Performance Optimization on Ascend, Biren, and Cambricon Training Course

Foundations of Performance: Concepts and Metrics

• Analysis of latency, throughput, power consumption, and resource utilization
• Distinguishing between system-level and model-level bottlenecks
• Profiling strategies tailored for inference versus training workflows

Profiling Strategies on Huawei Ascend

• Leveraging CANN Profiler and MindInsight for deep insights
• Diagnostic techniques for kernels and operators
• Understanding offload patterns and memory mapping mechanisms

Profiling Strategies on Biren GPU

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

Profiling Strategies on Cambricon MLU

• Employing BANGPy and Neuware performance utilities
• Gaining kernel-level visibility and interpreting diagnostic logs
• Integrating MLU profilers with various deployment frameworks

Advanced Graph and Model Optimization

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

Memory and Kernel Optimization Techniques

• Enhancing memory layout and reuse efficiency
• Implementing efficient buffer management across different chipsets
• Applying platform-specific kernel tuning methods

Best Practices for Cross-Platform Performance

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

Conclusion and Future Directions