Performance Optimization on Ascend, Biren, and Cambricon Training Course
Ascend, Biren, and Cambricon stand as premier AI hardware platforms in China, providing distinct acceleration and profiling utilities designed for large-scale AI workloads in production environments.
This instructor-led live training session, available both online and onsite, targets advanced AI infrastructure and performance engineers seeking to enhance model inference and training processes across various Chinese AI chip architectures.
Upon completing this course, participants will be capable of:
- Evaluating model performance on Ascend, Biren, and Cambricon systems.
- Recognizing system bottlenecks and inefficiencies in memory and computation.
- Implementing optimizations at the graph, kernel, and operator levels.
- Refining deployment pipelines to achieve superior throughput and reduced latency.
Course Format
- Engaging lectures and interactive discussions.
- Practical application of profiling and optimization tools across each platform.
- Supervised exercises centered on real-world tuning scenarios.
Customization Options
- For a tailored training experience based on your specific performance environment or model type, please reach out to us to arrange details.
Course Outline
Core Performance Concepts and Metrics
- Latency, throughput, power consumption, and resource utilization
- Distinguishing system-level versus model-level bottlenecks
- Profiling strategies for inference versus training phases
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 performance monitoring capabilities
- Mastering kernel fusion, memory alignment, and execution queues
- Conducting power and temperature-aware profiling
Profiling on Cambricon MLU
- Using BANGPy and Neuware performance tools
- Gaining kernel-level visibility and interpreting logs
- Integrating MLU profiler with deployment frameworks
Graph and Model-Level Optimization
- Strategies for graph pruning and quantization
- Operator fusion and computational graph restructuring
- Standardizing input sizes and tuning batch parameters
Memory and Kernel Optimization
- Enhancing memory layout and data reuse
- Managing buffers efficiently across different chipsets
- Applying platform-specific kernel tuning techniques
Cross-Platform Best Practices
- Achieving 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
- Hands-on experience with AI model training or deployment pipelines
- Comprehension of GPU/MLU computing principles and model optimization techniques
- Fundamental knowledge of performance profiling tools and key metrics
Target Audience
- Performance engineers
- Machine learning infrastructure teams
- AI system architects
Open Training Courses require 5+ participants.
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