POSET-RL: Phase ordering for Optimizing Size and Execution Time using Reinforcement Learning

Jain, Shalini and Andaluri, Yashas and VenkataKeerthy, S. and Upadrasta, Ramakrishna (2022) POSET-RL: Phase ordering for Optimizing Size and Execution Time using Reinforcement Learning. In: 2022 IEEE International Symposium on Performance Analysis of Systems and Software, ISPASS 2022, 22 May 2022 through 24 May 2022, Singapore.

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Abstract

The ever increasing memory requirements of several applications has led to increased demands which might not be met by embedded devices. Constraining the usage of memory in such cases is of paramount importance. It is important that such code size improvements should not have a negative impact on the runtime. Improving the execution time while optimizing for code size is a non-trivial but a significant task.The ordering of standard optimization sequences in modern compilers is fixed, and are heuristically created by the compiler domain experts based on their expertise. However, this ordering is sub-optimal, and does not generalize well across all the cases.We present a reinforcement learning based solution to the phase ordering problem, where the ordering improves both the execution time and code size. We propose two different approaches to model the sequences: one by manual ordering, and other based on a graph called Oz Dependence Graph (ODG). Our approach uses minimal data as training set, and is integrated with LLVM.We show results on x86 and AArch64 architectures on the benchmarks from SPEC-CPU 2006, SPEC-CPU 2017 and MiBench. We observe that the proposed model based on ODG outperforms the current Oz sequence both in terms of size and execution time by 6.19% and 11.99% in SPEC 2017 benchmarks, on an average. © 2022 IEEE.

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IITH Creators:

IITH Creators	ORCiD
Upadrasta, Ramakrishna	https://orcid.org/0000-0002-5290-3266

Item Type:

Conference or Workshop Item (Paper)

Additional Information:

This research is funded by the Department of Electronics Information Technologyand the Ministry of Communications Information Technology, Government of India. This work is partially supported by a Visvesvaraya PhD Scheme under the MEITY, GoI (PhD-MLA/04(02)/2015-16), an NSM research grant (MeitY/R&D/HPC/2(1)/2014), a Visvesvaraya Young Faculty Research Fellowship from MeitY, and a Google PhD Fellowship.

Uncontrolled Keywords:

Compiler Optimization; Phase Ordering; Reinforcement learning

Subjects:

Computer science

Divisions:

Department of Computer Science & Engineering

Depositing User:

. LibTrainee 2021

Date Deposited:

27 Jul 2022 10:17

Last Modified:

27 Jul 2022 10:17