19001

A Versatile Software Systolic Execution Model for GPU Memory-Bound Kernels

Peng Chen, Mohamed Wahib, Shinichiro Takizawa, Ryousei Takano, Satoshi Matsuoka
Tokyo Institute of Technology, AIST-Tokyo Tech Real World, Big-Data Computation Open Innovation Laboratory, National Institute of Advanced Industrial Science and Technology
arXiv:1907.06154 [cs.DC], (14 Jul 2019)

@misc{chen2019versatile,

   title={A Versatile Software Systolic Execution Model for GPU Memory-Bound Kernels},

   author={Peng Chen and Mohamed Wahib and Shinichiro Takizawa and Ryousei Takano and Satoshi Matsuoka},

   year={2019},

   eprint={1907.06154},

   archivePrefix={arXiv},

   primaryClass={cs.DC}

}

This paper proposes a versatile high-performance execution model, inspired by systolic arrays, for memory-bound regular kernels running on CUDA-enabled GPUs. We formulate a systolic model that shifts partial sums by CUDA warp primitives for the computation. We also employ register files as a cache resource in order to operate the entire model efficiently. We demonstrate the effectiveness and versatility of the proposed model for a wide variety of stencil kernels that appear commonly in HPC, and also convolution kernels (increasingly important in deep learning workloads). Our algorithm outperforms the top reported state-of-the-art stencil implementations, including implementations with sophisticated temporal and spatial blocking techniques, on the two latest Nvidia architectures: Tesla V100 and P100. For 2D convolution of general filter sizes and shapes, our algorithm is on average 2.5x faster than Nvidia’s NPP on V100 and P100 GPUs.
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