Migrating CUDA Applications to Chinese GPU Architectures Training Course
Domestic GPU solutions, including Huawei Ascend, Biren, and Cambricon MLUs, provide tailored alternatives to CUDA for the local AI and high-performance computing (HPC) sectors.
This live training session, facilitated by an expert instructor and available either online or on-site, targets advanced GPU developers and infrastructure specialists looking to migrate and optimize existing CUDA applications for deployment on Chinese hardware platforms.
Upon completion of this course, participants will be capable of:
- Assessing the compatibility of current CUDA workloads with domestic chip alternatives.
- Porting CUDA codebases to Huawei CANN, Biren SDK, and Cambricon BANGPy environments.
- Evaluating performance metrics and identifying optimization opportunities across different platforms.
- Resolving practical challenges related to cross-architecture support and deployment.
Course Format
- Interactive lectures and discussions.
- Practical labs involving code translation and performance benchmarking.
- Guided exercises focusing on multi-GPU adaptation strategies.
Customization Options
- To request a customized version of this training based on your specific platform or CUDA project, please contact us to arrange it.
Course Outline
Overview of the Domestic AI GPU Ecosystem
- Comparison of Huawei Ascend, Biren, and Cambricon MLU.
- Analysis of CUDA versus CANN, Biren SDK, and BANGPy programming models.
- Industry trends and vendor ecosystem dynamics.
Preparation for Migration
- Assessing the existing CUDA codebase.
- Identifying target platforms and necessary SDK versions.
- Installing toolchains and setting up the development environment.
Code Translation Techniques
- Porting CUDA memory access patterns and kernel logic.
- Mapping compute grid and thread models.
- Evaluating automated versus manual translation approaches.
Platform-Specific Implementations
- Utilizing Huawei CANN operators and writing custom kernels.
- Navigating the Biren SDK conversion pipeline.
- Rebuilding models using BANGPy (Cambricon).
Cross-Platform Testing and Optimization
- Profiling execution on each target platform.
- Conducting memory tuning and comparing parallel execution methods.
- Monitoring performance and iterating on optimizations.
Managing Mixed GPU Environments
- Implementing hybrid deployments across multiple architectures.
- Developing fallback strategies and device detection mechanisms.
- Using abstraction layers to ensure code maintainability.
Case Studies and Best Practices
- Porting computer vision and NLP models to Ascend or Cambricon.
- Integrating inference pipelines within Biren clusters.
- Addressing version mismatches and API discrepancies.
Summary and Next Steps
Requirements
- Experience in programming with CUDA or GPU-accelerated 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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