Performance Optimization on Ascend, Biren, and Cambricon Training Course
Ascend, Biren, and Cambricon stand out as premier AI hardware platforms in China, providing distinct acceleration and profiling capabilities tailored for large-scale AI operations.
This instructor-led live training, available both online and onsite, is designed for advanced AI infrastructure and performance engineers seeking to enhance model inference and training workflows across these prominent Chinese AI chip ecosystems.
Upon completion of this training, participants will be equipped to:
- Evaluate model performance on Ascend, Biren, and Cambricon platforms.
- Pinpoint system bottlenecks and identify inefficiencies in memory and compute resources.
- Implement optimizations at the graph, kernel, and operator levels.
- Refine deployment pipelines to enhance throughput and reduce latency.
Course Format
- Interactive lectures and discussions.
- Practical application of profiling and optimization tools on each respective platform.
- Guided exercises targeting real-world tuning scenarios.
Customization Options
- For customized training tailored to 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.
- Differentiating between system-level and model-level bottlenecks.
- Profiling strategies for inference versus training.
Profiling on Huawei Ascend
- Leveraging CANN Profiler and MindInsight.
- Diagnostics for kernels and operators.
- Understanding offload patterns and memory mapping.
Profiling on Biren GPU
- Utilizing Biren SDK performance monitoring features.
- Optimizing kernel fusion, memory alignment, and execution queues.
- Profiling with attention to power and temperature.
Profiling on Cambricon MLU
- Using BANGPy and Neuware performance tools.
- 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 of computational graphs.
- Standardizing input sizes and tuning batch parameters.
Memory and Kernel Optimization
- Optimizing memory layouts and reuse strategies.
- Managing buffers efficiently across different chipsets.
- Platform-specific kernel-level tuning techniques.
Cross-Platform Best Practices
- Performance portability through abstraction strategies.
- Developing shared tuning pipelines for multi-chip environments.
- Case study: Tuning an object detection model across Ascend, Biren, and MLU.
Summary and Next Steps
Requirements
- Experience with AI model training or deployment pipelines.
- 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.
Open Training Courses require 5+ participants.
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