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Precision Tuning the Rust Memory-Safe Programming Language

Authors Gabriele Magnani , Lev Denisov , Daniele Cattaneo , Giovanni Agosta , Stefano Cherubin

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Subject Classification

ACM Subject Classification
  • Software and its engineering → Compilers
  • Software and its engineering → Software safety
  • Software and its engineering → Software performance
  • Computer systems organization → Embedded software
Keywords
  • Approximate Computing
  • Memory Safety
  • Precision Tuning

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Abstract

Precision tuning is an increasingly common approach for exploiting the tradeoff between energy efficiency or speedup, and accuracy. Its effectiveness is particularly strong whenever the maximum performance must be extracted from a computing system, such as embedded platforms. In these contexts, current engineering practice sees a dominance of memory-unsafe programming languages such as C and C++. However, the unsafe nature of these languages has come under great scrutiny as it leads to significant software vulnerabilities. Hence, safer programming languages which prevent memory-related bugs by design have been proposed as a replacement. Amongst these safer programming languages, one of the most popular has been Rust. In this work we adapt a state-of-the-art precision tuning tool, TAFFO, to operate on Rust code. By porting the PolyBench/C benchmark suite to Rust, we show that the effectiveness of the precision tuning is not affected by the use of a safer programming language, and moreover the safety properties of the language can be successfully preserved. Specifically, using TAFFO and Rust we achieved up to a 15× speedup over the base Rust code, thanks to the use of precision tuning.

Cite As Get BibTex

Gabriele Magnani, Lev Denisov, Daniele Cattaneo, Giovanni Agosta, and Stefano Cherubin. Precision Tuning the Rust Memory-Safe Programming Language. In 15th Workshop on Parallel Programming and Run-Time Management Techniques for Many-Core Architectures and 13th Workshop on Design Tools and Architectures for Multicore Embedded Computing Platforms (PARMA-DITAM 2024). Open Access Series in Informatics (OASIcs), Volume 116, pp. 4:1-4:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024) https://doi.org/10.4230/OASIcs.PARMA-DITAM.2024.4

Author Details

Gabriele Magnani
  • DEIB - Politecnico di Milano, Italy
Lev Denisov
  • DEIB - Politecnico di Milano, Italy
Daniele Cattaneo
  • DEIB - Politecnico di Milano, Italy
Giovanni Agosta
  • DEIB - Politecnico di Milano, Italy
Stefano Cherubin
  • NTNU - Norwegian University of Science and Technology, Trondheim, Norway

Funding

The authors gratefully acknowledge funding from European Union’s Horizon 2020 Research and Innovation programme under the Marie Skłodowska Curie grant agreement No. 956090 (APROPOS: Approximate Computing for Power and Energy Optimisation, http://www.apropos.eu/). Also the Italian MISE project F/340043/01-03/X59 "Piattaforma ed ecosistema cooperativo, C- ITS ETSI standard per la mobilità digitale integrata" within the framework FCS - Accordi Innovazione Settore Automotive (DM 31/12/2021 e DD 10/10/2022).

References

  1. Daniele Cattaneo, Michele Chiari, Giovanni Agosta, and Stefano Cherubin. TAFFO: The compiler-based precision tuner. SoftwareX, 20:101238, 2022. URL: https://doi.org/10.1016/j.softx.2022.101238.
  2. Daniele Cattaneo, Michele Chiari, Nicola Fossati, Stefano Cherubin, and Giovanni Agosta. Architecture-aware precision tuning with multiple number representation systems. In 2021 58th ACM/IEEE Design Automation Conference (DAC), pages 673-678, 2021. URL: https://doi.org/10.1109/DAC18074.2021.9586303.
  3. Stefano Cherubin and Giovanni Agosta. Tools for reduced precision computation: a survey. ACM Computing Surveys, 53(2), April 2020. URL: https://doi.org/10.1145/3381039.
  4. Stefano Cherubin, Daniele Cattaneo, Michele Chiari, and Agosta Giovanni. Dynamic precision autotuning with TAFFO. ACM Transaction on Architecture and Code Optimization, 17(2), May 2020. URL: https://doi.org/10.1145/3388785.
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