Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Machine Learning in Compiler Optimisation   In the last decade, machine learning based compilation has moved from an an
obscure research niche to a mainstream activity. In this article, we describe
the relationship between machine learning and compiler optimisation and
introduce the main concepts of features, models, training and deployment. We
then provide a comprehensive survey and provide a road map for the wide variety
of different research areas. We conclude with a discussion on open issues in
the area and potential research directions. This paper provides both an
accessible introduction to the fast moving area of machine learning based
compilation and a detailed bibliography of its main achievements.

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The categories of this paper are: chao-dyn
32
The categories of this paper are: atom-ph
33
The categories of this paper are: plasm-ph
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
11
The categories of this paper are: hep-lat
25
The categories of this paper are: patt-sol
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The categories of this paper are: adap-org
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The categories of this paper are: dg-ga
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The categories of this paper are: eess
10
The categories of this paper are: q-bio
18
The categories of this paper are: econ
3
The categories of this paper are: physics
13
The categories of this paper are: quant-ph
36
The categories of this paper are: cmp-lg
31
The categories of this paper are: ao-sci