Migrating CUDA Applications to Chinese GPU Architectures Training Course

Chinese AI GPU Ecosystem Overview

• Comparative analysis of Huawei Ascend, Biren, and Cambricon MLU
• Contrasting CUDA with CANN, Biren SDK, and BANGPy paradigms
• Current industry trends and vendor ecosystem dynamics

Pre-Migration Preparation

• Auditing your existing CUDA codebase
• Defining target platforms and required SDK versions
• Installing toolchains and configuring the development environment

Code Translation Methodologies

• Translating CUDA memory access patterns and kernel logic
• Aligning compute grid and thread models
• Evaluating automated versus manual translation approaches

Implementation Specifics by Platform

• Leveraging Huawei CANN operators and custom kernels
• Utilizing the Biren SDK conversion pipeline
• Reconstructing models using BANGPy (Cambricon)

Cross-Platform Testing and Tuning

• Profiling execution metrics on each target platform
• Optimizing memory usage and comparing parallel execution strategies
• Tracking performance indicators and iterating on improvements

Oversight of Mixed GPU Environments

• Managing hybrid deployments across multiple architectures
• Implementing fallback mechanisms and device detection logic
• Introducing abstraction layers to enhance code maintainability

Case Studies and Industry Best Practices

• Transferring vision and NLP models to Ascend or Cambricon
• Adapting inference pipelines for Biren clusters
• Resolving version inconsistencies and API discrepancies

Conclusion and Forward-Looking Steps