Performance Optimization on Ascend, Biren, and Cambricon Training Course

Performance Fundamentals and Key Metrics

• Analyzing latency, throughput, energy consumption, and resource utilization.
• Distinguishing between system-level and model-level constraints.
• Differentiating profiling approaches for inference versus training.

Profiling Techniques on Huawei Ascend

• Leveraging CANN Profiler and MindInsight for detailed analysis.
• Diagnosing issues at the kernel and operator levels.
• Managing offload patterns and memory mapping strategies.

Profiling Techniques on Biren GPU

• Utilizing Biren SDK features for performance monitoring.
• Optimizing kernel fusion, memory alignment, and execution queues.
• Incorporating power and temperature data into profiling workflows.

Profiling Techniques on Cambricon MLU

• Applying BANGPy and Neuware performance tools.
• Interpreting kernel-level visibility and system logs.
• Integrating the MLU profiler with various deployment frameworks.

Graph and Model-Level Enhancement

• Strategies for graph pruning and quantization.
• Techniques for operator fusion and computational graph restructuring.
• Standardizing input sizes and tuning batch parameters.

Memory and Kernel Refinement

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

Cross-Platform Best Practices

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

Conclusion and Future Directions