Performance Optimization on Ascend, Biren, and Cambricon Training Course

Performance Fundamentals and Key Metrics

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

Profiling Techniques on Huawei Ascend

• Leveraging CANN Profiler and MindInsight tools
• Advanced diagnostics for kernels and operators
• Strategies for offload patterns and memory mapping

Profiling Techniques on Biren GPU

• Utilizing Biren SDK performance monitoring capabilities
• Exploring kernel fusion, memory alignment, and execution queue management
• Implementing power and temperature-aware profiling strategies

Profiling Techniques on Cambricon MLU

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

Graph and Model-Level Optimization Strategies

• Applying graph pruning and quantization methods
• Restructuring computational graphs through operator fusion
• Standardizing input sizes and optimizing batch parameters

Memory and Kernel Optimization Strategies

• Enhancing memory layout efficiency and data reuse
• Managing buffers effectively across diverse chipsets
• Applying platform-specific kernel-level tuning techniques

Cross-Platform Best Practices

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

Conclusion and Recommended Next Steps