Migrating CUDA Applications to Chinese GPU Architectures Training Course
Chinese GPU architectures, including Huawei Ascend, Biren, and Cambricon MLUs, provide alternatives to CUDA that are specifically designed for the local AI and high-performance computing (HPC) markets.
This instructor-led live training, available both online and onsite, is designed for advanced-level GPU programmers and infrastructure specialists who aim to migrate and optimize their existing CUDA applications for deployment on Chinese hardware platforms.
Upon completion of this training, participants will be capable of:
- Evaluating the compatibility of existing CUDA workloads with Chinese chip alternatives.
- Porting CUDA codebases to Huawei CANN, Biren SDK, and Cambricon BANGPy environments.
- Comparing performance across platforms and identifying key optimization areas.
- Addressing practical challenges related to cross-architecture support and deployment.
Course Format
- Interactive lectures and discussions.
- Hands-on labs involving code translation and performance comparisons.
- Guided exercises focused on multi-GPU adaptation strategies.
Course Customization Options
- To request customized training for this course tailored to your specific platform or CUDA project, please contact us to arrange.
Course Outline
Overview of the Chinese AI GPU Ecosystem
- Comparison of Huawei Ascend, Biren, and Cambricon MLU
- Comparison between CUDA and CANN, Biren SDK, and BANGPy models
- Industry trends and vendor ecosystems
Preparing for Migration
- Assessing your CUDA codebase
- Identifying target platforms and SDK versions
- Installing toolchains and setting up the environment
Code Translation Techniques
- Porting CUDA memory access and kernel logic
- Mapping compute grid and thread models
- Options for automated versus manual translation
Platform-Specific Implementations
- Utilizing Huawei CANN operators and custom kernels
- Understanding the Biren SDK conversion pipeline
- Rebuilding models with BANGPy (Cambricon)
Cross-Platform Testing and Optimization
- Profiling execution on each target platform
- Comparing memory tuning and parallel execution
- Performance tracking and iterative improvement
Managing Mixed GPU Environments
- Hybrid deployments involving multiple architectures
- Fallback strategies and device detection
- Implementing abstraction layers for code maintainability
Case Studies and Best Practices
- Porting vision and NLP models to Ascend or Cambricon
- Retrofitting inference pipelines on 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
Audience
- GPU programmers
- System architects
- Porting specialists
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