Optimizing small channel 3D convolution on GPU with tensor core-Reference-Cited by-同舟云学术

Optimizing small channel 3D convolution on GPU with tensor core

Published:2022-10 Issue: Volume:113 Page:102954
ISSN:0167-8191
Container-title:Parallel Computing
language:en
Short-container-title:Parallel Computing

Author:

Jiang Jiazhi,Huang Dan,Du Jiangsu,Lu Yutong,Liao Xiangke

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Graphics and Computer-Aided Design,Computer Networks and Communications,Hardware and Architecture,Theoretical Computer Science,Software

Reference27 articles.

1. D. Yan, W. Wang, X. Chu, Optimizing batched winograd convolution on GPUs, in: Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 2020, pp. 32–44.

2. Cudnn: Efficient primitives for deep learning;Chetlur,2014

3. Estimating cosmological parameters from the dark matter distribution;Ravanbakhsh,2016

4. CosmoFlow: Using deep learning to learn the universe at scale;Mathuriya,2018

5. Three-dimensional convolutional neural network (3D-CNN) for heterogeneous material homogenization;Rao;Comput. Mater. Sci.,2020

Cited by 6 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. High-Performance 3D convolution on the Latest Generation Sunway Processor;Proceedings of the 53rd International Conference on Parallel Processing;2024-08-12

2. MixRec: Orchestrating Concurrent Recommendation Model Training on CPU-GPU platform;2023 IEEE 41st International Conference on Computer Design (ICCD);2023-11-06

3. Unlocking the potential of edge computing for hyperspectral image classification: An efficient low-energy strategy;Future Generation Computer Systems;2023-10

4. Hierarchical Model Parallelism for Optimizing Inference on Many-core Processor via Decoupled 3D-CNN Structure;ACM Transactions on Architecture and Code Optimization;2023-07-19

5. Novel accelerated methods for convolution neural network with matrix core;The Journal of Supercomputing;2023-05-30