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An MPI-CUDA Implementation for Massively Parallel Incompressible
文件大小：
1161k
资源说明：MPI和CUDA结合实现并行
Modern graphics processing units (GPUs) with many-core architectures have emerged as general-purpose
parallel computing platforms that can accelerate simulation science applications tremendously. While multiGPU workstations with several TeraFLOPS of peak computing power are available to accelerate computational problems, larger problems require even more resources. Conventional clusters of central processing
units (CPU) are now being augmented with multiple GPUs in each compute-node to tackle large problems.
The heterogeneous architecture of a multi-GPU cluster with a deep memory hierarchy creates unique challenges in developing scalable and efficient simulation codes. In this study, we pursue mixed MPI-CUDA implementations and investigate three strategies to probe the efficiency and scalability of incompressible flow
computations on the Lincoln Tesla cluster at the National Center for Supercomputing Applications (NCSA).
We exploit some of the advanced features of MPI and CUDA programming to overlap both GPU data transfer
and MPI communications with computations on the GPU. We sustain approximately 2.4 TeraFLOPS on the
64 nodes of the NCSA Lincoln Tesla cluster using 128 GPUs with a total of 30,720 processing elements. Our
results demonstrate that multi-GPU clusters can substantially accelerate computational fluid dynamics (CFD)
simulations.
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