稀疏深度神经网络图挑战的GPU实现

2019 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2019-09-01 DOI:10.1109/HPEC.2019.8916223

M. Bisson, M. Fatica

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引用次数: 16

摘要

本文介绍了图形挑战最新添加的CUDA实现，该挑战是对大型稀疏深度神经网络集合的推理计算。一台Tesla V100可以以3.7 TeraEdges/s的速度计算推理。使用CUDA中可用的托管内存API可以在多gpu NVIDIA DGX-2服务器上简单有效地分配这些计算。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A GPU Implementation of the Sparse Deep Neural Network Graph Challenge

This paper presents a CUDA implementation of the latest addition to the Graph Challenge, the inference computation on a collection of large sparse deep neural networks. A single Tesla V100 can compute the inference at 3.7 TeraEdges/s. Using the managed memory API available in CUDA allows for simple and efficient distribution of these computations across a multiGPU NVIDIA DGX-2 server.

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2019 IEEE High Performance Extreme Computing Conference (HPEC)

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