Migrating CUDA Applications to Chinese GPU Architectures Training Course
Domestic GPU architectures in China, including Huawei Ascend, Biren, and Cambricon MLUs, provide CUDA alternatives specifically designed for the local AI and High-Performance Computing (HPC) sectors.
This guided, live training session (available online or on-site) is designed for advanced GPU developers and infrastructure experts looking to migrate and optimize their existing CUDA applications for Chinese hardware platforms.
Upon completion of this training, participants will be able to:
- Assess the compatibility of current CUDA workloads with Chinese chip alternatives.
- Transfer CUDA codebases to Huawei CANN, Biren SDK, and Cambricon BANGPy environments.
- Evaluate performance metrics and identify optimization opportunities across different platforms.
- Resolve practical challenges related to cross-architecture support and deployment.
Course Format
- Interactive lectures and discussions.
- Practical labs involving code translation and performance benchmarking.
- Instructor-led exercises focused on multi-GPU adaptation strategies.
Customization Options
- For tailored training based on your specific platform or CUDA project, please contact us to arrange a customized session.
Course Outline
Overview of the Chinese AI GPU Ecosystem
- Comparison of Huawei Ascend, Biren, and Cambricon MLU.
- Contrasting CUDA with CANN, Biren SDK, and BANGPy models.
- Industry trends and vendor ecosystems.
Preparing for Migration
- Assessing your existing CUDA codebase.
- Identifying target platforms and SDK versions.
- Installing the toolchain and setting up the environment.
Code Translation Techniques
- Porting CUDA memory access and kernel logic.
- Mapping compute grid and thread models.
- Evaluating automated versus manual translation options.
Platform-Specific Implementations
- Utilizing Huawei CANN operators and custom kernels.
- Implementing the Biren SDK conversion pipeline.
- Rebuilding models using BANGPy (Cambricon).
Cross-Platform Testing and Optimization
- Profiling execution on each target platform.
- Comparing memory tuning and parallel execution strategies.
- Tracking performance and iterating on improvements.
Managing Mixed GPU Environments
- Hybrid deployments involving multiple architectures.
- Implementing fallback strategies and device detection.
- Establishing abstraction layers to ensure code maintainability.
Case Studies and Best Practices
- Porting vision and NLP models to Ascend or Cambricon.
- Integrating inference pipelines within Biren clusters.
- Addressing version mismatches and API gaps.
Summary and Next Steps
Requirements
- Experience in programming with CUDA or GPU-based applications.
- Understanding of GPU memory models and compute kernels.
- Familiarity with AI model deployment or acceleration workflows.
Target Audience
- GPU developers.
- System architects.
- Porting specialists.
Open Training Courses require 5+ participants.
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