The C++ Programming Language
A hypergraph-partitioning approach for coarse-grain decomposition
Proceedings of the 2001 ACM/IEEE conference on Supercomputing
Fast Conjugate Gradients with Multiple GPUs
ICCS '09 Proceedings of the 9th International Conference on Computational Science: Part I
A Parallel Preconditioned Conjugate Gradient Solver for the Poisson Problem on a Multi-GPU Platform
PDP '10 Proceedings of the 2010 18th Euromicro Conference on Parallel, Distributed and Network-based Processing
A parallel algebraic multigrid solver on graphics processing units
HPCA'09 Proceedings of the Second international conference on High Performance Computing and Applications
Efficient AMG on heterogeneous systems
Facing the Multicore-Challenge II
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In this paper, we describe the implementation of an AMG solver for a hybrid cluster that exploits distributed and shared memory parallelization and uses the available GPU accelerators on each node. This solver has been written by using LAMA (Library for Accelerated Math Applications). This library does not only provide an easy-to-use framework for solvers that might run on different devices with different matrix formats, but also comes with features to optimize and hide communication and memory transfers between CPUs and GPUs. These features are explained and their impact on the efficiency of the AMG solver is shown in this paper. The benchmark results show that an efficient use of hybrid clusters is even possible for multi-level methods like AMG where fast solutions are needed on all levels for multiple problem sizes.