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basic-cuda-tutorial

You can find the code in https://github.com/eunomia-bpf/basic-cuda-tutorial

A collection of CUDA programming examples to learn GPU programming with NVIDIA CUDA.

Make sure to change the gpu architecture sm_61 to your own gpu architecture in Makefile

Examples and tutorials

  • 01-vector-addition.cu and 01-vector-addition.md: Introduction to CUDA programming with a vector addition example
  • 02-ptx-assembly.cu and 02-ptx-assembly.md: Demonstration of CUDA PTX inline assembly with a vector multiplication example
  • 03-gpu-programming-methods.cu and 03-gpu-programming-methods.md: Comprehensive comparison of GPU programming methods including CUDA, PTX, Thrust, Unified Memory, Shared Memory, CUDA Streams, and Dynamic Parallelism using matrix multiplication
  • 04-gpu-architecture.cu and 04-gpu-architecture.md: Detailed exploration of GPU organization hierarchy including hardware architecture, thread/block/grid structure, memory hierarchy, and execution model
  • 05-neural-network.cu and 05-neural-network.md: Implementing a basic neural network forward pass on GPU with CUDA
  • 06-cnn-convolution.cu and 06-cnn-convolution.md: GPU-accelerated convolution operations for CNN with shared memory optimization
  • 07-attention-mechanism.cu and 07-attention-mechanism.md: CUDA implementation of attention mechanism for transformer models
  • 08-profiling-tracing.cu and 08-profiling-tracing.md: Profiling and tracing CUDA applications with CUDA Events, NVTX, and CUPTI for performance optimization
  • 09-gpu-extension.cu and 09-gpu-extension.md: GPU application extension mechanisms for modifying behavior without source code changes, including API interception, memory management, kernel optimization, and error resilience
  • 10-cpu-gpu-profiling-boundaries.md: Analysis of CPU and GPU profiling boundaries, exploring which operations to measure on CPUs vs GPUs and utilizing techniques like eBPF for function hooking

Each tutorial includes comprehensive documentation explaining the concepts, implementation details, and optimization techniques used in ML/AI workloads on GPUs.

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