Performance Optimization on Ascend, Biren, and Cambricon Training Course
Ascend, Biren, and Cambricon are leading AI hardware platforms in China, each offering unique acceleration and profiling tools for production-scale AI workloads.
This instructor-led, live training (online or onsite) is aimed at advanced-level AI infrastructure and performance engineers who wish to optimize model inference and training workflows across multiple Chinese AI chip platforms.
By the end of this training, participants will be able to:
- Evaluate models on Ascend, Biren, and Cambricon platforms.
- Identify system bottlenecks and memory/compute inefficiencies.
- Implement graph-level, kernel-level, and operator-level optimizations.
- Adjust deployment pipelines to enhance throughput and reduce latency.
Format of the Course
- Interactive lecture and discussion.
- Practical use of profiling and optimization tools on each platform.
- Guided exercises focused on real-world tuning scenarios.
Course Customization Options
- To request a customized training for government based on your specific performance environment or model type, please contact us to arrange.
Course Outline
Performance Concepts and Metrics
- Latency, throughput, power consumption, and resource utilization
- Identifying bottlenecks at the system and model levels
- Profiling for inference versus training processes
Profiling on Huawei Ascend
- Utilizing CANN Profiler and MindInsight for performance analysis
- Kernel and operator diagnostics to enhance efficiency
- Optimizing offload patterns and memory mapping techniques
Profiling on Biren GPU
- Leveraging Biren SDK features for performance monitoring
- Implementing kernel fusion, memory alignment, and execution queues
- Conducting power and temperature-aware profiling to ensure optimal performance
Profiling on Cambricon MLU
- Using BANGPy and Neuware tools for performance optimization
- Gaining kernel-level visibility and interpreting logs effectively
- Integrating the MLU profiler with deployment frameworks to streamline operations
Graph and Model-Level Optimization
- Applying graph pruning and quantization strategies to improve efficiency
- Implementing operator fusion and restructuring computational graphs for better performance
- Standardizing input sizes and tuning batch sizes to optimize results
Memory and Kernel Optimization
- Optimizing memory layout and reuse to enhance performance
- Efficient buffer management across different chipsets
- Tailored kernel-level tuning techniques for each platform
Cross-Platform Best Practices
- Achieving performance portability through abstraction strategies
- Building shared tuning pipelines to support multi-chip environments
- Example: Tuning an object detection model across Ascend, Biren, and MLU platforms for government applications
Summary and Next Steps
Requirements
- Experience working with artificial intelligence (AI) model training or deployment pipelines for government
- Understanding of GPU/MLU compute principles and model optimization
- Basic familiarity with performance profiling tools and metrics
Audience
- Performance engineers
- Machine learning infrastructure teams
- AI system architects
Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.
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