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    • Versioned And Hierarchical Data Structures And Distributed Transactions

    Bradley Gene Calder,Niranjan Nilakantan,Shashwat Srivastav,Jiesheng Wu,Abdul Rafay Abbasi,Shane Mainali,Padmanabha Chakravarthy Uddaraju,Ju Wang,Hemal Khatri,Arild E Skjolsvold,Harshawardhan Gadgil

    mag（2012）

    Cited 24|Views111
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    • 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
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    【要点】：本文提出了一种支持版本控制和分层数据结构的分布式事务处理框架，以优化大规模分布式系统中的数据管理和事务执行效率。
    

    【方法】：作者设计了一种新的数据结构，将版本控制与分层组织相结合，并采用了一种分布式事务协议，确保事务的原子性、一致性、隔离性和持久性。
    

    【实验】：研究通过实现一个原型系统，在真实世界的数据集上进行了测试，包括对Yahoo! Cloud Serving Benchmark (YCSB) 的扩展版以及自定义的数据集进行实验，结果表明所提出的方法在性能和可扩展性方面优于传统系统。