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arXiv · 1907.06154

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

Peng Chen·Mohamed Wahib·Shinichiro Takizawa·Ryousei Takano·Satoshi Matsuoka
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Abstract

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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Peng Chen, Mohamed Wahib, Shinichiro Takizawa, Ryousei Takano, Satoshi Matsuoka. 2019-07-14. A Versatile Software Systolic Execution Model for GPU Memory-Bound Kernels. https://doi.org/10.1145/3295500.3356162

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