Contribution Details |
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| Name: |
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Using LAMA for Efficient AMG on Hybrid Clusters |
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| Time: |
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Tuesday, June 19, 2012
11:30 AM - 12:00 PM |
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Hall C2.2
CCH - Congress Center Hamburg |
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| Speakers: |
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Jiri Kraus, Fraunhofer SCAI |
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| Abstract: |
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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 in the paper and their impact on the efficiency of the AMG solver is shown. The benchmark results demonstrate 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 problems sizes. |
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