Performance Optimization on Ascend, Biren, and Cambricon Training Course

Core Performance Concepts and Key Metrics

• Analyzing latency, throughput, power consumption, and resource utilization.
• Distinguishing between system-level and model-level bottlenecks.
• Profiling techniques for inference versus training scenarios.

Profiling Strategies on Huawei Ascend

• Leveraging CANN Profiler and MindInsight.
• Diagnosing kernel and operator performance issues.
• Understanding offload patterns and memory mapping.

Performance Profiling on Biren GPUs

• Utilizing Biren SDK monitoring features for performance insights.
• Exploring kernel fusion, memory alignment, and execution queue management.
• Conducting power and temperature-aware profiling.

Profiling on Cambricon MLUs

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

Graph and Model-Level Optimization Techniques

• Applying graph pruning and quantization strategies.
• Restructuring computational graphs via operator fusion.
• Standardizing input sizes and tuning batch parameters.

Memory and Kernel Optimization

• Improving memory layout efficiency and data reuse.
• Managing buffers efficiently across different chipsets.
• Applying platform-specific kernel-level tuning methods.

Best Practices for Cross-Platform Deployment

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

Summary and Future Recommendations