Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: EmbRace: Accelerating Sparse Communication for Distributed Training of
  NLP Neural Networks   Distributed data-parallel training has been widely used for natural language
processing (NLP) neural network models. However, the embedding tables in NLP
models, holding a large portion of parameters and bringing dramatic sparsity in
communication, make it a big challenge to efficiently scale the distributed
training. Current distributed training frameworks mainly concentrate on dense
models but neglect the sparsity of NLP models, resulting in significant
communication overhead and relatively poor scalability.
  In this paper, we propose EmbRace, an efficient communication framework
designed to accelerate sparse communication of distributed NLP model training.
EmbRace introduces Sparsity-aware Hybrid Communication, which combines AlltoAll
and AllReduce to optimize the communication overhead for sparse and dense data
in NLP models. EmbRace further introduces a 2D Communication Scheduling
approach to thoroughly overlap communication with computation by optimizing
model computation procedure, relaxing the dependency of embeddings, and
scheduling communication with a priority queue.
  We implement EmbRace based on PyTorch and Horovod, and conduct comprehensive
evaluations with four representative NLP models on two high-performance GPU
clusters. Experimental results show that EmbRace achieves up to 30.66X speedup
on 16 GPUs clusters among four popular distributed training baselines.

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The categories of this paper are: hep-th
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The categories of this paper are: hep-ex
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The categories of this paper are: eess
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The categories of this paper are: dg-ga
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The categories of this paper are: plasm-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: cmp-lg
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The categories of this paper are: chem-ph
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The categories of this paper are: econ
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The categories of this paper are: hep-lat
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The categories of this paper are: nucl-th
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The categories of this paper are: math
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The categories of this paper are: supr-con
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The categories of this paper are: gr-qc
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The categories of this paper are: adap-org
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The categories of this paper are: funct-an