Performance Optimization on Ascend, Biren, and Cambricon Training Course

Core Performance Principles and Metrics

• Analysis of latency, throughput, energy consumption, and resource allocation
• Distinguishing between system-wide and model-specific constraints
• Techniques for profiling inference versus training workloads

Profiling Capabilities on Huawei Ascend

• Leveraging CANN Profiler and MindInsight for deep insights
• Diagnostic analysis of kernels and operators
• Managing offload patterns and memory mapping strategies

Performance Analysis on Biren GPU

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

Profiling Strategies for Cambricon MLU

• Application of BANGPy and Neuware performance utilities
• Interpreting logs for kernel-level visibility
• Integrating the MLU profiler with existing deployment frameworks

Advanced Graph and Model Optimization

• Strategies for graph pruning and quantization
• Reconstructing computational graphs via operator fusion
• Standardizing input dimensions and fine-tuning batch sizes

Memory and Kernel Enhancement

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

Best Practices for Cross-Platform Deployment

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

Conclusion and Future Directions