Performance Optimization on Ascend, Biren, and Cambricon Training Course
Ascend, Biren, and Cambricon are prominent AI hardware platforms in China, each providing distinct acceleration and profiling capabilities tailored for large-scale AI workloads.
This instructor-led, live training session (available online or onsite) is designed for advanced AI infrastructure and performance engineers seeking to enhance model inference and training workflows across various Chinese AI chip ecosystems.
Upon completion of this training, participants will be equipped to:
- Benchmark models on Ascend, Biren, and Cambricon platforms.
- Identify system bottlenecks and memory or compute inefficiencies.
- Implement optimizations at the graph, kernel, and operator levels.
- Tune deployment pipelines to enhance throughput and reduce latency.
Format of the Course
- Interactive lectures and discussions.
- Practical application of profiling and optimization tools on each platform.
- Guided exercises focused on real-world tuning scenarios.
Course Customization Options
- To request a customized training version based on your specific performance environment or model type, please contact us to make arrangements.
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
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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(*The final price may vary depending on the technical specialization of the course, the level of customization, the method of delivery and the number of learners)
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