The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Petuum: A New Platform for Distributed Machine Learning on Big Data
Open accessAbstract
What is a systematic way to efficiently apply a wide spectrum of advanced ML programs to industrial scale problems, using Big Models (up to 100 s of billions of parameters) on Big Data (up to terabytes or petabytes)? Modern parallelization strategies employ fine-grained operations and scheduling beyond the classic bulk-synchronous processing paradigm popularized by MapReduce, or even specialized graph-based execution that relies on graph representations of ML programs. The variety of approaches tends to pull systems and algorithms design in different directions, and it remains difficult to find a universal platform applicable to a wide range of ML programs at scale. We propose a general-purpose framework, Petuum, that systematically addresses data- and model-parallel challenges in large-scale ML, by observing that many ML programs are fundamentally optimization-centric and admit error-tolerant, iterative-convergent algorithmic solutions. This presents unique opportunities for an integrative system design, such as bounded-error network synchronization and dynamic scheduling based on ML program structure. We demonstrate the efficacy of these system designs versus well-known implementations of modern ML algorithms, showing that Petuum allows ML programs to run in much less time and at considerably larger model sizes, even on modestly-sized compute clusters.
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Cites
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Zhang, C., Xie, Y., Bai, H., Yu, B., Li, W., & Gao, Y. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.106775 · Cited by 1,899
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Exploiting Unintended Feature Leakage in Collaborative Learning
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Explainable AI (XAI): Core Ideas, Techniques, and Solutions
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Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training
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Metadata
- Authors
- Xing, E. P., Ho, Q., Dai, W., Kim, J. K., Wei, J., Lee, S., Zheng, X., Xie, P., Kumar, A., & Yu, Y.
- Venue
- IEEE Transactions on Big Data
- Published
- 2015-06-01
- Volume
- 1
- Issue
- 2
- Pages
- 49-67
- ISSN
- 2332-7790 (ISSN-L)
- OpenAlex
- W2579247884
- Cited by
- 388
- References
- 34
- FWCI
- 105.41
- Type
- journal article
- Language
- en
- Open access
- green
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- Xing, E. P., Ho, Q., Dai, W., Kim, J. K., Wei, J., Lee, S., Zheng, X., Xie, P., Kumar, A., & Yu, Y. (2015). Petuum: A New Platform for Distributed Machine Learning on Big Data. IEEE Transactions on Big Data. https://doi.org/10.1109/tbdata.2015.2472014
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