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.
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