@misc{BluehdornG2024,

title = {Local Adjoints for Simultaneous Preaccumulations with Shared Inputs},

author = {J. Blühdorn, N. R. Gauger},

url = {https://arxiv.org/abs/2405.07819},

year  = {2024},

date = {2024-05-13},

urldate = {2024-05-13},

howpublished = {arXiv:2405.07819 [cs.MS]},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{BluehdornGAG2024,

title = {Hybrid Parallel Discrete Adjoints in SU2},

author = {J. Blühdorn, P. Gomes, M. Aehle, N. R. Gauger},

url = {https://arxiv.org/abs/2405.06056},

year  = {2024},

date = {2024-05-09},

urldate = {2024-05-09},

howpublished = {arXiv:2405.06056 [cs.MS]},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{BG2023,

title = {Automatic Differentiation of OpenMP with OpDiLib},

author = {J. Blühdorn, N. R. Gauger},

url = {https://www.nhr-verein.de/sites/default/files/2024-01/NHR%20Conference%202023%20-%20Book%20of%20Abstracts.pdf},

year  = {2023},

date = {2023-09-18},

urldate = {2023-09-18},

howpublished = {NHR Conference '23 -- Book of Abstracts (extended version), pp. 176--177},

note = {poster contribution},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{nokey,

title = {Reverse-Mode Automatic Differentiation of Compiled Programs},

author = {M. Aehle, J. Blühdorn, M. Sagebaum, N.R. Gauger},

url = {https://arxiv.org/pdf/2212.13760.pdf},

year  = {2022},

date = {2022-12-28},

urldate = {2022-12-28},

howpublished = {arXiv:2212.13760},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{nokey,

title = {Forward-Mode Automatic Differentiation of Compiled Programs},

author = {M. Aehle, J. Blühdorn, M. Sagebaum, N. R. Gauger},

url = {https://arxiv.org/pdf/2209.01895},

year  = {2022},

date = {2022-09-05},

urldate = {2022-09-05},

abstract = {Algorithmic differentiation (AD) is a set of techniques to obtain accurate derivatives of a computer-implemented function in an automatic fashion. State-of-the-art AD tools rely on the source code of the implementation or internal representations of compilers building it.

We present the new AD tool Derivgrind, which augments the machine code of compiled programs with forward AD logic. Derivgrind leverages the Valgrind instrumentation framework for a structured access to the machine code, and a shadow memory tool to store dot values. Depending on the application scenario, no access to the source code is required at all, or the access is restricted to the parts defining input and output variables.

Derivgrind's versatility comes at the price of scaling the running time by a factor between 60 and 140, measured on a benchmark based on a PDE solver. Results of our extensive test suite indicate that Derivgrind produces correct results on GCC- and Clang-compiled programs, including a Python interpreter, with a small number of exceptions. While we provide a list of scenarios that Derivgrind does not handle correctly, most of them are academic examples or originate from highly optimized math libraries. We will therefore further study the potential of our tool in more complex software projects. },

howpublished = {arXiv:2209.01895},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{SIVERT2022,

title = {Derivatives in Proton CT},

author = {M. Aehle, J. Alme, G. Barnaföldi, J. Blühdorn, T. Bodova, V. Borshchov, A. van den Brink, M. Chaar, V. Eikeland, G. Feofilov, C. Garth, N.R. Gauger, et al.},

url = {https://arxiv.org/pdf/2202.05551v1.pdf},

year  = {2022},

date = {2022-02-14},

urldate = {2022-02-14},

abstract = {Algorithmic derivatives can be useful to quantify uncertainties and optimize parameters using computer simulations. Whether they actually are, depends on how "well-linearizable" the program is. Proton computed tomography (pCT) is a medical imaging technology with the potential to increase the spatial accuracy of the dose delivered in proton-beam radiotherapy. The Bergen pCT collaboration is developing and constructing a digital tracking calorimeter (DTC) to measure the position, direction and energy of protons after they passed through a patient, and a software pipeline to process these data into a pCT image. We revisit the software pipeline from the perspective of algorithmic differentiation (AD). In the early subprocedures, several obstacles such as discrete variables or frequent discontinuities were identified, and are probably tackled best by using surrogate models. The model-based iterative reconstruction (MBIR) subprocedure in the end seems to be AD-ready, and we propose changes in the AD workflow that can reduce the memory consumption in reverse mode.},

howpublished = {arXiv: 2202.05551v1},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{BluehdornSG2021,

title = {Event-Based Automatic Differentiation of OpenMP with OpDiLib},

author = {J. Blühdorn, M. Sagebaum, N. R. Gauger},

url = {https://arxiv.org/abs/2102.11572},

year  = {2021},

date = {2021-02-23},

howpublished = {preprint arXiv:2102.11572},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{sagebaum2021index,

title = {Index handling and assign optimization for Algorithmic Differentiation reuse index managers},

author = {M. Sagebaum, J. Blühdorn, N.R. Gauger},

url = {https://arxiv.org/abs/2006.12992},

year  = {2021},

date = {2021-01-01},

howpublished = {arXiv cs.MS 2006.12992},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{BluehdornGK2020,

title = {AutoMat -- Automatic Differentiation for Generalized Standard Materials on GPUs},

author = {J. Blühdorn, N. R. Gauger, M. Kabel},

url = {https://arxiv.org/abs/2006.04391},

year  = {2020},

date = {2020-10-06},

howpublished = {preprint arXiv:2006.04391},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}