Performance Optimization on Ascend, Biren, and Cambricon Training Course
Ascend, Biren, and Cambricon are prominent AI hardware platforms in China, providing distinct acceleration and profiling tools for AI workloads at scale.
This instructor-led live training, available online or onsite, targets advanced AI infrastructure and performance engineers seeking to optimize model inference and training workflows across various Chinese AI chip architectures.
Upon completion, participants will be able to:
- Evaluate models on Ascend, Biren, and Cambricon platforms.
- Pinpoint system bottlenecks and memory or compute inefficiencies.
- Implement graph-level, kernel-level, and operator-level optimizations.
- Refine deployment pipelines to enhance throughput and reduce latency.
Course Format
- Interactive lectures and discussions.
- Practical application of profiling and optimization tools on each platform.
- Guided exercises centered on real-world tuning scenarios.
Customization Options
- For tailored training 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
- Differentiating system-level vs. model-level bottlenecks
- Profiling techniques for inference versus training
Profiling on Huawei Ascend
- Utilizing CANN Profiler and MindInsight
- Diagnostics for kernels and operators
- Understanding offload patterns and memory mapping
Profiling on Biren GPU
- Performance monitoring via the Biren SDK
- Kernel fusion, memory alignment, and execution queues
- Power and temperature-aware profiling
Profiling on Cambricon MLU
- Using BANGPy and Neuware performance tools
- Kernel-level visibility and log interpretation
- Integrating the MLU profiler with deployment frameworks
Graph and Model-Level Optimization
- Strategies for graph pruning and quantization
- Operator fusion and restructuring the computational graph
- Standardizing input sizes and tuning batch parameters
Memory and Kernel Optimization
- Optimizing memory layout and reuse
- Efficient buffer management across different chipsets
- Platform-specific kernel tuning techniques
Cross-Platform Best Practices
- Performance portability: Abstraction strategies
- Creating shared tuning pipelines for multi-chip environments
- Example: Tuning an object detection model across Ascend, Biren, and MLU
Summary and Next Steps
Requirements
- Experience with AI model training or deployment pipelines
- Knowledge 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
Open Training Courses require 5+ participants.
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