Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: CHAOS: A Parallelization Scheme for Training Convolutional Neural
  Networks on Intel Xeon Phi   Deep learning is an important component of big-data analytic tools and
intelligent applications, such as, self-driving cars, computer vision, speech
recognition, or precision medicine. However, the training process is
computationally intensive, and often requires a large amount of time if
performed sequentially. Modern parallel computing systems provide the
capability to reduce the required training time of deep neural networks. In
this paper, we present our parallelization scheme for training convolutional
neural networks (CNN) named Controlled Hogwild with Arbitrary Order of
Synchronization (CHAOS). Major features of CHAOS include the support for thread
and vector parallelism, non-instant updates of weight parameters during
back-propagation without a significant delay, and implicit synchronization in
arbitrary order. CHAOS is tailored for parallel computing systems that are
accelerated with the Intel Xeon Phi. We evaluate our parallelization approach
empirically using measurement techniques and performance modeling for various
numbers of threads and CNN architectures. Experimental results for the MNIST
dataset of handwritten digits using the total number of threads on the Xeon Phi
show speedups of up to 103x compared to the execution on one thread of the Xeon
Phi, 14x compared to the sequential execution on Intel Xeon E5, and 58x
compared to the sequential execution on Intel Core i5.

16
The categories of this paper are: stat
35
The categories of this paper are: mtrl-th
23
The categories of this paper are: solv-int
25
The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
8
The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio
2
The categories of this paper are: math
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
7
The categories of this paper are: hep-th
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The categories of this paper are: nucl-ex
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The categories of this paper are: nlin
34
The categories of this paper are: chem-ph
20
The categories of this paper are: acc-phys