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MegaScale-Data: Scaling Dataloader for Multisource Large Foundation Model Training
arXiv – CS AI|Juntao Zhao, Qi Lu, Wei Jia, Borui Wan, Lei Zuo, Junda Feng, Jianyu Jiang, Yangrui Chen, Shuaishuai Cao, Jialing He, Kaihua Jiang, Yuanzhe Hu, Shibiao Nong, Yanghua Peng, Haibin Lin, Chuan Wu|
🤖AI Summary
Researchers developed MegaScale-Data, an industrial-grade distributed data loading architecture that significantly improves training efficiency for large foundation models using multiple data sources. The system achieves up to 4.5x training throughput improvement and 13.5x reduction in CPU memory usage through disaggregated preprocessing and centralized data orchestration.
Key Takeaways
- →MegaScale-Data addresses workload imbalance and memory inefficiency in multi-source large foundation model training.
- →The architecture features disaggregated data preprocessing with role-specific actors to eliminate redundant data access.
- →A centralized data plane enables dynamic orchestration of diverse data sources including multimodal and curriculum learning scenarios.
- →Multi-level auto-partitioning scales source loaders efficiently under varying preprocessing costs.
- →Performance improvements include 4.5x training throughput gains and 13.5x reduction in CPU memory usage.
#megascale-data#foundation-models#distributed-training#data-loading#machine-learning#scalability#performance-optimization#arxiv
Read Original →via arXiv – CS AI
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