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Course Outline
Performance Concepts and Metrics
- Latency, throughput, power consumption, and resource utilisation
- Distinguishing between system-level and model-level bottlenecks
- Profiling strategies for inference versus training
Profiling on Huawei Ascend
- Utilising CANN Profiler and MindInsight
- Kernel and operator diagnostics
- Offload patterns and memory mapping techniques
Profiling on Biren GPU
- Biren SDK performance monitoring capabilities
- 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 analysis
- Integration of the MLU profiler with deployment frameworks
Graph and Model-Level Optimisation
- Graph pruning and quantisation strategies
- Operator fusion and computational graph restructuring
- Input size standardisation and batch tuning
Memory and Kernel Optimisation
- Optimising memory layout and reuse strategies
- Efficient buffer management across different chipsets
- 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: tuning an object detection model across Ascend, Biren, and MLU
Summary and Next Steps
Requirements
- Hands-on experience with AI model training or deployment pipelines
- Working knowledge of GPU/MLU compute principles and model optimisation
- Basic familiarity with performance profiling tools and key metrics
Target Audience
- Performance engineers
- Machine learning infrastructure teams
- AI system architects
21 Hours
