Performance Optimization on Ascend, Biren, and Cambricon Training Course
Ascend, Biren, and Cambricon stand out as premier AI hardware platforms in China, each providing distinct acceleration and profiling capabilities tailored for large-scale AI production workloads.
This instructor-led live training, available either online or on-site, targets advanced AI infrastructure and performance engineers looking to streamline model inference and training processes across various Chinese AI chip architectures.
Upon completion of this training, participants will be equipped to:
- Conduct benchmarks for models running on Ascend, Biren, and Cambricon platforms.
- Pinpoint system bottlenecks alongside memory or compute inefficiencies.
- Implement optimizations at the graph, kernel, and operator levels.
- Refine deployment pipelines to enhance throughput and reduce latency.
Format of the Course
- Interactive lectures paired with group discussions.
- Practical application of profiling and optimization tools across each platform.
- Guided exercises designed around real-world tuning scenarios.
Course Customization Options
- To arrange customized training tailored to your specific performance environment or model type, please reach out to us.
Course Outline
Performance Concepts and Metrics
- Latency, throughput, power consumption, and resource utilization
- Distinguishing between system-level and model-level bottlenecks
- Profiling approaches for inference versus training
Profiling on Huawei Ascend
- Utilizing CANN Profiler and MindInsight
- Analyzing kernel and operator diagnostics
- Understanding offload patterns and memory mapping
Profiling on Biren GPU
- Leveraging Biren SDK for performance monitoring
- Optimizing kernel fusion, memory alignment, and execution queues
- Profiling with awareness of power and temperature metrics
Profiling on Cambricon MLU
- Using BANGPy and Neuware performance utilities
- Gaining kernel-level visibility and interpreting 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 tuning batch parameters
Memory and Kernel Optimization
- Enhancing memory layout and reuse efficiency
- Managing buffers effectively 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 setups
- Case Study: Tuning an object detection model across Ascend, Biren, and MLU
Summary and Next Steps
Requirements
- Hands-on experience with AI model training or deployment workflows
- Knowledge of GPU/MLU computing principles and model optimization techniques
- Foundational understanding of performance profiling tools and metrics
Audience
- Performance engineers
- Machine learning infrastructure teams
- AI system architects
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Performance Optimization on Ascend, Biren, and Cambricon Training Course - Enquiry
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