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 online or onsite) targets advanced AI infrastructure and performance engineers seeking to optimize both model inference and training workflows across these diverse Chinese AI chip ecosystems.
Upon completion of this training, participants will be equipped to:
- Conduct benchmarks for models across Ascend, Biren, and Cambricon platforms.
- Pinpoint system bottlenecks and inefficiencies related to memory and compute resources.
- Implement optimizations at the graph, kernel, and operator levels.
- Refine deployment pipelines to enhance both throughput and latency.
Course Format
- Engaging interactive lectures and discussions.
- Practical application of profiling and optimization tools on each respective platform.
- Guided exercises centered on real-world tuning scenarios.
Course Customization Options
- To arrange customized training tailored to your specific performance environment or model type, please reach out to us.
Course Outline
Performance Concepts and Metrics
- Analyzing latency, throughput, power consumption, and resource utilization
- Distinguishing between system-level and model-level bottlenecks
- Profiling strategies for inference versus training workloads
Profiling on Huawei Ascend
- Leveraging CANN Profiler and MindInsight
- Diagnostics for kernels and operators
- Understanding offload patterns and memory mapping
Profiling on Biren GPU
- Utilizing Biren SDK performance monitoring features
- Kernel fusion, memory alignment, and execution queue management
- Profiling with consideration for power and temperature
Profiling on Cambricon MLU
- Employing 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 restructuring of computational graphs
- Standardizing input sizes and tuning batch parameters
Memory and Kernel Optimization
- Optimizing memory layouts and facilitating reuse
- Managing buffers efficiently across different chipsets
- Platform-specific kernel-level tuning techniques
Cross-Platform Best Practices
- Ensuring performance portability through abstraction strategies
- Developing shared tuning pipelines for multi-chip environments
- Case Study: Optimizing an object detection model across Ascend, Biren, and MLU
Summary and Next Steps
Requirements
- Professional experience with AI model training or deployment pipelines
- Solid understanding of GPU/MLU compute principles and model optimization techniques
- Familiarity with basic 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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