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Introduction to CUDA Kernel Programming

Overview

Learn CUDA from the ground up by managing GPU memory, launching 1D and 2D kernels, optimizing with shared memory, and building practical image-processing pipelines for graphics-oriented parallel computing.

What you'll learn

  • Allocate GPU memory and transfer data safely between CPUs and GPUs
  • Configure CUDA blocks and grids for one- and two-dimensional workloads
  • Implement scalable kernels using linear indexing and grid-stride loops
  • Build naive and tiled matrix multiplication kernels
  • Optimize global memory access with shared memory and thread synchronization
  • Create GPU image-processing pipelines for grayscale conversion and edge detection
  • Validate kernel outputs and benchmark optimized implementations

Advantages and disadvantages

Advantages

  • University courses you can audit for free, with lectures, readings and practice quizzes.
  • A verified certificate from the university if you pay for it.
  • Self-paced: start any time.

Disadvantages

  • Graded assignments and the certificate need the paid track.
  • Audit access can expire a few weeks after the course ends.
  • Learning is free, but the certificate costs money.
  • Some parts (graded work, certificate) are paid.

Some points apply to every course of this kind; see how we rank.

Free to audit

  • Free: Choose "Audit this course" when you enrol: lectures, readings and practice are free.
  • Paid: Graded assignments and the verified certificate (Certificate $70). Audit access may end after the course closes.

Before you start

Basic C++ programming experience

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