This repository provides a curated collection of CUDA implementations for core deep learning operations and components, targeting engineers and researchers seeking to understand and optimize GPU-accelerated machine learning. It offers practical, optimized CUDA code for fundamental building blocks like matrix multiplication, attention mechanisms, and optimizers, enabling deeper insights into GPU performance.

How It Works

The project systematically implements various deep learning primitives in CUDA C/C++. It focuses on optimizing memory access patterns, parallelization strategies, and leveraging specific GPU architectures for performance gains. Key implementations include custom operators, memory reduction techniques, GEMM, and optimized CUDA kernels for Transformer components like LayerNorm, SoftMax, Cross Entropy, AdamW, and self-attention.

Quick Start & Requirements

• Install: Requires a CUDA-enabled GPU and a compatible NVIDIA driver. Compilation typically involves nvcc or a build system like CMake.
• Prerequisites: CUDA Toolkit, C++ compiler.
• Resources: Building and running CUDA code requires a development environment with the CUDA Toolkit installed.

Highlighted Details

• Implements optimized CUDA kernels for fundamental deep learning operations.
• Covers essential Transformer components including LayerNorm, SoftMax, and self-attention.
• Includes practical examples for memory optimization and reduction techniques.
• Demonstrates CUDA implementations for optimizers like AdamW.

Maintenance & Community

This is a personal learning project, with no explicit mention of community channels or active maintenance beyond the author's contributions.

Licensing & Compatibility

The repository does not specify a license.

Limitations & Caveats

The project is presented as a learning resource and may not be production-ready or include comprehensive error handling. Licensing is unspecified, which may impact commercial use.