Migrating CUDA Applications to Chinese GPU Architectures Training Course
Indigenous GPU architectures in China, including Huawei Ascend, Biren, and Cambricon MLUs, provide viable alternatives to CUDA, specifically designed for the domestic artificial intelligence and high-performance computing (HPC) sectors.
This instructor-led, live training session (available online or onsite) targets advanced GPU developers and infrastructure specialists seeking to migrate and optimise existing CUDA applications for deployment on Chinese hardware platforms.
Upon completion of this training, participants will be capable of:
- Evaluating the compatibility of current CUDA workloads with domestic chip alternatives.
- Translating CUDA codebases into Huawei CANN, Biren SDK, and Cambricon BANGPy environments.
- Comparing performance metrics and identifying key areas for optimisation across different platforms.
- Navigating practical challenges associated with cross-architecture support and deployment.
Course Delivery Format
- Interactive lectures and discussions.
- Practical labs involving code translation and performance benchmarking.
- Guided exercises focusing on multi-GPU adaptation strategies.
Customisation Options
- To arrange customised training for this course tailored to your specific platform or CUDA project, please contact us.
Course Outline
Overview of the Indigenous Chinese AI GPU Ecosystem
- Comparison of Huawei Ascend, Biren, and Cambricon MLU
- Understanding CUDA versus 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
- Toolchain installation and environment setup
Code Translation Techniques
- Translating CUDA memory access and kernel logic
- Mapping compute grid and thread models
- Options for automated versus manual translation
Platform-Specific Implementations
- Utilising Huawei CANN operators and custom kernels
- The Biren SDK conversion pipeline
- Rebuilding models with BANGPy (Cambricon)
Cross-Platform Testing and Optimisation
- Profiling execution on each target platform
- Memory tuning and parallel execution comparisons
- Performance tracking and iteration
Managing Mixed GPU Environments
- Hybrid deployments involving multiple architectures
- Fallback strategies and device detection
- Abstraction layers to ensure code maintainability
Case Studies and Best Practices
- Translating vision and NLP models to Ascend or Cambricon
- Adapting inference pipelines for Biren clusters
- Handling 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 programmers
- System architects
- Porting specialists
Open Training Courses require 5+ participants.
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