Performance Optimization on Ascend, Biren, and Cambricon Training Course

Performance Fundamentals and Key Metrics

• Analysis of latency, throughput, power consumption, and resource usage
• Distinguishing between system-wide and model-specific bottlenecks
• Tailored profiling strategies for inference versus training workloads

Profiling on Huawei Ascend

• Leveraging CANN Profiler and MindInsight tools
• Deep-dive diagnostics for kernels and operators
• Exploring offload patterns and memory mapping strategies

Profiling on Biren GPU

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

Profiling on Cambricon MLU

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

Graph and Model-Level Refinements

• Strategies for graph pruning and quantization
• Advanced operator fusion and computational graph restructuring
• Standardizing input sizes and optimizing batch tuning

Memory and Kernel Efficiency

• Improving memory layout and data reuse patterns
• Efficient buffer management across various chipsets
• Platform-specific kernel tuning methodologies

Cross-Platform Best Practices

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

Conclusion and Future Directions