Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Single stream parallelization of generalized LSTM-like RNNs on a GPU   Recurrent neural networks (RNNs) have shown outstanding performance on
processing sequence data. However, they suffer from long training time, which
demands parallel implementations of the training procedure. Parallelization of
the training algorithms for RNNs are very challenging because internal
recurrent paths form dependencies between two different time frames. In this
paper, we first propose a generalized graph-based RNN structure that covers the
most popular long short-term memory (LSTM) network. Then, we present a
parallelization approach that automatically explores parallelisms of arbitrary
RNNs by analyzing the graph structure. The experimental results show that the
proposed approach shows great speed-up even with a single training stream, and
further accelerates the training when combined with multiple parallel training
streams.

14
The categories of this paper are: math-ph
17
The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
2
The categories of this paper are: math
16
The categories of this paper are: stat
21
The categories of this paper are: adap-org
23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
19
The categories of this paper are: eess
24
The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
25
The categories of this paper are: patt-sol
7
The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an
8
The categories of this paper are: hep-ex