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Course Outline
Performance Fundamentals and Metrics
- Latency, throughput, power consumption, and resource utilization
- Distinguishing between system-level and model-level bottlenecks
- Profiling techniques for inference versus training
Profiling with Huawei Ascend
- Leveraging CANN Profiler and MindInsight
- Diagnosing kernels and operators
- Analyzing offload patterns and memory mapping
Profiling on Biren GPU
- Utilizing Biren SDK performance monitoring capabilities
- Exploring kernel fusion, memory alignment, and execution queues
- Conducting power and temperature-aware profiling
Profiling on Cambricon MLU
- Using BANGPy and Neuware performance utilities
- Gaining kernel-level visibility and interpreting logs
- Integrating the MLU profiler with deployment frameworks
Graph and Model Optimization
- Strategies for graph pruning and quantization
- Operator fusion and restructuring computational graphs
- Standardizing input sizes and tuning batch parameters
Memory and Kernel Optimization
- Improving memory layout and reuse
- Managing buffers efficiently across different chipsets
- Applying platform-specific kernel tuning techniques
Cross-Platform Best Practices
- Achieving performance portability through abstraction strategies
- Creating shared tuning pipelines for multi-chip environments
- Case study: optimizing an object detection model across Ascend, Biren, and MLU
Conclusion and Future Steps
Requirements
- Hands-on experience with AI model training or deployment pipelines
- Solid grasp of GPU/MLU compute principles and model optimization concepts
- Fundamental knowledge of performance profiling tools and key metrics
Target Audience
- Performance engineers
- Machine learning infrastructure teams
- AI system architects
21 Hours
