A survey on compiler autotuning using machine learning
Since the mid-1990s, researchers have been trying to use machine-learning-based
approaches to solve a number of different compiler optimization problems. These …
approaches to solve a number of different compiler optimization problems. These …
A review on prognostics methods for engineering systems
Due to the advancements in sensing technologies and computational capabilities, system
health assessment and prognostics have been extensively investigated in the literature …
health assessment and prognostics have been extensively investigated in the literature …
Neural code comprehension: A learnable representation of code semantics
T Ben-Nun, AS Jakobovits… - Advances in neural …, 2018 - proceedings.neurips.cc
With the recent success of embeddings in natural language processing, research has been
conducted into applying similar methods to code analysis. Most works attempt to process the …
conducted into applying similar methods to code analysis. Most works attempt to process the …
Machine learning in compiler optimization
Z Wang, M O'Boyle - Proceedings of the IEEE, 2018 - ieeexplore.ieee.org
In the last decade, machine-learning-based compilation has moved from an obscure
research niche to a mainstream activity. In this paper, we describe the relationship between …
research niche to a mainstream activity. In this paper, we describe the relationship between …
End-to-end deep learning of optimization heuristics
C Cummins, P Petoumenos, Z Wang… - 2017 26th …, 2017 - ieeexplore.ieee.org
Accurate automatic optimization heuristics are necessary for dealing with thecomplexity and
diversity of modern hardware and software. Machine learning is aproven technique for …
diversity of modern hardware and software. Machine learning is aproven technique for …
The emerging" big dimensionality"
The world continues to generate quintillion bytes of data daily, leading to the pressing needs
for new efforts in dealing with the grand challenges brought by Big Data. Today, there is a …
for new efforts in dealing with the grand challenges brought by Big Data. Today, there is a …
Optimizing existing software with genetic programming
WB Langdon, M Harman - IEEE Transactions on Evolutionary …, 2014 - ieeexplore.ieee.org
We show that the genetic improvement of programs (GIP) can scale by evolving increased
performance in a widely-used and highly complex 50000 line system. Genetic improvement …
performance in a widely-used and highly complex 50000 line system. Genetic improvement …
Synthesizing benchmarks for predictive modeling
C Cummins, P Petoumenos, Z Wang… - 2017 IEEE/ACM …, 2017 - ieeexplore.ieee.org
Predictive modeling using machine learning is an effective method for building compiler
heuristics, but there is a shortage of benchmarks. Typical machine learning experiments …
heuristics, but there is a shortage of benchmarks. Typical machine learning experiments …
[图书][B] Abstraction in Artificial Intelligence
One of the field in which models of abstraction have been proposed is Artificial Intelligence
(AI). This chapter has two parts: one presents an overview of the formal models, either …
(AI). This chapter has two parts: one presents an overview of the formal models, either …
Compiler-based graph representations for deep learning models of code
In natural language processing, novel methods in deep learning, like recurrent neural
networks (RNNs) on sequences of words, have been very successful. In contrast to natural …
networks (RNNs) on sequences of words, have been very successful. In contrast to natural …