HET
نویسندگان
چکیده
Embedding models have been an effective learning paradigm for high-dimensional data. However, one open issue of embedding is that their representations (latent factors) often result in large parameter space. We observe existing distributed training frameworks face a scalability since updating and retrieving the shared parameters from servers usually dominates cycle. In this paper, we propose HET, new system framework significantly improves huge model training. embrace skewed popularity distributions embeddings as performance opportunity leverage it to address communication bottleneck with cache. To ensure consistency across caches, incorporate into HET design, which provides fine-grained guarantees on per-embedding basis. Compared previous work only allows staleness read operations, also utilizes write operations. Evaluations six representative tasks show achieves up 88% reductions 20.68x speedup over state-of-the-art baselines.
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ژورنال
عنوان ژورنال: Proceedings of the VLDB Endowment
سال: 2021
ISSN: ['2150-8097']
DOI: https://doi.org/10.14778/3489496.3489511