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| title | draft |
|---|---|
| Writing a Convolutional Neural Network library with CUDA Support | true |
Straightforward project, learned a lot more than I expected.
"Just use cuBLAS, it'll be easier. You don't have to implement custom CUDA kernels.", they said. Actually, noone said that. I just thought that because I didn't do enough research.
Why not combine multiple challenging things into 1 (C++, cmake, CUDA, CNN)
Quickly discovering that without writing custom kernels, you can't really progress
- cuBLAS column major layout, macro
- cmake woes (findCUDA)
- google test
- padding kernel
- column major / row major headache
- removing cuBLAS -> just row major representation
- naive conv2d
- learning 3D memory representation
- optimizing conv2d
- softmax sum reduce
- softmax numerical stability - max reduce
- custom binary weights file - (safetensors - json parser vs csv) values overwritten by header
- tests passing -> implement AlexNet
- AlexNet cmake, opencv
- AlexNet crashing -> add cuda error checking to tests -> test crashing
- compute-sanitizer memecheck