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Course Outline
Performance Concepts and Metrics
- Latency, throughput, power consumption, and resource utilization
- System versus model-level bottlenecks
- Profiling for inference versus training phases
Profiling on Huawei Ascend
- Utilizing CANN Profiler and MindInsight
- Kernel and operator diagnostics
- Offload patterns and memory mapping
Profiling on Biren GPU
- Performance monitoring features via the Biren SDK
- 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
- Integration of the MLU profiler 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 different chipsets
- Platform-specific kernel-level tuning techniques
Cross-Platform Best Practices
- Performance portability: abstraction strategies
- Developing shared tuning pipelines for multi-chip environments
- Example: Tuning an object detection model across Ascend, Biren, and MLU
Summary and Next Steps
Requirements
- Experience in AI model training or deployment pipelines
- Understanding of GPU/MLU compute principles and model optimization techniques
- Basic familiarity with performance profiling tools and metrics
Audience
- Performance engineers
- Machine learning infrastructure teams
- AI system architects
21 Hours
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 3900 € + VAT*
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