A free stale synchronous parallel strategy for distributed machine learning
- Publisher:
- ACM Press
- Publication Type:
- Conference Proceeding
- Citation:
- ACM International Conference Proceeding Series, 2019, pp. 23-29
- Issue Date:
- 2019-06-11
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|---|---|---|---|---|---|
| 3341620.3341625.pdf | Published version | 656.56 kB |
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© 2019 Association for Computing Machinery. With the machine learning applications processing larger and more complex data, people tend to use multiple computing nodes to execute the machine learning tasks in distributed way. However, in real world, people always encounter a problem that a few nodes in system exhibit poor performance and drag down the efficiency of the whole system. In existing parallel strategies such as bulk synchronous parallel and stale synchronous parallel, these nodes with poor performance may not be monitored and found out in time. To address this problem, we proposed a free stale synchronous parallel (FSSP) strategy to free the system from the negative impact of those nodes. Our experimental results on some classical machine leaning algorithms and datasets demonstrated that FSSP strategy outperformed other existing parallel computing strategy.
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