📚LeetCUDA: Modern CUDA Learn Notes with PyTorch for Beginners🐑, 200+ CUDA Kernels, Tensor Cores, HGEMM, FA-2 MMA.🎉

Kernl lets you run PyTorch transformer models several times faster on GPU with a single line of code, and is designed to be easily hackable.

Fast, differentiable sorting and ranking in PyTorch

row-major matmul optimization

Direct NVIDIA CUDA access for PHP. GPU-accelerated tensors, JIT-compiled kernels, and async execution—no Python required

A performance comparison of standard matrix functions between CPU and GPU using Nvidia CUDA on Visual Studio using C++

A beginner's guide to CUDA programming

a custom CUDA kernel for windowed matrix multiplication

SNU CSE Scalable High Performance Computing (M1522.006700) - 2023 Autumn

Winning submission for StartHack 2024: HPC optimized multi-GPU/CPU inference

A collection of scripts on MLSys-related stuff

CUDA Concepts & Techniques: From skilled to expert

GPU-accelerated N-body simulation with CUDA and OpenGL. Supports Direct N², Barnes-Hut O(N log N), and Spatial Hash O(N) algorithms. Real-time visualization of up to 1 million particles with symplectic integration.

Custom PyTorch CUDA kernel implementing optimized ReLU activation with vectorization, performance profiling, and memory analysis on Tesla T4 GPU achieving 75% bandwidth efficiency.

CUDA-first C++ deep learning framework with custom NDArray, Tensor autograd, optimizers, and parallelized CUDA kernels for high-performance tensor operations.

🍭 Sweet GPU compute kernels in CUDA, wrapped via CuPy

🔍 Explore GEMM: a C/C++ library for efficient matrix multiplication using OpenMP, designed for parallel computing learners and practitioners.