Performance Optimization on Ascend, Biren, and Cambricon Training Course
Ascend, Biren, and Cambricon represent some of the foremost AI hardware ecosystems in China, each providing distinct acceleration capabilities and profiling utilities designed for large-scale AI production environments.
This instructor-led live training, available either online or onsite, targets experienced AI infrastructure and performance engineers who aim to enhance model inference and training workflows across various Chinese AI chip architectures.
Upon completing this training, participants will be equipped to:
- Execute benchmarks on Ascend, Biren, and Cambricon platforms.
- Diagnose system bottlenecks and identify inefficiencies in memory and compute resources.
- Implement optimizations at the graph, kernel, and operator levels.
- Refine deployment pipelines to significantly boost throughput and reduce latency.
Training Format
- Engaging lectures paired with interactive discussions.
- Practical application of profiling and optimization tools specific to each platform.
- Guided exercises centered on real-world tuning scenarios.
Customization Options
- For tailored training based on your specific performance environment or model architecture, please contact us to arrange a session.
Course Outline
Core Performance Concepts and Metrics
- Analysis of latency, throughput, power consumption, and resource utilization
- Distinguishing between system-level and model-level bottlenecks
- Differentiating profiling approaches for inference versus training
Profiling on Huawei Ascend
- Leveraging CANN Profiler and MindInsight
- Diagnostics for kernels and operators
- Analyzing offload patterns and memory mapping
Profiling on Biren GPUs
- Utilizing Biren SDK performance monitoring capabilities
- Optimizing kernel fusion, memory alignment, and execution queues
- Conducting power and temperature-aware profiling
Profiling on Cambricon MLU
- Employing BANGPy and Neuware performance utilities
- Gaining kernel-level visibility and interpreting logs
- Integrating the MLU profiler with deployment frameworks
Optimizing at the Graph and Model Level
- Strategies for graph pruning and quantization
- Operator fusion and restructuring computational graphs
- Standardizing input sizes and tuning batch configurations
Memory and Kernel Optimization
- Enhancing memory layout and reuse patterns
- Managing buffers efficiently across different chipsets
- Applying platform-specific kernel tuning techniques
Best Practices for Cross-Platform Environments
- Ensuring performance portability through abstraction strategies
- Developing shared tuning pipelines for multi-chip setups
- Case Study: Tuning an object detection model across Ascend, Biren, and MLU
Conclusion and Next Steps
Requirements
- Hands-on experience with AI model training or deployment pipelines
- Foundational knowledge of GPU/MLU compute principles and model optimization techniques
- Basic proficiency with performance profiling tools and metrics
Target Audience
- Performance engineers
- Machine learning infrastructure teams
- AI system architects
Open Training Courses require 5+ participants.
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