Performance Optimization on Ascend, Biren, and Cambricon Training Course

Core Performance Principles and Metrics

• Analysis of latency, throughput, power consumption, and resource efficiency
• Distinguishing between system-wide and model-specific performance limits
• Comparative profiling techniques for inference versus training scenarios

Profiling Workflows on Huawei Ascend

• Leveraging CANN Profiler and MindInsight for detailed analysis
• Diagnosing kernel and operator performance issues
• Managing offload patterns and memory mappings

Performance Analysis on Biren GPU

• Utilizing Biren SDK monitoring capabilities
• Optimizing kernel fusion, memory alignment, and execution queue management
• Incorporating power and thermal constraints into profiling strategies

Profiling on Cambricon MLU

• Applying BANGPy and Neuware diagnostic tools
• Interpreting kernel-level logs and enhancing visibility
• Integrating the MLU profiler with existing deployment frameworks

Advanced Graph and Model Optimization

• Implementing graph pruning and quantization methodologies
• Restructuring computational graphs through operator fusion
• Standardizing input sizes and optimizing batch configurations

Memory and Kernel Refinement

• Enhancing memory layout strategies and data reuse
• Streamlining buffer management across different chipset architectures
• Applying platform-specific kernel tuning techniques

Best Practices for Multi-Platform Environments

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

Conclusion and Forward Path