Migrating CUDA Applications to Chinese GPU Architectures Training Course

Chinese GPU architectures such as Huawei Ascend, Biren, and Cambricon MLUs offer CUDA alternatives tailored for 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.

By the end of this training, participants will be able to:

Evaluate compatibility of existing CUDA workloads with Chinese chip alternatives.
Port CUDA codebases to Huawei CANN, Biren SDK, and Cambricon BANGPy environments.
Compare performance and identify optimization points across platforms.
Address practical challenges in cross-architecture support and deployment.

Format of the Course

Interactive lecture and discussion.
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 platform or CUDA project, please contact us to arrange.

This course is available as onsite live training in United Kingdom or online live training.

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Course Outline

Overview of Chinese AI GPU Ecosystem

Comparison of Huawei Ascend, Biren, Cambricon MLU
CUDA vs CANN, Biren SDK, and BANGPy models
Industry trends and vendor ecosystems

Preparing for Migration

Assessing your CUDA codebase
Identifying target platforms and SDK versions
Toolchain installation and environment setup

Code Translation Techniques

Porting CUDA memory access and kernel logic
Mapping compute grid/thread models
Automated vs manual translation options

Platform-Specific Implementations

Using Huawei CANN operators and custom kernels
Biren SDK conversion pipeline
Rebuilding models with BANGPy (Cambricon)

Cross-Platform Testing and Optimization

Profiling execution on each target platform
Memory tuning and parallel execution comparisons
Performance tracking and iteration

Managing Mixed GPU Environments

Hybrid deployments with multiple architectures
Fallback strategies and device detection
Abstraction layers for code maintainability

Case Studies and Best Practices

Porting vision/NLP models to Ascend or Cambricon
Retrofitting inference pipelines on Biren clusters
Handling version mismatches and API gaps

Summary and Next Steps

Requirements

Experience 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

21 Hours

Delivery Options

Private Group Training

Our identity is rooted in delivering exactly what our clients need.

Pre-course call with your trainer
Customisation of the learning experience to achieve your goals -

Bespoke outlines
Practical hands-on exercises containing data / scenarios recognisable to the learners

Training scheduled on a date of your choice
Delivered online, onsite/classroom or hybrid by experts sharing real world experience

Private Group Prices RRP from £5700 online delivery, based on a group of 2 delegates, £1800 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.