Migrating CUDA Applications to Chinese GPU Architectures Training Course
Chinese GPU architectures, including Huawei Ascend, Biren, and Cambricon MLUs, offer CUDA alternatives specifically designed for the 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 for government use.
By the end of this training, participants will be able to:
- Evaluate the compatibility of existing CUDA workloads with Chinese chip alternatives.
- Port CUDA codebases to Huawei CANN, Biren SDK, and Cambricon BANGPy environments.
- Compare performance metrics and identify optimization opportunities across different platforms.
- Address practical challenges in supporting and deploying applications across multiple architectures.
Format of the Course
- Interactive lecture and discussion sessions.
- 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 specific platform or CUDA project, please contact us to arrange.
Course Outline
Overview of Chinese AI GPU Ecosystem for Government
- Comparison of Huawei Ascend, Biren, and Cambricon MLU
- CUDA vs. CANN, Biren SDK, and BANGPy models
- Industry trends and vendor ecosystems
Preparing for Migration
- Assessing your CUDA codebase for government applications
- Identifying target platforms and SDK versions suitable for government use
- Toolchain installation and environment setup for government systems
Code Translation Techniques
- Porting CUDA memory access and kernel logic to align with government requirements
- Mapping compute grid/thread models for government-specific tasks
- Evaluating automated versus manual translation options in a government context
Platform-Specific Implementations
- Using Huawei CANN operators and custom kernels for government projects
- Biren SDK conversion pipeline tailored for government workflows
- Rebuilding models with BANGPy (Cambricon) to meet government standards
Cross-Platform Testing and Optimization
- Profiling execution on each target platform for government applications
- Memory tuning and parallel execution comparisons for enhanced government performance
- Performance tracking and iteration to ensure optimal government outcomes
Managing Mixed GPU Environments
- Hybrid deployments with multiple architectures in government settings
- Fallback strategies and device detection methods for government systems
- Abstraction layers for code maintainability in government projects
Case Studies and Best Practices
- Porting vision/NLP models to Ascend or Cambricon for government use
- Retrofitting inference pipelines on Biren clusters for government operations
- Handling version mismatches and API gaps in a government context
Summary and Next Steps
Requirements
- Experience in programming with CUDA or GPU-based applications for government
- Understanding of GPU memory models and compute kernels
- Familiarity with AI model deployment or acceleration workflows
Audience
- GPU programmers
- System architects
- Porting specialists
Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.
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