The motivation for this project is the increasing reliance on Python in scientific computing and the
simultaneous rise of heterogeneous high-performance computing (HPC) platforms, such as GPUs and
FPGAs. While Python remains the dominant language for prototyping and experimentation, it often
fails to utilize the full potential of modern accelerators due to performance bottlenecks and limited
low-level hardware integration. Many scientists, especially those without formal training in HPC or
hardware programming, struggle to scale their solutions effectively.

The Py2HPC project aims to bridge this gap by fostering collaboration between domain scientists
and HPC experts. Py2HPC provides a structured environment and a growing library ecosystem where
scientists are supported by specialists who help them adapt and accelerate their Python-based
applications using the latest HPC tools and platforms.

The project emphasizes reproducibility, ease of use, and community-driven best practices. Tutorials,
case studies, and collaborative sprints serve to onboard new users, expand the user base, and
contribute to democratizing access to performance-efficient computing, accelerating scientific
discovery through interdisciplinary collaboration and targeted optimization