# PaddleJitLab/CUDATutorial

A self-learning tutorail for CUDA High Performance Programing.

Repository: https://github.com/PaddleJitLab/CUDATutorial
Canonical: https://ross.abutalabs.com/products/cudatutorial
Language: JavaScript
License: Apache-2.0
License Family: permissive
Topics: cuda-programming, deep-learning
Last push: 2026-01-14T10:44:24+00:00

## Health v2 (maintenance only)
Score: 60/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 62, release rhythm 35, longevity 100
- inputs: {"age_days": 1422, "days_push": 231, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1087, forks 113 (observed 2026-08-28T04:03:32.384298+00:00)

## What it is
A self-learning tutorial repository for CUDA high-performance programming, structured as progressive lesson series from beginner environment setup through advanced kernel optimization. It includes hands-on implementations and optimization guides for matrix multiplication, reduction, convolution, Triton kernels, and LLM inference techniques like Flash Attention and vLLM.

## Use cases
- learn CUDA programming from scratch
- optimize GPU kernels for matrix multiplication
- understand how Flash Attention works
- study vLLM source code and scheduling
- write faster reduction kernels avoiding bank conflicts
- get started with Triton kernel programming
- profile CUDA code with nvprof
- implement convolution operators with im2col and implicit GEMM

## When to choose
- you want a structured, hands-on path to learn CUDA and GPU kernel optimization
- you need worked examples of GEMM, reduce, and convolution optimization techniques
- you are preparing for deep learning inference systems work involving CUDA or Triton

## When to avoid
- you need production-ready CUDA libraries rather than educational material
- you want a general GPU computing course without a deep learning focus
- you cannot read Chinese, as the primary documentation is written in Chinese

## Facets
- artifact type: learning-resource
- maturity: active
- function: gpu-computing, developer-tools, benchmarking
- domain: gpu-computing, deep-learning, tutorials, large-language-models, performance
- platform: cross-platform
- tags: cuda-programming, tutorial, high-performance-computing, kernel-optimization, gemm, flash-attention, triton, vllm, matrix-multiplication, reduce-operations, convolution-optimization, notes, self-learning, chinese-language, gpu, cuda, linux

## Member repositories
- PaddleJitLab/CUDATutorial (main) score 60

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:32.384298+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:49:25.295838+00:00, confidence not recorded.
  - readme: https://github.com/PaddleJitLab/CUDATutorial (fetched 2026-08-28T04:03:32.384298+00:00, sha b98d3fdbb3c5)
- Data as of 2026-08-30T08:39:29.467469+00:00.