---
title: Writing a Convolutional Neural Network library with CUDA Support
draft: true
---

"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