Performance Optimization on Ascend, Biren, and Cambricon Training Course

Core Performance Concepts and Metrics

• Analyzing latency, throughput, power consumption, and resource utilization
• Distinguishing between system-wide and model-specific bottlenecks
• Differentiating profiling approaches for inference versus training

Profiling Techniques on Huawei Ascend

• Utilizing CANN Profiler and MindInsight
• Diagnosing kernel and operator performance
• Analyzing offload patterns and memory mapping

Performance Analysis on Biren GPU

• Leveraging Biren SDK performance monitoring capabilities
• Exploring kernel fusion, memory alignment, and execution queues
• Implementing power and temperature-aware profiling

Profiling on Cambricon MLU

• Using BANGPy and Neuware performance utilities
• Interpreting kernel-level logs for visibility
• Integrating the MLU profiler with deployment frameworks

Graph and Model-Level Optimization Strategies

• Implementing graph pruning and quantization techniques
• Applying operator fusion and computational graph restructuring
• Standardizing input sizes and tuning batch configurations

Memory and Kernel Efficiency

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

Best Practices for Cross-Platform Optimization

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

Conclusion and Recommended Next Steps