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Course Outline
Performance Concepts and Metrics
- Latency, throughput, power usage, resource utilization
- System vs model-level bottlenecks
- Profiling for inference vs training
Profiling on Huawei Ascend
- Using CANN Profiler and MindInsight
- Kernel and operator diagnostics
- Offload patterns and memory mapping
Profiling on Biren GPU
- Biren SDK performance monitoring features
- Kernel fusion, memory alignment, and execution queues
- Power and temperature-aware profiling
Profiling on Cambricon MLU
- BANGPy and Neuware performance tools
- Kernel-level visibility and log interpretation
- MLU profiler integration with deployment frameworks
Graph and Model-Level Optimization
- Graph pruning and quantization strategies
- Operator fusion and computational graph restructuring
- Input size standardization and batch tuning
Memory and Kernel Optimization
- Optimizing memory layout and reuse
- Efficient buffer management across chipsets
- Kernel-level tuning techniques per platform
Cross-Platform Best Practices
- Performance portability: abstraction strategies
- Building shared tuning pipelines for multi-chip environments
- Example: tuning an object detection model across Ascend, Biren, and MLU
Summary and Next Steps
Requirements
- Experience working with AI model training or deployment pipelines
- Understanding of GPU/MLU compute principles and model optimization
- Basic familiarity with performance profiling tools and metrics
Audience
- Performance engineers
- Machine learning infrastructure teams
- AI system architects
21 Hours
