Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient and Robust Parallel DNN Training through Model Parallelism on
  Multi-GPU Platform   The training process of Deep Neural Network (DNN) is compute-intensive, often
taking days to weeks to train a DNN model. Therefore, parallel execution of DNN
training on GPUs is a widely adopted approach to speed up the process nowadays.
Due to the implementation simplicity, data parallelism is currently the most
commonly used parallelization method. Nonetheless, data parallelism suffers
from excessive inter-GPU communication overhead due to frequent weight
synchronization among GPUs. Another approach is pipelined model parallelism,
which partitions a DNN model among GPUs, and processes multiple mini-batches
concurrently. This approach can significantly reduce inter-GPU communication
cost compared to data parallelism. However, pipelined model parallelism faces
the weight staleness issue; that is, gradients are computed with stale weights,
leading to training instability and accuracy loss. In this paper, we present a
pipelined model parallel execution method that enables high GPU utilization
while maintaining robust training accuracy via a novel weight prediction
technique, SpecTrain. Experimental results show that our proposal achieves up
to 8.91x speedup compared to data parallelism on a 4-GPU platform while
maintaining comparable model accuracy.

34
The categories of this paper are: chem-ph
16
The categories of this paper are: stat
15
The categories of this paper are: nucl-ex
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn
18
The categories of this paper are: econ
29
The categories of this paper are: funct-an
21
The categories of this paper are: adap-org
32
The categories of this paper are: atom-ph