Performance Optimization on Ascend, Biren, and Cambricon Training Course

Fundamentals of Performance and Key Metrics

• Analyzing latency, throughput, power consumption, and resource utilization
• Distinguishing between system-level and model-level bottlenecks
• Approaches to profiling for inference versus training tasks

Profiling on Huawei Ascend

• Leveraging CANN Profiler and MindInsight
• Conducting diagnostics at the kernel and operator levels
• Analyzing offload patterns and memory mapping strategies

Profiling on Biren GPUs

• Utilizing performance monitoring features within the Biren SDK
• Managing kernel fusion, memory alignment, and execution queues
• Implementing power and temperature-aware profiling techniques

Profiling on Cambricon MLU

• Employing BANGPy and Neuware performance utilities
• Gaining visibility into kernel operations and interpreting logs
• Integrating the MLU profiler with deployment frameworks

Optimization at the Graph and Model Levels

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

Memory and Kernel Optimization Techniques

• Enhancing memory layout efficiency and reuse strategies
• Managing buffers effectively across different chipsets
• Applying platform-specific tuning techniques for kernels

Cross-Platform Best Practices

• 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 architectures

Summary and Future Steps