9304

MPI Derived Datatypes Processing on Noncontiguous GPU-resident Data

John Jenkins, James Dinan, Pavan Balaji, Tom Peterka, Nagiza F. Samatova, Rajeev Thakur
Department of Computer Science, North Carolina State University
North Carolina State University, Preprint ANL/MCS-P4042-0313, 2013
@article{jenkins2013mpi,

   title={MPI Derived Datatypes Processing on Noncontiguous GPU-resident Data},

   author={Jenkins, John and Dinan, James and Balaji, Pavan and Peterka, Tom and Samatova, Nagiza F and Thakur, Rajeev},

   year={2013}

}

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Driven by the goals of efficient and generic communication of noncontiguous data layouts in GPU memory, for which solutions do not currently exist, we present a parallel, noncontiguous data-processing methodology through the MPI datatypes specification. Our processing algorithm utilizes a kernel on the GPU to pack arbitrary noncontiguous GPU data by enriching the datatypes encoding to expose a fine-grained, data-point level of parallelism. Additionally, the typically tree-based datatype encoding is preprocessed to enable efficient, cached access across GPU threads. Using CUDA, we show that the computational method outperforms DMA-based alternatives for several common data layouts as well as more complex data layouts for which reasonable DMA-based processing does not exist. Our method incurs low overhead for data layouts that closely match best-case DMA usage or that can be processed by layout-specific implementations. We additionally investigate usage scenarios for data packing that incur resource contention, identifying potential pitfalls for various packing strategies. We also demonstrate the efficacy of kernel-based packing in various communication scenarios, showing multifold improvement in point-topoint communication and evaluating packing within the context of the SHOC stencil benchmark and HACC mesh analysis.
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