JOSS paper

.github/workflows/draft-joss-paper.yml

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+name: Draft JOSS paper
+
+on:
+  push:
+    branches: [ "main" ]
+  pull_request:
+    branches: [ "main" ]
+
+jobs:
+  paper:
+    runs-on: ubuntu-latest
+    name: Paper Draft
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+        with:
+          repository: ${{ github.event.pull_request.head.repo.full_name }}
+          ref: ${{ github.event.pull_request.head.ref }}
+
+      - name: Build draft PDF
+        uses: openjournals/openjournals-draft-action@master
+        with:
+          journal: joss
+          # This should be the path to the paper within your repo.
+          paper-path: paper/paper.md
+
+      - name: Upload PDF
+        uses: actions/upload-artifact@v4
+        with:
+          name: paper
+          # This is the output path where Pandoc will write the compiled
+          # PDF. Note, this should be the same directory as the input
+          # paper.md
+          path: paper/paper.pdf
+
+      - name: Commit PDF to repository
+        uses: EndBug/add-and-commit@v9
+        with:
+          message: '(auto) paper draft'
+          add: 'paper/paper.pdf'

compilation.sh

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+#!/bin/bash
+sudo docker run --rm \
+    --volume $PWD/paper:/data \
+    --user $(id -u):$(id -g) \
+    --env JOURNAL=joss \
+    openjournals/inara
+

paper/paper.bib

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+@article{tapenade,
+  title     = {{The Tapenade automatic differentiation tool: principles, model, and specification}},
+  author    = {Hasco{\"e}t, Laurent and Pascual, Val{\'e}rie},
+  journal   = {ACM Transactions on Mathematical Software (TOMS)},
+  volume    = {39},
+  number    = {3},
+  pages     = {1--43},
+  year      = {2013},
+  doi       = {10.1145/2450153.2450158},
+  publisher = {ACM New York, NY, USA}
+}
+
+@book{griewank2008,
+  title     = {Evaluating derivatives: principles and techniques of algorithmic differentiation},
+  author    = {Griewank, Andreas and Walther, Andrea},
+  year      = {2008},
+  doi       = {10.1137/1.9780898717761},
+  publisher = {SIAM}
+}
+
+@article{HFBTHO,
+title   = {{HFBTHO-AD: Differentiation of a nuclear energy density functional code}},
+author  = {Laurent Hasco{\"e}t and Matt Menickelly and Sri Hari Krishna Narayanan and Jared O’Neal and Nicolas Schunck and Stefan M. Wild},
+journal = {Computer Physics Communications},
+volume  = {320},
+pages   = {109955},
+year    = {2026},
+doi     = {10.1016/j.cpc.2025.109955},
+}
+
+@article{ADOLC,
+title     = {{Algorithm 755: ADOL-C: a package for the automatic differentiation of algorithms written in C/C++}},
+author    = {Griewank, Andreas and Juedes, David and Utke, Jean},
+journal   = {ACM Transactions on Mathematical Software (TOMS)},
+volume    = {22},
+number    = {2},
+pages     = {131--167},
+year      = {1996},
+doi       = {10.1145/229473.229474},
+publisher = {Association for Computing Machinery},
+}
+
+@article{jonasson2020,
+  title     = {{Algorithm 1005: Fortran subroutines for reverse mode algorithmic differentiation of blas matrix operations}},
+  author    = {Jonasson, Kristjan and Sigurdsson, Sven and Yngvason, Hordur Freyr and Ragnarsson, Petur Orri and Melsted, Pall},
+  journal   = {ACM Transactions on Mathematical Software (TOMS)},
+  volume    = {46},
+  number    = {1},
+  pages     = {1--20},
+  year      = {2020},
+  doi       = {10.1145/3382191},
+  publisher = {ACM New York, NY, USA}
+}
+
+@article{giles2008,
+  title     = {An extended collection of matrix derivative results for forward and reverse mode automatic differentiation},
+  author    = {Giles, Mike},
+  year      = {2008},
+}
+
+@inproceedings{TAF,
+  title        = {{Applying TAF to generate efficient derivative code of Fortran 77-95 programs}},
+  author       = {Giering, Ralf and Kaminski, Thomas},
+  booktitle    = {{PAMM: Proceedings in Applied Mathematics and Mechanics}},
+  volume       = {2},
+  number       = {1},
+  pages        = {54--57},
+  year         = {2003},
+  doi          = {10.1002/pamm.200310014},
+  organization = {Wiley Online Library}
+}

paper/paper.md

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+---
+title: 'diffblas: algorithmically differentiated BLAS routines'
+tags:
+  - BLAS
+  - automatic differentiation
+  - numerical linear algebra
+  - scientific computing
+authors:
+  - name: Sri Hari Krishna Narayanan^[corresponding author]
+    orcid: 0000-0003-0388-5943
+    affiliation: 1
+  - name: Alexis Montoison
+    orcid: 0000-0002-3403-5450
+    affiliation: 1
+  - name: Jean-Luc Bouchot
+    orcid: 0000-0003-4523-3986
+    affiliation: 2
+affiliations:
+ - name: Mathematics and Computer Science Division, Argonne National Laboratory, USA
+   index: 1
+ - name: Inria de Saclay, Palaiseau, France
+   index: 2
+date: 22 February 2026
+bibliography: paper.bib
+
+---
+
+# Summary
+
+`diffblas` is a library that provides BLAS routines algorithmically differentiated using algorithmic differentiation principles [@griewank2008] from their reference implementations in [LAPACK](https://github.com/Reference-LAPACK/lapack) on GitHub using the automatic differentiation tool Tapenade [@tapenade].
+It supports four modes: tangent (`_d`), vector tangent (`_dv`), adjoint (`_b`), and vector adjoint (`_bv`).
+
+In addition to differentiating the standard Fortran-style `BLAS` interface, `diffblas` also provides differentiated `CBLAS` routines, facilitating interoperability with C and other languages.
+Its API mirrors BLAS / CBLAS, with additional arguments specifying differentiation variables, making integration into existing workflows straightforward.
+
+`diffblas` relies on the underlying standard BLAS implementation and is agnostic to the backend (OpenBLAS, BLIS, MKL, Apple Accelerate, or libblastrampoline), ensuring both high performance and portability.
+Precompiled artifacts are available on GitHub, simplifying installation and use across different programming environments.
+
+By providing efficient and accurate derivatives of linear algebra operations, `diffblas` facilitates gradient-based optimization, sensitivity analysis, and derivative-based workflows in scientific computing.
+
+# Statement of need
+
+Linear algebra routines such as those in LAPACK are widely used in scientific computing, optimization, and machine learning. However, they do not provide derivatives, which are often required for gradient-based algorithms.
+
+Existing approaches rely on hand-coded derivatives or generic automatic differentiation applied to high-level code, which can be inefficient or error-prone [@jonasson2020].
+
+`diffblas` addresses this gap by providing algorithmically differentiated BLAS routines directly from reference LAPACK implementations and following relevant differntiation rules [@giles2008].
+
+# State of the field
+
+Automatic source-to-source differentiation tools, such as Tapenade [@tapenade], ADOL-C [@ADOLC], or TAF [@TAF], provide general mechanisms to compute derivatives of code.
+However, naively applying AD to low-level BLAS or LAPACK routines can be inefficient due to loop structures, memory layout, and caching constraints [@jonasson2020].
+`diffblas` addresses these challenges by generating differentiated routines directly from reference LAPACK implementations, combining the reliability and performance of LAPACK with the efficiency of algorithmic differentiation.
+
+# Research impact statement
+
+This work was inspired in part by a need to differentiate a Fortran code [@HFBTHO] that uses BLAS and LAPACK routines, and to use the differentiated application for gradient-based optimization.
+
+Providing both the standard and CBLAS interfaces ensures that diffblas can be adopted across different programming environments, facilitating derivative computations in diverse scientific computing projects.
+Precompiled artifacts on GitHub further simplify integration, enabling rapid deployment in multiple languages and scientific computing projects.
+
+# Acknowledgements
+
+This work was supported in part by the Applied Mathematics activity within the U.S. Department of Energy, Office of Science, Office
+of Advanced Scientific Computing Research Applied Mathematics, and Office of Nuclear Physics SciDAC program under Contract No. DE-AC02-06CH11357. This work was supported in part by NSF CSSI grant 2104068.
+
+# AI usage disclosure
+
+Generative AI was used to ...
+ChatGPT was used to check spelling, grammar, and clarity of the English text in this paper.
+
+# References