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Duration 21 hours
Course Outline
Overview of the Chinese AI GPU Ecosystem
- Comparative analysis of Huawei Ascend, Biren, and Cambricon MLU
- Contrasting CUDA with CANN, Biren SDK, and BANGPy paradigms
- Industry trends and vendor ecosystems
Preparing for Migration
- Evaluating your current CUDA codebase
- Identifying target platforms and SDK versions
- Toolchain installation and environment configuration
Code Translation Techniques
- Translating CUDA memory access and kernel logic
- Mapping compute grid and thread models
- Exploring automated versus manual translation methods
Platform-Specific Implementations
- Leveraging Huawei CANN operators and custom kernels
- Utilizing the Biren SDK conversion pipeline
- Reconstructing models using BANGPy (Cambricon)
Cross-Platform Testing and Optimization
- Profiling execution on each target platform
- Comparing memory tuning and parallel execution
- Tracking performance and iterative refinement
Managing Mixed GPU Environments
- Hybrid deployments across multiple architectures
- Implementing fallback strategies and device detection
- Employing abstraction layers for enhanced code maintainability
Case Studies and Best Practices
- Porting vision and NLP models to Ascend or Cambricon
- Adapting inference pipelines for Biren clusters
- Resolving version mismatches and API gaps
Summary and Next Steps
Requirements
- Programming experience 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
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 3900 € + VAT*
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