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Documents authored by Cattaneo, Daniele

Document
DOI: 10.4230/OASIcs.PARMA-DITAM.2024.4
Precision Tuning the Rust Memory-Safe Programming Language

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

Published in: OASIcs, Volume 116, 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)

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

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)

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@InProceedings{magnani_et_al:OASIcs.PARMA-DITAM.2024.4,
  author =	{Magnani, Gabriele and Denisov, Lev and Cattaneo, Daniele and Agosta, Giovanni and Cherubin, Stefano},
  title =	{{Precision Tuning the Rust Memory-Safe Programming Language}},
  booktitle =	{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)},
  pages =	{4:1--4:12},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-307-2},
  ISSN =	{2190-6807},
  year =	{2024},
  volume =	{116},
  editor =	{Bispo, Jo\~{a}o and Xydis, Sotirios and Curzel, Serena and Sousa, Lu{\'\i}s Miguel},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.PARMA-DITAM.2024.4},
  URN =		{urn:nbn:de:0030-drops-196989},
  doi =		{10.4230/OASIcs.PARMA-DITAM.2024.4},
  annote =	{Keywords: Approximate Computing, Memory Safety, Precision Tuning}
}
Document
Complete Volume
DOI: 10.4230/OASIcs.NG-RES.2023
OASIcs, Volume 108, NG-RES 2023, Complete Volume

Authors: Federico Terraneo and Daniele Cattaneo

Published in: OASIcs, Volume 108, Fourth Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2023)

Abstract
OASIcs, Volume 108, NG-RES 2023, Complete Volume
Cite as

Fourth Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2023). Open Access Series in Informatics (OASIcs), Volume 108, pp. 1-98, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@Proceedings{terraneo_et_al:OASIcs.NG-RES.2023,
  title =	{{OASIcs, Volume 108, NG-RES 2023, Complete Volume}},
  booktitle =	{Fourth Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2023)},
  pages =	{1--98},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-268-6},
  ISSN =	{2190-6807},
  year =	{2023},
  volume =	{108},
  editor =	{Terraneo, Federico and Cattaneo, Daniele},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.NG-RES.2023},
  URN =		{urn:nbn:de:0030-drops-177300},
  doi =		{10.4230/OASIcs.NG-RES.2023},
  annote =	{Keywords: OASIcs, Volume 108, NG-RES 2023, Complete Volume}
}
Document
Front Matter
DOI: 10.4230/OASIcs.NG-RES.2023.0
Front Matter, Table of Contents, Preface, Conference Organization

Authors: Federico Terraneo and Daniele Cattaneo

Published in: OASIcs, Volume 108, Fourth Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2023)

Abstract
Front Matter, Table of Contents, Preface, Conference Organization
Cite as

Fourth Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2023). Open Access Series in Informatics (OASIcs), Volume 108, pp. 0:i-0:x, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{terraneo_et_al:OASIcs.NG-RES.2023.0,
  author =	{Terraneo, Federico and Cattaneo, Daniele},
  title =	{{Front Matter, Table of Contents, Preface, Conference Organization}},
  booktitle =	{Fourth Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2023)},
  pages =	{0:i--0:x},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-268-6},
  ISSN =	{2190-6807},
  year =	{2023},
  volume =	{108},
  editor =	{Terraneo, Federico and Cattaneo, Daniele},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.NG-RES.2023.0},
  URN =		{urn:nbn:de:0030-drops-177312},
  doi =		{10.4230/OASIcs.NG-RES.2023.0},
  annote =	{Keywords: Front Matter, Table of Contents, Preface, Conference Organization}
}
Document
DOI: 10.4230/OASIcs.NG-RES.2022.4
Ahead-Of-Real-Time (ART): A Methodology for Static Reduction of Worst-Case Execution Time

Authors: Daniele Cattaneo, Gabriele Magnani, Stefano Cherubin, and Giovanni Agosta

Published in: OASIcs, Volume 98, Third Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2022)

Abstract
Precision tuning is an approximate computing technique for trading precision with lower execution time, and it has been increasingly important in embedded and high-performance computing applications. In particular, embedded applications benefit from lower precision in order to reduce or remove the dependency on computationally-expensive data types such as floating point. Amongst such applications, an important fraction are mission-critical tasks, such as control systems for vehicles or medical use-cases. In this context, the usefulness of precision tuning is limited by concerns about verificability of real-time and quality-of-service constraints. However, with the introduction of optimisations techniques based on integer linear programming and rigorous WCET (Worst-Case Execution Time) models, these constraints not only can be verified automatically, but it becomes possible to use precision tuning to automatically enforce these constraints even when not previously possible. In this work, we show how to combine precision tuning with WCET analysis to enforce a limit on the execution time by using a constraint-based code optimisation pass with a state-of-the-art precision tuning framework.
Cite as

Daniele Cattaneo, Gabriele Magnani, Stefano Cherubin, and Giovanni Agosta. Ahead-Of-Real-Time (ART): A Methodology for Static Reduction of Worst-Case Execution Time. In Third Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2022). Open Access Series in Informatics (OASIcs), Volume 98, pp. 4:1-4:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{cattaneo_et_al:OASIcs.NG-RES.2022.4,
  author =	{Cattaneo, Daniele and Magnani, Gabriele and Cherubin, Stefano and Agosta, Giovanni},
  title =	{{Ahead-Of-Real-Time (ART): A Methodology for Static Reduction of Worst-Case Execution Time}},
  booktitle =	{Third Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2022)},
  pages =	{4:1--4:10},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-221-1},
  ISSN =	{2190-6807},
  year =	{2022},
  volume =	{98},
  editor =	{Bertogna, Marko and Terraneo, Federico and Reghenzani, Federico},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.NG-RES.2022.4},
  URN =		{urn:nbn:de:0030-drops-161120},
  doi =		{10.4230/OASIcs.NG-RES.2022.4},
  annote =	{Keywords: Approximate Computing, Precision Tuning, Worst-Case Execution Time}
}
Document
DOI: 10.4230/OASIcs.PARMA-DITAM.2022.5
Precision Tuning in Parallel Applications

Authors: Gabriele Magnani, Lev Denisov, Daniele Cattaneo, and Giovanni Agosta

Published in: OASIcs, Volume 100, 13th Workshop on Parallel Programming and Run-Time Management Techniques for Many-Core Architectures and 11th Workshop on Design Tools and Architectures for Multicore Embedded Computing Platforms (PARMA-DITAM 2022)

Abstract
Nowadays, parallel applications are used every day in high performance computing, scientific computing and also in everyday tasks due to the pervasiveness of multi-core architectures. However, several implementation challenges have so far stifled the integration of parallel applications and automatic precision tuning. First of all, tuning a parallel application introduces difficulties in the detection of the region of code that must be affected by the optimization. Moreover, additional challenges arise in handling shared variables and accumulators. In this work we address such challenges by introducing OpenMP parallel programming support to the TAFFO precision tuning framework. With our approach we achieve speedups up to 750% with respect to the same parallel application without precision tuning.
Cite as

Gabriele Magnani, Lev Denisov, Daniele Cattaneo, and Giovanni Agosta. Precision Tuning in Parallel Applications. In 13th Workshop on Parallel Programming and Run-Time Management Techniques for Many-Core Architectures and 11th Workshop on Design Tools and Architectures for Multicore Embedded Computing Platforms (PARMA-DITAM 2022). Open Access Series in Informatics (OASIcs), Volume 100, pp. 5:1-5:9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{magnani_et_al:OASIcs.PARMA-DITAM.2022.5,
  author =	{Magnani, Gabriele and Denisov, Lev and Cattaneo, Daniele and Agosta, Giovanni},
  title =	{{Precision Tuning in Parallel Applications}},
  booktitle =	{13th Workshop on Parallel Programming and Run-Time Management Techniques for Many-Core Architectures and 11th Workshop on Design Tools and Architectures for Multicore Embedded Computing Platforms (PARMA-DITAM 2022)},
  pages =	{5:1--5:9},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-231-0},
  ISSN =	{2190-6807},
  year =	{2022},
  volume =	{100},
  editor =	{Palumbo, Francesca and Bispo, Jo\~{a}o and Cherubin, Stefano},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.PARMA-DITAM.2022.5},
  URN =		{urn:nbn:de:0030-drops-161210},
  doi =		{10.4230/OASIcs.PARMA-DITAM.2022.5},
  annote =	{Keywords: Compilers, Parallel Programming, Precision Tuning}
}
Document
DOI: 10.4230/OASIcs.PARMA-DITAM.2022.7
Efficient Memory Management for Modelica Simulations

Authors: Michele Scuttari, Nicola Camillucci, Daniele Cattaneo, Federico Terraneo, and Giovanni Agosta

Published in: OASIcs, Volume 100, 13th Workshop on Parallel Programming and Run-Time Management Techniques for Many-Core Architectures and 11th Workshop on Design Tools and Architectures for Multicore Embedded Computing Platforms (PARMA-DITAM 2022)

Abstract
The ever increasing usage of simulations in order to produce digital twins of physical systems led to the creation of specialized equation-based modeling languages such as Modelica. However, compilers of such languages often generate code that exploits the garbage collection memory management paradigm, which introduces significant runtime overhead. In this paper we explain how to improve the memory management approach of the automatically generated simulation code. This is achieved by addressing two different aspects. One regards the reduction of the heap memory usage, which is obtained by modifying functions whose resulting arrays could instead be allocated on the stack by the caller. The other aspect regards the possibility of avoiding garbage collection altogether by performing all memory lifetime tracking statically. We implement our approach in a prototype Modelica compiler, achieving an improvement of the memory management overhead of over 10 times compared to a garbage collected solution, and an improvement of 56 times compared to the production-grade compiler OpenModelica.
Cite as

Michele Scuttari, Nicola Camillucci, Daniele Cattaneo, Federico Terraneo, and Giovanni Agosta. Efficient Memory Management for Modelica Simulations. In 13th Workshop on Parallel Programming and Run-Time Management Techniques for Many-Core Architectures and 11th Workshop on Design Tools and Architectures for Multicore Embedded Computing Platforms (PARMA-DITAM 2022). Open Access Series in Informatics (OASIcs), Volume 100, pp. 7:1-7:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{scuttari_et_al:OASIcs.PARMA-DITAM.2022.7,
  author =	{Scuttari, Michele and Camillucci, Nicola and Cattaneo, Daniele and Terraneo, Federico and Agosta, Giovanni},
  title =	{{Efficient Memory Management for Modelica Simulations}},
  booktitle =	{13th Workshop on Parallel Programming and Run-Time Management Techniques for Many-Core Architectures and 11th Workshop on Design Tools and Architectures for Multicore Embedded Computing Platforms (PARMA-DITAM 2022)},
  pages =	{7:1--7:13},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-231-0},
  ISSN =	{2190-6807},
  year =	{2022},
  volume =	{100},
  editor =	{Palumbo, Francesca and Bispo, Jo\~{a}o and Cherubin, Stefano},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.PARMA-DITAM.2022.7},
  URN =		{urn:nbn:de:0030-drops-161237},
  doi =		{10.4230/OASIcs.PARMA-DITAM.2022.7},
  annote =	{Keywords: Modelica, modeling \& simulation, memory management, garbage collection}
}
Document
DOI: 10.4230/OASIcs.PARMA-DITAM.2021.3
The Impact of Precision Tuning on Embedded Systems Performance: A Case Study on Field-Oriented Control

Authors: Gabriele Magnani, Daniele Cattaneo, Michele Chiari, and Giovanni Agosta

Published in: OASIcs, Volume 88, 12th Workshop on Parallel Programming and Run-Time Management Techniques for Many-core Architectures and 10th Workshop on Design Tools and Architectures for Multicore Embedded Computing Platforms (PARMA-DITAM 2021)

Abstract
Field Oriented Control (FOC) is an industry-standard strategy for controlling induction motors and other kinds of AC-based motors. This control scheme has a very high arithmetic intensity when implemented digitally - in particular it requires the use of trigonometric functions. This requirement contrasts with the necessity of increasing the control step frequency when required, and the minimization of power consumption in applications where conserving battery life is paramount such as drones. However, it also makes FOC well suited for optimization using precision tuning techniques. Therefore, we exploit the state-of-the-art FixM methodology to optimize a miniapp simulating a typical FOC application by applying precision tuning of trigonometric functions. The FixM approach itself was extended in order to implement additional algorithm choices to enable a trade-off between execution time and code size. With the application of FixM on the miniapp, we achieved a speedup up to 278%, at a cost of an error in the output less than 0.1%.
Cite as

Gabriele Magnani, Daniele Cattaneo, Michele Chiari, and Giovanni Agosta. The Impact of Precision Tuning on Embedded Systems Performance: A Case Study on Field-Oriented Control. In 12th Workshop on Parallel Programming and Run-Time Management Techniques for Many-core Architectures and 10th Workshop on Design Tools and Architectures for Multicore Embedded Computing Platforms (PARMA-DITAM 2021). Open Access Series in Informatics (OASIcs), Volume 88, pp. 3:1-3:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{magnani_et_al:OASIcs.PARMA-DITAM.2021.3,
  author =	{Magnani, Gabriele and Cattaneo, Daniele and Chiari, Michele and Agosta, Giovanni},
  title =	{{The Impact of Precision Tuning on Embedded Systems Performance: A Case Study on Field-Oriented Control}},
  booktitle =	{12th Workshop on Parallel Programming and Run-Time Management Techniques for Many-core Architectures and 10th Workshop on Design Tools and Architectures for Multicore Embedded Computing Platforms (PARMA-DITAM 2021)},
  pages =	{3:1--3:13},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-181-8},
  ISSN =	{2190-6807},
  year =	{2021},
  volume =	{88},
  editor =	{Bispo, Jo\~{a}o and Cherubin, Stefano and Flich, Jos\'{e}},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.PARMA-DITAM.2021.3},
  URN =		{urn:nbn:de:0030-drops-136390},
  doi =		{10.4230/OASIcs.PARMA-DITAM.2021.3},
  annote =	{Keywords: Approximate Computing, Field-oriented control, Precision Tuning}
}
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