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how-to-optim-algorithm-in-cuda is an open educational repository focused on teaching developers how to optimize algorithms for high-performance execution on GPUs using CUDA. The project combines technical notes, code examples, and practical experiments that demonstrate how common computational kernels can be optimized to improve speed and memory efficiency. Instead of presenting only theoretical explanations, the repository includes hand-written CUDA implementations of fundamental operations such as reductions, element-wise computations, softmax, and attention mechanisms. These examples show how different optimization techniques influence performance on modern GPU hardware and allow readers to experiment with real implementations. The repository also contains extensive learning notes that summarize CUDA programming concepts, GPU architecture details, and performance engineering strategies.
Features
- Collection of optimized CUDA kernel implementations for common algorithms
- Learning notes explaining GPU architecture and CUDA programming concepts
- Examples of optimization techniques for reduction, softmax, and element-wise operations
- Tutorial materials related to Triton, CUTLASS, and GPU performance engineering
- Experiments demonstrating bandwidth and performance improvements on GPUs
- Research notes covering GPU systems and large language model infrastructure
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