Inferring Needless Write Memory Accesses on Ethereum Bytecode
Authors: 
Elvira Albert, Jesús Correas, Pablo Gordillo, Guillermo Román-Díez, Albert RubioAuthors Info & Claims
Pages 448 - 466
Abstract
Efficiency is a fundamental property of any type of program, but it is even more so in the context of the programs executing on the blockchain (known as smart contracts). This is because optimizing smart contracts has direct consequences on reducing the costs of deploying and executing the contracts, as there are fees to pay related to their bytes-size and to their resource consumption (called gas). Optimizing memory usage is considered a challenging problem that, among other things, requires a precise inference of the memory locations being accessed. This is also the case for the Ethereum Virtual Machine (EVM) bytecode generated by the most-widely used compiler, solc, whose rather unconventional and low-level memory usage challenges automated reasoning. This paper presents a static analysis, developed at the level of the EVM bytecode generated by solc, that infers write memory accesses that are needless and thus can be safely removed. The application of our implementation on more than 19,000 real smart contracts has detected about 6,200 needless write accesses in less than 4 hours. Interestingly, many of these writes were involved in memory usage patterns generated by solc that can be greatly optimized by removing entire blocks of bytecodes. To the best of our knowledge, existing optimization tools cannot infer such needless write accesses, and hence cannot detect these inefficiencies that affect both the deployment and the execution costs of Ethereum smart contracts.
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Published In
Apr 2023
717 pages
ISBN:978-3-031-30822-2
DOI:10.1007/978-3-031-30823-9
- Editors:
- Sriram Sankaranarayanan,
- Natasha Sharygina
© The Author(s) 2023.
Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
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Published: 22 April 2023
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