Performance Optimization on Ascend, Biren, and Cambricon Training Course

Core Performance Concepts and Metrics

• Key indicators: latency, throughput, power consumption, and resource usage
• Distinguishing between system-wide and model-specific bottlenecks
• Profiling strategies for inference versus training phases

Profiling with Huawei Ascend

• Leveraging CANN Profiler and MindInsight for insights
• Analyzing kernel and operator behavior
• Understanding offload patterns and memory mapping

Profiling on Biren GPU

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

Profiling on Cambricon MLU

• Using BANGPy and Neuware performance utilities
• 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
• Reconstructing computational graphs and fusing operators
• Standardizing input sizes and tuning batch configurations

Memory and Kernel Optimization

• Refining memory layout and data reuse
• Managing buffers efficiently across different chipsets
• Applying platform-specific kernel tuning techniques

Best Practices Across Platforms

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

Wrap-up and Future Actions