Course Code: cudatocngpu
Duration: 21 hours
Prerequisites:
  • Experience programming with CUDA or GPU-based applications
  • Understanding of GPU memory models and compute kernels
  • Familiarity with AI model deployment or acceleration workflows

Audience

  • GPU programmers
  • System architects
  • Porting specialists
Overview:

Chinese GPU architectures such as Huawei Ascend, Biren, and Cambricon MLUs offer CUDA alternatives tailored for local AI and HPC markets.

This instructor-led, live training (online or onsite) is aimed at advanced-level GPU programmers and infrastructure specialists who wish to migrate and optimize existing CUDA applications for deployment on Chinese hardware platforms.

By the end of this training, participants will be able to:

  • Evaluate compatibility of existing CUDA workloads with Chinese chip alternatives.
  • Port CUDA codebases to Huawei CANN, Biren SDK, and Cambricon BANGPy environments.
  • Compare performance and identify optimization points across platforms.
  • Address practical challenges in cross-architecture support and deployment.

Format of the Course

  • Interactive lecture and discussion.
  • Hands-on code translation and performance comparison labs.
  • Guided exercises focused on multi-GPU adaptation strategies.

Course Customization Options

  • To request a customized training for this course based on your platform or CUDA project, please contact us to arrange.
Course Outline:

Overview of Chinese AI GPU Ecosystem

  • Comparison of Huawei Ascend, Biren, Cambricon MLU
  • CUDA vs CANN, Biren SDK, and BANGPy models
  • Industry trends and vendor ecosystems

Preparing for Migration

  • Assessing your CUDA codebase
  • Identifying target platforms and SDK versions
  • Toolchain installation and environment setup

Code Translation Techniques

  • Porting CUDA memory access and kernel logic
  • Mapping compute grid/thread models
  • Automated vs manual translation options

Platform-Specific Implementations

  • Using Huawei CANN operators and custom kernels
  • Biren SDK conversion pipeline
  • Rebuilding models with BANGPy (Cambricon)

Cross-Platform Testing and Optimization

  • Profiling execution on each target platform
  • Memory tuning and parallel execution comparisons
  • Performance tracking and iteration

Managing Mixed GPU Environments

  • Hybrid deployments with multiple architectures
  • Fallback strategies and device detection
  • Abstraction layers for code maintainability

Case Studies and Best Practices

  • Porting vision/NLP models to Ascend or Cambricon
  • Retrofitting inference pipelines on Biren clusters
  • Handling version mismatches and API gaps

Summary and Next Steps

Sites Published:

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