Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: MLComp: A Methodology for Machine Learning-based Performance Estimation
  and Adaptive Selection of Pareto-Optimal Compiler Optimization Sequences   Embedded systems have proliferated in various consumer and industrial
applications with the evolution of Cyber-Physical Systems and the Internet of
Things. These systems are subjected to stringent constraints so that embedded
software must be optimized for multiple objectives simultaneously, namely
reduced energy consumption, execution time, and code size. Compilers offer
optimization phases to improve these metrics. However, proper selection and
ordering of them depends on multiple factors and typically requires expert
knowledge. State-of-the-art optimizers facilitate different platforms and
applications case by case, and they are limited by optimizing one metric at a
time, as well as requiring a time-consuming adaptation for different targets
through dynamic profiling.
  To address these problems, we propose the novel MLComp methodology, in which
optimization phases are sequenced by a Reinforcement Learning-based policy.
Training of the policy is supported by Machine Learning-based analytical models
for quick performance estimation, thereby drastically reducing the time spent
for dynamic profiling. In our framework, different Machine Learning models are
automatically tested to choose the best-fitting one. The trained Performance
Estimator model is leveraged to efficiently devise Reinforcement Learning-based
multi-objective policies for creating quasi-optimal phase sequences.
  Compared to state-of-the-art estimation models, our Performance Estimator
model achieves lower relative error (<2%) with up to 50x faster training time
over multiple platforms and application domains. Our Phase Selection Policy
improves execution time and energy consumption of a given code by up to 12% and
6%, respectively. The Performance Estimator and the Phase Selection Policy can
be trained efficiently for any target platform and application domain.

27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
13
The categories of this paper are: quant-ph
34
The categories of this paper are: chem-ph
19
The categories of this paper are: eess
2
The categories of this paper are: math
4
The categories of this paper are: cond-mat
32
The categories of this paper are: atom-ph
25
The categories of this paper are: patt-sol
9
The categories of this paper are: nlin
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
18
The categories of this paper are: econ
22
The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th
20
The categories of this paper are: acc-phys