TY - JOUR

T1 - Reduced O3 subsequence labelling

T2 - a stepping stone towards optimisation sequence prediction

AU - You, Yi Ping

AU - Su, Yi Chiao

N1 - Publisher Copyright:
© 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

PY - 2022

Y1 - 2022

N2 - Finding a good compiler autotuning methodology, particularly for selecting the right set of optimisations and finding the best ordering of these optimisations for a given code fragment has been a long-standing problem. As the rapid development of machine learning techniques, tackling the problem of compiler autotuning using machine learning or deep learning has become increasingly common in recent years. There have been many deep learning models proposed to solve problems, such as predicting the optimal heterogeneous mapping or thread-coarsening factor; however, very few have revisited the problem of optimisation phase tuning. In this paper, we intend to revisit and tackle the problem using deep learning techniques. Unfortunately, the problem is too complex to be addressed in its full scope. We present a new problem, called reduced O3 subsequence labelling–a reduced O3 subsequence is defined as a subsequence of O3 optimisation passes that contains no useless passes, which is a simplified problem and expected to be a stepping stone towards solving the optimisation phase tuning problem. We formulated the problem and attempted to solve the problem by using genetic algorithms. We believe that with mature deep learning techniques, a machine learning model that predicts the reduced O3 subsequences or even the best O3 subsequence for a given code fragment could be developed and used to prune the search space of the problem of the optimisation phase tuning, thereby shortening the tuning process and also providing more effective tuning results.

AB - Finding a good compiler autotuning methodology, particularly for selecting the right set of optimisations and finding the best ordering of these optimisations for a given code fragment has been a long-standing problem. As the rapid development of machine learning techniques, tackling the problem of compiler autotuning using machine learning or deep learning has become increasingly common in recent years. There have been many deep learning models proposed to solve problems, such as predicting the optimal heterogeneous mapping or thread-coarsening factor; however, very few have revisited the problem of optimisation phase tuning. In this paper, we intend to revisit and tackle the problem using deep learning techniques. Unfortunately, the problem is too complex to be addressed in its full scope. We present a new problem, called reduced O3 subsequence labelling–a reduced O3 subsequence is defined as a subsequence of O3 optimisation passes that contains no useless passes, which is a simplified problem and expected to be a stepping stone towards solving the optimisation phase tuning problem. We formulated the problem and attempted to solve the problem by using genetic algorithms. We believe that with mature deep learning techniques, a machine learning model that predicts the reduced O3 subsequences or even the best O3 subsequence for a given code fragment could be developed and used to prune the search space of the problem of the optimisation phase tuning, thereby shortening the tuning process and also providing more effective tuning results.

KW - Compiler autotuning

KW - compiler optimisation phase tuning

KW - evolutionary computation

UR - http://www.scopus.com/inward/record.url?scp=85125914849&partnerID=8YFLogxK

U2 - 10.1080/09540091.2022.2044761

DO - 10.1080/09540091.2022.2044761

M3 - Comment/debate

AN - SCOPUS:85125914849

SN - 0954-0091

JO - Connection Science

JF - Connection Science

ER -