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data parallelism

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8 - Edge Learning Architecture Design for System Scalability
Song Guo, The Hong Kong Polytechnic University, Zhihao Qu, The Hong Kong Polytechnic University
Book:

Edge Learning for Distributed Big Data Analytics

Published online:

14 January 2022

Print publication:

10 February 2022, pp 131-158
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Summary

With the growth of model complexity and computational overhead, modern ML applications are usually handled by the distributed systems, where the training procedure is conducted in parallel. Basically, the datasets and models are partitioned to different workers in data parallelism and model parallelism, respectively.
In this chapter, we present the details of these two schemes. Moreover, considering some latest researches that handle distributed training via multiple primitives, we also discuss the extension of training parallelism, i.e., learning frameworks and efficient synchronization mechanisms over the hierarchical architecture.