Performance Optimization on Ascend, Biren, and Cambricon Training Course
The leading AI hardware platforms in China—Ascend, Biren, and Cambricon—provide distinctive acceleration and profiling tools tailored for large-scale AI workloads.
This instructor-led training session (conducted either online or on-site) is designed for advanced-level engineers specializing in AI infrastructure and performance. The goal is to enhance their ability to optimize model inference and training processes across various Chinese AI chip platforms.
Upon completion of this course, participants will be able to:
- Evaluate models using the Ascend, Biren, and Cambricon platforms.
- Detect system limitations and inefficiencies in memory and compute resources.
- Implement optimizations at the graph level, kernel level, and operator level.
- Refine deployment pipelines to boost throughput and reduce latency.
Course Format
- An interactive lecture combined with discussions.
- Practical use of profiling and optimization tools on each platform.
- Guided exercises centered around real-world tuning scenarios.
Customization Options for the Course
- If you require a customized training session based on your specific performance environment or model type, please contact us to arrange this.
Course Outline
Performance Concepts and Metrics
- Latency, throughput, power usage, resource utilization
- System vs model-level bottlenecks
- Profiling for inference vs training
Profiling on Huawei Ascend
- Using CANN Profiler and MindInsight
- Kernel and operator diagnostics
- Offload patterns and memory mapping
Profiling on Biren GPU
- Biren SDK performance monitoring features
- Kernel fusion, memory alignment, and execution queues
- Power and temperature-aware profiling
Profiling on Cambricon MLU
- BANGPy and Neuware performance tools
- Kernel-level visibility and log interpretation
- MLU profiler integration with deployment frameworks
Graph and Model-Level Optimization
- Graph pruning and quantization strategies
- Operator fusion and computational graph restructuring
- Input size standardization and batch tuning
Memory and Kernel Optimization
- Optimizing memory layout and reuse
- Efficient buffer management across chipsets
- Kernel-level tuning techniques per platform
Cross-Platform Best Practices
- Performance portability: abstraction strategies
- Building shared tuning pipelines for multi-chip environments
- Example: tuning an object detection model across Ascend, Biren, and MLU
Summary and Next Steps
Requirements
- Experience working with AI model training or deployment pipelines
- Understanding of GPU/MLU compute principles and model optimization
- Basic familiarity with performance profiling tools and metrics
Audience
- Performance engineers
- Machine learning infrastructure teams
- AI system architects
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