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Accelerating and Scaling Python for HPC

DescriptionPython is powering breakthrough exascale scientific discoveries—come learn how to program the world's largest supercomputers with it! In this interactive tutorial you’ll learn how to write, debug, profile, and optimize high-performance, multi-node GPU applications in Python. You'll learn and master: CuPy for drop-in GPU acceleration of NumPy workflows; Numba for writing custom kernels that match the performance of C++ and Fortran; and mpi4py for scaling across thousands of nodes. Along the way we’ll learn how to profile our code, debug tricky kernels, and leverage foundational and domain-specific accelerated libraries. Everything is hands-on: short and interactive lectures by expert instructors will be paired with guided Jupyter Notebooks that introduce each concept and have you immediately apply it in bite-sized exercises (2D heat equation, SVD image compression, Mandelbrot, etc.). The labs culminate in porting the miniWeather mini-app from serial Python to a hybrid MPI + GPU implementation. We'll use a web-based environment that requires no installs or setup—just a laptop and a browser. Whether you’re a domain scientist seeking faster turnaround or a software engineer evaluating portable acceleration strategies, you’ll leave with a roadmap, skills, and code for bringing scalable Python practices back to your own HPC facility.

Note for Attendees Please sign up for an account on https://brev.nvidia.com before the tutorial. The entire tutorial is web-based and no installations are needed, only a web browser with Javascript and WebRTC support. Google Chrome is recommended.

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