Performance Optimization on Ascend, Biren, and Cambricon Training Course
Ascend, Biren, and Cambricon represent the forefront of AI hardware platforms in China, each providing distinct acceleration and profiling capabilities designed for large-scale AI workloads.
This instructor-led live training session (available online or onsite) is tailored for advanced-level AI infrastructure and performance engineers looking to enhance model inference and training workflows across various Chinese AI chip architectures.
Upon completion of this training, participants will be equipped to:
- Evaluate and benchmark models across Ascend, Biren, and Cambricon environments.
- Pinpoint system bottlenecks and identify inefficiencies in memory and compute resources.
- Implement optimizations at the graph, kernel, and operator levels.
- Optimize deployment pipelines to achieve superior throughput and reduced latency.
Training Format
- Interactive lectures coupled with open discussions.
- Practical application of profiling and optimization tools on each specific platform.
- Guided exercises centered on real-world tuning scenarios.
Customization Options
- For tailored training sessions based on your specific performance environment or model requirements, please reach out to us to coordinate.
Course Outline
Core Performance Concepts and Metrics
- Understanding latency, throughput, power consumption, and resource utilization
- Differentiating between system-level and model-level bottlenecks
- Approaches to profiling for both inference and training phases
Profiling on Huawei Ascend
- Leveraging CANN Profiler and MindInsight
- Analyzing kernel and operator performance
- Understanding offload patterns and memory mapping
Profiling on Biren GPU
- Utilizing performance monitoring features within the Biren SDK
- Mastering kernel fusion, memory alignment, and execution queues
- Conducting power and temperature-aware profiling
Profiling on Cambricon MLU
- Employing BANGPy and Neuware performance utilities
- Gaining kernel-level visibility and interpreting logs
- Integrating MLU profiler with deployment frameworks
Graph and Model-Level Optimization
- Strategies for graph pruning and quantization
- Implementing operator fusion and restructuring computational graphs
- Standardizing input sizes and tuning batch parameters
Memory and Kernel Optimization
- Optimizing memory layout and facilitating 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
- Experience in AI model training or deployment workflows
- Foundational knowledge of GPU/MLU compute principles and model optimization techniques
- Basic proficiency with performance profiling tools and relevant metrics
Target Audience
- Performance engineers
- Machine learning infrastructure teams
- AI system architects
Open Training Courses require 5+ participants.
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