Tue, Apr 26, 2022

2 PM – 5 PM EDT (GMT-4)

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This workshop will introduce participants to high performance Python using techniques such as Just In Time (JIT) compilation through Numba. We will look at several problems, and develop solutions using several different techniques, and compare the performance gained by doing so with the (potential) loss in expressivity and clarity.

Learning objectives: Attendees will learn a collection of methods to accelerate Python code without resorting to GPUs, as well as have an idea of the potential drawbacks of requiring the needed libraries.

Knowledge prerequisites: Intermediate Python knowledge (including Numpy).  if you have not used Conda before, Intro to Conda is recommended.

Hardware/software prerequisites: (1) Bring a laptop which can connect to the eduroam wireless network. You will also need to be able to Duo authenticate to use campus resources. (2) Have an SSH client installed on your laptop.  (3) Register for an account on Adroit. This is the cluster we will use for demonstration purposes. Make sure you can SSH to Adroit before the workshop by following this guide. (4) Optional: Have the Anaconda Distribution installed on your laptop to run things locally.

Workshop format: Lecture and hands-on


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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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