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High-Performance Python for GPUs
Registration
Registration is now closed (this event already took place).
Details
This workshop will introduce participants to high-performance Python on GPUs using tools to provide simplified GPU programming, as well as offer a brief look into creating custom kernels by hand. The main software libraries include CuPy and Numba.
Workshop format: Presentation with hands-on exercises.
Target audience: Those with Python experience looking to improve the performance of their code using GPUs.
Knowledge prerequisites: Some experience with Python is required.
Hardware/software prerequisites: For this workshop, users must have an account on our Adroit cluster, and they should confirm that they can SSH into Adroit at least a few hours beforehand. Details on all of the above can be found in this guide: https://bit.ly/3QER9Sv
Learning objectives: Participants will come away with the ability to accelerate their Python code using GPUs.
Speakers
Henry Schreiner
Princeton University
Henry Schreiner is a Computational Physicist / Research Software Engineer in High Energy Physics. He received his Ph.D. in experimental high-energy physics from the University of Texas at Austin. Henry is working on a three year project to develop simpler compiled packages for Python using Scikit-build. He is also an admin of Scikit-HEP, and also the lead web developer for IRIS-HEP and Scikit-HEP. Henry is also a maintainer/core developer for pypa/build, scikit-build, cibuildwheel, pybind11, and Plumbum for Python, and primary author of CLI11 for C++. He is also the author of a variety of CMake, GPU, and Python training courses and classes. He is also currently co-teaching APC 524.
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Co-hosted with: GradFUTURES
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