Partners: Hartmut Kaiser, Rod Tohid, Andrew Lumsdaine 

SSEC Engineers: Don Setiawan, Ayush Nag 

Research Goals and Domain 

HPyX bridges the gap between Python and high-performance computing by providing Python bindings for the HPX C++ Parallelism Library. HPX implements modern C++ concurrency and parallelism features, allowing for scalable execution across multi-core and distributed systems. HPyX leverages Python 3.13’s free-threading capabilities and Nanobind to expose these features to Python developers, enabling scientific workflows that demand speed and scalability. 

Software Problem 

While widely used across scientific computing, Python struggles with true multi-threading due to the Global Interpreter Lock (GIL). This limitation reduces performance for large-scale simulations, model training, and data-intensive tasks. Existing solutions often require rewriting code in C++ or using complex frameworks, creating barriers for existing Python developers.

Software Solution 

HPyX removes these barriers by integrating HPX’s asynchronous and parallel execution primitives directly into Python. The project introduces free-threaded Python support for true concurrency, Nanobind-based bindings for efficient C++ interoperability, and exposes high-level APIs for parallel algorithms such as for_loop and async. The SSEC team also introduced robust packaging and documentation, releases to conda and PyPI, automated builds, and tutorials. HPyX further stabilizes cross-platform behavior with early Windows support, reorganizes configuration files for clarity, and adds a fully automated build‑and‑test workflow using pixi. Through a new MkDocs documentation system, expanded examples, and improved docstrings, HPyX delivers a better developer experience and a stronger foundation for distributed and multi-threaded Python execution.

Impact 

HPyX democratizes access to advanced parallel computing by opening HPX’s performance to the large Scientific Python community. Researchers can now scale workloads across cores and clusters without leaving Python, accelerating innovation in fields like climate modeling, genomics, and large language models. By making easy open-source collaboration and integrating best practices, HPyX strengthens the scientific software ecosystem and supports reproducible, high-impact research.