专注于 AI 基础设施与高性能计算的工程实践
🔬 Focus: CUDA Kernel Optimization · LLM Inference · GPU Computing
🌱 Exploring: AI inference acceleration & large-scale pipeline orchestration
🤝 Open to: Research collaboration & open-source co-building
I build AI infrastructure and high-performance systems with C++/CUDA, Python, and Go.
主要关注 AI 基础设施、GPU 算子优化与高性能计算等方向的工程实践。
- 🔥 GPU Kernel Engineering — CUDA/Triton kernel optimization, FlashAttention, GEMM, quantization
- 🧠 AI Inference Systems — LLM inference engines, model quantization (W8A16/FP8), KV Cache
- ⚡ High-Performance Computing — N-body simulation, ray tracing, image processing pipelines
- 🌐 Real-time Systems — WebRTC signaling, real-time detection, digital human platform
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Modern C++17/CUDA AI kernel library — Elementwise, GEMM, FlashAttention, Conv2D, SpMV, FP8 quantization
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From naive 3-loop to Tensor Core — progressive SGEMM optimization reaching 40% cuBLAS
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RMSNorm+RoPE fusion, Gated MLP fusion, FP8 GEMM with auto-tuning for Transformers
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CUDA LLM kernel library — FlashAttention (online softmax), FP16/INT8 GEMM with Tensor Core
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Lightweight LLM inference engine — W8A16 quantization, KV Cache, multi-sampling strategies
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7-level GEMM optimization (Naive→Tensor Core), reaching 72% cuBLAS with MNIST demo
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WebGPU micro inference engine — Conv2d, kernel fusion, Im2Col, MNIST classification
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Multi-model real-time vision — YOLO/DETR/OWL-ViT/BLIP with WebSocket streaming
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Pure CUDA ray tracer — Phong shading, path tracing, BVH acceleration, warp divergence optimization
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Million-particle GPU simulation — Direct N², Barnes-Hut, Spatial Hash with CUDA-OpenGL interop
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10K-particle real-time fluid simulation using WebGPU compute shaders with trail effects
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CUDA image processing library — convolution, morphology, geometric transforms, pipeline processing
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DAG-based heterogeneous image pipeline — multi-stream scheduling, pinned memory pool
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3D digital human platform — Three.js rendering, voice interaction, behavior control, emotion FSM
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Minimal WebRTC demo — Go WebSocket signaling, room management, peer-to-peer media
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E2E encrypted note sync — AES-256, 12-word mnemonic, real-time collaboration via WebSocket
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Browser-based memory training — N-back, spaced reinforcement, adaptive difficulty, PWA
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Background in communications engineering. / 通信与信息工程相关背景 |
Medical imaging, RTC & Genomic data. / 医疗、音视频与基因数据工程 |
| Category | Technologies |
|---|---|
| Languages | |
| AI & HPC | |
| System & DevOps | |
| Web & Frontend |
📈 More Stats / 更多数据
Feel free to reach out for collaboration, technical discussions, or open-source ideas.
欢迎通过邮箱与我交流技术想法、合作机会或开源项目。
