧重于GPU内存最少利用的混合AMG框架，并且与仅GPU仅实现的性能。可以调整混合AMG框架以明显较低的GPU记忆工作，因此可以解决大型代数系统。 BICG方法将混合AMG框架与Krylov子空间求解器（如共轭梯度）结合在一起，为解决大量问题提供了解决方案堆栈。分析了具有不同属性和大小的矩阵阵列的拟议混合AMG框架的性能。此外，将CPU-GPU算法的性能与仅GPU的实现进行了比较，以说明记忆要求明显较低。
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.
