Ever wanted to harness the power of your GPU for lightning-fast computations? This guide takes you from having no GPU knowledge to writing your own CUDA kernels that can speed up your code by 10-100x.
This isn't just another technical manual. We'll build your intuition step by step:
- Why GPUs are different from CPUs (and why that matters for your code)
- How to write parallel code that runs thousands of operations simultaneously
- GPU memory tricks to make your code run fast
- Common patterns for speeding up real algorithms
- Connecting GPU code to Python/PyTorch for machine learning
- You're comfortable with basic programming (loops, functions, arrays)
- You want to speed up computations (machine learning, simulations, data processing)
- You're tired of slow code and want to understand why it's slow
- You have a CUDA-capable GPU (NVIDIA graphics card)
No prior GPU knowledge required! We'll explain everything from the ground up.
Each chapter builds on the previous one. Start with Chapter 1 and work through in order.
- Read the explanations - we use simple analogies (like comparing CPUs to chefs and GPUs to assembly lines)
- Run the code examples - see the concepts in action
- Experiment - modify the code and see what happens
- Apply to your problems - adapt the patterns to your own code
The big picture: CPUs vs GPUs
- Why your gaming GPU can also do serious computing
- Simple analogy: chefs vs. assembly lines
- What problems GPUs excel at
Your first CUDA program
- Writing functions that run on the GPU
- Understanding threads, blocks, and grids
- Running parallel code and seeing results
Why memory is everything in GPU programming
- Different types of GPU memory and their speeds
- Patterns for fast memory access
- Why bad memory usage can make your code 10x slower
Ready-to-use techniques for parallel computing
- Element-wise operations (like vector addition)
- Summing large arrays quickly
- Image processing and neighborhood operations
Connect GPU code to real applications
- Writing custom operations for PyTorch
- Automatic gradients for machine learning
- Building and testing your GPU code
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Check your setup:
nvidia-smi # Should show your GPU nvcc --version # Should show CUDA toolkit
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Install requirements:
- CUDA Toolkit (free from NVIDIA)
- C++ compiler
- For Python integration: PyTorch with CUDA support
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Start coding! Each chapter has working code you can compile and run.
Most GPU guides throw technical terms at you. This guide:
- Uses everyday analogies you already understand
- Shows working code from the start
- Explains why things work the way they do
- Builds intuition before diving into details
By the end, you'll understand GPU programming deeply enough to write efficient code for your own problems.
Head to Chapter 1 to learn why GPUs can be so much faster than CPUs for the right problems.