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IR2VEC: LLVM IR Based Scalable Program Embeddings

Authors:

S. VenkataKeerthy,

Rohit Aggarwal,

Maunendra Sankar Desarkar,

Ramakrishna Upadrasta,

Y. N. SrikantAuthors Info & Claims

ACM Transactions on Architecture and Code Optimization (TACO), Volume 17, Issue 4

Article No.: 32, Pages 1 - 27

https://doi.org/10.1145/3418463

Published: 22 December 2020 Publication History

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Abstract

We propose IR2VEC, a Concise and Scalable encoding infrastructure to represent programs as a distributed embedding in continuous space. This distributed embedding is obtained by combining representation learning methods with flow information to capture the syntax as well as the semantics of the input programs. As our infrastructure is based on the Intermediate Representation (IR) of the source code, obtained embeddings are both language and machine independent. The entities of the IR are modeled as relationships, and their representations are learned to form a seed embedding vocabulary. Using this infrastructure, we propose two incremental encodings: Symbolic and Flow-Aware. Symbolic encodings are obtained from the seed embedding vocabulary, and Flow-Aware encodings are obtained by augmenting the Symbolic encodings with the flow information.

We show the effectiveness of our methodology on two optimization tasks (Heterogeneous device mapping and Thread coarsening). Our way of representing the programs enables us to use non-sequential models resulting in orders of magnitude of faster training time. Both the encodings generated by IR2VEC outperform the existing methods in both the tasks, even while using simple machine learning models. In particular, our results improve or match the state-of-the-art speedup in 11/14 benchmark-suites in the device mapping task across two platforms and 53/68 benchmarks in the thread coarsening task across four different platforms. When compared to the other methods, our embeddings are more scalable, is non-data-hungry, and has better Out-Of-Vocabulary (OOV) characteristics.

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Index Terms

IR2VEC: LLVM IR Based Scalable Program Embeddings
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
  2. Machine learning
2. Software and its engineering
  1. Software notations and tools
    1. Compilers
    2. General programming languages

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Published In

cover image ACM Transactions on Architecture and Code Optimization

ACM Transactions on Architecture and Code Optimization Volume 17, Issue 4

December 2020

430 pages

ISSN:1544-3566

EISSN:1544-3973

DOI:10.1145/3427420

Editor:
David Kaeli
Northeastern University, USA

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Copyright © 2020 ACM.

© 2020 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Publication History

Published: 22 December 2020

Accepted: 01 August 2020

Revised: 01 July 2020

Received: 01 February 2020

Published in TACO Volume 17, Issue 4

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Visvesvaraya Young Faculty Research Fellowship from MeitY
NSM
Visvesvaraya PhD Scheme under the MEITY, GoI
AMD
Department of Electronics 8 Information Technology and the Ministry of Communications 8 Information Technology, Government of India

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