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2019 GTC San Jose
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DLIW906 - Instructor-Led Workshop: Fundamentals of Accelerated Computing with CUDA Python

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Session Description

Prerequisites: Basic Python competency including familiarity with variable types, loops, conditional statements, functions, and array manipulations. NumPy competency including the use of ndarrays and ufuncs. CUDA programming knowledge is not required.

Certification: Available upon completion of code-based assessment

How To Prepare: All attendees must bring their own laptop and charger. We recommend using a current version of Chrome, Firefox, or Safari for an optimal experience. Create an account at http://courses.nvidia.com/join before you arrive.

This course explores how to use Numba—the just-in-time, type-specializing Python function compiler—to accelerate Python programs to run on massively parallel NVIDIA GPUs. You’ll learn how to:

  • Use Numba to compile CUDA kernels from NumPy universal functions (ufuncs)
  • Use Numba to create and launch custom CUDA kernels
  • Apply key GPU memory management techniques

Upon completion, you’ll be able to use Numba to compile and launch CUDA kernels to accelerate your Python applications on NVIDIA GPUs.

 


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Instructor-Led Workshop
8 Hours
Session Schedule
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