@article{domke_unifying_2025,
  title = {A {{Unifying Framework}} to {{Enable Artificial Intelligence}} in {{High-Performance Computing Workflows}}},
  author = {Domke, Jens and Wahib, Mohamed and Dubey, Anshu and Ben-Nun, Tal and Draeger, Erik W.},
  date = {2025-05-28},
  journaltitle = {Computing in Science \& Engineering},
  volume = {27},
  number = {01},
  pages = {73--78},
  issn = {1558-366X},
  doi = {10.1109/MCSE.2025.3543940},
  abstract = {Current trends point to a future where large-scale scientific applications are tightly coupled high-performance computing/artificial intelligence (HPC/AI) hybrids. Hence, we urgently need to invest in creating a seamless, scalable framework where HPC and AI/machine learning can efficiently work together and adapt to novel hardware and vendor libraries without starting from scratch every few years. The current ecosystem and sparsely connected community are not sufficient to tackle these challenges, and we require a breakthrough catalyst for science similar to what PyTorch enabled for AI.},
  keywords = {Artificial intelligence,Computational intelligence,Ecosystems,Hardware,High performance computing,Large scale integration,Machine learning,Market research},
  file = {/home/domke/Documents/Zotero/storage/RN9EM59M/Domke et al. - 2025 - A Unifying Framework to Enable Artificial Intellig.pdf}
}