A Versatile Software Systolic Execution Model for GPU Memory-Bound Kernels
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.
July 21, 2019 by hgpu
