WeChat Mini Program
Old Version Features

    • Scalable Distributed Training of Recommendation Models: an ASTRA-SIM + NS3 Case-Study with TCP/IP Transport.

    Saeed Rashidi,Pallavi Shurpali,Srinivas Sridharan, Naader Hassani,Dheevatsa Mudigere,Krishnakumar Nair, Misha Smelyanski,Tushar Krishna

    IEEE Symposium on High-Performance Interconnects（2020）

    Georgia Inst Technol

    Cited 10|Views43
    Abstract
    Recommendation model DNNs have gained significant attention due to their vital role in recommending the best content to the user. However, in order to further increase accuracy, DNNs are becoming more complex with more data to be trained, making them infeasible for training on a single node. Distributed training is a solution to tackle this problem by employing multiple nodes for training. The importance of recommendation models necessitates to design customized HW/SW platforms for training such networks in order to minimize the communication overheads among different nodes. However, exploring this design space is difficult due to the presence of many HW/SW parameters and the limitations to change the HW parameters in real systems. In this paper, we port the previously proposed ASTRA-SIM simulation platform on top of the versatile NS3 network simulator by introducing a portable network interface for ASTRA-SIM. Using NS3 enables modeling a wide variety of networks with much better accuracy. Furthermore, we enhance NS3 with detailed modeling of TCP/IP. Finally, we study various HW/SW platforms for the DLRM recommendation model with TCP/IP as the network protocol and analyze the communication overheads in the presence of various interconnect configurations.
    More
    Translated text
    Key words
    distributed training,collective communication,recommendation models
    求助PDF
    上传PDF
    View via Publisher
    Bibtex
    AI Read Science
    AI Summary
    AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
    Example
    Background
    Key content
    Introduction
    Methods
    Results
    Related work
    Fund
    Key content
    • Pretraining has recently greatly promoted the development of natural language processing (NLP)
    • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
    • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
    • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
    • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
    Upload PDF to Generate Summary
    Must-Reading Tree
    Example
    Generate MRT to find the research sequence of this paper
    Data Disclaimer
    The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
    Chat Paper
    Summary is being generated by the instructions you defined