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This workshop will give you hands-on experience accelerating Python codes with NVIDIA GPUs. We will utilize code samples in two main categories to introduce you to Python GPU accelerated computing. First, we will explore drop-in replacements for SciPy and NumPy codes through the CuPy library. Then we’ll cover NVIDIA RAPIDS, which provides GPU acceleration for end-to-end data science workloads. We'll finish with an end-to-end example incorporating all the tools introduced to tackle a geospatial problem. By the end of the workshop, you'll have the skills to start accelerating your Python codes with NVIDIA GPUs!
Prerequisites: Basic familiarity with Python. Familiarity with NumPy, and/or Scikit-learn is beneficial.
Instructor Bio: Zoe Ryan is a solutions architect at NVIDIA, working with higher education institutions and researchers. She joined NVIDIA in 2020 after studying computer science and mathematics at Harvey Mudd College. She's worked in embedded systems/simulations and GPU-accelerated high-performance computing. Now, she is focused on helping researchers adopt and fully utilize GPU technology across domains.
Zoom coordinates will be sent prior to the workshop