Performance Optimization on Ascend, Biren, and Cambricon Training Course
Ascend, Biren, and Cambricon stand out as prominent AI hardware ecosystems in China, each providing specialized acceleration and profiling capabilities for large-scale AI production workloads.
This instructor-led live training, available online or onsite, targets advanced AI infrastructure and performance engineers seeking to enhance model inference and training workflows across these diverse Chinese AI chip architectures.
Upon completion of this training, participants will be equipped to:
- Execute benchmarking procedures 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 achieve superior throughput and reduced latency.
Course Format
- Interactive lectures and group discussions.
- Practical application of profiling and optimization tools across each platform.
- Guided exercises designed around real-world tuning scenarios.
Customization Options
- For tailored training aligned with your specific performance environment or model architecture, please reach out to us to make arrangements.
Course Outline
Core Performance Concepts and Metrics
- Latency, throughput, power consumption, and resource utilization
- Distinguishing between system-level and model-level bottlenecks
- Profiling methodologies for inference versus training phases
Profiling Techniques for Huawei Ascend
- Leveraging CANN Profiler and MindInsight
- Analyzing kernel and operator diagnostics
- Understanding offload patterns and memory mapping
Profiling Techniques for Biren GPU
- Utilizing Biren SDK for performance monitoring
- Optimizing kernel fusion, memory alignment, and execution queues
- Conducting power and temperature-aware profiling
Profiling Techniques for Cambricon MLU
- Using BANGPy and Neuware performance utilities
- Gaining kernel-level visibility and interpreting logs
- Integrating the MLU profiler with deployment frameworks
Optimization at Graph and Model Levels
- Strategies for graph pruning and quantization
- Operator fusion and restructuring computational graphs
- Standardizing input sizes and fine-tuning batch parameters
Memory and Kernel Optimization
- Optimizing memory layouts and reuse patterns
- Managing buffers efficiently across different chipsets
- Applying platform-specific kernel tuning techniques
Cross-Platform Best Practices
- Achieving performance portability through abstraction strategies
- Developing unified 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
- Understanding of GPU/MLU compute principles and model optimization techniques
- Familiarity with 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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