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Duration 21 hours
Course Outline
Performance Concepts and Metrics
- Latency, throughput, power consumption, and resource utilization
- Distinguishing between system-level and model-level bottlenecks
- Profiling techniques 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
- Utilizing Biren SDK performance monitoring features
- Addressing kernel fusion, memory alignment, and execution queues
- Implementing power and temperature-aware profiling
Profiling on Cambricon MLU
- Applying BANGPy and Neuware performance tools
- Gaining kernel-level visibility and interpreting logs
- Integrating the MLU profiler with deployment frameworks
Graph and Model-Level Optimization
- Strategies for graph pruning and quantization
- Operator fusion and restructuring computational graphs
- Standardizing input sizes and tuning batches
Memory and Kernel Optimization
- Optimizing memory layout and reuse patterns
- Managing buffers efficiently 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
- Practical experience with AI model training or deployment pipelines
- A solid grasp of GPU/MLU compute principles and model optimization concepts
- Fundamental knowledge of performance profiling tools and associated metrics
Target Audience
- Performance engineers
- Machine learning infrastructure teams
- AI system architects
