Fundamentals of Accelerated Computing with CUDA Python (FACCP)

 

Course Overview

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.

Please note that once a booking has been confirmed, it is non-refundable. This means that after you have confirmed your seat for an event, it cannot be cancelled and no refund will be issued, regardless of attendance.

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
  • No previous knowledge of CUDA programming is required

Course Objectives

At the conclusion of the workshop, you’ll have an understanding of the fundamental tools and techniques for GPU-accelerated Python applications with CUDA and Numba:

  • GPU-accelerate NumPy ufuncs with a few lines of code.
  • Configure code parallelization using the CUDA thread hierarchy.
  • Write custom CUDA device kernels for maximum performance and flexibility.
  • Use memory coalescing and on-device shared memory to increase CUDA kernel bandwidth.

Prijs & Delivery methods

Online training

Duur
1 dag

Prijs
  • Op Aanvraag
Klassikale training

Duur
1 dag

Prijs
  • Op Aanvraag

Op dit moment is deze training niet beschikbaar in het open rooster. De kans is echter groot dat wij u toch een passende oplossing kunnen bieden. Wij horen graag wat uw specifieke wensen zijn. U bereikt ons via 030 658 2131 of info@flane.nl. We helpen u graag!