English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 113822/144841 (79%)
Visitors : 51821523      Online Users : 558
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 >  Item 140.119/152416
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/152416


    Title: 應用多顆 GPU 以及 PyTorch 指令進行平行處理於加速學習演算法之執行速度
    Applying multiple GPUs and PyTorch commands for parallel processing to accelerate the execution speed of learning algorithms
    Authors: 方凱柔
    Fang, Kai-Rou
    Contributors: 蔡瑞煌
    林怡伶

    Tsaih, Rua-Huan
    Lin, Yi-Ling

    方凱柔
    Fang, Kai-Rou
    Keywords: 學習演算法
    自適應神經網路
    PyTorch
    數據平行處理
    Learning algorithm
    Adaptive neural networks
    PyTorch
    Data parallelism
    Date: 2024
    Issue Date: 2024-08-05 12:07:57 (UTC+8)
    Abstract: 在平行處理和多GPU應用方面,對於兩層自適應神經網路的研究相對較少。本研究旨在利用PyTorch框架及其相關指令,結合多個GPU,探討兩層自適應神經網路的數據平行處理。此外,我們將運用Pupil Learning Mechanism演算法,實現在多GPU環境下更高效的計算。以銅價預測數據集為基礎,我們將透過一系列實驗來驗證這一方法,並分析多GPU平行處理對模型訓練速度和準確性的影響,以全面了解和評估所提方法的實際效果和應用價值。預期能提供一個簡單的平行處理模組,讓未來使用兩層自適應神經網路的研究得以快速且簡單地進行平行處理。
    Research on the parallel processing and multi-GPU application of two-layer adaptive neural networks (2LANN) is relatively scarce. This study aims to explore the data parallel processing and model parallel processing of 2LANN by leveraging the PyTorch framework and its related instructions, combined with multiple GPUs. The Pupil Learning Mechanism algorithm is employed to achieve more efficient computation in a multi-GPU environment. Based on a copper price prediction dataset, a series of experiments is conducted to validate this approach and analyze the impact of multi-GPU parallel processing on model training speed and accuracy. This aims to comprehensively understand and evaluate the practical effectiveness and application value of the proposed method. It is expected to provide a simple parallel processing module, facilitating future research on 2LANN to conduct parallel processing quickly and easily.
    Reference: Bahrampour, S., Ramakrishnan, N., Schott, L., & Shah, M. (2015). Comparative Study of Deep Learning Software Frameworks. ArXiv Preprint ArXiv: 1511.06435
    DataParallel — PyTorch 2.2 documentation. (2024). Retrieved March 6, 2024, from https://pytorch.org/docs/stable/generated/torch.nn.DataParallel.html
    Distributed communication package - torch.distributed — PyTorch 2.3 documentation. (2024). Retrieved June 3, 2024, from https://pytorch.org/docs/stable/distributed.html
    Distributed Data Parallel — PyTorch 2.3 documentation. (2024). Retrieved June 2, 2024, from https://pytorch.org/docs/stable/notes/ddp.html
    Distributed data parallel training using Pytorch on AWS | Telesens. (2019). Retrieved May 31, 2024, from https://www.telesens.co/2019/04/04/distributed-data-parallel-training-using-pytorch-on-aws/
    DistributedDataParallel — PyTorch 2.3 documentation. (2024). Retrieved June 1, 2024, from https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html
    Fan, S., Rong, Y., Meng, C., Cao, Z., Wang, S., Zheng, Z., Wu, C., Long, G., Yang, J., Xia, L., Diao, L., Liu, X., & Lin, W. (2021). DAPPLE: A pipelined data parallel approach for training large models. Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP, 431–445.
    Geng, J., Li, D., & Wang, S. (2019). ElasticPipe: An efficient and dynamic model-parallel solution to DNN training. ScienceCloud 2019 - Proceedings of the 10th Workshop on Scientific Cloud Computing, Co-Located with HPDC 2019, 5–9.
    Hara, K., Saito, D., & Shouno, H. (2015). Analysis of function of rectified linear unit used in deep learning. 2015 International Joint Conference on Neural Networks (IJCNN), 1–8.
    Harlap, A., Narayanan, D., Phanishayee, A., Seshadri, V., Devanur, N., Ganger, G., & Gibbons, P. (2018). PipeDream: Fast and Efficient Pipeline Parallel DNN Training. Preprint ArXiv: 1806.03377
    Ketkar, N., & Moolayil, J. (2021). Deep learning with python: Learn Best Practices of Deep Learning Models with PyTorch. In Deep Learning with Python: Learn Best Practices of Deep Learning Models with PyTorch. Apress Media LLC. https://doi.org/10.1007/978-1-4842-5364-9
    Khomenko, V., Shyshkov, O., Radyvonenko, O., & Bokhan, K. (2016). Accelerating recurrent neural network training using sequence bucketing and multi-GPU data parallelization. 2016 IEEE First International Conference on Data Stream Mining & Processing (DSMP), 100–103.
    Krizhevsky, A. (2014). One weird trick for parallelizing convolutional neural networks. ArXiv Preprint ArXiv: 1404.5997
    Lee, S., Kang, Q., Madireddy, S., Balaprakash, P., Agrawal, A., Choudhary, A., Archibald, R., & Liao, W. (2019). Improving Scalability of Parallel CNN Training by Adjusting Mini-Batch Size at Run-Time. 2019 IEEE International Conference on Big Data (Big Data), 830–839.
    Nguyen, T. D. T., Park, J. H., Hossain, M. I., Hossain, M. D., Lee, S.-J., Jang, J. W., Jo, S. H., Huynh, L. N. T., Tran, T. K., & Huh, E.-N. (2019). Performance Analysis of Data Parallelism Technique in Machine Learning for Human Activity Recognition Using LSTM. 2019 IEEE International Conference on Cloud Computing Technology and Science (CloudCom), 387–391.
    Optional: Data Parallelism — PyTorch Tutorials 2.2.0+cu121 documentation. (2024). Retrieved February 21, 2024, from https://pytorch.org/tutorials/beginner/blitz/data_parallel_tutorial.html
    Owens, J. D., Houston, M., Luebke, D., Green, S., Stone, J. E., & Phillips, J. C. (2008). GPU Computing. Proceedings of the IEEE, 96(5), 879–899.
    Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., Facebook, Z. D., Research, A. I., Lin, Z., Desmaison, A., Antiga, L., Srl, O., & Lerer, A. (2017). Automatic differentiation in PyTorch. NIPS 2017 Workshop on Autodiff.
    Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., … Chintala, S. (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library. ArXiv Preprint ArXiv: 1912.01703
    Pérez-Sánchez, B., Fontenla-Romero, O., & Guijarro-Berdiñas, B. (2018). A review of adaptive online learning for artificial neural networks. Artificial Intelligence Review, 49(2), 281–299.
    PyTorch Distributed Overview — PyTorch Tutorials 2.3.0+cu121 documentation. (2024). Retrieved June 1, 2024, from https://pytorch.org/tutorials/beginner/dist_overview.html
    Ren-Han, Y. (2022). An adaptive learning-based model for copper price forecasting. Master's thesis, Department of Information Management, National Chengchi University, 1–78.
    Sanders, J., Kandrot, E., & Jacoboni, E. (2011). CUDA par l’exemple [une introduction à la programmation parallèle de GPU]. Pearson.
    torch.nn — PyTorch 2.2 documentation. (2024). Retrieved March 5, 2024, from https://pytorch.org/docs/stable/nn.html
    torch.nn.parallel.data_parallel — PyTorch 2.3 documentation. (2024). Retrieved June 2, 2024, from https://pytorch.org/docs/stable/_modules/torch/nn/parallel/data_parallel.html
    torch.utils.data — PyTorch 2.3 documentation. (2024). Retrieved June 3, 2024, from https://pytorch.org/docs/stable/data.html#single-and-multi-process-data-loading
    Tsai, Y.-H., Jheng, Y.-J., & Tsaih, R.-H. (2019). The Cramming, Softening and Integrating Learning Algorithm with Parametric ReLU Activation Function for Binary Input/Output Problems. 2019 International Joint Conference on Neural Networks (IJCNN), 1–7.
    Tsaih, R. R. (1998). An Explanation of Reasoning Neural Networks. In Mathematical and Computer Modelling (Vol. 28, Issue 2).
    Tsaih, R.-H., Chien, Y.-H., & Chien, S.-Y. (2023). Pupil Learning Mechanism. ArXiv Preprint ArXiv: 2307.16141
    Description: 碩士
    國立政治大學
    資訊管理學系
    111356049
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0111356049
    Data Type: thesis
    Appears in Collections:[資訊管理學系] 學位論文

    Files in This Item:

    File Description SizeFormat
    604901.pdf3115KbAdobe PDF0View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback