Abstract:Parameter servers (PSs) ease the implementation of distributed training for large machine learning (ML) tasks by providing primitives for shared parameter access. Especially for ML tasks that access parameters sparsely, PSs can achieve high efficiency and scalability. To do so, they employ a number of techniques-such as replication or relocation-to reduce communication cost and/or latency of parameter accesses. A suitable choice and parameterization of these techniques is crucial to realize these gains, howeve… Show more
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