Performance Optimization on Ascend, Biren, and Cambricon Training Course

Foundational Performance Metrics and Concepts

• Analysis of latency, throughput, power consumption, and resource utilization
• Distinguishing between system-wide and model-specific bottlenecks
• Profiling methodologies for inference versus training phases

Profiling Techniques for Huawei Ascend

• Leveraging CANN Profiler and MindInsight tools
• Diagnostic analysis of kernels and operators
• Examining offload patterns and memory mapping strategies

Performance Analysis on Biren GPU

• Utilizing Biren SDK monitoring capabilities
• Exploring kernel fusion, memory alignment, and execution queue management
• Implementing power and temperature-aware profiling

Optimizing Cambricon MLU Performance

• Using BANGPy and Neuware performance utilities
• Gaining kernel-level visibility and interpreting diagnostic logs
• Integrating the MLU profiler with deployment frameworks

Graph and Model Architecture Optimization

• Strategies for graph pruning and quantization
• Operator fusion and restructuring computational graphs
• Standardizing input sizes and optimizing batch settings

Memory and Kernel-Level Enhancements

• Refining memory layout and data reuse patterns
• Managing buffers efficiently across different chipsets
• Applying platform-specific kernel tuning techniques

Best Practices for Cross-Platform Development

• Achieving performance portability through abstraction strategies
• Constructing shared tuning pipelines for multi-chip environments
• Case study: Tuning an object detection model across Ascend, Biren, and MLU architectures

Conclusion and Future Recommendations