Performance Optimization on Ascend, Biren, and Cambricon Training Course

Foundational Performance Concepts and Metrics

• Key indicators such as latency, throughput, power consumption, and resource utilization
• Distinguishing between system-level and model-level bottlenecks
• Differentiating profiling approaches for inference versus training

Profiling Strategies on Huawei Ascend

• Leveraging CANN Profiler and MindInsight for analysis
• Diagnosing kernels and operators
• Analyzing offload patterns and memory mapping

Profiling Strategies on Biren GPU

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

Profiling Strategies on Cambricon MLU

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

Advanced Graph and Model Optimization

• Applying graph pruning and quantization strategies
• Executing operator fusion and restructuring computational graphs
• Standardizing input sizes and tuning batch configurations

Optimizing Memory and Kernels

• Improving memory layout and resource reuse
• Managing buffers efficiently across different chipsets
• Applying specific kernel-level tuning techniques for each platform

Best Practices for Cross-Platform Efficiency

• Ensuring 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

Conclusions and Future Directions