How-To-Optim-Algorithm-In-Cuda

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how-to-optim-algorithm-in-cuda is an Open Source resource for optimizing algorithms using CUDA. It provides code implementations and learning notes for various GPU and large language model related optimizations.

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Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is how-to-optim-algorithm-in-cuda?

how-to-optim-algorithm-in-cuda is a comprehensive open-source repository dedicated to optimizing algorithms using CUDA. It offers a wealth of resources including code implementations for fundamental CUDA operators like reduce, softmax, and elementwise operations, as well as detailed learning notes and blog translations related to GPU and large language models. The project covers advanced topics such as CUTLASS, CuTe DSL, Triton, and PTX ISA, making it an invaluable learning tool for developers aiming to enhance the performance of their CUDA code. It also includes notes on large language model inference/training optimization and GPU/AI system papers.

Best used for

Ideal for developers who need to optimize CUDA algorithms, learn about GPU programming, and improve deep learning framework performance. Especially valuable for those interested in large language model optimization and understanding advanced GPU architectures like CUTLASS and Triton.

Common actions

optimize CUDA algorithms

learn GPU programming

improve deep learning performance

study large language models

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Capabilities

Key features

CUDA kernel optimization implementations
CUTLASS/CuTe DSL learning notes
Triton code and notes
Large language model optimization
GPU/AI system paper reviews
PyTorch CUDA tech notes

Target Audience

developerprofessorteacher

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What kind of algorithms can be optimized using this repository?

This repository focuses on optimizing common algorithms such as reduce, softmax, elementwise operations, GEMV, fast atomic add, upsample-nearest2d, indexing, and linear attention. It provides specific CUDA kernel implementations and detailed explanations for each.

Does this project include resources for large language model optimization?

Yes, the repository includes a dedicated section for large language model (LLM) optimization. It contains notes on LLM inference and training optimization, as well as relevant papers on GPU and AI systems.

Are there learning materials for advanced CUDA topics like CUTLASS and Triton?

Absolutely. The project features extensive learning notes and code examples for CUTLASS and CuTe DSL, covering topics like GEMM, TMA, WGMMA, and Swizzle mechanisms. It also provides code and notes for Triton, including Flash Attention and LayerNorm implementations.

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Research & Education › Course Creation Research & Education › Scientific Computing

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