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Straggler-Aware Distributed Learning: Communication–Computation Latency Trade-Off
When gradient descent (GD) is scaled to many parallel workers for large-scale machine learning applications, its per-iteration computation time is limited by straggling workers. Straggling workers can be tolerated by assigning redundant computations and/or coding across data and computations, but in...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
MDPI
2020
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7517046/ https://www.ncbi.nlm.nih.gov/pubmed/33286316 http://dx.doi.org/10.3390/e22050544 |