gpu-mode / resource-stream
GPU programming related news and material links
☆1,447Updated 3 months ago
Alternatives and similar repositories for resource-stream:
Users that are interested in resource-stream are comparing it to the libraries listed below
- Puzzles for learning Triton☆1,566Updated 4 months ago
- An ML Systems Onboarding list☆750Updated 2 months ago
- Material for gpu-mode lectures☆4,222Updated 2 months ago
- Fast CUDA matrix multiplication from scratch☆683Updated last year
- Flash Attention in ~100 lines of CUDA (forward pass only)☆774Updated 3 months ago
- What would you do with 1000 H100s...☆1,035Updated last year
- Tile primitives for speedy kernels☆2,251Updated this week
- Training materials associated with NVIDIA's CUDA Training Series (www.olcf.ornl.gov/cuda-training-series/)☆738Updated 7 months ago
- Building blocks for foundation models.☆477Updated last year
- Minimalistic 4D-parallelism distributed training framework for education purpose☆982Updated last month
- Mirage: Automatically Generating Fast GPU Kernels without Programming in Triton/CUDA☆798Updated this week
- Domain-specific language designed to streamline the development of high-performance GPU/CPU/Accelerators kernels☆971Updated this week
- A subset of PyTorch's neural network modules, written in Python using OpenAI's Triton.☆529Updated last month
- FlashInfer: Kernel Library for LLM Serving☆2,611Updated this week
- UNet diffusion model in pure CUDA☆600Updated 9 months ago
- Awesome resources for GPUs☆556Updated last year
- A library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit floating point (FP8) precision on Hopper and Ada GPUs…☆2,346Updated this week
- Pipeline Parallelism for PyTorch☆762Updated 7 months ago
- PyTorch native quantization and sparsity for training and inference☆1,944Updated this week
- ☆153Updated last year
- ☆148Updated 8 months ago
- Step-by-step optimization of CUDA SGEMM☆304Updated 3 years ago
- Slides, notes, and materials for the workshop☆322Updated 10 months ago
- CUDA Learning guide☆357Updated 9 months ago
- Thunder gives you PyTorch models superpowers for training and inference. Unlock out-of-the-box optimizations for performance, memory and …☆1,320Updated this week
- ☆1,027Updated 3 months ago
- This is a series of GPU optimization topics. Here we will introduce how to optimize the CUDA kernel in detail. I will introduce several…☆1,003Updated last year
- Puzzles for exploring transformers☆342Updated last year
- An implementation of the transformer architecture onto an Nvidia CUDA kernel☆178Updated last year
- 100 days of building GPU kernels!☆336Updated this week