LeetGPU -- browser-based CUDA playground, no setup needed

• https://leetgpu.com
• Sign in with Google or GitHub
• Paste any .cu file, submit, and see results + generated PTX
• Free tier: 5 submissions/day

Compiler Explorer (Godbolt) -- interactive CUDA to PTX/SASS viewer

• https://godbolt.org
• Select CUDA as language, pick a GPU target (e.g. sm_80 for A100)
• Type code on the left, see PTX/SASS on the right in real time
• Great for understanding what the compiler does with your code

PTX ISA Reference -- the full instruction set documentation

• https://docs.nvidia.com/cuda/parallel-thread-execution/index.html

CUDA Programming Guide

• https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html

If you have an NVIDIA GPU and nvcc installed:

# Build a single exercise
nvcc -O2 01_vec_add_solution.cu -o vec_add && ./vec_add

# Generate PTX output
nvcc -O2 -ptx 01_vec_add_solution.cu -o vec_add.ptx

# Or use the Makefile
make vec_add           # compile
make vec_add_cpu       # CPU baseline (needs gcc + OpenMP)
make ptx_vec_add       # inline PTX exercise
make 01_vec_add_solution.ptx   # generate PTX file from any .cu

Triton exercises require pip install triton torch and a GPU:

python3 05_triton_vec_add.py
python3 06_triton_softmax.py
python3 07_triton_matmul.py

Solutions have the _solution.cu suffix.

• After each LeetGPU submission, click the PTX tab to see the generated assembly
• Use nvcc -ptx file.cu to generate PTX locally
• On Godbolt, add -arch=sm_80 to compiler flags for A100-targeted PTX
• Compare naive matmul PTX (all ld.global) vs tiled matmul PTX (ld.shared in inner loop)
• Look for mma.sync in PTX when Tensor Cores are used