Skip to main content
Beta
This lesson is in the beta phase, which means that it is ready for teaching by instructors outside of the original author team.
Light
Dark
Auto
Instructor View
Learner View
Menu
GPU Programming
GPU Programming
Key Points
Instructor Notes
Extract All Images
More
Reference
Search the All In One page
GPU Programming
Toggle Theme
Light
Dark
Auto
Instructor View
Learner View
EPISODES
Summary and Schedule
1. Introduction
2. Using your GPU with CuPy
3. Accelerate your Python code with Numba
4. A Better Look at the GPU
5. Your First GPU Kernel
6. Registers, Global, and Local Memory
7. Shared Memory and Synchronization
8. Constant Memory
9. Concurrent access to the GPU
RESOURCES
Key Points
Instructor Notes
Extract All Images
Reference
See all in one page
Instructor Notes
This is a placeholder file. Please add content here.
IntroductionGraphics Processing Unit
Parallel by Design
Speed Benefits
Using your GPU with CuPyIntroduction to CuPy
Convolutions in Python
A scientific application: image processing for radio astronomy
Accelerate your Python code with NumbaUsing Numba to execute Python code on the GPU
A Better Look at the GPUThe GPU, a High Level View at the Hardware
How Programs are Executed
Different Memories
Additional Material
Your First GPU KernelSumming Two Vectors in Python
Summing Two Vectors in CUDA
Running Code on the GPU with CuPy
Understanding the CUDA Code
Computing Hierarchy in CUDA
Vectors of Arbitrary Size
Registers, Global, and Local MemoryRegisters
Global Memory
Local Memory
Shared Memory and SynchronizationShared Memory
Thread Synchronization
Constant MemoryConstant Memory
Concurrent access to the GPUConcurrently execute two kernels on the same GPU
Stream synchronization
Measure execution time using streams and events
Back
To Top