Performance Optimization on Ascend, Biren, and Cambricon Training Course

Performance Concepts and Metrics

• Key metrics including latency, throughput, power consumption, and resource utilization
• Distinguishing between system-level and model-level bottlenecks
• Profiling techniques for inference versus training workloads

Profiling on Huawei Ascend

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

Profiling on Biren GPU

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

Profiling on Cambricon MLU

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

Graph and Model-Level Optimization

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

Memory and Kernel Optimization

• Optimizing memory layout and data reuse
• Managing buffers efficiently across different chipsets
• Platform-specific kernel-level tuning methods

Cross-Platform Best Practices

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

Summary and Next Steps