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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
- Leveraging CANN Profiler and MindInsight
- Diagnosing kernels and operators
- Managing offload patterns and memory mapping
Profiling on Biren GPU
- Utilising Biren SDK performance monitoring capabilities
- Addressing kernel fusion, memory alignment, and execution queues
- Conducting power and temperature-aware profiling
Profiling on Cambricon MLU
- Using BANGPy and Neuware performance tools
- Gaining kernel-level visibility and interpreting logs
- Integrating the MLU profiler with deployment frameworks
Graph and Model-Level Optimisation
- Strategies for graph pruning and quantization
- Operator fusion and restructuring computational graphs
- Standardising input sizes and tuning batch parameters
Memory and Kernel Optimisation
- Refining memory layout and reuse strategies
- Efficient buffer management across different 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: tuning an object detection model across Ascend, Biren, and MLU
Summary and Next Steps
Requirements
- Practical experience with AI model training or deployment pipelines
- Knowledge of GPU/MLU compute principles and model optimisation
- Foundational understanding of performance profiling tools and key metrics
Target Audience
- Performance engineers
- Machine learning infrastructure teams
- AI system architects
21 Hours