Performance Optimization on Ascend, Biren, and Cambricon Training Course

Performance Concepts and Metrics

• Latency, throughput, power consumption, and resource utilization
• Differentiating system-level vs. model-level bottlenecks
• Profiling techniques for inference versus training

Profiling on Huawei Ascend

• Utilizing CANN Profiler and MindInsight
• Diagnostics for kernels and operators
• Understanding offload patterns and memory mapping

Profiling on Biren GPU

• Performance monitoring via the Biren SDK
• Kernel fusion, memory alignment, and execution queues
• Power and temperature-aware profiling

Profiling on Cambricon MLU

• Using BANGPy and Neuware performance tools
• Kernel-level visibility and log interpretation
• Integrating the MLU profiler with deployment frameworks

Graph and Model-Level Optimization

• Strategies for graph pruning and quantization
• Operator fusion and restructuring the computational graph
• Standardizing input sizes and tuning batch parameters

Memory and Kernel Optimization

• Optimizing memory layout and reuse
• Efficient buffer management across different chipsets
• Platform-specific kernel tuning techniques

Cross-Platform Best Practices

• Performance portability: Abstraction strategies
• Creating shared tuning pipelines for multi-chip environments
• Example: Tuning an object detection model across Ascend, Biren, and MLU

Summary and Next Steps