Performance Optimization on Ascend, Biren, and Cambricon Training Course

Performance Concepts and Metrics

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

Profiling on Huawei Ascend

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

Profiling on Biren GPU

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

Profiling on Cambricon MLU

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

Graph and Model-Level Optimization

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

Memory and Kernel Optimization

• Improving memory layout and data reuse efficiency
• Managing buffers effectively across different chipsets
• Applying platform-specific kernel-level tuning methods

Cross-Platform Best Practices

• 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

Summary and Next Steps