research-article

ADiJaC -- Automatic Differentiation of Java Classfiles

Authors:

Emil I. Sluşanschi,

Vlad DumitrelAuthors Info & Claims

ACM Transactions on Mathematical Software (TOMS), Volume 43, Issue 2

Article No.: 9, Pages 1 - 33

https://doi.org/10.1145/2904901

Published: 02 September 2016 Publication History

Abstract

This work presents the current design and implementation of ADiJaC, an automatic differentiation tool for Java classfiles. ADiJaC uses source transformation to generate derivative codes in both the forward and the reverse modes of automatic differentiation. We describe the overall architecture of the tool and present various details and examples for each of the two modes of differentiation. We emphasize the enhancements that have been made over previous versions of ADiJaC and illustrate their influence on the generality of the tool and on the performance of the generated derivative codes. The ADiJaC tool has been used to generate derivatives for a variety of problems, including real-world applications. We evaluate the performance of such codes and compare it to derivatives generated by Tapenade, a well-established automatic differentiation tool for Fortran and C/C++. Additionally, we present a more detailed performance analysis of a real-world application. Apart from being the only general-purpose automatic differentiation tool for Java bytecode, we argue that ADiJaC’s features and performance are comparable to those of similar mature tools for other programming languages such as C/C++ or Fortran.

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Cited By

Lee WPark SAiken AKrause ABrunskill ECho KEngelhardt BSabato SScarlett J(2023)On the correctness of automatic differentiation for neural networks with machine-representable parametersProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3619197(19094-19140)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.5555/3618408.3619197
Hascoët L(2023)Data-flow Reversal and Garbage CollectionACM Transactions on Mathematical Software10.1145/362753750:1(1-20)Online publication date: 18-Nov-2023
https://dl.acm.org/doi/10.1145/3627537
Andelfinger P(2023)Towards Differentiable Agent-Based SimulationACM Transactions on Modeling and Computer Simulation10.1145/356581032:4(1-26)Online publication date: 11-Jan-2023
https://dl.acm.org/doi/10.1145/3565810
Show More Cited By

Index Terms

ADiJaC -- Automatic Differentiation of Java Classfiles
1. Mathematics of computing
  1. Mathematical analysis
    1. Mathematical optimization
    2. Numerical analysis
      1. Automatic differentiation
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization
  2. Semantics and reasoning
    1. Program reasoning
      1. Program analysis
    2. Program semantics
      1. Operational semantics

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Published In

ACM Transactions on Mathematical Software Volume 43, Issue 2

June 2017

200 pages

ISSN:0098-3500

EISSN:1557-7295

DOI:10.1145/2988256

Editor:
Michael A. Heroux
Sandia National Laboratories, USA

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Copyright © 2016 ACM.

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Publication History

Published: 02 September 2016

Accepted: 01 March 2016

Revised: 01 February 2016

Received: 01 August 2015

Published in TOMS Volume 43, Issue 2

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Cited By

Lee WPark SAiken AKrause ABrunskill ECho KEngelhardt BSabato SScarlett J(2023)On the correctness of automatic differentiation for neural networks with machine-representable parametersProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3619197(19094-19140)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.5555/3618408.3619197
Hascoët L(2023)Data-flow Reversal and Garbage CollectionACM Transactions on Mathematical Software10.1145/362753750:1(1-20)Online publication date: 18-Nov-2023
https://dl.acm.org/doi/10.1145/3627537
Andelfinger P(2023)Towards Differentiable Agent-Based SimulationACM Transactions on Modeling and Computer Simulation10.1145/356581032:4(1-26)Online publication date: 11-Jan-2023
https://dl.acm.org/doi/10.1145/3565810
Andelfinger PDiallo STolk AGiabbanelli P(2021)Differentiable Agent-Based Simulation for Gradient-Guided Simulation-Based OptimizationProceedings of the 2021 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation10.1145/3437959.3459261(27-38)Online publication date: 21-May-2021
https://dl.acm.org/doi/10.1145/3437959.3459261
Peñuñuri FPeón RGonzález–Sánchez DEscalante Soberanis M(2020)Dual Numbers and Automatic Differentiation to Efficiently Compute Velocities and AccelerationsActa Applicandae Mathematicae: an international survey journal on applying mathematics and mathematical applications10.1007/s10440-020-00351-9170:1(649-659)Online publication date: 1-Dec-2020
https://dl.acm.org/doi/10.1007/s10440-020-00351-9
Shen YDai Y(2018)Fast Automatic Differentiation for Large Scale Bundle AdjustmentIEEE Access10.1109/ACCESS.2018.28121736(11146-11153)Online publication date: 2018
https://doi.org/10.1109/ACCESS.2018.2812173
Baydin APearlmutter BRadul ASiskind J(2017)Automatic differentiation in machine learningThe Journal of Machine Learning Research10.5555/3122009.324201018:1(5595-5637)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.5555/3122009.3242010

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