📚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
A performance comparison of standard matrix functions between CPU and GPU using Nvidia CUDA on Visual Studio using C++
Direct NVIDIA CUDA access for PHP. GPU-accelerated tensors, JIT-compiled kernels, and async execution—no Python required
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
Custom PyTorch CUDA kernel implementing optimized ReLU activation with vectorization, performance profiling, and memory analysis on Tesla T4 GPU achieving 75% bandwidth efficiency.
A simple Python vector addition project using Numba CUDA, demonstrating CPU vs GPU parallel computing. Compares sequential CPU vector addition with GPU-accelerated Numba implementation. Shows how to write GPU kernels with @cuda.jit, manage device memory, and compare CPU vs GPU performance.
A simple CUDA C++ vector addition project demonstrating CPU vs GPU parallel computing. Compares sequential CPU vector addition with GPU-accelerated CUDA implementation. shows how to write GPU kernels, manage memory, and compare CPU vs GPU performance.
🔍 Explore GEMM: a C/C++ library for efficient matrix multiplication using OpenMP, designed for parallel computing learners and practitioners.
NYCU 2025 Spring Computer Architecture 劉志尉 計算機結構
Add a description, image, and links to the cuda-kernel topic page so that developers can more easily learn about it.
To associate your repository with the cuda-kernel topic, visit your repo's landing page and select "manage topics."