Project EREN: My Journey to Building a CUDA-Powered LLM Inference Engine

说明与用途

建立了CUDA LLM推理引擎的基础设施：项目结构、CMake、基本核函数、错误处理和测试，为后续GPT‑2参考实现奠定基础。

问题

Understand and implement the components that run large language models on GPU, rather than relying on existing inference frameworks.

结果

Established a reliable C++/CUDA development foundation with basic kernels, error handling, and testing, enabling progression to a GPT‑2 reference implementation.

工作流

• Set up repository structure and CMake build system
• Implemented basic CUDA vector addition kernel to validate memory allocation and kernel launch
• Implemented shared‑memory multi‑stage reduction kernel
• Created reusable CUDA error‑checking helper
• Added CTest smoke tests and initial reduction benchmark
• Verified clean build and passing tests via terminal output

证据

My first milestone was establishing a reliable C++/CUDA development foundation.

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Implemented a basic vector addition kernel to validate device memory allocation, host-to-device transfers, kernel launches, and result verification.

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Implemented a shared-memory, multi-stage reduction baseline.

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Created a reusable CUDA error-checking helper to make runtime failures easier to identify and debug.

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Added smoke tests using CTest and an initial reduction benchmark target.

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