We present an implementation of AMG with simple aggregation techniques on multiple GPUs. It supports the parallel matrix representations typically used for finite volume discretisation. We employ the ICRS sparse matrix format and the asynchronous exchange mechanism of MPI on CPUs that has been modified to make it suitable for the GPU coprocessors. We show that the solution phase of the standard v-cycle AMG with simple aggregation is accelerated by a factor of up to 12. The solution phase of the more advanced Krylov-accelerated AMG runs faster by a factor of up to 7 on Nvidia TESLA C2070 compared to calculation on Intel X5650 CPUs.
展开▼
机译：我们在多个GPU上呈现了具有简单聚合技术的AMG的实现。它支持通常用于有限体积离散的并行矩阵表示。我们采用ICRS稀疏矩阵格式和MPI对CPU的异步交换机制已被修改为使其适用于GPU协处理器。我们表明，具有简单聚集的标准V周期AMG的解决方案阶段加速了最多12的因子。更先进的Krylov-Concelerated AMG的溶液阶段在NVIDIA Tesla C2070上的倍数最高7倍。与Intel X5650 CPU的计算相比。
展开▼
