Tongkaio/CUDA_Kernel_Samples resource

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1095 stars · 117 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A curated collection of hand-written CUDA kernel samples covering common operators like elementwise add, reduction, softmax, sgemm, and transpose, with naive and optimized implementations. It serves as an interview preparation guide for CUDA programming, pairing each operator with GPU architecture knowledge points and optimization strategies.

Use cases

• prepare for CUDA kernel interview questions
• learn to write optimized sgemm kernels
• study reduction and softmax kernel optimization
• understand vectorized memory access with float4
• learn how to avoid bank conflicts in transpose kernels
• practice writing CUDA operators from naive to optimized versions

When to choose

• you are preparing for GPU/CUDA programming interviews
• you want annotated, progressive examples of kernel optimization techniques
• you need a quick reference for common CUDA operator implementations

When to avoid

• you need a production-ready CUDA operator library like cuBLAS or CUTLASS
• you are looking for a general CUDA tutorial rather than operator-focused interview prep
• you need kernels for domains beyond the covered operators (e.g., convolutions, attention)

Facets

learning-resource · maturity active

gpu-computing developer-tools benchmarking gpu-computing machine-learning tutorials education cpp cross-platform cuda gpu-kernels interview-preparation performance-optimization sgemm reduction deep-learning-operators gpu linux

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