Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Core Performance Concepts and Metrics
- Latency, throughput, power consumption, and resource utilization
- Distinguishing between system-level and model-level bottlenecks
- Profiling methodologies for inference versus training
Profiling on Huawei Ascend
- Leveraging CANN Profiler and MindInsight
- Diagnosing kernels and operators
- Exploring offload patterns and memory mapping
Profiling on Biren GPU
- Utilizing Biren SDK performance monitoring features
- Examining kernel fusion, memory alignment, and execution queues
- Conducting power and temperature-aware profiling
Profiling on Cambricon MLU
- Employing BANGPy and Neuware performance tools
- Interpreting kernel-level visibility and 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 batch tuning
Memory and Kernel Optimization
- Optimizing memory layout and data reuse
- Managing buffers efficiently across different chipsets
- Applying platform-specific kernel tuning techniques
Cross-Platform Best Practices
- Ensuring performance portability through abstraction strategies
- Developing shared tuning pipelines for multi-chip environments
- Case study: optimizing 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 understanding of GPU/MLU compute principles and model optimization
- Basic proficiency with performance profiling tools and metrics
Target Audience
- Performance engineers
- Machine learning infrastructure teams
- AI system architects
21 Hours
