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 tailored for large-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 these diverse Chinese AI chip ecosystems.
Upon completion of this training, participants will be equipped to:
- Conduct benchmarking of models on Ascend, Biren, and Cambricon platforms.
- Diagnose system bottlenecks and identify memory or compute inefficiencies.
- 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 platform.
- Guided exercises centered on real-world tuning scenarios.
Customization Options
- To arrange customized training tailored to your specific performance environment or model architecture, please contact us.
Course Outline
Performance Concepts and Metrics
- Latency, throughput, power consumption, and resource utilization.
- Distinguishing between system-level and model-level bottlenecks.
- Profiling strategies for inference versus training.
Profiling on Huawei Ascend
- Leveraging CANN Profiler and MindInsight.
- Kernel and operator diagnostics.
- Understanding offload patterns and memory mapping.
Profiling on Biren GPU
- Utilizing Biren SDK for performance monitoring.
- Analyzing 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 the 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
- Optimizing memory layout and reuse strategies.
- Managing buffers efficiently across different chipsets.
- Applying platform-specific kernel-level 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 with AI model training or deployment pipelines.
- Understanding of GPU/MLU compute principles and model optimization techniques.
- Basic familiarity with performance profiling tools and metrics.
Target Audience
- Performance engineers.
- Machine learning infrastructure teams.
- AI system architects.
Open Training Courses require 5+ participants.
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