Performance Optimization on Ascend, Biren, and Cambricon Training Course

Core Performance Concepts and Metrics

• Latency, throughput, power consumption, and resource utilization
• Distinguishing between system-level and model-level bottlenecks
• Profiling methodologies for inference versus training phases

Profiling Techniques for Huawei Ascend

• Leveraging CANN Profiler and MindInsight
• Analyzing kernel and operator diagnostics
• Understanding offload patterns and memory mapping

Profiling Techniques for Biren GPU

• Utilizing Biren SDK for performance monitoring
• Optimizing kernel fusion, memory alignment, and execution queues
• Conducting power and temperature-aware profiling

Profiling Techniques for Cambricon MLU

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

Optimization at Graph and Model Levels

• Strategies for graph pruning and quantization
• Operator fusion and restructuring computational graphs
• Standardizing input sizes and fine-tuning batch parameters

Memory and Kernel Optimization

• Optimizing memory layouts and reuse patterns
• Managing buffers efficiently across different chipsets
• Applying platform-specific kernel tuning techniques

Cross-Platform Best Practices

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

Summary and Next Steps