Continue the JOSS paper

Author: amontoison

.github/workflows/draft-pdf.yml

@@ -13,18 +13,28 @@ jobs:
     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
+          paper-path: joss/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
+          path: joss/paper.pdf
 
+      - name: Commit PDF to repository
+        uses: EndBug/add-and-commit@v9
+        with:
+          message: '(auto) paper draft'
+          add: 'joss/paper.pdf'

paper/paper.bib

@@ -1,10 +1,59 @@
 @article{tapenade,
-  title     = {The Tapenade automatic differentiation tool: principles, model, and specification},
-  author    = {Hascoet, Laurent and Pascual, Val{\'e}rie},
+  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},
+  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},
+  publisher = {Unspecified}
+}

paper/paper.md

@@ -1,37 +1,71 @@
 ---
 title: 'diffblas: algorithmically differentiated BLAS routines'
 tags:
+  - BLAS
   - automatic differentiation
-  - linear algebra
+  - numerical linear algebra
+  - scientific computing
 authors:
-  - name: Alexis Montoison^[corresponding author]
-    orcid: 0000-0002-3403-5450
-    affiliation: 1
-  - name: Sri Hari Krishna Narayanan
+  - 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: Argonne National Laboratory, Lemont, IL, USA.
+ - name: Mathematics and Computer Science Division, Argonne National Laboratory, USA
    index: 1
- - name: Inria de Saclay, Palaiseau, France.
+ - name: Inria de Saclay, Palaiseau, France
    index: 2
-date: 6 February 2026
+date: 22 February 2026
 bibliography: paper.bib
 
 ---
 
 # Summary
 
-`diffblas`is a library that  provides (algorithmically) differentiated BLAS routines from their reference implementation in LAPACK using the automatic differentiation tool Tapenade [@tapenade] in four modes: forward (`_d`), vector forward (`_dv`), reverse (`_b`), and vector reverse (`_bv`).
+`diffblas` is a library that provides algorithmically differentiated [@griewank2008] BLAS routines 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: forward (`_d`), vector forward (`_dv`), reverse (`_b`), and vector reverse (`_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 it straightforward to integrate into existing workflows.
+
+`diffblas` calls the underlying standard `BLAS `implementation, and is agnostic to the backend (OpenBLAS, BLIS, MKL, Apple Accelerate), ensuring both performance and portability.
+
+By providing accurate and efficient derivatives of linear algebra operations, `diffblas` facilitates gradient-based optimization, sensitivity analysis, and a wide range of scientific computing applications
 
 # Statement of need
 
+Linear algebra routines such as those in LAPACK are fundamental to scientific computing, optimization, and machine learning.
+However, they do not provide derivatives, which are often required for gradient-based algorithms.
+Current approaches either rely on hand-coded derivatives or generic automatic differentiation applied to high-level code, which can be inefficient or error-prone.
+`diffblas` addresses this gap by providing algorithmically differentiated BLAS routines directly from reference LAPACK implementations and following relevant differntiation rules [@giles2008].
+This enables accurate and efficient computation of derivatives while preserving compatibility with existing BLAS-based codes.
+
+# State of the field
+
+Automatic differentiation (AD) tools such as Tapenade [@tapenade], ADOL-C [@ADOLC], or Taf provide general mechanisms to compute derivatives of code.
+However, applying AD naively to low-level BLAS or LAPACK routines can be inefficient due to loops, memory layout, and caching issues [@jonasson2020].
+Specialized libraries like diffblas that generate differentiated routines directly from reference implementations combine the reliability of LAPACK with the efficiency of AD, bridging a gap in current scientific computing workflows.
+
+# 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