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 tools designed for production-scale AI workloads.
This instructor-led live training, available both online and onsite, is designed for advanced AI infrastructure and performance engineers seeking to optimize model inference and training workflows across various Chinese AI chip platforms.
Upon completing this training, participants will be equipped to:
- Conduct model benchmarking on Ascend, Biren, and Cambricon platforms.
- Identify system bottlenecks and memory/compute inefficiencies.
- Implement graph-level, kernel-level, and operator-level optimizations.
- Tune deployment pipelines to enhance throughput and reduce latency.
Course Format
- Interactive lectures and discussions.
- Hands-on practice using profiling and optimization tools on each platform.
- Guided exercises centered on practical tuning scenarios.
Course Customization Options
- To arrange customized training tailored to your specific performance environment or model type, please contact us.
Course Outline
Performance Concepts and Metrics
- Latency, throughput, power usage, and resource utilization.
- Distinction between system and model-level bottlenecks.
- Profiling approaches for inference versus training.
Profiling on Huawei Ascend
- Utilizing CANN Profiler and MindInsight.
- Kernel and operator diagnostics.
- Offload patterns and memory mapping.
Profiling on Biren GPU
- Performance monitoring features of the Biren SDK.
- Kernel fusion, memory alignment, and execution queues.
- Power and temperature-aware profiling.
Profiling on Cambricon MLU
- BANGPy and Neuware performance tools.
- Kernel-level visibility and log interpretation.
- Integration of the MLU profiler with deployment frameworks.
Graph and Model-Level Optimization
- Strategies for graph pruning and quantization.
- Operator fusion and computational graph restructuring.
- Input size standardization and batch tuning.
Memory and Kernel Optimization
- Optimizing memory layout and reuse.
- Efficient buffer management across different chipsets.
- Platform-specific kernel-level tuning techniques.
Cross-Platform Best Practices
- Performance portability: abstraction strategies.
- Building shared tuning pipelines for multi-chip environments.
- Example: 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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