publications

2026

1. SC ’26

   ADELIA: Automatic Differentiation for Efficient Laplace Inference Approximations

   Afif Boudaoud, Lisa Gaedke-Merzhäuser, Alexandros Nikolaos Ziogas, and 6 more authors

   arXiv preprint arXiv:2605.06392. Under review at SC ’26. , 2026

   Abs arXiv Bib HTML

   Spatio-temporal Bayesian inference drives environmental and health sciences using latent Gaussian models. INLA enables inference for these models at HPC scale but relies on derivative-based optimization over d hyperparameters. State-of-the-art INLA implementations approximate derivatives via central finite differences (2d+1 evaluations). ADELIA replaces this with reverse-mode automatic differentiation on a multi-GPU backward pass, achieving 4.2–7.9x per-gradient speedups and 5–8x energy savings over DALIA while enabling convergence where finite differences stall.

   @article{boudaoud2026adelia,
     title = {ADELIA: Automatic Differentiation for Efficient Laplace Inference Approximations},
     author = {Boudaoud, Afif and Gaedke-Merzh{\"a}user, Lisa and Ziogas, Alexandros Nikolaos and Maillou, Vincent and Calotoiu, Alexandru and Copik, Marcin and Rue, H{\aa}vard and Luisier, Mathieu and Hoefler, Torsten},
     journal = {arXiv preprint arXiv:2605.06392},
     year = {2026},
   }

2025

1. CLUSTER ’25

   DaCe AD: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing

   Afif Boudaoud, Alexandru Calotoiu, Marcin Copik, and 1 more author

   In Proceedings of the IEEE International Conference on Cluster Computing (CLUSTER), 2025

   Abs arXiv Bib HTML

   A general-purpose automatic differentiation engine that requires no code modifications and uses a novel ILP-based algorithm to balance storing vs. recomputing intermediates under a memory budget. Benchmarked on NPBench, it is on average over 92x faster than JAX on gradient computation for scientific computing patterns.

   @inproceedings{boudaoud2025daceAD,
     title = {{DaCe AD}: Unifying High-Performance Automatic Differentiation for Machine Learning and Scientific Computing},
     author = {Boudaoud, Afif and Calotoiu, Alexandru and Copik, Marcin and Hoefler, Torsten},
     booktitle = {Proceedings of the IEEE International Conference on Cluster Computing (CLUSTER)},
     year = {2025},
   }

2. SC ’25

   PerfDojo: Automated ML Library Generation for Heterogeneous Architectures

   Andrei Ivanov, Siyuan Shen, Gioele Gottardo, and 5 more authors

   In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC), 2025

   DOI arXiv Bib HTML

   @inproceedings{ivanov2025perfdojo,
     title = {{PerfDojo}: Automated ML Library Generation for Heterogeneous Architectures},
     author = {Ivanov, Andrei and Shen, Siyuan and Gottardo, Gioele and Chrapek, Marcin and Boudaoud, Afif and Schneider, Timo and Benini, Luca and Hoefler, Torsten},
     booktitle = {Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC)},
     year = {2025},
     doi = {10.1145/3712285.3759900},
   }

3. PACT ’25

   LOOPer: A Learned Automatic Code Optimizer for Polyhedral Compilers

   Massinissa Merouani, Afif Boudaoud, Iheb Nassim Aouadj, and 7 more authors

   In Proceedings of the International Conference on Parallel Architectures and Compilation Techniques (PACT), 2025

   arXiv Bib HTML

   @inproceedings{merouani2025looper,
     title = {{LOOPer}: A Learned Automatic Code Optimizer for Polyhedral Compilers},
     author = {Merouani, Massinissa and Boudaoud, Afif and Aouadj, Iheb Nassim and Tchoulak, Nassim and Bernou, Islem Kara and Benyamina, Hamza and Benbouzid-Si Tayeb, Fatima and Benatchba, Karima and Leather, Hugh and Baghdadi, Riyadh},
     booktitle = {Proceedings of the International Conference on Parallel Architectures and Compilation Techniques (PACT)},
     year = {2025},
   }

4. dataset

   LOOPerSet: A Large-Scale Dataset for Data-Driven Polyhedral Compiler Optimization

   Massinissa Merouani, Afif Boudaoud, and Riyadh Baghdadi

   arXiv preprint arXiv:2510.10209, 2025

   Abs arXiv Bib HTML

   An open-source dataset of 28 million labeled data points derived from 220,000 synthetically generated polyhedral programs, linking transformations to execution-time measurements for training learned cost models and auto-schedulers.

   @article{merouani2025looperset,
     title = {{LOOPerSet}: A Large-Scale Dataset for Data-Driven Polyhedral Compiler Optimization},
     author = {Merouani, Massinissa and Boudaoud, Afif and Baghdadi, Riyadh},
     journal = {arXiv preprint arXiv:2510.10209},
     year = {2025},
   }